diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 213c6973..23ebfdb1 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -16,6 +16,9 @@ jobs: with: python-version: 3.8 + - name: Remove solaris + run: sudo rm -rf ./docker + - name: Clone Ramp run: git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code diff --git a/.github/workflows/upload.yml b/.github/workflows/upload.yml index 5ebaff88..ea523c74 100644 --- a/.github/workflows/upload.yml +++ b/.github/workflows/upload.yml @@ -3,7 +3,7 @@ name: Build and Publish on: push: tags: - - 'v*' + - "v*" jobs: build: @@ -18,16 +18,20 @@ jobs: - name: Checkout code uses: actions/checkout@v2 + - name: Remove solaris + run: | + sudo rm -rf docker + - name: Install dependencies run: | python -m pip install --upgrade pip pip install setuptools wheel twine - name: Build package - run: python setup.py sdist + run: python setup.py sdist - name: Publish to PyPI env: TWINE_USERNAME: __token__ TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} - run: twine upload dist/* \ No newline at end of file + run: twine upload dist/* diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..319eba1e --- /dev/null +++ b/Dockerfile @@ -0,0 +1,43 @@ +FROM tensorflow/tensorflow:2.9.2-gpu-jupyter + +RUN apt-get update && apt-get install -y python3-opencv +RUN add-apt-repository ppa:ubuntugis/ppa && apt-get update +RUN apt-get update +RUN apt-get install -y gdal-bin +RUN apt-get install -y libgdal-dev +ENV CPLUS_INCLUDE_PATH=/usr/include/gdal +ENV C_INCLUDE_PATH=/usr/include/gdal + +#install numpy before gdal +RUN pip install numpy==1.23.5 + +# pip install dependencies. +RUN pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version` + +COPY docker/ramp/docker-requirements.txt docker-requirements.txt +RUN pip install -r docker-requirements.txt + +# pip install solaris -- try with tmp-free build +# COPY docker/ramp/solaris /tmp/solaris +COPY docker/solaris/solaris /tmp/solaris/solaris +COPY docker/solaris/requirements.txt /tmp/solaris/requirements.txt +COPY docker/solaris/setup.py /tmp/solaris/setup.py + +RUN pip install /tmp/solaris --use-feature=in-tree-build + +RUN pip install scikit-fmm --use-feature=in-tree-build + +RUN pip install setuptools --upgrade + +ENV RAMP_HOME=/tf + +# Install the package in development mode +COPY setup.py ./setup.py +COPY hot_fair_utilities ./hot_fair_utilities +RUN pip install -e . + +# install ramp-fair +RUN pip install ramp-fair mercantile pandas==1.5.3 + +## Copy Sample data +COPY ramp-data ./ramp-data \ No newline at end of file diff --git a/README.md b/README.md index 3c75454a..d20bf015 100644 --- a/README.md +++ b/README.md @@ -1,94 +1,39 @@ # hot_fair_utilities ( Utilities for AI Assisted Mapping fAIr ) -Initially lib was developed during Open AI Challenge with [Omdeena](https://omdena.com/). Learn more about challenge from [here](https://www.hotosm.org/tech-blog/hot-tech-talk-open-ai-challenge/) -## `hot_fair_utilities` Installation - -hot_fair_utilities is collection of utilities which contains core logic for model data prepration , training and postprocessing . It can support multiple models , Currently ramp is supported. - -1. To get started clone this repo first : - ``` - git clone https://github.com/hotosm/fAIr-utilities.git - ``` -2. Setup your virtualenv with ```python 3.8``` ( Ramp is tested with python 3.8 ) - -3. Install tensorflow ```2.9.2``` from [here] (https://www.tensorflow.org/install/pip) According to your os - -#### Setup Ramp : - -4. Copy your basemodel : Basemodel is derived from ramp basemodel - ``` - git clone https://github.com/radiantearth/model_ramp_baseline.git - ``` - -5. Clone ramp working dir - - ``` - git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code - ``` - -6. Copy base model to ramp-code - ``` - cp -r model_ramp_baseline/data/input/checkpoint.tf ramp-code/ramp/checkpoint.tf - ``` +Initially lib was developed during Open AI Challenge with [Omdeena](https://omdena.com/). Learn more about challenge from [here](https://www.hotosm.org/tech-blog/hot-tech-talk-open-ai-challenge/) -7. Install native bindings - - Install Numpy - ``` - pip install numpy==1.23.5 - ``` - - Install [gdal](https://gdal.org/index.html) . - - for eg : on Ubuntu - ``` - sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update - sudo apt-get install gdal-bin - sudo apt-get install libgdal-dev - export CPLUS_INCLUDE_PATH=/usr/include/gdal - export C_INCLUDE_PATH=/usr/include/gdal - pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version` - ``` - on conda : - ``` - conda install -c conda-forge gdal - ``` - - Install rasterio +## `hot_fair_utilities` Installation - for eg: on ubuntu : - ``` - sudo apt-get install -y python3-rasterio - ``` - on conda : - ``` - conda install -c conda-forge rasterio - ``` +Installing all libraries could be pain so we suggest you to use docker , If you like to do it bare , You can follow `.github/build.yml` -8. Install ramp requirements +Clone repo - Install necessary requirements for ramp and hot_fair_utilites - - ``` - cd ramp-code && cd colab && make install && cd ../.. && pip install -e . - ``` +``` +git clone https://github.com/hotosm/fAIr-utilities.git +``` +Build Docker +``` +docker build --tag fairutils . +``` -## Conda Virtual Environment -Create from env fle +Run Container with default Jupyter Notebook , Or add `bash` at end to see terminal ``` -conda env create -f environment.yml +docker run -it --rm --gpus=all -p 8888:8888 fairutils ``` -Create your own + +By Default tf is set as Ramp_Home , You can change that attaching your volume to container as tf ``` -conda create -n fAIr python=3.8 -conda activate fAIr -conda install -c conda-forge gdal -conda install -c conda-forge geopandas -pip install pyogrio rasterio tensorflow -pip install -e hot_fair_utilities +-v /home/hotosm/fAIr-utilities:/tf ``` -## Test Installation and workflow +## Test Installation and workflow + +You can run `package_test.ipynb` on your notebook from docker to test the installation and workflow with sample data provided , Or open with [collab and connect your runtime locally](https://research.google.com/colaboratory/local-runtimes.html#:~:text=In%20Colab%2C%20click%20the%20%22Connect,connected%20to%20your%20local%20runtime.) + +## Get started with development -You can run ```package_test.ipynb``` to your pc to test the installation and workflow with sample data provided +Now you can play with your data , use your own data , use different models for testing and also Help me Improve me ! diff --git a/docker/ramp/docker-requirements.txt b/docker/ramp/docker-requirements.txt new file mode 100644 index 00000000..51f119b2 --- /dev/null +++ b/docker/ramp/docker-requirements.txt @@ -0,0 +1,78 @@ +affine==2.3.1 +albumentations==1.0.3 +attrs==21.4.0 +branca==0.4.2 +brotlipy==0.7.0 +certifi==2021.10.8 +cffi==1.15.0 +charset-normalizer==2.0.4 +click==8.1.0 +click-plugins==1.1.1 +cligj==0.7.2 +cloudpickle==2.0.0 +cryptography==36.0.0 +cycler==0.11.0 +cytoolz==0.11.2 +dask +efficientnet==1.0.0 +Fiona==1.8.21 +folium==0.12.1.post1 +fonttools==4.31.2 +fsspec==2022.2.0 +GDAL +geojson==2.5.0 +geopandas==0.10.2 +h5py==3.6.0 +idna==3.3 +image-classifiers==1.0.0 +imagecodecs +imageio==2.16.1 +imgaug==0.4.0 +Jinja2==3.1.1 +joblib==1.1.0 +Keras-Applications==1.0.8 +kiwisolver==1.4.2 +locket==0.2.0 +mapclassify==2.4.3 +MarkupSafe==2.1.1 +matplotlib==3.5.1 +munch==2.5.0 +munkres==1.1.4 +networkx +numpy +opencv-python==4.5.5.64 +packaging==21.3 +pandas +partd==1.2.0 +Pillow==9.0.1 +pip==21.2.4 +pycosat==0.6.3 +pycparser==2.21 +pyOpenSSL==21.0.0 +pyparsing==3.0.7 +pyproj +PySocks==1.7.1 +python-dateutil==2.8.2 +pytz==2022.1 +PyWavelets==1.3.0 +PyYAML==6.0 +rasterio==1.2.10 +requests==2.27.1 +Rtree==0.9.7 +scikit-image==0.19.2 +scikit-learn==1.0.2 +scipy +segmentation-models==1.0.1 +setuptools==58.0.4 +Shapely==1.8.0 +six==1.16.0 +snuggs==1.4.7 +threadpoolctl==3.1.0 +tifffile +tinydb==4.7.0 +toolz==0.11.2 +tqdm==4.62.3 +unicodedata2==14.0.0 +urllib3==1.26.7 +wheel==0.37.1 +xyzservices==2022.3.0 \ No newline at end of file diff --git a/docker/solaris/.gitattributes b/docker/solaris/.gitattributes new file mode 100644 index 00000000..d9d68857 --- /dev/null +++ b/docker/solaris/.gitattributes @@ -0,0 +1,3 @@ +*.ipynb filter=nbstripout + +*.ipynb diff=ipynb diff --git a/docker/solaris/.github/ISSUE_TEMPLATE/bug_report.md b/docker/solaris/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..9dfb3319 --- /dev/null +++ b/docker/solaris/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,47 @@ +--- +name: Bug report +about: Report an error or other suspected bug behavior +title: "[BUG]: (your description here)" +labels: 'Status: Review Needed, Type: bug' +assignees: '' + +--- + +_Thank you for helping us improve `solaris`!_ + +## Summary of the bug +A clear and concise description of what the bug is. + +## Steps to reproduce the bug +Please either paste sample code used to generate the buggy behavior below, or provide step-by-step instructions to reproduce the problem. + +``` +Paste bug-causing code here +``` + +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +## Buggy behavior and/or error message +Please describe the buggy behavior and/or paste output here. +``` +Paste output here +``` + +## Expected behavior +A clear and concise description of what you expected to happen. + +## Screenshots +If applicable, add screenshots to help explain your problem. + +## Environment information +- OS: +- `solaris` version: +- python version: +- version of any relevant dependencies (optional - we may ask for this information later if not provided) + +## Additional context +Add any other context about the problem here. diff --git a/docker/solaris/.github/ISSUE_TEMPLATE/documentation.md b/docker/solaris/.github/ISSUE_TEMPLATE/documentation.md new file mode 100644 index 00000000..9cc2efe5 --- /dev/null +++ b/docker/solaris/.github/ISSUE_TEMPLATE/documentation.md @@ -0,0 +1,18 @@ +--- +name: Documentation +about: Request for additional documentation, clarification of existing documentation, or tutorials +title: "[DOCS]" +labels: 'Status: Review Needed, Type: Documentation' +assignees: '' + +--- + +_Thank you for helping to improve `solaris`!_ + +## Documentation request summary + +A short summary of the changes you'd like made to the documentation. + +## Task detail and notes + +List any additional information, links, or other content here that is needed to make the changes you're requesting. diff --git a/docker/solaris/.github/ISSUE_TEMPLATE/feature_request.md b/docker/solaris/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..e2752103 --- /dev/null +++ b/docker/solaris/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: "[FEATURE]: (short description)" +labels: 'Status: Review Needed, Type: Enhancement' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/docker/solaris/.github/ISSUE_TEMPLATE/maintenance.md b/docker/solaris/.github/ISSUE_TEMPLATE/maintenance.md new file mode 100644 index 00000000..84598c0d --- /dev/null +++ b/docker/solaris/.github/ISSUE_TEMPLATE/maintenance.md @@ -0,0 +1,22 @@ +--- +name: Maintenance +about: Request for maintenance, refactoring, or related tasks +title: "[MAINT]" +labels: 'Status: Review Needed, Type: Maintenance' +assignees: '' + +--- + +## Maintenance request summary + +A short summary of the maintenance to perform, including why it needs to be done. + +## Task detail and notes + +Any additional information: + +- What other code will this impact? + +- Do new tests need to be written or do existing tests need to be updated? + +- Will this impact documentation? diff --git a/docker/solaris/.gitignore b/docker/solaris/.gitignore new file mode 100644 index 00000000..ea88187b --- /dev/null +++ b/docker/solaris/.gitignore @@ -0,0 +1,127 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# MacOS stuff: +.DS_Store + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Text editor backups: +*~ + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Project-specific +sandbox.ipynb +solaris/nets/weights +model_weights diff --git a/docker/solaris/.readthedocs-environment.yml b/docker/solaris/.readthedocs-environment.yml new file mode 100644 index 00000000..8b70d329 --- /dev/null +++ b/docker/solaris/.readthedocs-environment.yml @@ -0,0 +1,8 @@ +name: solaris +dependencies: + - python + - pip + - gdal + - pip: + - sphinx_bootstrap_theme + - nbsphinx diff --git a/docker/solaris/.readthedocs.yml b/docker/solaris/.readthedocs.yml new file mode 100644 index 00000000..ac3c539c --- /dev/null +++ b/docker/solaris/.readthedocs.yml @@ -0,0 +1,17 @@ +version: 2 +formats: + - htmlzip + +python: + version: 3.6 + install: + - method: pip + path: . + - requirements: .rtfd-requirements.txt + + +conda: + environment: .readthedocs-environment.yml + +build: + image: latest diff --git a/docker/solaris/.rtfd-requirements.txt b/docker/solaris/.rtfd-requirements.txt new file mode 100644 index 00000000..59aa86cc --- /dev/null +++ b/docker/solaris/.rtfd-requirements.txt @@ -0,0 +1 @@ +sphinx_bootstrap_theme diff --git a/docker/solaris/.travis.yml b/docker/solaris/.travis.yml new file mode 100644 index 00000000..9ce7d4ce --- /dev/null +++ b/docker/solaris/.travis.yml @@ -0,0 +1,47 @@ +language: python +sudo: required +dist: xenial +cache: false +python: + - "3.6" + - "3.7" + - "3.8" + +# command to install dependencies +install: + - sudo apt-get update + # We do this conditionally because it saves us some downloading if the + # version is the same. + - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then + wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; + else + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; + fi + - bash miniconda.sh -b -p $HOME/miniconda + - export PATH="$HOME/miniconda/bin:$PATH" + - hash -r + - conda config --set always_yes yes --set changeps1 no + # Useful for debugging any issues with conda + - conda install pip # workaround to avoid miniconda 4.7.12 bugs + - conda update conda -c conda-forge + - conda info -a + # switch python version spec in environment.yml to match TRAVIS_PYTHON_VERSION + # annoying workaround to `conda env create python=$TRAVIS_PYTHON_VERSION` not working + - sed -i -E 's/(python=)(.*)/\1'$TRAVIS_PYTHON_VERSION'/' ./environment.yml + - conda env create -n solaris -f environment.yml + - conda list -n solaris + - source activate solaris + - python --version + - pip install --user -r requirements.txt -vv + - pip install .[test] -vv + - pip install codecov pytest-cov pytest==5.3.1 +# command to run tests +script: + - python -m pytest --cov=./ + +after_success: + - codecov + +jobs: + allow_failures: + - python: "3.8" diff --git a/docker/solaris/CHANGELOG.md b/docker/solaris/CHANGELOG.md new file mode 100644 index 00000000..3794dc8b --- /dev/null +++ b/docker/solaris/CHANGELOG.md @@ -0,0 +1,106 @@ +# Changelog + +## Instructions + +Anytime you add something new to this project, add a new item under the appropriate sub-heading of the [Unreleased](#unreleased) portion of this document. That item should be formatted as follows: +``` +- [Date (ISO format)], [GitHub username]: [Short description of change] [(PR number)] +``` +e.g. +``` +- 20190930, nrweir: Added changelog (#259) +``` +Consistent with the "one PR per task" paradigm, we recommend having only one changelog entry per PR whenever possible; however, multiple entries can be included for a single PR if needed to capture the full changeset. + +When a new version of `solaris` is released, all of the changes in the Unreleased portion will be moved to the newest release version. + +## Unreleased + +### Added +### Removed +### Changed +### Fixed +### Deprecated +### Security + +--- + +## Version 0.4.0 + +### Added +- 20200803, jshermeyer: Added mAP metric +- 20200701, dphogan: Added SpaceNet Change and Object Tracking (SCOT) metric +### Removed +### Changed +### Fixed +### Deprecated +### Security + +--- + +## Version 0.3.0 + +### Added +- 20200701, dphogan: Added multimodal preprocessing subpackage (#360) +- 20200716, dphogan: Added three-part tutorial for preprocessing subpackage +### Fixed +- 20200630, dphogan: Remove edge case numeric values from loss function tests (#358) +- 20200706, dphogan: Added API reference entry for utils.data +- 20200706, roshanr11: fixed `checkpoint_frequency` parameter in YAML files to work as intended +- 20200706, roshanr11: tqdm progress bar fixed, follow-up on previous issue (#335) + +--- + +## Version 0.2.2 + +### Changed +- 20200401, rbavery: new tiler method `fill_all_nodata` to fill nodata with `"mean"` or custom value (#328) +- 20200401, rbavery: option to ignore MultiPolygon and GeometryCollection types in `geojson2coco` since these cannot be converted to COCO. +- 20200401, rbavery: new function `solaris.vector.mask.geojsons_to_masks_and_fill_nodata`, which rasterized vector labels according to raster tile extents. Fills nodata areas in raster tile and corresponding rasterized label raster. +- 20200401, rbavery: new test/example of tiling and creating instance masks with nodata values filled in tile outputs + +### Fixed +- 20200401, rbavery: restrict_to_aoi implemented, sets values outside aoi to nodata value (#327, #240) +- 20200401, rbavery: tqdm prints correctly in notebook and lab (if ipywidgets is enabled according to https://github.com/tqdm/tqdm/issues/394#issuecomment-384743637) (#335) +- 20200401, rbavery: fixed bug where aoi boundary was not intersected with src_img extent prior to tiling +- 20200401, rbavery/nrweir: adapted `_check_crs` to convert `pyproj.CRS` or pass through `rasterio.crs.CRS` class when rasterio crs object is required (for example, reprojecting in the tilers) +- 20200414, zaburo-ch: fixed `val_datagen` to point to the correct augmentation pipeline + +--- + +## Version 0.2.1 + +### Changed +- 20200103, nrweir: Updated version pins for proj6 compatibility, also relaxed version pins for many dependencies (#321) +### Fixed +- 20200103, nrweir: Fixed various places where CRS wasn't passed correctly from rasterio CRS object (#319, #322) +- 20200103, nrweir: Fixed axis length check for axis ordering in sol.utils.raster.reorder_axes() (#318) + +--- + +## Version 0.2.0 + +### Added +- 20190930, nrweir: Added CHANGELOG.md (#259) +- 20190930, nrweir: Add contributing guidelines, CONTRIBUTING.md (#260) +- 20191003, nrweir: Added `solaris.vector.mask.instance_mask()` (#261) +- 20191009, nrweir: Added `solaris.data.coco` and some label utility functions (#265) +- 20191009, nrweir: Added `solaris.data.coco` API documentation and a usage tutorial (#266) +- 20191122, dphogan: Added option to take sigmoid of input in TorchDiceLoss (#281) +- 20191122, dphogan: Inferer calls now take default DataFrame path from config dictionary (#282) +- 20191125, nrweir: Added `solaris.utils.data.make_dataset_csv()` (#241) +- 20191202, dphogan: Added fixed nodata value of 0 for mask files (#295) +- 20191203: dphogan: Added filename argument to vector tiler's tile() (#297) +- 20191211: rbavery: Tilers also accept rasterio CRS objects, `RasterTiler.tile` returns CRS object for vector tiler (#294) +- 20191214: rbavery: tiler argument `aoi_bounds` is now `aoi_boundary` and can accept polygons besides boxes. functionaility for this moved to `solaris.utils.geo.split_geom` (#298) +- 20191217: dphogan: Added support for custom loss functions (#308) + +### Fixed +- 20191123, dphogan: Fixed issue in mask_to_poly_geojson() with empty GeoDataFrames. +- 20191204, dphogan: Fixed issue with file output from footprint_mask() and contact_mask() (#301) +- 20191212, jshermeyer: Fixed issue with vector tiling: could not load in list of sublists previously. Corrected comments for appropriate order as well. (#306) +- 20191219: rbavery: In `solaris.utils.geo.split_geom`, tile bounds that fall within `aoi_boundary` but not `src_img` are not returned. `solaris.vector.mask.instance_mask` only rasterizes geojsons where `reference_im` has values (nodata pixels won't have corresponding labels) (#315) + + +--- +_The changelog for solaris was not implemented until after version 0.1.3, therefore no previous changes are recorded here. See the [GitHub releases](https://github.com/CosmiQ/solaris/releases) for available change records._ diff --git a/docker/solaris/CONTRIBUTING.md b/docker/solaris/CONTRIBUTING.md new file mode 100644 index 00000000..4a473357 --- /dev/null +++ b/docker/solaris/CONTRIBUTING.md @@ -0,0 +1,172 @@ +_These contributing guidelines are adapted from [scikit-image](https://github.com/scikit-image/scikit-image) - Copyright 2019, the scikit-image team._ + +# How to contribute to `Solaris` + +We welcome contributions from the open source community! From creating issues to describe bugs or request new features, to PRs to improve the codebase or documentation, we encourage you to dive in, even if you're a novice. + +- To find things to work on, check out the [open issues on GitHub](https://github.com/cosmiq/solaris/issues?state=open) +- The technical detail of the development process is summed up below. + +## Contributing through issues to identify bugs or request features + +We welcome bug reports or feature requests through issues. + +1. Go to https://github.com/cosmiq/solaris/issues and search the issues to see if your bug/feature is already present in the list. If not, +2. Create a new issue, using the template appropriate for the type of issue you're creating (bug report/feature request/etc.) + - Please don't change the labels associated with the issue when you create it - maintainers will do so during triage. + - If you wish to work on resolving the issue yourself, you're welcome to do so! proceed to the next session for guidelines. + +## Contributing through pull requests (PRs) to improve the codebase + +1. If you are a first-time contributor: + - Go to [https://github.com/cosmiq/solaris](https://github.com/cosmiq/solaris) and click the "fork" button to create your own copy of the project. + - Clone the project to your local computer: + ``` + git clone https://github.com/your-username/solaris.git + ``` + - Change the directory: + ``` + cd solaris + ``` + - Add the upstream repository: + ``` + git remote add upstream https://github.com/cosmiq/solaris.git + ``` + - Now, you have remote repositories named: + - `upstream`, which refers to the CosmiQ repository + - `origin`, which refers to your personal fork + +2. Develop your contribution: + - Pull the latest changes from upstream's `dev` branch: + ``` + git checkout dev + git pull upstream dev + ``` + - Create a branch for the issue that you want to work on. (If there isn't already an issue for the bug or feature that you want to implement, create that issue first). We recommend formatting the branch name as `ISS[number]_[short description]`, e.g. `ISS42_meaning`. To do so, run: + ``` + git checkout -b ISS42_meaning + ``` + - Commit locally as you progress (``git add`` and ``git commit``) +3. To submit your contribution: + - Push your changes back to your fork on GitHub: + ``` + git push origin ISS42_meaning + ``` + - Enter your GitHub username and password if requested. + - Go to GitHub. The new branch will show up with a green Pull Request button - click it. Fill out the Pull Request form and click "Submit Pull Request". + - Monitor the CI tests and debug your code if necessary to ensure that all tests pass. + - If your PR reduces coverage after tests pass, you may be asked to add new unit tests or extend existing tests. For more, see [Unit Tests](#unit-tests) below. +4. Review process: + - Core contributors may write inline and/or general comments on your Pull Request (PR) to help you improve its implementation, documentation, and style. This is intended as a friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don't let the review discourage you from contributing: its only aim is to improve the quality of the project, not to criticize (we are, after all, very grateful for the time you're donating!). + - To update your pull request, make your changes on your local repository, commit, and push to the same branch on your fork of the repository. As soon as those changes are pushed up (to the same branch as before) the pull request will update automatically. + +### Continuing integration +`Travis-CI ` (soon to be replaced with GitHub actions), a continuous integration service, is triggered after each Pull Request or new branch push. CI runs unit tests, measures code coverage, and checks coding style (PEP8) of your branch. The Travis tests must pass before your PR can be merged. If Travis fails, you can find out why by clicking on the "failed" icon (red cross) and inspecting the build and test log. The PR will not be merged until the CI run succeeds. + +A pull request must be approved by a core team members before merging. + +### Unit tests + +Our codebase is tested by `pytest` unit tests [in the tests directory](https://github.com/CosmiQ/solaris/tree/master/tests). Those tests run during the pull request CI, and if they fail, the CI fails and the PR will not be merged until it is fixed. When adding new functionality, you are encouraged to extend existing tests or implement new tests to test the functionality you added. As a rule of thumb, any PR should increase code coverage on the repository. If substantial changes are made without accompanying tests, maintainers may ask you to add tests before a PR is merged. + +### Document changes + +Every pull request must include an update to the "Unreleased" portion of [the changelog](https://github.com/CosmiQ/solaris/blob/master/CHANGELOG.md). + +Divergence between ``upstream master`` and your feature branch +-------------------------------------------------------------- + +If GitHub indicates that the branch of your Pull Request can no longer +be merged automatically, merge the CosmiQ dev branch into yours: +``` +git fetch upstream dev +git merge upstream/dev +``` + +If any conflicts occur, they need to be fixed before continuing. See +which files are in conflict using `git status`. This will yield a message like: +``` +Unmerged paths: + (use "git add ..." to mark resolution) + + both modified: file_with_conflict.txt +``` + +Inside the conflicted file, you'll find sections like these: +``` +<<<<<<< HEAD +The way the text looks in your branch +======= +The way the text looks in the master branch +>>>>>>> dev +``` +Choose one version of the text that should be kept, and delete the +rest: +``` +The way the text looks in your branch +``` +Finally, add the fixed files, commit, and push. + +## Guidelines + +- All code should have tests (see `test coverage`_ below for more details). +- All code should be documented, to the same + [standard](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard) as NumPy and SciPy +- No changes are ever merged into `dev` without review and approval by a maintainer. Maintainers closely monitor pull requests and will usually respond within 24 hours on weekdays, if not faster. __Never merge your own pull request.__ + +### Stylistic Guidelines + +- Follow [PEP008](https://www.python.org/dev/peps/pep-0008/). Check code with pyflakes / flake8. + +### Testing + +`solaris` has an extensive test suite that ensures correct execution on your system. The test suite has to pass before a pull request can be merged, and tests should be added to cover any modifications to the code base. + +We make use of the [pytest](https://docs.pytest.org/en/latest/) +testing framework, with tests located in the various ``solaris/tests/submodule`` folders. If adding new tests, make sure to add them to the appropriate submodule folder and test script. + +### Test coverage + +Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%. At a minimum, newly added code should not reduce coverage across the library. To measure the test coverage, install [pytest-cov](https://pytest-cov.readthedocs.io/en/latest/) and then run: +``` +$ make coverage +``` + +This will print a report with one line for each file in `solaris`, +detailing the test coverage. + +### Activate Travis-CI for your fork (optional) + +Travis-CI checks all unit tests in the project to prevent breakage. + +Before sending a pull request, you may want to check that Travis-CI +successfully passes all tests. To do so, + +- Go to [Travis-CI](https://travis-ci.org/) and follow the Sign In link at + the top + +- Go to your [profile page](https://travis-ci.org/profile) and switch on + your `solaris` fork + +As soon as you push your code to your fork, it will trigger Travis-CI, +and you will receive an email notification when the process is done. + +Every time Travis is triggered, it also calls on [Codecov](https://codecov.io) to inspect the current test overage. + + +### Building docs + +Sphinx[http://www.sphinx-doc.org/en/stable/] is needed to build the documentation. +You can install it and other necessary packages with conda. +``` +conda install -c conda-forge sphinx sphinx_bootstrap_theme nbsphinx +``` + +To build docs, run ``make`` from the ``doc`` directory. ``make help`` lists +all targets. For example, to build the HTML documentation, you can run `make html`. Then, all the HTML files will be generated in `solaris/docs/_build/html`. To rebuild a full clean documentation, run: +``` +make clean +make html +``` + +If you have any questions, create an issue. diff --git a/docker/solaris/LICENSE.txt b/docker/solaris/LICENSE.txt new file mode 100644 index 00000000..2eaa2e9b --- /dev/null +++ b/docker/solaris/LICENSE.txt @@ -0,0 +1,201 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. Definitions. + +"License" shall mean the terms and conditions for use, reproduction, +and distribution as defined by Sections 1 through 9 of this document. + +"Licensor" shall mean the copyright owner or entity authorized by +the copyright owner that is granting the License. + +"Legal Entity" shall mean the union of the acting entity and all +other entities that control, are controlled by, or are under common +control with that entity. For the purposes of this definition, +"control" means (i) the power, direct or indirect, to cause the +direction or management of such entity, whether by contract or +otherwise, or (ii) ownership of fifty percent (50%) or more of the +outstanding shares, or (iii) beneficial ownership of such entity. + +"You" (or "Your") shall mean an individual or Legal Entity +exercising permissions granted by this License. + +"Source" form shall mean the preferred form for making modifications, +including but not limited to software source code, documentation +source, and configuration files. + +"Object" form shall mean any form resulting from mechanical +transformation or translation of a Source form, including but +not limited to compiled object code, generated documentation, +and conversions to other media types. + +"Work" shall mean the work of authorship, whether in Source or +Object form, made available under the License, as indicated by a +copyright notice that is included in or attached to the work +(an example is provided in the Appendix below). + +"Derivative Works" shall mean any work, whether in Source or Object +form, that is based on (or derived from) the Work and for which the +editorial revisions, annotations, elaborations, or other modifications +represent, as a whole, an original work of authorship. For the purposes +of this License, Derivative Works shall not include works that remain +separable from, or merely link (or bind by name) to the interfaces of, +the Work and Derivative Works thereof. + +"Contribution" shall mean any work of authorship, including +the original version of the Work and any modifications or additions +to that Work or Derivative Works thereof, that is intentionally +submitted to Licensor for inclusion in the Work by the copyright owner +or by an individual or Legal Entity authorized to submit on behalf of +the copyright owner. For the purposes of this definition, "submitted" +means any form of electronic, verbal, or written communication sent +to the Licensor or its representatives, including but not limited to +communication on electronic mailing lists, source code control systems, +and issue tracking systems that are managed by, or on behalf of, the +Licensor for the purpose of discussing and improving the Work, but +excluding communication that is conspicuously marked or otherwise +designated in writing by the copyright owner as "Not a Contribution." + +"Contributor" shall mean Licensor and any individual or Legal Entity +on behalf of whom a Contribution has been received by Licensor and +subsequently incorporated within the Work. + +2. Grant of Copyright License. Subject to the terms and conditions of +this License, each Contributor hereby grants to You a perpetual, +worldwide, non-exclusive, no-charge, royalty-free, irrevocable +copyright license to reproduce, prepare Derivative Works of, +publicly display, publicly perform, sublicense, and distribute the +Work and such Derivative Works in Source or Object form. + +3. Grant of Patent License. Subject to the terms and conditions of +this License, each Contributor hereby grants to You a perpetual, +worldwide, non-exclusive, no-charge, royalty-free, irrevocable +(except as stated in this section) patent license to make, have made, +use, offer to sell, sell, import, and otherwise transfer the Work, +where such license applies only to those patent claims licensable +by such Contributor that are necessarily infringed by their +Contribution(s) alone or by combination of their Contribution(s) +with the Work to which such Contribution(s) was submitted. If You +institute patent litigation against any entity (including a +cross-claim or counterclaim in a lawsuit) alleging that the Work +or a Contribution incorporated within the Work constitutes direct +or contributory patent infringement, then any patent licenses +granted to You under this License for that Work shall terminate +as of the date such litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the +Work or Derivative Works thereof in any medium, with or without +modifications, and in Source or Object form, provided that You +meet the following conditions: + +(a) You must give any other recipients of the Work or +Derivative Works a copy of this License; and + +(b) You must cause any modified files to carry prominent notices +stating that You changed the files; and + +(c) You must retain, in the Source form of any Derivative Works +that You distribute, all copyright, patent, trademark, and +attribution notices from the Source form of the Work, +excluding those notices that do not pertain to any part of +the Derivative Works; and + +(d) If the Work includes a "NOTICE" text file as part of its +distribution, then any Derivative Works that You distribute must +include a readable copy of the attribution notices contained +within such NOTICE file, excluding those notices that do not +pertain to any part of the Derivative Works, in at least one +of the following places: within a NOTICE text file distributed +as part of the Derivative Works; within the Source form or +documentation, if provided along with the Derivative Works; or, +within a display generated by the Derivative Works, if and +wherever such third-party notices normally appear. The contents +of the NOTICE file are for informational purposes only and +do not modify the License. You may add Your own attribution +notices within Derivative Works that You distribute, alongside +or as an addendum to the NOTICE text from the Work, provided +that such additional attribution notices cannot be construed +as modifying the License. + +You may add Your own copyright statement to Your modifications and +may provide additional or different license terms and conditions +for use, reproduction, or distribution of Your modifications, or +for any such Derivative Works as a whole, provided Your use, +reproduction, and distribution of the Work otherwise complies with +the conditions stated in this License. + +5. Submission of Contributions. Unless You explicitly state otherwise, +any Contribution intentionally submitted for inclusion in the Work +by You to the Licensor shall be under the terms and conditions of +this License, without any additional terms or conditions. +Notwithstanding the above, nothing herein shall supersede or modify +the terms of any separate license agreement you may have executed +with Licensor regarding such Contributions. + +6. Trademarks. This License does not grant permission to use the trade +names, trademarks, service marks, or product names of the Licensor, +except as required for reasonable and customary use in describing the +origin of the Work and reproducing the content of the NOTICE file. + +7. Disclaimer of Warranty. Unless required by applicable law or +agreed to in writing, Licensor provides the Work (and each +Contributor provides its Contributions) on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or +implied, including, without limitation, any warranties or conditions +of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A +PARTICULAR PURPOSE. You are solely responsible for determining the +appropriateness of using or redistributing the Work and assume any +risks associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, +whether in tort (including negligence), contract, or otherwise, +unless required by applicable law (such as deliberate and grossly +negligent acts) or agreed to in writing, shall any Contributor be +liable to You for damages, including any direct, indirect, special, +incidental, or consequential damages of any character arising as a +result of this License or out of the use or inability to use the +Work (including but not limited to damages for loss of goodwill, +work stoppage, computer failure or malfunction, or any and all +other commercial damages or losses), even if such Contributor +has been advised of the possibility of such damages. + +9. Accepting Warranty or Additional Liability. While redistributing +the Work or Derivative Works thereof, You may choose to offer, +and charge a fee for, acceptance of support, warranty, indemnity, +or other liability obligations and/or rights consistent with this +License. However, in accepting such obligations, You may act only +on Your own behalf and on Your sole responsibility, not on behalf +of any other Contributor, and only if You agree to indemnify, +defend, and hold each Contributor harmless for any liability +incurred by, or claims asserted against, such Contributor by reason +of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS + +APPENDIX: How to apply the Apache License to your work. + +To apply the Apache License to your work, attach the following +boilerplate notice, with the fields enclosed by brackets "[]" +replaced with your own identifying information. (Don't include +the brackets!) The text should be enclosed in the appropriate +comment syntax for the file format. We also recommend that a +file or class name and description of purpose be included on the +same "printed page" as the copyright notice for easier +identification within third-party archives. + +Copyright 2019 CosmiQ Works, an IQT Lab + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/docker/solaris/MANIFEST.in b/docker/solaris/MANIFEST.in new file mode 100644 index 00000000..cfe28cdb --- /dev/null +++ b/docker/solaris/MANIFEST.in @@ -0,0 +1,10 @@ +include *.txt +include *.md +include *json +include *.csv +include *.pkl +include *.tif +include *.txt +include *.npy + +graft solaris/data diff --git a/docker/solaris/PULL_REQUEST_TEMPLATE.md b/docker/solaris/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000..0a1c4249 --- /dev/null +++ b/docker/solaris/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,35 @@ +Thank you for submitting your PR. Please read the template below, fill it out as appropriate, and make additional changes to your code as needed. Please feel free to submit your PR even if it doesn't satisfy all of the requirements below - simply prepend [WIP] to the PR title until it is ready for review by a maintainer. If you need assistance or review from a maintainer, add the label __Status: Help Needed__ or __Status: Review Needed__ respectively. After review, a maintainer will add the label __Status: Revision Needed__ if further work is required for the PR to be merged. + +# Description + +Please include a summary of the change and which issue is resolved. Please also include relevant motivation and context. List any dependencies that are required for this change. + +Fixes # (issue) + +## Type of change + +Please delete options that are not relevant. + +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] __Breaking change__ (fix or feature that would cause existing functionality to not work as expected - these changes will not be merged until major releases!) + + + +# How Has This Been Tested? + +Please describe tests that you added to the pytest codebase (if applicable). + +# Checklist: + +- [ ] My PR has a descriptive title +- [ ] My code follows PEP8 +- [ ] I have performed a self-review of my own code +- [ ] I have commented my code, particularly in hard-to-understand areas +- [ ] I have made corresponding changes to the documentation +- [ ] My changes generate no new errors +- [ ] I have added tests that prove my fix is effective or that my feature works +- [ ] My PR passes Travis CI tests +- [ ] My PR does not reduce coverage in Codecov + +_If your PR does not fulfill all of the requirements in the checklist above, that's OK!_ Just prepend [WIP] to the PR title until they are all satisfied. If you need help, @-mention a maintainer and/or add the __Status: Help Needed__ label. diff --git a/docker/solaris/README.md b/docker/solaris/README.md new file mode 100644 index 00000000..2fcbe663 --- /dev/null +++ b/docker/solaris/README.md @@ -0,0 +1,95 @@ +

+Solaris +

+

An open source ML pipeline for overhead imagery by CosmiQ Works

+

+PyPI python version +PyPI + +build +docs +license + + +

+ +## This is a beta version of Solaris which may continue to develop. Please report any bugs through issues! + +- [Documentation](#documentation) +- [Installation Instructions](#installation-instructions) +- [Dependencies](#dependencies) +- [License](#license) +--- + +This repository provides the source code for the CosmiQ Works `solaris` project, which provides software tools for: +- Tiling large-format overhead images and vector labels +- Converting between geospatial raster and vector formats and machine learning-compatible formats +- Performing semantic and instance segmentation, object detection, and related tasks using deep learning models designed specifically for overhead image analysis +- Evaluating performance of deep learning model predictions + +## Documentation +The full documentation for `solaris` can be found at https://solaris.readthedocs.io, and includes: +- A summary of `solaris` +- Installation instructions +- API Documentation +- Tutorials for common uses + +The documentation is still being improved, so if a tutorial you need isn't there yet, check back soon or post an issue! + +## Installation Instructions + +_coming soon_: One-command installation from conda-forge. + +We recommend creating a `conda` environment with the dependencies defined in [environment.yml](./environment.yml) before installing `solaris`. After cloning the repository: +``` +cd solaris +``` + +If you're installing on a system with GPU access: +``` +conda env create -n solaris -f environment-gpu.yml +``` +Otherwise: +``` +conda env create -n solaris -f environment.yml +``` + +Finally, regardless of your installation environment: +``` +conda activate solaris +pip install . +``` + +#### pip + + +The package also exists on[ PyPI](https://pypi.org), but note that some of the dependencies, specifically [rtree](https://github.com/Toblerity/rtree) and [gdal](https://www.gdal.org), are challenging to install without anaconda. We therefore recommend installing at least those dependencies using `conda` before installing from PyPI. + +``` +conda install -c conda-forge rtree gdal=2.4.1 +pip install solaris +``` + +If you don't want to use `conda`, you can [install libspatialindex](https://libspatialindex.org), then `pip install rtree`. Installing GDAL without conda can be very difficult and approaches vary dramatically depending upon the build environment and version, but [the rasterio install documentation](https://rasterio.readthedocs.io/en/stable/installation.html) provides OS-specific install instructions. Simply follow their install instructions, replacing `pip install rasterio` with `pip install solaris` at the end. + + + + + +## Dependencies +All dependencies can be found in the requirements file [./requirements.txt](requirements.txt) or +[environment.yml](./environment.yml) + +## License +See [LICENSE](./LICENSE.txt). + diff --git a/docker/solaris/docker/cpu/Dockerfile b/docker/solaris/docker/cpu/Dockerfile new file mode 100644 index 00000000..03a91d54 --- /dev/null +++ b/docker/solaris/docker/cpu/Dockerfile @@ -0,0 +1,79 @@ +FROM nvidia/cuda:9.2-devel-ubuntu16.04 +LABEL maintainer="nweir " + +ARG solaris_branch='master' + + +# prep apt-get and cudnn +RUN apt-get update && apt-get install -y --no-install-recommends \ + apt-utils && \ + rm -rf /var/lib/apt/lists/* + +# install requirements +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + bc \ + bzip2 \ + ca-certificates \ + curl \ + git \ + libgdal-dev \ + libssl-dev \ + libffi-dev \ + libncurses-dev \ + libgl1 \ + jq \ + nfs-common \ + parallel \ + python-dev \ + python-pip \ + python-wheel \ + python-setuptools \ + unzip \ + vim \ + wget \ + build-essential \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +SHELL ["/bin/bash", "-c"] +ENV PATH /opt/conda/bin:$PATH + +# install anaconda +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh -O ~/miniconda.sh && \ + /bin/bash ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh && \ + /opt/conda/bin/conda clean -tipsy && \ + ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate base" >> ~/.bashrc + +# prepend pytorch and conda-forge before default channel +RUN conda update conda && \ + conda config --prepend channels conda-forge && \ + conda config --prepend channels pytorch + +# get dev version of solaris and create conda environment based on its env file +WORKDIR /tmp/ +RUN git clone https://github.com/cosmiq/solaris.git && \ + cd solaris && \ + git checkout ${solaris_branch} && \ + conda env create -f environment.yml +ENV PATH /opt/conda/envs/solaris/bin:$PATH + +RUN cd solaris && pip install . + +# install various conda dependencies into the space_base environment +RUN conda install -n solaris \ + jupyter \ + jupyterlab \ + ipykernel + +# add a jupyter kernel for the conda environment in case it's wanted +RUN source activate solaris && python -m ipykernel.kernelspec \ + --name solaris --display-name solaris + +# open ports for jupyterlab and tensorboard +EXPOSE 8888 6006 + +RUN ["/bin/bash"] diff --git a/docker/solaris/docker/gpu/Dockerfile b/docker/solaris/docker/gpu/Dockerfile new file mode 100644 index 00000000..ed3dc5b7 --- /dev/null +++ b/docker/solaris/docker/gpu/Dockerfile @@ -0,0 +1,84 @@ +FROM nvidia/cuda:9.2-devel-ubuntu16.04 +LABEL maintainer="nweir " + +ENV CUDNN_VERSION 7.3.0.29 +LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}" +ARG solaris_branch='master' + + +# prep apt-get and cudnn +RUN apt-get update && apt-get install -y --no-install-recommends \ + apt-utils \ + libcudnn7=$CUDNN_VERSION-1+cuda9.0 \ + libcudnn7-dev=$CUDNN_VERSION-1+cuda9.0 && \ + apt-mark hold libcudnn7 && \ + rm -rf /var/lib/apt/lists/* + +# install requirements +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + bc \ + bzip2 \ + ca-certificates \ + curl \ + git \ + libgdal-dev \ + libssl-dev \ + libffi-dev \ + libncurses-dev \ + libgl1 \ + jq \ + nfs-common \ + parallel \ + python-dev \ + python-pip \ + python-wheel \ + python-setuptools \ + unzip \ + vim \ + wget \ + build-essential \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +SHELL ["/bin/bash", "-c"] +ENV PATH /opt/conda/bin:$PATH + +# install anaconda +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh -O ~/miniconda.sh && \ + /bin/bash ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh && \ + /opt/conda/bin/conda clean -tipsy && \ + ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate base" >> ~/.bashrc + +# prepend pytorch and conda-forge before default channel +RUN conda update conda && \ + conda config --prepend channels conda-forge && \ + conda config --prepend channels pytorch + +# get dev version of solaris and create conda environment based on its env file +WORKDIR /tmp/ +RUN git clone https://github.com/cosmiq/solaris.git && \ + cd solaris && \ + git checkout ${solaris_branch} && \ + conda env create -f environment-gpu.yml +ENV PATH /opt/conda/envs/solaris/bin:$PATH + +RUN cd solaris && pip install . + +# install various conda dependencies into the space_base environment +RUN conda install -n solaris \ + jupyter \ + jupyterlab \ + ipykernel + +# add a jupyter kernel for the conda environment in case it's wanted +RUN source activate solaris && python -m ipykernel.kernelspec \ + --name solaris --display-name solaris + +# open ports for jupyterlab and tensorboard +EXPOSE 8888 6006 + +RUN ["/bin/bash"] diff --git a/docker/solaris/docs/Makefile b/docker/solaris/docs/Makefile new file mode 100644 index 00000000..298ea9e2 --- /dev/null +++ b/docker/solaris/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docker/solaris/docs/_static/custom_styles.css b/docker/solaris/docs/_static/custom_styles.css new file mode 100644 index 00000000..ff858af8 --- /dev/null +++ b/docker/solaris/docs/_static/custom_styles.css @@ -0,0 +1,15 @@ +p { + font-size: 1.1em; +} + +h1 { + font-size: 2.5em; +} + +h2 { + font-size: 2.2em; +} + +h3 { + font-size: 1.75em; +} diff --git a/docker/solaris/docs/_static/solaris_logo.png b/docker/solaris/docs/_static/solaris_logo.png new file mode 100644 index 00000000..abf94841 Binary files /dev/null and b/docker/solaris/docs/_static/solaris_logo.png differ diff --git a/docker/solaris/docs/_static/solaris_logo_tiny_black.png b/docker/solaris/docs/_static/solaris_logo_tiny_black.png new file mode 100644 index 00000000..ba8fc46a Binary files /dev/null and b/docker/solaris/docs/_static/solaris_logo_tiny_black.png differ diff --git a/docker/solaris/docs/_templates/autosummary/base.rst b/docker/solaris/docs/_templates/autosummary/base.rst new file mode 100644 index 00000000..b7556ebf --- /dev/null +++ b/docker/solaris/docs/_templates/autosummary/base.rst @@ -0,0 +1,5 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} diff --git a/docker/solaris/docs/_templates/autosummary/class.rst b/docker/solaris/docs/_templates/autosummary/class.rst new file mode 100644 index 00000000..8861b79a --- /dev/null +++ b/docker/solaris/docs/_templates/autosummary/class.rst @@ -0,0 +1,29 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + + {% block methods %} + .. automethod:: __init__ + + {% if methods %} + .. rubric:: Methods + + .. autosummary:: + {% for item in methods %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block attributes %} + {% if attributes %} + .. rubric:: Attributes + + .. autosummary:: + {% for item in attributes %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} diff --git a/docker/solaris/docs/_templates/autosummary/module.rst b/docker/solaris/docs/_templates/autosummary/module.rst new file mode 100644 index 00000000..6ec89e05 --- /dev/null +++ b/docker/solaris/docs/_templates/autosummary/module.rst @@ -0,0 +1,36 @@ +{{ fullname | escape | underline}} + +.. automodule:: {{ fullname }} + + {% block functions %} + {% if functions %} + .. rubric:: Functions + + .. autosummary:: + {% for item in functions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block classes %} + {% if classes %} + .. rubric:: Classes + + .. autosummary:: + {% for item in classes %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block exceptions %} + {% if exceptions %} + .. rubric:: Exceptions + + .. autosummary:: + {% for item in exceptions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} diff --git a/docker/solaris/docs/_templates/base/base.rst b/docker/solaris/docs/_templates/base/base.rst new file mode 100644 index 00000000..45c8dd7f --- /dev/null +++ b/docker/solaris/docs/_templates/base/base.rst @@ -0,0 +1,7 @@ +.. {{ obj.type }}:: {{ obj.name }} + + {% if summary %} + + {{ obj.summary }} + + {% endif %} diff --git a/docker/solaris/docs/_templates/custom_sidebar.html b/docker/solaris/docs/_templates/custom_sidebar.html new file mode 100644 index 00000000..8ff3907c --- /dev/null +++ b/docker/solaris/docs/_templates/custom_sidebar.html @@ -0,0 +1 @@ +
    {{ toctree(collapse=False,includehidden=theme_globaltoc_includehidden|tobool) }}
diff --git a/docker/solaris/docs/_templates/dotnet/base_detail.rst b/docker/solaris/docs/_templates/dotnet/base_detail.rst new file mode 100644 index 00000000..a197d68b --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/base_detail.rst @@ -0,0 +1,88 @@ +{% block title %} + +{{ obj.short_name }} {{ obj.type.title()}} +{{ "=" * (obj.short_name|length + obj.type|length + 1) }} + +{% endblock %} + +{% block summary %} + {% if obj.summary %} + +{{ obj.summary }} + + {% endif %} +{% endblock %} + +{% if obj.namespace %} +Namespace + :dn:ns:`{{ obj.namespace }}` + +{% endif %} +{% if obj.assemblies %} +Assemblies + {% for assembly in obj.assemblies %} + * {{ assembly }} + {% endfor %} +{% endif %} + +---- + +.. contents:: + :local: + +{% block inheritance %} + +{% if obj.inheritance %} + +Inheritance Hierarchy +--------------------- + +{% for item in obj.inheritance %} +* :dn:{{ item.ref_directive }}:`{{ item.ref_name }}` + {% endfor %} +* :dn:{{ obj.ref_directive }}:`{{ obj.ref_name }}` +{% endif %} + +{% endblock %} + +{% block syntax %} + +{% if obj.example %} + +Syntax +------ + +.. code-block:: csharp + + {{ obj.example|indent(4) }} + +{% endif %} + +{% endblock %} + + +{% block content %} + +.. dn:{{ obj.ref_type }}:: {{ obj.definition }} + :hidden: + +.. dn:{{ obj.ref_type }}:: {{ obj.name }} + +{% for item_type in obj.item_map.keys() %} +{% if item_type in obj.item_map %} + +{{ item_type.title() }} +{{ "-" * item_type|length }} + +.. dn:{{ obj.ref_type }}:: {{ obj.name }} + :noindex: + :hidden: + + {% for obj_item in obj.item_map.get(item_type, []) %} + {{ obj_item.render()|indent(4) }} + {% endfor %} + +{% endif %} +{% endfor %} + +{% endblock %} diff --git a/docker/solaris/docs/_templates/dotnet/base_embed.rst b/docker/solaris/docs/_templates/dotnet/base_embed.rst new file mode 100644 index 00000000..62ca04a0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/base_embed.rst @@ -0,0 +1,29 @@ +.. dn:{{ obj.ref_type }}:: {{ obj.name }} + + {% if obj.summary %} + {{ obj.summary|indent(4) }} + + {% endif %} + + {% for param in obj.parameters %} + + {% if param.desc %} + :param {{ param.name }}: {{ param.desc|indent(8) }} + {% endif %} + {% if param.type %} + :type {{ param.name }}: {{ param.type|indent(8) }} + {% endif %} + {% endfor %} + + {% if obj.returns.type %} + :rtype: {{ obj.returns.type|indent(8) }} + {% endif %} + {% if obj.returns.description %} + :return: {{ obj.returns.description|indent(8) }} + {% endif %} + + {% if obj.example %} + .. code-block:: csharp + + {{ obj.example|indent(8) }} + {% endif %} diff --git a/docker/solaris/docs/_templates/dotnet/base_list.rst b/docker/solaris/docs/_templates/dotnet/base_list.rst new file mode 100644 index 00000000..f1635272 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/base_list.rst @@ -0,0 +1,66 @@ +{% block title %} + +{{ obj.name }} {{ obj.type.title() }} +{{ "=" * (obj.name|length + obj.type|length + 1) }} + +{% endblock %} + +{% block toc %} + +{% if obj.children %} + +.. toctree:: + :hidden: + :maxdepth: 2 + + {% for item in obj.children|sort %} + {% if item.type != 'namespace' %} + {{ item.include_path }} + {% endif %} + {% endfor %} + + +{% endif %} + +{% if obj.references %} + +.. toctree:: + :hidden: + :maxdepth: 2 + + {% for item in obj.references|sort %} + {% if item.type != 'namespace' %} + {{ item.include_path }} + {% endif %} + {% endfor %} + +{% endif %} + +{% endblock %} + + +{% block content %} + +{% macro display_type(item_type) %} + + .. rubric:: {{ item_type.title() }} + +{% for obj_item in obj.item_map.get(item_type, []) %} +{% macro render() %}{{ obj_item.summary }}{% endmacro %} + + {{ obj_item.type }} :dn:{{ obj_item.ref_directive }}:`{{ obj_item.ref_short_name }}` + .. object: type={{ obj_item.type }} name={{ obj_item.ref_name }} + + {{ render()|indent(8) }} + +{% endfor %} +{% endmacro %} + +.. dn:{{ obj.ref_type }}:: {{ obj.name }} + +{% for item_type in obj.item_map.keys() %} +{{ display_type(item_type) }} +{% endfor %} + + +{% endblock %} diff --git a/docker/solaris/docs/_templates/dotnet/class.rst b/docker/solaris/docs/_templates/dotnet/class.rst new file mode 100644 index 00000000..556f99b0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/class.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_detail.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/constructor.rst b/docker/solaris/docs/_templates/dotnet/constructor.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/constructor.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/delegate.rst b/docker/solaris/docs/_templates/dotnet/delegate.rst new file mode 100644 index 00000000..556f99b0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/delegate.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_detail.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/enum.rst b/docker/solaris/docs/_templates/dotnet/enum.rst new file mode 100644 index 00000000..556f99b0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/enum.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_detail.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/event.rst b/docker/solaris/docs/_templates/dotnet/event.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/event.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/field.rst b/docker/solaris/docs/_templates/dotnet/field.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/field.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/interface.rst b/docker/solaris/docs/_templates/dotnet/interface.rst new file mode 100644 index 00000000..556f99b0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/interface.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_detail.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/method.rst b/docker/solaris/docs/_templates/dotnet/method.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/method.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/namespace.rst b/docker/solaris/docs/_templates/dotnet/namespace.rst new file mode 100644 index 00000000..fa9ffc27 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/namespace.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_list.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/operator.rst b/docker/solaris/docs/_templates/dotnet/operator.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/operator.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/property.rst b/docker/solaris/docs/_templates/dotnet/property.rst new file mode 100644 index 00000000..c9188602 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/property.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_embed.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/dotnet/struct.rst b/docker/solaris/docs/_templates/dotnet/struct.rst new file mode 100644 index 00000000..556f99b0 --- /dev/null +++ b/docker/solaris/docs/_templates/dotnet/struct.rst @@ -0,0 +1 @@ +{% extends "dotnet/base_detail.rst" %} \ No newline at end of file diff --git a/docker/solaris/docs/_templates/go/base_member.rst b/docker/solaris/docs/_templates/go/base_member.rst new file mode 100644 index 00000000..ad6e5b68 --- /dev/null +++ b/docker/solaris/docs/_templates/go/base_member.rst @@ -0,0 +1,26 @@ +.. go:{{ obj.ref_type }}:: {{ obj.name }} +{% if obj.type == 'func' %} + {% set argjoin = joiner(', ') %} + ({% for param in obj.parameters %} + {{ argjoin() }}{{ param.name }} {{ param.type }} + {% endfor %}) +{% endif %} + + {% macro render() %}{{ obj.docstring }}{% endmacro %} + {{ render()|indent(4) }} + + {# Don't define parameter description here, that can be done in the block + above #} + {% for param in obj.parameters %} + :type {{ param.name }}: {{ param.type }} + {% endfor %} + {% if obj.returns %} + :rtype: {{ obj.returns.type }} + {% endif %} + + {% if obj.children %} + {% for child in obj.children|sort %} + {% macro render_child() %}{{ child.render() }}{% endmacro %} + {{ render_child()|indent(4) }} + {% endfor %} + {% endif %} diff --git a/docker/solaris/docs/_templates/go/const.rst b/docker/solaris/docs/_templates/go/const.rst new file mode 100644 index 00000000..04663550 --- /dev/null +++ b/docker/solaris/docs/_templates/go/const.rst @@ -0,0 +1 @@ +{% extends "go/base_member.rst" %} diff --git a/docker/solaris/docs/_templates/go/func.rst b/docker/solaris/docs/_templates/go/func.rst new file mode 100644 index 00000000..04663550 --- /dev/null +++ b/docker/solaris/docs/_templates/go/func.rst @@ -0,0 +1 @@ +{% extends "go/base_member.rst" %} diff --git a/docker/solaris/docs/_templates/go/method.rst b/docker/solaris/docs/_templates/go/method.rst new file mode 100644 index 00000000..04663550 --- /dev/null +++ b/docker/solaris/docs/_templates/go/method.rst @@ -0,0 +1 @@ +{% extends "go/base_member.rst" %} diff --git a/docker/solaris/docs/_templates/go/package.rst b/docker/solaris/docs/_templates/go/package.rst new file mode 100644 index 00000000..85cf0579 --- /dev/null +++ b/docker/solaris/docs/_templates/go/package.rst @@ -0,0 +1,32 @@ +.. go:package:: {{ obj.name }} + +{{ obj.name }} +{{ "=" * obj.name|length }} + +{% block toc %} + {% if obj.children %} + +{# TODO Make this work +.. toctree:: + :maxdepth: 4 + + {% for item in obj.children|sort %} + /autoapi/{{ item.id.split('.')|join('/') }}/index + {% endfor %} +#} + + {% endif %} +{% endblock %} + +{% if obj.docstring %} +{{ obj.docstring }} +{% endif %} + +{% block content %} + {% for obj_item in obj.children|sort %} + +{% macro render() %}{{ obj_item.render() }}{% endmacro %} +{{ render()|indent(0) }} + + {% endfor %} +{% endblock %} diff --git a/docker/solaris/docs/_templates/go/type.rst b/docker/solaris/docs/_templates/go/type.rst new file mode 100644 index 00000000..04663550 --- /dev/null +++ b/docker/solaris/docs/_templates/go/type.rst @@ -0,0 +1 @@ +{% extends "go/base_member.rst" %} diff --git a/docker/solaris/docs/_templates/go/var.rst b/docker/solaris/docs/_templates/go/var.rst new file mode 100644 index 00000000..04663550 --- /dev/null +++ b/docker/solaris/docs/_templates/go/var.rst @@ -0,0 +1 @@ +{% extends "go/base_member.rst" %} diff --git a/docker/solaris/docs/_templates/index.rst b/docker/solaris/docs/_templates/index.rst new file mode 100644 index 00000000..f8c78f0a --- /dev/null +++ b/docker/solaris/docs/_templates/index.rst @@ -0,0 +1,15 @@ +API Reference +============= + +This page contains auto-generated API reference documentation [#f1]_. + +.. toctree:: + :titlesonly: + + {% for page in pages %} + {% if page.top_level_object and page.display %} + {{ page.include_path }} + {% endif %} + {% endfor %} + +.. [#f1] Created with `sphinx-autoapi `_ diff --git a/docker/solaris/docs/_templates/javascript/class.rst b/docker/solaris/docs/_templates/javascript/class.rst new file mode 100644 index 00000000..7a44aab0 --- /dev/null +++ b/docker/solaris/docs/_templates/javascript/class.rst @@ -0,0 +1,20 @@ +.. js:class:: {{ obj.name }}{% if obj.args %}({{ obj.args|join(',') }}){% endif %} + + {% if obj.docstring %} + + .. rubric:: Summary + + {{ obj.docstring|indent(3) }} + + {% endif %} + + {% if obj.methods %} + + {% for method in obj.methods %} + + {% macro render() %}{{ method.render() }}{% endmacro %} + {{ render()|indent(3) }} + + {%- endfor %} + + {% endif %} diff --git a/docker/solaris/docs/_templates/javascript/function.rst b/docker/solaris/docs/_templates/javascript/function.rst new file mode 100644 index 00000000..88835ab2 --- /dev/null +++ b/docker/solaris/docs/_templates/javascript/function.rst @@ -0,0 +1,14 @@ +{# Identention in this file is important #} + +{% if is_method %} +{# Slice self off #} +.. method:: {{ obj.name.split('.')[-1] }}({{ args[1:]|join(',') }}) +{% else %} +.. function:: {{ obj.name.split('.')[-1] }}({{ args|join(',') }}) +{% endif %} + + {% if obj.docstring %} + {{ obj.docstring|indent(3) }} + {% endif %} + + diff --git a/docker/solaris/docs/_templates/javascript/member.rst b/docker/solaris/docs/_templates/javascript/member.rst new file mode 100644 index 00000000..8e2d86a6 --- /dev/null +++ b/docker/solaris/docs/_templates/javascript/member.rst @@ -0,0 +1,7 @@ +{# Identention in this file is important #} + +.. {{ obj.type }}:: {{ obj.name }} + + {{ obj.docstring|indent(3) }} + + diff --git a/docker/solaris/docs/_templates/javascript/module.rst b/docker/solaris/docs/_templates/javascript/module.rst new file mode 100644 index 00000000..7f344ab4 --- /dev/null +++ b/docker/solaris/docs/_templates/javascript/module.rst @@ -0,0 +1,52 @@ +{{ obj.name }} +{{ "-" * obj.name|length }} + +{% block toc %} + +{% if obj.children %} + +.. toctree:: + :maxdepth: 4 + + {% for item in obj.children|sort %} + /autoapi/{{ item.pathname }}/index + {%- endfor %} + +{% endif %} + +{% endblock %} + +{% if obj.docstring %} + +.. rubric:: Summary + +{{ obj.docstring }} + +{% endif %} + +.. js:module:: {{ obj.name }} + + + +{% block content %} + +{%- macro display_type(item_type) %} + +{{ item_type.title() }} +{{ "*" * item_type|length }} + +{%- for obj_item in obj.item_map.get(item_type, []) %} +{% macro render() %}{{ obj_item.render() }}{% endmacro %} + + {{ render()|indent(4) }} + +{%- endfor %} +{%- endmacro %} + +{%- for item_type in obj.item_map.keys() %} +{% if item_type.lower() != 'module' %} +{{ display_type(item_type) }} +{% endif %} +{%- endfor %} + +{% endblock %} diff --git a/docker/solaris/docs/_templates/python/attribute.rst b/docker/solaris/docs/_templates/python/attribute.rst new file mode 100644 index 00000000..ebaba555 --- /dev/null +++ b/docker/solaris/docs/_templates/python/attribute.rst @@ -0,0 +1 @@ +{% extends "python/data.rst" %} diff --git a/docker/solaris/docs/_templates/python/class.rst b/docker/solaris/docs/_templates/python/class.rst new file mode 100644 index 00000000..1c082dd3 --- /dev/null +++ b/docker/solaris/docs/_templates/python/class.rst @@ -0,0 +1,25 @@ +{% if obj.display %} +.. py:{{ obj.type }}:: {{ obj.short_name }}{% if obj.args %}({{ obj.args }}){% endif %} + + + {% if obj.bases %} + Bases: {% for base in obj.bases %}:class:`{{ base }}`{% if not loop.last %}, {% endif %}{% endfor %} + + + {% endif %} + {% if obj.docstring %} + {{ obj.docstring|prepare_docstring|indent(3) }} + {% endif %} + {% set visible_classes = obj.classes|selectattr("display")|list %} + {% for klass in visible_classes %} + {{ klass.rendered|indent(3) }} + {% endfor %} + {% set visible_attributes = obj.attributes|selectattr("display")|list %} + {% for attribute in visible_attributes %} + {{ attribute.rendered|indent(3) }} + {% endfor %} + {% set visible_methods = obj.methods|selectattr("display")|list %} + {% for method in visible_methods %} + {{ method.rendered|indent(3) }} + {% endfor %} +{% endif %} diff --git a/docker/solaris/docs/_templates/python/data.rst b/docker/solaris/docs/_templates/python/data.rst new file mode 100644 index 00000000..2ce3e1ef --- /dev/null +++ b/docker/solaris/docs/_templates/python/data.rst @@ -0,0 +1,7 @@ +{% if obj.display %} +.. {{ obj.type }}:: {{ obj.name }} + {%+ if obj.value is not none or obj.annotation is not none %}:annotation:{% if obj.annotation %} :{{ obj.annotation }}{% endif %}{% if obj.value is not none %} = {{ obj.value }}{% endif %}{% endif %} + + + {{ obj.docstring|prepare_docstring|indent(3) }} +{% endif %} diff --git a/docker/solaris/docs/_templates/python/exception.rst b/docker/solaris/docs/_templates/python/exception.rst new file mode 100644 index 00000000..92f3d38f --- /dev/null +++ b/docker/solaris/docs/_templates/python/exception.rst @@ -0,0 +1 @@ +{% extends "python/class.rst" %} diff --git a/docker/solaris/docs/_templates/python/function.rst b/docker/solaris/docs/_templates/python/function.rst new file mode 100644 index 00000000..af1d122d --- /dev/null +++ b/docker/solaris/docs/_templates/python/function.rst @@ -0,0 +1,8 @@ +{% if obj.display %} +.. function:: {{ obj.short_name }}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %} + + {% if obj.docstring %} + + {{ obj.docstring|prepare_docstring|indent(3) }} + {% endif %} +{% endif %} diff --git a/docker/solaris/docs/_templates/python/method.rst b/docker/solaris/docs/_templates/python/method.rst new file mode 100644 index 00000000..be336195 --- /dev/null +++ b/docker/solaris/docs/_templates/python/method.rst @@ -0,0 +1,9 @@ +{%- if obj.display %} + +.. {{ obj.method_type }}:: {{ obj.short_name }}({{ obj.args }}) + + {% if obj.docstring %} + {{ obj.docstring|prepare_docstring|indent(3) }} + {% endif %} + +{% endif %} diff --git a/docker/solaris/docs/_templates/python/module.rst b/docker/solaris/docs/_templates/python/module.rst new file mode 100644 index 00000000..55df3f71 --- /dev/null +++ b/docker/solaris/docs/_templates/python/module.rst @@ -0,0 +1,94 @@ +{% if not obj.display %} +:orphan: + +{% endif %} +:mod:`{{ obj.name }}` +======={{ "=" * obj.name|length }} + +.. py:module:: {{ obj.name }} + +{% if obj.docstring %} +.. autoapi-nested-parse:: + + {{ obj.docstring|prepare_docstring|indent(3) }} + +{% endif %} + +{% block subpackages %} +{% set visible_subpackages = obj.subpackages|selectattr("display")|list %} +{% if visible_subpackages %} +Subpackages +----------- +.. toctree:: + :titlesonly: + :maxdepth: 3 + +{% for subpackage in visible_subpackages %} + {{ subpackage.short_name }}/index.rst +{% endfor %} + + +{% endif %} +{% endblock %} +{% block submodules %} +{% set visible_submodules = obj.submodules|selectattr("display")|list %} +{% if visible_submodules %} +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + +{% for submodule in visible_submodules %} + {{ submodule.short_name }}/index.rst +{% endfor %} + + +{% endif %} +{% endblock %} +{% block content %} +{% set visible_children = obj.children|selectattr("display")|list %} +{% if visible_children %} +{{ obj.type|title }} Contents +{{ "-" * obj.type|length }}--------- + +{% set visible_classes = visible_children|selectattr("type", "equalto", "class")|list %} +{% set visible_functions = visible_children|selectattr("type", "equalto", "function")|list %} +{% if include_summaries and (visible_classes or visible_functions) %} +{% block classes %} +{% if visible_classes %} +Classes +~~~~~~~ + +.. autoapisummary:: + +{% for klass in visible_classes %} + {{ klass.id }} +{% endfor %} + + +{% endif %} +{% endblock %} + +{% block functions %} +{% if visible_functions %} +Functions +~~~~~~~~~ + +.. autoapisummary:: + +{% for function in visible_functions %} + {{ function.id }} +{% endfor %} + + +{% endif %} +{% endblock %} +{% endif %} +{% for obj_item in visible_children %} +{% if obj.all is none or obj_item.short_name in obj.all %} +{{ obj_item.rendered|indent(0) }} +{% endif %} +{% endfor %} +{% endif %} +{% endblock %} diff --git a/docker/solaris/docs/_templates/python/package.rst b/docker/solaris/docs/_templates/python/package.rst new file mode 100644 index 00000000..fb9a6496 --- /dev/null +++ b/docker/solaris/docs/_templates/python/package.rst @@ -0,0 +1 @@ +{% extends "python/module.rst" %} diff --git a/docker/solaris/docs/api/data.rst b/docker/solaris/docs/api/data.rst new file mode 100644 index 00000000..0b1f6b88 --- /dev/null +++ b/docker/solaris/docs/api/data.rst @@ -0,0 +1,12 @@ +.. title:: solaris.data API reference + +``solaris.data`` API reference +=============================== + +.. contents:: + +``solaris.data.coco`` COCO label format management +-------------------------------------------------- + +.. automodule:: solaris.data.coco + :members: diff --git a/docker/solaris/docs/api/eval.rst b/docker/solaris/docs/api/eval.rst new file mode 100644 index 00000000..b86edda3 --- /dev/null +++ b/docker/solaris/docs/api/eval.rst @@ -0,0 +1,32 @@ +.. title:: solaris.eval API reference + +``solaris.eval`` API reference +============================== + +.. contents:: + + +``solaris.eval.base`` Base evaluator class +---------------------------------------------- + +.. automodule:: solaris.eval.base + :members: + +``solaris.eval.pixel`` Pixel-wise scoring functions +--------------------------------------------------- + +.. automodule:: solaris.eval.pixel + :members: + + +``solaris.eval.iou`` IoU scoring functions +------------------------------------------ + +.. automodule:: solaris.eval.iou + :members: + +``solaris.eval.challenges`` SpaceNet Challenge scoring functionality +-------------------------------------------------------------------- + +.. automodule:: solaris.eval.challenges + :members: diff --git a/docker/solaris/docs/api/index.rst b/docker/solaris/docs/api/index.rst new file mode 100644 index 00000000..f0267a84 --- /dev/null +++ b/docker/solaris/docs/api/index.rst @@ -0,0 +1,37 @@ +.. _api_index: + +.. title:: API reference contents + +################### +Solaris API summary +################### + +Complete submodule documentation +================================ +* `solaris.tile `_: Tiling functionality for imagery and vector labels +* `solaris.raster `_: Raster (imagery) coordinate management and formatting +* `solaris.vector `_: Vector (label) management and format interconversion +* `solaris.preproc `_: Preprocessing workflows for imagery and vector labels +* `solaris.nets `_: Deep learning model ingestion, creation, training, and inference +* `solaris.eval `_: Deep learning model performance evaluation +* `solaris.utils `_: Utility functions for the above toolsets +* `solaris.data `_: Data management and format interconversion + +Submodule summaries +=================== + +.. toctree:: + :maxdepth: 3 + + tile + raster + vector + preproc + nets + eval + utils + data + +CLI commands +============ +See `Tutorials <../tutorials/index.html>`_. diff --git a/docker/solaris/docs/api/nets.rst b/docker/solaris/docs/api/nets.rst new file mode 100644 index 00000000..9c1052e5 --- /dev/null +++ b/docker/solaris/docs/api/nets.rst @@ -0,0 +1,76 @@ +.. title:: solaris.nets API reference + +``solaris.nets`` API reference +============================== + +.. contents:: + + +``solaris.nets.callbacks`` Keras-like callbacks +----------------------------------------------- + +.. automodule:: solaris.nets.callbacks + :members: + +.. automodule:: solaris.nets.torch_callbacks + :members: + +``solaris.nets.losses`` Loss functions for Geo CV model training +---------------------------------------------------------------- + +.. automodule:: solaris.nets.losses + :members: + +.. automodule:: solaris.nets._keras_losses + :members: + +.. automodule:: solaris.nets._torch_losses + :members: + +``solaris.nets.transform`` Augmentation pipeline prep for Geo imagery +--------------------------------------------------------------------- + +.. automodule:: solaris.nets.transform + :members: + +``solaris.nets.optimizers`` Model training optimizer management +--------------------------------------------------------------- + +.. automodule:: solaris.nets.optimizers + :members: + +``solaris.nets.model_io`` Model I/O and model weight management +--------------------------------------------------------------- + +.. automodule:: solaris.nets.model_io + :members: + +``solaris.nets.datagen`` Data generators for model training +----------------------------------------------------------- + +.. automodule:: solaris.nets.datagen + :members: + +``solaris.nets.metrics`` Metrics for evaluating model performance +----------------------------------------------------------------- + +.. automodule:: solaris.nets.metrics + :members: + +``solaris.nets.zoo`` Model definitions for geospatial image analysis +-------------------------------------------------------------------- + +.. automodule:: solaris.nets.zoo + :members: + +``solaris.nets.train`` Model training functionality +--------------------------------------------------- + +.. automodule:: solaris.nets.train + :members: + +``solaris.nets.infer`` Prediction with Geo CV models +---------------------------------------------------- + +.. automodule:: solaris.nets.infer + :members: diff --git a/docker/solaris/docs/api/preproc.rst b/docker/solaris/docs/api/preproc.rst new file mode 100644 index 00000000..55a58acf --- /dev/null +++ b/docker/solaris/docs/api/preproc.rst @@ -0,0 +1,31 @@ +.. title:: solaris.preproc API reference + +``solaris.preproc`` API reference +================================= + +.. contents:: + + +``solaris.preproc.pipesegment`` Preprocessing base class and control structures +------------------------------------------------------------------------------- + +.. automodule:: solaris.preproc.pipesegment + :members: + +``solaris.preproc.image`` Preprocessing of geospatial imagery +------------------------------------------------------------- + +.. automodule:: solaris.preproc.image + :members: + +``solaris.preproc.sar`` Preprocessing of SAR imagery +---------------------------------------------------- + +.. automodule:: solaris.preproc.sar + :members: + +``solaris.preproc.label`` Preprocessing of vector labels +---------------------------------------------------------- + +.. automodule:: solaris.preproc.label + :members: diff --git a/docker/solaris/docs/api/raster.rst b/docker/solaris/docs/api/raster.rst new file mode 100644 index 00000000..c51e2ba6 --- /dev/null +++ b/docker/solaris/docs/api/raster.rst @@ -0,0 +1,13 @@ +.. title:: solaris.raster API reference + +``solaris.raster`` API reference +================================ + +.. contents:: + + +``solaris.raster.image`` Image pre- and post-processing +------------------------------------------------------- + +.. automodule:: solaris.raster.image + :members: diff --git a/docker/solaris/docs/api/tile.rst b/docker/solaris/docs/api/tile.rst new file mode 100644 index 00000000..219679ad --- /dev/null +++ b/docker/solaris/docs/api/tile.rst @@ -0,0 +1,19 @@ +.. title:: solaris.tile API reference + +``solaris.tile`` API reference +============================== + +.. contents:: + + +``solaris.tile.raster_tile`` Raster image tiling functionality +-------------------------------------------------------------- + +.. automodule:: solaris.tile.raster_tile + :members: + +``solaris.tile.vector_tile`` Vector tiling functionality +--------------------------------------------------------- + +.. automodule:: solaris.tile.vector_tile + :members: diff --git a/docker/solaris/docs/api/utils.rst b/docker/solaris/docs/api/utils.rst new file mode 100644 index 00000000..c73ac8df --- /dev/null +++ b/docker/solaris/docs/api/utils.rst @@ -0,0 +1,48 @@ +.. title:: solaris.utils API reference + +``solaris.utils`` API reference +=============================== + +.. contents:: + +``solaris.utils.core`` Core utilities +------------------------------------- + +.. automodule:: solaris.utils.core + :members: + +``solaris.utils.config`` Configuration file utilities +----------------------------------------------------- + +.. automodule:: solaris.utils.config + :members: + +``solaris.utils.io`` Imagery and vector I/O utilities +----------------------------------------------------- + +.. automodule:: solaris.utils.io + :members: + +``solaris.utils.geo`` Geographic coordinate system management utilities +----------------------------------------------------------------------- + +.. automodule:: solaris.utils.geo + :members: + +``solaris.utils.tile`` Tiling utilities +--------------------------------------- + +.. automodule:: solaris.utils.tile + :members: + +``solaris.utils.raster`` Raster image and array management utilities +-------------------------------------------------------------------- + +.. automodule:: solaris.utils.raster + :members: + +``solaris.utils.data`` Dataset CSV utilities +-------------------------------------------- + +.. automodule:: solaris.utils.data + :members: diff --git a/docker/solaris/docs/api/vector.rst b/docker/solaris/docs/api/vector.rst new file mode 100644 index 00000000..a4ff2aba --- /dev/null +++ b/docker/solaris/docs/api/vector.rst @@ -0,0 +1,24 @@ +.. title:: solaris.vector API reference + +``solaris.vector`` API reference +================================ + +.. contents:: + +``solaris.vector.polygon`` vector polygon management +---------------------------------------------------- + +.. automodule:: solaris.vector.polygon + :members: + +``solaris.vector.graph`` graph and road network analysis +-------------------------------------------------------- + +.. automodule:: solaris.vector.graph + :members: + +``solaris.vector.mask`` vector <-> training mask interconversion +---------------------------------------------------------------- + +.. automodule:: solaris.vector.mask + :members: diff --git a/docker/solaris/docs/conf.py b/docker/solaris/docs/conf.py new file mode 100644 index 00000000..c428e370 --- /dev/null +++ b/docker/solaris/docs/conf.py @@ -0,0 +1,195 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('..')) +import sphinx_bootstrap_theme +import numpy as np +# -- Project information ----------------------------------------------------- + +project = 'solaris' +copyright = '2019, CosmiQ Works' +author = 'CosmiQ Works' +license = 'Apache 2.0' +import time +copyright = u'2018-{}, CosmiQ Works: an IQT Lab'.format(time.strftime("%Y")) + +# The full version, including alpha/beta/rc tags +release = '0.4.0' +version = '0.4.0' + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.napoleon', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.autosummary', + 'sphinx.ext.coverage', + 'sphinx.ext.imgmath', + 'sphinx.ext.viewcode', + 'sphinx.ext.githubpages', + # 'autoapi.extension', + 'sphinx.ext.autosectionlabel', + 'nbsphinx' +] + +# autoapi_type = 'python' +# autoapi_template_dir = '_templates/' +# autoapi_dirs = ['../solaris'] +# autoapi_options = ['members', 'undoc-members', 'special-members'] +# autoapi_ignore = ['*data*', +# '*bin*', +# '*migrations*'] +# autoapi_root = 'api' + +autodoc_mock_imports = ['shapely', 'fiona', 'pandas', 'geopandas', 'cv2', + 'numpy', 'gdal', 'tqdm', 'rtree', 'networkx', + 'rasterio', 'scipy', 'skimage', 'tensorflow', 'torch', + 'torchvision', 'yaml', 'affine', 'albumentations', + 'rio_tiler', 'PIL', 'matplotlib', 'rio_cogeo', + 'pyproj'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = None + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'bootstrap' +html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() + +html_theme_options = { + 'source_link_position': "footer", + 'bootswatch_theme': "paper", + 'bootstrap_version': "3", + 'navbar_links': [ + ("Tutorials", "tutorials/index"), + ("API", "api/index") + ], + + } + + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] +html_logo = 'solaris_logo_tiny_black.png' + +def setup(app): + app.add_stylesheet("custom_styles.css") + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +html_sidebars = {'**': ['custom_sidebar.html', 'sourcelink.html', 'searchbox.html']} + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'solarisdoc' + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'solaris', 'solaris Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'solaris', 'solaris Documentation', + author, 'solaris'), +] + + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + + +# -- Extension configuration ------------------------------------------------- + +# -- Options for intersphinx extension --------------------------------------- + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = { + "python": ('https://docs.python.org/', None), + "rasterio": ('https://rasterio.readthedocs.io/en/latest/', None), + "pandas": ('http://pandas.pydata.org/pandas-docs/stable/', None), + "geopandas": ('http://geopandas.org/', None), + "rtree": ('http://toblerity.org/rtree/', None), + "shapely": ('https://shapely.readthedocs.io/en/stable/', None), + 'numpy': ('https://numpy.org/doc/stable/', None), + 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), + 'PyTorch': ('http://pytorch.org/docs/master/', None) + } diff --git a/docker/solaris/docs/index.rst b/docker/solaris/docs/index.rst new file mode 100644 index 00000000..373ecbe7 --- /dev/null +++ b/docker/solaris/docs/index.rst @@ -0,0 +1,55 @@ +| +| + +.. image:: _static/solaris_logo.png + :width: 500px + :align: center + :alt: solaris_logo + +############################################################################################################ +`solaris `__ |release| by `CosmiQ Works `__ +############################################################################################################ + +*************************************************************** +An open source machine learning pipeline for geospatial imagery +*************************************************************** + + +.. toctree:: + :name: mastertoc + :maxdepth: 3 + :glob: + :hidden: + + installation + intro + pretrained_models + api/index + tutorials/index + + +========== + +User Guide +========== +* :ref:`What is solaris? ` +* :ref:`Installation ` +* :ref:`Pretrained models available in solaris ` +* :ref:`Tutorials and recipes ` + +Reference +========= +* :ref:`API reference ` + +Index +===== +* :ref:`genindex` +* :ref:`modindex` + + +**License:** `Apache 2.0`__. + +.. __: https://github.com/CosmiQ/solaris/blob/master/LICENSE.txt + +Follow us at our blog `The DownlinQ `_ or +`on Twitter `_ for updates! diff --git a/docker/solaris/docs/installation.rst b/docker/solaris/docs/installation.rst new file mode 100644 index 00000000..6340bf2a --- /dev/null +++ b/docker/solaris/docs/installation.rst @@ -0,0 +1,75 @@ +.. _installation: + +###################### +Installing ``solaris`` +###################### + +There are several methods available for installing `solaris `_: + +* :ref:`github-install` +* :ref:`pip-only` *use at your own risk!* + +---------- + +Prerequisites +============= + +Regardless of installation method, you'll need Python version 3.6 or greater. +More details on installing Python can be found +`here `_. Additionally, if you +plan to use the SpaceNet dataset with ``solaris`` (it features prominently in +many of the tutorials), you'll need `a free Amazon Web Services account `_ +and the AWS CLI `installed `_ +and `configured `_. +If you're just going to work with your own data, you can skip these steps. + +-------------- + +.. _github-install: + +Installing from GitHub using a ``conda`` environment and ``pip`` +================================================================ +If you wish to install a bleeding-edge version of ``solaris`` that isn't available +on conda-forge yet, you can install from GitHub. You'll need +`anaconda`_ for this installation as well. + +From a terminal, run:: + + git clone https://github.com/cosmiq/solaris.git + cd solaris + git checkout [branch_name] # for example, git checkout dev for bleeding-edge + +If you have access to a GPU where you're installing ``solaris``, use the following:: + + conda env create -f environment-gpu.yml + +If you don't have access to a GPU:: + + conda env create -f environment.yml + +Finally, run the last two lines (for installs both with or without GPU):: + + conda activate solaris + pip install . + +The above installation will create a new conda environment called ``solaris`` +containing your desired version of solaris and all of its dependencies. + +---------- + +.. _pip-only: + +Installing with only ``pip`` +============================ +*Use this method at your own risk!* + +If you have already installed the dependencies with underlying binaries that +don't install well with ``pip`` (i.e. ``GDAL`` and ``rtree``), you can easily +``pip install`` the rest:: + + pip install solaris + +Note that this will raise an error if you don't already have ``GDAL`` installed. + + +.. _anaconda: https://docs.anaconda.com/anaconda/install/ diff --git a/docker/solaris/docs/intro.rst b/docker/solaris/docs/intro.rst new file mode 100644 index 00000000..feaa1431 --- /dev/null +++ b/docker/solaris/docs/intro.rst @@ -0,0 +1,98 @@ +.. _intro: + + +############################## +An introduction to ``solaris`` +############################## + +-------------- + +What is ``solaris``? +==================== + +``solaris`` is a Python library with two main purposes: + +#. Run existing geospatial computer vision models on any overhead imagery with + a single line of code + +#. Accelerate research in the geospatial computer vision domain by providing + efficient implementations of common utility functions: + + * Imagery and vector-formatted label tiling + * Interconversion between geospatial and machine learning data formats + * Loss functions common in geospatial computer vision applications + * Standardized evaluation of model performance on geospatial analysis tasks + * *And more!* + +-------------- + +Why should I use ``solaris``? +============================= +Most geospatial machine learning researchers discover early that they need to +write custom code to massage their data into a machine learning-compatible +format. This poses three major problems: + +#. It is very challenging to evaluate models developed elsewhere or using different + data, precluding deployment of geospatial ML solutions. + +#. Researchers must have deep expertise in both GIS concepts and computer vision + to advance the field, meaning less research gets done, slowing progress. + +#. Every geospatial ML practitioner uses different data formats, + imagery normalization methods, and machine learning frameworks during algorithm + development. This makes comparison between models and application to new data + time-consuming, if not impossible. + +``solaris`` aims to overcome these obstacles by providing a single, centralized, +open source tool suite that can: + +#. Accommodate any geospatial imagery and label formats, + +#. prepare data for use in machine learning in a standardized fashion, + +#. train computer vision models and generate predictions on geospatial imagery + data using common deep learning frameworks, and + +#. score model performance using domain-relevant metrics in a reproducible + manner. + +-------------- + +How do I use ``solaris``? +========================= +After `installing solaris `_, there are two usage +modes: + +Command line: train or test models performance with a single command +-------------------------------------------------------------------- +``solaris`` will provide a command line interface (CLI) tool to run an entire +geospatial imagery analysis pipeline from raw, un-chipped imagery, through model +training (if applicable) and prediction, to vector-formatted outputs. If you +provide ground truth labels over your prediction area, ``solaris`` can generate +quality metrics for the predictions. See +`an introduction to the solaris CLI `_ for more. + + +Python API: Use ``solaris`` to accelerate model development +----------------------------------------------------------- +Alongside the simple CLI, all of ``solaris``'s functionality is accessible via +the Python API. The entirely open source codebase provides classes and functions +to: + +* Tile imagery and labels +* Convert geospatial raster and vector data to formats compatible with machine + learning frameworks +* Train deep learning models using PyTorch and Tensorflow (Keras) - more + frameworks coming soon! +* Generate predictions on any geospatial imagery using your own models or + existing pre-trained models from past `SpaceNet `_ + challenges +* Convert model outputs to geospatial raster or vector formats +* Score model performance using standardized, geospatial-specific metrics + +The ``solaris`` Python API documentation can be found `here `_, and +`we have provided tutorials for common use cases `_. +The open source codebase is available `on GitHub `_. + +Follow us at our blog `The DownlinQ `_ or +`on Twitter `_ for updates! diff --git a/docker/solaris/docs/make.bat b/docker/solaris/docs/make.bat new file mode 100644 index 00000000..27f573b8 --- /dev/null +++ b/docker/solaris/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docker/solaris/docs/pretrained_models.rst b/docker/solaris/docs/pretrained_models.rst new file mode 100644 index 00000000..b5d7b5a3 --- /dev/null +++ b/docker/solaris/docs/pretrained_models.rst @@ -0,0 +1,62 @@ +.. _pretrained_models: + +########################################## +Pretrained models available in ``solaris`` +########################################## + +``solaris`` provides access to a number of pre-trained models from +`the SpaceNet challenges `_. See the table below for a +summary. Note that the model name in the first column should be used as the +``"model_name"`` argument in +`the config file `_ if you wish to use that model with ``solaris``. Note that we re-trained the + competitors' models for compatibility with ``solaris`` and the training parameters, + inputs, and performance may vary slightly from their original models. + +Model details +============= + ++------------------------------------+---------------------+-----------------------+----------------+-------------+-------------+---------------------------------+---------------------------------------+ +| Model name | Model type | Model details | # Parameters | Input shape |Output shape | Config file | Weights file | ++====================================+=====================+=======================+================+=============+=============+=================================+=======================================+ +| xdxd_spacenet4 | Segmentation UNet | Encoder: VGG16 | 29.3M | 3x512x512 | 1x512x512 | `link `_ | `link `_ (117 MB) | ++------------------------------------+---------------------+-----------------------+----------------+-------------+-------------+---------------------------------+---------------------------------------+ +| selimsef_spacenet4_resnet34unet | Segmentation UNet | Encoder: ResNet-34 | 30.0M | 4x416x416 | 3x416x416 | `link `_ | `link `_ (120 MB) | ++------------------------------------+---------------------+-----------------------+----------------+-------------+-------------+---------------------------------+---------------------------------------+ +| selimsef_spacenet4_densenet121unet | Segmentation UNet | Encoder: DenseNet-121 | 15.6M | 3x384x384 | 3x384x384 | `link `_ | `link `_ (63 MB) | ++------------------------------------+---------------------+-----------------------+----------------+-------------+-------------+---------------------------------+---------------------------------------+ +| selimsef_spacenet4_densenet161unet | Segmentation UNet | Encoder: DenseNet-161 | 41.1M | 3x384x384 | 3x384x384 | `link `_ | `link `_ (158 MB) | ++------------------------------------+---------------------+-----------------------+----------------+-------------+-------------+---------------------------------+---------------------------------------+ + +Training details +================ + +Below is a summary of the training hyperparameters for each model. For image +pre-processing and augmentation pipelines see the config files linked above. +*Note that our hyperparameters may differ from the competitors' original values.* +See `their solution descriptions `_ for +more on their implementations. + ++------------------------------------+-------------------------+-------------------+---------------+------------------------+-----------------+------------+-----------------+---------------------+ +| Model name | Loss function | Optimizer | Learning Rate | Training input | Training mask | Batch size | Training Epochs | Pre-trained weights | ++====================================+=========================+===================+===============+========================+=================+============+=================+=====================+ +| xdxd_spacenet4 | BCE + | Adam | 1e-4 | SpaceNet 4 | Footprints only | 12 | 60 | None | +| | Jaccard (4:1) | default params | with decay | Pan-sharpened RGB | | | | | ++------------------------------------+-------------------------+-------------------+---------------+------------------------+-----------------+------------+-----------------+---------------------+ +| selimsef_spacenet4_resnet34unet | Focal + Dice | AdamW | 2e-4 | SpaceNet 4 | 3-channel (FP, | 42 | 70 | ImageNet (encoder | +| | (1:1) | 1e-3 weight decay | with decay | Pan-sharpened RGB+NIR | (edge, contact) | | | only) | ++------------------------------------+-------------------------+-------------------+---------------+------------------------+-----------------+------------+-----------------+---------------------+ +| selimsef_spacenet4_densenet121unet | Focal + Dice | AdamW | 2e-4 | SpaceNet 4 | 3-channel (FP, | 32 | 70 | ImageNet (encoder | +| | (1:1) | 1e-3 weight decay | with decay | Pan-sharpened RGB | (edge, contact) | | | only) | ++------------------------------------+-------------------------+-------------------+---------------+------------------------+-----------------+------------+-----------------+---------------------+ +| selimsef_spacenet4_densenet161unet | Focal + Dice | AdamW | 2e-4 | SpaceNet 4 | 3-channel (FP, | 20 | 60 | ImageNet (encoder | +| | (1:1) | 1e-3 weight decay | with decay | Pan-sharpened RGB | (edge, contact) | | | only) | ++------------------------------------+-------------------------+-------------------+---------------+------------------------+-----------------+------------+-----------------+---------------------+ + +.. _XDXDconfig: https://github.com/CosmiQ/solaris/blob/master/solaris/nets/configs/xdxd_spacenet4.yml +.. _ssresnet34config: https://github.com/CosmiQ/solaris/blob/master/solaris/nets/configs/selimsef_resnet34unet_spacenet4.yml +.. _ssdense121config: https://github.com/CosmiQ/solaris/blob/master/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml +.. _ssdense161config: https://github.com/CosmiQ/solaris/blob/master/solaris/nets/configs/selimsef_densenet161unet_spacenet4.yml +.. _XDXDweights: https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/xdxd_spacenet4_solaris_weights.pth +.. _ssresnet34weights: https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_resnet34unet_solaris_weights.pth +.. _ssdense121weights: https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_densenet121unet_solaris_weights.pth +.. _ssdense161weights: https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_densenet161unet_solaris_weights.pth diff --git a/docker/solaris/docs/tutorials/index.rst b/docker/solaris/docs/tutorials/index.rst new file mode 100644 index 00000000..72d03a3f --- /dev/null +++ b/docker/solaris/docs/tutorials/index.rst @@ -0,0 +1,89 @@ +.. _tutorials_index: + +############################## +Solaris Tutorials and Cookbook +############################## + +.. toctree:: + :maxdepth: 3 + :glob: + :hidden: + + cli* + notebooks/* + + +There are two different ways to use ``solaris``: + +* :ref:`The Command Line Interface` (Simple use with existing models) +* :ref:`The Python API` (Python users who wish to develop their own models) + +Here we provide a brief introduction to these two approaches, links to tutorials, +and usage recipes to complete common tasks. If there's a common use case not +covered here, `submit an issue on the GitHub repo to request its inclusion. `_ + +--------------- + +The command line interface +========================== +The command line interface (CLI) is the simplest way to use Solaris. Using the CLI, +you can run training and/or prediction on overhead imagery using `SpaceNet `_ models +without writing a single line of python code. + +After :doc:`installing Solaris <../installation>`, you can run simple commands from a +terminal or command prompt for standard operations, from creating training masks +using vector labels to running an entire deep learning pipeline through +evaluating model performance. Instead of having to write code to help ``solaris`` +find your data, you just make basic edits to a configuration file template, then +``solaris`` does all the work to make your analysis pipeline fit together. Tutorials +on creating configuration files and running the CLI can be found below. + +* `Creating the .yml config file `_ +* `Creating reference files to help solaris find your imagery `_ +* `Creating training masks with the solaris CLI `_ +* `Running a full deep learning pipeline using the solaris CLI `_ +* `Evaluating prediction quality on SpaceNet data with the solaris CLI `_ +* `Mapping vehicles with the cowc dataset `_ + +If these relatively narrow use cases don't cover your needs, the ``solaris`` python +API can help! + +The Python API +============== +The ``solaris`` Python API provides every functionality needed to perform deep learning +analysis of overhead imagery data: + +* Customizable imagery and vector label tiling, with different size and coordinate system options. +* Training mask creation functions, with the option to create custom width edge masks, building footprint masks, road network masks, multi-class masks, and even masks which label narrow spaces between objects. +* All required deep learning functionality, from augmentation (including >3 channel imagery tools!) to data ingestion to model training and inference to evaluation during training. These functions are currently implemented with both PyTorch and TensorFlow backends. +* The ability to use pre-trained or freshly initialized `SpaceNet `_ models, as well as your own custom models +* Model performance evaluation tools for the SpaceNet IoU metric (APLS coming soon!) + +The :doc:`Python API Reference <../api/index>` provides full documentation of +everything described above and more. For usage examples to get you started, see +the tutorials below. + +* `Tiling imagery `_ +* `Creating training masks `_ +* `Training a SpaceNet model `_ +* `Inference with a pre-trained SpaceNet model `_ +* `Training a custom model `_ +* `Converting pixel masks to vector labels `_ +* `Scoring your model's performance with the solaris Python API `_ +* `Creating COCO-formatted datasets `_ +* `Preprocessing Part 1: Pipelines `_ +* `Preprocessing Part 2: Branching `_ +* `Preprocessing Part 3: SAR `_ + +Reference +========= +* :doc:`API reference <../api/index>` + +Index +===== +* :ref:`genindex` +* :ref:`modindex` + + +Check back here and `follow us on Twitter `_ or +on our blog, `The DownlinQ `_ for updates! diff --git a/docker/solaris/docs/tutorials/notebooks/api_coco_tutorial.ipynb b/docker/solaris/docs/tutorials/notebooks/api_coco_tutorial.ipynb new file mode 100644 index 00000000..a7036962 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_coco_tutorial.ipynb @@ -0,0 +1,1898 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Converting GeoJSON labels to COCO-formatted labels using `Solaris`\n", + "\n", + "Now, you can automatically generate COCO .jsons from GeoJSON vector labels and georegistered image files. Let's look at a couple of exmaples of how to do so. All of these cases use the [solaris.data.coco.geojson2coco()](../../api/data.rst#solaris.data.coco.geojson2coco) function. For more information about the COCO specification, see [the COCO dataset website](http://cocodataset.org/#format-data).\n", + "\n", + "## Syntax\n", + "The [solaris.data.coco.geojson2coco()](../../api/data.rst#solaris.data.coco.geojson2coco) takes the following arguments:\n", + "\n", + "- `image_src`: a `str` or `list` or `dict` defining source image(s) to use in the dataset. These are required not only to list as part of the dataset, but also to convert georegistered labels to pixel coordinates. This argument can be: \n", + "\n", + " 1. a string path to an image (e.g. `\"path/to/a/geotiff.tif\"`)\n", + " 2. the path to a directory containing a bunch of images (e.g. `\"/path/to/geotiff/dir/\"`)\n", + " 3. a list of image paths (e.g. `[\"path/to/geotiff_1.tif\", \"path/to/geotiff_2.tif\"]`)\n", + " 4. a dictionary corresponding to COCO-formatted image records (e.g.\n", + " ```\n", + " [\n", + " {\n", + " \"id\": 1,\n", + " \"file_name\": \"path/to/geotiff.tif\",\n", + " \"height\": 640,\n", + " \"width\": 640,\n", + " },\n", + " {etc.}\n", + " ]\n", + " ```\n", + " 5. a string path to a COCO JSON containing image records (e.g. `\"path/to/coco_dataset.json\"`)\n", + "\n", + " If `image_src` is a directory, the `recursive` flag will be used to determine whetheror not to descend into sub-directories.\n", + "\n", + "\n", + "- `label_src`: `str` or `list` of source labels to use in the dataset. This can be a string path to a geojson, the path to a directory containing multiple geojsons, or a list of geojson file paths. If a directory, the `recursive` flag will determine whether or not to descend into sub-directories.\n", + "- `output_path` : an optional `str` path to save the JSON-formatted COCO records to. If not provided, the records will only be returned as a dict, and not saved to file.\n", + "- `image_ext`: The string extension to use to identify images when searching directories. Only has an effect if `image_src` is a directory path. Defaults to `\".tif\"`.\n", + "- `matching_re` : A regular expression pattern to match filenames between `image_src` and `label_src` if both are directories of multiple files. This has no effect if those arguments do not both correspond to directories or lists of files. If this isn't provided, it is assumed that label filenames and image filenames differ _only in their extensions_, and filenames will be compared for identity to find matches.\n", + "- `category_attribute`: The `str` name of an attribute in the geojson that specifies which category a given instance corresponds to. If not provided, it's assumed that only one class of object is present in the dataset, which will be termed `\"other\"` in the output json.\n", + "- `preset_categories`: An optional pre-set `list` of `dict`s of categories to use for labels. These categories should\n", + " be formatted per [the COCO category specification](http://cocodataset.org/#format-data).\n", + "- `include_other`: A boolean which, if set to `True`, and `preset_categories` is provided, causes objects that don't fall into the specified categories to be kept in the dataset. They will be passed into a category named `\"other\"` with its own associated category `id`. If `False`, objects whose categories don't match a category from `preset_categories` will be dropped.\n", + "- `info_dict`: An optional `dict` with the following key-value pairs:\n", + "\n", + " - `\"year\"`: `int` year of creation\n", + " - `\"version\"`: `str` version of the dataset\n", + " - `\"description\"`: `str` string description of the dataset\n", + " - `\"contributor\"`: `str` who contributed the dataset\n", + " - `\"url\"`: `str` URL where the dataset can be found\n", + " - `\"date_created\"`: `datetime.datetime` when the dataset was created\n", + "\n", + " If `info_dict` isn't provided, it will be left out of the .json created by `solaris`.\n", + "\n", + "- license_dict:\n", + " An optional `dict` containing the licensing information for the dataset, with\n", + " the following key-value pairs:\n", + "\n", + " - `\"name\"`: `str` the name of the license.\n", + " - `\"url\"`: `str` a link to the dataset's license.\n", + "\n", + " __Note__: This implementation assumes that all of the data uses one license. If multiple licenses are provided, the image records will not be assigned a license ID.\n", + "- recursive: If `image_src` and/or `label_src` are directories, setting this flag to `True` will induce solaris to descend into subdirectories to find files. By default, solaris does not traverse the directory tree.\n", + "- verbose : Verbose text output. By default, none is provided; if `True` or `1`, information-level outputs are provided; if `2`, extremely verbose text is output.\n", + "\n", + "## Examples\n", + "\n", + "See the two examples below for usage of this function.\n", + "\n", + "#### Example 1: A dataset with one image and one json (for example, untiled geospatial imagery files)\n", + "\n", + "In this example, we'll load in a single image and geojson. Because there's only one file for each, labels will be converted to their pixel coordinates within the only image included. In addition, we'll specify a property of the items in the geojson, `\"truncated\"`, to separate into two classes. Note that we won't include any license information or info metadata since we're not providing that during dataset creation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1 [00:00" + ] + }, + "execution_count": 5, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import JSON\n", + "JSON(coco_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case the above doesn't render for you, the raw text is below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'annotations': [{'id': 1, 'image_id': 1, 'category_id': 1, 'segmentation': [0.0, 2.845103836618364, 7.787239895900711, 7.813573766499758, 6.348949391860515, 21.166115891188383, 5.487595358863473, 29.24418894201517, 19.3797596283257, 37.85056554712355, 18.118415302364156, 57.70217224024236, 0.0, 54.131107677705586, 0.0, 2.845103836618364], 'area': 608.3880075917921, 'bbox': [0.0, 2.845103836618364, 19.3797596283257, 54.857068403624], 'iscrowd': 0}, {'id': 2, 'image_id': 1, 'category_id': 2, 'segmentation': [27.38481539185159, 226.1645903000608, 34.46586190746166, 226.48033855389804, 34.72251786501147, 221.01391235832125, 44.8147500208579, 221.47823364380747, 44.453276831656694, 229.49973394535482, 56.44128756551072, 230.05102432798594, 54.999366192379966, 261.5376432267949, 46.934077847748995, 267.3053462980315, 25.54191842698492, 266.33953956048936, 27.38481539185159, 226.1645903000608], 'area': 1175.2086036457465, 'bbox': [25.54191842698492, 221.01391235832125, 30.8993691385258, 46.29143393971026], 'iscrowd': 0}, {'id': 3, 'image_id': 1, 'category_id': 1, 'segmentation': [60.03418597159907, 884.8732050526887, 73.8337494416628, 900.0, 51.516283753560856, 900.0, 47.80893106292933, 895.9360736850649, 60.03418597159907, 884.8732050526887], 'area': 214.14410906402435, 'bbox': [47.80893106292933, 884.8732050526887, 26.02481837873347, 15.126794947311282], 'iscrowd': 0}, {'id': 4, 'image_id': 1, 'category_id': 2, 'segmentation': [65.83512698789127, 443.34588148258626, 86.05315328529105, 444.11831593420357, 84.12356285331771, 493.6842159954831, 63.905484846793115, 492.9117766721174, 65.83512698789127, 443.34588148258626], 'area': 1003.6164099476883, 'bbox': [63.905484846793115, 443.34588148258626, 22.14766843849793, 50.338334512896836], 'iscrowd': 0}, {'id': 5, 'image_id': 1, 'category_id': 2, 'segmentation': [87.2731370574329, 72.93001714255661, 106.98580074869096, 84.21334314905107, 97.70029512513429, 100.08772260416299, 91.15104462415911, 98.73803176078945, 53.36824434134178, 78.81699287053198, 59.59959887806326, 67.12329848110676, 79.5907216486521, 77.6452063396573, 87.2731370574329, 72.93001714255661], 'area': 832.0140045611614, 'bbox': [53.36824434134178, 67.12329848110676, 53.617556407349184, 32.96442412305623], 'iscrowd': 0}, {'id': 6, 'image_id': 1, 'category_id': 2, 'segmentation': [87.33356586564332, 502.7506626434624, 90.79576571006328, 511.5002211164683, 93.23485574219376, 550.6385944513604, 70.7970443549566, 553.24962748494, 70.8928169994615, 544.9904495924711, 70.136441974435, 526.1424378501251, 69.54070870671421, 501.6971649955958, 87.33356586564332, 502.7506626434624], 'area': 1055.98129961667, 'bbox': [69.54070870671421, 501.6971649955958, 23.694147035479546, 51.55246248934418], 'iscrowd': 0}, {'id': 7, 'image_id': 1, 'category_id': 2, 'segmentation': [89.06576380180195, 561.6383464429528, 91.58933665603399, 579.8661990063265, 94.85523002082482, 592.7489638356492, 93.41836131247692, 606.9227179316804, 85.23654213640839, 610.96199104283, 73.38412117748521, 610.6515495320782, 71.78515014378354, 605.9850031817332, 72.83866719854996, 588.2692635813728, 72.19266184815206, 561.761005807668, 89.06576380180195, 561.6383464429528], 'area': 965.97420241684, 'bbox': [71.78515014378354, 561.6383464429528, 23.07007987704128, 49.3236445998773], 'iscrowd': 0}, {'id': 8, 'image_id': 1, 'category_id': 2, 'segmentation': [73.42513769492507, 652.7116207033396, 73.87162248673849, 640.5596289457753, 73.96795534505509, 634.6088027460501, 89.67092586541548, 635.2249299148098, 95.65740334033035, 635.5673428568989, 95.31320596928708, 642.0125183537602, 90.37084288243204, 643.375933191739, 86.37565372907557, 643.2291440004483, 83.40025364165194, 643.7899634344503, 78.97129776002839, 645.6513671381399, 77.39582740026526, 652.6148558100685, 73.42513769492507, 652.7116207033396], 'area': 226.76552441704672, 'bbox': [73.42513769492507, 634.6088027460501, 22.232265645405278, 18.102817957289517], 'iscrowd': 0}, {'id': 9, 'image_id': 1, 'category_id': 2, 'segmentation': [104.26538560027257, 379.3509592106566, 95.71053624199703, 432.8294323347509, 83.6166091109626, 427.17569901794195, 71.57171053020284, 424.2952412031591, 74.16042645578273, 415.48699966818094, 82.98187345149927, 415.6049499800429, 84.05773156136274, 402.6163706937805, 81.36960026691668, 392.8713309513405, 80.55370765388943, 388.34107194375247, 84.53470388124697, 378.76643434073776, 104.26538560027257, 379.3509592106566], 'area': 991.4279262138889, 'bbox': [71.57171053020284, 378.76643434073776, 32.69367507006973, 54.06299799401313], 'iscrowd': 0}, {'id': 10, 'image_id': 1, 'category_id': 2, 'segmentation': [105.87982941744849, 313.81173481605947, 127.49495613621548, 320.8758743349463, 111.21164846420288, 370.3255268894136, 89.93423808692023, 363.40850543417037, 96.1564225088805, 344.47919271234423, 106.8362458597403, 336.29499020427465, 112.88286360329948, 326.15949539188296, 113.55506858509034, 319.46216831356287, 105.87982941744849, 313.81173481605947], 'area': 946.9628953122234, 'bbox': [89.93423808692023, 313.81173481605947, 37.56071804929525, 56.513792073354125], 'iscrowd': 0}, {'id': 11, 'image_id': 1, 'category_id': 2, 'segmentation': [129.5470552511979, 257.30139927752316, 162.33337076054886, 274.0591311287135, 154.6747992991004, 288.89506167545915, 124.72422339068726, 273.46653464064, 123.76762919081375, 268.4957895213738, 129.5470552511979, 257.30139927752316], 'area': 606.3783174309181, 'bbox': [123.76762919081375, 257.30139927752316, 38.56574156973511, 31.593662397935987], 'iscrowd': 0}, {'id': 12, 'image_id': 1, 'category_id': 2, 'segmentation': [133.95478952932172, 97.67253606952727, 150.39837071765214, 108.12547634728253, 153.35539799532853, 113.66894138418138, 158.35767206153832, 117.05394644103944, 162.1479135560803, 127.83751212060452, 171.07018301589414, 132.85823319572955, 166.4350645239465, 144.5352517813444, 156.12809146079235, 140.59148615878075, 151.76094630081207, 135.0823942553252, 140.6975575806573, 129.0484553426504, 133.83045004075393, 121.51388919819146, 124.8308775019832, 113.32103253901005, 133.95478952932172, 97.67253606952727], 'area': 905.6790360714577, 'bbox': [124.8308775019832, 97.67253606952727, 46.23930551391095, 46.862715711817145], 'iscrowd': 0}, {'id': 13, 'image_id': 1, 'category_id': 2, 'segmentation': [213.84757142001763, 865.6317731337622, 231.50301338662393, 882.3587670447305, 216.42948372568935, 899.1955095911399, 210.5498044961132, 889.5282784951851, 214.30827019084245, 884.5313852354884, 213.079774370417, 877.5474507408217, 203.49479921744205, 874.2963477959856, 213.84757142001763, 865.6317731337622], 'area': 392.57584507364606, 'bbox': [203.49479921744205, 865.6317731337622, 28.008214169181883, 33.56373645737767], 'iscrowd': 0}, {'id': 14, 'image_id': 1, 'category_id': 2, 'segmentation': [231.21248426428065, 330.48871645797044, 248.18980536190793, 356.7320148414001, 241.67447824659757, 364.39305018913, 225.53970709559508, 372.71031646989286, 220.28613743791357, 362.82807022240013, 226.06819452391937, 359.3577359961346, 222.16971050621942, 354.03699261229485, 211.39998442842625, 359.29360383190215, 203.3048722210806, 347.08347506821156, 231.21248426428065, 330.48871645797044], 'area': 942.4941668682975, 'bbox': [203.3048722210806, 330.48871645797044, 44.88493314082734, 42.22160001192242], 'iscrowd': 0}, {'id': 15, 'image_id': 1, 'category_id': 2, 'segmentation': [226.15747217368335, 126.1882931953296, 227.64633786818013, 117.96173016168177, 236.09353622514755, 119.48706156853586, 234.58611978427507, 127.7140777958557, 226.15747217368335, 126.1882931953296], 'area': 71.7025184022421, 'bbox': [226.15747217368335, 117.96173016168177, 9.9360640514642, 9.75234763417393], 'iscrowd': 0}, {'id': 16, 'image_id': 1, 'category_id': 2, 'segmentation': [237.77231920976192, 153.3169838031754, 243.4796773325652, 156.68477841839194, 245.5267063616775, 153.83819461707026, 253.28920675627887, 154.69214213639498, 252.36845179693773, 160.33013889566064, 273.4036889746785, 171.02616648748517, 271.2190176327713, 181.93320011347532, 268.2431232582312, 190.08504440169781, 263.83925927826203, 197.53924131486565, 260.0654399082996, 201.13819517660886, 252.25840627076104, 199.2199343442917, 229.10111278295517, 187.5324132423848, 221.94777155457996, 183.48959933500737, 237.77231920976192, 153.3169838031754], 'area': 1507.8715455558022, 'bbox': [221.94777155457996, 153.3169838031754, 51.45591742009856, 47.82121137343347], 'iscrowd': 0}, {'id': 17, 'image_id': 1, 'category_id': 2, 'segmentation': [392.3872257217299, 671.1492497138679, 417.30380885861814, 671.2074872627854, 418.62818518141285, 684.4039134653285, 393.0598451150581, 685.027445490472, 392.3872257217299, 671.1492497138679], 'area': 341.9972755053627, 'bbox': [392.3872257217299, 671.1492497138679, 26.240959459682927, 13.878195776604116], 'iscrowd': 0}, {'id': 18, 'image_id': 1, 'category_id': 1, 'segmentation': [415.6500815402251, 870.6108930064365, 423.3889202498831, 878.856587799266, 425.6205423306674, 893.4736175602302, 417.6680606456939, 900.0, 385.6889950442128, 900.0, 415.6500815402251, 870.6108930064365], 'area': 640.7200900905971, 'bbox': [385.6889950442128, 870.6108930064365, 39.93154728645459, 29.389106993563473], 'iscrowd': 0}, {'id': 19, 'image_id': 1, 'category_id': 2, 'segmentation': [407.2164936910849, 293.369396366179, 427.77757996553555, 294.5104229282588, 424.95420863106847, 345.4521803893149, 401.8091458447743, 344.1744022862986, 404.039300782606, 303.9233255367726, 406.6232181608211, 304.0600705072284, 407.2164936910849, 293.369396366179], 'area': 1155.038723289968, 'bbox': [401.8091458447743, 293.369396366179, 25.96843412076123, 52.0827840231359], 'iscrowd': 0}, {'id': 20, 'image_id': 1, 'category_id': 2, 'segmentation': [432.1206162075978, 225.95247913245112, 430.60763758723624, 245.3663629340008, 426.6382694914937, 244.7529080240056, 425.1836831646506, 258.9493404906243, 429.14439756423235, 259.2078733071685, 428.2809057792183, 275.56508298031986, 414.204479301814, 274.6432346571237, 414.4758333056234, 263.69406074192375, 411.21745124668814, 255.69423871394247, 405.99594805110246, 248.6523243561387, 406.9461998385377, 242.70285607129335, 410.4586205475498, 239.0436596525833, 410.1156435646117, 232.59303102549165, 406.6664985958487, 231.23442135937512, 407.21412571519613, 224.76207193825394, 432.1206162075978, 225.95247913245112], 'area': 926.2819276108769, 'bbox': [405.99594805110246, 224.76207193825394, 26.124668156495318, 50.80301104206592], 'iscrowd': 0}, {'id': 21, 'image_id': 1, 'category_id': 2, 'segmentation': [412.0414752406068, 165.4036012943834, 432.5184705699794, 166.1471375450492, 430.66934231179766, 216.68781219702214, 410.19229355221614, 215.94427104014903, 412.0414752406068, 165.4036012943834], 'area': 1036.2973770508409, 'bbox': [410.19229355221614, 165.4036012943834, 22.326177017763257, 51.28421090263873], 'iscrowd': 0}, {'id': 22, 'image_id': 1, 'category_id': 2, 'segmentation': [436.6716912172269, 114.92877714522183, 435.17816135426983, 145.7952524824068, 428.3110607606359, 155.7733131237328, 420.42894698819146, 155.34407423250377, 418.76928842114285, 153.5201010480523, 419.0462323431857, 149.65126746241003, 420.8805788680911, 143.41388408094645, 419.8326982872095, 136.22578839305788, 416.34996173810214, 131.20568466931581, 416.69879815378226, 115.06078892573714, 436.6716912172269, 114.92877714522183], 'area': 674.2933167606384, 'bbox': [416.34996173810214, 114.92877714522183, 20.32172947912477, 40.844535978510976], 'iscrowd': 0}, {'id': 23, 'image_id': 1, 'category_id': 2, 'segmentation': [459.1644711194094, 47.61499526724219, 455.6476237687748, 70.11859888583422, 450.8766771061346, 69.36933278851211, 446.62103112763725, 96.59647608082741, 426.43874416314065, 93.47081579640508, 434.2112922635861, 43.74008092097938, 459.1644711194094, 47.61499526724219], 'area': 1137.873532469484, 'bbox': [426.43874416314065, 43.74008092097938, 32.72572695626877, 52.856395159848034], 'iscrowd': 0}, {'id': 24, 'image_id': 1, 'category_id': 1, 'segmentation': [484.2024364131503, 0.0, 479.75414649397135, 9.601744243875146, 477.463500038255, 8.547827863134444, 464.7478086431511, 36.00354308541864, 446.46081846160814, 27.615649731829762, 459.25223012291826, 0.0, 484.2024364131503, 0.0], 'area': 730.4737448745893, 'bbox': [446.46081846160814, 0.0, 37.74161795154214, 36.00354308541864], 'iscrowd': 0}, {'id': 25, 'image_id': 1, 'category_id': 2, 'segmentation': [446.38990870770067, 842.2273999303579, 481.3434248256963, 828.2793587576598, 488.85468638362363, 846.9848243454471, 453.91915302863345, 860.910242264159, 446.38990870770067, 842.2273999303579], 'area': 758.0660568429099, 'bbox': [446.38990870770067, 828.2793587576598, 42.46477767592296, 32.63088350649923], 'iscrowd': 0}, {'id': 26, 'image_id': 1, 'category_id': 2, 'segmentation': [482.2356772432104, 357.92745217029005, 495.83988205646165, 360.03715515416116, 493.9617549048271, 372.0909278737381, 527.8789904229343, 377.3452287474647, 524.7309722411446, 397.46488589048386, 492.0437040710822, 392.40253333747387, 491.48141598375514, 395.98978219833225, 476.6471859868616, 393.6881181783974, 482.2356772432104, 357.92745217029005], 'area': 1201.892037899198, 'bbox': [476.6471859868616, 357.92745217029005, 51.23180443607271, 39.5374337201938], 'iscrowd': 0}, {'id': 27, 'image_id': 1, 'category_id': 2, 'segmentation': [536.0753469388001, 150.17036613915116, 535.8976141829044, 157.3439671061933, 537.3988791736774, 162.56776288338006, 536.2554783222731, 165.92503716237843, 539.3879669941962, 178.65563245117664, 533.924685027916, 182.25146871525794, 530.728569818195, 184.41585112269968, 524.4869354791008, 188.82972381450236, 524.3672584414016, 192.2951983232051, 522.0256408345886, 192.1969511229545, 522.3906255022157, 182.04453698452562, 525.8205245367717, 178.80905124824494, 530.0610870544333, 171.51414068136364, 528.5654665878974, 168.04368018638343, 524.6151287727989, 165.1658039363101, 525.0040782545693, 149.90775556955487, 536.0753469388001, 150.17036613915116], 'area': 404.6692002570957, 'bbox': [522.0256408345886, 149.90775556955487, 17.36232615960762, 42.38744275365025], 'iscrowd': 0}, {'id': 28, 'image_id': 1, 'category_id': 2, 'segmentation': [523.4165055262856, 198.2224921071902, 544.9352911333553, 199.05147654097527, 542.4260684649926, 249.9190295347944, 522.5057537995744, 248.40736349392682, 520.1315930527635, 239.36497628502548, 518.9120450657792, 233.5128228161484, 526.6003856104799, 229.04146504867822, 528.8170812996104, 220.97468123119324, 522.6861530637834, 212.42346664890647, 523.4165055262856, 198.2224921071902], 'area': 1021.3360981644006, 'bbox': [518.9120450657792, 198.2224921071902, 26.02324606757611, 51.6965374276042], 'iscrowd': 0}, {'id': 29, 'image_id': 1, 'category_id': 2, 'segmentation': [526.6318989666179, 261.5354417562485, 545.283115554601, 262.0126638803631, 544.4942456730641, 292.8397702910006, 552.0921549452469, 293.03174194227904, 551.5256321858615, 314.70872772019356, 517.8084108987823, 313.8443781072274, 518.1340601162519, 301.317967700772, 525.6015503380913, 301.4909356869757, 526.6318989666179, 261.5354417562485], 'area': 1238.4757250715768, 'bbox': [517.8084108987823, 261.5354417562485, 34.28374404646456, 53.17328596394509], 'iscrowd': 0}, {'id': 30, 'image_id': 1, 'category_id': 2, 'segmentation': [567.6196071440354, 79.48379767127335, 573.6413344931789, 77.47242644708604, 573.9580853967927, 84.36761443130672, 576.7988056694157, 88.18258353415877, 583.9389728535898, 90.16137413866818, 588.2799405395053, 90.78792378865182, 553.5269071373623, 129.635154761374, 549.2492460440844, 126.27696036919951, 545.6672062769067, 115.66594129707664, 547.9324978117365, 101.98241242859513, 567.6196071440354, 79.48379767127335], 'area': 1005.3323860159348, 'bbox': [545.6672062769067, 77.47242644708604, 42.61273426259868, 52.16272831428796], 'iscrowd': 0}, {'id': 31, 'image_id': 1, 'category_id': 2, 'segmentation': [545.0001820274629, 404.13968645595014, 545.5456960839219, 383.8837510570884, 561.4482773041818, 384.3170414939523, 561.6076893680729, 378.67540270090103, 573.1816837256774, 378.9923253301531, 573.0370411262847, 384.47823309339583, 594.233580631204, 385.0264944685623, 593.6913648874033, 405.41553003899753, 545.0001820274629, 404.13968645595014], 'area': 1054.6293162692457, 'bbox': [545.0001820274629, 378.67540270090103, 49.23339860374108, 26.7401273380965], 'iscrowd': 0}, {'id': 32, 'image_id': 1, 'category_id': 2, 'segmentation': [594.5955353775062, 341.2696067793295, 592.6359737580642, 346.1783115705475, 595.5670321371872, 351.4116119751707, 594.498773291707, 355.56610705144703, 595.03770820098, 359.39284333679825, 596.8756023284514, 362.4332278929651, 590.6566405876074, 362.45178297907114, 590.6322764432989, 356.1265549827367, 588.5902138527017, 356.1319851242006, 588.5435697012581, 341.2840873301029, 594.5955353775062, 341.2696067793295], 'area': 113.72690001497435, 'bbox': [588.5435697012581, 341.2696067793295, 8.332032627193257, 21.18217619974166], 'iscrowd': 0}, {'id': 33, 'image_id': 1, 'category_id': 2, 'segmentation': [642.5548559394665, 605.4954561004415, 650.1750435382128, 639.3135964740068, 630.8239523877855, 643.6478302329779, 625.8465431011282, 621.5068183001131, 613.9918430529069, 624.1488145207986, 611.3485644147731, 612.4494963856414, 642.5548559394665, 605.4954561004415], 'area': 833.1100217257011, 'bbox': [611.3485644147731, 605.4954561004415, 38.82647912343964, 38.15237413253635], 'iscrowd': 0}, {'id': 34, 'image_id': 1, 'category_id': 1, 'segmentation': [664.1072873058729, 0.0, 665.963440204272, 5.102927703410387, 663.4489023594651, 4.764764592982829, 657.1336445596535, 3.8756691990420222, 655.4026131848805, 1.4097766196355224, 653.3671769341454, 3.967581197619438, 653.9054305306636, 8.526797778904438, 655.4832516363822, 13.08284202683717, 651.7876941068098, 16.08068347070366, 648.9221955472603, 18.858505848795176, 649.2320157396607, 14.056635465472937, 646.6069010677747, 9.94786886498332, 646.0617618362885, 4.3456138940528035, 644.300418858882, 0.6374908359721303, 644.1158141889609, 0.0, 664.1072873058729, 0.0], 'area': 163.2888762358447, 'bbox': [644.1158141889609, 0.0, 21.847626015311107, 18.858505848795176], 'iscrowd': 0}, {'id': 35, 'image_id': 1, 'category_id': 2, 'segmentation': [719.5465582867619, 598.6448629098013, 720.3695895529818, 606.5043138191104, 724.4469213041011, 610.7773993844166, 725.2147377748042, 616.3742183251306, 723.0049092427362, 620.1570535134524, 718.3882041969337, 619.6482330150902, 713.9281129532028, 616.4276928380132, 703.0521553403232, 615.0506034400314, 697.5230599956121, 619.7578771309927, 691.4791235984303, 620.1051252679899, 691.3655090769753, 598.7110763099045, 719.5465582867619, 598.6448629098013], 'area': 591.1230200188784, 'bbox': [691.3655090769753, 598.6448629098013, 33.84922869782895, 21.512190603651106], 'iscrowd': 0}, {'id': 36, 'image_id': 1, 'category_id': 2, 'segmentation': [766.4948697979562, 219.65857510454953, 766.9394415735733, 203.64448958076537, 774.5338656448293, 199.1309275366366, 779.6678650518879, 196.49748022854328, 780.0790439015254, 206.49778971262276, 792.9675920906011, 206.67150652222335, 792.7963749796618, 220.19298242591321, 766.4948697979562, 219.65857510454953], 'area': 436.3985394080503, 'bbox': [766.4948697979562, 196.49748022854328, 26.472722292644903, 23.695502197369933], 'iscrowd': 0}, {'id': 37, 'image_id': 1, 'category_id': 2, 'segmentation': [794.3443439039402, 800.0892576370388, 808.1741715462413, 800.0180635405704, 811.0787292337045, 810.2460591299459, 808.0733455095906, 813.3824441283941, 807.4013454001397, 820.0798055976629, 768.3534275970887, 822.9638589080423, 759.5865474676248, 819.0049897767603, 760.6663332274184, 806.1938135968521, 774.9641209535766, 796.3894291333854, 783.0687794568948, 796.0362568320706, 791.1689595563803, 796.2602832280099, 794.3443439039402, 800.0892576370388], 'area': 1091.069005525542, 'bbox': [759.5865474676248, 796.0362568320706, 51.49218176607974, 26.927602075971663], 'iscrowd': 0}, {'id': 38, 'image_id': 1, 'category_id': 2, 'segmentation': [818.6449922658503, 145.89386002346873, 802.3041800016072, 146.51470147818327, 802.9269792409614, 162.90226284973323, 777.9049582753796, 163.82376996334642, 776.5785818777513, 128.4980932334438, 817.9413526556455, 126.95574354380369, 818.6449922658503, 145.89386002346873], 'area': 1195.1484685924327, 'bbox': [776.5785818777513, 126.95574354380369, 42.06641038809903, 36.86802641954273], 'iscrowd': 0}, {'id': 39, 'image_id': 1, 'category_id': 2, 'segmentation': [794.6951001100242, 2.116394373588264, 816.6974766298663, 1.4683247059583664, 818.0185485305265, 47.98086807690561, 804.6792487849016, 48.35088091529906, 795.6887790956534, 36.73992804996669, 794.6951001100242, 2.116394373588264], 'area': 972.2084233562225, 'bbox': [794.6951001100242, 1.4683247059583664, 23.323448420502245, 46.88255620934069], 'iscrowd': 0}, {'id': 40, 'image_id': 1, 'category_id': 2, 'segmentation': [816.4727658838965, 58.42847666423768, 817.5045415207278, 105.2588225658983, 796.3883123914711, 105.70768600795418, 795.4426827779971, 62.4043871788308, 803.5142542636022, 62.22955618426204, 806.4344622618519, 58.65132196247578, 816.4727658838965, 58.42847666423768], 'area': 955.3143319089635, 'bbox': [795.4426827779971, 58.42847666423768, 22.061858742730692, 47.2792093437165], 'iscrowd': 0}, {'id': 41, 'image_id': 1, 'category_id': 2, 'segmentation': [820.6129307732917, 800.003008636646, 838.7254208496306, 798.1625695805997, 841.7378241324332, 806.7232382101938, 859.6126089137979, 806.553273351863, 864.2543455683626, 813.4094758052379, 835.6097001582384, 818.8363996865228, 830.4454396180809, 813.3912856318057, 821.766802502796, 813.04821888078, 820.6129307732917, 800.003008636646], 'area': 511.57672611563873, 'bbox': [820.6129307732917, 798.1625695805997, 43.64141479507089, 20.673830105923116], 'iscrowd': 0}, {'id': 42, 'image_id': 1, 'category_id': 2, 'segmentation': [877.9025507906917, 363.2765975808725, 838.2836305517703, 366.3079837486148, 837.0310469446704, 349.9801886640489, 850.4099756779615, 348.9433259088546, 850.0006144659128, 343.5819130791351, 854.7069255011156, 343.20067395456135, 854.3236986373086, 338.14936562720686, 865.5350079541095, 337.29858210776, 865.9971866649576, 343.30238648783416, 884.2128435778432, 341.9032686809078, 885.3848932385445, 357.21201885771006, 877.9025507906917, 363.2765975808725], 'area': 983.4834268683098, 'bbox': [837.0310469446704, 337.29858210776, 48.353846293874085, 29.009401640854776], 'iscrowd': 0}, {'id': 43, 'image_id': 1, 'category_id': 1, 'segmentation': [886.5125979208387, 820.9232407584786, 886.2984008654021, 802.261728647165, 900.0, 802.1414167098701, 900.0, 818.7495461180806, 894.7975760672707, 816.548164521344, 888.9884018914308, 818.9095647959039, 886.5125979208387, 820.9232407584786], 'area': 216.4340088998764, 'bbox': [886.2984008654021, 802.1414167098701, 13.701599134597927, 18.78182404860854], 'iscrowd': 0}], 'categories': [{'id': 1, 'name': 1.0}, {'id': 2, 'name': 0.0}], 'images': [{'id': 1, 'file_name': 'sample_geotiff.tif', 'width': 900, 'height': 900}]}\n" + ] + } + ], + "source": [ + "print(coco_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And, to show the image with the labels overlaid:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "from matplotlib import patches\n", + "import skimage" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "im = skimage.io.imread(sample_image)\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(im, cmap='gray')\n", + "colors = ['', 'r', 'b']\n", + "for anno in coco_dict['annotations']:\n", + " patch = patches.Rectangle((anno['bbox'][0], anno['bbox'][1]), anno['bbox'][2], anno['bbox'][3], linewidth=1, edgecolor=colors[anno['category_id']], facecolor='none')\n", + " ax.add_patch(patch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's a little tough to see here, but building bounding boxes from the COCO dataset are boxed, with truncated buildings (at the edge of the image) in a different category.\n", + "\n", + "#### Example 2: A dataset with multiple images and geojsons (for example, tiled SpaceNet datasets)\n", + "\n", + "To use multiple images and geojsons, `solaris` needs a way to match them to one another. This can be done one of two ways:\n", + "1. If the images and their corresponding geojsons have the exact same filenames once extension and directory information are removed, then `solaris` can match them without any help.\n", + "2. You can provide a regex to extract substrings from image and geojson filenames that should be identical between matching files.\n", + "\n", + "Since 2. is more complicated, we'll show an example of doing that here. We'll also include license information to show what that looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 17.27it/s]\n" + ] + } + ], + "source": [ + "sample_geojsons = [os.path.join(data_dir, 'vectortile_test_expected/geoms_733601_3724734.geojson'),\n", + " os.path.join(data_dir, 'vectortile_test_expected/geoms_733601_3724869.geojson')]\n", + "sample_images = [os.path.join(data_dir, 'rastertile_test_expected/sample_geotiff_733601_3724734.tif'),\n", + " os.path.join(data_dir, 'rastertile_test_expected/sample_geotiff_733601_3724869.tif')]\n", + "\n", + "coco_dict = sol.data.coco.geojson2coco(sample_images,\n", + " sample_geojsons,\n", + " matching_re=r'(\\d+_\\d+)',\n", + " license_dict={'CC-BY 4.0': 'https://creativecommons.org/licenses/by/4.0/'},\n", + " verbose=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, we'll display the json to show what the output looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "annotations": [ + { + "area": 214.14410906402435, + "bbox": [ + 47.80893106292933, + 74.87320505268872, + 26.02481837873347, + 15.126794947311282 + ], + "category_id": 1, + "id": 1, + "image_id": 1, + "iscrowd": 0, + "segmentation": [ + 60.03418597159907, + 74.87320505268872, + 73.8337494416628, + 90, + 51.516283753560856, + 90, + 47.80893106292933, + 85.93607368506491, + 60.03418597159907, + 74.87320505268872 + ] + }, + { + "area": 232.6028019573394, + "bbox": [ + 70.69254911504686, + 0, + 19.30745088495314, + 13.249627484939992 + ], + "category_id": 1, + "id": 2, + "image_id": 2, + "iscrowd": 0, + "segmentation": [ + 90, + 11.015026673674583, + 70.7970443549566, + 13.249627484939992, + 70.8928169994615, + 4.990449592471123, + 70.69254911504686, + 0, + 90, + 0, + 90, + 11.015026673674583 + ] + }, + { + "area": 853.8212747899074, + "bbox": [ + 71.78515014378354, + 21.638346442952752, + 18.21484985621646, + 49.3236445998773 + ], + "category_id": 1, + "id": 3, + "image_id": 2, + "iscrowd": 0, + "segmentation": [ + 89.06576380180195, + 21.638346442952752, + 90, + 28.386366279795766, + 90, + 68.61032488476485, + 85.23654213640839, + 70.96199104283005, + 73.38412117748521, + 70.6515495320782, + 71.78515014378354, + 65.98500318173319, + 72.83866719854996, + 48.2692635813728, + 72.19266184815206, + 21.76100580766797, + 89.06576380180195, + 21.638346442952752 + ] + } + ], + "categories": [ + { + "id": 1, + "name": "other" + } + ], + "images": [ + { + "file_name": "sample_geotiff_733601_3724734.tif", + "height": 90, + "id": 1, + "license": 1, + "width": 90 + }, + { + "file_name": "sample_geotiff_733601_3724869.tif", + "height": 90, + "id": 2, + "license": 1, + "width": 90 + } + ], + "licenses": [ + { + "id": 1, + "name": "CC-BY 4.0", + "url": "https://creativecommons.org/licenses/by/4.0/" + } + ] + }, + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "JSON(coco_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'annotations': [{'id': 1, 'image_id': 1, 'category_id': 1, 'segmentation': [60.03418597159907, 74.87320505268872, 73.8337494416628, 90.0, 51.516283753560856, 90.0, 47.80893106292933, 85.93607368506491, 60.03418597159907, 74.87320505268872], 'area': 214.14410906402435, 'bbox': [47.80893106292933, 74.87320505268872, 26.02481837873347, 15.126794947311282], 'iscrowd': 0}, {'id': 2, 'image_id': 2, 'category_id': 1, 'segmentation': [90.0, 11.015026673674583, 70.7970443549566, 13.249627484939992, 70.8928169994615, 4.990449592471123, 70.69254911504686, 0.0, 90.0, 0.0, 90.0, 11.015026673674583], 'area': 232.6028019573394, 'bbox': [70.69254911504686, 0.0, 19.30745088495314, 13.249627484939992], 'iscrowd': 0}, {'id': 3, 'image_id': 2, 'category_id': 1, 'segmentation': [89.06576380180195, 21.638346442952752, 90.0, 28.386366279795766, 90.0, 68.61032488476485, 85.23654213640839, 70.96199104283005, 73.38412117748521, 70.6515495320782, 71.78515014378354, 65.98500318173319, 72.83866719854996, 48.2692635813728, 72.19266184815206, 21.76100580766797, 89.06576380180195, 21.638346442952752], 'area': 853.8212747899074, 'bbox': [71.78515014378354, 21.638346442952752, 18.21484985621646, 49.3236445998773], 'iscrowd': 0}], 'categories': [{'id': 1, 'name': 'other'}], 'licenses': [{'name': 'CC-BY 4.0', 'url': 'https://creativecommons.org/licenses/by/4.0/', 'id': 1}], 'images': [{'id': 1, 'file_name': 'sample_geotiff_733601_3724734.tif', 'width': 90, 'height': 90, 'license': 1}, {'id': 2, 'file_name': 'sample_geotiff_733601_3724869.tif', 'width': 90, 'height': 90, 'license': 1}]}\n" + ] + } + ], + "source": [ + "print(coco_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Still have questions?\n", + "\n", + "Check the API documentation for [sol.data.coco.geojson2coco](../../api/data.rst#solaris.data.coco.geojson2coco) or open an issue in [the Solaris GitHub repo](https://github.com/cosmiq/solaris)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_evaluation_tutorial.ipynb b/docker/solaris/docs/tutorials/notebooks/api_evaluation_tutorial.ipynb new file mode 100644 index 00000000..5378b883 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_evaluation_tutorial.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scoring model performance with the `solaris` python API\n", + "\n", + "This tutorial describes how to run evaluation of a proposal (CSV or .geojson) for a single chip against a ground truth (CSV or .geojson) for the same chip.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CSV Eval\n", + "\n", + "### Steps\n", + "1. Imports\n", + "2. Load ground truth CSV\n", + "3. Load proposal CSV\n", + "4. Perform evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Imports \n", + "\n", + "For this test case we will use the `eval` submodule within `solaris`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import solaris as sol\n", + "from solaris.data import data_dir\n", + "import pandas as pd # just for visualizing the outputs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "\n", + "### Load ground truth CSV\n", + "\n", + "We will first instantiate an `Evaluator()` object, which is the core class `eval` uses for comparing predicted labels to ground truth labels. `Evaluator()` takes one argument - the path to the CSV or .geojson ground truth label object. It can alternatively accept a pre-loaded `GeoDataFrame` of ground truth label geometries." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Evaluator sample_truth.csv" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ground_truth_path = os.path.join(data_dir, 'sample_truth.csv')\n", + "\n", + "evaluator = sol.eval.base.Evaluator(ground_truth_path)\n", + "evaluator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, `evaluator` has the following attributes:\n", + "\n", + "- `ground_truth_fname`: the filename corresponding to the ground truth data. This is simply `'GeoDataFrame'` if a GDF was passed during instantiation.\n", + "\n", + "- `ground_truth_GDF`: GeoDataFrame-formatted geometries for the ground truth polygon labels.\n", + "\n", + "- `ground_truth_GDF_Edit`: A deep copy of `eval_object.ground_truth_GDF` which is edited during the process of matching ground truth label polygons to proposals.\n", + "\n", + "- `ground_truth_sindex`: The RTree/libspatialindex spatial index for rapid spatial referencing.\n", + "\n", + "- `proposal_GDF`: An _empty_ GeoDataFrame instantiated to hold proposals later.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Load proposal CSV\n", + "\n", + "Next we will load in the proposal CSV file. Note that the `proposalCSV` flag must be set to true for CSV data. If the CSV contains confidence column(s) that indicate confidence in proprosals, the name(s) of the column(s) should be passed as a list of strings with the `conf_field_list` argument; because no such column exists in this case, we will simply pass `conf_field_list=[]`. There are additional arguments available (see [the method documentation](https://cw-eval.readthedocs.io/en/latest/api.html#cw_eval.baseeval.EvalBase.load_proposal)) which can be used for multi-class problems; those will be covered in another recipe. The defaults suffice for single-class problems." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "proposals_path = os.path.join(data_dir, 'sample_preds.csv')\n", + "evaluator.load_proposal(proposals_path, proposalCSV=True, conf_field_list=[])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "\n", + "### Perform evaluation\n", + "\n", + "Evaluation iteratively steps through the proposal polygons in `eval_object.proposal_GDF` and determines if any of the polygons in `eval_object.ground_truth_GDF_Edit` have IoU overlap > `miniou` (see [the method documentation](https://cw-eval.readthedocs.io/en/latest/api.html#cw_eval.baseeval.EvalBase.eval_iou)) with that proposed polygon. If one does, that proposal polygon is scored as a true positive. The matched ground truth polygon with the highest IoU (in case multiple had IoU > `miniou`) is removed from `eval_object.ground_truth_GDF_Edit` so it cannot be matched against another proposal. If no ground truth polygon matches with IoU > `miniou`, that proposal polygon is scored as a false positive. After iterating through all proposal polygons, any remaining ground truth polygons in `eval_object.ground_truth_GDF_Edit` are scored as false negatives.\n", + "\n", + "There are several additional arguments to this method related to multi-class evaluation which will be covered in a later recipe. See [the method documentation](https://cw-eval.readthedocs.io/en/latest/api.html#cw_eval.baseeval.EvalBase.eval_iou) for usage.\n", + "\n", + "The prediction outputs a `list` of `dict`s for each class evaluated (only one `dict` in this single-class case). The `dict`(s) have the following keys:\n", + "\n", + "- `'class_id'`: The class being scored in the dict, `'all'` for single-class scoring.\n", + "\n", + "- `'iou_field'`: The name of the column in `eval_object.proposal_GDF` for the IoU score for this class. See [the method documentation](https://cw-eval.readthedocs.io/en/latest/api.html#cw_eval.baseeval.EvalBase.eval_iou) for more information.\n", + "\n", + "- `'TruePos'`: The number of polygons in `eval_object.proposal_GDF` that matched a polygon in `eval_object.ground_truth_GDF_Edit`.\n", + "\n", + "- `'FalsePos'`: The number of polygons in `eval_object.proposal_GDF` that had no match in `eval_object.ground_truth_GDF_Edit`.\n", + "\n", + "- `'FalseNeg'`: The number of polygons in `eval_object.ground_truth_GDF_Edit` that had no match in `eval_object.proposal_GDF`.\n", + "\n", + "- `'Precision'`: The [precision statistic](https://en.wikipedia.org/wiki/Precision_and_recall) for IoU between the proposals and the ground truth polygons.\n", + "\n", + "- `'Recall'`: The [recall statistic](https://en.wikipedia.org/wiki/Precision_and_recall) for IoU between the proposals and the ground truth polygons.\n", + "\n", + "- `'F1Score'`: Also known as the [SpaceNet Metric](https://medium.com/the-downlinq/the-spacenet-metric-612183cc2ddb), the [F1 score](https://en.wikipedia.org/wiki/F1_score) for IoU between the proposals and the ground truth polygons." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "151it [00:00, 153.44it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'class_id': 'all',\n", + " 'iou_field': 'iou_score_all',\n", + " 'TruePos': 151,\n", + " 'FalsePos': 0,\n", + " 'FalseNeg': 0,\n", + " 'Precision': 1.0,\n", + " 'Recall': 1.0,\n", + " 'F1Score': 1.0}]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator.eval_iou(calculate_class_scores=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, the score is perfect because the polygons in the ground truth CSV and the proposal CSV are identical. At this point, a new proposal CSV can be loaded (for example, for a new nadir angle at the same chip location) and scoring can be repeated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## GeoJSON Eval\n", + "\n", + "The same operation can be completed with .geojson-formatted ground truth and proposal files. See the example below, and see the detailed explanation above for a description of each step's operations." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "28it [00:00, 76.20it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'class_id': 'all',\n", + " 'iou_field': 'iou_score_all',\n", + " 'TruePos': 8,\n", + " 'FalsePos': 20,\n", + " 'FalseNeg': 20,\n", + " 'Precision': 0.2857142857142857,\n", + " 'Recall': 0.2857142857142857,\n", + " 'F1Score': 0.2857142857142857}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ground_truth_geojson = os.path.join(data_dir, 'gt.geojson')\n", + "proposal_geojson = os.path.join(data_dir, 'pred.geojson')\n", + "\n", + "evaluator = sol.eval.base.Evaluator(ground_truth_geojson)\n", + "evaluator.load_proposal(proposal_geojson, proposalCSV=False, conf_field_list=[])\n", + "evaluator.eval_iou(calculate_class_scores=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note that the above comes from a different chip location and different proposal than the CSV example, hence the difference in scores)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_mask_to_vector.ipynb b/docker/solaris/docs/tutorials/notebooks/api_mask_to_vector.ipynb new file mode 100644 index 00000000..ad29c461 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_mask_to_vector.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Converting model outputs to vector format using the Python API\n", + "\n", + "To use segmentation masks in a geospatial application, one often needs to convert to a vector format. This is a non-trivial task in many cases and a lot of science goes into finding the best way to convert a pixel mask to vector-formatted outputs, but we've provided a basic implementation in `solaris` for users to build from.\n", + "\n", + "Let's begin with an image showing some predicted building footprints:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import solaris as sol\n", + "import os\n", + "import skimage\n", + "import matplotlib.pyplot as plt\n", + "\n", + "mask_image = skimage.io.imread(os.path.join(sol.data.data_dir, 'sample_fbc_from_df2px.tif'))\n", + "\n", + "f, ax = plt.subplots(figsize=(10, 8))\n", + "plt.imshow(mask_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(900, 900, 3)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask_image.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This image has the value 255 in the first channel anywhere a building footprint is predicted, 255 in the second channel where an edge is predicted, and 255 in the third channel anywhere two buildings are very near one another:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axarr = plt.subplots(3, 1, figsize=(8, 24))\n", + "\n", + "axarr[0].imshow(mask_image[:, :, 0], cmap='gray')\n", + "axarr[0].set_title('footprints', fontsize=16)\n", + "axarr[1].imshow(mask_image[:, :, 1], cmap='gray')\n", + "axarr[1].set_title('edges', fontsize=16)\n", + "axarr[2].imshow(mask_image[:, :, 2], cmap='gray')\n", + "axarr[2].set_title('contacts', fontsize=16);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll work with just the first channel for the simplest case of going from footprints to polygons. We'll use the `solaris.vector.mask.mask_to_poly_geojson()` function. This function has a number of arguments for customizing function:\n", + "\n", + "- pred_arr: The prediction array (in this case, `mask_image`)\n", + "- channel_scaling: Scaling factors to use if using a multi-channel mask; see the next example.\n", + "- reference_im: A georeferenced image that has the same extent as `mask_image` to use for georeferencing polygons. This is optional.\n", + "- output_path: The path to the file to save. If not provided, the geometries are returned in a geopandas `GeoDataFrame`, but no file is saved.\n", + "- output_type: Should the saved file be a `'csv'` or a `'geojson'`?\n", + "- min_area: Use this argument to set a minimum area for geometries to be retained. This can be useful to eliminate speckling or very small, erroneous predictions.\n", + "- bg_threshold: The value to set to separate background from foreground pixels in the mask. In this example, we'll use `1` because anything >0 is foreground.\n", + "- simplify: A boolean to indicate whether or not you'd like to use the Douglas-Peucker algorithm to simplify geometries. This can _dramatically_ accelerate processing of geometries later, and can also make your geometries look nicer!\n", + "- tolerance: The tolerance parameter for the Douglas-Peucker simplification algorithm. Only has an effect if `simplify=True`.\n", + "\n", + "Let's convert the first channel of the above mask to georegistered polygons." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometryvalue
0POLYGON ((542 0, 542 1, 545 1, 545 2, 546 2, 5...255.0
1POLYGON ((202 52, 202 53, 201 53, 190 53, 190 ...255.0
2POLYGON ((339 54, 339 72, 340 72, 340 78, 352 ...255.0
3POLYGON ((548 55, 548 56, 547 56, 542 56, 542 ...255.0
4POLYGON ((261 59, 261 60, 260 60, 248 60, 248 ...255.0
\n", + "
" + ], + "text/plain": [ + " geometry value\n", + "0 POLYGON ((542 0, 542 1, 545 1, 545 2, 546 2, 5... 255.0\n", + "1 POLYGON ((202 52, 202 53, 201 53, 190 53, 190 ... 255.0\n", + "2 POLYGON ((339 54, 339 72, 340 72, 340 78, 352 ... 255.0\n", + "3 POLYGON ((548 55, 548 56, 547 56, 542 56, 542 ... 255.0\n", + "4 POLYGON ((261 59, 261 60, 260 60, 248 60, 248 ... 255.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geoms = sol.vector.mask.mask_to_poly_geojson(mask_image[:, :, 0])\n", + "geoms.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's the output! We'll use a shapely convenience function to visualize them:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from shapely.ops import cascaded_union\n", + "cascaded_union(geoms['geometry'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And there the geometries are! Just like the input mask (flipped vertically because they count up instead of down; if you georeference your outputs, this won't matter.)\n", + "\n", + "What if we want to use some complicated logic around a multi-channel mask to generate predictions? For example, what if we want to predict where edges and contact points are, then subtract those values to make sure we separate buildings well (a common challenge for building footprint extraction algorithms!) To do so, we'll use the `channel_scaling` argument, which allows you to specify the following operation:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$mask(x, y) = \\sum_{c}^{ } mask[x, y, c]\\times channel\\_scaling[c]$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where c is the channel index. So, in this example, let's say we want to subtract the edges and contact layers from the footprint - we will set `channel_scaling=[1, -1, -1]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geoms = sol.vector.mask.mask_to_poly_geojson(mask_image, channel_scaling=[1, -1, -1])\n", + "cascaded_union(geoms['geometry'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Though not readily apparent in this particular example, this can be extremely useful with imperfect predictions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_masks_tutorial.ipynb b/docker/solaris/docs/tutorials/notebooks/api_masks_tutorial.ipynb new file mode 100644 index 00000000..ac990cc3 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_masks_tutorial.ipynb @@ -0,0 +1,426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating training masks with the `solaris` python API\n", + "\n", + "You can create training masks from geojson-formatted labels with a single `solaris` command. Here, we'll go through creating masks using a sample image and geojson provided with `solaris`.\n", + "\n", + "Solaris enables creation of four types of masks:\n", + "\n", + "- [Polygon footprints](#polygon-footprints), for buildings or other objects\n", + "- [Polygon outlines](#polygon-edges), for outlines of buildings or other target objects\n", + "- [Polygon contact points](#polygon-contact-points), for places where polygons are closely apposed to one another (e.g. buildings in suburbs)\n", + "- [Road network masks](#road-network-masks), from linestring-formatted road networks\n", + "\n", + "The first three options here can also be combined to make multi-channel training targets, [as many of the SpaceNet 4 competitors did](https://medium.com/the-downlinq/a-deep-dive-into-the-spacenet-4-winning-algorithms-8d611a5dfe25).\n", + "\n", + "Let's start with creating building footprints." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polygon footprints\n", + "\n", + "The [solaris.vector.mask.footprint_mask()](../../api/vector.rst#solaris.vector.mask.footprint_mask) function creates footprints from polygons, with 0s on the outside of the polygon and _burn\\_value_ on the inside. The function's arguments:\n", + "\n", + "- `df`: A `pandas.DataFrame` or `geopandas.GeoDataFrame` containing polygons in one column.\n", + "- `out_file`: An optional argument to specify a filepath to save outputs to.\n", + "- `reference_im`: A georegistered image covering the same geography as `df`. This is optional, but if it's not provided and you wish to convert the polygons from a geospatial CRS to pixel coordinates, you must provide `affine_obj`.\n", + "- `geom_col`: An optional argument specifying which column holds polygons in `df`. This defaults to `\"geometry\"`, the default geometry column in GeoDataFrames.\n", + "- `do_transform`: Should polygons be transformed from a geospatial CRS to pixel coordinates? `solaris` will try to infer whether or not this is necessary, but you can force behavior by providing `True` or `False` with this argument.\n", + "- `affine_obj`: An [affine](https://pypi.org/project/affine/) object specifying a transform to convert polygons from a geospatial CRS to pixel coordinates. This optional (unless `do_transform=True` and you don't provide `reference_im`), and is superceded by `reference_im` if that argument is also provided.\n", + "- `shape`: Optionally, the `(X, Y)` bounds of the output image. If `reference_im` is provided, the shape of that image supercedes this argument.\n", + "- `out_type`: Either `int` or `float`, the dtype of the output. Defaults to `int`.\n", + "- `burn_value`: The value that pixels falling within the polygon will be set to. Defaults to `255`.\n", + "- `burn_field`: Optionally, a column within `df` that specifies the value to set for each individual polygon. This can be used if you have multiple classes.\n", + "\n", + "Let's make a mask! We'll use `sample_geotiff.tif` from the sample data and `geotiff_labels.geojson` for polygons. First, we'll open those two objects up so you can see what we're working with." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import solaris as sol\n", + "from solaris.data import data_dir\n", + "import os\n", + "import skimage\n", + "import geopandas as gpd\n", + "from matplotlib import pyplot as plt\n", + "from shapely.ops import cascaded_union\n", + "\n", + "image = skimage.io.imread(os.path.join(data_dir, 'sample_geotiff.tif'))\n", + "f, axarr = plt.subplots(figsize=(10, 10))\n", + "plt.imshow(image, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a panchromatic image of an area in Atlanta. Can you pick out the buildings?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf = gpd.read_file(os.path.join(data_dir, 'geotiff_labels.geojson'))\n", + "cascaded_union(gdf.geometry.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a convenient visualization built into Jupyter notebooks, but what are the objects it's showing?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POLYGON ((733633.9175634939 3724917.327059259, 733644.0265766426 3724916.940842033, 733643.0617814267 3724892.157892002, 733632.9527424234 3724892.544111664, 733633.9175634939 3724917.327059259))\n", + "POLYGON ((733653.1326928001 3724949.324520395, 733648.855268121 3724922.585283833, 733642.8083045555 3724925.412150491, 733636.7858552651 3724926.852379398, 733638.0802132279 3724931.256500166, 733642.4909367257 3724931.19752501, 733643.0288657807 3724937.691814653, 733641.6848001335 3724942.564334524, 733641.2768538269 3724944.829464028, 733643.2673519406 3724949.61678283, 733653.1326928001 3724949.324520395))\n", + "POLYGON ((733614.6924076959 3725025.91770485, 733618.2329309537 3725025.759830723, 733618.3612589325 3725028.493043821, 733623.4073750104 3725028.260883178, 733623.2266384158 3725024.250133027, 733629.2206437828 3725023.974487836, 733628.4996830962 3725008.231178387, 733624.4670389239 3725005.347326851, 733613.7709592135 3725005.83023022, 733614.6924076959 3725025.91770485))\n" + ] + } + ], + "source": [ + "for geom in gdf.geometry[0:3]:\n", + " print(geom)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A bunch of Well-Known-Text (WKT)-formatted strings. Let's convert these into something a deep learning model can use." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fp_mask = sol.vector.mask.footprint_mask(df=os.path.join(data_dir, 'geotiff_labels.geojson'),\n", + " reference_im=os.path.join(data_dir, 'sample_geotiff.tif'))\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "plt.imshow(fp_mask, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a `numpy` array of shape `(900, 900)` with `0` at non-building pixels and `255` at building pixels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polygon outlines\n", + "\n", + "What if we want to find all of the edges of these buildings? In that case, we can use the [solaris.vector.mask.boundary_mask()](../../api/vector.rst#solaris.vector.mask.boundary_mask) function. There are a few arguments to this function:\n", + "\n", + "- `footprint_msk`: Instead of providing `df` as you did for [footprint_mask()](../../api/vector.rst#solaris.vector.mask.footprint_mask), for this function you can provide the footprint output. (Don't worry, if you didn't make one yet, you can still just provide the `df` argument and other [footprint_mask()](../../api/vector.rst#solaris.vector.mask.footprint_mask) arguments - `solaris` will create it behind the scenes)\n", + "- `out_file`: Same as earlier.\n", + "- `reference_im`: Same as earlier.\n", + "- `boundary_width`: The width, in pixel units, of the outline around the polygon. Defaults to `3`.\n", + "- `boundary_type`: Should the boundary be inside the polygon (`\"inner\"`, the default value) or outside (`\"outer\"`)?\n", + "- `burn_value`: Same as earlier, still defaults to `255`.\n", + "\n", + "You can also provide additional keyword arguments to pass to [footprint_mask()](../../api/vector.rst#solaris.vector.mask.footprint_mask) if that needs to be made first." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAJCCAYAAADKjmNEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dbax1Z3kf+P81OEBCOxiTjEVtz0AUKxGqJmA/So0SVS00HWCqmA+IAXUGC3n0fMlMk7ZSh8xIM4o0HxqpKg2aERorpDVRhoTSMLZQlJQxSJUqQeInEAI4lCcvYFsGpwk40yKldXvPh7O2vZ7tfc7Z73u9/H7S1tl77X3OWWvvvdb6r+u+77WqtRYAAM78J6eeAQCAIRGOAAB6hCMAgB7hCACgRzgCAOgRjgAAeg4SjqrqzVX15aq6XlXvPcT/AAA4hNr3eY6q6kVJ/lWSH03yRJLfTPKu1tqX9vqPAAAO4BCVox9Kcr219vuttX+X5JeS3HuA/wMAsHc3HeBv3pbk8d7jJ5L8pYt+oaqcphvYm7vvvjtJcu3ataP8Hvt13uewmN5/7u677/Z5sYt/3Vr7nuWJhwhHa6mqq0munur/A9P16KOPJkmqau3fWXQx2OR3OIxr167d8Hn0u38sP+5PZzvbdK+Z0Pv91VUTD9Gs9mSSO3qPb++m3aC19kBr7Upr7crdd9+91YcDsGyXkDOhDf4kLT6fqnrutmAfspv+e3rZbQ4OEY5+M8mdVfWaqnpxkncmefgA/weAmZvbTpvj2HuzWmvt2ar6H5L8epIXJfn51toXL/qdRXuxsjawD+tuQ1QbxuG8pjQ4lL0P5d9qJnodsgUkYFubbj9aa7Y1A7bc78hndRibvrcT+yyutdauLE8c5BmyJ/SmA0fiwGr6hnAwzzwMMhwBHJod7fAJupyKcARMxqZ9jex8x8HnxbEJR8DoqQIB+3Syk0AC7JOq0XT5rDi2QVWOHP0Bm9pkuyEYjY/PilMYVDgCADg14QgYLZUg4BCEIwCAHuEIGKVtzoa9yeuB+RKOgFFrra3dKVswAtYhHAGjtBx0LgpIRsICmxhkOLIhA9ZRVc/dktVVJM1pwKYGdRLIVRs4GzRgHRcFJIBNDLJyBLAtB1TArgZVOVo47wjQRg9YR39b0Vqz7QA2MujKUb8vQaIvErA5wQgupzn6RoMORws2bgBwGPaxLzSKcJT48ADgUPrdWRhROAIADkdAep5wBADQM8jRagCXHb1qaof9q6rnOmfPeR0TjoBBO+8yIReFpzlv1GFX/YA0V8IRMCqbXFMN2M7cDzCEI2Cw1tlAC0vAvg2iQ/bdd99tgwYADMKgKkcuFQIAnFoNoWJTVTfMhI6WMG+LbcA26/vcR9kAG7nWWruyPHFQlaOF8zZsQwhywHFsG4wAdjWIPkfrcjQIXMZ2Ag5jTsP7B1k5AgBO67wgNIema+EIAGZs036+c6gejapZDeA8c9hgwzFNvTp0EZUjAJi5OQehVYQjYFA2qQCpFgGHoFltBRtcAJgvlaMlq674rdwIx7XpOmcdBfZJ5ajnvLPyqiQBwHyoHJ2jH5CWT3x12VHqHM4BAUNhXQP2TTjqXHQtp8W05Sa3iypMmuVgc6q0wBBoVttAVb2gorTq/jIbfNg/6xVwKCpHS9ZpEquqjTpu95vlVJFgf6xP67H9gc0IR53lwLNOQDrU3wbYB9U12I5mtZ6LOmHv42/3+y7ZaAGHpN8jbE84WrKqX9G+Q9Kh/jZMgR357vrNaN5P2JxmtXPsMpR/3b8tGAH7dlH/Is36sB6VozU4KSQwBrZNsB/C0ZoW5el99RsyegRuZMe+m8ua0lSsYX2a1bawKiBtEnIEI1ht3XXCDv5G674fi5Gzq15vewTPUznawXknhAQOz878hdZ5T5ar4Au2YfA8laMdndfMdtEJIYHVLls/BKIb7TpQpH8ONu8tPE842pN1r7923jSYu3Uu6OzgYrVttymCEax2abNaVf18VT1dVV/oTbulqj5RVV/pfr6im15V9f6qul5Vn6+quw4580N00fXXgO31m4PszHcPNoIRnG+dPkf/JMmbl6a9N8kjrbU7kzzSPU6StyS5s7tdTfKB/czm+Kwa2XaooOSIGuZFMILDujQctdb+RZI/WZp8b5IHu/sPJnlbb/qH2plPJ7m5ql61r5kdo+Wj3UNujAQkmL59BSPgfNuOVru1tfZUd//rSW7t7t+W5PHe657opr1AVV2tqker6tEt54EVVJFgug51KSPgRjt3yG6ttaraeI1trT2Q5IEk2eb3ObN8FNnvEG7jB9Ozj4qRbQOXmftB9raVo28smsu6n093059Mckfvdbd30ziAVeX1fZ7FG5gGwYjL9PvGLp9t/ZjdQ4Zi23D0cJL7uvv3JXmoN/3d3ai1e5I802t+44iMmAOWzWGnxubmGoAucmmzWlV9OMlfSfLdVfVEkv8tyd9P8pGquj/JV5O8o3v5ryZ5a5LrSb6d5D0HmGeyXqfMXS9zAoyfdR82V0OoKuhztJltS+Q2kjBum/YltM7Dpa611q4sT3RttRGzwYP52PRAdggHvjBWwtGMCFMwbuuuw8sdaoHNCEcjtu2RoVFsMF3WbdidC8+OUP9K2pv2QVj+3cU0OJZNd96+n8+7bH03ZB/2QzgaqV1Goi2fMHIshLnpWOczHNv389D6BzarWD9gfzSrjdwuG8IxbUT7O4VDX8gXgHkTjiZg10rQWEKGk5PBaqpGsF/C0URsG5DGvDEd87zDtlZVURPrA+yTcDQhY+1LtK6pLtec+Ax3c946LhjBfglHEzW1E8a5mC7cSMUIDsdotYnZZhTbYhTMpqcFOLYhzxscS38dt05wnkMfRE79uyccTdS250KywQUgOT8AzaF6LxxN2CZVpF3OmwTrGkuVcgy8f1zGd2R7+hzNQH/4+2V9dpZfC/tmgw0MnXA0I/2d0mXBxw6MQxO+gaESjmZm06t6wyEI38CQCUczdFkTmz5HAMyZcDRTmzSxwaFo3gWGyGi1GXNiRQB4IZUjVl7Q1RE7AOeZ+gG1yhE3EIoA5mvqoWddwhEwODbQcDoOkjWrASdw0YhIoyWBUxOOgMFQMQKGQLMacBIXVYamXjW67BI+wGkJR8BJnBcQ5hQOlpdV5QyGQThi1Fb1T1newcxpZzsWVbUyCPisgCEQjpiE8wLR4gSXdrrD4zMBhkqHbCbNDhiATakcMVqGfDNFKp1wesIRo7RpMLLDAeZMZ//NCEfMhoAEzIlAtD3hiNHZtGq03Dl7eTrA1Ni+7UY4YpS2WfGXh/vrs3Q82xzBTvlzWZzKYNV3cMrLDWNhtBqjsq8y8UXnReL05hAQzltG30c4PZUjRmPflZ5VzW1z2Cmfyiad5+eiX0Hy3YPhUDli9qrq3H5JAMyPcAQdR+4AJMIRI6HZiyla/l77fsMwCEeMxrF2HHZQHNOq75umXTgt4YjBs6MA4JiMVmPQNKcxRasCvxOUwnAIR8BBqfzdaDkECUUwPJrVGKxj71TtxDkmQQiGSzhisE5xFms7LI5h1eVDfPdgODSrMWjHumisqhHHpC8dDJvKEaOwvBMRZhg7wQiGS+WI0VhuZtvX0bejeI7Ndw2GTThilPbd3GZnBcCCZjVGTXPbtMzh8xPEYfhUjhi985rblp9bNocdMQCbu7RyVFV3VNWnqupLVfXFqvqJbvotVfWJqvpK9/MV3fSqqvdX1fWq+nxV3XXohYCF5SHRy2HpvN8BgIV1mtWeTfJ3W2uvTXJPkh+vqtcmeW+SR1prdyZ5pHucJG9Jcmd3u5rkA3ufa7jEqpDUp2oETJlt3G4uDUettadaa7/V3f//kjyW5LYk9yZ5sHvZg0ne1t2/N8mH2plPJ7m5ql619zmHNfRD0qKK5MR7nFL/e7juDdblO7MfG/U5qqpXJ3l9ks8kubW19lT31NeT3Nrdvy3J471fe6Kb9lRvWqrqas4qS3BwQhBDssn30Y6OdVx0zT42t/Zotar6c0n+WZKfbK39af+5dvYpbPRJtNYeaK1daa1d2eT3AIDnuXjx/q0VjqrqO3IWjH6xtfYr3eRvLJrLup9Pd9OfTHJH79dv76YBAHukm8BhrDNarZJ8MMljrbV/2Hvq4ST3dffvS/JQb/q7u1Fr9yR5ptf8BgDskVC0f+v0OfrhJP9dkt+pqs910/7nJH8/yUeq6v4kX03yju65X03y1iTXk3w7yXv2OscAwKWcxmR7NYROW1V1+pkADmKTa9fN4Tp3rbWNO2RP+f1gN9t+P3yvnnNtVd9nlw8BDs5GGBgT4QjgyIZQsQfO59pqwMGtEwbmUl1anINGswYMl8oRMAiqKcBQqBwBB6U68kKqRzBswhHACQhI7GpRbVV13T/hCABGSKg+nMmGo/OStC8TAHCRyYaj5IVBSOkRGBoHbDA8kw5HwOmsezAiHABDM4lwtM5GWNUIju+i4HNRZ1KB6YztFpzGJMLRgg0qjMd56+tcAoHKGgzXpMLROi7aINkIwX7N4UKy25hLAISxmk04umzjbGMF+7VY5zZZt6a+HvaXT2CE4ZpNOAI4JVU0GA/XVgMA6BGOAAB6hCMAgB7hCDiYqXewBqZJOAIOQgfkG3kfYDyMVgMOZp1AMLfqUmtNUIKBE46Avdsm8MwhMFTV7MIgjNFkwtEcNqwwVXNbfwUkGDZ9jgCOaG5BEMZoMpWjXTiKg/1aNB/pX7Oa9wSGTeWoY2MF+7XNtdUAhkA4Ag5GQALGSDgCAOgRjkbM0ThjoHoEjI1wNHJ2OACwX7MPR2MNF/35XowKgqG6bMCD7zAwJLMeyj+FjXG/yaK/PEbfMRZzbnZbd5mtz3Bcs6wc9YNEVY1uw7Pqgp7LyzDHHQ0A7MNkKkdzu5bTqnnvT1sOgMAwrdPkCBzXJMLRnHb+m5bhNbfBMAk9MFyzbFabE2EIADYzicoRF1NFYujmWkWxDsIwqRyNzC4je5Y7nxs+DQAvpHI0Ytte8Xw5YLlyOscijANjIByN0L6ayZb/joDEIRmVBYyFZrURW9VMtsvfglPyHQSGQjiagF36ETlaB4AbCUcTsU1nayeJBIAX0udoYpwlGwB2o3I0YRdVkoQmAFhN5WjiVg3bX34OAHiecDQT+xrVBgBTp1lthlSMAOB8wtFMCUgAsJpwBADQo88RcBBGRO7Pqn6C3lc4nEsrR1X10qr6jar67ar6YlX9dDf9NVX1maq6XlW/XFUv7qa/pHt8vXv+1YddBGDIFqeRWOfGCy1O8Lp8olfgcNZpVvuzJG9srf1gktcleXNV3ZPkZ5K8r7X2fUm+meT+7vX3J/lmN/193euAGVresZ93AxiSS8NRO/Nvuoff0d1akjcm+Wg3/cEkb+vu39s9Tvf8m8rWDwAYibU6ZFfVi6rqc0meTvKJJL+X5FuttWe7lzyR5Lbu/m1JHk+S7vlnkrxynzMNTI9jKGAo1uqQ3Vr7D0leV1U3J/lYkh/Y9R9X1dUkV3f9OwBDpz8VjMtGQ/lba99K8qkkb0hyc1UtwtXtSZ7s7j+Z5I4k6Z5/eZI/XvG3HmitXWmtXdly3gFGQ18rGI91Rqt9T1cxSlV9Z5IfTfJYzkLS27uX3Zfkoe7+w93jdM9/sjlsgtkw8gwYu3Wa1V6V5MGqelHOwtRHWmsfr6ovJfmlqvrfk3w2yQe7138wyS9U1fUkf5LknQeYb2DAVEWAMashHOFV1elnAtiL1trG4Wib3xmTfS6fk2vCXl1b1b3H5UMAAHqEIwCAHtdWAziCIXRhANYjHA2EfgQwXdZrGBfNagOx2HgaBs3c+L4DQ6NyNCAXBSRHnkyZ7zcwJCpHA9bfYSwCk6NsADgs4WjgVl1mQEACgMMRjkbCtZgA4DiEoxExog0ADk84AgDoEY4GRn8iADgtQ/kHZJ1mM01qAHBYKkcjoaJ0xvsAwKEJRyMy56pR/xxPAtJw+WyAKdCsNhBGoq3W39n2zyDOcPkOA2OncjQgdio3WhWMVj0HAPukcnRi6+zk5xYELgpFi2mLZjaBEmA6LtrfHXN7r3J0QpeFgL65hIBN3hMAOASVoxPYJADMpWq0aShSPYLDWN7mWL84psW2/dRdKYSjI1MZed6uG2EBaZjmEujnwHrFsey7i8mu313h6IgEo/N5P6bh1Ed77I91krHZ54hm4ehIthmqP8Xh/fsu2aseDZvPBFjXPq8Oses+QYfsgZvyzmVfy+b8R7B/1ifGah/7FpUjjmpR6QFgvoa+H1A54ugOUelRPYL9sT6xb4vuD/1LQVXVDbchUTk6gl36G03VeX2FVi33uu+b/kcAwzWm7bLK0YCN6Yu0D+d11p56UIShsc5xCGP6XglHR6Jq9EL98LOqzLrr3wQ2N7eDMo7jmN+rfWz/hSNOarHCnBeINg07Uzz9Acez3CcCGI/lA+1dCEcHZiN7uX1dV857zT4JSjBfOmQfwdxP/LitTXZK3jf2aXmQgLPbwzAd6lqAwtEBLT60bS6qypl13gvBiEPqN+3O5Ts2l+Xk9PZRmT3E91U4OqBV/WUu2pEr32/OewYwHttss08R1oWjI1h1Mc5DlQKnZtXRuveOQ/J9gsMY07olHJ3AOmGJF57UUb8PTsX6CfNitNoA9Iex2+mvJhhxaAIQsCAcDYid/gv135MhXn+HafH9AhLNaoyAHRZDscu1/4DxEI4ALnHRmdoNEIDpEY4A1nRZ8FlncIXwBMMnHAHsyUXBZ9uTwgLHJxzt2aYjXmwcYR6WL0lyKOf9bdsaWJ9wtEebXsbC0GHgEC47cSpwMUP5T8TGCoZjKuvjVJYDTk3laM82KV0rc8NwDH19XDf4nLccc7pwLuxKONoTR2zAJjYJKps22a/6X9tcpkiYYq6EI4AZ2DToOOBjzvQ52iMdsWF8rI/AMpWjPdKmD9NzUXjaZn0XxmD4hCOAXHxyxkWfnYt+Z9XvAeMkHO3JeRtPYNhWnZxxmwvMbnLpECEKhm3tPkdV9aKq+mxVfbx7/Jqq+kxVXa+qX66qF3fTX9I9vt49/+rDzDrAflXVytsuv9v/Gw6gYBw26ZD9E0ke6z3+mSTva619X5JvJrm/m35/km9209/XvY4ljhxhXjYNW8DprBWOqur2JP91kp/rHleSNyb5aPeSB5O8rbt/b/c43fNvqhltDRwZAsC4rVs5+kdJ/l6S/9g9fmWSb7XWnu0eP5Hktu7+bUkeT5Lu+We619+gqq5W1aNV9eiW8z4462RA4QkAhu3ScFRVfyPJ0621a/v8x621B1prV1prV/b5dwGmatHp+7wbsB/rjFb74SQ/VlVvTfLSJP9pkp9NcnNV3dRVh25P8mT3+ieT3JHkiaq6KcnLk/zx3ud8xGbUygjswbpVaQEJ9uPSylFr7adaa7e31l6d5J1JPtla+5tJPpXk7d3L7kvyUHf/4e5xuuc/2ayxAAd12Wi5bW4wV7tcPuR/SvJ3qup6zvoUfbCb/sEkr+ym/50k791tFgEAjqeGUNSpqtPPxB6sc+VslxgBgMG4tqrvswvP7pngAwDjJhwBAPQIR0c0hCZMAOBiLjy7pW2DjmY3ABg2lSMAgB6Voy2sMyoNABgnlaMtbRqM9DcCgHEQjgAAejSrbWlVJUgzGwCMn3C0hVUhaPmij4ISAIyTZrU9Wb5QoytkA8A4qRzt2UUBSTUJAIZP5eiAhCEAGB/h6MCWm9sAgGETjo5EQAKAcRCOAAB6hCMAgB7hCACgRzgCAOgRjgAAepwEcg2bnunayDQAGC+VIwCAHpWjNa1bDXI9NQAYN5UjAIAe4QgAoEc4uoRmMgCYF+FoDUafAcB8CEcAAD3C0R5pggOA8ROO9kwTHACMm3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPTedegbGoLV26lkAAI5EOLpEVV36GuEJAKZjrWa1qvrDqvqdqvpcVT3aTbulqj5RVV/pfr6im15V9f6qul5Vn6+quw65AENQVc/dAIBx26TP0V9trb2utXale/zeJI+01u5M8kj3OEnekuTO7nY1yQf2NbMAAIe2S4fse5M82N1/MMnbetM/1M58OsnNVfWqHf4PAMDRrBuOWpJ/XlXXqupqN+3W1tpT3f2vJ7m1u39bksd7v/tENw0AYPDW7ZD9I621J6vqP0vyiar63f6TrbVWVRv1Su5C1tVLXzggl3W81ucIAMZvrcpRa+3J7ufTST6W5IeSfGPRXNb9fLp7+ZNJ7uj9+u3dtOW/+UBr7UqvD9PotdaeuwEA43RpOKqql1XVn1/cT/LXk3whycNJ7utedl+Sh7r7Dyd5dzdq7Z4kz/Sa3yahPzpt1Q0AGK91mtVuTfKxbqd/U5L/u7X2a1X1m0k+UlX3J/lqknd0r//VJG9Ncj3Jt5O8Z+9zPQKtNUEJAEaohtAEtGl/pVNZvFfrnhhSOAKAQbu2qnuPa6sBAPQIRwc0hKocALAZ4ehANKkBwDgJRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3C0YEZzg8A4yIcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9wtEGXEwWAKZPOAIA6BGOAAB6hCMAgB7h6ID0UQKA8RGOAAB6hCMAgB7hCACgRzgCAOi56dQzMFattVPPAgBwAMLRFi4LRkapAcB4CUcbEnwAYNr0OQIA6BGOAAB6hCMAgB7hCACgRzgCAOgxWm0L657jyMg2ABgf4WhLlwUfJ4kEgHHSrAYA0CMcAQD0CEcAAD3CEQBAj3B0QDplA8D4CEcHYhg/AIyTcAQA0CMcAQD0CEcHpt8RAIyLcAQA0CMcAQD0CEcAAD3C0QEZzg8A4yMcHYFO2QAwHsLRgakeAcC4CEcAAD3CEQBAj3AEANCzVjiqqpur6qNV9btV9VhVvaGqbqmqT1TVV7qfr+heW1X1/qq6XlWfr6q7DrsIAAD7s27l6GeT/Fpr7QeS/GCSx5K8N8kjrbU7kzzSPU6StyS5s7tdTfKBvc4xAMABXRqOqurlSf5ykg8mSWvt37XWvpXk3iQPdi97MMnbuvv3JvlQO/PpJDdX1av2PucAsCettZ1vTMc6laPXJPmjJP+4qj5bVT9XVS9Lcmtr7anuNV9Pcmt3/7Ykj/d+/4lu2g2q6mpVPVpVj24/+wCwH1W19Y1pWScc3ZTkriQfaK29Psm/zfNNaEmSdhaZN4rNrbUHWmtXWmtXNvk9AIBDWiccPZHkidbaZ7rHH81ZWPrGorms+/l09/yTSe7o/f7t3bRZUmoFgHG5NBy11r6e5PGq+v5u0puSfCnJw0nu66bdl+Sh7v7DSd7djVq7J8kzvea32ei3QSu5AlxM3x2G5KY1X/c/JvnFqnpxkt9P8p6cBauPVNX9Sb6a5B3da381yVuTXE/y7e61s2LlBrjc1LaVrTUHwxNRQ/hyVtXpZ2ID67xnVhCA55233exvK09Zbd9HsNFaMErXVvV9XrdyRI8vPsBqFx08Tn3bWVXPNQ1OfVmnTjgCYCf7CkSLcAGnJhwBsJNFqNlXtWQolZd1g1p/XgW8aRCOANiLfYSaIYSL/v+/bHmWRyafet7Zj3WvrQYAszKE6hWnIRwBsLMpBIltzk/Xv3yI0WrToVkNgL25rFlpqMFhH82BTIdwBMBBrDqHEYyBcATAXlxUPdm0s/JQRqwxT/ocATAoQhGnJhwBcDSbVo/gFIQjAI5CRYixEI4AAHqEIwCAHuEIgMHRBMcpGcq/pW06ClrZATYbpm9IP6cgHO1gk5UbABgH4ehIHPkAnH8yyPMOIm07OQXhaI9UiADWs2p7KQgxFMLRHghFANsRiBgi4WhH/WBkJQe4nG0lQycc7WARjKzoADAdznMEANAjHO1I1QgApkU42oIO2AAwXcIRAECPDtlb0JQGANOlcgQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPS4fAiwsV0uvuzyO8DQCUfAxqoqrbWNg05rbavfAzgmzWoAAD3CEQBAj3AEANAjHAEA9AhHwFHsMsIN4JiMVgO2tmngMUoNGAPhCNiKoANMlWY1AIAe4QgAoEc4AgDoEY4AAHqEIwCAnkvDUVV9f1V9rnf706r6yaq6pao+UVVf6X6+ont9VdX7q+p6VX2+qu46/GIAAOzHpeGotfbl1trrWmuvS3J3km8n+ViS9yZ5pLV2Z5JHusdJ8pYkd3a3q0k+cIgZBwA4hE2b1d6U5Pdaa19Ncm+SB7vpDyZ5W3f/3iQfamc+neTmqnrVXuYWAODANg1H70zy4e7+ra21p7r7X09ya3f/tiSP937niW7aDarqalU9WlWPbjgPAAAHs3Y4qqoXJ/mxJP90+bl2dg2Bja4j0Fp7oLV2pbV2ZZPfAwA4pE0qR29J8luttW90j7+xaC7rfj7dTX8yyR2937u9mwYAMHibhKN35fkmtSR5OMl93f37kjzUm/7ubtTaPUme6TW/AQAMWq1zVe2qelmSryX53tbaM920Vyb5SJL/PMlXk7yjtfYndXY1yv8jyZtzNrLtPa21C/sVVdVml/YGANjdtVXde9YKR4cmHAEAJ7AyHDlDNgBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9AhHAAA9whEAQI9wBADQIxwBAPQIRwAAPcIRAECPcAQA0CMcAQD0CEcAAD3CEQBAj3AEANAjHAEA9KwVjqrqb1fVF6vqC1X14ap6aVW9pqo+U1XXq+qXq+rF3Wtf0j2+3j3/6kMuAADAPl0ajqrqtiR/K8mV1tpfTPKiJO9M8jNJ3nOlOe8AAAqWSURBVNda+74k30xyf/cr9yf5Zjf9fd3rAIADa62tdeNi6zar3ZTkO6vqpiTfleSpJG9M8tHu+QeTvK27f2/3ON3zb6qq2s/sAgCr9ENPVZ17W37tMeZrbIHs0nDUWnsyyT9I8rWchaJnklxL8q3W2rPdy55Iclt3/7Ykj3e/+2z3+lfud7YBgOTG8NEPQOc5VECaUpVqnWa1V+SsGvSaJH8hycuSvHnXf1xVV6vq0ap6dNe/BQBztFwtWlc/IO3SBLf8uqFUq3Z10xqv+WtJ/qC19kdJUlW/kuSHk9xcVTd11aHbkzzZvf7JJHckeaJrhnt5kj9e/qOttQeSPND9zfG8YwBwYtuGor51fm+dQLPO36mqk4Sjy/7nefO+Tp+jryW5p6q+q+s79KYkX0ryqSRv715zX5KHuvsPd4/TPf/JNqa4CAADto9gNCeX9b9a+TtrpsKfTvLfJHk2yWeT/Pc561v0S0lu6ab9t621P6uqlyb5hSSvT/InSd7ZWvv9S/6+8AQAl+g3Xw3h/7XW1p6XY8/74n9e8v+utdauLE9cKxwdmnAEABc7Vbi46H9uOk+bZo5dl3XbcLROnyMAgJ1tEnYWHb1P0XQoHAEAL7BuZ+yLRrXtEmz6f/vYAUk4AgBWWnck2rJNm8+G0MWnTzgCAA7ioqrPtsPsj0E4AgD2avkkk+u89lAu+v/n/W/hCABG5JB9cPbdvLXoN3Tq8zFt2vQnHAHACPTPMr3NsP5T9esZYjC6aHoiHAHAaCxfp+zY5w0am22XVzgCgJGZW8g5tnWurQYAMBvCEQBAj3AEANAjHAGzt865WID50CEbmKVVYWgI52MBTk/lCJid5WAkEAF9KkfALFwWiE55BXBgWIQjYLJUiIBtCEfA5AhFwC6EI2AS9hGINK0BiXAEjNghKkT9i3sC82S0GjBKms6AQxGOgFGrqoMEI9UjmC/hCBilRSDad4jp/z0BCeZJOALoaKoDEh2ygZkTiIBlwhEwWtsOvReIgIsIR8CobTL0XigC1iEcAZMmEAGbEo6ASeg3rQlEwC6EI2D0+n2PlqcDbEo4AiZFIAJ2JRwBkyAUAfviJJAAAD3CEQBAj3DEILiGFQBDIRxxcotgJCABMATCESezaui1gATAqQlHnEQ/BFXVc7fl5wDg2IQjTuq84dcC0vSsqhQCDJFwxFEt7yAvOqOxHel0XPSZAwyNcMRR9ZvQ+s1oq5rZFs8xbovP0OcKjIVwxEn1d5jnsSMdr1WfnYAEDJ1wxGBoYpuWVRWjBQEJGDLhiEETkMbJZwWMmXDE4OmrMi7L/cfO40KxwFDddOoZgHX0m2gYpnVDEcDQqRytoDoxLIIRAMekctQjFJ3GRe+7YDQOu3xOrTWfLzAowlHnvGt82Wgfx6r32WcwfLs2pVWVgxJgcDSrLVnnvDscnmA0Lj4nYEpmH476Z2fub+CNjjqu/ucgGI3DPj4n6xcwRLMOR5c1CZx3eQv2y7mMxuW8A4pt/s6ufwPgEGYbjpyLZdi878OnYgRM1WzD0SYbdhWkw1vVpMnw7FrtWa46+ayBIZptOEq2C0gcjp3lcO2rKW3B5wwM2VCG8v+bJF8+xT8+YUD67iT/ep9/cODmtrzJhJZ5ze/+pcs7sVA0mc93A3Nb5rktbzK/Zf4vVk0cSjj6cmvtyqln4piq6tE5LfPcljeZ3zJb3umb2zLPbXmTeS7zKrNuVgMAWCYcAQD0DCUcPXDqGTiBuS3z3JY3md8yW97pm9syz215k3ku8wuUoekAAM8bSuUIAGAQTh6OqurNVfXlqrpeVe899fzsQ1X9fFU9XVVf6E27pao+UVVf6X6+opteVfX+bvk/X1V3nW7Ot1NVd1TVp6rqS1X1xar6iW76lJf5pVX1G1X1290y/3Q3/TVV9Zlu2X65ql7cTX9J9/h69/yrTzn/26qqF1XVZ6vq493jqS/vH1bV71TV56rq0W7alL/XN1fVR6vqd6vqsap6w8SX9/u7z3Zx+9Oq+smJL/Pf7rZZX6iqD3fbskmvx9s4aTiqqhcl+T+TvCXJa5O8q6pee8p52pN/kuTNS9Pem+SR1tqdSR7pHidny35nd7ua5ANHmsd9ejbJ322tvTbJPUl+vPscp7zMf5bkja21H0zyuiRvrqp7kvxMkve11r4vyTeT3N+9/v4k3+ymv6973Rj9RJLHeo+nvrxJ8ldba6/rDW+e8vf6Z5P8WmvtB5L8YM4+68kub2vty91n+7okdyf5dpKPZaLLXFW3JflbSa601v5ikhcleWfmsR5vZnHm21Pckrwhya/3Hv9Ukp865TztcdleneQLvcdfTvKq7v6rcnZupyT5v5K8a9XrxnpL8lCSH53LMif5riS/leQv5ezkaTd105/7fif59SRv6O7f1L2uTj3vGy7n7TnbUbwxyceT1JSXt5v3P0zy3UvTJvm9TvLyJH+w/DlNdXlXLP9fT/Ivp7zMSW5L8niSW7r18uNJ/qupr8fb3E7drLb4oBae6KZN0a2ttae6+19Pcmt3f1LvQVd2fX2Sz2Tiy9w1MX0uydNJPpHk95J8q7X2bPeS/nI9t8zd888keeVx53hn/yjJ30vyH7vHr8y0lzdJWpJ/XlXXqupqN22q3+vXJPmjJP+4azr9uap6Waa7vMvemeTD3f1JLnNr7ckk/yDJ15I8lbP18lqmvx5v7NThaJbaWQyf3DDBqvpzSf5Zkp9srf1p/7kpLnNr7T+0s3L87Ul+KMkPnHiWDqaq/kaSp1tr1049L0f2I621u3LWnPLjVfWX+09O7Ht9U5K7knygtfb6JP82zzcnJZnc8j6n62PzY0n+6fJzU1rmru/UvTkLwn8hycvywi4g5PTh6Mkkd/Qe395Nm6JvVNWrkqT7+XQ3fRLvQVV9R86C0S+21n6lmzzpZV5orX0ryadyVo6+uaoWl+XpL9dzy9w9//Ikf3zkWd3FDyf5sar6wyS/lLOmtZ/NdJc3yXNH2mmtPZ2zvig/lOl+r59I8kRr7TPd44/mLCxNdXn73pLkt1pr3+geT3WZ/1qSP2it/VFr7d8n+ZWcrduTXo+3cepw9JtJ7ux6yr84Z2XNh088T4fycJL7uvv35axfzmL6u7tREPckeaZXzh2FqqokH0zyWGvtH/aemvIyf09V3dzd/86c9bF6LGch6e3dy5aXefFevD3JJ7sj0lForf1Ua+321tqrc7aefrK19jcz0eVNkqp6WVX9+cX9nPVJ+UIm+r1urX09yeNV9f3dpDcl+VImurxL3pXnm9SS6S7z15LcU1Xf1W23F5/xZNfjrZ2601OStyb5Vznrr/G/nHp+9rRMH85Ze+6/z9nR2P05a6d9JMlXkvy/SW7pXls5G7H3e0l+J2ejCE6+DBsu74/krOz8+SSf625vnfgy/5dJPtst8xeS/K/d9O9N8htJruesRP+SbvpLu8fXu+e/99TLsMOy/5UkH5/68nbL9tvd7YuL7dPEv9evS/Jo973+f5K8YsrL2y3Hy3JWDXl5b9pklznJTyf53W679QtJXjLl9XjbmzNkAwD0nLpZDQBgUIQjAIAe4QgAoEc4AgDoEY4AAHqEIwCAHuEIAKBHOAIA6Pn/AYvqZ3QUfMAfAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "b_mask = sol.vector.mask.boundary_mask(fp_mask, boundary_width=5)\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(b_mask, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the SpaceNet competitors have found this type of mask useful for helping separate nearby objects.\n", + "\n", + "## Polygon contact points\n", + "\n", + "What about training a model to specifically find places where buildings are near each other? This can be helpful for the same purpose as the edges. The [solaris.vector.mask.contact_mask()](../../api/vector.rst#solaris.vector.mask.contact_mask) function creates this.\n", + "\n", + "Its arguments:\n", + "\n", + "\n", + "- `contact_spacing`: An `int` specifying how close objects have to be for contact points to be labeled. Default value is 10.\n", + "- `meters`: A boolean argument indicating whether or not the `contact_spacing` argument is in meters. Defaults to `False`, in which case it's in pixels.\n", + "\n", + "This function also takes `df` (__required__), `out_file`, `reference_im`, `geom_col`, `do_transform`, `affine_obj`, `shape`, `out_type`, and `burn_value` as arguments, which have the same meanings as in [solaris.vector.mask.footprint_mask()](../../api/vector.rst#solaris.vector.mask.footprint_mask).\n", + "\n", + "Let's create some contact points!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "c_mask = sol.vector.mask.contact_mask(df=os.path.join(data_dir, 'geotiff_labels.geojson'),\n", + " reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\n", + " contact_spacing=10, meters=True)\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(c_mask, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's create a three-channel mask containing all three of these layers! The [solaris.vector.mask.df_to_px_mask()](../../api/vector.rst#solaris.vector.mask.df_to_px_mask) function takes all of the same arguments as above, plus `channels`, a list specifying which of the three types of masks should be included. We'll make one that has them all!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fbc_mask = sol.vector.mask.df_to_px_mask(df=os.path.join(data_dir, 'geotiff_labels.geojson'),\n", + " channels=['footprint', 'boundary', 'contact'],\n", + " reference_im=os.path.join(data_dir, 'sample_geotiff.tif'),\n", + " boundary_width=5, contact_spacing=10, meters=True)\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(fbc_mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That mask looks ready to train a solid model!\n", + "\n", + "## Road network masks\n", + "\n", + "Finally, we can use linestrings to create a road network. Here we're going to use a different sample input image and geojson alongside the [solaris.vector.mask.road_mask()](../../api/vector.rst#solaris.vector.mask.road_mask). This function takes many of the same arguments as the earlier functions: `df` (__required__), `meters`, `out_file`, `reference_im`, `geom_col`, `do_transform`, `affine_obj`, `shape`, `out_type`, `burn_value`, and `burn_field`.\n", + "\n", + "This function also takes `width`, which specifies the width of the road network (in pixels unless `meters=True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "road_mask = sol.vector.mask.road_mask(os.path.join(data_dir, 'sample_roads_for_masking.geojson'),\n", + " reference_im=os.path.join(data_dir, 'road_mask_input.tif'),\n", + " width=4, meters=True)\n", + "\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(road_mask, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And there we go! Enjoy creating masks!! If you want to do this in batch without running python code, [the make_masks CLI function](cli_mask_creation.ipynb) allows you to do so, with the option of parallelizing some aspects." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_tiling_tutorial.ipynb b/docker/solaris/docs/tutorials/notebooks/api_tiling_tutorial.ipynb new file mode 100644 index 00000000..c06e2dbd --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_tiling_tutorial.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tiling imagery and labels using the `solaris` Python API\n", + "\n", + "This tutorial will walk you through an example case of using the `solaris` Python API to tile one of the SpaceNet cities - in this case, Rio de Janeiro. We'll assume that you have already [installed solaris](https://solaris.readthedocs.io/en/master/installation.html).\n", + "\n", + "__First__, downloaded and extracted two files from the `spacenet-dataset` AWS S3 bucket:\n", + "\n", + "1. Imagery: https://s3.amazonaws.com/spacenet-dataset/AOIs/AOI_1_Rio/PS-RGB/PS-RGB_mosaic_013022223133.tif\n", + "2. Vector labels: https://spacenet-dataset.s3.amazonaws.com/AOIs/AOI_1_Rio/srcData/buildingLabels/Rio_Buildings_Public_AOI_v2.geojson\n", + "\n", + "Move both of these files to your working directory or alter the paths below to point to the files at the downloaded location.\n", + "\n", + "As you're getting started, your directory should have the following in it:\n", + "\n", + "- A directory named 3band which contains the imagery files\n", + "- A directory named geojson which contains two files: Rio_Buildings_Public_AOI_v2.geojson and Rio_OUTLINE_Public_AOI.geojson (we only need the first of those two).\n", + "\n", + "Feel free to open up the imagery/vector labels in QGIS or another browser and explore to see what you're looking at." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tiling the imagery\n", + "\n", + "For this working example we'll tile into 500-by-500-pixel chips beginning at the top left corner. Note that you can also tile based on the metric units covered by an image - for example, we could specify 250 meter-by-250 meter chips (which is the same size in this case). See the documentation for `sol.tile.raster_tile.RasterTiler()` for more details.\n", + "\n", + "Initialize the `RasterTiler` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/opt/miniconda3/envs/solaris/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing Tiler...\n", + "Tiler initialized.\n", + "dest_dir: rio_chips\n", + "dest_crs will be inferred from source data.\n", + "src_tile_size: (500, 500)\n", + "tile size units metric: False\n" + ] + } + ], + "source": [ + "import solaris as sol\n", + "import os\n", + "\n", + "raster_tiler = sol.tile.raster_tile.RasterTiler(dest_dir='rio_chips', # the directory to save images to\n", + " src_tile_size=(500, 500), # the size of the output chips\n", + " verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This object can be re-used with the same parameters for multiple images if desired. This way, you can tile multiple images collected over the same geography with the same settings. There are additional arguments that you can provide (for example, the destination coordinate reference system).\n", + "\n", + "To tile the imagery, pass the image file to the tiler's `tile()` method, which returns the CRS of the source raster for vector tiling:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]The following warnings were found:\n", + "- The file is greater than 512xH or 512xW, it is recommended to include internal overviews\n", + "\n", + "The following errors were found:\n", + "- The offset of the main IFD should be 8 for ClassicTIFF or 16 for BigTIFF. It is 93844024 instead\n", + "- The offset of the first block of the image should be after its IFD\n", + "3it [00:00, 23.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beginning tiling...\n", + "Checking input data...\n", + "COG: False\n", + "[1, 2, 3]\n", + "Source CRS: EPSG:4326\n", + "Destination CRS: EPSG:4326\n", + "Inputs OK.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1600it [01:04, 24.86it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tiling complete. Cleaning up...\n", + "Done.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "raster_bounds_crs = raster_tiler.tile('/Users/nweir/code/cosmiq_repos/solaris/PS-RGB_mosaic_013022223133.tif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This should throw a few warnings/errors about the input file, which you can ignore. You'll create 1600 files in your \"rio_chips\" subdirectory, one for each 500x500 tile. The filenames are in the format `[src-filename]\\_[longitude]\\_[latitude].tif`. Reprojection takes a while, so be patient.\n", + "\n", + "Once that process finishes, we'll use these auto-generated tile boundaries, which are stored in `raster_tiler`, to create vector tiles." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These bounds are in the format `[left, bottom, right, top]` in the input file CRS. The following line prints the first set of bounds (there are 1600 in the list):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(-43.681640625, -22.939453125, -43.67939668543198, -22.937209185431986)\n" + ] + } + ], + "source": [ + "print(raster_tiler.tile_bounds[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`raster_tiler.tile_bounds` is passed as an argument into the `VectorTiler` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparing the tiler...\n", + "Initialization done.\n", + "Num tiles: 1600\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1600it [02:16, 11.74it/s]\n" + ] + } + ], + "source": [ + "vector_tiler = sol.tile.vector_tile.VectorTiler(dest_dir='rio_labels',\n", + " verbose=True)\n", + "vector_tiler.tile('/Users/nweir/code/cosmiq_repos/solaris/Rio_Buildings_Public_AOI_v2.geojson',\n", + " tile_bounds=raster_tiler.tile_bounds,\n", + " tile_bounds_crs=raster_bounds_crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "...And you're done! Simple as that. For more details, check out the [tiling API docs](https://solaris.readthedocs.io/en/master/api.html)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_training_custom.ipynb b/docker/solaris/docs/tutorials/notebooks/api_training_custom.ipynb new file mode 100644 index 00000000..d256bcca --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_training_custom.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training your own custom model using `solaris`\n", + "\n", + "If you want to go beyond using [the pretrained models in solaris](../../pretrained_models.html), you can train your own. Here's a primer for how to do so, where we'll walk through training the [SpaceNet 4 Baseline model](https://github.com/cosmiq/cosmiq_sn4_baseline) fresh. If you want to use one of the existing models in `solaris`, [check out this tutorial](api_training_spacenet.ipynb).\n", + "\n", + "First, you'll need to [create a YAML config file](creating_the_yaml_config_file.ipynb) for your model. This config should differ from a pre-trained model in a couple of key places:\n", + "\n", + "- model_name: Don't use one of the model names for a pre-trained model in solaris; give it another name.\n", + "- model_path: If you have pre-trained weights to load in, put the path to those weights here; otherwise, leave it blank.\n", + "\n", + "Fill out all of the model-specific parameters (width/height of inputs, mask channels, the neural network framework, optimizer, learning rate, etc.) according to the model you plan to use.\n", + "\n", + "Next, you'll need to create your model. See below for the SpaceNet 4 Baseline example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose\n", + "from tensorflow.keras.layers import concatenate, BatchNormalization, Dropout\n", + "from tensorflow.keras import Model\n", + "\n", + "def cosmiq_sn4_baseline(input_shape=(512, 512, 3), base_depth=64):\n", + " \"\"\"Keras implementation of untrained TernausNet model architecture.\n", + "\n", + " Arguments:\n", + " ----------\n", + " input_shape (3-tuple): a tuple defining the shape of the input image.\n", + " base_depth (int): the base convolution filter depth for the first layer\n", + " of the model. Must be divisible by two, as the final layer uses\n", + " base_depth/2 filters. The default value, 64, corresponds to the\n", + " original TernausNetV1 depth.\n", + "\n", + " Returns:\n", + " --------\n", + " An uncompiled Keras Model instance with TernausNetV1 architecture.\n", + "\n", + " \"\"\"\n", + " inputs = Input(input_shape)\n", + " conv1 = Conv2D(base_depth, 3, activation='relu', padding='same')(inputs)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + "\n", + " conv2_1 = Conv2D(base_depth*2, 3, activation='relu',\n", + " padding='same')(pool1)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2_1)\n", + "\n", + " conv3_1 = Conv2D(base_depth*4, 3, activation='relu',\n", + " padding='same')(pool2)\n", + " conv3_2 = Conv2D(base_depth*4, 3, activation='relu',\n", + " padding='same')(conv3_1)\n", + " pool3 = MaxPooling2D(pool_size=(2, 2))(conv3_2)\n", + "\n", + " conv4_1 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(pool3)\n", + " conv4_2 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(conv4_1)\n", + " pool4 = MaxPooling2D(pool_size=(2, 2))(conv4_2)\n", + "\n", + " conv5_1 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(pool4)\n", + " conv5_2 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(conv5_1)\n", + " pool5 = MaxPooling2D(pool_size=(2, 2))(conv5_2)\n", + "\n", + " conv6_1 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(pool5)\n", + "\n", + " up7 = Conv2DTranspose(base_depth*4, 2, strides=(2, 2), activation='relu',\n", + " padding='same')(conv6_1)\n", + " concat7 = concatenate([up7, conv5_2])\n", + " conv7_1 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(concat7)\n", + "\n", + " up8 = Conv2DTranspose(base_depth*4, 2, strides=(2, 2), activation='relu',\n", + " padding='same')(conv7_1)\n", + " concat8 = concatenate([up8, conv4_2])\n", + " conv8_1 = Conv2D(base_depth*8, 3, activation='relu',\n", + " padding='same')(concat8)\n", + "\n", + " up9 = Conv2DTranspose(base_depth*2, 2, strides=(2, 2), activation='relu',\n", + " padding='same')(conv8_1)\n", + " concat9 = concatenate([up9, conv3_2])\n", + " conv9_1 = Conv2D(base_depth*4, 3, activation='relu',\n", + " padding='same')(concat9)\n", + "\n", + " up10 = Conv2DTranspose(base_depth, 2, strides=(2, 2), activation='relu',\n", + " padding='same')(conv9_1)\n", + " concat10 = concatenate([up10, conv2_1])\n", + " conv10_1 = Conv2D(base_depth*2, 3, activation='relu',\n", + " padding='same')(concat10)\n", + "\n", + " up11 = Conv2DTranspose(int(base_depth/2), 2, strides=(2, 2),\n", + " activation='relu', padding='same')(conv10_1)\n", + " concat11 = concatenate([up11, conv1])\n", + "\n", + " out = Conv2D(1, 1, activation='sigmoid', padding='same')(concat11)\n", + "\n", + " return Model(inputs=inputs, outputs=out)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, you'll pass that model to a custom model dictionary for the `solaris` model trainer." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "custom_model_dict = {'model_name': 'cosmiq_sn4_baseline',\n", + " 'weight_path': None,\n", + " 'weight_url': None,\n", + " 'arch': cosmiq_sn4_baseline}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can follow roughly the same process as for a pre-trained model: load in the config file, then create your trainer. The major difference here is that you'll pass an additional argument to the trainer, `custom_model_dict`, which provides the model architecture to the trainer:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import solaris as sol\n", + "\n", + "config = sol.utils.config.parse('/Users/nweir/code/cosmiq_repos/solaris/cosmiq_sn4_baseline.yml')\n", + "trainer = sol.nets.train.Trainer(config, custom_model_dict=custom_model_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, you can treat training as you would a pre-trained model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/api_training_spacenet.ipynb b/docker/solaris/docs/tutorials/notebooks/api_training_spacenet.ipynb new file mode 100644 index 00000000..ac8f0b45 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/api_training_spacenet.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training included SpaceNet models with the `solaris` Python API\n", + "\n", + "We've included a number of SpaceNet models with `solaris`, including pre-trained model weights. You can find more information about your model choices [here](../pretrained_models.html) and the original competitors' code for the models [here](https://github.com/spacenetchallenge/spacenet_off_nadir_solutions).\n", + "\n", + "For this tutorial we'll walk through training a model using XD_XD's SpaceNet 4 model. We'll use the config file for that model, which you can find [here](https://github.com/CosmiQ/solaris/blob/master/solaris/nets/configs/xdxd_spacenet4.yml).\n", + "\n", + "You'll also need to [create training masks](api_masks_tutorial.ipynb) and [create the image reference files](creating_im_reference_csvs.ipynb) before you start.\n", + "\n", + "Once you've completed those steps, you can get down to model training! First, let's load in the configuration:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'xdxd_spacenet4',\n", + " 'model_path': None,\n", + " 'train': False,\n", + " 'infer': True,\n", + " 'pretrained': True,\n", + " 'nn_framework': 'torch',\n", + " 'batch_size': 12,\n", + " 'data_specs': {'width': 512,\n", + " 'height': 512,\n", + " 'image_type': 'zscore',\n", + " 'rescale': False,\n", + " 'rescale_minima': 'auto',\n", + " 'rescale_maxima': 'auto',\n", + " 'channels': 4,\n", + " 'label_type': 'mask',\n", + " 'is_categorical': False,\n", + " 'mask_channels': 1,\n", + " 'val_holdout_frac': 0.2,\n", + " 'data_workers': None},\n", + " 'training_data_csv': '/path/to/training_df.csv',\n", + " 'validation_data_csv': None,\n", + " 'inference_data_csv': '/path/to/test_df.csv',\n", + " 'training_augmentation': {'augmentations': {'DropChannel': {'idx': 3,\n", + " 'axis': 2},\n", + " 'HorizontalFlip': {'p': 0.5},\n", + " 'RandomRotate90': {'p': 0.5},\n", + " 'RandomCrop': {'height': 512, 'width': 512, 'p': 1.0},\n", + " 'Normalize': {'mean': [0.006479, 0.009328, 0.01123],\n", + " 'std': [0.004986, 0.004964, 0.00495],\n", + " 'max_pixel_value': 65535.0,\n", + " 'p': 1.0}},\n", + " 'p': 1.0,\n", + " 'shuffle': True},\n", + " 'validation_augmentation': {'augmentations': {'DropChannel': {'idx': 3,\n", + " 'axis': 2},\n", + " 'CenterCrop': {'height': 512, 'width': 512, 'p': 1.0},\n", + " 'Normalize': {'mean': [0.006479, 0.009328, 0.01123],\n", + " 'std': [0.004986, 0.004964, 0.00495],\n", + " 'max_pixel_value': 65535.0,\n", + " 'p': 1.0}},\n", + " 'p': 1.0},\n", + " 'inference_augmentation': {'augmentations': {'DropChannel': {'idx': 3,\n", + " 'axis': 2,\n", + " 'p': 1.0},\n", + " 'Normalize': {'mean': [0.006479, 0.009328, 0.01123],\n", + " 'std': [0.004986, 0.004964, 0.00495],\n", + " 'max_pixel_value': 65535.0,\n", + " 'p': 1.0}},\n", + " 'p': 1.0},\n", + " 'training': {'epochs': 60,\n", + " 'steps_per_epoch': None,\n", + " 'optimizer': 'Adam',\n", + " 'lr': 0.0001,\n", + " 'opt_args': None,\n", + " 'loss': {'bcewithlogits': None, 'jaccard': None},\n", + " 'loss_weights': {'bcewithlogits': 10, 'jaccard': 2.5},\n", + " 'metrics': {'training': None, 'validation': None},\n", + " 'checkpoint_frequency': 10,\n", + " 'callbacks': {'model_checkpoint': {'filepath': 'xdxd_best.pth',\n", + " 'monitor': 'val_loss'}},\n", + " 'model_dest_path': 'xdxd.pth',\n", + " 'verbose': True},\n", + " 'inference': {'window_step_size_x': None,\n", + " 'window_step_size_y': None,\n", + " 'output_dir': 'inference_out/'}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import solaris as sol\n", + "\n", + "config = sol.utils.config.parse('/path/to/xdxd_spacenet4.yml')\n", + "config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the YAML gets parsed into a set of nested dictionaries by `solaris`. Relevant pieces of that config then get read during training.\n", + "\n", + "Let's assume that all of the paths in that config are correct. Now, to run training, you can run the following line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = sol.nets.train.Trainer(config)\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's all there is to it! With just four lines of Python code, you can train a model for geospatial ML!\n", + "\n", + "If you wish to use a custom ML model not provided by `solaris`, check out [the custom model training tutorial](api_training_custom.ipynb)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/cli_mask_creation.ipynb b/docker/solaris/docs/tutorials/notebooks/cli_mask_creation.ipynb new file mode 100644 index 00000000..8613ccc6 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/cli_mask_creation.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the `solaris` CLI to make training masks\n", + "\n", + "Geospatial data labels are rarely in the form of pixel masks; however, such masks are essential for training neural networks for segmentation tasks. We've provided functions and a CLI to standardize training mask creation so that users can convert their geospatial-format vector labels into ML-compatible training masks. If you'd prefer the Python API implementation, [there's a tutorial here](api_masks_tutorial.ipynb).\n", + "\n", + "There are two ways to create these masks with the CLI: one file at a time, or in batch based on a reference file. Each is described below. We'll start with describing the simple single-file mask creation case, then describe how to complete batch processing. If you have any questions about what the different types of masks look like, [check out the Python API tutorial for mask creation](api_masks_tutorial.ipynb).\n", + "\n", + "## Single mask creation with the CLI\n", + "\n", + "Once you have [installed solaris](../../installation.html), you will have access to the `make_masks` command in your command line prompt. This command has a number of possible arguments to control mask creation, described below. If you need a refresher on these within your command line, you can always run `make_masks -h` for usage instructions.\n", + "\n", + "### `make_masks` arguments\n", + "\n", + "- __--source\\_file__, __-s__: \\[str\\] The full path to a vector file file to create a mask from.\n", + "- __--reference\\_image__, __-r__, \\[str\\] The full path to a georegistered image in the same coordinate system (for conversion to pixels) or in the target coordinate system (for conversion to a geographic coordinate reference system)\n", + "- __--output\\_path__, __-o__: \\[str\\] The full path to the output file for the generated mask image.\n", + "- __--geometry\\_column__, __-g__: \\[str\\] (default: `'geometry'`) The column containing footprint polygons to transform.\n", + "- __--transform__, __-t__: Use this flag if the geometries are in a georeferenced coordinate system and need to be converted to pixel coordinates.\n", + "- __--value__, __-v__: \\[int\\] (default: `255`) The value to set for labeled pixels in the mask.\n", + "- __--footprint__, __-f__: If this flag is set, the mask will include filled-in building footprints as a channel.\n", + "- __--edge__, __-e__: If this flag is set, the mask will include the building edges as a channel.\n", + "- __--edge\\_width__, __-ew__: \\[int\\] (default: `3`) The pixel thickness of the edges in the edge mask. Only has an effect if __--edge__ or __-e__ is used.\n", + "- __--edge\\_type__, __-et__: \\[str\\] (default: `inner`) Type of edge: either `'inner'` or `'outer'`. Only has an effect if __--edge__ or __-e__ is used.\n", + "- __--contact__, __-c__: If this flag is set, the mask will include contact points between buildings as a channel.\n", + "- __--contact\\_spacing__, __-cs__: \\[int\\] (default: `10`) Sets the maximum distance between two buildings, in pixel units unless __--metric_widths__ is provided, that will be identified as a contact. Only has an effect if __--contact__ or __-c__ is used.\n", + "- __--metric\\_widths__, __-m__: Use this flag if widths should be in metric units instead of pixel units.\n", + "- __--batch__, __-b__: Use this flag if you wish to operate on multiple files in batch. In this case, __--argument\\_csv__ must be provided. See the batch processing section below for more details.\n", + "- __--argument\\_csv__, __-a__: \\[str\\] The reference file for variable values for batch processing. It must contain columns to pass the source_file and reference_image arguments, and can additionally contain columns providing other arguments if you wish to define them differently for items in the batch. Only has an effect if the __--batch__ or __-b__ arguments are used. These columns must have the same names as the corresponding arguments. See the next section for more details on batch processing.\n", + "- __--workers__, __-w__: \\[int\\] (default: `1`) The number of parallel processing workers to use for batch processing. This should not exceed the number of CPU cores available. See the next section for more details on batch processing.\n", + "\n", + "### `make_masks` CLI usage examples\n", + "\n", + "Assume you have fies for a GeoTIFF, `image.tif`, and georegistered building footprint labels, `building_labels.geojson`:\n", + "\n", + "_Creating building footprint labels:_\n", + "\n", + "```console\n", + "$ make_masks --source_file building_labels.geojson --reference_image image.tif --footprint --transform\n", + "```\n", + "\n", + "Let's change the burn value to 1 for the footprints instead of 255:\n", + "\n", + "```console\n", + "$ make_masks --source_file building_labels.geojson --reference_image image.tif --footprint --transform --value 1\n", + "```\n", + "\n", + "\n", + "What if your building labels are already in pixel coordinates in a CSV named `building_labels.csv`, and the geometries are in a column named `WKT_Pix`?\n", + "\n", + "```console\n", + "$ make_masks --source_file building_labels.csv --reference_image image.tif --footprint --geometry_column WKT_Pix\n", + "```\n", + "\n", + "What if you have the same CSV file as above, but instead of making just building footprints, you want outer borders of width 10 and also contact points for anything within 10 meters?\n", + "\n", + "```console\n", + "$ make_masks --source_file building_labels.csv --reference_image image.tif --geometry_column WKT_Pix --footprint --edge --edge-type outer --edge-width 10 --contact --contact_spacing 10 --metric_widths \n", + "```\n", + "\n", + "## Batch mask creation using the `solaris` CLI\n", + "\n", + "There's one additional requirement for batch mask creation: a CSV specifying the location of the label files, the reference images, and optionally any other arguments that you wish to modify on a mask-by-mask basis.\n", + "\n", + "### Creating the argument CSV\n", + "\n", + "The reference CSV has three required columns, which must be named __exactly__ as below:\n", + "\n", + "- __source_file__: the paths to vector-formatted label files that you wish to transform to masks.\n", + "- __reference_image__: The paths to images that correspond to the same geographies as the vector labels that you're using.\n", + "- __output_path__: The paths to save the output masks to.\n", + "The values in these two columns must be matching geographies across the row, or you'll get empty masks! For both cases, we recommend using the absolute path to the files in each column rather than a relative path for consistency and clarity.\n", + "\n", + "If you wish to use different values for the other arguments to `make_masks` (e.g., if you wish to have different burn values for different masks), you can provide those values in the CSV as well. Just create a column with the same name as the argument that you're replacing, and make sure to provide a value for every row.\n", + "\n", + "### `make_masks` CLI batch processing examples\n", + "\n", + "Assume you have a CSV `mask_reference.csv` that specifies the path to your .geojson labels, matching reference images, and where you want those files saved, as described in the last section.\n", + "\n", + "Let's create footprint masks:\n", + "\n", + "```console\n", + "$ make_masks --batch --argument_csv mask_reference.csv --footprint\n", + "```\n", + "\n", + "What if you have a _lot_ of masks to make and you want to parallelize over four CPUs? (Make sure you have access to four CPU cores first!)\n", + "\n", + "```console\n", + "$ make_masks --batch --argument_csv mask_reference.csv --footprint --workers 4\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/cli_ml_pipeline.ipynb b/docker/solaris/docs/tutorials/notebooks/cli_ml_pipeline.ipynb new file mode 100644 index 00000000..1300d57d --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/cli_ml_pipeline.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running a deep learning pipeline with the `solaris` CLI\n", + "\n", + "Running a full ML pipeline with `solaris` only takes a single line of code! After [installing solaris](../../installation.rst), all you need to do is [set up your YAML config file](creating_the_yaml_config_file.ipynb). Then, run the following line from a command prompt:\n", + "\n", + "```console\n", + "$ solaris_run_ml [path_to_your_config]\n", + "```\n", + "\n", + "You can try it now! [Download the config file for XD_XD's SpaceNet 4 Model](https://github.com/CosmiQ/solaris/blob/dev/solaris/nets/configs/xdxd_spacenet4.yml), point it to your data, and then run:\n", + "\n", + "```console\n", + "$ solaris_run_ml xdxd_spacenet4.yml\n", + "```\n", + "\n", + "That's all there is to it!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/cli_spacenet_evaluation.ipynb b/docker/solaris/docs/tutorials/notebooks/cli_spacenet_evaluation.ipynb new file mode 100644 index 00000000..a283972f --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/cli_spacenet_evaluation.ipynb @@ -0,0 +1,488 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the `solaris` CLI to score model performance\n", + "\n", + "Once you have [generated your model predictions](cli_ml_pipeline.ipynb) and [converted predictions to vector format](api_mask_to_vector.ipynb), you'll be ready to score your predictions! Let's go through a test case for some \"predictions\" from the SpaceNet 4 dataset. Just to show you what those look like:\n", + "\n", + "## Ground truth and prediction data formats\n", + "\n", + "#### Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ImageIdBuildingIdPolygonWKT_PixConfidence
0Atlanta_nadir8_catid_10300100023BC100_743501_3...0POLYGON ((0.00 712.83, 158.37 710.28, 160.59 6...1
1Atlanta_nadir8_catid_10300100023BC100_743501_3...1POLYGON ((665.82 0.00, 676.56 1.50, 591.36 603...1
2Atlanta_nadir8_catid_10300100023BC100_743501_3...0POLYGON ((182.62 324.15, 194.25 323.52, 197.97...1
3Atlanta_nadir8_catid_10300100023BC100_743501_3...1POLYGON ((92.99 96.94, 117.20 99.64, 114.72 12...1
4Atlanta_nadir8_catid_10300100023BC100_743501_3...2POLYGON ((0.82 29.96, 3.48 40.71, 2.80 51.00, ...1
\n", + "
" + ], + "text/plain": [ + " ImageId BuildingId \\\n", + "0 Atlanta_nadir8_catid_10300100023BC100_743501_3... 0 \n", + "1 Atlanta_nadir8_catid_10300100023BC100_743501_3... 1 \n", + "2 Atlanta_nadir8_catid_10300100023BC100_743501_3... 0 \n", + "3 Atlanta_nadir8_catid_10300100023BC100_743501_3... 1 \n", + "4 Atlanta_nadir8_catid_10300100023BC100_743501_3... 2 \n", + "\n", + " PolygonWKT_Pix Confidence \n", + "0 POLYGON ((0.00 712.83, 158.37 710.28, 160.59 6... 1 \n", + "1 POLYGON ((665.82 0.00, 676.56 1.50, 591.36 603... 1 \n", + "2 POLYGON ((182.62 324.15, 194.25 323.52, 197.97... 1 \n", + "3 POLYGON ((92.99 96.94, 117.20 99.64, 114.72 12... 1 \n", + "4 POLYGON ((0.82 29.96, 3.48 40.71, 2.80 51.00, ... 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import solaris as sol\n", + "import os\n", + "\n", + "preds = pd.read_csv(os.path.join(sol.data.data_dir, 'sample_preds_competition.csv'))\n", + "preds.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The file shows the image ID, the polygon geometry in WKT format, and a `BuildingId` counter to distinguish between buildings in a single image. The `Confidence` field in this case has no meaning, but can be provided if desired.\n", + "\n", + "#### Ground Truth" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ImageIdBuildingIdPolygonWKT_PixPolygonWKT_Geo
0Atlanta_nadir8_catid_10300100023BC100_743501_3...0POLYGON ((476.88 884.61, 485.59 877.64, 490.50...1
1Atlanta_nadir8_catid_10300100023BC100_743501_3...1POLYGON ((459.45 858.97, 467.41 853.09, 463.37...1
2Atlanta_nadir8_catid_10300100023BC100_743501_3...2POLYGON ((407.34 754.17, 434.90 780.55, 420.27...1
3Atlanta_nadir8_catid_10300100023BC100_743501_3...3POLYGON ((311.00 760.22, 318.38 746.78, 341.02...1
4Atlanta_nadir8_catid_10300100023BC100_743501_3...4POLYGON ((490.49 742.67, 509.81 731.14, 534.12...1
\n", + "
" + ], + "text/plain": [ + " ImageId BuildingId \\\n", + "0 Atlanta_nadir8_catid_10300100023BC100_743501_3... 0 \n", + "1 Atlanta_nadir8_catid_10300100023BC100_743501_3... 1 \n", + "2 Atlanta_nadir8_catid_10300100023BC100_743501_3... 2 \n", + "3 Atlanta_nadir8_catid_10300100023BC100_743501_3... 3 \n", + "4 Atlanta_nadir8_catid_10300100023BC100_743501_3... 4 \n", + "\n", + " PolygonWKT_Pix PolygonWKT_Geo \n", + "0 POLYGON ((476.88 884.61, 485.59 877.64, 490.50... 1 \n", + "1 POLYGON ((459.45 858.97, 467.41 853.09, 463.37... 1 \n", + "2 POLYGON ((407.34 754.17, 434.90 780.55, 420.27... 1 \n", + "3 POLYGON ((311.00 760.22, 318.38 746.78, 341.02... 1 \n", + "4 POLYGON ((490.49 742.67, 509.81 731.14, 534.12... 1 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "truth = pd.read_csv(os.path.join(sol.data.data_dir, 'sample_truth_competition.csv'))\n", + "truth.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More or less the same thing. So, how does scoring work?\n", + "\n", + "\n", + "## Scoring functions in the `solaris` CLI\n", + "\n", + "Once you have [installed solaris](../../installation.html), you will have access to the `spacenet_eval` command in your command line prompt. This command has a number of possible arguments to control mask creation, described below. If you need a refresher on these within your command line, you can always run `spacenet_eval -h` for usage instructions.\n", + "\n", + "### `spacenet_eval` arguments\n", + "\n", + "- __--proposal\\_csv__, __-p__: \\[str\\] The full path to a CSV-formatted proposal file containing the same columns shown above.\n", + "- __--truth\\_csv__, __-t__: \\[str\\] The full path to a CSV-formatted ground truth file containing the same columns shown above.\n", + "- __--challenge__, __-c__, \\[str, one of `('off-nadir', 'spacenet-buildings2')` \\] The challenge being scored. Because the SpaceNet Off-Nadir Building Footprint Extraction Challenge was scored slightly differently from previous challenges to accommodate the different look angles, the challenge type must be specified here.\n", + "- __--output\\_file__, __-o__: \\[str\\] The path to the output files to be saved. Two files will be saved: the summary file with the name provided in this argument, and one with `'_full'` added before the `'.csv'` extension, which contains the image-by-image breakdown of scores.\n", + "\n", + "### `spacenet_eval` CLI usage example\n", + "\n", + "Assuming you have the two files shown above as your examples:\n", + "\n", + "\n", + "```console\n", + "$ spacenet_eval --proposal_csv /path/to/sample_preds_competition.csv --truth_csv /path/to/sample_truth_competition.csv --challenge 'off-nadir' --output_file /path/to/outputs.csv\n", + "```\n", + "\n", + "Let's look at what the outputs would look like:\n", + "\n", + "#### Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
F1ScoreFalseNegFalsePosPrecisionRecallTruePos
01.0001.01.02319
\n", + "
" + ], + "text/plain": [ + " F1Score FalseNeg FalsePos Precision Recall TruePos\n", + "0 1.0 0 0 1.0 1.0 2319" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_summary = pd.read_csv(os.path.join(sol.data.data_dir, 'competition_test_results.csv'))\n", + "result_summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, the score is perfect because the predictions and ground truth were literally identical.\n", + "\n", + "Here's the image-by-image breakout:\n", + "\n", + "#### Detailed results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
F1ScoreFalseNegFalsePosPrecisionRecallTruePosimageIDiou_fieldnadir-category
01.0001.01.080Atlanta_nadir8_catid_10300100023BC100_743501_3...iou_scoreNadir
11.0001.01.0112Atlanta_nadir8_catid_10300100023BC100_743501_3...iou_scoreNadir
21.0001.01.072Atlanta_nadir8_catid_10300100023BC100_743501_3...iou_scoreNadir
31.0001.01.01Atlanta_nadir8_catid_10300100023BC100_743501_3...iou_scoreNadir
41.0001.01.052Atlanta_nadir8_catid_10300100023BC100_743501_3...iou_scoreNadir
\n", + "
" + ], + "text/plain": [ + " F1Score FalseNeg FalsePos Precision Recall TruePos \\\n", + "0 1.0 0 0 1.0 1.0 80 \n", + "1 1.0 0 0 1.0 1.0 112 \n", + "2 1.0 0 0 1.0 1.0 72 \n", + "3 1.0 0 0 1.0 1.0 1 \n", + "4 1.0 0 0 1.0 1.0 52 \n", + "\n", + " imageID iou_field nadir-category \n", + "0 Atlanta_nadir8_catid_10300100023BC100_743501_3... iou_score Nadir \n", + "1 Atlanta_nadir8_catid_10300100023BC100_743501_3... iou_score Nadir \n", + "2 Atlanta_nadir8_catid_10300100023BC100_743501_3... iou_score Nadir \n", + "3 Atlanta_nadir8_catid_10300100023BC100_743501_3... iou_score Nadir \n", + "4 Atlanta_nadir8_catid_10300100023BC100_743501_3... iou_score Nadir " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_result = pd.read_csv(os.path.join(sol.data.data_dir, 'competition_test_results_full.csv'))\n", + "full_result.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are five rows from the full result file, where each row indicates the scores for a single image chip." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/creating_im_reference_csvs.ipynb b/docker/solaris/docs/tutorials/notebooks/creating_im_reference_csvs.ipynb new file mode 100644 index 00000000..2e892925 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/creating_im_reference_csvs.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating reference CSVs for model training and inference\n", + "\n", + "When you train models with `solaris`, it uses reference CSV files to find images and matching labels. Let's go through what those are and what they should include. You'll create (up to) three different reference files:\n", + "\n", + "- [Training data](#Training-Data-CSV): Required for Training\n", + "- [Epoch-wise validation data](#Validation-Data-CSV): Optional\n", + "- [Inference data](#Inference-Data-CSV): Required for inference\n", + "- [Using these files](#Using-these-files)\n", + "\n", + "## Training Data CSV\n", + "\n", + "Your training data CSV must have two columns with the __exact__ names below:\n", + "\n", + "- __image__: The `image` column defines the paths to each image file to be used during training, one path per row. You can use either the absolute path to the file or the path relative to the path that you run code in - we recommend using the absolute path for consistency.\n", + "- __label__: The `label` column defines the paths to the label (mask) files. If you need to create masks first, [check out the Python API tutorial](api_masks_tutorial.ipynb) or the [CLI tutorial](../cli_mask_creation.html).\n", + "\n", + "__The image and label in each row must match!__ This is how `solaris` matches your training images to the expected outputs.\n", + "\n", + "If you choose to have `solaris` split validation data out for you, it will randomly select a fraction of the rows for validation. The fraction used for validation is defined in the config YAML file - for more on how to do so, [see the YAML config reference](creating_the_yaml_config_file.ipynb).\n", + "\n", + "For more control over what data is used for training vs. validation, you can create a separate validation CSV.\n", + "\n", + "## Validation Data CSV\n", + "\n", + "This CSV is the same as the Training Data CSV, but specifies images and masks to be used for epoch-wise validation. Make sure there's no overlap between your training and validation sets - you don't want any data leaks! If you want `solaris` to split the validation data out of the training data automatically, you don't need to provide this.\n", + "\n", + "## Inference Data CSV\n", + "\n", + "This reference file points to the image files that you wish to make predictions on. It therefore only needs to contain one column: __image__.\n", + "\n", + "## Using these files\n", + "\n", + "Once you have made these labels, provide the paths to them [in your configuration file](creating_the_yaml_config_file.ipynb); they'll automatically be loaded into your config when you call [solaris.utils.config.parse()](../../api/utils.rst#solaris.utils.config.parse)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/creating_the_yaml_config_file.ipynb b/docker/solaris/docs/tutorials/notebooks/creating_the_yaml_config_file.ipynb new file mode 100644 index 00000000..bc73a0d9 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/creating_the_yaml_config_file.ipynb @@ -0,0 +1,116 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating the YAML Configuration File\n", + "\n", + "`solaris` uses a YAML-formatted config file to specify all of the parameters required for data pre-processing, model training, inference, and more. Here, we'll go through every single element of that file and what it means, as well as how to create your own or modify an existing file for your use.\n", + "\n", + "## Helpful Resources\n", + "\n", + "- [Link to the YAML configuration skeleton](https://github.com/CosmiQ/solaris/blob/dev/solaris/nets/configs/config_skeleton.yml)\n", + "- [Link to a sample YAML config file for XD_XD's SpaceNet 4 solution model](https://github.com/CosmiQ/solaris/blob/dev/solaris/nets/configs/xdxd_spacenet4.yml)\n", + "\n", + "## The elements of the Config file\n", + "\n", + "#### Top-level arguments\n", + "\n", + "- __model\\_name:__ \\[str\\] The name of the model being used. This will be cross-referenced against a list of possible options provided by `solaris`, and if it's not in that list, the user will be expected to provide the model. _Note_: currently, using user-provided models requires use of the Python API.\n", + "- __model\\_path__: \\[str\\] Leave this blank unless you're using a custom model not native to solaris. solaris will automatically find your model.\n", + "- __train__: \\[bool\\] Should `solaris` execute model training?\n", + "- __infer__: \\[bool\\] Should `solaris` execute model inference?\n", + "- __pretrained__: \\[bool\\] Do you wish to use pretrained weights with the model? This must be `true` if `train` is `false`.\n", + "- __nn\\_framework__: \\[str\\] Which neural network framework are you using? This should either be `\"torch\"` or `\"keras\"` (more to be added later!)\n", + "- __batch\\_size__: \\[int\\] What's the batch size for model training/inference?\n", + "\n", + "#### Data specs\n", + "- __width:__ \\[int\\] The pixel width of the model inputs.\n", + "- __height:__ \\[int\\] The pixel height of the model inputs.\n", + "- __image_type:__ \\[str\\] One of `\"normalized\"` (0-1 range), `\"zscore\"`, `\"8bit\"`, `\"16bit\"`. The data type that the model ingests.\n", + "- __rescale:__ \\[bool\\] Should image values be rescaled prior to post-processing?\n", + "- __rescale\\_minima:__ \\[str or list\\] Either `\"auto\"` (in which case Solaris automatically determines this) or a value or list of values to set the minimum to.\n", + "- __rescale\\_maxima:__ \\[str or list\\] Either `\"auto\"` (in which case Solaris automatically determines this) or a value or list of values to set the maximum to.\n", + "- __channels:__ \\[int\\] The number of channels in the input.\n", + "- __label\\_type:__ \\[str\\] currently the only possible value to this argument is `\"mask\"`.\n", + "- __is\\_categorical:__ \\[bool\\] Currently this argument has no effect. When classification/object detection is added, it will be implemented.\n", + "- __mask\\_channels:__ \\[int\\] The number of channels in the training mask to be used as a training target.\n", + "- __val\\_holdout\\_frac:__ \\[float\\] The fraction of the training data to hold out for validation. Note that this argument has no effect if __validation\\_data\\_csv__ (below) is specified. Otherwise, the a random subset of the samples in the training CSV will be held back for end-of-epoch validation.\n", + "- __data\\_workers:__ \\[int\\] This argument is currently unused.\n", + "\n", + "#### Data reference files\n", + "\n", + "See [Creating reference files to help solaris find your imagery](../cli_im_ref) for details on what these files must include.\n", + "\n", + "- __training\\_data\\_csv:__ \\[str\\] The path to the training data CSV. See the link above for more details.\n", + "- __validation\\_data\\_csv:__ \\[str\\] The path to the validation data CSV. See the link above for more details. If you are splitting your training data for validation using __val\\_holdout\\_frac__, ignore this argument.\n", + "- __inference\\_data\\_csv:__ \\[str\\] The path to the inference data CSV. See the link above for more details.\n", + "\n", + "#### Training augmentation\n", + "\n", + "Augmentation is critical in training many models, particularly for geospatial data. If you perform data normalization during your augmentation pipeline, you can also specify that here. See [XD_XD's SpaceNet 4 augmentation pipeline](https://github.com/CosmiQ/solaris/blob/dev/solaris/nets/configs/xdxd_spacenet4.yml) for an example of a pipeline.\n", + "\n", + "- __augmentations:__ \\[dict\\] The augmentations to run. The majority of augmentations implemented in [albumentations](https://albumentations.readthedocs.io/) are available here, either using that implementation or a custom version to enable >3-channel imagery ingestion. Pass the name of the augmentation as keys in this dictionary, and `kwarg: value` pairs as sub-dicts. See the sample linked above if this is unclear.\n", + "- __p:__ \\[float\\] The probability that the augmentation pipeline will be applied to images in a batch.\n", + "- __shuffle:__ \\[bool\\] Should the order of training images be shuffled as they're fed into the model? Defaults to `true`.\n", + "\n", + "#### Validation augmentation\n", + "\n", + "The same arguments are valid here as for `training_augmentation`.\n", + "\n", + "#### Inference augmentation\n", + "\n", + "The same arguments are valid here as for `training_augmentation`.\n", + "\n", + "#### Training\n", + "\n", + "This set of parameters define the actual training process.\n", + "\n", + "- __epochs:__ \\[int\\] The number of epochs to train for.\n", + "- __steps\\_per\\_epoch:__ \\[int\\] The number of batches to train for in each epoch. This is determined automatically if not provided.\n", + "- __optimizer:__ \\[str\\] The name of the optimizer to use for training. Options are `\"Adam\"`, `\"SGD\"`, `\"adadelta\"`, `\"RMSProp\"`, `\"Adamax\"`, `\"Nadam\"` (Keras only), `\"Adagrad\"` (Keras only), `\"SparseAdam\"` (Torch only), or `\"ASGD\"` (Torch only). Pass arguments for these optimizers to __opt\\_args__ (see below).\n", + "- __lr:__ \\[float\\] The learning rate to use (at least at the start of the training process).\n", + "- __opt\\_args:__ \\[dict\\] A dictionary of `kwarg: value` pairs to pass to the optimizer.\n", + "- __loss:__ \\[dict\\] A dictionary of loss function name(s). This allows you to create composite loss functions with ease. If there are any arguments that must be passed to the loss function upon initialization (e.g. the gamma parameter for focal loss), pass them as subdicts here.\n", + "- __loss\\_weights:__ \\[dict\\] A dictionary of `loss_name: weight` pairs. If provided, the same names must be passed here as were passed in __loss__. If not provided, the different losses will be weighted equally. Weight values can be ints or floats.\n", + "- __metrics:__ \\[dict\\] A dict of `training: [list of training metrics], validation: [list of validation metrics]`. See the linked example for what this can look like. Note that this only currently has an effect for Keras models.\n", + "- __checkpoint\\_frequency:__ \\[int\\] The frequency at which model checkpoints should be saved.\n", + "- __callbacks:__ \\[dict\\] A dict of callback names, whose values are subdicts defining any arguments for the callback. See [callbacks](../api/nets.rst#module-solaris.nets.callbacks) for options.\n", + "- __model\\_dest\\_path:__ \\[str\\] The path to save the final, trained model to.\n", + "- __verbose:__ \\[bool\\] Verbose text output during training.\n", + "\n", + "#### Inference\n", + "\n", + "- __window\\_step\\_size\\_x:__ \\[int\\] If your model takes in an image smaller than your inference chips, this argument will specify how far in the x direction each tile should step. Set to the same value as __width__ if you don't want any overlap between tiles; if you have overlap, `solaris` will average predictions across the overlapping areas to give you more robust estimates.\n", + "- __window\\_step\\_size\\_y:__ \\[int\\] If your model takes in an image smaller than your inference chips, this argument will specify how far in the y direction each tile should step. Set to the same value as __height__ if you don't want any overlap between tiles; if you have overlap, `solaris` will average predictions across the overlapping areas to give you more robust estimates.\n", + "\n", + "\n", + "## How the config file is processed\n", + "\n", + "`solaris` contains a utility function, [solaris.utils.config.parse()](../../api/utils.rst#solaris.utils.config.parse) which takes one argument: the path to a YAML config file to read in. [parse()](../../api/utils.rst#solaris.utils.config.parse) will check to make sure necessary values are present. This is called automatically by CLI functions that take the config file as an argument, but you can also call it with the Python API to use the config as an argument in [solaris.nets](../../api/nets.rst) functions.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "solaris", + "language": "python", + "name": "solaris" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/map_vehicles_cowc.ipynb b/docker/solaris/docs/tutorials/notebooks/map_vehicles_cowc.ipynb new file mode 100644 index 00000000..704fc710 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/map_vehicles_cowc.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mapping vehicles with `solaris` and the `cowc` dataset\n", + "\n", + "`solaris` can assist with tasks beyond foundational mapping. Here, we'll go through a full exercise of preprocessing the Cars Overhead With Context (`cowc`) dataset, training a segmentation model with `solaris`, mapping vehicles in a previously unseen test city, and finally scoring our results.\n", + "\n", + "\n", + "Let's start with downloading our data. The `cowc` dataset can be [downloaded here.](ftp://gdo152.ucllnl.org/cowc/datasets/ground_truth_sets/) Save it to a location where you will be able to preprocess the data and enable your GPU to find it.\n", + "\n", + "Once all the data in the `ground_truth_sets/` directory is downloaded, we'll import our packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import solaris as sol\n", + "import os\n", + "import glob\n", + "import gdal\n", + "from tqdm import tqdm\n", + "import cv2\n", + "import shutil\n", + "import pandas as pd\n", + "import numpy as np\n", + "from skimage.morphology import square, dilation\n", + "from matplotlib import pyplot as plt\n", + "from solaris.eval.iou import calculate_iou\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify our directories for pre processing" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "root= \".../cowc/datasets/ground_truth_sets/\" ##cowc ground_truth_sets location after download\n", + "masks_out= \".../cowc/masks\" ##output location for your masks for training\n", + "images_out= \".../cowc/tiles\" ##output location for your tiled images for testing\n", + "masks_test_out= \".../cowc/masks_test\" ##output location for your masks for testing\n", + "images_test_out= \".../cowc/tiles_test\" ##output location for your tiled images for testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize a tiling function\n", + "Below is a function for tiling, solaris presently does not handle non-georeferenced pngs, but will in the future. This is a hold-over function until then." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def geo_tile(untiled_image_dir, tiles_out_dir, tile_size=544,\n", + " overlap=0.2, search=\".png\",Output_Channels=[1,2,3]):\n", + " \"\"\"Function to tile a set of images into smaller square chunks with embedded georeferencing info\n", + " allowing an end user to specify the size of the tile, the overlap of each tile, and when to discard\n", + " a tile if it contains blank data.\n", + " Arguments\n", + " ---------\n", + " untiled_image_dir : str\n", + " Directory containing full or partial image strips that are untiled.\n", + " Imagery must be georeferenced.\n", + " tiles_out_dir : str\n", + " Output directory for tiled imagery.\n", + " tile_size : int\n", + " Extent of each tile in both X and Y directions in units of pixels.\n", + " Defaults to ``544`` .\n", + " overlap : float\n", + " The amount of overlap of each tile in float format. Should range between 0 and <1.\n", + " Defaults to ``0.2`` .\n", + " search : str\n", + " A string with a wildcard to search for files by type\n", + " Defaults to \".png\"\n", + " Output_Channels : list\n", + " A list of the number of channels to output, 1 indexed.\n", + " Defaults to ``[1,2,3]`` .\n", + " Returns\n", + " -------\n", + " Tiled imagery directly output to the tiles_out_dir\n", + " \"\"\"\n", + " if not os.path.exists(tiles_out_dir):\n", + " os.makedirs(tiles_out_dir)\n", + "\n", + " os.chdir(untiled_image_dir)\n", + " search2 = \"*\" + search\n", + " images = glob.glob(search2)\n", + " tile_size = int(tile_size)\n", + "\n", + " for stackclip in images:\n", + " print(stackclip)\n", + " interp = gdal.Open(os.path.abspath(stackclip))\n", + " width = int(interp.RasterXSize)\n", + " height = int(interp.RasterYSize)\n", + " count = 0\n", + " for i in range(0, width, int(tile_size * (1 - overlap))):\n", + " for j in range(0, height, int(tile_size * (1 - overlap))):\n", + " Chip = [i, j, tile_size, tile_size]\n", + " count += 1\n", + " Tileout = tiles_out_dir + \"/\" + \\\n", + " stackclip.split(search)[0] + \"_tile_\" + str(count) + \".tif\"\n", + " output = gdal.Translate(Tileout, stackclip, srcWin=Chip, bandList=Output_Channels)\n", + " del output\n", + " print(\"Done\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Orgainze our data\n", + "The `cowc` dataset is a bit cluttered to start, some reogranization helps us down the road for smoother pre-processing of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir(root)\n", + "dirs=glob.glob(\"*/\")\n", + "\n", + "for directory in dirs:\n", + " os.chdir(directory)\n", + " if not os.path.exists(\"Images\"):\n", + " os.makedirs(\"Images\")\n", + " os.makedirs(\"Masks\")\n", + " os.makedirs(\"Extras\")\n", + " xcfs=glob.glob(\"*.xcf\")\n", + " txts=glob.glob(\"*.txt\")\n", + " os.chdir(\"Images\")\n", + " negatives=glob.glob(\"*Negatives.png\")\n", + " masks=glob.glob(\"*Annotated_Cars.png\")\n", + " for xcf in xcfs:\n", + " shutil.move(xcf,os.path.join(root,directory,\"Extras\",xcf))\n", + " for txt in txts:\n", + " shutil.move(txt,os.path.join(root,directory,\"Extras\",txt))\n", + " for negative in negatives:\n", + " shutil.move(negative,os.path.join(root,directory,\"Extras\",negative))\n", + " for mask in masks:\n", + " shutil.move(mask,os.path.join(root,directory,\"Masks\",mask))\n", + " images=glob.glob(\"*.png\")\n", + " for image in images:\n", + " shutil.move(image,os.path.join(root,directory,\"Images\",image))\n", + " os.chdir(root)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tile our masks and convert them to GeoTiffs\n", + "Presently `solaris` works with GeoTiffs exclusively, so converting pngs into this tifs is required to start. Furthermore tiling is required to feed our neural network. We will tile our masks and images sequentially, this process may take some time depending on your compute resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for directory in dirs:\n", + " if directory != \"Utah_AGRC\":\n", + " directory = os.path.join(root,directory,\"Masks\")\n", + " print(directory)\n", + " geo_tile(directory, masks_out, tile_size=512, overlap=0.1,search=\"*.png\",Output_Channels=[1])\n", + " else:\n", + " directory = os.path.join(root,directory,\"Masks\")\n", + " print(directory)\n", + " geo_tile(directory, masks_out, tile_size=512, overlap=0,search=\"*.png\",Output_Channels=[1]) #No overlap for testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for directory in dirs:\n", + " if directory != \"Utah_AGRC\":\n", + " directory = os.path.join(root,directory,\"Images\")\n", + " print(directory)\n", + " geo_tile(directory, images_out, tile_size=512, overlap=0.1,search=\"*.png\",Output_Channels=[1,2,3])\n", + " else:\n", + " directory = os.path.join(root,directory,\"Images\")\n", + " print(directory)\n", + " geo_tile(directory, images_out, tile_size=512, overlap=0,search=\"*.png\",Output_Channels=[1,2,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dialate our masks to increase the size of our labels\n", + "Here we will perform a [simple morphological dialation filter](https://scikit-image.org/docs/dev/auto_examples/applications/plot_morphology.html#dilation) to increase our label size. This will make our masks large enough for our neural network to detect, but not large enough so they start to overlap one another when cars are located in close proximity to one another." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "driver = gdal.GetDriverByName(\"GTiff\")\n", + "os.chdir(masks_out)\n", + "images=glob.glob(\"*.tif\")\n", + "for image in tqdm(images):\n", + " band=gdal.Open(image)\n", + " band = band.ReadAsArray()\n", + " band=dilation(band, square(9))\n", + " im_out = driver.Create(image,band.shape[1],band.shape[0],1,gdal.GDT_Byte)\n", + " im_out.GetRasterBand(1).WriteArray(band)\n", + " del im_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate some basic statistics for z-scoring (normalizing) our imagery\n", + "[Z-scoring](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/z-score/) and normalizing our imagery can help to standardize it, improve generalizability, and potentially the transferability of a model to another location. It also helps to soften overly bright or dark areas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "M1=[]\n", + "M2=[]\n", + "M3=[]\n", + "S1=[]\n", + "S2=[]\n", + "S3=[]\n", + "driver = gdal.GetDriverByName(\"GTiff\")\n", + "os.chdir(images_out)\n", + "images=glob.glob(\"*.tif\")\n", + "for image in images:\n", + " band=gdal.Open(image).ReadAsArray()\n", + " M1.append(np.mean(band[0,:,:]))\n", + " M2.append(np.mean(band[1,:,:]))\n", + " M3.append(np.mean(band[2,:,:]))\n", + " S1.append(np.std(band[0,:,:]))\n", + " S2.append(np.std(band[1,:,:]))\n", + " S3.append(np.std(band[2,:,:]))\n", + "\n", + "print(\"Save these numbers for your solaris.yml file for training and z-scoring (normalizing) your imagery\")\n", + "print(np.mean(M1)/255)\n", + "print(np.mean(M2)/255)\n", + "print(np.mean(M3)/255)\n", + "print(np.mean(S1)/255)\n", + "print(np.mean(S2)/255)\n", + "print(np.mean(S3)/255)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hold out a city for testing\n", + "Here we hold out Salt Lake City, Utah as a test city, and do some simple data reogranization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.exists(images_test_out):\n", + " os.makedirs(images_test_out)\n", + "os.chdir(images_out)\n", + "images = glob.glob(\"12TVL*\")\n", + "for image in tqdm(images):\n", + " output = os.path.join(images_test_out,image)\n", + " shutil.move(image, output)\n", + "\n", + "if not os.path.exists(masks_test_out):\n", + " os.makedirs(masks_test_out)\n", + "os.chdir(masks_out)\n", + "images = glob.glob(\"12TVL*\")\n", + "for image in tqdm(images):\n", + " output = os.path.join(masks_test_out,image)\n", + " shutil.move(image, output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Review some of our masks and images\n", + "We can now review some our masks and images, you can change the integer value in the filename to see different tiles easily." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image = os.path.join(images_test_out,\"12TVL240120_tile_518.tif\")\n", + "mask = os.path.join(masks_test_out,\"12TVL240120_Annotated_Cars_tile_518.tif\")\n", + "image = gdal.Open(image).ReadAsArray()\n", + "mask = gdal.Open(mask).ReadAsArray()\n", + "\n", + "fig, ax = plt.subplots(ncols=2, figsize=(10, 5))\n", + "ax[0].imshow(np.moveaxis(image,0,2))\n", + "ax[0].set_title('Image')\n", + "ax[1].imshow(mask)\n", + "ax[1].set_title('Mask')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a csv file that lists our images and our masks for training and testing\n", + "`solaris` requires our images and masks to be organized coherently and documents where each image is in a tabular csv format. These csvs will be linked to in our yml file and can then be used to read in the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "images = []\n", + "image_folder=images_out\n", + "label_folder=masks_out\n", + "os.chdir(label_folder)\n", + "labels=glob.glob(\"*.tif\")\n", + "for x in labels:\n", + " z = x.split('_Annotated_Cars')[0] + x.split('_Annotated_Cars')[1]\n", + " os.chdir(image_folder)\n", + " image=glob.glob(z)\n", + " if len(image) != 1:\n", + " os.chdir(label_folder)\n", + " os.remove(x) \n", + " else:\n", + " images.append(image[0])\n", + " \n", + "for image, label in zip(images,labels):\n", + " image = os.path.join(image_folder,image)\n", + " label = os.path.join(label_folder,label)\n", + " data.append((image, label))\n", + "\n", + "df = pd.DataFrame(data, columns=['image', 'label'])\n", + "df.to_csv(os.path.join(root,\"train_data_cowc2.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "images = []\n", + "image_folder=images_test_out\n", + "label_folder=masks_test_out\n", + "os.chdir(label_folder)\n", + "labels=glob.glob(\"*.tif\")\n", + "for x in labels:\n", + " z = x.split('_Annotated_Cars')[0] + x.split('_Annotated_Cars')[1]\n", + " os.chdir(image_folder)\n", + " image=glob.glob(z)\n", + " if len(image) != 1:\n", + " os.chdir(label_folder)\n", + " os.remove(x) \n", + " else:\n", + " images.append(image[0])\n", + " \n", + "for image, label in zip(images,labels):\n", + " image = os.path.join(image_folder,image)\n", + " label = os.path.join(label_folder,label)\n", + " data.append((image, label))\n", + "\n", + "df = pd.DataFrame(data, columns=['image', 'label'])\n", + "df.to_csv(os.path.join(root,\"test_data_cowc2.csv\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Edit your .yml file and begin training your model\n", + "An [example yml file is provided here](https://github.com/jshermeyer/solaris_cowc/blob/master/xdxd_vehicleDetection.yml). Be sure the train paramenter is set to `True`. Furthermore, be sure to pass the paths to your csvs that you just created into the `training_data_csv` and `inference_data_csv` prompts. Optionally you may want to alter the `batch_size` or `val_holdout_frac` which is the fraction of images randomly sampled out of your training set to help your model learn as it trains. Also ensure that your `Normalize` values are correct.\n", + "\n", + "When you're ready, set the path you your modified yml file and run the prompt below. Training time can take multiple hours." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = sol.utils.config.parse('/path/to/yml/xdxd_vehicleDetection.yml')\n", + "trainer = sol.nets.train.Trainer(config)\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time to inference\n", + "Be sure to edit your yml to enable infer mode and then we can start inferencing to find the vehicles in Salt Lake City." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inf_df = sol.nets.infer.get_infer_df(config)\n", + "inferer = sol.nets.infer.Inferer(config)\n", + "inferer(inf_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify our directories for post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "inference_output=\".../cowc/inference_out\" ## The location that you output inference results to as specified in your yml.\n", + "inference_output_bin=\".../cowc/inference_out_binary\" ## A location to store binarized outputs\n", + "inference_polygon_dir=\".../cowc/inference_polys\" ## Outputs polygonized\n", + "ground_truth_polygon_dir=\".../cowc/ground_truth_polys\" ## Ground Truth Outputs polygonized" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Post-processing- binarize our masks and convert them to polygons\n", + "Here we will binarize our outputs and convert a mask to a 1/0 value for car/no car. Following this we will convert the masks to polygons. You may want to adjust the cutoff value for binarization based on your own experiments or change this if you chose not to z-score your imagery." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from solaris.vector.mask import mask_to_poly_geojson\n", + "driver = gdal.GetDriverByName(\"GTiff\")\n", + "\n", + "os.chdir(inference_output_bin)\n", + "images=glob.glob(\"*.tif\")\n", + "for image in tqdm(images):\n", + " band=gdal.Open(image)\n", + " band = band.ReadAsArray()\n", + " band[np.where((band > 0))] = 1 ### Note that these values may be model specific and change slightly due to reimplementations. For simplicity I threshold at 0.\n", + " band[np.where((band <= 0))] = 0\n", + " im_out = driver.Create(os.path.join(inference_output_bin,image),band.shape[1],band.shape[0],1,gdal.GDT_Byte)\n", + " im_out.GetRasterBand(1).WriteArray(band)\n", + " del im_out\n", + " output=os.path.join(inference_polygon_dir,image.split(\".\")[0]+\".geojson\") \n", + " gdf=mask_to_poly_geojson(band,reference_im=os.path.join(images_test_out,image),min_area=1,simplify=True)\n", + " if not gdf.empty:\n", + " gdf.to_file(output, driver='GeoJSON')\n", + " \n", + " \n", + "os.chdir(masks_test_out)\n", + "images=glob.glob(\"*.tif\")\n", + "for image in tqdm(images):\n", + " band=gdal.Open(image)\n", + " band = band.ReadAsArray()\n", + " output=os.path.join(ground_truth_polygon_dir,image.split(\".\")[0]+\".geojson\") \n", + " image=image.split(\"_Annotated_Cars\")[0]+image.split(\"_Annotated_Cars\")[1]\n", + " gdf=mask_to_poly_geojson(band,reference_im=os.path.join(images_test_out,image),min_area=1,simplify=True)\n", + " if not gdf.empty:\n", + " gdf.to_file(output, driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check out our results\n", + "Below we can inspect our results, and compare vs. the ground truth mask. Again the integer value can be changed in each filename to inspect different tiles." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image = os.path.join(images_test_out,\"12TVL240120_tile_519.tif\")\n", + "mask = os.path.join(masks_test_out,\"12TVL240120_Annotated_Cars_tile_519.tif\")\n", + "prediction = os.path.join(inference_output_bin,\"12TVL240120_tile_519.tif\")\n", + "image = gdal.Open(image).ReadAsArray()\n", + "mask = gdal.Open(mask).ReadAsArray()\n", + "prediction = gdal.Open(prediction).ReadAsArray()\n", + "\n", + "fig, ax = plt.subplots(ncols=3, figsize=(15, 5))\n", + "ax[0].imshow(np.moveaxis(image,0,2))\n", + "ax[0].set_title('Image')\n", + "ax[1].imshow(mask)\n", + "ax[1].set_title('Ground Truth Mask')\n", + "ax[2].imshow(prediction)\n", + "ax[2].set_title('Prediction')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize a few more functions for scoring our results. \n", + "Here we will work with the [solaris.eval.iou.calculate_iou()](../../api/eval.rst#solaris.eval.iou) function to calculate [intersection over union](https://en.wikipedia.org/wiki/Jaccard_index) and from this [precision and recall.](https://en.wikipedia.org/wiki/Precision_and_recall) This can be used to score how well our model is performing at detecting cars.\n", + "The calculate_ious function's arguments:\n", + "\n", + "- `pred_poly` : A `shapely.Polygon`. This is a prediction polygon to test.\n", + "- `test_data_GDF` : A `geopandas.GeoDataFrame`. This is GeoDataFrame of ground truth polygons to test ``pred_poly`` against.\n", + "\n", + "Using the function as is will calculate precision, but we can actually \"invert\" the inputs to calculate recall as well. For the recall caluclation instead of supplying a prediciton polygon we will supply a ground-truth polygon. Furthermore, instead of supplying a ground truth geodataframe containing all of our ground truth polygons for a tile we will supply a geodataframe containing all of the prediction polygons for a tile.\n", + "\n", + "We can initialize the functions below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Wrapper functions for calculate_iou\n", + "\n", + "def precision_calc(proposal_polygons_dir,groundtruth_polygons_dir,file_format=\"geojson\"):\n", + " ious=[]\n", + " os.chdir(proposal_polygons_dir)\n", + " search = \"*\" + file_format\n", + " proposal_geojsons=glob.glob(search)\n", + " for geojson in tqdm(proposal_geojsons):\n", + " ground_truth_poly = os.path.join(groundtruth_polygons_dir,geojson)\n", + " if os.path.exists(ground_truth_poly):\n", + " ground_truth_gdf=gpd.read_file(ground_truth_poly)\n", + " proposal_gdf=gpd.read_file(geojson)\n", + " for index, row in (proposal_gdf.iterrows()):\n", + " iou=calculate_iou(row.geometry, ground_truth_gdf)\n", + " if 'iou_score' in iou.columns:\n", + " iou=iou.iou_score.max()\n", + " ious.append(iou)\n", + " else:\n", + " iou=0\n", + " ious.append(iou)\n", + " return ious\n", + "\n", + "def recall_calc(proposal_polygons_dir,groundtruth_polygons_dir,file_format=\"geojson\"):\n", + " ious=[]\n", + " os.chdir(groundtruth_polygons_dir)\n", + " search = \"*\" + file_format\n", + " gt_geojsons=glob.glob(search)\n", + " for geojson in tqdm(gt_geojsons):\n", + " proposal_poly = os.path.join(proposal_polygons_dir,geojson)\n", + " if os.path.exists(proposal_poly):\n", + " proposal_gdf=gpd.read_file(proposal_poly)\n", + " gt_gdf=gpd.read_file(geojson)\n", + " for index, row in (gt_gdf.iterrows()):\n", + " iou=calculate_iou(row.geometry, proposal_gdf)\n", + " if 'iou_score' in iou.columns:\n", + " iou=iou.iou_score.max()\n", + " ious.append(iou)\n", + " else:\n", + " iou=0\n", + " ious.append(iou)\n", + " return ious\n", + " \n", + "def f1_score(precision_ious,recall_ious,threshold=0.5):\n", + " items=[]\n", + " for i in precision_ious:\n", + " if i >=threshold:\n", + " items.append(1)\n", + " else:\n", + " items.append(0)\n", + " \n", + " precision= np.mean(items)\n", + " \n", + " items=[]\n", + " for i in recall_ious:\n", + " if i >=threshold:\n", + " items.append(1)\n", + " else:\n", + " items.append(0)\n", + " recall= np.mean(items)\n", + " \n", + " f1 = 2* precision * recall/(precision + recall)\n", + " return f1\n", + "\n", + "def simple_average_precision(precisions_ious,threshold=0.5):\n", + " items=[]\n", + " for i in precision_ious:\n", + " if i >=threshold:\n", + " items.append(1)\n", + " else:\n", + " items.append(0)\n", + " \n", + " precision= np.mean(items)\n", + " return precision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Score our results\n", + "As a final step we can score our outputs against the ground truth to report scores. Without these scores all you really have is a pretty picture. We report both an [F1 Score](https://en.wikipedia.org/wiki/F1_score) and an [average precision score](https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Score our results\n", + "precision_ious = precision_calc(inference_polygon_dir,ground_truth_polygon_dir,file_format=\"geojson\")\n", + "recall_ious = recall_calc(inference_polygon_dir,ground_truth_polygon_dir,file_format=\"geojson\")\n", + "print(f1_score(precision_ious,recall_ious,threshold=0.25), \"F1 Score@0.25\")\n", + "print(f1_score(precision_ious,recall_ious,threshold=0.5), \"F1 Score@0.5\") ## The traditional SpaceNet metric\n", + "print(simple_average_precision(precision_ious,threshold=0.25), \"AP@0.25\") ## Acceptable for small objects like cars!\n", + "print(simple_average_precision(precision_ious,threshold=0.5), \"AP@0.5\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docker/solaris/docs/tutorials/notebooks/preprocessing_branching.ipynb b/docker/solaris/docs/tutorials/notebooks/preprocessing_branching.ipynb new file mode 100644 index 00000000..a6489710 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/preprocessing_branching.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solaris Multimodal Preprocessing Library\n", + "# Tutorial Part 2: Branching" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "\n", + "import solaris.preproc.pipesegment as pipesegment\n", + "import solaris.preproc.image as image\n", + "import solaris.preproc.sar as sar\n", + "import solaris.preproc.optical as optical\n", + "import solaris.preproc.label as label\n", + "\n", + "plt.rcParams['figure.figsize'] = [4, 4]\n", + "datadir = '../../../solaris/data/preproc_tutorial'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the second example of the preprocessing library, let's implement a basic pansharpening algorithm. In pansharpening, a high-resolution grayscale image (\"panchromatic\") is combined with a low-resolution color image of the same region (\"multispectral\") to create a high-resolution color image (\"pansharpened\"). As always, a good starting point is to imagine the flowchart of the desired task:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purpose of this tutorial, the details of how this works are not important, nor is the fact that better pansharpening algorithms are available. What matters is that different branches split apart and come together in this flowchart, making this a more complex workflow than the simple pipeline of Example 1.\n", + "\n", + "The code will be set up as a reusable class, following the template in the Example 1 Follow-Up." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Pansharpening(pipesegment.PipeSegment):\n", + " def __init__(self, ms_path, pan_path, output_path):\n", + " super().__init__()\n", + " load_ms = image.LoadImage(ms_path) \\\n", + " * image.ShowImage(bands=[2,1,0], vmin=0, vmax=250, width=12)\n", + " resize_ms = image.Resize(600, 600)\n", + " color_ms = optical.RGBToHSV(rband=2, gband=1, bband=0)\n", + " load_pan = image.LoadImage(pan_path)\n", + " stack1 = image.MergeToStack()\n", + " get_hs = image.SelectBands((0, 1))\n", + " get_v = sar.BandMath(lambda x: x[3] * np.mean(x[2]) / np.mean(x[3]))\n", + " stack2 = image.MergeToStack()\n", + " color_output = optical.HSVToRGB(hband=0, sband=1, vband=2) \\\n", + " * image.ShowImage(vmin=0, vmax=250, width=12)\n", + " save_output = image.SaveImage(output_path)\n", + " self.feeder = (load_ms * resize_ms * color_ms + load_pan) * stack1 \\\n", + " * (get_hs + get_v) * stack2 * color_output * save_output\n", + "\n", + "ms_path = os.path.join(datadir, 'ms1.tif')\n", + "pan_path = os.path.join(datadir, 'pan1.tif')\n", + "output_path = os.path.join(datadir, 'output2.tif')\n", + "pansharpen = Pansharpening(ms_path, pan_path, output_path)\n", + "pansharpen()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The important thing to notice here is the `+` signs in the step specifying how all the parts are wired together:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# self.feeder = (load_ms * resize_ms * color_ms + load_pan) * stack1 \\\n", + "# * (get_hs + get_v) * stack2 * color_output * save_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two objects or pipelines connected by a `+` sign can get their input from the same place and/or send their output to the same place. For example, the section `stack1 * (get_hs + get_v) * stack2` means that the output of `stack1` is fed into both `get_hs` and `get_v`, while the outputs of both are in turn fed into `stack2`. Many classes in `preproc.image`, `preproc.sar`, and `preproc.optical` expect a single image as input, but `MergeToStack` expects a tuple of images, and the `+` sign combines the two pipelines' outputs into a tuple." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that two different `MergeToStack` objects were defined in the code (`stack1` and `stack2`). In contrast, if the same variable appears twice in the expression, the software assumes that both references refer to the exact same block in the flowchart (its input may be specified at either place it appears, or redundantly at both -- they just can't contradict each other). Sometimes multiple references to the same block are exactly what we want. But here we had two different blocks that just happened to be of the same class, so two different objects had to be instantiated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notation for building up workflows with `*` and `+` symbols is very flexible. Any directed acyclic graph (i.e., any flowchart without loops) can be defined this way. The `preproc.pipesegment` module includes classes to handle cycles, but in practice that's rarely needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Technical side note*: In this example, it's assumed that we knew in advance how big the panchromatic image was, so the arguments to the `Resize` object could be hardcoded. However, if we need the code to work for any image size, we could wrap the `image.Resize` class with a `pipesegment.PipeArgs` class to pipe in constructor arguments at runtime." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 2 Follow-Up: Parallel Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's often necessary to repeat the same analysis on many images. For example, perhaps a large image has been tiled into thousands of small ones, and we want to do the same image processing for each tile. On a multicore computer, there can be a speed improvement from running a number of jobs in parallel.\n", + "\n", + "The classes in the `preproc` library are `PipeSegment` subclasses. They inherit a method called `parallel` that handles parallel processing automatically. Because user-defined classes also inherit from `PipeSegment`, they have the same method as well.\n", + "\n", + "The code below uses the `parallel` method inherited by the `Pansharpening` class in Example 2 to pansharpen three images. On a multicore computer, up to three cores are used at once." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('../../../solaris/data/preproc_tutorial/ms1.tif', '../../../solaris/data/preproc_tutorial/pan1.tif', '../../../solaris/data/preproc_tutorial/output2a.tif'), ('../../../solaris/data/preproc_tutorial/ms2.tif', '../../../solaris/data/preproc_tutorial/pan2.tif', '../../../solaris/data/preproc_tutorial/output2b.tif'), ('../../../solaris/data/preproc_tutorial/ms3.tif', '../../../solaris/data/preproc_tutorial/pan3.tif', '../../../solaris/data/preproc_tutorial/output2c.tif')]\n" + ] + }, + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Specify all the file paths\n", + "ms_paths = [os.path.join(datadir, file) for file in ['ms1.tif', 'ms2.tif', 'ms3.tif']]\n", + "pan_paths = [os.path.join(datadir, file) for file in ['pan1.tif', 'pan2.tif', 'pan3.tif']]\n", + "output_paths = [os.path.join(datadir, file) for file in ['output2a.tif', 'output2b.tif', 'output2c.tif']]\n", + "input_args = list(zip(ms_paths, pan_paths, output_paths))\n", + "print(input_args)\n", + "\n", + "#Run the jobs in parallel\n", + "Pansharpening.parallel(input_args)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/preprocessing_pipelines.ipynb b/docker/solaris/docs/tutorials/notebooks/preprocessing_pipelines.ipynb new file mode 100644 index 00000000..fc98d459 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/preprocessing_pipelines.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solaris Multimodal Preprocessing Library\n", + "# Tutorial Part 1: Pipelines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Preprocessing imagery for geospatial deep learning can involve many steps. Tiling large images into manageably-sized tiles, as can be done with the `solaris` `tile` subpackage, is one important step. However, it is often necessary or desirable to modify the imagery itself to bring out key features or combine data from multiple sources. To do that, the `solaris` `preproc` subpackage provides a powerful syntax for executing image processing workflows.\n", + "\n", + "The `preproc` subpackage contains more than 60 classes, each of which does some specific data manipulation task. By connecting them together, the user can build out image processing tasks of arbitrary complexity. The syntax for doing so uses only two symbols: the `*` symbol to create pipelines, and the `+` symbol for data branching. These are further discussed below.\n", + "\n", + "The classes can work together like this because they are all subclasses of the `PipeSegment` base class, which handles all the details behind the scenes. For example, intermediate results are stored in RAM (making the library RAM-intensive but quite fast) and these intermediate results are deleted as soon as they are no longer needed. In this functional programming approach, the user just specifies what data processing he or she wants to happen, and the implementation is taken care of automatically.\n", + "\n", + "This tutorial will show different ways of using the `preproc` subpackage with three realistic examples. In the first example, a pipeline will be created to compute an image's normalized difference vegetation index. In the second example, a workflow with branching will be created to do pansharpening. In the final example, complex synthetic aperture radar data will be processed into readily-interpretable imagery." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "\n", + "import solaris.preproc.pipesegment as pipesegment\n", + "import solaris.preproc.image as image\n", + "import solaris.preproc.sar as sar\n", + "import solaris.preproc.optical as optical\n", + "import solaris.preproc.label as label\n", + "\n", + "plt.rcParams['figure.figsize'] = [4, 4]\n", + "datadir = '../../../solaris/data/preproc_tutorial'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1: A Simple Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the first example, let's create an image processing pipeline that will take a multispectral image and generate its normalized difference vegetation index (NDVI), a measurement that shows where the plants are. It helps to start by picturing the task as a flowchart:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a pipeline where the output of each block is fed in as the input to the next block. Here's the code:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mypipeline = (\n", + " image.LoadImage(os.path.join(datadir, 'ms1.tif'))\n", + " * sar.BandMath(lambda x: (x[3] - x[2]) / (x[3] + x[2] + 0.1))\n", + " * image.SaveImage(os.path.join(datadir, 'output1.tif'))\n", + ")\n", + "\n", + "mypipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first command, `mypipeline = (...)`, creates an object in memory (actually, a linked list of objects) describing the desired workflow. Once that's done, the computer knows what work to do, but the work hasn't actually happened yet. The work all happens when the object is called with the second command, `mypipeline()`.\n", + "\n", + "Let's look at the first command more closely. The new object `mypipeline` is built out of three objects representing each of the three steps in the flowchart. These three objects are all instances of various classes in the `preproc` library.\n", + "\n", + "Objects of the `LoadImage` and `SaveImage` classes load and save a geotiff file, including available metadata. The `BandMath` class does calculations with the different bands of an image. The NDVI is defined as the difference of the near-infrared and red bands divided by their sum, and in our sample image the near-infrared and red bands are bands 3 and 2, respectively, which is why the formula looks the way it does. The 0.1 in the denominator is just there to prevent division by zero.\n", + "\n", + "The `*` symbol plays a special role with classes that inherit from the `PipeSegment` base class, as mentioned in the introduction. (Except for a couple special cases, every class in `preproc` is a subclass of `PipeSegment`.) The `*` symbol tells the computer that we want to use the output of the preceeding object as the input to the following object. In this way, the `mypipeline = (...)` command is a complete description of the flowchart above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In any case, did the code actually work? To find out, we could look at the output image, `output1.tif`. With more complicated workflows it's often convenient to check intermediate results without the hassle of writing things to disk and opening them from there. That's where the `ShowImage` and `ImageStats` classes come in. The former will display the image and the latter will print descriptive statistics about it. These classes pass on their input images unchanged, so they can be inserted anywhere into the pipeline that this information would be helpful." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Visible Spectrum\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Difference Vegetation Index\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ms1: 1 bands, 300x300, float64, {'geotransform': (593270.2919143771, 1.0000483155950517, 0.0, 5747657.4158721585, 0.0, -1.0000483155950517), 'projection_ref': 'PROJCS[\"WGS 84 / UTM zone 31N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199...\n", + "\n", + " min max mean median std pos zero neg nan\n", + "0 -0.972502 0.997769 0.440329 0.430321 0.35757 83584 288 6128 0\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_pipeline_with_output = (\n", + " image.LoadImage(os.path.join(datadir, 'ms1.tif'))\n", + " * image.ShowImage(bands=[2,1,0], vmin=0, vmax=250, caption='Visible Spectrum')\n", + " * sar.BandMath(lambda x: (x[3] - x[2]) / (x[3] + x[2] + 0.1))\n", + " * image.SaveImage(os.path.join(datadir, 'output1.tif'))\n", + " * image.ShowImage(caption='Normalized Difference Vegetation Index')\n", + " * image.ImageStats()\n", + ")\n", + "\n", + "my_pipeline_with_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It worked! The NDVI ranges from -1 to 1 as expected, with vegetation indicated in white and everything else in gray or black.\n", + "\n", + "Note that `output1.tif` will only contain the result of the calculation, not the original image. Creating an output image with both of those things uses branching, which is shown in Example 2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1 Follow-Up: Building a Reusable Class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code snippets above, we created objects describing workflows, then called them to do the work. It's often better to start by defining a new class, from which such objects can be created. For the pipeline in this example, a class can be created like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class MyPipeline(pipesegment.PipeSegment):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.feeder = (\n", + " image.LoadImage(os.path.join(datadir, 'ms1.tif'))\n", + " * sar.BandMath(lambda x: (x[3] - x[2]) / (x[3] + x[2] + 0.1))\n", + " * image.SaveImage(os.path.join(datadir, 'output1.tif'))\n", + " )\n", + "\n", + "my_pipeline_instance = MyPipeline()\n", + "my_pipeline_instance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class's constructor has a boilerplate `super().__init__()` command, then sets `self.feeder` to the desired workflow. However, this class isn't very useful in real life, because the input and output paths are hardwired into the code. To preserve flexibility, let's send them in as arguments to the constructor. We'll also give variable names to all the different blocks of the flowchart, to more cleanly separate the descriptions of the individual blocks from the logic of how they are wired together." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class MyBestPipeline(pipesegment.PipeSegment):\n", + " def __init__(self, input_path, output_path):\n", + " super().__init__()\n", + " load = image.LoadImage(input_path)\n", + " ndvi = sar.BandMath(lambda x: (x[3] - x[2]) / (x[3] + x[2] + 0.1))\n", + " save = image.SaveImage(output_path)\n", + " self.feeder = load * ndvi * save\n", + "\n", + "my_input_path = os.path.join(datadir, 'ms1.tif')\n", + "my_output_path = os.path.join(datadir, 'output1.tif')\n", + "my_best_pipeline_instance = MyBestPipeline(my_input_path, my_output_path)\n", + "my_best_pipeline_instance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best part about defining a class, instead of just creating the object directly, is that the class is also a subclass of the `PipeSegment` base class. That gives us the option of using our new class as a building block when building up a more-complicated workflow in the future." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/docs/tutorials/notebooks/preprocessing_sar.ipynb b/docker/solaris/docs/tutorials/notebooks/preprocessing_sar.ipynb new file mode 100644 index 00000000..64c3a3b9 --- /dev/null +++ b/docker/solaris/docs/tutorials/notebooks/preprocessing_sar.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solaris Multimodal Preprocessing Library\n", + "# Tutorial Part 3: SAR" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/home/sol/conda/envs/solaris/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "\n", + "import solaris.preproc.pipesegment as pipesegment\n", + "import solaris.preproc.image as image\n", + "import solaris.preproc.sar as sar\n", + "import solaris.preproc.optical as optical\n", + "import solaris.preproc.label as label\n", + "\n", + "plt.rcParams['figure.figsize'] = [4, 4]\n", + "datadir = '../../../solaris/data/preproc_tutorial'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the third example of the preprocessing library, we'll look at synthetic aperture radar (SAR) data. SAR requires a number of specialized processing steps to go from complex data to the most widely-used finished products. A typical workflow might be:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's a class to do that, with `ShowImage` objects added to illustrate some of the steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intensity\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO2da4wk13Xf/6equqof0/PY2QcHu1wul6KWFBfCkiEohLIJWYps0pZDSkAc0pJBxFJIAxQcIIARyRJiI4GBCLHjL5Fs0I4gJZApEQH0gCBEJggljoRIFmmv+ZJWS9JLcR/cx3B2Zmd6Zvp18qH71t6urvejq6r7/oDGTFfX495bVeeee+655xAzQ6FQzC5a3gVQKBT5ooSAQjHjKCGgUMw4SggoFDOOEgIKxYyjhIBCMeNkJgSI6H4iOkVErxLRp7K6jkKhSAZl4SdARDqAnwH4IICzAH4M4BFmfiX1iykUikRkpQncA+BVZn6dmdsAvgrgwYyupVAoEmBkdN6DAN6Uvp8F8B6vnYlIuS0qCk+z2cS1a9fyLkYSrjDzPufGrDQBctk28qIT0WNE9BwRPTd2MLkdHrEAKZxjWlBtER4igqZprm22tbWV+Pzz8/NYXFxMfJ6YvOG2MStN4CyAG6XvhwCcl3dg5icBPAkMNAFN09Dv98VvYyckItftXsj7Oo8VNzjM+aJet2gQESzLQqfT8W3faSbKPWRmz31F+yXh2rVr0DQN8vOeN1lpAj8GcCsR3UxEJoCHAXzL7wC3htd1HYZh2NLZDfl3XddDndvvRocpV9lot9vo9/tjvdusaAhFuofMjF6vZwsAIkK9Xs+1TJkIAWbuAvgkgO8C+AmAp5n55YBjxrb1+33Mz8/j9ttvBzODiNBsNu3fndpDUSRrkRDtInpD0c5e2pEMEc2MoMgLZka73c61DJn5CTDzd5j5ncx8CzP/UcxzYGdnB2tra+j3+yMvvWmaAAaCYm5uDpZleUp8oS2Iv5VKxVOzmFac2o8sDOTvfscovCGiEU2UiHD33XePPGdetoZutzuRMnpR+Deh1WrhwoULAK4LBeeDKz+ouq6jWq3CMAwsLS3Z2sO9996LSqUytv+s4DXcmsW2yAKnEADGX25hCxDPb6VSwcrKysTK6EXhhYCAiNDv921NoN1u2w/w1tYWdnd37d7eNE3ouo5arQZg0NiHDx+2z9XtdgOHDtOuBitV35247eIcjjIzTp48icXFRVsbcApcTdPQbDZzvw+ZeAxGLsTQT8DNihvFsiskLQD0ej1omgZmtocOnU5nZIwsn995DV3X0ev1klVMMfO8+93vxqlTp+xnz+05m5ubw/r6+iSK8zwz3+3cWBhNwM1Qpeu6p8XfjX6/P9LL9/t9GIaBTqeDbrc7IgB0XYdpmrAsC4YxOlNKRIUWALquJ+49vManilGSttFPfvIT7O7uwjRNVztUpVLBXXfdlegaSSmUJgAMGkWeQol4HvsF1zTNHpMxMyzLws7Ojv0dgH1TnEbH+fl5bG5uRhIGZfYnkMte5nqUlQm2ebE1AYHs1BIWWVozM7rdLjqdDogIDzzwAJgZ29vbgRZyYNDLbmxs2PO5YSnziyNPu5a5HkUh6syT23M4SQojBJwqeRREI1YqFVSrVVQqFczNzaHX6+F73/ue/bvbNYT/gWmaqFQqI34Hs6AuG4YR6A47re0wNzc3UrewRsGgfeROTLY9BeHUUKOyvLyMubm5yMcVRgikMVfabrexs7ODXq+HVqtlq/PCrtDtdlGtVkcaWdgJdnd3AQDvec97YBgG9u/fn7g8ZaDb7Y5oPPLDWqlUbKNqnmQlhHZ3d8c0wzCaUBz39aBj5GdSDE/n5+exZ88eNBoNGIaBarWKm266CQsLC66CwrIsexo8ClmtHZgYzvGUU40XdgD5OxFhbm4O/X7fFhbC2La8vIxer4fV1dWZV407nQ6AwQMqDKx5kNV9mITxN8waATH9Lf4HBs5wrVbL3kcI67Nnz6Lf77vOor399tuedfIrR+EMgzGPBzD+sPgZXMT0ofy7mLcVNgFlJLuO6Ik2NzfzLkokhHeo0PSyJI3npVqt2jNZ4nzixQ5a+Cb/LoY2jhe/HIbBOHipcX6N5SZN+/0+1tfXPVcxzipiWDWJFylter1eZA0mrl0gSACIWSs/nGWNMlyRtYhqtRq4NkQwFUIgKnFXEM6yIABgz7iUCdnLVKZWq3nWJW27gNsxmqbBsizX/YQG4NZRBZ2fiFCtVtFoNFxnwtyYeiEQxdlI4CXlZ3Vo4PUwlXkRVtx7mUQIOgWSVxmYGQsLC7aRT/aE9SuDeEa73W6klYmFu4v1en1kKk9UOOwDp2ma3XhucQj8bqLypx9Fbo9pGyLt7Owk7s1lorZFv993fVF7vR6OHDmC+fl5uwMTcTXCXIuZ0Wq1sLGxEboshRICYsWfaZr2Ayh7AYp9/DAMw144BFwfY9XrdU/DjfOcRezx83jh3LQfed7babDKkzhl8DpGGEGjnCdpG8jH33HHHdjc3ESr1RoJrBN0XFwKJQSYGZcuXbJXBMrjIvEyB72gziAagsOHD/sODcRvYQRAHhpDULkmVR5RDrktDcNIbWgQtx5pCu5ms4kbbrgh0rWTBrSRy//Nb34Tq6urAIBGowHTNEeMss5r+Tk8hTJyFqHXiztFKKb5gFHXS5epEef1xiynpmmOLE9WFJ+4U3J+x4WZbtZ1PbTRLkj7ZGZUKpWRdS5h6xBmf4d/wPRNEYob4Zx6cZN+zvUFTjqdjlpZF4MgG0vQsc59orR/XAGwd+9e33O6nXd5edn+/84778TevXtDGZ39/FSEN2av17PrLbz+grTNpaWlUFOOoTQUUemoHwyiCX8PgxiCLwP4N8PtfwjgHICTw8+vhjgXA+ChRpDLR9O0sTKIbV6fPMtblA8NIkXHOi7vex22nM1mM/RxUc6taRpblsWGYYRuEyJiXdf5ox/9KJ84cYIrlQovLS2xruth2vo5t/cv9nCAiFYArDDz3xFRE8DzAB4C8BsANpn5jyOcK14hUqQM3oFFLqPoueSeNKzKOmmiBIwRPXa73YZlWXbkZqHGa5qGffv2YXFxES+99FKke+QculqWZcfEcLZhtVrF3r170Wq1cPXq1dGXOPw1XYcDsdcOMPMFABeG/18jop9gkHkoNkGVCVtZMU0YxcMtaNxWBIpSDi/cVM+5ublIWXvixOM3DCPSArSwAkDXdezZswe7u7vodrv2OpRqtYrd3V1bGLz11lt2HMyo5T5w4IB9bLvdHhsCiDUtnU4Hb731lmusjaTPRSo2ASI6AuBOAD8abvokEb1ARF8koqWw5wlj+QeCfQb6/T6q1Soef/xxUb7QrqDyFNi0OAhNwtYh2skwDFiWZRtto2btccuPEEQUARDl3L1eD2tra9jc3LR7Z2bG7u6uPY0ogtfI06VenoBO+v0+Ll26ZC/QqlardtsJH5d+v49er2e7P0cVkHNzc/jd3/1d/53i2gSkF2QOg6HAR4bfDwDQMRAwfwTgix7HPQbgueEn0ZjUbbthGHzkyJHcxp2z+tF1nSuVSmHtJV42n0qlYo+rRR2cx5imaY/JxT66rtu/ExEbhuE7Pvcqk67rXKvVWNd1Nk2TdV1nTdPsT9z6GobBt9xyi/ierk0AAIioAuDbAL7LzP/F5fcjAL7NzMcDzhO7EIZhoNfr2WMj6ZyuUrPI4+ppQB5CibYuatBW2bouenRmHglWSzTIELS1tWVnuXLWRfYTkEPWueEcYoq4Aevr69A0zY5/IYYaQecLcw2JdKcIaXCl/wbgJ7IAGBoMBR8G8FLca4QhygutBED2eA2hkg5J0hrOyNNvcjlFR+K8plwfOXr1ysoK6vX62DFu5whCvPBCuMhlmcSQNElQkfcC+C0ALxLRyeG23wfwCBGdwED9OAPg8UQlDECWynJjeRn6lBAYJcs2YWZbU3NqaVGvKebk3V7WMAiHMF3Xsbi4iNXVVXtBj9NKL9uGNE1Dq9Wy16N0u13UajU7toJbD+3Xazs1VgBjfv6yJhCnrlGPSTI78H3ANQX5d+KecxK4PYBCzZOnZmaFLOsrXrzt7W07ZFbcB1sYyoB4oejE0t12u419+/bh8uXL9m/OMjmNfDs7O2g0Grh69Sre+9734vTp03jjjTdc4xQEaQbyNhquiXFef9IZiwvvMZimVdsrLFOlUsH8/LwtpcPOJGRRxmlCWNIFYReBudHpdOypujj0ej1cu3YN7XYbp0+fHvvdy8tUlFloDJ///Ofxjne8I9YSdTe63e7YbJdIVOI2hIq7RsPXs7MIPZ+XYTDMOgBFMQmj8mc1FBHqtPMa4nm69dZb8dOf/jTUeRYXF7G2tmZvMwzDHr+HNTx7bcshbqOrYbDQQmD4W+CDklTNFBShLaYFodIGqbaTcsgSDmSilxVDlXa7jVqtht3dXc+oy4cPH4Zpmnj99ddtbbLRaNjBO8J0Ul7PsbCZABN5/sq1gEhEU5Ebxk2lcRtTRUG2wsooFT8ZPHSr9XpBRPo3YY8JIun9EM9IpVKxp/SEK/D29rb9vIn0dOKlNU0TP//5z/Gzn/0M3W4XRIRbbrnF1gbCqudez2e/34dpmvjEJz4xUtc0Us2FpfCaQMBxiYYLceZg5WsXoe0myaQNVgK5reV7FvceOI8TEX6B68JCHpMLt11x3L333otTp05hbW1tJOVdGF8IcU5Rfjk3ZtyZjwiUSxMABr1Fo9EA4O76KztphMEptcWxcRI2zJoAAOK59AYR5nxyW8vG3bD3QJ5ZcDtOJKwRH/FciN5YvLhCMDz//PO48cYbsXfvXntI0Ov1YJomjhw5Eqo+stFROCTJtotJUmgh0G637QQMhmHgne98JxYWFuxG8gu7JCP2caaHEpI4jexHSSjT0GMSwi/N9iAiHDt2DA8++KDvfs4hJfMgYKcc8EP2JDx16pSdoEYc1+l0cO7cOXtf8Yxpmobjx49jeXnZFgCGYYw8j61Wa0TYyOXP+vkoZAYiN2NRt9vFuXPn7DFdUHQXuTH95mq9fp8keV8/T4Lujd8xYdyRmRlnz57F+vo6gGjDOKFpyh6GzIxarWZrD879hbVfHq4sLi7i/Pnz2Nrawm/+5m+i0WjgL//yL20hIJ5jYaiM0hZpUEgh4PVgbG5u2tFYgsI7hbE6O/dxTid5HSv8CkQcOEU+hJ2i29zctDMnuTmKeQ0p5ZdaWPGFl2GYl1Pss729bc8ivPjii64GU2bGbbfdhldeeWVkSDKJ2ZNCDwfcEJIyieuoWxhyYR2u1+sAgrMkT3JBTJmGC5PETwtMC7cXP2wsSvEM7ezs2C/1Cy+8gOeff951/3vvvTew7FnkeiiUEEi7gs4XWTbyiGvJ0zFimCGmiNwQluKrV6+mWlZFOkSdLg5jWBb2AXmhT5hrRBVGt91221iZkkx/h6VQQiDNuVEeLl6R6ff79lhOjPOEQBASf3NzE9VqdcyTy81o40YWvfYs2wzKTFRvwLNnz7ouMJLJYoq21H4CaSO0ArfZApHgUfjCu3kauq1Gk3+fBJP2Xyiyv0QST1KZSddRjpGRMunGGJxGhNonI7QJYYsQGoGu6+h0Ona8OdnJRD6f0DQm5WQz6ReyqAIASK/XnHQdJz1lXZjhQNSVe26/ZaGKd7tdezwIDLLZNptN2zi0vb2NarWKer0Oy7LGhjRRHZoU2RtCy2RoDeNWXa/XE9nTCqMJRJW2wokjqvdYEvr9Plqt1ti1xOIT0eNH8WFQjJN125Sp7eWhpbBdCcTzJhzqZMQS+YWFBVy5csW3zoXRBLyYn5/HiRMn8Au/8AsArkvxMCqT8MyKi5ersvy77O4pbpi4Ubquu0rxoEVR08Ik65bkPpcBMSshExTBqN1u4/Lly8HG7CJIRWEY9Ft3DVxPFbaysoLz58+P7Ssv5hDIvXPYsVa9Xsf29rY9ppf/yudtNBrY2dnxzSMnbAh+fgVKMyguk7w3E7hW8RcQufUcwmtLdse8dOmS6/Fy9hbx4vZ6PTsQhHwdv15KCACv8h0/fhxPPfUUfu3Xfm3kem77CzVOeDq61VMJgHzxexYmeW/yeg4SCQEiOkNELxLRSSJ6brhtDxE9Q0Snh39DJx9xnNvz5nhNnzhfRlk9l+PGiX2d15PP4/xfPvf6+jpee+01/N7v/Z5vHYRRsNvtjswZq5e+WMz6/UhDE/glZj4hqRmfAvAsM98K4Nnh91D4eUrJL6mfN5/bfvI8fr1eHxunO9WwWq3m2ztcuXIF3/jGN/D9738/sE5uU4dpMYkVZorpJ4vhwIMAvjz8/8sYJClNlbAum04hIHpmrxj4YlvQy7Wzs4NXXnkFTz/99Mhxk2YSLqWK6SepEGAAf01EzxPRY8NtB3iQrBTDv/vdDiSix4joOTGMiIKbVdQwDDvUmB9iNZeX+i/2cbpvWpZlj+srlQrm5uZw6tQpuT5Rq6FQFIKkQuC9zHwXgAcAPEFE94U9kJmfZOa7ZWulVw8c5gWrVCquawWc55EXgzgRGoIw9snXrVarthBoNpu4/fbbR1JEK/JlGoRwXnVIJASY+fzw7yUAXwdwD4CLNExFNvzrbsp3wasRwrxo29vbdupocYzznEFWfNM0x6YBxfHr6+v2mvRqtYobbrhhZMpRCYJ8KUr7x/HcC1pjkrVwSJKLsEFETfE/gF/GIO/gtwA8OtztUQDfDHEue+FNUM46L+Of177OhvWb+nPO5Xv5FWxvb9thpJIS9gZnsY58mplktF6ZOC7ifgJMDHMF8hJ4+W8iZOt1lA+AowD+Yfh5GcBnhtuXMZgVOD38uyfoXCSli15YWOBqteqa2pqIeGlpiffv3x+YrpmI2LIs333CftzKIrb7lSPo9yiflZWVwqb7nvZP2drdp7zppyZPC3kpsbyM0s0DEEBo1c8wDDtWfKDrJF1PQW1Z1kj6LHmfKNd/4IEHsGfPHnzlK19xPZdzCrQI96LM5NGGbtfMKzR7CMqZgWj4+9gLYxgG9u3bh7ffftvOKuPmWyAb+Pxchw3DwPLyMi5evJhGlezrA+GFRtT9FeMU+AUsAsV3G3Yixv9uL3an08Hly5dHwj7t378fN910k+2qCwws+czsqlHIyK7GaZHVzEFe490y4CcA3OwqQe04C+1cCk0gLPLwQYQmF9N9YXoIrxDWsjCaosUkigIxoftd7MhCaTSCvEZA+OqLc4ZREcViIzcbxKRfSCUAZos873dhhgNhVfGweeGjvrhJ8hJmiZyqahZUU4U78hBXJo2p48IIAcBdGsp+A3KW4iB/grh4CQEx1MiCMEtZlQBQeK2cTUqhhEAY5HBLab4YQQalONc6dOgQbrnllsD9/G6kKJfXiko30mgXpXkUCy/NNuxiOj8NuvBCQI7VJ7+oIitMGNLowaNcT3DDDTfg8OHDiV4mt+XQQaTROygBMD3QMN6g5+9FMEBFmR2I6mQj1gS4Of+ERaQoiyMIkiLXT3avViic+L0LQ4N3sf0EZF9oZ1pneR9nrx7GEzCOoJNXJIroQFFfPk3TXFc3hkVeGm0YxkTWDygNYPKk1eZ+z7mfj0xhhIBIFFqr1eye17IsWJaFSqVib3vHO95h9+7A9V7aqyH7/f5YuucwOIOVRr1R4qUVeQu88Duv7LMg/B2y1gLCCsxKpaIERkqkqY3HseUUZjhQqVTQ6XTsZKDigRcGkSQP//79+z2Dk2aJMMjouu45HImSKks5EE0nSe5r2GOH+xV7OCCcezqdzojTz8LCAo4ePRraP0AgXkDTNHHp0iUsLCxMtOeSA5T42SPC5roHvHsMeZiUR++sNIJkRBEAzrZO+uwABRIC8nhXrKEmIuzs7GB9fR233357JEEgnI+EcNnY2AgtMdOYHovjV1CtVvHII4/gC1/4wtj15ZDlfuQRd0C0tRIG2ZOFJliI4YCmaSz8+8UDxcyo1+toNpvo9XpYX1+3VwuGSeiRNl5ql5ubsdgfiC7l6/U6arUarly5Yp9fHhaFLVeWWJaFXq9n11utfiwNxV9K7HygNU2zE4fIeQeDFvQIo2K73c5MUAgV3M2ZJ41zm6aJZrOJ1dVV32kfYNyIycyoVqvY2dnxPFbTNNTrdTtkWtTyAeqlLyHFtgkA4w+VsOwLDYCIcNNNN8GyrLFjhQFO13XUajVYlpXpQ+o0XAZ5ZUWl2+16ZkIS9Hq9MYOp2D/I2MjMsX0n8lhQpYYa2VEoIRCGVqvlmoFIfjDl3jnMwxP3BXYrQxowD5JPBr2kzlkTWZOSMx55HRu0j2I2iL2UmIiOAfiatOkogH8PYBHAvwZwebj995n5O7FLKMHMuHz5sutvIpcAM4/4BUyqx8qid4xqT5CHSNPmVaiGHtmRik2AiHQA5wC8B8C/ArDJzH8c4fjU7rDc88/Cg+MMoSbXWdf1SFOQRUV4Tk6bYMuBTG0CHwDwGjO/kcbJnGGWoyB6wrItwY07xeZnAyi7V58wvlqWpUKuZ0haLfswgKek758koheI6IvkkZWYHGnI5PUCe/fuRaPRSKVgcf32J00Ur0Hni+32oouFRl7TimkS93zCkOslAEUymFar5et6rUhG4uEAEZkAzgO4g5kvEtEBAFcwiHP+HwGsMPNvB5zDniIkItRqNXS7XTsnoMv+mau4ZZgGk6MhielKrziJSRG9stu5G40Gdnd3I7+ozWYTALC5uRm4EjSrhWIzRmYxBh8A8HfMfBEAxF8AIKK/APDtKCcTkj9oeitryvBACXVfTr8m8jakjZixcGNrayvWOa9du+b7u58viFv5FPFIYzjwCKShAA3zEA75MAapyUJRxrF8Hoi2abfb2N3dRb1ex8GDBwEgUdyEpGVK855l2bNPw7OValsnaWgiqgN4E8BRZl4fbvsfAE5gMBw4A+BxHqYq9zkP12o1e97azQ9AMY7fYpIow4IoL5zbvtMyCzEDpD8cYOYWBrkH5W2/FedcYrnv7u6ur3uweLg1TcPx48dx+vRpbG9vJ6lGaQnyJkzjPGH2DXO8WCquKB6FmXd56623bPdgeQWeLAA0TcPBgwft386cORNb/VWr3tJDDv7qRNyrSS32Et6fWd9bv2uU7bkqzPyZ8PKTnULcepi1tTUws92zzKIKKiz1RXKg8XLRFuWbVDnlNR1ZX8frGmV7JgsjBOSG85oWNAzDnk4SbsJpXK+MyAuXxPe8KUIZgMmUI02hlvf0ZmGGA2ERD36/3w+tCZRNPQvCyyty2urphRxJSZGc0rSkiBIkHoAg6Znmst6iIoKZymPyvXv3TuTaaQmcWIExC2zPSeL67XW+rOtaCiEghgLyy+83JjMMA3Nzc/Z3t/2ShAKfBH5GJ9EGhmGMhB0zDCNUxqM0iKq++tVH7tW9/peR8z/EeUGyvO9R2iWMNuP2nKctFAotBOQHRLgQM18PQ+ZFpVLB0pLrkgW7AZeXlz33KQJhPCZ3dnZGpkfb7TZ+9KMfZV62OMj1kUO49/t9e+ZA07SRNSNebSA/F24viGy5d/akmqblet9FWQzDQLVaDX1MltpAocKLBeyDarU6EtfOK5+A/ACENeCIkF6iPeLkKsgaoQHIQlB+meR9gs4xSbyu6cwE7fzu5YSUpjE0b6PcJJDqmNnagVSoVqsjjkJOmNnu9SqVChYWFuz4e/IxYugA+GddcbK8vIzPfvazWF9fh2VZ+MxnPjPRQKZhEPWUBZvsRNVoNLCxsRHqHJPE65pOAS1mfMRD69X+aSwmirIuISlRPTKjdF5hEMZ0T82qCFIwzaAiXoiovW6NaxgGFhYWsLW1hU6ng7m5Oayvr2ddpEhYluXqGCVU4ywCnubN/v37ceXKlZF7JhzJkngfur0QUZLAZE2G2klxow2LkON5Uga1UH5Qg1TirJYUlwFhF3CuQXHe4zLc8zCIlaMh6lLcaMPOwn/gAx/Abbfdlsq5xTjTLzWzWxmKiDw+Duq1iigA3KJEZwEzj+VEENud+yUhTo7KLJDD8cehEELAyd69e0em+JIgGqfb7abuO5D3AxB0/aL5SsjG1knMf2et3k/LysnCGAZlfvCDH0QKVOE33pd7zrT91/N+AIKuX5R1BX6I6bxutwtN07CwsIC1tbW8i+WKbHuJQtGHHYUUAmfPnh3b5teQXg4Vaat/ZaNo9ZXvidewpmhllily2ZJQSCHgh9vLXcTxr2Ict5dI7ln7/X7sUGWTII6XZFFmHPwonE3Aa5wonGTCjiOL3vCzSJh7Mm2BR8qw0KkwJQya8kpzwUpZEJGXxf9lpuzlj4NY35Gpy28K5w4UAjTIHXCJiF6Stu0homeI6PTw75L026eJ6FUiOkVEvxK2IF5BKZz7RDHMCFdjmUlEnQlDmB5C0zQcPXoUQPk1m7KXPy5eYfPj4hdXMi5hNIEvAbjfse1TAJ5l5lsBPDv8DiJ6FwaJSO4YHvMFGqQoC0XQohn59zAvs67r+OAHPziyLWhOdVICIoww6/V6ePnllydQmnFmUfMqA5kIU3lZrtcHwBEAL0nfT2GQVAQAVgCcGv7/aQCflvb7LoB/GuL8nMaHiHjogmx/l/8CYE3TxvZznkPTtFTKI5/P63qz8PFr71n/TKJdpGs85/b+xbUJHOBhGPHh3/3D7QcxCEEuODvcNhHcppt0XUetVrPX3YtsPV7UarWRFXlJx3RuQ5j5+fnY5ysjQUu/i0iaGoyf01acGYeoBF0jbcOgWwldS0COXIRZ0ev10Gq1RrzVhJ+1WHwjCwV5kQ4zY2FhIXQQCqcXnPxdvhFBK/2KRBovQ9kEAJBumdOcwo5TriDP0bhC4CINMw0N/14abj8L4EZpv0MY5Ckcg5mfZOa73RY0BCEs5kkRvbTcU/d6vZEH/+rVq2NLkpvNpuvaBrcblPVsR1ySTrVGKb/zfuVd90lRlHoG2Z/iCoFvAXh0+P+jAL4pbX+YiCwiuhnArQD+NuY1PMk61Zb84Pd6PSwtLaFer9s9e6vVwhtvjGZhF9I2rPdb3r1j0utHOd6ZHKYoC2+yJqiNJtUGgfcqhNHuKQAXAHQw6Ok/jkHWoWcBnB7+3SPt/xkAr2FgPHwgpOExd/vzzMUAABMcSURBVAON38c0TdZ13fN3wzBmwvjlNLrGre+0t1OB28HVMFiIeAKTCCqSJWJhidOl2Rkuq6zI0X5E/eIuplFcxzfaTzZh4oobTyAJRVArpzGqj2VZgTEYFMkoyvOihECG5/Za3lwGDMMYsXM4186nUa9ZsQ04CVPnIAGRpgAppRAQjZh2QEbnzSnD4o+saLVa2NnZcf0tTW/CWRMCYQSf8/es26mUT7mQgm7zn0kde+TzzPISZWdb+P0Wt817vV5pNaW4uGmHQesBnLaYtCmlEBC4hRRPS00qynitCLi1hewEpdoqGV7tK3+AQae3sLCQ+vVLLQSikrYUnTVVNi5e7TSLwwFgkGNDvMxe3nxCuOq6bruuM7NrUpykz3XpIguFRWSvEY1XqVTQbrdTjfcmzpNkqqesCLfrJHWbxnYJQ7vdtoOnBA2HZG2XmbG1tTXS7mnYxaZWE+j3+zBN0xYEotEn/eBN04NerVZx//3XV5WHrds0tUEayPkX47hlu3mliqX1cTSrqXYWcnNy8WOWE3aExTTNSHkap1UTygq3BWdhjhHOWwHHTaezkB+iQcPmMIj6sIqxWL1ej1awguJcUelGGAGgaZq9dFsJgGhomoZjx45FWiTHzGEzELlfM9ZRJUFIxu3t7VBzs2FTRWuaZnvTEVHmC5omhVvsgyTnUkSn3+/j/PnzE82KPdVCABg8jGHTNIWNdDuy+GLoTyBSbJmmWVono7ReXHnMq3BHBKxxwszY2NiYaPuV82nNAGaOJASE+iVbaYtMpVKBruuo1+tYXFzM/Hqz5gQUlyJ0GPmXYIh4iZrNZikWrgj3TyEEhPrW6XQK+QKItQCGYYQe9iiS49U5iI6kCJ1HYYSAkIh79uyZWPbauGiaNhZ7UI5LWIQb66TdbqPb7WJjYwNvvfUWgOJrL9OA3NM7PQCFIMibQk0Ryg+ls1xqqik/4kxbKcafWTGXLwzWObSn6xRhoTwGMwiiMFHKUMa4TGu9ssTZZs5evyiBWQozHAjC2VhOu8Hc3BwajcYkizRG3BfFMIxCGIi8cKtXpVKJPJxQw48BsnNPEYibhuw/E9FPiegFIvo6ES0Otx8hom0iOjn8/HlWBX/iiSdsJx2xLsAZ0LIsdLtdzweiCC8OEUHX9RFB1el0Igs9pU0MSNMfIxVCBAG9D8BdGM1A9MsAjOH/nwPwueH/R+T9wn4QI2himlmCivypVCq+9aWIGZMqlQpXq9WxrExptfksBFyN+9F13bMN/QLZpviJl4GImf8GwNuObX/NzGJ50w8xyC8wUQolSTNErCLzqm/UXqXT6dgRg4SWEfZ4sZ+bk4tpmrj//vvx0EMP4YYbbghdnmlHzAbIvv1u2l2eswRpGAZ/G8DXpO83E9HfA9gA8Flm/r9RTkZEaDQa6HQ6U+OOmwRn9OIiuPW6BXPpdDr4wQ9+YOdl8GKajaduOBelham7ZVmpZzP2I5EQIKLPAOgC+Mpw0wUAh5l5lYj+CYBvENEdzDyWd4uIHgPwmHM7M2NnZ2fiPX3ehhqRr8/vxicpX9bhz5kZm5ubI9dzu9YsCQAAI56lYe07E3/24x5IRI8C+BCAj7IY2DPvMvPq8P/nMUhC8k6349knDZmfoSwr8n44JzFvvLS0hOXl5UzOLWY4cpr/Lixua/+DEJqDX0SmNIklBIjofgD/DsA/Z+aWtH0fEenD/49ikIbs9dCFyXGaLM8HdxIqsvBOy3LsKddhVmw2cZFfZPF/XiHYA4cDRPQUgPcB2EtEZwH8AYBPA7AAPDMs9A+Z+XcwmEn4D0TUBdAD8DvM/Lbrid2vFbkC08CkhMDVq1dTOZdbed3sBLNC1PsnB7uRjw+KOizvm2YAnEK5DSvCk6Yrb9SHWEVguo54ocNoPkH3LOg+iFkGsXQ9huF89iILTTNJxt7iYZLPFQUlAK7jnKL1G9IG3bOg+yAvOEpz5kwJgRlEfnDDDsHkcWscvEJrlx3nqlFn+xBRLBfrSaKEwIwTVgsQ+8U1+E2r9uDs3b18KIpMqYRAkaVpWYgT7yBMfEY/nB6G03wfvYx7k7S9LSwsRPLaLNRS4iCYOXOnl2kmrjHRa3/DMNDtdgMNWs7esQjG6CT41dfLoh+0j0xSx7X19XWsr6+H3r9UmgDg7ytfqVQKH5Uoa4KSVtxzzz342Mc+FupclmXBsiw7urLz3OLlnpubK0VIuLSIIsTESlfHgjlXoq7l8EMO+x7E1E0RzppvelRqtRpM0/TtKURPJGsOfu0apueatfsi6uvXNs42EZpV0DmjlkGi+JGF0mCWHrQ4bG9vB8ZdEA9tWJfXMD3XrN2XMIZUZ5vIxlO3Fz5IGAed34vSDQcUk2Xfvn04cuSI7z5RpxkV7oQRulkIUyUEZgivmQG/uf9ut4tOp4Nms4l7773XdZ8kPVNRozPnQbPZTO1cUWwCSggofFlbW8O5c+eg6zqWlpZin8fvRU9DCJRJkCwvL+Po0aNj251h7JPg9Ar13bcIYzW1dmB6mTWDYBiOHTuGw4cP45lnnnFtnwzbzNUwqISAQjFBcs7hoBYQFQE5m7FievFSxcNGGRKJSiaBEgITRuSfk3sCJRCmj6B7GqQJiLBkk0AJgQnjtpquCEOyJCwtLY1ZoqMYpsqMZVmuCV7jLphyiziUNVPnLFR0ir6iLA5ra2tj22Yl1qBYOyFwixgUBBHBsizs7Oy4+grEsSNEMS5Ov6hWKHxI2tsKtV2slHS+xG7nFxmdZNrttmdZ4ghUsX+YuIVx05D9IRGdo+vpxn5V+u3TRPQqEZ0iol+JVPIpRtM0zM3NgYhw6NDEc7UocN0xqVar2S9hGtpKv9/3VP+9zu/MRSASkwQhXmi/SMSaptkfeeWtF2E0gS8BuN9l+58y84nh5zvDArwLwMMA7hge8wURfVgxgJmxurqadzEKS5bjYNGj7u7uprJSzzRNVCqVUD213CMnETxh4hOI8gjBEmSfiJWGzIcHAXyVB/kH/hHAqwDuCXnsVNPv9+30X2VNnDoJJmFHCNvremFZFkzTDPWCOY/Lesl1nHolsQl8kgZZib9IRMKf9CCAN6V9zg63KTDbYbmniUOHDsGyLPR6vUh5HOVchEWKuRhXCPwZgFsAnMAg9difDLe76XKuoomIHiOi54jouZhlUJSUsvtFEFEkASBot9tjswlpI1LIR9EIYk0RMvNF8T8R/QWAbw+/ngVwo7TrIQDnPc7xJIAnh+eY/rmkKcZrOspvmqrMawpeffXVWMfJNoGswuMJDSPKMCVuGrIV6euHAYiZg28BeJiILCK6GYM0ZH8b5xqK8uAlANxSmIv9w4TZKipxyyfaJIuhgChTu91Gu92OdGzcNGTvI6ITGKj6ZwA8DgDM/DIRPQ3gFQyyFT/BzNMZa1rhCRHBNE2YphnLOaroGkJQ+by0HGEXyALDMGINUQC1ilCRAfv378fRo0fxwx/+MO+ixELTNOi6XkjvzjCpygBPQaWWEismR5nH/GmRNHR43GsCnrEN1VJixWRYWlrC7bffnncxxpi0rSGP3BhxhhxqAZEiVYgocvKLSeGmmei6nth5qOwoTUCRCWXIEJUkZuKkCLMAyAvTNHHw4EEQkW9aMiUEFKliGEZpshFtbW1NNIJPHKJoKcePH8eRI0dGfAWuXLkCZvbVzNRwQJEqcaep8qDdbsMwjKkZCly8eBHtdtt2Te73+9jd3QUAe92KG0oIKFKlLAJAEDeMl1iym6X3XxDO2YfLly/b/ztnZ/zqqIYDitQoslrtRVwtQPS2Ra1zlHopIaBIlcAoNlMUd7Db7caOJZgGaWkg03NHFLkTJrhGtVqdKkEwDai7oVDMOEoIKEKTdPxrGAZarVbpjIdpUzQ7ghICilA0Gg3ceeeddtAK5zLhMA92nuPnIlG0KUklBBSBaJqGTqeDF198Ef1+H6ZpjoVKC/NgR3n4ixR+a9pRQkARiDD4iWmx48ePZ67SynHzFdmiWlgRin6/D8MwcOONN+Lll1/23M8wDJimaQfTlIVFlN5d2A3c7Ae6rrum/lLEQ3kMKnwRATaAwQu+vr4+li5L0zTUajV0Oh10Oh074YUQAmJ5a1oGwX6/HzmElsIbpQkofGFm3HXXXfjc5z6HhYUFHDt2DMw8skiImdFut/Hrv/7r+NCHPoRKpYLFxUU7sq4zp55AeNxFHVrEcdUlotIsbJo0cdOQfU1KQXaGiE4Otx8hom3ptz/PsvCKybCysoL3v//9aLVa2NraAjCqpjMzOp0Ozp07hwsXLqDX69lZfmTffHmc7xzru2XjTdstV5S5aFN0eRNmOPAlAP8VwH8XG5j5X4r/iehPAMjrFF9j5hNpFVCRL8yMM2fO4Gtf+xquXr1qr0pzTvc1Gg288MIL2N3dRa/XQ6fTsfMvbmxs2OcCRhe3yD74XhqDWFPvXFYbJYRZ1GxBs0SgEGDmvyGiI26/0eBu/QaA96dbLEWROHnyJE6ePAlgfEmqeBGr1Sr6/T46nY79shERGo0GNjY2MDc3h83NTQCDHlloAl4vvtPmYBgGut3uiAYiaxO9Xm9kVZ1Y4ecmJLwEh0gtVsQAo1mS1CbwiwAuMvNpadvNRPT3RPR/iOgXE55fUTDklwy4bvFfXV3F2tqa/QKJLD0XLlwAABw4cMB+0TVNg2maI4Kg1+uBiGBZ1pja3uv1oOs6dF23g5aIF7zZbNqzEZVKxT7GMIzI04vNZhP1ej1225SVpELgEQBPSd8vADjMzHcC+LcA/oqI5t0OVGnIyo14Ud3yK+q6DsuyRra99tprdg/c6/Wwvb09dqymaThw4ID9Xe6xhY2hXq9jbm7Ovs6BAwewvb2NSqViz0wAGNNIguwARITV1dVCxkbMHNkRxOsD4AiAlxzbDAAXARzyOe5/A7g7xPlZfWbjMwwvH+s457FExNVqdWSbruusaRrPz8+zZVme19d1feS8lUqFNU3LvX0y/jzn9v4l0QT+GYCfMvNZsYGI9hGRPvz/KAZpyF5PcA3FlOE3Hl9ZWXH9TaTvcqr3zIydnR17HYMcc39jYwO7u7v2EEKcRw69Jc7hzN7jtjZimgkzRfgUgP8H4BgRnSWijw9/ehijQwEAuA/AC0T0DwD+J4DfYea30yywYjppt9u2/UDGNE0Ao+q9EzGs6Pf7+MhHPoL5edcR6MiMhHj5geszHbJzExFhz549ySpVElQGIkVpcUu5peu6q7AQbswrKyt48803oes69uzZY0fjdSOPDEIZozIQKaYLtynARqOBWq3mOnTodrs4f/48gIHWsLq6CmaGZVnQNA0PPfQQ7rvvPvuYKRMAnihNQDFV6Lru61bs1rsLW8Hy8jK63e40zxC4agKzY/1QzARBXoF+zkOrq6uZlKnoqOGAYqYoguZbNJQQUChmHCUEFIoZRwkBhWLGUUJAoZhxlBBQKGYcJQQUihlHCQGFYsZRQkChmHGUEFAoZhwlBBSKGUcJAYVixlFCQKGYcZQQUChmHCUEFIoZRwkBhWLGUUJAoZhxlBBQKGYcJQQUihmnKDEGrwDYGv6dNvZiOusFTG/dprVeN7ltLES0YQAgoufcIqGWnWmtFzC9dZvWenmhhgMKxYyjhIBCMeMUSQg8mXcBMmJa6wVMb92mtV6uFMYmoFAo8qFImoBCociB3IUAEd1PRKeI6FUi+lTe5UkKEZ0hoheJ6CQRPTfctoeIniGi08O/S3mXMwgi+iIRXSKil6RtnvUgok8P7+EpIvqVfEodDo+6/SERnRvet5NE9KvSb6WpWxxyFQJEpAP4PIAHALwLwCNE9K48y5QSv8TMJ6Rppk8BeJaZbwXw7PB70fkSgPsd21zrMbxnDwO4Y3jMF4b3tqh8CeN1A4A/Hd63E8z8HaCUdYtM3prAPQBeZebXmbkN4KsAHsy5TFnwIIAvD///MoCHcixLKJj5bwC87djsVY8HAXyVmXeZ+R8BvIrBvS0kHnXzolR1i0PeQuAggDel72eH28oMA/hrInqeiB4bbjvAzBcAYPh3f26lS4ZXPablPn6SiF4YDhfEUGda6uZJ3kKAXLaVfbrivcx8FwZDnCeI6L68CzQBpuE+/hmAWwCcAHABwJ8Mt09D3XzJWwicBXCj9P0QgPM5lSUVmPn88O8lAF/HQHW8SEQrADD8eym/EibCqx6lv4/MfJGZe8zcB/AXuK7yl75uQeQtBH4M4FYiupmITAwMMN/KuUyxIaIGETXF/wB+GcBLGNTp0eFujwL4Zj4lTIxXPb4F4GEisojoZgC3AvjbHMoXGyHchnwYg/sGTEHdgsh1FSEzd4nokwC+C0AH8EVmfjnPMiXkAICvExEwaNu/Yub/RUQ/BvA0EX0cwM8B/IscyxgKInoKwPsA7CWiswD+AMB/gks9mPllInoawCsAugCeYOZeLgUPgUfd3kdEJzBQ9c8AeBwoX93ioDwGFYoZJ+/hgEKhyBklBBSKGUcJAYVixlFCQKGYcZQQUChmHCUEFIoZRwkBhWLGUUJAoZhx/j9d9AqONCIkRAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multilook (Boxcar Filter)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversion to Decibels\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Orthorectification\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class SARClass(pipesegment.PipeSegment):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.feeder = (\n", + " image.LoadImage(os.path.join(datadir, 'sar_hh.tif'))\n", + " * sar.CapellaScaleFactor()\n", + " * sar.Intensity() * image.ShowImage(caption='Intensity')\n", + " * sar.Multilook(2) * image.ShowImage(caption='Multilook (Boxcar Filter)')\n", + " * sar.Decibels() * image.ShowImage(caption='Conversion to Decibels')\n", + " * sar.Orthorectify(projection = 32631, row_res=3, col_res=3) * image.ShowImage(caption='Orthorectification')\n", + " * image.SaveImage(os.path.join(datadir, 'output3a.tif'))\n", + " )\n", + "\n", + "sar_processing = SARClass()\n", + "sar_processing()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The API reference lists possible arguments for these various classes. Things like the kernel size of the `Multilook` filter and how the `Decibel` class handles zeros can be adjusted through arguments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also in `preproc.sar` are classes for several polarimetric decompositions, including Freeman-Durden (for quad pol) and H-Alpha (for dual pol). A Pauli decomposition is shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class SARDecomposition(pipesegment.PipeSegment):\n", + " def __init__(self):\n", + " super().__init__()\n", + " loadlist = [image.LoadImage(os.path.join(datadir, file)) \n", + " * sar.CapellaScaleFactor() for file in \n", + " ['sar_hh.tif', 'sar_hv.tif', 'sar_vh.tif', 'sar_vv.tif']]\n", + " stack = np.sum(loadlist) * image.MergeToStack()\n", + " self.feeder = (\n", + " stack\n", + " * sar.DecompositionPauli(hh_band=0, vv_band=3, xx_band=1)\n", + " * sar.Multilook(2)\n", + " * sar.Decibels()\n", + " * sar.Orthorectify(projection = 32631, row_res=3, col_res=3)\n", + " * image.SaveImage(os.path.join(datadir, 'output3b.tif'))\n", + " * image.ShowImage(bands=[1,2,0], vmin=-20, vmax=0)\n", + " )\n", + "\n", + "sar_decomposition = SARDecomposition()\n", + "sar_decomposition()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, note how `np.sum()` respects the special meaning of `+` for `PipeSegment` subclasses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3 Follow-Up: Masking and Multimodal Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multimodal datasets contain multiple types of data covering the same area. A common task when preparing such datasets is taking a mask from one data source and applying it to another. \n", + "\n", + "Suppose we have two equal-sized images covering the same area: a SAR image which is masked, and an optical image which isn't. To apply the mask from the former to the latter:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class TransferMask(pipesegment.PipeSegment):\n", + " def __init__(self, masked_path, unmasked_path, output_path):\n", + " super().__init__()\n", + " load_masked = image.LoadImage(masked_path) * image.ShowImage(bands=[0])\n", + " load_unmasked = image.LoadImage(unmasked_path) * image.ShowImage()\n", + " get_mask = image.GetMask()\n", + " set_mask = image.SetMask(0)\n", + " save_output = image.SaveImage(output_path) * image.ShowImage()\n", + " self.feeder = (load_unmasked + load_masked * get_mask) * set_mask * save_output\n", + "\n", + "masked_path = os.path.join(datadir, 'sar_masked.tif')\n", + "unmasked_path = os.path.join(datadir, 'rgb_unmasked.tif')\n", + "output_path = os.path.join(datadir, 'output3c.tif')\n", + "transfer_mask = TransferMask(masked_path, unmasked_path, output_path)\n", + "transfer_mask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `SetMask` class expects two inputs: the first is the image to be masked, and the second is the mask to use. An optional argument to `SetMask` allows for reversing the order of the arguments. By default, `preproc` uses `NaN` (the `float` value that's \"not a number\") as the mask value, but this too can be modified with arguments to `GetMask` and `SetMask`.\n", + "\n", + "For imagery labels and for data types beyond imagery, the `preproc.labels` module contains classes for working with vector labels and other data in GeoPandas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Epilogue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classes in the preprocessing library can be used in different ways. They can be used one-at-a-time within a traditional (\"imperative\") program, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#Read in an image, and store it in the variable 'myimage'\n", + "myimage = image.LoadImage(os.path.join(datadir, 'ms1.tif'))()\n", + "\n", + "#Now resize the image, and store the result in a different variable\n", + "mythumbnail = (myimage * image.Resize(50,50))()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note the empty parentheses at the end of each line -- these lines instantiate an object and call it immediately.)\n", + "\n", + "But these classes are more powerful when they are attached to each other to build up more complicated workflows. That can be done by defining a one-time-use object:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "thumbnail_pipeline = image.LoadImage(os.path.join(datadir, 'ms1.tif')) \\\n", + " * image.Resize(50,50)\n", + "mythumbnail = thumbnail_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or by defining a reusable class:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class Thumbnail(pipesegment.PipeSegment):\n", + " def __init__(self):\n", + " super().__init__()\n", + " load = image.LoadImage(os.path.join(datadir, 'ms1.tif'))\n", + " size = image.Resize(50,50)\n", + " self.feeder = load * size\n", + "thumbnail_pipeline = Thumbnail()\n", + "mythumbnail = thumbnail_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By defining a workflow, the program takes care of details like figuring out the order in which to do the different calculations and remembering where each intermediate result is stored. To see what the program is actually doing when the instance is called, replace `mythumbnail = thumbnail_pipeline()` with `mythumbnail = thumbnail_pipeline(verbose=1)`. Or set the `verbose` argument to 2 or 3 for additional information. This is useful for debugging." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, this tutorial has focused on creating new capabilities by combining and using the library's existing classes. For capabilities that can't be built out of the existing `preproc` classes, one can create a new class directly from the `PipeSegment` base class. The new class should be given a `transform` method that takes some input, does a calculation, and returns some output. The whole `preproc` source code is mostly classes of this type and is a ready source of examples.\n", + "\n", + "For completeness, here's a short example. This class takes a string as input and returns the string in all capital letters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'THANKS FOR READING!'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Uppercase(pipesegment.PipeSegment):\n", + " def transform(self, pin):\n", + " return pin.upper()\n", + "\n", + "(pipesegment.LoadSegment('Thanks for reading!') * Uppercase())()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docker/solaris/environment-gpu.yml b/docker/solaris/environment-gpu.yml new file mode 100644 index 00000000..37313573 --- /dev/null +++ b/docker/solaris/environment-gpu.yml @@ -0,0 +1,36 @@ +name: solaris +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - python>=3.6 + - pip>=19.0.3 + - affine>=2.3.0 + - albumentations=0.4.3 + - fiona>=1.7.13 + - gdal=3.0.3 + - geopandas>=0.7.0 + - matplotlib>=3.1.2 + - networkx>=2.4 + - opencv>=4.1 + - pandas>=0.25.3 + - pyproj>=2.1 + - pytorch>=1.3.1 + - pyyaml=5.2 + - rasterio>=1.0.23 + - requests=2.22.0 +# - rio-cogeo>=1.1.6 + - rtree>=0.9.3 + - scikit-image>=0.16.2 + - scipy>=1.3.2 + - torchvision>=0.5.0 + - cudatoolkit=9.2 + - tqdm>=4.40.0 + - urllib3>=1.25.7 + - tensorflow-gpu=1.13.1 + - cuda100 + - shapely + #- pip + #- pip: + # - git+https://github.com/Toblerity/shapely.git@master#egg=shapely-1.7.1dev # temporary, dev required for numpy array support diff --git a/docker/solaris/environment.yml b/docker/solaris/environment.yml new file mode 100644 index 00000000..20035ac2 --- /dev/null +++ b/docker/solaris/environment.yml @@ -0,0 +1,35 @@ +name: solaris +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - python>=3.6 + - pip>=19.0.3 + - affine>=2.3.0 + - albumentations=0.4.3 + - fiona>=1.7.13 + - gdal=3.0.3 + - geopandas>=0.7.0 + - matplotlib>=3.1.2 + - networkx>=2.4 + - numpy>=1.17.3 + - opencv>=4.1 + - pandas>=0.25.3 + - pyproj>=2.1 + - pytorch>=1.3.1 + - pyyaml=5.2 + - rasterio>=1.0.23 + - requests=2.22.0 +# - rio-cogeo>=1.1.6 + - rtree>=0.9.3 + - scikit-image>=0.16.2 + - scipy>=1.3.2 + - shapely>=1.6.4 + - tensorflow=1.13.1 + - torchvision>=0.5.0 + - cudatoolkit=9.2 + - tqdm>=4.40.0 + - urllib3>=1.25.7 + - pip: + - git+https://github.com/Toblerity/shapely.git@master#egg=shapely-1.7.1dev # temporary, 1.8dev required for numpy array support diff --git a/docker/solaris/renovate.json b/docker/solaris/renovate.json new file mode 100644 index 00000000..3ea65c78 --- /dev/null +++ b/docker/solaris/renovate.json @@ -0,0 +1,13 @@ +{ + "extends": [ + "config:base" + ], + "pip_setup": { + "enabled": true + }, + "assignees": ["nrweir"], + "baseBranches": ["dev"], + "ignoreDeps": ["GDAL", "tensorflow"], + "labels": ["dependencies"], + "reviewers": ["nrweir"] +} diff --git a/docker/solaris/requirements.txt b/docker/solaris/requirements.txt new file mode 100644 index 00000000..697119bc --- /dev/null +++ b/docker/solaris/requirements.txt @@ -0,0 +1,24 @@ +pip>=19.0.3 +affine>=2.3.0 +albumentations==0.4.3 +fiona>=1.7.13 +gdal>=3.0.2 +geopandas>=0.7.0 +matplotlib>=3.1.2 +networkx>=2.4 +numpy>=1.17.3 +opencv-python>=4.1 +pandas>=0.25.3 +pyproj>=2.1 +torch>=1.3.1 +pyyaml==5.2 +rasterio>=1.0.23 +requests==2.22.0 +rtree>=0.9.3 +scikit-image>=0.16.2 +scipy>=1.3.2 +git+git://github.com/toblerity/shapely.git@master#egg=shapely-1.7.1dev +torchvision>=0.5.0 +tqdm>=4.40.0 +urllib3>=1.25.7 +tensorflow==1.13.1 diff --git a/docker/solaris/setup.py b/docker/solaris/setup.py new file mode 100644 index 00000000..b8922be5 --- /dev/null +++ b/docker/solaris/setup.py @@ -0,0 +1,122 @@ +import os +import sys +import subprocess +import logging +from setuptools import setup, find_packages +import re + + +def get_version(): + VERSIONFILE = os.path.join('solaris', '__init__.py') + initfile_lines = open(VERSIONFILE, 'rt').readlines() + VSRE = r'^__version__ = [\"\']*([\d\w.]+)[\"\']' + for line in initfile_lines: + mo = re.search(VSRE, line, re.M) + if mo: + return mo.group(1) + raise RuntimeError('Unable to find version string in %s.' % (VERSIONFILE,)) + + +logging.basicConfig(stream=sys.stderr, level=logging.INFO) +log = logging.getLogger() + + +def check_output(cmd): + # since subprocess.check_output doesn't exist in 2.6 + # we wrap it here. + try: + out = subprocess.check_output(cmd) + return out.decode('utf') + except AttributeError: + # For some reasone check_output doesn't exist + # So fall back on Popen + p = subprocess.Popen(cmd, stdout=subprocess.PIPE) + out, err = p.communicate() + return out + + +# check GDAL install +include_dirs = [] +library_dirs = [] +libraries = [] +extra_link_args = [] +gdal2plus = False +gdal_output = [None] * 4 +gdalversion = None + +try: + gdal_version = subprocess.check_output( + ['gdal-config', '--version']).decode('utf') + gdal_config = os.environ.get('GDAL_CONFIG', 'gdal-config') + +except Exception: + sys.exit("GDAL must be installed to use `solaris`. See the documentation " + "for more info. We recommend installing GDAL within a conda " + "environment first, then installing solaris there.") + + +on_rtd = os.environ.get('READTHEDOCS') == 'True' +if on_rtd: + inst_reqs = ['sphinx_bootstrap_theme'] +else: + inst_reqs = ['pip>=19.0.3', + 'affine>=2.3.0', + 'albumentations>=0.4.3', + 'fiona>=1.7.13', + 'gdal', + 'geopandas>=0.7.0', + 'matplotlib>=3.1.2', + 'networkx>=2.4', + 'numpy>=1.17.3', + 'opencv-python>=4.1', + 'pandas>=0.25.3', + 'pyproj>=2.1', + 'pyyaml>=5.2', + 'rasterio>=1.0.23', + 'requests>=2.22.0', + 'rtree>=0.9.3', + 'scikit-image>=0.16.2', + 'scipy>=1.3.2', + 'shapely>=1.7.1dev', + 'tqdm>=4.40.0', + 'urllib3>=1.25.7', + ] + + +extra_reqs = { + 'test': ['mock', 'pytest', 'pytest-cov', 'codecov']} + +# workaround until new shapely release is out +os.system('pip install git+git://github.com/toblerity/shapely@master') + + +project_name = 'solaris' +setup(name='solaris', + version=get_version(), + description="CosmiQ Works Geospatial Machine Learning Analysis Toolkit", + classifiers=[ + 'Intended Audience :: Information Technology', + 'Intended Audience :: Science/Research', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Topic :: Scientific/Engineering :: GIS'], + author=u"CosmiQ Works", + author_email='nweir@iqt.org', + url='https://github.com/CosmiQ/solaris', + license='Apache-2.0', + packages=find_packages(exclude=['ez_setup', 'examples', 'tests']), + zip_safe=False, + include_package_data=True, + install_requires=inst_reqs, + extras_require=extra_reqs, + dependency_links=['https://github.com/toblerity/shapely/tarball/master#egg=shapely-1.7.1dev'], + entry_points={'console_scripts': [ + 'geotransform_footprints = solaris.bin.geotransform_footprints:main', + 'make_graphs = solaris.bin.make_graphs:main', + 'make_masks = solaris.bin.make_masks:main', + 'mask_to_polygons = solaris.bin.mask_to_polygons:main', + 'spacenet_eval = solaris.bin.spacenet_eval:main', + 'solaris_run_ml = solaris.bin.solaris_run_ml:main' + ] + } + ) diff --git a/docker/solaris/solaris/__init__.py b/docker/solaris/solaris/__init__.py new file mode 100644 index 00000000..91672fcb --- /dev/null +++ b/docker/solaris/solaris/__init__.py @@ -0,0 +1,3 @@ +from . import bin, data, eval, preproc, raster, tile, utils, vector + +__version__ = "0.4.0" diff --git a/docker/solaris/solaris/bin/__init__.py b/docker/solaris/solaris/bin/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/bin/geotransform_footprints.py b/docker/solaris/solaris/bin/geotransform_footprints.py new file mode 100644 index 00000000..f6249961 --- /dev/null +++ b/docker/solaris/solaris/bin/geotransform_footprints.py @@ -0,0 +1,143 @@ +import argparse +import pandas as pd +from tqdm import tqdm +from multiprocessing import Pool +from ..vector.polygon import geojson_to_px_gdf +from ..vector.polygon import georegister_px_df +from ..utils.cli import _func_wrapper +from itertools import repeat + + +def main(): + + parser = argparse.ArgumentParser( + description='Interconvert footprints between pixel and geographic ' + + 'coordinate systems.', argument_default=None) + + parser.add_argument('--source_file', '-s', type=str, + help='Full path to file to transform') + parser.add_argument('--reference_image', '-r', type=str, + help='Full path to a georegistered image in the same' + + ' coordinate system (for conversion to pixels) or in' + + ' the target coordinate system (for conversion to a' + + ' geographic coordinate reference system).') + parser.add_argument('--output_path', '-o', type=str, + help='Full path to the output file for the converted' + + 'footprints.') + parser.add_argument('--to_pixel', '-p', action='store_true', default=False, + help='Use this argument if you wish to convert' + + ' footprints in --source-file to pixel coordinates.') + parser.add_argument('--to_geo', '-g', action='store_true', default=False, + help='Use this argument if you wish to convert' + + ' footprints in --source-file to a geographic' + + ' coordinate system.') + parser.add_argument('--geometry_column', '-c', type=str, + default='geometry', help='The column containing' + + ' footprint polygons to transform. If not provided,' + + ' defaults to "geometry".') + parser.add_argument('--decimal_precision', '-d', type=int, + help='The number of decimals to round to in the' + + ' final footprint geometries. If not provided, they' + + ' will be rounded to float32 precision.') + parser.add_argument('--batch', '-b', action='store_true', default=False, + help='Use this flag if you wish to operate on' + + ' multiple files in batch. In this case,' + + ' --argument-csv must be provided. See help' + + ' for --argument_csv and the codebase docs at' + + ' https://solaris.readthedocs.io for more info.') + parser.add_argument('--argument_csv', '-a', type=str, + help='The reference file for variable values for' + + ' batch processing. It must contain columns to pass' + + ' the source_file and reference_image arguments, and' + + ' can additionally contain columns providing the' + + ' geometry_column and decimal_precision arguments' + + ' if you wish to define them differently for items' + + ' in the batch. These columns must have the same' + + ' names as the corresponding arguments. See the ' + + ' usage recipes at https://cw-geodata.readthedocs.io' + + ' for examples.') + parser.add_argument('--workers', '-w', type=int, default=1, + help='The number of parallel processing workers to' + + ' use. This should not exceed the number of CPU' + + ' cores available.') + + args = parser.parse_args() + # check that the necessary set of arguments are provided. + if args.batch and args.argument_csv is None: + raise ValueError( + 'To perform batch processing, you must provide both --batch and' + + ' --argument_csv.') + if args.argument_csv is None and args.source_file is None: + raise ValueError( + 'You must provide a source file using either --source_file or' + + ' --argument_csv.') + if args.argument_csv is None and args.reference_image is None: + raise ValueError( + 'You must provide a reference image using either' + + ' --reference_image or --argument_csv.') + if args.to_pixel == args.to_geo: + raise ValueError( + 'One, and only one, of --to_pixel and --to_geo must be specified.') + + if args.argument_csv is not None: + arg_df = pd.read_csv(args.argument_csv) + else: + arg_df = pd.DataFrame({}) + + if args.batch: + # add values from individual arguments to the argument df + if args.source_file is not None: + arg_df['source_file'] = args.source_file + if args.reference_image is not None: + arg_df['reference_image'] = args.reference_image + if args.geometry_column is not None: + arg_df['geometry_column'] = args.geometry_column + if args.decimal_precision is not None: + arg_df['decimal_precision'] = args.decimal_precision + else: + # add values from individual arguments to the argument df + if args.source_file is not None: + arg_df['source_file'] = [args.source_file] + if args.reference_image is not None: + arg_df['reference_image'] = [args.reference_image] + if args.geometry_column is not None: + arg_df['geometry_column'] = [args.geometry_column] + if args.decimal_precision is not None: + arg_df['decimal_precision'] = [args.decimal_precision] + if args.output_path is not None: + arg_df['output_path'] = [args.output_path] + + if args.to_pixel: + # rename argument columns for compatibility with the target func + arg_df = arg_df.rename(columns={'source_file': 'geojson', + 'reference_image': 'im_path', + 'decimal_precision': 'precision', + 'geometry_column': 'geom_col'}) + arg_dict_list = arg_df[ + ['geojson', 'im_path', 'precision', 'geom_col', 'output_path'] + ].to_dict(orient='records') + func_to_call = geojson_to_px_gdf + elif args.to_geo: + # rename argument columns for compatibility with the target func + arg_df = arg_df.rename(columns={'source_file': 'df', + 'reference_image': 'im_path', + 'decimal_precision': 'precision', + 'geometry_column': 'geom_col'}) + arg_dict_list = arg_df[ + ['df', 'im_path', 'precision', 'geom_col', 'output_path'] + ].to_dict(orient='records') + func_to_call = georegister_px_df + + if not args.batch: + result = func_to_call(**arg_dict_list[0]) + if not args.output_path: + return result + else: + with Pool(processes=args.workers) as pool: + result = tqdm(pool.starmap(_func_wrapper, zip(repeat(func_to_call), + arg_dict_list))) + pool.close() + + +if __name__ == '__main__': + main() diff --git a/docker/solaris/solaris/bin/make_graphs.py b/docker/solaris/solaris/bin/make_graphs.py new file mode 100644 index 00000000..f697a23a --- /dev/null +++ b/docker/solaris/solaris/bin/make_graphs.py @@ -0,0 +1,111 @@ +import argparse +import pandas as pd +from tqdm.auto import tqdm +from multiprocessing import Pool +from ..vector.graph import geojson_to_graph +from ..utils.cli import _func_wrapper +from itertools import repeat + + +def main(): + + parser = argparse.ArgumentParser( + description='Create training pixel masks from vector data', + argument_default=None) + + parser.add_argument('--source_file', '-s', type=str, + help='Full path to file to create graph from.') + parser.add_argument('--output_path', '-o', type=str, + help='Full path to the output file for the graph' + + ' object.') + parser.add_argument('--road_type_field', '-r', type=str, + help='The name of the column in --source_file that' + + ' defines the road type of each linestring.') + parser.add_argument('--first_edge_idx', '-e', type=int, default=0, + help='The numeric index to use for the first edge in' + + ' the graph. Defaults to 0.') + parser.add_argument('--first_node_idx', '-n', type=int, default=0, + help='The numeric index to use for the first node in' + + ' the graph. Defaults to 0.') + parser.add_argument('--weight_norm_field', '-wn', type=str, + help='The name of a column in --source_file to' + + ' weight edges with. If not provided, edge weights' + + ' are determined only by Euclidean distance. If' + + ' provided, edge weights are distance*weight.') + parser.add_argument('--batch', '-b', action='store_true', default=False, + help='Use this flag if you wish to operate on' + + ' multiple files in batch. In this case,' + + ' --argument-csv must be provided. See help' + + ' for --argument_csv and the codebase docs at' + + ' https://cw-geodata.readthedocs.io for more info.') + parser.add_argument('--argument_csv', '-a', type=str, + help='The reference file for variable values for' + + ' batch processing. It must contain columns to pass' + + ' the source_file and reference_image arguments, and' + + ' can additionally contain columns providing the' + + ' footprint_column and decimal_precision arguments' + + ' if you wish to define them differently for items' + + ' in the batch. These columns must have the same' + + ' names as the corresponding arguments. See the ' + + ' usage recipes at https://cw-geodata.readthedocs.io' + + ' for examples.') + parser.add_argument('--workers', '-w', type=int, default=1, + help='The number of parallel processing workers to' + + ' use. This should not exceed the number of CPU' + + ' cores available.') + + args = parser.parse_args() + + if args.batch and args.argument_csv is None: + raise ValueError( + 'To perform batch processing, you must provide both --batch and' + + ' --argument_csv.') + if args.argument_csv is None and args.source_file is None: + raise ValueError( + 'You must provide a source file using either --source_file or' + + ' --argument_csv.') + + if args.argument_csv is not None: + arg_df = pd.read_csv(args.argument_csv) + else: + arg_df = pd.DataFrame({}) + if args.batch: + if args.source_file is not None: + arg_df['source_file'] = args.source_file + if args.output_path is not None: + arg_df['output_path'] = args.output_path + if args.road_type_field is not None: + arg_df['road_type_field'] = args.road_type_field + arg_df['first_node_idx'] = args.first_node_idx + arg_df['first_edge_idx'] = args.first_edge_idx + if args.weight_norm_field is not None: + arg_df['weight_norm_field'] = args.weight_norm_field + else: + arg_df['source_file'] = [args.source_file] + arg_df['output_path'] = [args.output_path] + arg_df['road_type_field'] = [args.road_type_field] + arg_df['first_node_idx'] = [args.first_node_idx] + arg_df['first_edge_idx'] = [args.first_edge_idx] + arg_df['weight_norm_field'] = [args.weight_norm_field] + + arg_df = arg_df.rename(columns={'source_file': 'geojson', + 'first_edge_idx': 'edge_idx'}) + arg_dict_list = arg_df[['geojson', 'output_path', 'road_type_field', + 'weight_norm_field', 'edge_idx', 'first_node_idx', + 'output_path'] + ].to_dict(orient='records') + if not args.batch: + result = geojson_to_graph(**arg_dict_list[0]) + if not args.output_path: + return result + + else: + with Pool(processes=args.workers) as pool: + result = tqdm(pool.starmap(_func_wrapper, + zip(repeat(geojson_to_graph), + arg_dict_list))) + pool.close() + + +if __name__ == '__main__': + main() diff --git a/docker/solaris/solaris/bin/make_masks.py b/docker/solaris/solaris/bin/make_masks.py new file mode 100644 index 00000000..8ecf10d6 --- /dev/null +++ b/docker/solaris/solaris/bin/make_masks.py @@ -0,0 +1,182 @@ +import argparse +import pandas as pd +from tqdm.auto import tqdm +from multiprocessing import Pool +from ..vector.mask import df_to_px_mask +from ..utils.cli import _func_wrapper +from itertools import repeat + + +def main(): + + parser = argparse.ArgumentParser( + description='Create training pixel masks from vector data', + argument_default=None) + + parser.add_argument('--source_file', '-s', type=str, + help='Full path to file to create mask from.') + parser.add_argument('--reference_image', '-r', type=str, + help='Full path to a georegistered image in the same' + ' coordinate system (for conversion to pixels) or in' + ' the target coordinate system (for conversion to a' + ' geographic coordinate reference system).') + parser.add_argument('--output_path', '-o', type=str, + help='Full path to the output file for the converted' + 'footprints.') + parser.add_argument('--geometry_column', '-g', type=str, + default='geometry', help='The column containing' + ' footprint polygons to transform. If not provided,' + ' defaults to "geometry".') + parser.add_argument('--transform', '-t', action='store_true', + default=False, help='Use this flag if the geometries' + ' are in a georeferenced coordinate system and' + ' need to be converted to pixel coordinates.') + parser.add_argument('--value', '-v', type=int, default=255, + help='The value to set for labeled pixels in the' + ' mask. Defaults to 255.') + parser.add_argument('--footprint', '-f', action='store_true', + default=False, help='If this flag is set, the mask' + ' will include filled-in building footprints as a' + ' channel.') + parser.add_argument('--edge', '-e', action='store_true', + default=False, help='If this flag is set, the mask' + ' will include the building edges as a channel.') + parser.add_argument('--edge_width', '-ew', type=int, default=3, + help='Pixel thickness of the edges in the edge mask.' + ' Defaults to 3 if not provided.') + parser.add_argument('--edge_type', '-et', type=str, default='inner', + help='Type of edge: either inner or outer. Defaults' + ' to inner if not provided.') + parser.add_argument('--contact', '-c', action='store_true', + default=False, help='If this flag is set, the mask' + ' will include contact points between buildings as a' + ' channel.') + parser.add_argument('--contact_spacing', '-cs', type=int, default=10, + help='Sets the maximum distance between two' + ' buildings, in source file units, that will be' + ' identified as a contact. Defaults to 10.') + parser.add_argument('--metric_widths', '-m', action='store_true', + default=False, help='Use this flag if any widths ' + '(--contact-spacing specifically) should be in metric ' + 'units instead of pixel units.') + parser.add_argument('--batch', '-b', action='store_true', default=False, + help='Use this flag if you wish to operate on' + ' multiple files in batch. In this case,' + ' --argument-csv must be provided. See help' + ' for --argument_csv and the codebase docs at' + ' https://solaris.readthedocs.io for more info.') + parser.add_argument('--argument_csv', '-a', type=str, + help='The reference file for variable values for' + ' batch processing. It must contain columns to pass' + ' the source_file and reference_image arguments, and' + ' can additionally contain columns providing the' + ' footprint_column and decimal_precision arguments' + ' if you wish to define them differently for items' + ' in the batch. These columns must have the same' + ' names as the corresponding arguments. See the ' + ' usage recipes at https://solaris.readthedocs.io' + ' for examples.') + parser.add_argument('--workers', '-w', type=int, default=1, + help='The number of parallel processing workers to' + ' use. This should not exceed the number of CPU' + ' cores available.') + + args = parser.parse_args() + + if args.batch and args.argument_csv is None: + raise ValueError( + 'To perform batch processing, you must provide both --batch and' + + ' --argument_csv.') + if args.argument_csv is None and args.source_file is None: + raise ValueError( + 'You must provide a source file using either --source_file or' + + ' --argument_csv.') + if args.argument_csv is None and args.reference_image is None: + raise ValueError( + 'You must provide a reference image using either' + + ' --reference_image or --argument_csv.') + if not args.footprint and not args.edge and not args.contact: + raise ValueError( + 'You must specify --footprint, --edge, and/or --contact. See' + + ' make_masks --help or the CLI documentation at' + + ' cw-geodata.readthedocs.io.') + + if args.argument_csv is not None: + arg_df = pd.read_csv(args.argument_csv) + else: + arg_df = pd.DataFrame({}) + + # generate the channels argument for df_to_px_mask + channels = [] + if args.footprint: + channels.append('footprint') + if args.edge: + channels.append('boundary') + if args.contact: + channels.append('contact') + if len(arg_df) < 2: + arg_df['channels'] = [channels] + else: + arg_df['channels'] = [channels]*len(arg_df) # all channels in each row + + if args.batch: + if args.source_file is not None: + arg_df['source_file'] = args.source_file + if args.reference_image is not None: + arg_df['reference_image'] = args.reference_image + if args.output_path is not None and not args.batch: + arg_df['output_path'] = args.output_path + if args.geometry_column is not None: + arg_df['geometry_column'] = args.geometry_column + if args.transform: + arg_df['transform'] = True + if 'value' not in arg_df.columns: + arg_df['value'] = args.value + if 'edge_width' not in arg_df.columns: + arg_df['edge_width'] = args.edge_width + if 'edge_type' not in arg_df.columns: + arg_df['edge_type'] = args.edge_type + if 'contact_spacing' not in arg_df.columns: + arg_df['contact_spacing'] = args.contact_spacing + else: + arg_df['source_file'] = [args.source_file] + arg_df['reference_image'] = [args.reference_image] + arg_df['output_path'] = [args.output_path] + arg_df['geometry_column'] = [args.geometry_column] + arg_df['transform'] = [args.transform] + arg_df['metric'] = [args.metric_widths] + arg_df['value'] = [args.value] + arg_df['edge_width'] = [args.edge_width] + arg_df['edge_type'] = [args.edge_type] + arg_df['contact_spacing'] = [args.contact_spacing] + + # rename arguments to match API + arg_df = arg_df.rename(columns={'source_file': 'df', + 'output_path': 'out_file', + 'reference_image': 'reference_im', + 'geometry_column': 'geom_col', + 'transform': 'do_transform', + 'value': 'burn_value', + 'edge_width': 'boundary_width', + 'edge_type': 'boundary_type'}) + + arg_dict_list = arg_df[['df', 'out_file', 'reference_im', 'geom_col', + 'do_transform', 'channels', 'burn_value', + 'boundary_width', 'boundary_type', + 'contact_spacing'] + ].to_dict(orient='records') + if not args.batch: + result = df_to_px_mask(**arg_dict_list[0]) + if not args.output_path: + return result + + else: + with Pool(processes=args.workers) as pool: + result = tqdm(pool.starmap(_func_wrapper, + zip(repeat(df_to_px_mask), + arg_dict_list))) + pool.close() + + +if __name__ == '__main__': + main() diff --git a/docker/solaris/solaris/bin/mask_to_polygons.py b/docker/solaris/solaris/bin/mask_to_polygons.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/bin/solaris_run_ml.py b/docker/solaris/solaris/bin/solaris_run_ml.py new file mode 100644 index 00000000..1bedb2d7 --- /dev/null +++ b/docker/solaris/solaris/bin/solaris_run_ml.py @@ -0,0 +1,38 @@ +import argparse +import os +import pandas as pd +from ..utils.config import parse +from ..nets.train import Trainer +from ..nets.infer import Inferer + + +def main(): + + parser = argparse.ArgumentParser( + description='Run a Solaris ML pipeline based on a config YAML', + argument_default=None) + + parser.add_argument('--config', '-c', type=str, required=True, + help="Full path to a YAML-formatted config file " + "specifying parameters for model training and/or " + "inference.") + + args = parser.parse_args() + + if not os.path.exists(args.config): + raise ValueError('The configuration file cannot be found at the path ' + 'specified.') + + config = parse(args.config) + + if config['train']: + trainer = Trainer(config) + trainer.train() + if config['infer']: + inferer = Inferer(config) + inf_df = pd.read_csv(config['inference_data_csv']) + inferer(inf_df) + + +if __name__ == '__main__': + main() diff --git a/docker/solaris/solaris/bin/spacenet_eval.py b/docker/solaris/solaris/bin/spacenet_eval.py new file mode 100644 index 00000000..2b09c921 --- /dev/null +++ b/docker/solaris/solaris/bin/spacenet_eval.py @@ -0,0 +1,56 @@ +"""Script for executing eval for SpaceNet challenges.""" +from ..eval.challenges import off_nadir_buildings +from ..eval.challenges import spacenet_buildings_2 +import argparse +import pandas as pd +supported_challenges = ['off-nadir', 'spacenet-buildings2'] +# , 'spaceNet-buildings1', 'spacenet-roads1', 'buildings', 'roads'] + + +def main(): + parser = argparse.ArgumentParser( + description='Evaluate SpaceNet Competition CSVs') + parser.add_argument('--proposal_csv', '-p', type=str, + help='Proposal CSV') + parser.add_argument('--truth_csv', '-t', type=str, + help='Truth CSV') + parser.add_argument('--challenge', '-c', type=str, + default='off-nadir', + choices=supported_challenges, + help='SpaceNet Challenge eval type') + parser.add_argument('--output_file', '-o', type=str, + default='Off-Nadir', + help='Output file To write results to CSV') + args = parser.parse_args() + + truth_file = args.truth_csv + prop_file = args.proposal_csv + + if args.challenge.lower() == 'off-nadir': + evalSettings = {'miniou': 0.5, + 'min_area': 20} + results_DF, results_DF_Full = off_nadir_buildings( + prop_csv=prop_file, truth_csv=truth_file, **evalSettings) + elif args.challenge.lower() == 'spaceNet-buildings2'.lower(): + evalSettings = {'miniou': 0.5, + 'min_area': 20} + results_DF, results_DF_Full = spacenet_buildings_2( + prop_csv=prop_file, truth_csv=truth_file, **evalSettings) + + with pd.option_context('display.max_rows', None, + 'display.max_columns', None): + print(results_DF) + + if args.output_file: + print("Writing summary results to {}".format( + args.output_file.rstrip('.csv') + '.csv')) + results_DF.to_csv(args.output_file.rstrip('.csv') + '.csv', + index=False) + print("Writing full results to {}".format( + args.output_file.rstrip('.csv')+"_full.csv")) + results_DF_Full.to_csv(args.output_file.rstrip('.csv')+"_full.csv", + index=False) + + +if __name__ == '__main__': + main() diff --git a/docker/solaris/solaris/data/SN2_sample_iou_by_building.csv b/docker/solaris/solaris/data/SN2_sample_iou_by_building.csv new file mode 100644 index 00000000..9eba82f2 --- /dev/null +++ b/docker/solaris/solaris/data/SN2_sample_iou_by_building.csv @@ -0,0 +1,173 @@ +,ImageId,BuildingId,PolygonWKT_Pix,PolygonWKT_Geo,geometry,iou_score +0,AOI_2_Vegas_img3457,1,"POLYGON ((230.11 542.07 0,204.78 541.94 0,204.61 564.45 0,177.46 564.32 0,177.42 568.76 0,164.43 568.69 0,164.2 597.85 0,178.24 597.92 0,178.12 613.19 0,200.64 613.3 0,200.75 599.42 0,229.66 599.56 0,230.11 542.07 0))","POLYGON ((-115.21739131 36.181239121000033 0,-115.217392504999964 36.181083877000049 0,-115.217470583999955 36.181084268000063 0,-115.217470871999978 36.181046782000067 0,-115.217531680999969 36.181047088000071 0,-115.217531363999967 36.181088314000078 0,-115.217569252999965 36.181088504000058 0,-115.21756864799994 36.181167229000039 0,-115.217533557999957 36.181167053000024 0,-115.217533464999974 36.181179047000057 0,-115.217460161999952 36.181178679000027 0,-115.217459693999956 36.181239464000043 0,-115.21739131 36.181239121000033 0))","POLYGON Z ((230.11 542.0700000000001 0, 204.78 541.9400000000001 0, 204.61 564.45 0, 177.46 564.3200000000001 0, 177.42 568.76 0, 164.43 568.6900000000001 0, 164.2 597.85 0, 178.24 597.92 0, 178.12 613.1900000000001 0, 200.64 613.3 0, 200.75 599.42 0, 229.66 599.5599999999999 0, 230.11 542.0700000000001 0))",0.6969060813200635 +1,AOI_2_Vegas_img3457,2,"POLYGON ((101.94 537.25 0,72.09 537.8 0,72.95 567.68 0,97.98 567.21 0,98.71 592.53 0,137.68 591.8 0,136.25 541.85 0,102.09 542.49 0,101.94 537.25 0))","POLYGON ((-115.21773737 36.181252136000069 0,-115.217736963999982 36.18123797700008 0,-115.21764473799999 36.18123969800007 0,-115.217640873999983 36.181104831000027 0,-115.217746083999941 36.18110286700005 0,-115.217748041999982 36.181171229000029 0,-115.217815634999965 36.181169968000063 0,-115.217817944999979 36.181250633000047 0,-115.21773737 36.181252136000069 0))","POLYGON Z ((101.94 537.25 0, 72.09 537.8 0, 72.95 567.6799999999999 0, 97.98 567.21 0, 98.70999999999999 592.53 0, 137.68 591.8 0, 136.25 541.85 0, 102.09 542.49 0, 101.94 537.25 0))",0.8192251875474869 +2,AOI_2_Vegas_img3457,3,"POLYGON ((51.41 536.38 0,19.18 537.57 0,20.07 553.14 0,10.38 553.5 0,10.82 561.39 0,0.0 561.79 0,0.0 592.28 0,54.47 590.27 0,51.41 536.38 0))","POLYGON ((-115.217873800999939 36.181254469000066 0,-115.217865543999949 36.181108972000061 0,-115.218012599821591 36.181103536019876 0,-115.218012599821591 36.181185860899824 0,-115.21798337499996 36.181186942000068 0,-115.217984584999954 36.18120826300003 0,-115.217958421999981 36.181209231000025 0,-115.217960805999951 36.181251253000028 0,-115.217873800999939 36.181254469000066 0))","POLYGON Z ((51.41 536.38 0, 19.18 537.5700000000001 0, 20.07 553.14 0, 10.38 553.5 0, 10.82 561.39 0, 0 561.79 0, 0 592.28 0, 54.47 590.27 0, 51.41 536.38 0))",0.8453880146144677 +3,AOI_2_Vegas_img3457,4,"POLYGON ((282.81 520.66 0,245.8 534.0 0,280.51 596.7 0,340.87 574.93 0,332.63 560.04 0,339.82 557.45 0,330.14 539.98 0,322.96 542.57 0,315.9 529.81 0,292.54 538.24 0,282.81 520.66 0))","POLYGON ((-115.217249008999943 36.181296929000041 0,-115.217222733999961 36.181249464000075 0,-115.217159678999963 36.181272204000038 0,-115.217140614999948 36.181237764000059 0,-115.217121214999963 36.181244760000027 0,-115.217095097999959 36.181197581000049 0,-115.217114497999944 36.181190584000035 0,-115.21709224599999 36.181150388000049 0,-115.217255232999946 36.181091608000031 0,-115.21734894 36.181260890000033 0,-115.217249008999943 36.181296929000041 0))","POLYGON Z ((282.81 520.66 0, 245.8 534 0, 280.51 596.7 0, 340.87 574.9299999999999 0, 332.63 560.04 0, 339.82 557.45 0, 330.14 539.98 0, 322.96 542.5700000000001 0, 315.9 529.8099999999999 0, 292.54 538.24 0, 282.81 520.66 0))",0.8322470066180069 +4,AOI_2_Vegas_img3457,5,"POLYGON ((382.07 477.73 0,324.11 459.67 0,311.11 486.86 0,305.0 484.95 0,294.39 507.15 0,323.59 516.24 0,327.26 508.57 0,350.92 515.94 0,356.67 503.9 0,367.88 507.39 0,382.07 477.73 0))","POLYGON ((-115.216981011999962 36.181412830000056 0,-115.217019310999945 36.181332737000048 0,-115.21704957899999 36.181342166000036 0,-115.217065118999983 36.18130966800004 0,-115.217129006999983 36.181329572000038 0,-115.217138914999964 36.18130885000005 0,-115.217217740999956 36.181333407000068 0,-115.217189090999966 36.181393323000066 0,-115.217172606999952 36.181388187000039 0,-115.21713750899994 36.181461584000033 0,-115.216981011999962 36.181412830000056 0))","POLYGON Z ((382.07 477.73 0, 324.11 459.67 0, 311.11 486.86 0, 305 484.95 0, 294.39 507.15 0, 323.59 516.24 0, 327.26 508.57 0, 350.92 515.9400000000001 0, 356.67 503.9 0, 367.88 507.39 0, 382.07 477.73 0))",0.8205606371371863 +5,AOI_2_Vegas_img3457,6,"POLYGON ((159.33 438.89 0,159.33 426.1 0,183.28 426.1 0,183.28 393.42 0,120.95 393.42 0,120.95 414.17 0,108.98 414.17 0,108.98 438.89 0,159.33 438.89 0))","POLYGON ((-115.217582400999959 36.181517688000042 0,-115.217718353999942 36.181517688000042 0,-115.217718353999942 36.181584450000059 0,-115.21768603 36.181584450000059 0,-115.21768603 36.181640468000069 0,-115.217517752999981 36.181640468000069 0,-115.217517752999981 36.181552220000071 0,-115.217582400999959 36.181552220000071 0,-115.217582400999959 36.181517688000042 0))","POLYGON Z ((159.33 438.89 0, 159.33 426.1 0, 183.28 426.1 0, 183.28 393.42 0, 120.95 393.42 0, 120.95 414.17 0, 108.98 414.17 0, 108.98 438.89 0, 159.33 438.89 0))",0.7741803910087685 +6,AOI_2_Vegas_img3457,7,"POLYGON ((52.89 409.31 0,70.25 408.77 0,69.51 393.3 0,52.15 393.84 0,0.0 395.46 0,0.0 420.85 0,5.35 420.68 0,6.62 447.2 0,54.62 445.71 0,52.89 409.31 0))","POLYGON ((-115.217869804999964 36.181597575000069 0,-115.217865120999988 36.181499271000064 0,-115.217994738999948 36.181495248000033 0,-115.21799815199995 36.181566861000078 0,-115.218012599821591 36.181566412621173 0,-115.218012599821591 36.18163495942656 0,-115.217871794999951 36.181639331000042 0,-115.217824922999966 36.18164078600006 0,-115.217822932999979 36.181599030000029 0,-115.217869804999964 36.181597575000069 0))","POLYGON Z ((52.89 409.31 0, 70.25 408.77 0, 69.51000000000001 393.3 0, 52.15 393.84 0, 0 395.46 0, 0 420.85 0, 5.35 420.68 0, 6.62 447.2 0, 54.62 445.71 0, 52.89 409.31 0))",0.7845801888705872 +7,AOI_2_Vegas_img3457,8,"POLYGON ((366.29 388.17 0,295.58 390.42 0,296.76 414.71 0,325.41 413.8 0,326.81 442.4 0,368.87 441.07 0,366.29 388.17 0))","POLYGON ((-115.21702363 36.181654639000044 0,-115.217016663999971 36.181511821000072 0,-115.217130214999941 36.181508213000029 0,-115.21713398199995 36.181585440000049 0,-115.217211341999985 36.181582982000066 0,-115.217214540999976 36.181648573000075 0,-115.21702363 36.181654639000044 0))","POLYGON Z ((366.29 388.17 0, 295.58 390.42 0, 296.76 414.71 0, 325.41 413.8 0, 326.81 442.4 0, 368.87 441.07 0, 366.29 388.17 0))",0.6486550994785412 +8,AOI_2_Vegas_img3457,9,"POLYGON ((50.7 376.93 0,50.7 344.61 0,59.07 344.61 0,59.07 329.68 0,50.7 329.68 0,5.37 329.68 0,5.37 353.49 0,0.0 353.49 0,0.0 376.93 0,50.7 376.93 0))","POLYGON ((-115.217875697999943 36.181684976000042 0,-115.218012599821591 36.181684976000042 0,-115.218012599821591 36.181748283000047 0,-115.217998102999957 36.181748283000047 0,-115.217998102999957 36.181812551000064 0,-115.217875697999943 36.181812551000064 0,-115.21785311799999 36.181812551000064 0,-115.21785311799999 36.181772264000074 0,-115.217875697999943 36.181772264000074 0,-115.217875697999943 36.181684976000042 0))","POLYGON Z ((50.7 376.93 0, 50.7 344.61 0, 59.07 344.61 0, 59.07 329.68 0, 50.7 329.68 0, 5.37 329.68 0, 5.37 353.49 0, 0 353.49 0, 0 376.93 0, 50.7 376.93 0))",0.8102709980170206 +9,AOI_2_Vegas_img3457,10,"POLYGON ((149.12 322.04 0,98.33 323.53 0,99.65 353.05 0,111.2 352.71 0,112.24 375.95 0,184.95 373.83 0,183.93 351.13 0,150.47 352.11 0,149.12 322.04 0))","POLYGON ((-115.217609970999945 36.18183317900008 0,-115.21760633699995 36.18175201300005 0,-115.217515980999963 36.181754648000037 0,-115.217513235999945 36.181693353000071 0,-115.217709555999988 36.181687625000052 0,-115.217712365999944 36.181750371000078 0,-115.217743542999983 36.181749461000038 0,-115.217747112999973 36.181829177000054 0,-115.217609970999945 36.18183317900008 0))","POLYGON Z ((149.12 322.04 0, 98.33 323.53 0, 99.65000000000001 353.05 0, 111.2 352.71 0, 112.24 375.95 0, 184.95 373.83 0, 183.93 351.13 0, 150.47 352.11 0, 149.12 322.04 0))",0.7536719383249613 +10,AOI_2_Vegas_img3457,11,"POLYGON ((324.42 373.42 0,324.23 368.6 0,366.64 367.49 0,364.64 317.43 0,316.98 318.67 0,317.9 341.53 0,307.94 341.79 0,308.25 349.6 0,296.07 349.91 0,297.03 374.13 0,324.42 373.42 0))","POLYGON ((-115.217136668999956 36.181694463000042 0,-115.217210610999985 36.18169253800005 0,-115.217213223999977 36.18175793100005 0,-115.21718032 36.181758788000025 0,-115.217181162999964 36.181779856000048 0,-115.217154278999942 36.181780556000035 0,-115.217156745999944 36.181842278000033 0,-115.217028072999938 36.181845628000076 0,-115.217022670999938 36.181710464000048 0,-115.21713719 36.181707482000036 0,-115.217136668999956 36.181694463000042 0))","POLYGON Z ((324.42 373.42 0, 324.23 368.6 0, 366.64 367.49 0, 364.64 317.43 0, 316.98 318.67 0, 317.9 341.53 0, 307.94 341.79 0, 308.25 349.6 0, 296.07 349.91 0, 297.03 374.13 0, 324.42 373.42 0))",0.7009871867920193 +11,AOI_2_Vegas_img3457,12,"POLYGON ((62.56 252.91 0,0.0 251.95 0,0.0 302.69 0,49.53 303.45 0,50.14 277.44 0,61.97 277.62 0,62.56 252.91 0))","POLYGON ((-115.217843696999978 36.182019841000056 0,-115.21784527599999 36.181953126000053 0,-115.21787722 36.18195361800008 0,-115.21787888199998 36.181883386000038 0,-115.218012599821591 36.181885447936899 0,-115.218012599821591 36.182022445894503 0,-115.217843696999978 36.182019841000056 0))","POLYGON Z ((62.56 252.91 0, 0 251.95 0, 0 302.69 0, 49.53 303.45 0, 50.14 277.44 0, 61.97 277.62 0, 62.56 252.91 0))",0.7702480026439098 +12,AOI_2_Vegas_img3457,13,"POLYGON ((168.31 303.79 0,167.72 274.94 0,179.1 274.79 0,178.61 250.88 0,146.31 251.31 0,146.52 261.27 0,107.49 261.79 0,108.36 304.59 0,168.31 303.79 0))","POLYGON ((-115.21755817199994 36.181882463000079 0,-115.217720016999976 36.181880303000071 0,-115.217722383999956 36.181995872000073 0,-115.217617000999951 36.18199727800004 0,-115.217617551999979 36.182024173000059 0,-115.217530348999958 36.182025336000038 0,-115.217529026999955 36.181960762000074 0,-115.217559767999944 36.181960351000043 0,-115.21755817199994 36.181882463000079 0))","POLYGON Z ((168.31 303.79 0, 167.72 274.94 0, 179.1 274.79 0, 178.61 250.88 0, 146.31 251.31 0, 146.52 261.27 0, 107.49 261.79 0, 108.36 304.59 0, 168.31 303.79 0))",0.7946446059847826 +13,AOI_2_Vegas_img3457,14,"POLYGON ((401.28 242.41 0,391.84 241.91 0,390.77 254.9 0,400.2 255.41 0,401.28 242.41 0))","POLYGON ((-115.216929151999977 36.182048185000042 0,-115.21693205 36.182013106000056 0,-115.216957526999977 36.182014477000052 0,-115.216954628999986 36.182049556000038 0,-115.216929151999977 36.182048185000042 0))","POLYGON Z ((401.28 242.41 0, 391.84 241.91 0, 390.77 254.9 0, 400.2 255.41 0, 401.28 242.41 0))",0.0 +14,AOI_2_Vegas_img3457,15,"POLYGON ((380.48 231.74 0,345.72 231.93 0,345.85 247.73 0,301.94 247.96 0,297.71 247.98 0,297.92 274.37 0,302.16 274.35 0,327.49 274.21 0,327.65 293.42 0,380.99 293.14 0,380.48 231.74 0))","POLYGON ((-115.216985290999958 36.182076990000041 0,-115.21698393 36.181911223000043 0,-115.217127948999973 36.181910453000057 0,-115.217128373999969 36.181962331000079 0,-115.21719677599998 36.181961966000074 0,-115.217208208999978 36.181961905000037 0,-115.217208792999941 36.182033145000048 0,-115.21719736099999 36.182033206000028 0,-115.21707880299999 36.182033840000031 0,-115.21707915199994 36.182076488000064 0,-115.216985290999958 36.182076990000041 0))","POLYGON Z ((380.48 231.74 0, 345.72 231.93 0, 345.85 247.73 0, 301.94 247.96 0, 297.71 247.98 0, 297.92 274.37 0, 302.16 274.35 0, 327.49 274.21 0, 327.65 293.42 0, 380.99 293.14 0, 380.48 231.74 0))",0.6877396015592816 +15,AOI_2_Vegas_img3457,16,"POLYGON ((56.03 235.52 0,55.02 180.53 0,15.03 181.01 0,15.26 193.27 0,9.07 193.35 0,9.35 208.39 0,0.0 208.5 0,0.0 236.19 0,56.03 235.52 0))","POLYGON ((-115.21786132699998 36.182066807000069 0,-115.218012599821591 36.182064997877973 0,-115.218012599821591 36.182139748220848 0,-115.217987362999963 36.182140050000044 0,-115.217988108999975 36.182180660000029 0,-115.217971400999943 36.182180860000074 0,-115.217972007999947 36.182213969000031 0,-115.21786405099999 36.182215260000078 0,-115.21786132699998 36.182066807000069 0))","POLYGON Z ((56.03 235.52 0, 55.02 180.53 0, 15.03 181.01 0, 15.26 193.27 0, 9.07 193.35 0, 9.35 208.39 0, 0 208.5 0, 0 236.19 0, 56.03 235.52 0))",0.8216986318227297 +16,AOI_2_Vegas_img3457,17,"POLYGON ((177.9 202.26 0,156.18 202.15 0,156.36 179.56 0,109.25 179.31 0,109.0 209.97 0,100.12 209.92 0,99.91 234.68 0,177.63 235.09 0,177.9 202.26 0))","POLYGON ((-115.217532275999986 36.182156590000034 0,-115.217533002999971 36.182067955000036 0,-115.217742834999967 36.182069076000062 0,-115.217742286999965 36.182135911000046 0,-115.217718308999963 36.182135782000046 0,-115.21771763 36.182218555000077 0,-115.217590421999944 36.182217875000049 0,-115.217590920999953 36.182156903000077 0,-115.217532275999986 36.182156590000034 0))","POLYGON Z ((177.9 202.26 0, 156.18 202.15 0, 156.36 179.56 0, 109.25 179.31 0, 109 209.97 0, 100.12 209.92 0, 99.91 234.68 0, 177.63 235.09 0, 177.9 202.26 0))",0.8229957939886354 +17,AOI_2_Vegas_img3457,18,"POLYGON ((329.54 232.05 0,329.46 227.66 0,364.95 227.26 0,364.08 177.32 0,320.85 177.81 0,321.31 204.44 0,293.18 204.76 0,293.66 232.46 0,329.54 232.05 0))","POLYGON ((-115.217122847999974 36.182076173000041 0,-115.21721972099999 36.182075071000042 0,-115.21722102699999 36.182149842000058 0,-115.217145057999971 36.182150706000073 0,-115.217146312999944 36.182222614000068 0,-115.217029577999938 36.182223941000075 0,-115.217027224999981 36.182089111000039 0,-115.217123054999945 36.182088021000027 0,-115.217122847999974 36.182076173000041 0))","POLYGON Z ((329.54 232.05 0, 329.46 227.66 0, 364.95 227.26 0, 364.08 177.32 0, 320.85 177.81 0, 321.31 204.44 0, 293.18 204.76 0, 293.66 232.46 0, 329.54 232.05 0))",0.7086097569016502 +18,AOI_2_Vegas_img3457,19,"POLYGON ((106.12 183.46 0,106.12 174.22 0,92.04 174.22 0,92.04 183.46 0,106.12 183.46 0))","POLYGON ((-115.21772607299999 36.18220735400007 0,-115.217764100999943 36.18220735400007 0,-115.217764100999943 36.182232293000027 0,-115.21772607299999 36.182232293000027 0,-115.21772607299999 36.18220735400007 0))","POLYGON Z ((106.12 183.46 0, 106.12 174.22 0, 92.04000000000001 174.22 0, 92.04000000000001 183.46 0, 106.12 183.46 0))",0.0 +19,AOI_2_Vegas_img3457,20,"POLYGON ((148.67 159.67 0,147.83 143.73 0,181.55 142.58 0,179.67 106.78 0,107.84 109.24 0,109.8 146.6 0,86.83 147.39 0,87.58 161.76 0,148.67 159.67 0))","POLYGON ((-115.21761119699994 36.182271588000049 0,-115.217776120999986 36.182265947000076 0,-115.217778157999987 36.182304760000079 0,-115.217716137999957 36.182306881000045 0,-115.217721433999941 36.182407758000068 0,-115.21752749699999 36.182414391000066 0,-115.21752242299999 36.182317730000079 0,-115.217613455999981 36.182314617000031 0,-115.21761119699994 36.182271588000049 0))","POLYGON Z ((148.67 159.67 0, 147.83 143.73 0, 181.55 142.58 0, 179.67 106.78 0, 107.84 109.24 0, 109.8 146.6 0, 86.83 147.39 0, 87.58 161.76 0, 148.67 159.67 0))",0.48289772994961905 +20,AOI_2_Vegas_img3457,21,"POLYGON ((372.49 104.12 0,289.43 104.56 0,289.61 125.9 0,300.24 125.84 0,300.47 153.54 0,362.23 153.22 0,362.0 125.92 0,372.66 125.87 0,372.49 104.12 0))","POLYGON ((-115.217006881999964 36.182421569000041 0,-115.217006406999985 36.182362861000058 0,-115.217035187999954 36.182362709000074 0,-115.217034590999958 36.182289015000038 0,-115.217201341999953 36.182288135000078 0,-115.217201947999968 36.182362928000032 0,-115.217230663999942 36.182362776000048 0,-115.21723113 36.182420386000047 0,-115.217006881999964 36.182421569000041 0))","POLYGON Z ((372.49 104.12 0, 289.43 104.56 0, 289.61 125.9 0, 300.24 125.84 0, 300.47 153.54 0, 362.23 153.22 0, 362 125.92 0, 372.66 125.87 0, 372.49 104.12 0))",0.7555968809328473 +21,AOI_2_Vegas_img3457,22,"POLYGON ((9.42 102.04 0,0.0 101.92 0,0.0 161.97 0,57.54 162.7 0,58.56 110.44 0,9.27 109.81 0,9.42 102.04 0))","POLYGON ((-115.217987170999947 36.182427184000062 0,-115.217987582999967 36.182406224000033 0,-115.21785447799999 36.182404518000055 0,-115.217857253999966 36.182263398000032 0,-115.218012599821591 36.182265387663136 0,-115.218012599821591 36.18242750942143 0,-115.217987170999947 36.182427184000062 0))","POLYGON Z ((9.42 102.04 0, 0 101.92 0, 0 161.97 0, 57.54 162.7 0, 58.56 110.44 0, 9.27 109.81 0, 9.42 102.04 0))",0.7039614396761557 +22,AOI_2_Vegas_img3457,23,"POLYGON ((79.4 99.18 0,79.4 89.59 0,70.16 89.59 0,70.16 99.18 0,79.4 99.18 0))","POLYGON ((-115.217798214999959 36.18243491800007 0,-115.217823170999964 36.18243491800007 0,-115.217823170999964 36.182460817000049 0,-115.217798214999959 36.182460817000049 0,-115.217798214999959 36.18243491800007 0))","POLYGON Z ((79.40000000000001 99.18000000000001 0, 79.40000000000001 89.59 0, 70.16 89.59 0, 70.16 99.18000000000001 0, 79.40000000000001 99.18000000000001 0))",0.6060585509864553 +23,AOI_2_Vegas_img3457,24,"POLYGON ((583.77 61.96 0,546.46 62.58 0,546.97 82.56 0,553.12 82.46 0,553.33 90.94 0,584.49 90.43 0,583.77 61.96 0))","POLYGON ((-115.216436415999965 36.182535396000048 0,-115.21643447299999 36.18245853600007 0,-115.216518605999966 36.182457149000072 0,-115.216519184999981 36.182480067000029 0,-115.216535786999941 36.182479793000027 0,-115.216537150999955 36.182533737000028 0,-115.216436415999965 36.182535396000048 0))","POLYGON Z ((583.77 61.96 0, 546.46 62.58 0, 546.97 82.56 0, 553.12 82.45999999999999 0, 553.33 90.94 0, 584.49 90.43000000000001 0, 583.77 61.96 0))",0.6703215354533573 +24,AOI_2_Vegas_img3457,25,"POLYGON ((650.0 147.27 0,650.0 59.74 0,601.57 59.74 0,601.57 111.46 0,568.47 111.46 0,568.47 147.27 0,650.0 147.27 0))","POLYGON ((-115.216257599818093 36.182305062000069 0,-115.216477730999941 36.182305062000069 0,-115.216477730999941 36.182401751000043 0,-115.216388363999954 36.182401751000043 0,-115.216388363999954 36.182541411000045 0,-115.216257599818093 36.182541411000045 0,-115.216257599818093 36.182305062000069 0))","POLYGON Z ((650 147.27 0, 650 59.74 0, 601.5700000000001 59.74 0, 601.5700000000001 111.46 0, 568.47 111.46 0, 568.47 147.27 0, 650 147.27 0))",0.6520543074631249 +25,AOI_2_Vegas_img3457,26,"POLYGON ((68.4 38.78 0,3.9 38.07 0,3.4 67.46 0,0.0 67.43 0,0.0 91.27 0,49.19 91.81 0,49.69 62.72 0,67.99 62.92 0,68.4 38.78 0))","POLYGON ((-115.217827923999948 36.182597983000051 0,-115.217829030999951 36.182532822000042 0,-115.21787844499994 36.182533368000065 0,-115.21787977799994 36.182454807000056 0,-115.218012599821591 36.182456275648939 0,-115.218012599821591 36.182520647445202 0,-115.218003428999964 36.182520546000035 0,-115.218002080999952 36.182599908000043 0,-115.217827923999948 36.182597983000051 0))","POLYGON Z ((68.40000000000001 38.78 0, 3.9 38.07 0, 3.4 67.45999999999999 0, 0 67.43000000000001 0, 0 91.27 0, 49.19 91.81 0, 49.69 62.72 0, 67.98999999999999 62.92 0, 68.40000000000001 38.78 0))",0.8310821212182624 +26,AOI_2_Vegas_img3457,27,"POLYGON ((385.87 43.57 0,363.07 43.25 0,363.29 33.05 0,326.62 32.53 0,326.2 51.91 0,307.39 51.65 0,307.11 64.21 0,294.16 64.02 0,293.66 87.08 0,384.9 88.37 0,385.87 43.57 0))","POLYGON ((-115.216970752999941 36.182585054000072 0,-115.21697336699998 36.182464111000058 0,-115.21721972099999 36.182467579000047 0,-115.217218376999938 36.182529837000061 0,-115.21718339 36.182529344000045 0,-115.217182656999967 36.182563256000037 0,-115.21713185599998 36.182562541000038 0,-115.217130724999947 36.182614871000055 0,-115.217031708999968 36.182613477000075 0,-115.217032304999975 36.182585920000065 0,-115.216970752999941 36.182585054000072 0))","POLYGON Z ((385.87 43.57 0, 363.07 43.25 0, 363.29 33.05 0, 326.62 32.53 0, 326.2 51.91 0, 307.39 51.65 0, 307.11 64.20999999999999 0, 294.16 64.02 0, 293.66 87.08 0, 384.9 88.37 0, 385.87 43.57 0))",0.7736462655792273 +27,AOI_2_Vegas_img3457,28,"POLYGON ((130.9 29.9 0,113.29 29.9 0,113.29 39.85 0,106.69 39.85 0,106.69 72.18 0,144.54 72.18 0,144.54 91.36 0,179.76 91.36 0,179.76 67.56 0,166.55 67.56 0,166.55 39.49 0,130.9 39.49 0,130.9 29.9 0))","POLYGON ((-115.217659171999969 36.182621963000031 0,-115.217659171999969 36.182596064000052 0,-115.217562910999959 36.182596064000052 0,-115.217562910999959 36.182520287000045 0,-115.217527259999898 36.182520287000045 0,-115.217527259999898 36.182456020000075 0,-115.217622330999973 36.182456020000075 0,-115.217622330999973 36.182507818000033 0,-115.217724533999956 36.182507818000033 0,-115.217724533999956 36.182595106000065 0,-115.21770670799998 36.182595106000065 0,-115.21770670799998 36.182621963000031 0,-115.217659171999969 36.182621963000031 0))","POLYGON Z ((130.9 29.9 0, 113.29 29.9 0, 113.29 39.85 0, 106.69 39.85 0, 106.69 72.18000000000001 0, 144.54 72.18000000000001 0, 144.54 91.36 0, 179.76 91.36 0, 179.76 67.56 0, 166.55 67.56 0, 166.55 39.49 0, 130.9 39.49 0, 130.9 29.9 0))",0.7015900054404083 +28,AOI_2_Vegas_img3457,29,"POLYGON ((337.84 24.97 0,337.84 16.09 0,327.27 16.09 0,327.27 24.97 0,337.84 24.97 0))","POLYGON ((-115.217100443999982 36.182635288000029 0,-115.217128964999972 36.182635288000029 0,-115.217128964999972 36.182659268000066 0,-115.217100443999982 36.182659268000066 0,-115.217100443999982 36.182635288000029 0))","POLYGON Z ((337.84 24.97 0, 337.84 16.09 0, 327.27 16.09 0, 327.27 24.97 0, 337.84 24.97 0))",0.0 +29,AOI_2_Vegas_img3457,30,"POLYGON ((67.97 12.23 0,67.6 -0.0 0,51.97 -0.0 0,52.36 12.55 0,67.97 12.23 0))","POLYGON ((-115.217829072999962 36.182669667000027 0,-115.217871234999961 36.182668827000043 0,-115.217872271975864 36.18270269983821 0,-115.21783008340249 36.18270269983821 0,-115.217829072999962 36.182669667000027 0))","POLYGON Z ((67.97 12.23 0, 67.59999999999999 -0 0, 51.97 -0 0, 52.36 12.55 0, 67.97 12.23 0))",0.0 +30,AOI_2_Vegas_img3457,31,"POLYGON ((44.44 -0.0 0,11.65 -0.0 0,11.85 14.04 0,44.63 13.74 0,44.44 -0.0 0))","POLYGON ((-115.217892623097768 36.18270269983821 0,-115.21789209799999 36.182665607000047 0,-115.217980600999965 36.182664790000047 0,-115.217981137925307 36.18270269983821 0,-115.217892623097768 36.18270269983821 0))","POLYGON Z ((44.44 -0 0, 11.65 -0 0, 11.85 14.04 0, 44.63 13.74 0, 44.44 -0 0))",0.530201633205296 +31,AOI_2_Vegas_img3457,32,"POLYGON ((169.61 -0.0 0,110.63 -0.0 0,110.99 18.35 0,157.19 17.75 0,156.88 1.63 0,156.87 1.4 0,169.63 1.23 0,169.61 -0.0 0))","POLYGON ((-115.217554658840029 36.18270269983821 0,-115.217554592999988 36.182699371000069 0,-115.217589047999979 36.182698927000047 0,-115.21758903599999 36.182698286000061 0,-115.217588174999946 36.182654766000041 0,-115.217712924999944 36.18265315900004 0,-115.217713904619245 36.18270269983821 0,-115.217554658840029 36.18270269983821 0))","POLYGON Z ((169.61 -0 0, 110.63 -0 0, 110.99 18.35 0, 157.19 17.75 0, 156.88 1.63 0, 156.87 1.4 0, 169.63 1.23 0, 169.61 -0 0))",0.8654193502744998 +32,AOI_2_Vegas_img3457,33,"POLYGON ((355.98 -0.0 0,317.43 -0.0 0,317.43 8.21 0,355.98 8.21 0,355.98 -0.0 0))","POLYGON ((-115.21705144399999 36.18270269983821 0,-115.21705144399999 36.182680523000045 0,-115.21715554799999 36.182680523000045 0,-115.21715554799999 36.18270269983821 0,-115.21705144399999 36.18270269983821 0))","POLYGON Z ((355.98 -0 0, 317.43 -0 0, 317.43 8.210000000000001 0, 355.98 8.210000000000001 0, 355.98 -0 0))",0.49040778379315847 +33,AOI_2_Vegas_img3457,34,"POLYGON ((650.0 31.29 0,650 0 0,566.98 -0.0 0,567.34 31.9 0,650.0 31.29 0))","POLYGON ((-115.216257599818093 36.182618216998584 0,-115.216480781999962 36.182616579000069 0,-115.21648175195908 36.18270269983821 0,-115.216257599818093 36.18270269983821 0,-115.216257599818093 36.182618216998584 0))","POLYGON Z ((650 31.29 0, 650 0 0, 566.98 -0 0, 567.34 31.9 0, 650 31.29 0))",0.7223641975150352 +34,AOI_2_Vegas_img5979,1,"POLYGON ((650.0 132.22 0,650 0 0,482.84 -0.0 0,482.84 132.22 0,650.0 132.22 0))","POLYGON ((-115.160097599706134 36.171815699000035 0,-115.160548934999952 36.171815699000035 0,-115.160548934999952 36.172172699817217 0,-115.160097599706134 36.172172699817217 0,-115.160097599706134 36.171815699000035 0))","POLYGON Z ((650 132.22 0, 650 0 0, 482.84 -0 0, 482.84 132.22 0, 650 132.22 0))",0.770812603972309 +35,AOI_2_Vegas_img5979,2,"POLYGON ((650.0 419.12 0,650.0 220.42 0,545.53 220.42 0,545.53 288.64 0,536.02 288.64 0,536.02 300.16 0,530.21 300.16 0,530.21 327.44 0,545.53 327.44 0,545.53 419.12 0,650.0 419.12 0))","POLYGON ((-115.160097599706134 36.171041089000028 0,-115.160379674999945 36.171041089000028 0,-115.160379674999945 36.171288599000036 0,-115.160421031999988 36.171288599000036 0,-115.160421031999988 36.171362277000071 0,-115.160405344999958 36.171362277000071 0,-115.160405344999958 36.171393360000025 0,-115.160379674999945 36.171393360000025 0,-115.160379674999945 36.171577553000077 0,-115.160097599706134 36.171577553000077 0,-115.160097599706134 36.171041089000028 0))","POLYGON Z ((650 419.12 0, 650 220.42 0, 545.53 220.42 0, 545.53 288.64 0, 536.02 288.64 0, 536.02 300.16 0, 530.21 300.16 0, 530.21 327.44 0, 545.53 327.44 0, 545.53 419.12 0, 650 419.12 0))",0.6544761822280079 +36,AOI_2_Vegas_img5979,3,"POLYGON ((40.43 609.78 0,0.0 609.66 0,0 650 0,40.23 650.0 0,40.43 609.78 0))","POLYGON ((-115.161743448999971 36.17052628700003 0,-115.161743970522906 36.170417699813719 0,-115.161852599709633 36.170417699813719 0,-115.161852599709633 36.17052662851922 0,-115.161743448999971 36.17052628700003 0))","POLYGON Z ((40.43 609.78 0, 0 609.66 0, 0 650 0, 40.23 650 0, 40.43 609.78 0))",0.5652159129062819 +37,AOI_2_Vegas_img5979,4,"POLYGON ((129.76 650.0 0,300.27 650.0 0,300.27 638.97 0,310.73 632.15 0,300.27 625.3 0,300.27 610.2 0,295.41 605.98 0,209.05 605.98 0,129.76 605.98 0,129.76 650.0 0))","POLYGON ((-115.161502248999966 36.170417699813719 0,-115.161502248999966 36.17053654800003 0,-115.161288153999976 36.17053654800003 0,-115.16105499 36.17053654800003 0,-115.161041863999969 36.170525166000061 0,-115.161041863999969 36.170484385000066 0,-115.161013632999982 36.170465896000053 0,-115.161041863999969 36.170447476000049 0,-115.161041863999969 36.170417699813719 0,-115.161502248999966 36.170417699813719 0))","POLYGON Z ((129.76 650 0, 300.27 650 0, 300.27 638.97 0, 310.73 632.15 0, 300.27 625.3 0, 300.27 610.2 0, 295.41 605.98 0, 209.05 605.98 0, 129.76 605.98 0, 129.76 650 0))",0.7856157030104057 +38,AOI_2_Vegas_img5979,5,"POLYGON ((252.81 501.09 0,280.83 524.28 0,309.51 501.69 0,281.5 478.51 0,252.81 501.09 0))","POLYGON ((-115.161170003999985 36.170819754000036 0,-115.16109255799995 36.170880731000068 0,-115.161016917999973 36.170818127000075 0,-115.16109436499994 36.170757149000053 0,-115.161170003999985 36.170819754000036 0))","POLYGON Z ((252.81 501.09 0, 280.83 524.28 0, 309.51 501.69 0, 281.5 478.51 0, 252.81 501.09 0))",0.7237064854922469 +39,AOI_2_Vegas_img5979,6,"POLYGON ((195.56 502.36 0,224.14 524.85 0,252.81 501.09 0,224.23 478.61 0,195.56 502.36 0))","POLYGON ((-115.161324596999975 36.170816319000039 0,-115.16124717 36.170880458000056 0,-115.161170003999985 36.170819754000036 0,-115.16124743099999 36.170755614000029 0,-115.161324596999975 36.170816319000039 0))","POLYGON Z ((195.56 502.36 0, 224.14 524.85 0, 252.81 501.09 0, 224.23 478.61 0, 195.56 502.36 0))",0.8041912367055439 +40,AOI_2_Vegas_img5979,7,"POLYGON ((167.09 478.51 0,140.74 499.01 0,169.21 522.86 0,195.56 502.36 0,167.09 478.51 0))","POLYGON ((-115.161401446999946 36.170880713000031 0,-115.161324596999975 36.170816319000039 0,-115.161395742999957 36.170760987000051 0,-115.161472592999985 36.170825381000043 0,-115.161401446999946 36.170880713000031 0))","POLYGON Z ((167.09 478.51 0, 140.74 499.01 0, 169.21 522.86 0, 195.56 502.36 0, 167.09 478.51 0))",0.8040345991669146 +41,AOI_2_Vegas_img5979,8,"POLYGON ((275.65 116.75 0,275.65 121.9 0,285.77 121.9 0,285.77 116.75 0,275.65 116.75 0))","POLYGON ((-115.161108343999956 36.171857469000031 0,-115.16108101 36.171857469000031 0,-115.16108101 36.171843558000035 0,-115.161108343999956 36.171843558000035 0,-115.161108343999956 36.171857469000031 0))","POLYGON Z ((275.65 116.75 0, 275.65 121.9 0, 285.77 121.9 0, 285.77 116.75 0, 275.65 116.75 0))",0.0 +42,AOI_5_Khartoum_img130,1,"POLYGON ((227.83 650.0 0,249.16 650.0 0,248.72 647.08 0,227.83 650.0 0))","POLYGON ((32.505758537123519 15.519076199937246 0,32.505814932000142 15.519084092000131 0,32.505816121577858 15.519076199937246 0,32.505758537123519 15.519076199937246 0))","POLYGON Z ((227.83 650 0, 249.16 650 0, 248.72 647.08 0, 227.83 650 0))",0.0 +43,AOI_5_Khartoum_img130,2,"POLYGON ((312.89 643.21 0,262.19 649.94 0,262.2 650.0 0,313.86 650.0 0,312.89 643.21 0))","POLYGON ((32.505988199000058 15.519094523000126 0,32.505990818706096 15.519076199937246 0,32.505851346163126 15.519076199937246 0,32.505851324000062 15.519076354999989 0,32.505988199000058 15.519094523000126 0))","POLYGON Z ((312.89 643.21 0, 262.19 649.9400000000001 0, 262.2 650 0, 313.86 650 0, 312.89 643.21 0))",0.0 +44,AOI_5_Khartoum_img130,3,"POLYGON ((643.01 650.0 0,650 650 0,650.0 648.87 0,643.01 650.0 0))","POLYGON ((32.506879519596467 15.519076199937246 0,32.506898400015778 15.519079244935215 0,32.506898400015778 15.519076199937246 0,32.506879519596467 15.519076199937246 0))","POLYGON Z ((643.01 650 0, 650 650 0, 650 648.87 0, 643.01 650 0))",0.0 +45,AOI_5_Khartoum_img130,4,"POLYGON ((418.81 650.0 0,455.82 650.0 0,453.12 627.27 0,416.59 631.3 0,418.81 650.0 0))","POLYGON ((32.506274185670868 15.519076199937246 0,32.506268188000114 15.519126688000076 0,32.506366825000072 15.519137566999989 0,32.506374114377337 15.519076199937246 0,32.506274185670868 15.519076199937246 0))","POLYGON Z ((418.81 650 0, 455.82 650 0, 453.12 627.27 0, 416.59 631.3 0, 418.81 650 0))",0.7108285706265539 +46,AOI_5_Khartoum_img130,5,"POLYGON ((458.28 650.0 0,478.05 650.0 0,474.38 623.6 0,454.96 626.1 0,458.28 650.0 0))","POLYGON ((32.50638075850712 15.519076199937246 0,32.506371795000057 15.519140718000129 0,32.506424224000078 15.519147480000075 0,32.506434126255328 15.519076199937246 0,32.50638075850712 15.519076199937246 0))","POLYGON Z ((458.28 650 0, 478.05 650 0, 474.38 623.6 0, 454.96 626.1 0, 458.28 650 0))",0.0 +47,AOI_5_Khartoum_img130,6,"POLYGON ((490.78 650.0 0,541.71 650.0 0,537.3 614.94 0,487.11 620.8 0,490.78 650.0 0))","POLYGON ((32.506468518218028 15.519076199937246 0,32.506458602000066 15.51915502900005 0,32.506594120000038 15.519170856000112 0,32.506606027267551 15.519076199937246 0,32.506468518218028 15.519076199937246 0))","POLYGON Z ((490.78 650 0, 541.71 650 0, 537.3 614.9400000000001 0, 487.11 620.8 0, 490.78 650 0))",0.7094316341074808 +48,AOI_5_Khartoum_img130,7,"POLYGON ((326.56 650.0 0,384.12 650.0 0,379.49 614.09 0,322.8 620.87 0,326.56 650.0 0))","POLYGON ((32.506025099951302 15.519076199937246 0,32.506014961000055 15.519154845000038 0,32.50616802600009 15.519173167000076 0,32.506180527906395 15.519076199937246 0,32.506025099951302 15.519076199937246 0))","POLYGON Z ((326.56 650 0, 384.12 650 0, 379.49 614.09 0, 322.8 620.87 0, 326.56 650 0))",0.0 +49,AOI_5_Khartoum_img130,8,"POLYGON ((442.51 624.52 0,440.78 607.0 0,417.15 609.17 0,418.87 626.68 0,442.51 624.52 0))","POLYGON ((32.50633817300006 15.519145002000103 0,32.506274356000112 15.519139162000108 0,32.506269695000078 15.519186450000133 0,32.506333512000062 15.519192290000076 0,32.50633817300006 15.519145002000103 0))","POLYGON Z ((442.51 624.52 0, 440.78 607 0, 417.15 609.17 0, 418.87 626.6799999999999 0, 442.51 624.52 0))",0.0 +50,AOI_5_Khartoum_img130,9,"POLYGON ((475.15 619.54 0,473.4 603.7 0,449.89 606.11 0,451.65 621.96 0,475.15 619.54 0))","POLYGON ((32.506426312000038 15.519158439 0,32.506362851000084 15.519151912000039 0,32.506358111000083 15.519194694000131 0,32.5064215720001 15.519201222000042 0,32.506426312000038 15.519158439 0))","POLYGON Z ((475.15 619.54 0, 473.4 603.7 0, 449.89 606.11 0, 451.65 621.96 0, 475.15 619.54 0))",0.0 +51,AOI_5_Khartoum_img130,10,"POLYGON ((583.78 589.25 0,552.38 592.37 0,553.71 604.81 0,546.36 605.54 0,550.93 648.29 0,601.2 643.29 0,596.62 600.54 0,585.11 601.68 0,583.78 589.25 0))","POLYGON ((32.50671960800004 15.519240220000098 0,32.506723203000028 15.519206654000085 0,32.506754276000073 15.519209743000019 0,32.506766635000055 15.519094320000015 0,32.506630920000092 15.519080828000074 0,32.506618560000042 15.51919625200002 0,32.506638412000015 15.519198225000121 0,32.506634818000059 15.51923179000004 0,32.50671960800004 15.519240220000098 0))","POLYGON Z ((583.78 589.25 0, 552.38 592.37 0, 553.71 604.8099999999999 0, 546.36 605.54 0, 550.9299999999999 648.29 0, 601.2 643.29 0, 596.62 600.54 0, 585.11 601.6799999999999 0, 583.78 589.25 0))",0.6462022646967056 +52,AOI_5_Khartoum_img130,11,"POLYGON ((530.4 604.28 0,528.57 585.69 0,495.12 588.74 0,496.95 607.33 0,530.4 604.28 0))","POLYGON ((32.506575484000088 15.519199648 0,32.506485166000076 15.519191403000089 0,32.506480231000111 15.519241596 0,32.506570549000052 15.519249840000123 0,32.506575484000088 15.519199648 0))","POLYGON Z ((530.4 604.28 0, 528.5700000000001 585.6900000000001 0, 495.12 588.74 0, 496.95 607.33 0, 530.4 604.28 0))",0.0 +53,AOI_5_Khartoum_img130,12,"POLYGON ((635.8 608.73 0,631.73 574.81 0,599.78 578.38 0,603.85 612.3 0,635.8 608.73 0))","POLYGON ((32.506860072000073 15.519187620000071 0,32.50677379400004 15.519178001000077 0,32.506762797000128 15.519269582000108 0,32.506849075000034 15.519279201000115 0,32.506860072000073 15.519187620000071 0))","POLYGON Z ((635.8 608.73 0, 631.73 574.8099999999999 0, 599.78 578.38 0, 603.85 612.3 0, 635.8 608.73 0))",0.0 +54,AOI_5_Khartoum_img130,13,"POLYGON ((650.0 595.78 0,650.0 557.43 0,642.68 558.7 0,649.16 595.92 0,650.0 595.78 0))","POLYGON ((32.506898400015778 15.519222597241322 0,32.506896142000123 15.519222204000119 0,32.506878642000046 15.519322704999988 0,32.506898400015778 15.519326145280065 0,32.506898400015778 15.519222597241322 0))","POLYGON Z ((650 595.78 0, 650 557.4299999999999 0, 642.6799999999999 558.7 0, 649.16 595.92 0, 650 595.78 0))",0.0 +55,AOI_5_Khartoum_img130,14,"POLYGON ((6.07 512.42 0,0.0 513.15 0,0.0 590.42 0,20.58 587.96 0,16.35 555.14 0,11.64 555.7 0,6.07 512.42 0))","POLYGON ((32.505159779000067 15.519447660000099 0,32.505174821000054 15.519330802000029 0,32.505187548000066 15.519332323000066 0,32.50519895700009 15.51924369500005 0,32.505143400015463 15.519237054876516 0,32.505143400015463 15.51944570258228 0,32.505159779000067 15.519447660000099 0))","POLYGON Z ((6.07 512.42 0, 0 513.15 0, 0 590.42 0, 20.58 587.96 0, 16.35 555.14 0, 11.64 555.7 0, 6.07 512.42 0))",0.5195114141803822 +56,AOI_5_Khartoum_img130,15,"POLYGON ((256.52 548.23 0,244.99 476.11 0,194.36 483.63 0,205.9 555.75 0,256.52 548.23 0))","POLYGON ((32.505836013000113 15.519350967000058 0,32.50569933000002 15.519330667000075 0,32.5056681780001 15.519525411000048 0,32.505804860000062 15.519545711000083 0,32.505836013000113 15.519350967000058 0))","POLYGON Z ((256.52 548.23 0, 244.99 476.11 0, 194.36 483.63 0, 205.9 555.75 0, 256.52 548.23 0))",0.0 +57,AOI_5_Khartoum_img130,16,"POLYGON ((189.65 471.87 0,188.32 464.46 0,177.51 466.28 0,178.84 473.69 0,189.65 471.87 0))","POLYGON ((32.505655466000043 15.51955714100014 0,32.505626278000094 15.519552250000107 0,32.505622667000047 15.519572256000112 0,32.505651856000036 15.519577147000042 0,32.505655466000043 15.51955714100014 0))","POLYGON Z ((189.65 471.87 0, 188.32 464.46 0, 177.51 466.28 0, 178.84 473.69 0, 189.65 471.87 0))",0.0 +58,AOI_5_Khartoum_img130,17,"POLYGON ((349.99 452.4 0,322.36 455.52 0,315.73 456.26 0,319.34 486.01 0,325.97 485.26 0,329.93 517.92 0,356.44 514.93 0,355.96 510.98 0,361.44 507.28 0,363.38 500.15 0,369.66 493.43 0,381.42 492.11 0,379.85 479.2 0,377.05 456.12 0,350.8 459.08 0,349.99 452.4 0))","POLYGON ((32.506088364000071 15.519609710000038 0,32.506090550000117 15.519591687000117 0,32.506161441000053 15.519599671000012 0,32.506168998000106 15.519537372000057 0,32.50617322800008 15.519502507000052 0,32.506141491000044 15.519498933000069 0,32.506124523000061 15.51948079400014 0,32.506119294000094 15.519461557000142 0,32.50610448500003 15.519451556000137 0,32.506105781000052 15.519440877000017 0,32.506034205000056 15.519432816000077 0,32.506023509000023 15.519520987000019 0,32.50600561100002 15.519518971000016 0,32.505995867000038 15.519599293000095 0,32.506013766000038 15.519601308000082 0,32.506088364000071 15.519609710000038 0))","POLYGON Z ((349.99 452.4 0, 322.36 455.52 0, 315.73 456.26 0, 319.34 486.01 0, 325.97 485.26 0, 329.93 517.92 0, 356.44 514.9299999999999 0, 355.96 510.98 0, 361.44 507.28 0, 363.38 500.15 0, 369.66 493.43 0, 381.42 492.11 0, 379.85 479.2 0, 377.05 456.12 0, 350.8 459.08 0, 349.99 452.4 0))",0.7140800811700151 +59,AOI_5_Khartoum_img130,18,"POLYGON ((542.01 498.92 0,539.74 481.49 0,561.16 478.9 0,559.02 462.53 0,574.52 460.66 0,570.66 431.06 0,505.43 438.95 0,509.29 468.54 0,511.42 484.92 0,513.69 502.35 0,542.01 498.92 0))","POLYGON ((32.506606821000069 15.519484105000119 0,32.506530364000056 15.519474859000107 0,32.506524233000071 15.519521927000069 0,32.506518475000099 15.519566130000126 0,32.506508068000088 15.519646031000079 0,32.506684188000065 15.519667330000088 0,32.506694596000074 15.51958742800009 0,32.506652761000048 15.51958237 0,32.506658519000048 15.519538166000141 0,32.506600689000088 15.519531173000068 0,32.506606821000069 15.519484105000119 0))","POLYGON Z ((542.01 498.92 0, 539.74 481.49 0, 561.16 478.9 0, 559.02 462.53 0, 574.52 460.66 0, 570.66 431.06 0, 505.43 438.95 0, 509.29 468.54 0, 511.42 484.92 0, 513.6900000000001 502.35 0, 542.01 498.92 0))",0.7472064817382343 +60,AOI_5_Khartoum_img130,19,"POLYGON ((635.87 428.05 0,597.26 432.94 0,603.07 478.73 0,612.27 477.56 0,613.95 490.85 0,650.0 486.28 0,650.0 435.45 0,637.02 437.1 0,635.87 428.05 0))","POLYGON ((32.506860244000045 15.519675476000085 0,32.50686334400006 15.519651031 0,32.506898400015778 15.519655477098608 0,32.506898400015778 15.519518250458857 0,32.50680106900009 15.519505906000139 0,32.506796519000027 15.51954177800013 0,32.506771678000071 15.519538627000079 0,32.506755999000092 15.519662255000057 0,32.506860244000045 15.519675476000085 0))","POLYGON Z ((635.87 428.05 0, 597.26 432.94 0, 603.0700000000001 478.73 0, 612.27 477.56 0, 613.95 490.85 0, 650 486.28 0, 650 435.45 0, 637.02 437.1 0, 635.87 428.05 0))",0.36838398028116426 +61,AOI_5_Khartoum_img130,20,"POLYGON ((80.25 471.74 0,94.85 468.52 0,89.04 401.34 0,43.87 407.8 0,45.79 430.01 0,37.5 432.43 0,38.17 440.48 0,38.34 442.57 0,32.45 443.24 0,35.83 482.23 0,80.25 471.74 0))","POLYGON ((32.505360085000106 15.519557511000096 0,32.505240131000043 15.519529176000061 0,32.505231025000043 15.519634458000095 0,32.505246912000068 15.519636268000102 0,32.505246446000093 15.519641899000073 0,32.505244645000097 15.519663649000037 0,32.505267034000084 15.519670175000041 0,32.505261848000075 15.519730142000064 0,32.505383816000069 15.519747569000119 0,32.505399503000049 15.519566190000106 0,32.505360085000106 15.519557511000096 0))","POLYGON Z ((80.25 471.74 0, 94.84999999999999 468.52 0, 89.04000000000001 401.34 0, 43.87 407.8 0, 45.79 430.01 0, 37.5 432.43 0, 38.17 440.48 0, 38.34 442.57 0, 32.45 443.24 0, 35.83 482.23 0, 80.25 471.74 0))",0.5017826829568274 +62,AOI_5_Khartoum_img130,21,"POLYGON ((132.81 467.69 0,173.49 462.16 0,164.51 400.84 0,123.83 406.37 0,127.43 430.92 0,112.19 432.99 0,117.57 469.76 0,132.81 467.69 0))","POLYGON ((32.505501987000066 15.519568432000098 0,32.505460846000069 15.519562839000109 0,32.505446308000067 15.519662129000029 0,32.505487449000029 15.51966772200017 0,32.505477743000057 15.519734009000111 0,32.505587570000053 15.519748938000138 0,32.505611813000044 15.519583361000112 0,32.505501987000066 15.519568432000098 0))","POLYGON Z ((132.81 467.69 0, 173.49 462.16 0, 164.51 400.84 0, 123.83 406.37 0, 127.43 430.92 0, 112.19 432.99 0, 117.57 469.76 0, 132.81 467.69 0))",0.5458645466491461 +63,AOI_5_Khartoum_img130,22,"POLYGON ((370.52 437.32 0,364.0 384.91 0,310.74 391.06 0,317.26 443.47 0,370.52 437.32 0))","POLYGON ((32.506143798000089 15.519650447000032 0,32.506000001000096 15.519633844000044 0,32.505982405000069 15.519775331000124 0,32.506126201000065 15.519791934000125 0,32.506143798000089 15.519650447000032 0))","POLYGON Z ((370.52 437.32 0, 364 384.91 0, 310.74 391.06 0, 317.26 443.47 0, 370.52 437.32 0))",0.5367048681832752 +64,AOI_5_Khartoum_img130,23,"POLYGON ((261.49 380.06 0,237.68 382.58 0,245.33 449.77 0,269.15 447.26 0,279.22 446.19 0,302.49 443.73 0,297.35 398.65 0,274.09 401.11 0,272.91 390.79 0,262.84 391.85 0,261.49 380.06 0))","POLYGON ((32.505849435000115 15.519805033000106 0,32.505853062000035 15.519773194000052 0,32.50588025600009 15.519776069999981 0,32.505883431000015 15.51974819800011 0,32.505946246000086 15.519754841000102 0,32.505960110000039 15.519633128000127 0,32.505897295000068 15.519626485000085 0,32.505870100000024 15.519623610000044 0,32.505805796000061 15.519616809000123 0,32.505785130000099 15.519798233000087 0,32.505849435000115 15.519805033000106 0))","POLYGON Z ((261.49 380.06 0, 237.68 382.58 0, 245.33 449.77 0, 269.15 447.26 0, 279.22 446.19 0, 302.49 443.73 0, 297.35 398.65 0, 274.09 401.11 0, 272.91 390.79 0, 262.84 391.85 0, 261.49 380.06 0))",0.7172596426969207 +65,AOI_5_Khartoum_img130,24,"POLYGON ((394.93 361.36 0,373.1 363.72 0,375.22 381.99 0,380.76 429.63 0,439.05 423.34 0,433.51 375.7 0,397.06 379.63 0,394.93 361.36 0))","POLYGON ((32.506209721000104 15.519855531000118 0,32.506215456000085 15.519806197000017 0,32.506313871000032 15.519816818000089 0,32.506328823000054 15.519688192000075 0,32.506171456000047 15.519671209 0,32.506156505000028 15.519799836000042 0,32.506150770000083 15.519849169 0,32.506209721000104 15.519855531000118 0))","POLYGON Z ((394.93 361.36 0, 373.1 363.72 0, 375.22 381.99 0, 380.76 429.63 0, 439.05 423.34 0, 433.51 375.7 0, 397.06 379.63 0, 394.93 361.36 0))",0.8457456718473334 +66,AOI_5_Khartoum_img130,25,"POLYGON ((615.41 384.53 0,642.29 381.13 0,637.93 346.68 0,589.11 352.84 0,593.31 387.32 0,605.54 385.77 0,607.15 396.54 0,616.77 395.32 0,615.41 384.53 0))","POLYGON ((32.50680500900009 15.519792975000062 0,32.506808691000089 15.519763831000091 0,32.506782697000141 15.519760547000097 0,32.506778369000081 15.519789608000043 0,32.506745346000095 15.519785436000083 0,32.506734001000055 15.51987851900004 0,32.506865823000069 15.51989517600002 0,32.506877577000076 15.519802144000078 0,32.50680500900009 15.519792975000062 0))","POLYGON Z ((615.41 384.53 0, 642.29 381.13 0, 637.9299999999999 346.68 0, 589.11 352.84 0, 593.3099999999999 387.32 0, 605.54 385.77 0, 607.15 396.54 0, 616.77 395.32 0, 615.41 384.53 0))",0.4877430780592595 +67,AOI_5_Khartoum_img130,26,"POLYGON ((170.36 359.91 0,168.94 341.85 0,151.6 343.11 0,153.02 361.17 0,170.36 359.91 0))","POLYGON ((32.505603359000062 15.519859452000093 0,32.505556551000069 15.519856048000058 0,32.505552733000108 15.519904797000112 0,32.505599540000041 15.519908201000147 0,32.505603359000062 15.519859452000093 0))","POLYGON Z ((170.36 359.91 0, 168.94 341.85 0, 151.6 343.11 0, 153.02 361.17 0, 170.36 359.91 0))",0.0 +68,AOI_5_Khartoum_img130,27,"POLYGON ((444.63 367.06 0,440.37 339.53 0,406.47 344.41 0,410.74 371.93 0,444.63 367.06 0))","POLYGON ((32.506343906000048 15.519840146000034 0,32.506252399000068 15.519826978000095 0,32.506240881000032 15.519901289000126 0,32.506332388000047 15.519914457000104 0,32.506343906000048 15.519840146000034 0))","POLYGON Z ((444.63 367.06 0, 440.37 339.53 0, 406.47 344.41 0, 410.74 371.93 0, 444.63 367.06 0))",0.0 +69,AOI_5_Khartoum_img130,28,"POLYGON ((402.74 319.57 0,400.09 303.91 0,358.28 310.48 0,360.93 326.14 0,402.74 319.57 0))","POLYGON ((32.506230807000101 15.519968372000044 0,32.506117909000096 15.51995062400009 0,32.506110751000037 15.519992894000076 0,32.506223649000077 15.520010643000033 0,32.506230807000101 15.519968372000044 0))","POLYGON Z ((402.74 319.57 0, 400.09 303.91 0, 358.28 310.48 0, 360.93 326.14 0, 402.74 319.57 0))",0.0 +70,AOI_5_Khartoum_img130,29,"POLYGON ((144.43 341.39 0,136.8 294.96 0,83.46 303.11 0,91.09 349.53 0,104.53 347.48 0,108.86 373.86 0,148.77 367.77 0,144.43 341.39 0))","POLYGON ((32.505533371000098 15.519909450000027 0,32.505545081000037 15.519838228000095 0,32.505437334000092 15.519821781000157 0,32.505425625000086 15.519893003000062 0,32.505389337000054 15.519887465000071 0,32.505368729000061 15.520012809000034 0,32.50551276300007 15.520034795000146 0,32.505533371000098 15.519909450000027 0))","POLYGON Z ((144.43 341.39 0, 136.8 294.96 0, 83.45999999999999 303.11 0, 91.09 349.53 0, 104.53 347.48 0, 108.86 373.86 0, 148.77 367.77 0, 144.43 341.39 0))",0.6121494007641477 +71,AOI_5_Khartoum_img130,30,"POLYGON ((190.87 284.89 0,149.74 290.13 0,156.15 336.85 0,197.28 331.61 0,196.75 327.75 0,214.44 325.5 0,209.16 286.96 0,191.46 289.21 0,190.87 284.89 0))","POLYGON ((32.50565874600003 15.520061991000066 0,32.505660346000056 15.520050322000102 0,32.505708121000097 15.52005640699999 0,32.505722397000092 15.519952349000041 0,32.505674621000033 15.519946265000016 0,32.505676049000087 15.519935860000052 0,32.505565005000037 15.519921718000074 0,32.505547702000051 15.520047848000097 0,32.50565874600003 15.520061991000066 0))","POLYGON Z ((190.87 284.89 0, 149.74 290.13 0, 156.15 336.85 0, 197.28 331.61 0, 196.75 327.75 0, 214.44 325.5 0, 209.16 286.96 0, 191.46 289.21 0, 190.87 284.89 0))",0.9217234816132825 +72,AOI_5_Khartoum_img130,31,"POLYGON ((573.35 333.62 0,566.51 280.23 0,523.85 285.69 0,525.33 297.93 0,509.84 315.27 0,511.36 327.11 0,505.23 327.9 0,507.05 342.11 0,573.35 333.62 0))","POLYGON ((32.506691434000068 15.519930429000089 0,32.506512441000069 15.519907500000095 0,32.506507525000103 15.519945874000053 0,32.506524075000058 15.519947994000049 0,32.506519980000107 15.519979965000118 0,32.50656179800005 15.520026781000116 0,32.506557802000046 15.520059834000067 0,32.506672968000018 15.520074587000133 0,32.506691434000068 15.519930429000089 0))","POLYGON Z ((573.35 333.62 0, 566.51 280.23 0, 523.85 285.69 0, 525.33 297.93 0, 509.84 315.27 0, 511.36 327.11 0, 505.23 327.9 0, 507.05 342.11 0, 573.35 333.62 0))",0.0 +73,AOI_5_Khartoum_img130,32,"POLYGON ((627.75 277.82 0,579.11 283.75 0,585.98 340.15 0,634.66 334.51 0,650.0 332.88 0,650.0 321.25 0,648.13 305.87 0,631.42 307.9 0,627.75 277.82 0))","POLYGON ((32.506838327000011 15.520081076000057 0,32.506848226000052 15.519999859000032 0,32.506893339000037 15.520005357000148 0,32.506898400015778 15.519963832860016 0,32.506898400015778 15.519932410483392 0,32.506856981000034 15.519928023000064 0,32.506725553000059 15.519912783000056 0,32.506706994000055 15.520065069000077 0,32.506838327000011 15.520081076000057 0))","POLYGON Z ((627.75 277.82 0, 579.11 283.75 0, 585.98 340.15 0, 634.66 334.51 0, 650 332.88 0, 650 321.25 0, 648.13 305.87 0, 631.42 307.9 0, 627.75 277.82 0))",0.16814659189473705 +74,AOI_5_Khartoum_img130,33,"POLYGON ((154.0 284.41 0,152.43 274.16 0,142.92 275.52 0,144.5 285.77 0,154.0 284.41 0))","POLYGON ((32.505559210000058 15.520063283000116 0,32.50553354000008 15.520059619000042 0,32.505529283000058 15.520087307000079 0,32.505554952000104 15.520090971000101 0,32.505559210000058 15.520063283000116 0))","POLYGON Z ((154 284.41 0, 152.43 274.16 0, 142.92 275.52 0, 144.5 285.77 0, 154 284.41 0))",0.0 +75,AOI_5_Khartoum_img130,34,"POLYGON ((262.02 328.0 0,266.82 325.96 0,270.64 321.76 0,273.15 316.68 0,274.01 311.97 0,284.03 310.65 0,279.78 280.73 0,278.58 272.27 0,254.56 275.43 0,255.76 283.89 0,234.62 286.67 0,224.83 287.96 0,233.52 349.26 0,243.31 347.97 0,243.74 350.97 0,264.88 348.19 0,262.02 328.0 0))","POLYGON ((32.505850841000075 15.519945601000106 0,32.505858574000072 15.519891097000114 0,32.505801490000074 15.519883579000142 0,32.505800342000036 15.51989167000003 0,32.505773908000059 15.519888189000076 0,32.505750429000038 15.520053697000119 0,32.505776862000076 15.520057178000096 0,32.505833946000088 15.520064697000061 0,32.505830705000072 15.520087541000127 0,32.505895564000106 15.520096084000064 0,32.505898806000054 15.520073239000094 0,32.505910269000076 15.519992436000088 0,32.505883218000086 15.519988873000155 0,32.505880915000091 15.519976176000061 0,32.505874141000056 15.51996243500013 0,32.505863802000114 15.519951099000068 0,32.505850841000075 15.519945601000106 0))","POLYGON Z ((262.02 328 0, 266.82 325.96 0, 270.64 321.76 0, 273.15 316.68 0, 274.01 311.97 0, 284.03 310.65 0, 279.78 280.73 0, 278.58 272.27 0, 254.56 275.43 0, 255.76 283.89 0, 234.62 286.67 0, 224.83 287.96 0, 233.52 349.26 0, 243.31 347.97 0, 243.74 350.97 0, 264.88 348.19 0, 262.02 328 0))",0.7678532834558965 +76,AOI_5_Khartoum_img130,35,"POLYGON ((214.01 276.98 0,212.29 265.18 0,173.87 270.37 0,175.58 282.17 0,214.01 276.98 0))","POLYGON ((32.505721224000069 15.520083344000117 0,32.505617475000058 15.520069339000051 0,32.505612842000104 15.52010120700011 0,32.505716591000116 15.520115212000112 0,32.505721224000069 15.520083344000117 0))","POLYGON Z ((214.01 276.98 0, 212.29 265.18 0, 173.87 270.37 0, 175.58 282.17 0, 214.01 276.98 0))",0.0 +77,AOI_5_Khartoum_img130,36,"POLYGON ((333.19 299.31 0,340.79 294.29 0,345.74 282.65 0,340.7 249.5 0,288.41 256.88 0,293.45 290.03 0,295.65 304.53 0,298.61 304.11 0,299.48 309.8 0,317.46 307.27 0,333.19 299.31 0))","POLYGON ((32.506043020000043 15.520023062000057 0,32.506000537000041 15.520001582000088 0,32.505951996000086 15.519994732000109 0,32.505949660000098 15.52001010300004 0,32.505941657000079 15.520008974000046 0,32.505935706000045 15.520048125000137 0,32.505922102000078 15.520137616000094 0,32.506063284000028 15.520157540000081 0,32.50607688700007 15.520068049000074 0,32.506063522000048 15.520036621000086 0,32.506043020000043 15.520023062000057 0))","POLYGON Z ((333.19 299.31 0, 340.79 294.29 0, 345.74 282.65 0, 340.7 249.5 0, 288.41 256.88 0, 293.45 290.03 0, 295.65 304.53 0, 298.61 304.11 0, 299.48 309.8 0, 317.46 307.27 0, 333.19 299.31 0))",0.7403148158727656 +78,AOI_5_Khartoum_img130,37,"POLYGON ((380.32 228.03 0,349.13 233.24 0,346.65 233.66 0,342.81 234.3 0,334.24 235.73 0,335.94 245.2 0,344.51 243.77 0,351.15 280.62 0,354.99 279.97 0,359.05 302.51 0,361.91 304.2 0,399.45 297.93 0,391.08 251.49 0,384.74 252.55 0,380.32 228.03 0))","POLYGON ((32.506170273000059 15.520215530000049 0,32.506182198000033 15.520149314000127 0,32.506199326000058 15.520152178000115 0,32.506221905000061 15.520026793000081 0,32.50612056500006 15.52000985000007 0,32.5061128400001 15.520014430000096 0,32.506101883000056 15.520075274000071 0,32.506091503000093 15.52007353800016 0,32.506073589000074 15.520173016000134 0,32.506050450000039 15.520169147000118 0,32.506045843000031 15.520194727 0,32.506068983000063 15.52019859600013 0,32.506079363000026 15.520200331000126 0,32.50608606100009 15.520201451000066 0,32.506170273000059 15.520215530000049 0))","POLYGON Z ((380.32 228.03 0, 349.13 233.24 0, 346.65 233.66 0, 342.81 234.3 0, 334.24 235.73 0, 335.94 245.2 0, 344.51 243.77 0, 351.15 280.62 0, 354.99 279.97 0, 359.05 302.51 0, 361.91 304.2 0, 399.45 297.93 0, 391.08 251.49 0, 384.74 252.55 0, 380.32 228.03 0))",0.5666698092198986 +79,AOI_5_Khartoum_img130,38,"POLYGON ((0.87 252.82 0,0.0 245.59 0,0.0 252.92 0,0.87 252.82 0))","POLYGON ((32.505145745000064 15.520148575000054 0,32.505143400015463 15.520148313694859 0,32.505143400015463 15.520168114550797 0,32.505145745000064 15.520148575000054 0))","POLYGON Z ((0.87 252.82 0, 0 245.59 0, 0 252.92 0, 0.87 252.82 0))",0.0 +80,AOI_5_Khartoum_img130,39,"POLYGON ((553.42 261.76 0,546.66 213.41 0,490.11 221.32 0,496.66 269.7 0,553.42 261.76 0))","POLYGON ((32.506637632000071 15.520124437000016 0,32.506484395000037 15.520103021000111 0,32.506466693000057 15.520233648000099 0,32.506619388000033 15.520254988000103 0,32.506637632000071 15.520124437000016 0))","POLYGON Z ((553.42 261.76 0, 546.66 213.41 0, 490.11 221.32 0, 496.66 269.7 0, 553.42 261.76 0))",0.7227968096800156 +81,AOI_5_Khartoum_img130,40,"POLYGON ((606.63 247.91 0,611.25 247.28 0,615.17 244.78 0,613.5 229.75 0,627.9 228.26 0,624.69 199.4 0,583.35 203.66 0,584.8 216.72 0,565.01 218.76 0,568.44 249.61 0,582.63 248.14 0,584.2 262.24 0,589.8 261.66 0,599.8 260.63 0,598.48 248.69 0,603.33 248.3 0,606.63 247.91 0))","POLYGON ((32.506781293000074 15.520161831000113 0,32.506772381000069 15.520160801000062 0,32.506759285000065 15.520159743000022 0,32.506762871000056 15.520127498000051 0,32.506735865000046 15.520124710000077 0,32.506720742000027 15.520123148000094 0,32.506716508000054 15.520161218000053 0,32.506678189000048 15.520157263000053 0,32.506668929000078 15.520240535000044 0,32.506722371000052 15.520246052000054 0,32.506718451000026 15.520281307000083 0,32.506830051000051 15.520292828000096 0,32.506838717000065 15.52021489400005 0,32.506799844000106 15.520210881000077 0,32.506804358000153 15.520170288 0,32.506793772000023 15.520163549000184 0,32.506781293000074 15.520161831000113 0))","POLYGON Z ((606.63 247.91 0, 611.25 247.28 0, 615.17 244.78 0, 613.5 229.75 0, 627.9 228.26 0, 624.6900000000001 199.4 0, 583.35 203.66 0, 584.8 216.72 0, 565.01 218.76 0, 568.4400000000001 249.61 0, 582.63 248.14 0, 584.2 262.24 0, 589.8 261.66 0, 599.8 260.63 0, 598.48 248.69 0, 603.33 248.3 0, 606.63 247.91 0))",0.4603291616701668 +82,AOI_5_Khartoum_img130,41,"POLYGON ((548.1 201.82 0,546.95 191.48 0,538.2 192.37 0,539.35 202.72 0,548.1 201.82 0))","POLYGON ((32.506623260000083 15.520286275000057 0,32.506599634000082 15.520283849000029 0,32.506596544000033 15.520311788000104 0,32.506620170000105 15.520314214000145 0,32.506623260000083 15.520286275000057 0))","POLYGON Z ((548.1 201.82 0, 546.95 191.48 0, 538.2 192.37 0, 539.35 202.72 0, 548.1 201.82 0))",0.0 +83,AOI_5_Khartoum_img130,42,"POLYGON ((178.66 182.32 0,137.7 189.32 0,142.4 214.88 0,137.45 215.72 0,138.76 222.81 0,145.92 261.71 0,202.72 252.0 0,195.56 213.11 0,186.83 214.6 0,185.53 207.51 0,183.37 207.88 0,178.66 182.32 0))","POLYGON ((32.50562578800006 15.520338944000068 0,32.505638491000084 15.520269930000051 0,32.505644328000059 15.520270928000084 0,32.505647851000056 15.520251786000074 0,32.505671423000038 15.520255813000071 0,32.505690752000056 15.52015079700003 0,32.505537372000063 15.52012458900006 0,32.505518044000077 15.520229605000065 0,32.505514520000084 15.520248747000075 0,32.505527890000089 15.520251032 0,32.505515188000061 15.520320046000077 0,32.50562578800006 15.520338944000068 0))","POLYGON Z ((178.66 182.32 0, 137.7 189.32 0, 142.4 214.88 0, 137.45 215.72 0, 138.76 222.81 0, 145.92 261.71 0, 202.72 252 0, 195.56 213.11 0, 186.83 214.6 0, 185.53 207.51 0, 183.37 207.88 0, 178.66 182.32 0))",0.6740213930775178 +84,AOI_5_Khartoum_img130,43,"POLYGON ((547.8 189.15 0,547.01 179.65 0,534.94 180.58 0,535.73 190.08 0,547.8 189.15 0))","POLYGON ((32.506622451000034 15.520320491000071 0,32.506589879000103 15.520317980000099 0,32.506587750000037 15.520343621000054 0,32.50662032200011 15.520346132000062 0,32.506622451000034 15.520320491000071 0))","POLYGON Z ((547.8 189.15 0, 547.01 179.65 0, 534.9400000000001 180.58 0, 535.73 190.08 0, 547.8 189.15 0))",0.0 +85,AOI_5_Khartoum_img130,44,"POLYGON ((324.42 185.84 0,320.27 158.81 0,268.51 166.19 0,272.65 193.21 0,274.71 206.61 0,293.29 203.96 0,294.24 210.11 0,327.42 205.38 0,324.42 185.84 0))","POLYGON ((32.506019322000071 15.52032944399999 0,32.506027424000031 15.52027668100005 0,32.505937844000144 15.520263910000127 0,32.505935296000054 15.520280502000089 0,32.505885121000084 15.520273349000128 0,32.505879566000047 15.520309521000138 0,32.505868364000072 15.520382477000048 0,32.506008119000072 15.520402401000124 0,32.506019322000071 15.52032944399999 0))","POLYGON Z ((324.42 185.84 0, 320.27 158.81 0, 268.51 166.19 0, 272.65 193.21 0, 274.71 206.61 0, 293.29 203.96 0, 294.24 210.11 0, 327.42 205.38 0, 324.42 185.84 0))",0.858882463254696 +86,AOI_5_Khartoum_img130,45,"POLYGON ((408.06 151.96 0,338.49 159.91 0,339.28 166.28 0,332.05 167.1 0,339.89 230.77 0,413.04 222.4 0,407.75 179.46 0,411.81 179.0 0,409.25 158.28 0,408.85 158.32 0,408.06 151.96 0))","POLYGON ((32.506245168000092 15.520420905000114 0,32.506247283000093 15.520403730000076 0,32.506248386000046 15.520403856000078 0,32.506255275000079 15.520347901000049 0,32.506244321000096 15.520346648000125 0,32.506258597000098 15.520230709000046 0,32.506061098000053 15.520208132000102 0,32.506039932000128 15.520380026000096 0,32.506059443000012 15.520382257000076 0,32.506057328000068 15.520399431000033 0,32.506245168000092 15.520420905000114 0))","POLYGON Z ((408.06 151.96 0, 338.49 159.91 0, 339.28 166.28 0, 332.05 167.1 0, 339.89 230.77 0, 413.04 222.4 0, 407.75 179.46 0, 411.81 179 0, 409.25 158.28 0, 408.85 158.32 0, 408.06 151.96 0))",0.44842903552554153 +87,AOI_5_Khartoum_img130,46,"POLYGON ((479.09 134.8 0,451.29 138.87 0,460.2 195.36 0,480.2 192.43 0,481.67 201.76 0,527.08 195.11 0,525.61 185.78 0,524.19 176.75 0,529.31 176.0 0,524.59 146.08 0,519.47 146.83 0,501.87 149.41 0,499.65 135.36 0,479.64 138.29 0,479.09 134.8 0))","POLYGON ((32.506436932000078 15.520467232000106 0,32.50643841800008 15.520457809000064 0,32.506492456000068 15.520465723000022 0,32.50649844000003 15.520427789999985 0,32.506545960000068 15.520434750000099 0,32.506559798000104 15.520436776000071 0,32.506572541000025 15.520356005000059 0,32.506558702000092 15.520353979000109 0,32.506562550000091 15.520329584000072 0,32.506566523000039 15.520304405000035 0,32.506443906000072 15.520286446000036 0,32.506439934000085 15.520311626000103 0,32.506385941000083 15.520303718000033 0,32.506361881000068 15.520456239000019 0,32.506436932000078 15.520467232000106 0))","POLYGON Z ((479.09 134.8 0, 451.29 138.87 0, 460.2 195.36 0, 480.2 192.43 0, 481.67 201.76 0, 527.08 195.11 0, 525.61 185.78 0, 524.1900000000001 176.75 0, 529.3099999999999 176 0, 524.59 146.08 0, 519.47 146.83 0, 501.87 149.41 0, 499.65 135.36 0, 479.64 138.29 0, 479.09 134.8 0))",0.8089902586431665 +88,AOI_5_Khartoum_img130,47,"POLYGON ((161.59 159.84 0,159.14 145.1 0,177.1 142.31 0,175.47 132.54 0,182.73 131.42 0,179.55 112.36 0,189.56 110.81 0,183.46 74.26 0,116.11 84.7 0,122.21 121.24 0,129.48 164.81 0,161.59 159.84 0))","POLYGON ((32.505579706000084 15.520399645000015 0,32.505493003000069 15.520386213000119 0,32.505473375000065 15.520503840000107 0,32.505456908000042 15.520602518000087 0,32.50563873300009 15.520630686000073 0,32.505655200000049 15.520532008000092 0,32.505628193000049 15.520527824000116 0,32.505636779000056 15.520476366000066 0,32.505617178000065 15.520473329000088 0,32.505621580000053 15.520446950000091 0,32.505573067000064 15.520439434000103 0,32.505579706000084 15.520399645000015 0))","POLYGON Z ((161.59 159.84 0, 159.14 145.1 0, 177.1 142.31 0, 175.47 132.54 0, 182.73 131.42 0, 179.55 112.36 0, 189.56 110.81 0, 183.46 74.26000000000001 0, 116.11 84.7 0, 122.21 121.24 0, 129.48 164.81 0, 161.59 159.84 0))",0.3779066243032705 +89,AOI_5_Khartoum_img130,48,"POLYGON ((578.23 88.8 0,575.94 67.93 0,567.0 68.84 0,569.29 89.71 0,578.23 88.8 0))","POLYGON ((32.506704615000054 15.520591446000045 0,32.506680490000115 15.520588989000066 0,32.506674311000083 15.520645326000082 0,32.506698436000086 15.52064778300006 0,32.506704615000054 15.520591446000045 0))","POLYGON Z ((578.23 88.8 0, 575.9400000000001 67.93000000000001 0, 567 68.84 0, 569.29 89.70999999999999 0, 578.23 88.8 0))",0.0 +90,AOI_5_Khartoum_img130,49,"POLYGON ((0.0 49.55 0,0.0 89.38 0,16.73 87.57 0,12.14 48.23 0,0.0 49.55 0))","POLYGON ((32.505143400015463 15.520697421738316 0,32.505176177000081 15.52070097600008 0,32.505188583000105 15.520594765000112 0,32.505143400015463 15.520589865493031 0,32.505143400015463 15.520697421738316 0))","POLYGON Z ((0 49.55 0, 0 89.38 0, 16.73 87.56999999999999 0, 12.14 48.23 0, 0 49.55 0))",0.3649197702930348 +91,AOI_5_Khartoum_img130,50,"POLYGON ((61.97 14.78 0,25.9 18.86 0,33.29 79.59 0,69.36 75.51 0,92.47 72.9 0,87.56 32.59 0,64.45 35.21 0,61.97 14.78 0))","POLYGON ((32.505310710000046 15.520791284000035 0,32.50531742400009 15.520736142999976 0,32.505379812000086 15.520743196000092 0,32.505393063000113 15.520634364000097 0,32.505330675000117 15.520627311000082 0,32.505233284000042 15.520616302000125 0,32.505213319000077 15.520780275000078 0,32.505310710000046 15.520791284000035 0))","POLYGON Z ((61.97 14.78 0, 25.9 18.86 0, 33.29 79.59 0, 69.36 75.51000000000001 0, 92.47 72.90000000000001 0, 87.56 32.59 0, 64.45 35.21 0, 61.97 14.78 0))",0.15072869548248846 +92,AOI_5_Khartoum_img130,51,"POLYGON ((579.72 28.37 0,577.57 13.74 0,512.96 22.56 0,515.11 37.19 0,520.72 36.43 0,524.53 62.29 0,539.08 60.31 0,539.91 65.96 0,584.36 59.89 0,583.53 54.24 0,579.72 28.37 0))","POLYGON ((32.506708652000057 15.52075459200011 0,32.50671892200009 15.520684755000055 0,32.506721167000066 15.520669485000049 0,32.506601156000059 15.520653101000079 0,32.506598911000012 15.520668371 0,32.506559625000101 15.520663008000064 0,32.506549355000082 15.52073284500003 0,32.506534195000043 15.520730775000086 0,32.506528386000099 15.520770284000015 0,32.506702843000021 15.520794101 0,32.506708652000057 15.52075459200011 0))","POLYGON Z ((579.72 28.37 0, 577.5700000000001 13.74 0, 512.96 22.56 0, 515.11 37.19 0, 520.72 36.43 0, 524.53 62.29 0, 539.08 60.31 0, 539.91 65.95999999999999 0, 584.36 59.89 0, 583.53 54.24 0, 579.72 28.37 0))",0.5739617594749962 +93,AOI_5_Khartoum_img130,52,"POLYGON ((85.78 29.36 0,83.93 3.15 0,64.76 4.4 0,66.61 30.61 0,85.78 29.36 0))","POLYGON ((32.505375013000076 15.520751939000089 0,32.505323241000035 15.520748549000132 0,32.50531825000008 15.52081931400004 0,32.505370021000026 15.520822704000112 0,32.505375013000076 15.520751939000089 0))","POLYGON Z ((85.78 29.36 0, 83.93000000000001 3.15 0, 64.76000000000001 4.4 0, 66.61 30.61 0, 85.78 29.36 0))",0.0 +94,AOI_5_Khartoum_img130,53,"POLYGON ((650.0 77.73 0,650 0 0,609.69 -0.0 0,586.11 2.81 0,596.81 83.01 0,632.63 80.2 0,636.14 104.78 0,650.0 102.8 0,650.0 96.42 0,647.38 78.09 0,650.0 77.73 0))","POLYGON ((32.506898400015778 15.520621331813562 0,32.506891331000084 15.520620350000067 0,32.506898400015778 15.520570867635795 0,32.506898400015778 15.520553632296748 0,32.50686098900011 15.520548288000072 0,32.506851507000071 15.520614661000087 0,32.506754792000052 15.52060707600009 0,32.506725888000069 15.520823612000129 0,32.506789575501678 15.520831199937557 0,32.506898400015778 15.520831199937557 0,32.506898400015778 15.520621331813562 0))","POLYGON Z ((650 77.73 0, 650 0 0, 609.6900000000001 -0 0, 586.11 2.81 0, 596.8099999999999 83.01000000000001 0, 632.63 80.2 0, 636.14 104.78 0, 650 102.8 0, 650 96.42 0, 647.38 78.09 0, 650 77.73 0))",0.28332572949840074 +95,AOI_5_Khartoum_img130,54,"POLYGON ((280.64 21.78 0,281.73 21.68 0,279.75 1.37 0,285.6 0.84 0,285.52 -0.0 0,209.13 -0.0 0,202.41 0.61 0,205.14 28.62 0,196.4 29.41 0,199.48 61.0 0,233.49 57.92 0,232.91 51.97 0,273.65 48.28 0,273.36 45.32 0,280.08 44.71 0,281.74 61.69 0,284.51 61.43 0,296.32 60.37 0,293.8 34.59 0,281.99 35.66 0,280.64 21.78 0))","POLYGON ((32.505901127000051 15.520772394000092 0,32.505904781000019 15.520734926000038 0,32.505936670000018 15.520737813000057 0,32.505943456000089 15.520668214000107 0,32.505911568000087 15.520665327000076 0,32.505904085000061 15.520664649000038 0,32.505899614000079 15.520710489000063 0,32.505881474000027 15.520708847000034 0,32.505882254 15.520700848000057 0,32.505772254000107 15.520690890000095 0,32.505773822000073 15.520674807999976 0,32.505682009000061 15.520666496000073 0,32.50567369000008 15.520751803000104 0,32.505697270000034 15.52075393800008 0,32.505689896000064 15.520829555999986 0,32.50570805494521 15.520831199937557 0,32.505914291058474 15.520831199937557 0,32.505914512000068 15.520828934000154 0,32.505898720000069 15.520827504000085 0,32.505904068000049 15.52077266000002 0,32.505901127000051 15.520772394000092 0))","POLYGON Z ((280.64 21.78 0, 281.73 21.68 0, 279.75 1.37 0, 285.6 0.84 0, 285.52 -0 0, 209.13 -0 0, 202.41 0.61 0, 205.14 28.62 0, 196.4 29.41 0, 199.48 61 0, 233.49 57.92 0, 232.91 51.97 0, 273.65 48.28 0, 273.36 45.32 0, 280.08 44.71 0, 281.74 61.69 0, 284.51 61.43 0, 296.32 60.37 0, 293.8 34.59 0, 281.99 35.66 0, 280.64 21.78 0))",0.07191542589009947 +96,AOI_5_Khartoum_img130,55,"POLYGON ((575.41 3.04 0,574.96 -0.0 0,507.87 -0.0 0,509.61 11.94 0,575.41 3.04 0))","POLYGON ((32.506696999000063 15.520823005000098 0,32.506519343000029 15.52079895500008 0,32.506514641427877 15.520831199937557 0,32.506695804072599 15.520831199937557 0,32.506696999000063 15.520823005000098 0))","POLYGON Z ((575.41 3.04 0, 574.96 -0 0, 507.87 -0 0, 509.61 11.94 0, 575.41 3.04 0))",0.0 +97,AOI_5_Khartoum_img130,56,"POLYGON ((22.28 252.28 0,0.0 254.86 0,0.0 287.08 0,0.19 288.48 0,74.26 278.97 0,72.15 262.11 0,67.93 229.1 0,20.17 234.72 0,22.28 252.28 0))","POLYGON ((32.505203558000083 15.520150050000073 0,32.505197869000021 15.520197459000061 0,32.505326823000068 15.520212630000072 0,32.505338201000029 15.520123500000032 0,32.505343890000091 15.520077987 0,32.505143903000068 15.520052299000081 0,32.505143400015463 15.520056081510267 0,32.505143400015463 15.520143085152903 0,32.505203558000083 15.520150050000073 0))","POLYGON Z ((22.28 252.28 0, 0 254.86 0, 0 287.08 0, 0.19 288.48 0, 74.26000000000001 278.97 0, 72.15000000000001 262.11 0, 67.93000000000001 229.1 0, 20.17 234.72 0, 22.28 252.28 0))",0.5729751692122188 +98,AOI_5_Khartoum_img1306,1,"POLYGON ((231.95 623.11 0,218.53 625.09 0,213.96 625.77 0,217.23 646.31 0,221.8 645.64 0,222.5 650.0 0,273.24 650.0 0,271.38 638.31 0,235.22 643.65 0,231.95 623.11 0))","POLYGON ((32.560174652000114 15.541963800000048 0,32.560183484000078 15.541908332000064 0,32.560281133000075 15.541922765000082 0,32.560286158723564 15.541891199941301 0,32.560149146802658 15.541891199941301 0,32.560147271000069 15.541902980000042 0,32.560134927000043 15.541901156000099 0,32.56012609400004 15.541956623000116 0,32.56013843900007 15.541958447000022 0,32.560174652000114 15.541963800000048 0))","POLYGON Z ((231.95 623.11 0, 218.53 625.09 0, 213.96 625.77 0, 217.23 646.3099999999999 0, 221.8 645.64 0, 222.5 650 0, 273.24 650 0, 271.38 638.3099999999999 0, 235.22 643.65 0, 231.95 623.11 0))",0.2886179984529358 +99,AOI_5_Khartoum_img1306,2,"POLYGON ((307.33 620.22 0,297.41 621.77 0,301.72 650.0 0,311.89 650.0 0,307.33 620.22 0))","POLYGON ((32.560378198 15.541971596000078 0,32.560390490074617 15.541891199941301 0,32.560363050688828 15.541891199941301 0,32.560351398000101 15.541967409000151 0,32.560378198 15.541971596000078 0))","POLYGON Z ((307.33 620.22 0, 297.41 621.77 0, 301.72 650 0, 311.89 650 0, 307.33 620.22 0))",0.0 +100,AOI_5_Khartoum_img1306,3,"POLYGON ((331.18 650.0 0,353.92 650.0 0,348.75 622.23 0,326.65 625.68 0,331.18 650.0 0))","POLYGON ((32.560442576077932 15.541891199941301 0,32.56043036800002 15.541956858000017 0,32.560490035000093 15.541966180000045 0,32.560503977192859 15.541891199941301 0,32.560442576077932 15.541891199941301 0))","POLYGON Z ((331.18 650 0, 353.92 650 0, 348.75 622.23 0, 326.65 625.6799999999999 0, 331.18 650 0))",0.2711400295189301 +101,AOI_5_Khartoum_img1306,4,"POLYGON ((281.17 632.19 0,279.27 613.05 0,267.64 614.13 0,269.54 633.26 0,281.17 632.19 0))","POLYGON ((32.560307563000066 15.54193929800012 0,32.56027614900006 15.541936400000115 0,32.560271015000083 15.541988060000017 0,32.560302429000046 15.541990958000099 0,32.560307563000066 15.54193929800012 0))","POLYGON Z ((281.17 632.1900000000001 0, 279.27 613.05 0, 267.64 614.13 0, 269.54 633.26 0, 281.17 632.1900000000001 0))",0.6193895825863917 +102,AOI_5_Khartoum_img1306,5,"POLYGON ((64.85 632.31 0,55.67 576.54 0,0.0 584.93 0,0.0 636.26 0,16.89 633.68 0,19.15 647.44 0,43.91 643.66 0,42.6 635.71 0,47.01 635.03 0,50.38 634.52 0,64.85 632.31 0))","POLYGON ((32.559723487000085 15.541938965000115 0,32.559684417000035 15.54193299700011 0,32.559675335000087 15.541931611000065 0,32.559663427000068 15.541929791000076 0,32.559666961000048 15.541908318000038 0,32.559600106000048 15.541898107000181 0,32.559593993000078 15.541935261000088 0,32.559548400025243 15.541928296858449 0,32.559548400025243 15.542066890148424 0,32.55969871000012 15.542089534000109 0,32.559723487000085 15.541938965000115 0))","POLYGON Z ((64.84999999999999 632.3099999999999 0, 55.67 576.54 0, 0 584.9299999999999 0, 0 636.26 0, 16.89 633.6799999999999 0, 19.15 647.4400000000001 0, 43.91 643.66 0, 42.6 635.71 0, 47.01 635.03 0, 50.38 634.52 0, 64.84999999999999 632.3099999999999 0))",0.5802352604058351 +103,AOI_5_Khartoum_img1306,6,"POLYGON ((399.51 618.17 0,450.43 611.69 0,445.5 575.73 0,394.58 582.21 0,395.92 591.98 0,379.68 594.05 0,383.27 620.24 0,399.51 618.17 0))","POLYGON ((32.560627083000085 15.541977136000117 0,32.560583235000031 15.541971556000087 0,32.560573542000043 15.542042269000039 0,32.560617388000011 15.54204784800009 0,32.560613773000057 15.542074227000063 0,32.560751238000087 15.542091719000057 0,32.560764548000087 15.541994628000099 0,32.560627083000085 15.541977136000117 0))","POLYGON Z ((399.51 618.17 0, 450.43 611.6900000000001 0, 445.5 575.73 0, 394.58 582.21 0, 395.92 591.98 0, 379.68 594.05 0, 383.27 620.24 0, 399.51 618.17 0))",0.49545296382982384 +104,AOI_5_Khartoum_img1306,7,"POLYGON ((143.79 624.11 0,133.58 566.78 0,65.72 578.01 0,75.93 635.34 0,143.79 624.11 0))","POLYGON ((32.559936637000092 15.541961107000137 0,32.559753418000106 15.541930780000094 0,32.559725846000092 15.542085576000121 0,32.55990906600011 15.542115902000122 0,32.559936637000092 15.541961107000137 0))","POLYGON Z ((143.79 624.11 0, 133.58 566.78 0, 65.72 578.01 0, 75.93000000000001 635.34 0, 143.79 624.11 0))",0.5694726775354048 +105,AOI_5_Khartoum_img1306,8,"POLYGON ((504.93 614.34 0,524.9 611.86 0,516.94 552.21 0,448.84 560.64 0,456.8 620.29 0,382.06 629.55 0,373.51 630.6 0,376.09 650.0 0,534.77 650.0 0,531.4 624.77 0,506.73 627.82 0,504.93 614.34 0))","POLYGON ((32.560911703000045 15.541987495000114 0,32.56091655900007 15.541951085000113 0,32.560983191000069 15.541959333 0,32.560992277643727 15.541891199941301 0,32.560563847749656 15.541891199941301 0,32.560556864000041 15.54194356799999 0,32.560579957000066 15.541946427000049 0,32.560781751000064 15.541971407000133 0,32.560760270000067 15.542132479000029 0,32.560944143000043 15.542155241000154 0,32.560965625000037 15.541994169000105 0,32.560911703000045 15.541987495000114 0))","POLYGON Z ((504.93 614.34 0, 524.9 611.86 0, 516.9400000000001 552.21 0, 448.84 560.64 0, 456.8 620.29 0, 382.06 629.55 0, 373.51 630.6 0, 376.09 650 0, 534.77 650 0, 531.4 624.77 0, 506.73 627.8200000000001 0, 504.93 614.34 0))",0.15003848471962594 +106,AOI_5_Khartoum_img1306,9,"POLYGON ((85.5 565.78 0,82.64 548.18 0,71.38 549.88 0,74.24 567.48 0,85.5 565.78 0))","POLYGON ((32.559779245000101 15.542118588000115 0,32.559748850000105 15.542114002000023 0,32.55974112500008 15.542161517000139 0,32.55977152 15.542166104000083 0,32.559779245000101 15.542118588000115 0))","POLYGON Z ((85.5 565.78 0, 82.64 548.1799999999999 0, 71.38 549.88 0, 74.23999999999999 567.48 0, 85.5 565.78 0))",0.0 +107,AOI_5_Khartoum_img1306,10,"POLYGON ((116.89 556.06 0,113.36 530.19 0,98.48 532.07 0,102.0 557.94 0,116.89 556.06 0))","POLYGON ((32.559863992000025 15.542144847000049 0,32.559823810000054 15.542139770000073 0,32.559814303000095 15.542209612000075 0,32.559854485000102 15.542214689000089 0,32.559863992000025 15.542144847000049 0))","POLYGON Z ((116.89 556.0599999999999 0, 113.36 530.1900000000001 0, 98.48 532.0700000000001 0, 102 557.9400000000001 0, 116.89 556.0599999999999 0))",0.0 +108,AOI_5_Khartoum_img1306,11,"POLYGON ((96.89 545.12 0,94.3 529.95 0,79.56 532.28 0,82.14 547.45 0,96.89 545.12 0))","POLYGON ((32.559809993000044 15.542174376000094 0,32.559770182000058 15.542168079000117 0,32.559763202000056 15.54220903900007 0,32.559803014000074 15.542215336000062 0,32.559809993000044 15.542174376000094 0))","POLYGON Z ((96.89 545.12 0, 94.3 529.95 0, 79.56 532.28 0, 82.14 547.45 0, 96.89 545.12 0))",0.0 +109,AOI_5_Khartoum_img1306,12,"POLYGON ((43.87 529.32 0,48.27 528.65 0,52.7 527.98 0,50.51 514.68 0,46.08 515.35 0,34.94 517.05 0,35.16 518.42 0,20.11 520.71 0,22.64 536.08 0,0.0 539.54 0,0.0 572.84 0,50.34 565.15 0,49.45 559.73 0,53.29 559.14 0,58.24 558.39 0,56.67 548.84 0,51.72 549.59 0,47.32 550.27 0,43.87 529.32 0))","POLYGON ((32.55966685100001 15.542217025000038 0,32.55967615600003 15.542160481000034 0,32.559688031000064 15.542162295000047 0,32.559701400000044 15.542164337000029 0,32.55970564200004 15.54213855300002 0,32.559692274000099 15.54213651100005 0,32.559681917000027 15.542134929000065 0,32.55968432500007 15.542120293000087 0,32.559548400025243 15.542099531776843 0,32.559548400025243 15.542189451260317 0,32.559609530000046 15.54219878800002 0,32.559602704000042 15.542240270000102 0,32.559643341000026 15.542246477000026 0,32.559642735000111 15.542250152000099 0,32.55967281800006 15.542254747000117 0,32.559684778000118 15.542256574000062 0,32.559690686000096 15.542220665000054 0,32.55967872600008 15.542218839000038 0,32.55966685100001 15.542217025000038 0))","POLYGON Z ((43.87 529.3200000000001 0, 48.27 528.65 0, 52.7 527.98 0, 50.51 514.6799999999999 0, 46.08 515.35 0, 34.94 517.05 0, 35.16 518.42 0, 20.11 520.71 0, 22.64 536.08 0, 0 539.54 0, 0 572.84 0, 50.34 565.15 0, 49.45 559.73 0, 53.29 559.14 0, 58.24 558.39 0, 56.67 548.84 0, 51.72 549.59 0, 47.32 550.27 0, 43.87 529.3200000000001 0))",0.6436925875232891 +110,AOI_5_Khartoum_img1306,13,"POLYGON ((404.67 511.82 0,374.48 516.04 0,375.6 523.46 0,380.95 559.01 0,440.95 550.63 0,435.6 515.08 0,405.79 519.24 0,404.67 511.82 0))","POLYGON ((32.56064102000007 15.542264296000047 0,32.560644039000081 15.542244245000067 0,32.560724522000037 15.542255492000091 0,32.56073897400006 15.542159500000079 0,32.560576971000081 15.542136862000124 0,32.560562520000083 15.54223285400011 0,32.560559502000039 15.542252905000066 0,32.56064102000007 15.542264296000047 0))","POLYGON Z ((404.67 511.82 0, 374.48 516.04 0, 375.6 523.46 0, 380.95 559.01 0, 440.95 550.63 0, 435.6 515.08 0, 405.79 519.24 0, 404.67 511.82 0))",0.5027818359239599 +111,AOI_5_Khartoum_img1306,14,"POLYGON ((33.64 509.17 0,44.78 507.46 0,36.55 457.43 0,0.0 463.01 0,0.0 514.3 0,33.64 509.17 0))","POLYGON ((32.559639231000091 15.542271451000069 0,32.559548400025243 15.542257577675533 0,32.559548400025243 15.542396060607647 0,32.559647084000098 15.542411133 0,32.559669313000015 15.542276046000113 0,32.559639231000091 15.542271451000069 0))","POLYGON Z ((33.64 509.17 0, 44.78 507.46 0, 36.55 457.43 0, 0 463.01 0, 0 514.3 0, 33.64 509.17 0))",0.7512782849065058 +112,AOI_5_Khartoum_img1306,15,"POLYGON ((71.89 522.8 0,69.73 509.68 0,68.94 504.88 0,100.76 500.02 0,91.81 445.63 0,43.6 452.99 0,44.14 456.27 0,52.38 506.3 0,53.67 514.19 0,55.5 525.3 0,71.89 522.8 0))","POLYGON ((32.559742514000064 15.542234641000102 0,32.559698258000033 15.542227881000024 0,32.559693322000065 15.542257879000044 0,32.559689817000049 15.542279178000065 0,32.55966758800006 15.542414265000117 0,32.559666130000068 15.542423123000091 0,32.559796289000069 15.542443004000086 0,32.559820453000086 15.54229615600007 0,32.559734550000044 15.542283035000091 0,32.559736683000025 15.542270076000023 0,32.559742514000064 15.542234641000102 0))","POLYGON Z ((71.89 522.8 0, 69.73 509.68 0, 68.94 504.88 0, 100.76 500.02 0, 91.81 445.63 0, 43.6 452.99 0, 44.14 456.27 0, 52.38 506.3 0, 53.67 514.1900000000001 0, 55.5 525.3 0, 71.89 522.8 0))",0.6675131354262643 +113,AOI_5_Khartoum_img1306,16,"POLYGON ((122.53 451.76 0,121.37 440.86 0,111.07 441.87 0,112.23 452.78 0,122.53 451.76 0))","POLYGON ((32.559879234000093 15.542426447000066 0,32.559851426000094 15.542423699999986 0,32.559848291000058 15.542453141000063 0,32.559876100000068 15.542455889000033 0,32.559879234000093 15.542426447000066 0))","POLYGON Z ((122.53 451.76 0, 121.37 440.86 0, 111.07 441.87 0, 112.23 452.78 0, 122.53 451.76 0))",0.0 +114,AOI_5_Khartoum_img1306,17,"POLYGON ((456.67 487.95 0,504.45 481.21 0,497.4 434.88 0,430.14 444.38 0,437.19 490.71 0,438.91 501.97 0,443.0 536.1 0,444.3 546.97 0,498.21 540.97 0,496.91 530.11 0,512.4 528.38 0,508.31 494.25 0,458.52 499.79 0,456.67 487.95 0))","POLYGON ((32.560781407000029 15.542328723000045 0,32.560786415000095 15.542296776000072 0,32.560920847000055 15.542311731000041 0,32.56093189200007 15.542219566000069 0,32.560890061000109 15.542214913000169 0,32.560893577000051 15.542185574000047 0,32.5607480090001 15.542169381000072 0,32.560744492000019 15.542198719000087 0,32.560733446000071 15.542290884000185 0,32.560728819000055 15.542321296000107 0,32.560709786000103 15.542446377000042 0,32.560891374000065 15.54247202300016 0,32.56091040600009 15.542346942000098 0,32.560781407000029 15.542328723000045 0))","POLYGON Z ((456.67 487.95 0, 504.45 481.21 0, 497.4 434.88 0, 430.14 444.38 0, 437.19 490.71 0, 438.91 501.97 0, 443 536.1 0, 444.3 546.97 0, 498.21 540.97 0, 496.91 530.11 0, 512.4 528.38 0, 508.31 494.25 0, 458.52 499.79 0, 456.67 487.95 0))",0.46275584946810755 +115,AOI_5_Khartoum_img1306,18,"POLYGON ((68.46 325.05 0,0.0 335.5 0,0.0 451.93 0,48.67 444.49 0,109.75 435.16 0,94.05 339.8 0,71.46 343.26 0,68.46 325.05 0))","POLYGON ((32.559733251000097 15.542768575000107 0,32.559741342000102 15.542719410000101 0,32.55980234400004 15.542728728000085 0,32.55984471200005 15.542471257000065 0,32.559679803000087 15.542446069000063 0,32.559548400025243 15.54242599906927 0,32.559548400025243 15.542740341026278 0,32.559733251000097 15.542768575000107 0))","POLYGON Z ((68.45999999999999 325.05 0, 0 335.5 0, 0 451.93 0, 48.67 444.49 0, 109.75 435.16 0, 94.05 339.8 0, 71.45999999999999 343.26 0, 68.45999999999999 325.05 0))",0.8779767395302545 +116,AOI_5_Khartoum_img1306,19,"POLYGON ((90.41 336.71 0,87.4 322.97 0,95.08 321.41 0,92.71 310.58 0,73.7 314.45 0,76.07 325.28 0,79.08 339.01 0,90.41 336.71 0))","POLYGON ((32.559792510000122 15.542737092000046 0,32.559761912000077 15.542730869000074 0,32.559753788000059 15.542767945000097 0,32.559747379000058 15.542797192000039 0,32.559798718000046 15.542807634000058 0,32.559805126000057 15.542778387000077 0,32.5597843850001 15.54277416900006 0,32.559792510000122 15.542737092000046 0))","POLYGON Z ((90.41 336.71 0, 87.40000000000001 322.97 0, 95.08 321.41 0, 92.70999999999999 310.58 0, 73.7 314.45 0, 76.06999999999999 325.28 0, 79.08 339.01 0, 90.41 336.71 0))",0.0 +117,AOI_5_Khartoum_img1306,20,"POLYGON ((471.72 282.72 0,469.69 268.58 0,468.29 258.75 0,451.83 260.94 0,453.23 270.76 0,246.62 298.22 0,245.09 287.53 0,231.56 289.32 0,233.09 300.02 0,157.15 310.11 0,156.56 305.99 0,119.21 310.96 0,119.8 315.07 0,123.19 338.73 0,160.48 333.78 0,162.07 344.89 0,169.74 343.87 0,171.5 356.22 0,163.84 357.23 0,135.65 360.98 0,140.83 397.11 0,131.72 398.32 0,135.58 425.26 0,137.95 441.84 0,158.31 439.14 0,159.91 450.31 0,208.85 443.8 0,207.25 432.63 0,206.29 425.91 0,200.61 386.26 0,208.88 385.16 0,206.21 366.5 0,216.58 365.12 0,272.77 357.65 0,274.31 368.38 0,218.11 375.85 0,219.25 383.79 0,230.52 382.29 0,233.7 404.5 0,247.77 402.63 0,250.27 420.07 0,277.65 416.43 0,279.04 426.18 0,346.37 417.23 0,344.35 403.14 0,412.86 394.04 0,412.07 388.52 0,485.47 378.76 0,483.01 361.61 0,457.12 365.05 0,456.37 359.77 0,418.55 364.8 0,416.9 353.28 0,454.72 348.26 0,453.72 341.24 0,479.6 337.8 0,477.99 326.49 0,471.72 282.72 0))","POLYGON ((32.560822042000048 15.542882843000086 0,32.560838961000066 15.542764667000039 0,32.560843330000118 15.54273415000007 0,32.560773433000044 15.542724861000103 0,32.560776146000094 15.542705911000091 0,32.560674034000087 15.542692342000079 0,32.560678486000072 15.542661247000135 0,32.560780598000044 15.542674817000011 0,32.560782637000067 15.542660571000118 0,32.56085253400007 15.542669860000085 0,32.560859166000114 15.542623543000049 0,32.560660996000031 15.542597209000089 0,32.560663132000066 15.542582291000073 0,32.560478147000069 15.542557709000048 0,32.560483592000061 15.542519678000113 0,32.560301817000081 15.542495523000049 0,32.560298049000025 15.542521840000134 0,32.560224133000162 15.542512018000066 0,32.560217391000045 15.542559110000099 0,32.560179382000086 15.542554060000054 0,32.560170796000072 15.542614022000105 0,32.560140376000092 15.542609979000073 0,32.560137308000044 15.542631403000078 0,32.560289028000042 15.542651565000018 0,32.560284881000058 15.542680539000026 0,32.56013316100006 15.542660377000113 0,32.560105171000046 15.5426566570001 0,32.560112386000078 15.542606259000047 0,32.560090054000064 15.542603292000084 0,32.560105381000028 15.542496237000098 0,32.560107980000076 15.542478086000139 0,32.560112296000042 15.542447934000053 0,32.55998016200008 15.542430376000102 0,32.55997584600005 15.542460528000074 0,32.559920871000038 15.542453222000102 0,32.559914459000076 15.542498003000045 0,32.559904049000075 15.54257072400004 0,32.55992863500007 15.542573991000047 0,32.559914667000029 15.542671556000132 0,32.559990763000052 15.542681668000034 0,32.560011456000034 15.542684418000063 0,32.560006685000126 15.542717744000051 0,32.559985992000087 15.542714994000148 0,32.559981697000055 15.542744997000115 0,32.559881014000027 15.542731617000074 0,32.559871868000094 15.542795505000123 0,32.559870277000101 15.54280661900005 0,32.559971108000106 15.542820019000066 0,32.559972699000056 15.542808904 0,32.560177733000117 15.542836151000083 0,32.560173600000056 15.542865025000017 0,32.560210140000059 15.542869881000069 0,32.560214274000018 15.54284100600009 0,32.560772125000064 15.542915137000065 0,32.560768329000034 15.542941657000069 0,32.560812777000031 15.542947563000046 0,32.560816573000068 15.542921044 0,32.560822042000048 15.542882843000086 0))","POLYGON Z ((471.72 282.72 0, 469.69 268.58 0, 468.29 258.75 0, 451.83 260.94 0, 453.23 270.76 0, 246.62 298.22 0, 245.09 287.53 0, 231.56 289.32 0, 233.09 300.02 0, 157.15 310.11 0, 156.56 305.99 0, 119.21 310.96 0, 119.8 315.07 0, 123.19 338.73 0, 160.48 333.78 0, 162.07 344.89 0, 169.74 343.87 0, 171.5 356.22 0, 163.84 357.23 0, 135.65 360.98 0, 140.83 397.11 0, 131.72 398.32 0, 135.58 425.26 0, 137.95 441.84 0, 158.31 439.14 0, 159.91 450.31 0, 208.85 443.8 0, 207.25 432.63 0, 206.29 425.91 0, 200.61 386.26 0, 208.88 385.16 0, 206.21 366.5 0, 216.58 365.12 0, 272.77 357.65 0, 274.31 368.38 0, 218.11 375.85 0, 219.25 383.79 0, 230.52 382.29 0, 233.7 404.5 0, 247.77 402.63 0, 250.27 420.07 0, 277.65 416.43 0, 279.04 426.18 0, 346.37 417.23 0, 344.35 403.14 0, 412.86 394.04 0, 412.07 388.52 0, 485.47 378.76 0, 483.01 361.61 0, 457.12 365.05 0, 456.37 359.77 0, 418.55 364.8 0, 416.9 353.28 0, 454.72 348.26 0, 453.72 341.24 0, 479.6 337.8 0, 477.99 326.49 0, 471.72 282.72 0))",0.08647114993412355 +118,AOI_5_Khartoum_img1306,21,"POLYGON ((612.75 244.25 0,499.14 259.25 0,501.11 273.04 0,494.15 273.96 0,500.58 319.11 0,507.53 318.2 0,562.74 310.9 0,564.45 322.91 0,518.49 328.98 0,525.92 381.17 0,570.75 375.25 0,571.8 382.64 0,587.32 380.59 0,587.68 383.14 0,589.1 393.09 0,528.75 401.06 0,534.8 443.61 0,527.26 444.6 0,528.6 453.98 0,536.14 452.99 0,537.72 464.08 0,525.37 465.71 0,530.3 500.34 0,530.75 503.51 0,539.26 502.39 0,560.26 650.0 0,650 650 0,650.0 626.45 0,636.15 628.28 0,634.68 617.95 0,650.0 615.93 0,650.0 510.14 0,646.42 485.01 0,650.0 484.53 0,650.0 461.11 0,635.85 462.98 0,632.64 440.41 0,634.63 440.15 0,633.09 429.26 0,650.0 427.03 0,650.0 396.4 0,649.25 391.14 0,650.0 391.04 0,650.0 348.32 0,646.32 322.47 0,622.59 325.6 0,621.4 317.21 0,593.9 320.84 0,592.74 312.68 0,612.0 310.14 0,611.66 307.75 0,611.76 307.74 0,610.11 296.14 0,618.25 295.06 0,641.98 291.93 0,637.68 261.71 0,615.65 264.62 0,612.75 244.25 0))","POLYGON ((32.561202819000115 15.542986733000136 0,32.561210646000042 15.542931721000093 0,32.561270132000047 15.542939577000066 0,32.561281740000055 15.542857987000033 0,32.561217671000065 15.542849526000076 0,32.561195707000046 15.542846626 0,32.56120016200002 15.542815307000076 0,32.561199879000078 15.542815269000055 0,32.561200796000044 15.542808819000092 0,32.561148790000075 15.542801951000143 0,32.561151925000104 15.542779922000053 0,32.561226179000066 15.542789727000144 0,32.561229401000077 15.54276708000002 0,32.561293470000066 15.542775541000017 0,32.561303400025558 15.542705748243776 0,32.561303400025558 15.54259038864968 0,32.561301381000114 15.542590122000098 0,32.561303400025558 15.542575930939897 0,32.561303400025558 15.542493224901211 0,32.561257734000066 15.542487195000023 0,32.561261914000063 15.54245780500009 0,32.5612565220001 15.542457093000115 0,32.5612651920001 15.54239615300007 0,32.561303400025558 15.542401198776028 0,32.561303400025558 15.54233795909229 0,32.561293744000068 15.542336684000103 0,32.561303400025558 15.542268816303114 0,32.561303400025558 15.541983186729858 0,32.561262027000097 15.54197772300007 0,32.561265994000074 15.541949837000136 0,32.561303400025558 15.541954776847133 0,32.561303400025558 15.541891199941301 0,32.561061111597326 15.541891199941301 0,32.561004408000031 15.542289751000069 0,32.560981424000019 15.542286715000047 0,32.560980205000078 15.542295278000037 0,32.56096690300005 15.54238878000006 0,32.561000232000076 15.542393180999982 0,32.560995971000068 15.542423131000088 0,32.560975610000071 15.542420443000012 0,32.560972006000036 15.542445772000097 0,32.56099236700004 15.542448461000115 0,32.560976023 15.542563342000117 0,32.561138965000055 15.542584860000105 0,32.56113514500003 15.542611709 0,32.561134164000045 15.54261860300006 0,32.561092269000021 15.542613070000142 0,32.561089430000052 15.542633026000097 0,32.560968383000038 15.542617040000048 0,32.560948334000081 15.542757952000025 0,32.561072408000015 15.542774337000063 0,32.561067796000017 15.542806757000118 0,32.560918732000047 15.542787072000102 0,32.560899953000032 15.542784592000119 0,32.560882608000057 15.542906504000022 0,32.560901387000072 15.542908984000082 0,32.560896089000025 15.542946226000046 0,32.561202819000115 15.542986733000136 0))","POLYGON Z ((612.75 244.25 0, 499.14 259.25 0, 501.11 273.04 0, 494.15 273.96 0, 500.58 319.11 0, 507.53 318.2 0, 562.74 310.9 0, 564.45 322.91 0, 518.49 328.98 0, 525.92 381.17 0, 570.75 375.25 0, 571.8 382.64 0, 587.3200000000001 380.59 0, 587.6799999999999 383.14 0, 589.1 393.09 0, 528.75 401.06 0, 534.8 443.61 0, 527.26 444.6 0, 528.6 453.98 0, 536.14 452.99 0, 537.72 464.08 0, 525.37 465.71 0, 530.3 500.34 0, 530.75 503.51 0, 539.26 502.39 0, 560.26 650 0, 650 650 0, 650 626.45 0, 636.15 628.28 0, 634.6799999999999 617.95 0, 650 615.9299999999999 0, 650 510.14 0, 646.42 485.01 0, 650 484.53 0, 650 461.11 0, 635.85 462.98 0, 632.64 440.41 0, 634.63 440.15 0, 633.09 429.26 0, 650 427.03 0, 650 396.4 0, 649.25 391.14 0, 650 391.04 0, 650 348.32 0, 646.3200000000001 322.47 0, 622.59 325.6 0, 621.4 317.21 0, 593.9 320.84 0, 592.74 312.68 0, 612 310.14 0, 611.66 307.75 0, 611.76 307.74 0, 610.11 296.14 0, 618.25 295.06 0, 641.98 291.93 0, 637.6799999999999 261.71 0, 615.65 264.62 0, 612.75 244.25 0))",0.0918037052783937 +119,AOI_5_Khartoum_img1306,22,"POLYGON ((30.61 236.48 0,29.12 221.6 0,2.71 224.05 0,4.2 238.92 0,10.33 238.36 0,11.28 247.95 0,31.56 246.08 0,30.61 236.48 0))","POLYGON ((32.559631035000073 15.543007698000086 0,32.559633616000049 15.542981790000121 0,32.559578862000045 15.542976726000129 0,32.559576281000027 15.5430026330001 0,32.559559731000078 15.543001103000098 0,32.559555728000092 15.543041274000068 0,32.559627032000051 15.543047868000089 0,32.559631035000073 15.543007698000086 0))","POLYGON Z ((30.61 236.48 0, 29.12 221.6 0, 2.71 224.05 0, 4.2 238.92 0, 10.33 238.36 0, 11.28 247.95 0, 31.56 246.08 0, 30.61 236.48 0))",0.0 +120,AOI_5_Khartoum_img1306,23,"POLYGON ((465.56 149.51 0,461.1 122.32 0,460.28 117.33 0,442.81 119.99 0,443.63 124.98 0,338.56 140.98 0,343.02 168.17 0,465.56 149.51 0))","POLYGON ((32.560805408000107 15.543242514000086 0,32.560474557000099 15.543192138000084 0,32.560462513000047 15.543265559000167 0,32.560746203000051 15.543308755000059 0,32.560743993000088 15.543322230000101 0,32.560791152000043 15.543329411000164 0,32.560793363000101 15.543315936000123 0,32.560805408000107 15.543242514000086 0))","POLYGON Z ((465.56 149.51 0, 461.1 122.32 0, 460.28 117.33 0, 442.81 119.99 0, 443.63 124.98 0, 338.56 140.98 0, 343.02 168.17 0, 465.56 149.51 0))",0.7835430151409475 +121,AOI_5_Khartoum_img1306,24,"POLYGON ((620.65 139.12 0,616.11 110.73 0,594.14 113.99 0,598.67 142.38 0,620.65 139.12 0))","POLYGON ((32.561224144000029 15.543270568000048 0,32.561164819000098 15.543261775000143 0,32.561152580000055 15.54333842200011 0,32.561211904000061 15.543347216 0,32.561224144000029 15.543270568000048 0))","POLYGON Z ((620.65 139.12 0, 616.11 110.73 0, 594.14 113.99 0, 598.67 142.38 0, 620.65 139.12 0))",0.0 +122,AOI_5_Khartoum_img1306,25,"POLYGON ((15.24 108.95 0,0.0 110.31 0,0.0 176.6 0,1.56 192.77 0,4.41 222.25 0,26.0 220.32 0,26.06 221.0 0,32.56 220.42 0,31.69 211.47 0,58.61 209.05 0,55.73 154.88 0,55.54 152.89 0,61.16 152.39 0,58.08 120.52 0,16.72 124.23 0,15.24 108.95 0))","POLYGON ((32.559589554000112 15.543352045000127 0,32.559593538000136 15.54331078000013 0,32.559705221000051 15.543320789000122 0,32.559713528000039 15.543234752000032 0,32.559698359000031 15.543233393 0,32.559698877000102 15.543228030000051 0,32.559706655000056 15.543081758000092 0,32.559633968000099 15.543075244000065 0,32.559636302000051 15.543051074000047 0,32.559618766000028 15.543049502000082 0,32.559618588000042 15.543051340000051 0,32.559560302000108 15.543046116000054 0,32.559552617000115 15.543125712000119 0,32.559548400025243 15.543169389249563 0,32.559548400025243 15.543348356732697 0,32.559589554000112 15.543352045000127 0))","POLYGON Z ((15.24 108.95 0, 0 110.31 0, 0 176.6 0, 1.56 192.77 0, 4.41 222.25 0, 26 220.32 0, 26.06 221 0, 32.56 220.42 0, 31.69 211.47 0, 58.61 209.05 0, 55.73 154.88 0, 55.54 152.89 0, 61.16 152.39 0, 58.08 120.52 0, 16.72 124.23 0, 15.24 108.95 0))",0.42403484146737436 +123,AOI_5_Khartoum_img1306,26,"POLYGON ((93.32 144.58 0,137.19 139.91 0,136.27 131.94 0,131.57 90.99 0,59.16 98.7 0,63.86 139.65 0,71.95 210.2 0,100.5 207.16 0,144.8 202.45 0,139.64 157.5 0,95.34 162.22 0,93.32 144.58 0))","POLYGON ((32.559800364000061 15.543255833000039 0,32.559805828000059 15.543208208000097 0,32.559925429000074 15.543220944000089 0,32.559939350000036 15.543099597000026 0,32.559819749000091 15.543086861000072 0,32.559742667000023 15.543078653000135 0,32.559720815000034 15.543269134000115 0,32.559708128000103 15.543379717000104 0,32.559903649000056 15.54340053700013 0,32.559916336000093 15.543289954000116 0,32.559918804000077 15.543268445000079 0,32.559800364000061 15.543255833000039 0))","POLYGON Z ((93.31999999999999 144.58 0, 137.19 139.91 0, 136.27 131.94 0, 131.57 90.98999999999999 0, 59.16 98.7 0, 63.86 139.65 0, 71.95 210.2 0, 100.5 207.16 0, 144.8 202.45 0, 139.64 157.5 0, 95.34 162.22 0, 93.31999999999999 144.58 0))",0.35351595595968255 +124,AOI_5_Khartoum_img1306,27,"POLYGON ((578.11 117.5 0,562.79 86.46 0,539.69 97.04 0,555.01 128.09 0,578.11 117.5 0))","POLYGON ((32.561109303000016 15.543328946000017 0,32.561046937000086 15.543300369000049 0,32.561005560000055 15.543384184000047 0,32.561067928000057 15.543412761000129 0,32.561109303000016 15.543328946000017 0))","POLYGON Z ((578.11 117.5 0, 562.79 86.45999999999999 0, 539.6900000000001 97.04000000000001 0, 555.01 128.09 0, 578.11 117.5 0))",0.7408359542328506 +125,AOI_5_Khartoum_img1306,28,"POLYGON ((236.43 62.69 0,234.26 48.74 0,184.96 55.86 0,187.13 69.81 0,236.43 62.69 0))","POLYGON ((32.560186759000054 15.543476934000152 0,32.560053658000086 15.543457700000015 0,32.560047793000038 15.543495368000103 0,32.560180893000101 15.54351460300013 0,32.560186759000054 15.543476934000152 0))","POLYGON Z ((236.43 62.69 0, 234.26 48.74 0, 184.96 55.86 0, 187.13 69.81 0, 236.43 62.69 0))",0.6846344975947855 +126,AOI_5_Khartoum_img1306,29,"POLYGON ((259.52 110.85 0,278.27 108.45 0,269.12 42.29 0,250.37 44.7 0,257.43 95.72 0,242.3 97.67 0,244.39 112.8 0,259.52 110.85 0))","POLYGON ((32.560249105000054 15.543346892000109 0,32.560208253000077 15.543341644000066 0,32.560202599000029 15.543382502 0,32.560243450000016 15.543387748999976 0,32.560224386000094 15.543525502 0,32.560275015000045 15.543532005000058 0,32.560299733000072 15.543353396000102 0,32.560249105000054 15.543346892000109 0))","POLYGON Z ((259.52 110.85 0, 278.27 108.45 0, 269.12 42.29 0, 250.37 44.7 0, 257.43 95.72 0, 242.3 97.67 0, 244.39 112.8 0, 259.52 110.85 0))",0.24289953500376474 +127,AOI_5_Khartoum_img1306,30,"POLYGON ((445.6 29.06 0,323.34 45.35 0,325.71 61.84 0,332.35 60.96 0,334.33 74.78 0,376.02 69.23 0,375.74 67.28 0,440.53 58.65 0,454.18 56.83 0,452.47 44.96 0,447.97 45.56 0,445.6 29.06 0))","POLYGON ((32.560751515000078 15.543567727000065 0,32.560757906000077 15.543523195000024 0,32.560770081000065 15.543524816000135 0,32.560774680000094 15.543492765000044 0,32.560737825000025 15.543487857000125 0,32.560562896000086 15.543464557000016 0,32.560563653000038 15.543459279000064 0,32.560451094000022 15.543444287000117 0,32.560445737000066 15.543481617000101 0,32.560427816000072 15.543479230000022 0,32.560421425000072 15.543523762000063 0,32.560751515000078 15.543567727000065 0))","POLYGON Z ((445.6 29.06 0, 323.34 45.35 0, 325.71 61.84 0, 332.35 60.96 0, 334.33 74.78 0, 376.02 69.23 0, 375.74 67.28 0, 440.53 58.65 0, 454.18 56.83 0, 452.47 44.96 0, 447.97 45.56 0, 445.6 29.06 0))",0.7972590263933044 +128,AOI_5_Khartoum_img1306,31,"POLYGON ((565.45 38.54 0,560.34 8.96 0,484.28 21.18 0,489.4 50.75 0,565.45 38.54 0))","POLYGON ((32.561075124000084 15.543542150000084 0,32.560869768000011 15.543509175000118 0,32.560855955000058 15.543589026000035 0,32.561061311000095 15.543622 0,32.561075124000084 15.543542150000084 0))","POLYGON Z ((565.45 38.54 0, 560.34 8.960000000000001 0, 484.28 21.18 0, 489.4 50.75 0, 565.45 38.54 0))",0.0 +129,AOI_5_Khartoum_img1306,32,"POLYGON ((105.67 -0.0 0,0 0 0,0.0 39.91 0,3.22 39.38 0,7.08 38.74 0,11.78 65.1 0,50.24 58.74 0,46.27 36.45 0,110.28 25.86 0,105.67 -0.0 0))","POLYGON ((32.559833703199736 15.543646199941612 0,32.559846144000055 15.543576370000038 0,32.559673321000055 15.543547791000035 0,32.559684046000022 15.543487593000041 0,32.559580201000053 15.54347042100008 0,32.559567519000097 15.543541598000049 0,32.559557101000095 15.543539875000031 0,32.559548400025243 15.543538436219842 0,32.559548400025243 15.543646199941612 0,32.559833703199736 15.543646199941612 0))","POLYGON Z ((105.67 -0 0, 0 0 0, 0 39.91 0, 3.22 39.38 0, 7.08 38.74 0, 11.78 65.09999999999999 0, 50.24 58.74 0, 46.27 36.45 0, 110.28 25.86 0, 105.67 -0 0))",0.622328484127778 +130,AOI_5_Khartoum_img1306,33,"POLYGON ((583.37 23.65 0,598.17 22.94 0,597.67 12.36 0,582.86 13.06 0,583.37 23.65 0))","POLYGON ((32.561123486000056 15.54358235600005 0,32.561122125000047 15.543610935000025 0,32.561162100000033 15.543612839000055 0,32.561163460000081 15.543584259000072 0,32.561123486000056 15.54358235600005 0))","POLYGON Z ((583.37 23.65 0, 598.17 22.94 0, 597.67 12.36 0, 582.86 13.06 0, 583.37 23.65 0))",0.45217615752342644 +131,AOI_5_Khartoum_img1301,1,"POLYGON ((134.59 647.52 0,132.74 635.82 0,123.11 637.23 0,124.96 648.93 0,134.59 647.52 0))","POLYGON ((32.559911783000103 15.53312289300003 0,32.559885786000066 15.533119081000107 0,32.559880795000019 15.533150683000049 0,32.559906792000056 15.533154495000085 0,32.559911783000103 15.53312289300003 0))","POLYGON Z ((134.59 647.52 0, 132.74 635.8200000000001 0, 123.11 637.23 0, 124.96 648.9299999999999 0, 134.59 647.52 0))",0.6025199178177894 +132,AOI_5_Khartoum_img1301,2,"POLYGON ((90.19 628.48 0,59.2 631.12 0,60.93 650.0 0,111.14 650.0 0,110.38 641.7 0,91.55 643.3 0,90.19 628.48 0))","POLYGON ((32.559791914000101 15.533174295000054 0,32.559795584000014 15.533134286000109 0,32.559846416000035 15.533138614000093 0,32.559848471492401 15.533116199939741 0,32.559712914923942 15.533116199939741 0,32.559708240000063 15.533167171000109 0,32.559791914000101 15.533174295000054 0))","POLYGON Z ((90.19 628.48 0, 59.2 631.12 0, 60.93 650 0, 111.14 650 0, 110.38 641.7 0, 91.55 643.3 0, 90.19 628.48 0))",0.0 +133,AOI_5_Khartoum_img1301,3,"POLYGON ((110.61 554.25 0,101.45 550.72 0,97.76 559.62 0,106.91 563.15 0,110.61 554.25 0))","POLYGON ((32.5598470360001 15.533374738000141 0,32.559837053000074 15.533350694000049 0,32.559812343000019 15.533360217000064 0,32.559822326000109 15.53338426100008 0,32.5598470360001 15.533374738000141 0))","POLYGON Z ((110.61 554.25 0, 101.45 550.72 0, 97.76000000000001 559.62 0, 106.91 563.15 0, 110.61 554.25 0))",0.0 +134,AOI_5_Khartoum_img1301,4,"POLYGON ((4.46 528.39 0,2.49 528.55 0,0.58 529.05 0,0.0 529.31 0,0.0 549.35 0,0.58 549.61 0,2.49 550.1 0,4.46 550.27 0,6.43 550.1 0,8.35 549.61 0,10.14 548.8 0,11.76 547.71 0,13.16 546.36 0,14.3 544.8 0,15.13 543.07 0,15.64 541.23 0,15.82 539.33 0,15.64 537.43 0,15.13 535.59 0,14.3 533.86 0,13.16 532.3 0,11.76 530.95 0,10.14 529.85 0,8.34 529.05 0,6.43 528.55 0,4.46 528.39 0))","POLYGON ((32.559560445000038 15.533444552000118 0,32.55956576905934 15.533444103257006 0,32.559570931356674 15.533442770586618 0,32.559575775038276 15.533440594481423 0,32.559580152931311 15.533437641061282 0,32.559583932015713 15.533434000064302 0,32.559586997465921 15.53342978212034 0,32.559589256139787 15.533425115389424 0,32.55959063940864 15.533420141667822 0,32.559591105242575 15.533415012079498 0,32.559590639487432 15.533409882484406 0,32.55958925629497 15.533404908742716 0,32.559586997692776 15.53340024197904 0,32.559583932307348 15.533396023990676 0,32.559580153278858 15.533392382939093 0,32.559575775431185 15.533389429455838 0,32.559570931783014 15.533387253281026 0,32.559565769506143 15.533385920536658 0,32.559560445453741 15.533385471717519 0,32.559555121394446 15.533385920460745 0,32.559549959097097 15.533387253131515 0,32.559548400025243 15.533387953570875 0,32.559548400025243 15.533442070162417 0,32.559549958670758 15.533442770437107 0,32.559555120947635 15.533444103181106 0,32.559560445000038 15.533444552000118 0))","POLYGON Z ((4.46 528.39 0, 2.49 528.55 0, 0.58 529.05 0, 0 529.3099999999999 0, 0 549.35 0, 0.58 549.61 0, 2.49 550.1 0, 4.46 550.27 0, 6.43 550.1 0, 8.35 549.61 0, 10.14 548.8 0, 11.76 547.71 0, 13.16 546.36 0, 14.3 544.8 0, 15.13 543.0700000000001 0, 15.64 541.23 0, 15.82 539.33 0, 15.64 537.4299999999999 0, 15.13 535.59 0, 14.3 533.86 0, 13.16 532.3 0, 11.76 530.95 0, 10.14 529.85 0, 8.34 529.05 0, 6.43 528.55 0, 4.46 528.39 0))",0.0 +135,AOI_5_Khartoum_img1301,5,"POLYGON ((479.12 481.1 0,442.5 472.15 0,434.89 501.04 0,471.51 510.0 0,479.12 481.1 0))","POLYGON ((32.56084202400006 15.53357222 0,32.560821468000036 15.533494199000106 0,32.560722593000051 15.533518382000056 0,32.560743149000082 15.533596402000022 0,32.56084202400006 15.53357222 0))","POLYGON Z ((479.12 481.1 0, 442.5 472.15 0, 434.89 501.04 0, 471.51 510 0, 479.12 481.1 0))",0.0 +136,AOI_5_Khartoum_img1301,6,"POLYGON ((432.39 491.17 0,437.17 470.55 0,400.53 462.65 0,395.74 483.27 0,415.84 487.61 0,413.87 496.09 0,430.42 499.66 0,432.39 491.17 0))","POLYGON ((32.560715842000072 15.533545033000124 0,32.560710522000065 15.533522122000095 0,32.560665843000017 15.533531752000114 0,32.560671162000062 15.533554663000105 0,32.560616894000063 15.533566358000098 0,32.560629824000074 15.533622050000073 0,32.560728771000086 15.533600726000014 0,32.560715842000072 15.533545033000124 0))","POLYGON Z ((432.39 491.17 0, 437.17 470.55 0, 400.53 462.65 0, 395.74 483.27 0, 415.84 487.61 0, 413.87 496.09 0, 430.42 499.66 0, 432.39 491.17 0))",0.0 +137,AOI_5_Khartoum_img1301,7,"POLYGON ((637.17 548.43 0,642.66 529.49 0,645.93 518.21 0,646.95 514.68 0,650.0 504.15 0,650.0 466.58 0,613.11 456.66 0,614.15 453.05 0,590.04 446.57 0,580.72 478.76 0,584.71 479.83 0,579.84 496.64 0,566.86 541.47 0,586.98 546.88 0,596.68 549.49 0,633.96 559.51 0,637.17 548.43 0))","POLYGON ((32.561268768000055 15.533390433000037 0,32.561260101000109 15.533360512000035 0,32.561159427000057 15.533387581000113 0,32.561133233000064 15.533394624 0,32.561078916000056 15.533409228000028 0,32.561113977000041 15.533530277000171 0,32.561127120000087 15.533575653000078 0,32.561116332000026 15.533578554000083 0,32.561141507000059 15.533665471000139 0,32.561206613000088 15.533647966000046 0,32.561203791000068 15.533638223000072 0,32.561303400025558 15.533611440335612 0,32.561303400025558 15.533510001653005 0,32.561295162000093 15.533481560000135 0,32.561292403000031 15.533472035000072 0,32.561283580000065 15.533441574000053 0,32.561268768000055 15.533390433000037 0))","POLYGON Z ((637.17 548.4299999999999 0, 642.66 529.49 0, 645.9299999999999 518.21 0, 646.95 514.6799999999999 0, 650 504.15 0, 650 466.58 0, 613.11 456.66 0, 614.15 453.05 0, 590.04 446.57 0, 580.72 478.76 0, 584.71 479.83 0, 579.84 496.64 0, 566.86 541.47 0, 586.98 546.88 0, 596.6799999999999 549.49 0, 633.96 559.51 0, 637.17 548.4299999999999 0))",0.3497703486038566 +138,AOI_5_Khartoum_img1301,8,"POLYGON ((551.57 503.04 0,520.77 493.32 0,533.98 454.47 0,539.35 438.69 0,502.29 426.99 0,496.92 442.78 0,473.08 512.89 0,477.64 514.33 0,487.92 517.58 0,487.5 518.81 0,509.72 525.83 0,540.52 535.55 0,551.57 503.04 0))","POLYGON ((32.561037628000079 15.533512982000056 0,32.561007791000115 15.533425225000027 0,32.560924642000025 15.533451469000111 0,32.560864660000078 15.533470401000066 0,32.560865794000094 15.533473738000025 0,32.560838024000098 15.533482503000146 0,32.560825722000061 15.533486386000112 0,32.560890092000065 15.533675705000036 0,32.560904582000035 15.533718321000096 0,32.561004636000085 15.533686742000084 0,32.560990146000051 15.533644125000043 0,32.560954479000017 15.533539225000059 0,32.561037628000079 15.533512982000056 0))","POLYGON Z ((551.5700000000001 503.04 0, 520.77 493.32 0, 533.98 454.47 0, 539.35 438.69 0, 502.29 426.99 0, 496.92 442.78 0, 473.08 512.89 0, 477.64 514.33 0, 487.92 517.58 0, 487.5 518.8099999999999 0, 509.72 525.83 0, 540.52 535.55 0, 551.5700000000001 503.04 0))",0.3358398188777739 +139,AOI_5_Khartoum_img1301,9,"POLYGON ((427.18 410.01 0,430.6 401.81 0,410.4 393.98 0,389.28 444.52 0,409.47 452.36 0,406.21 460.17 0,421.8 466.22 0,425.06 458.41 0,454.8 469.95 0,464.84 445.93 0,472.66 448.96 0,479.99 431.41 0,472.17 428.38 0,472.5 427.59 0,473.54 425.09 0,467.85 422.88 0,470.26 417.12 0,440.29 405.49 0,436.84 413.76 0,427.18 410.01 0))","POLYGON ((32.560701774000052 15.533764179000153 0,32.560727859000067 15.533754059000012 0,32.560737184000089 15.533776372000013 0,32.56081809100003 15.533744982 0,32.560811587000067 15.533729421000036 0,32.56082697100004 15.533723452000078 0,32.560824149000048 15.533716701000092 0,32.560823262000056 15.533714576000087 0,32.560844372000098 15.533706386000041 0,32.56082456900004 15.533659002000102 0,32.560803457000063 15.533667192999987 0,32.560776354000048 15.533602346000134 0,32.56069606300013 15.53363349600005 0,32.560687249000061 15.533612406000053 0,32.560645164000093 15.533628733000093 0,32.560653980000048 15.533649824000145 0,32.560599444000061 15.533670983000038 0,32.560656487000102 15.533807466000079 0,32.56071102300006 15.533786307000137 0,32.560701774000052 15.533764179000153 0))","POLYGON Z ((427.18 410.01 0, 430.6 401.81 0, 410.4 393.98 0, 389.28 444.52 0, 409.47 452.36 0, 406.21 460.17 0, 421.8 466.22 0, 425.06 458.41 0, 454.8 469.95 0, 464.84 445.93 0, 472.66 448.96 0, 479.99 431.41 0, 472.17 428.38 0, 472.5 427.59 0, 473.54 425.09 0, 467.85 422.88 0, 470.26 417.12 0, 440.29 405.49 0, 436.84 413.76 0, 427.18 410.01 0))",0.7431303983021704 +140,AOI_5_Khartoum_img1301,10,"POLYGON ((650.0 425.27 0,650.0 384.01 0,630.74 380.77 0,629.27 388.91 0,591.35 382.53 0,583.88 423.75 0,590.99 424.94 0,589.15 435.14 0,634.5 442.77 0,635.91 434.97 0,644.87 436.47 0,646.99 424.76 0,650.0 425.27 0))","POLYGON ((32.561303400025558 15.53372297579989 0,32.561295273000056 15.533724342 0,32.561289547000051 15.533692722 0,32.561265367000026 15.533696788000064 0,32.56126155100003 15.533675721000122 0,32.561139096000026 15.533696309000133 0,32.56114408400002 15.533723854000117 0,32.561124883000083 15.533727082000048 0,32.561145037000095 15.533838365000085 0,32.561247417000061 15.533821153000121 0,32.561251397000021 15.533843125000054 0,32.561303400025558 15.533834382159755 0,32.561303400025558 15.53372297579989 0))","POLYGON Z ((650 425.27 0, 650 384.01 0, 630.74 380.77 0, 629.27 388.91 0, 591.35 382.53 0, 583.88 423.75 0, 590.99 424.94 0, 589.15 435.14 0, 634.5 442.77 0, 635.91 434.97 0, 644.87 436.47 0, 646.99 424.76 0, 650 425.27 0))",0.5815233829908292 +141,AOI_5_Khartoum_img1301,11,"POLYGON ((650.0 372.07 0,650.0 358.49 0,608.42 352.63 0,606.4 365.92 0,650.0 372.07 0))","POLYGON ((32.561303400025558 15.533866622498707 0,32.56118568300009 15.533883220000124 0,32.561191134000097 15.53391910900004 0,32.561303400025558 15.533903280062058 0,32.561303400025558 15.533866622498707 0))","POLYGON Z ((650 372.07 0, 650 358.49 0, 608.42 352.63 0, 606.4 365.92 0, 650 372.07 0))",0.0 +142,AOI_5_Khartoum_img1301,12,"POLYGON ((555.6 414.78 0,569.52 368.96 0,558.38 365.82 0,559.66 361.6 0,517.81 349.8 0,516.53 354.02 0,502.61 399.83 0,509.99 401.91 0,506.31 414.03 0,551.92 426.89 0,555.6 414.78 0))","POLYGON ((32.561048516000014 15.533751307000037 0,32.561038576000072 15.533718588000131 0,32.560915444000052 15.533753310000135 0,32.560925384000029 15.533786030000032 0,32.560905456000036 15.533791649000099 0,32.560943032000118 15.533915346000112 0,32.560946496000071 15.53392674600007 0,32.561059483000051 15.533894886000073 0,32.561056020000031 15.533883485 0,32.56108609300005 15.533875005000041 0,32.561048516000014 15.533751307000037 0))","POLYGON Z ((555.6 414.78 0, 569.52 368.96 0, 558.38 365.82 0, 559.66 361.6 0, 517.8099999999999 349.8 0, 516.53 354.02 0, 502.61 399.83 0, 509.99 401.91 0, 506.31 414.03 0, 551.92 426.89 0, 555.6 414.78 0))",0.27728640643940716 +143,AOI_5_Khartoum_img1301,13,"POLYGON ((151.55 354.3 0,144.48 351.29 0,149.04 341.34 0,139.44 337.26 0,130.11 357.61 0,139.71 361.7 0,146.78 364.71 0,151.55 354.3 0))","POLYGON ((32.559957589000128 15.533914590000062 0,32.559944710000011 15.533886496000058 0,32.559925615000061 15.533894622000053 0,32.559899706000067 15.533905646000054 0,32.559924901000038 15.533960607000068 0,32.559950808000032 15.533949582000012 0,32.559938493000047 15.533922717000076 0,32.559957589000128 15.533914590000062 0))","POLYGON Z ((151.55 354.3 0, 144.48 351.29 0, 149.04 341.34 0, 139.44 337.26 0, 130.11 357.61 0, 139.71 361.7 0, 146.78 364.71 0, 151.55 354.3 0))",0.0 +144,AOI_5_Khartoum_img1301,14,"POLYGON ((460.79 333.21 0,432.9 325.88 0,426.22 349.48 0,421.75 348.3 0,417.18 364.46 0,412.07 363.12 0,408.11 377.1 0,413.22 378.45 0,412.07 382.51 0,416.54 383.68 0,444.43 391.01 0,460.79 333.21 0))","POLYGON ((32.560792535000111 15.533971534000033 0,32.560748357000094 15.533815466000085 0,32.560673060000042 15.533835252000118 0,32.560660993000042 15.533838423000043 0,32.560664100000096 15.53384939700007 0,32.560650296000048 15.533853025000058 0,32.560660985000069 15.533890787000129 0,32.560674789000117 15.53388715900009 0,32.56068713600007 15.53393078 0,32.560699203000077 15.533927609000111 0,32.560717237000105 15.533991320000078 0,32.560792535000111 15.533971534000033 0))","POLYGON Z ((460.79 333.21 0, 432.9 325.88 0, 426.22 349.48 0, 421.75 348.3 0, 417.18 364.46 0, 412.07 363.12 0, 408.11 377.1 0, 413.22 378.45 0, 412.07 382.51 0, 416.54 383.68 0, 444.43 391.01 0, 460.79 333.21 0))",0.7255622326538651 +145,AOI_5_Khartoum_img1301,15,"POLYGON ((420.22 324.52 0,384.06 315.33 0,371.98 359.46 0,408.14 368.65 0,420.22 324.52 0))","POLYGON ((32.56068298800006 15.533994999000056 0,32.560650371000072 15.533875844000049 0,32.560552739000094 15.533900652000074 0,32.560585356000075 15.534019808000021 0,32.56068298800006 15.533994999000056 0))","POLYGON Z ((420.22 324.52 0, 384.06 315.33 0, 371.98 359.46 0, 408.14 368.65 0, 420.22 324.52 0))",0.68040010172903 +146,AOI_5_Khartoum_img1301,16,"POLYGON ((307.04 376.6 0,292.76 370.32 0,307.98 338.24 0,252.9 313.98 0,225.86 370.97 0,280.94 395.23 0,286.37 383.8 0,300.65 390.09 0,307.04 376.6 0))","POLYGON ((32.560377420000059 15.53385436800006 0,32.560360148000036 15.533817966000083 0,32.560321592000044 15.533834947000051 0,32.560306941000064 15.533804069000027 0,32.560158221000073 15.533869572000134 0,32.560231236000092 15.5340234590001 0,32.56037995700008 15.533957956000094 0,32.560338864000059 15.533871349000092 0,32.560377420000059 15.53385436800006 0))","POLYGON Z ((307.04 376.6 0, 292.76 370.32 0, 307.98 338.24 0, 252.9 313.98 0, 225.86 370.97 0, 280.94 395.23 0, 286.37 383.8 0, 300.65 390.09 0, 307.04 376.6 0))",0.6148045217497515 +147,AOI_5_Khartoum_img1301,17,"POLYGON ((382.84 304.94 0,358.95 295.34 0,338.58 342.4 0,362.47 352.0 0,382.84 304.94 0))","POLYGON ((32.560582069000112 15.534047853000134 0,32.560527070000042 15.533920804000083 0,32.560462563000087 15.533946727000153 0,32.560517563000083 15.534073774000092 0,32.560582069000112 15.534047853000134 0))","POLYGON Z ((382.84 304.94 0, 358.95 295.34 0, 338.58 342.4 0, 362.47 352 0, 382.84 304.94 0))",0.5331202946917262 +148,AOI_5_Khartoum_img1301,18,"POLYGON ((420.39 289.73 0,396.56 280.24 0,385.85 305.21 0,409.69 314.7 0,420.39 289.73 0))","POLYGON ((32.560683456000056 15.53408893 0,32.560654554000074 15.534021513000033 0,32.560590203000103 15.534047122000096 0,32.560619104000061 15.534114539000074 0,32.560683456000056 15.53408893 0))","POLYGON Z ((420.39 289.73 0, 396.56 280.24 0, 385.85 305.21 0, 409.69 314.7 0, 420.39 289.73 0))",0.3252802987861806 +149,AOI_5_Khartoum_img1301,19,"POLYGON ((224.04 352.87 0,243.27 319.37 0,247.39 312.19 0,214.39 293.59 0,181.29 275.95 0,174.49 287.8 0,157.49 317.4 0,180.16 329.48 0,175.58 337.45 0,173.26 341.49 0,177.16 343.57 0,175.0 347.33 0,192.56 356.69 0,192.95 356.02 0,196.43 357.88 0,198.21 354.79 0,203.16 357.42 0,205.48 353.39 0,219.46 360.84 0,224.04 352.87 0))","POLYGON ((32.56015329800006 15.533918464000102 0,32.560140935000092 15.533896930000052 0,32.56010318900011 15.533917048000033 0,32.560096934000065 15.533906154000054 0,32.560083568000046 15.533913278000124 0,32.560078774000019 15.533904929000093 0,32.560069358000099 15.533909948000085 0,32.560068317000038 15.533908136000132 0,32.560020899000037 15.533933408000097 0,32.56002673400009 15.533943570000112 0,32.560016210000065 15.533949179000032 0,32.560022464000042 15.533960073000038 0,32.560034827000081 15.533981606000044 0,32.559973633000034 15.534014222000105 0,32.56001952400004 15.534094147000078 0,32.560037893000064 15.534126141000037 0,32.560127261000083 15.5340785090001 0,32.560216350000118 15.534028278000058 0,32.560205220000043 15.534008894000102 0,32.56015329800006 15.533918464000102 0))","POLYGON Z ((224.04 352.87 0, 243.27 319.37 0, 247.39 312.19 0, 214.39 293.59 0, 181.29 275.95 0, 174.49 287.8 0, 157.49 317.4 0, 180.16 329.48 0, 175.58 337.45 0, 173.26 341.49 0, 177.16 343.57 0, 175 347.33 0, 192.56 356.69 0, 192.95 356.02 0, 196.43 357.88 0, 198.21 354.79 0, 203.16 357.42 0, 205.48 353.39 0, 219.46 360.84 0, 224.04 352.87 0))",0.0 +150,AOI_5_Khartoum_img1301,20,"POLYGON ((138.62 331.45 0,131.53 328.1 0,132.11 326.99 0,156.87 289.46 0,152.75 286.94 0,163.46 270.71 0,110.29 238.14 0,99.58 254.37 0,103.04 256.49 0,87.34 280.28 0,104.33 290.68 0,100.5 296.48 0,103.81 317.26 0,114.86 331.7 0,126.63 337.43 0,133.84 340.84 0,138.62 331.45 0))","POLYGON ((32.559922677000024 15.533976280000028 0,32.559909765000086 15.533950932000073 0,32.559890306000092 15.533960134000125 0,32.559858522000113 15.533975610000065 0,32.559828681000091 15.53401461000013 0,32.559819755000071 15.534070696000043 0,32.559830086000062 15.534086354000062 0,32.559784213000093 15.534114453000102 0,32.559826599000083 15.53417868800004 0,32.559817264000095 15.534184406000024 0,32.559846175000089 15.53422821900002 0,32.559989733000094 15.534140285000062 0,32.559960822000029 15.534096471000099 0,32.55997195500003 15.53408965099999 0,32.559905094000051 15.533988325000088 0,32.559903526000049 15.533985337000081 0,32.559922677000024 15.533976280000028 0))","POLYGON Z ((138.62 331.45 0, 131.53 328.1 0, 132.11 326.99 0, 156.87 289.46 0, 152.75 286.94 0, 163.46 270.71 0, 110.29 238.14 0, 99.58 254.37 0, 103.04 256.49 0, 87.34 280.28 0, 104.33 290.68 0, 100.5 296.48 0, 103.81 317.26 0, 114.86 331.7 0, 126.63 337.43 0, 133.84 340.84 0, 138.62 331.45 0))",0.3653502576978149 +151,AOI_5_Khartoum_img1301,21,"POLYGON ((360.62 253.29 0,323.04 238.05 0,302.44 229.69 0,292.25 253.01 0,289.14 260.12 0,274.87 254.34 0,257.97 293.01 0,272.23 298.8 0,288.78 305.51 0,304.03 270.62 0,308.79 259.72 0,312.86 261.37 0,318.22 263.54 0,293.83 319.36 0,322.2 330.87 0,346.59 275.05 0,350.43 276.61 0,360.62 253.29 0))","POLYGON ((32.560522065000036 15.534187304000131 0,32.560494556000116 15.534124355000039 0,32.560484202000033 15.534128555000127 0,32.560418338000026 15.533977843000052 0,32.560341731000101 15.534008920000057 0,32.560407595000115 15.534159633000044 0,32.560393110000092 15.534165509000049 0,32.560382144000087 15.534169957000124 0,32.560369281000085 15.534140523000065 0,32.56032811300004 15.534046321000108 0,32.56028343400007 15.534064446000089 0,32.560244926000045 15.53408006700009 0,32.56029056200002 15.534184490000051 0,32.560329069000048 15.534168869000075 0,32.560337465000039 15.534188083000133 0,32.56036497500002 15.534251031000082 0,32.56042062000008 15.534228457000049 0,32.560522065000036 15.534187304000131 0))","POLYGON Z ((360.62 253.29 0, 323.04 238.05 0, 302.44 229.69 0, 292.25 253.01 0, 289.14 260.12 0, 274.87 254.34 0, 257.97 293.01 0, 272.23 298.8 0, 288.78 305.51 0, 304.03 270.62 0, 308.79 259.72 0, 312.86 261.37 0, 318.22 263.54 0, 293.83 319.36 0, 322.2 330.87 0, 346.59 275.05 0, 350.43 276.61 0, 360.62 253.29 0))",0.4610077120445046 +152,AOI_5_Khartoum_img1301,22,"POLYGON ((650.0 257.03 0,650.0 202.88 0,604.19 198.25 0,598.35 251.81 0,650.0 257.03 0))","POLYGON ((32.561303400025558 15.534177214264373 0,32.561163951000069 15.534191318000095 0,32.561179708000054 15.534335938000044 0,32.561303400025558 15.534323427911589 0,32.561303400025558 15.534177214264373 0))","POLYGON Z ((650 257.03 0, 650 202.88 0, 604.1900000000001 198.25 0, 598.35 251.81 0, 650 257.03 0))",0.790713630481732 +153,AOI_5_Khartoum_img1301,23,"POLYGON ((582.15 192.7 0,535.31 187.28 0,531.77 215.68 0,527.4 215.17 0,524.45 238.78 0,528.82 239.29 0,528.36 243.01 0,575.19 248.43 0,583.28 249.37 0,585.81 229.16 0,577.71 228.22 0,582.15 192.7 0))","POLYGON ((32.561120193000114 15.534350900000032 0,32.561108219000104 15.534255007000013 0,32.561130084000077 15.534252473000089 0,32.56112326900007 15.534197897000139 0,32.561101405000059 15.534200431000048 0,32.560974959000099 15.534215086000138 0,32.560976213000053 15.534225127000111 0,32.560964410000089 15.534226495000048 0,32.560972370000044 15.53429024300007 0,32.560984172000083 15.534288875000055 0,32.560993748000051 15.534365555 0,32.561120193000114 15.534350900000032 0))","POLYGON Z ((582.15 192.7 0, 535.3099999999999 187.28 0, 531.77 215.68 0, 527.4 215.17 0, 524.45 238.78 0, 528.8200000000001 239.29 0, 528.36 243.01 0, 575.1900000000001 248.43 0, 583.28 249.37 0, 585.8099999999999 229.16 0, 577.71 228.22 0, 582.15 192.7 0))",0.6110531478763146 +154,AOI_5_Khartoum_img1301,24,"POLYGON ((287.31 220.15 0,227.43 183.9 0,186.76 268.88 0,250.45 297.17 0,287.31 220.15 0))","POLYGON ((32.560324124000047 15.534276792000036 0,32.560224605 15.534068838000161 0,32.560052662000018 15.534145221000147 0,32.560162469000055 15.534374678000132 0,32.560324124000047 15.534276792000036 0))","POLYGON Z ((287.31 220.15 0, 227.43 183.9 0, 186.76 268.88 0, 250.45 297.17 0, 287.31 220.15 0))",0.33832062186646095 +155,AOI_5_Khartoum_img1301,25,"POLYGON ((3.65 171.54 0,0.0 177.03 0,0.0 234.58 0,0.59 234.94 0,4.56 237.39 0,12.83 224.97 0,20.99 230.01 0,30.01 216.46 0,34.3 219.11 0,48.76 197.38 0,36.3 189.69 0,21.85 211.42 0,17.87 208.96 0,32.98 186.25 0,13.52 174.24 0,11.92 176.64 0,3.65 171.54 0))","POLYGON ((32.559558267000106 15.534408050000076 0,32.559580589000085 15.534394263000101 0,32.559584913000023 15.534400762000059 0,32.559637450000025 15.534368315000121 0,32.55959665700005 15.534307002000018 0,32.559607383000085 15.534300376999957 0,32.55964641500006 15.534359040000066 0,32.559680043000064 15.534338270000077 0,32.55964101200005 15.53427960700003 0,32.559629429000069 15.534286760000031 0,32.559605081000022 15.534250165000115 0,32.559583035000074 15.534263781000075 0,32.559560715000053 15.534230234000033 0,32.559549988000057 15.534236860000124 0,32.559548400025243 15.534237840768091 0,32.559548400025243 15.534393220742379 0,32.559558267000106 15.534408050000076 0))","POLYGON Z ((3.65 171.54 0, 0 177.03 0, 0 234.58 0, 0.59 234.94 0, 4.56 237.39 0, 12.83 224.97 0, 20.99 230.01 0, 30.01 216.46 0, 34.3 219.11 0, 48.76 197.38 0, 36.3 189.69 0, 21.85 211.42 0, 17.87 208.96 0, 32.98 186.25 0, 13.52 174.24 0, 11.92 176.64 0, 3.65 171.54 0))",0.5370270299699298 +156,AOI_5_Khartoum_img1301,26,"POLYGON ((186.32 199.99 0,170.04 191.43 0,172.16 187.68 0,131.74 166.41 0,104.62 214.24 0,102.95 217.2 0,124.74 228.67 0,125.13 227.98 0,127.53 229.24 0,125.57 232.7 0,141.8 241.24 0,138.6 246.89 0,161.67 259.02 0,168.11 247.65 0,161.33 244.08 0,186.32 199.99 0))","POLYGON ((32.560051451000056 15.534331219000032 0,32.559983986000084 15.534212192000037 0,32.560002309000097 15.534202551000082 0,32.55998490000006 15.534171836000127 0,32.559922622000101 15.53420460400009 0,32.559931258000042 15.534219842000127 0,32.559887438000018 15.534242898000091 0,32.559892736000073 15.534252244000127 0,32.559886243000072 15.534255660000039 0,32.559885189000056 15.534253799000066 0,32.55982635400008 15.534284756000057 0,32.559830884000078 15.534292747000098 0,32.559904089000042 15.534421902000016 0,32.560013236000039 15.534364474000053 0,32.560007496000011 15.5343543450001 0,32.560051451000056 15.534331219000032 0))","POLYGON Z ((186.32 199.99 0, 170.04 191.43 0, 172.16 187.68 0, 131.74 166.41 0, 104.62 214.24 0, 102.95 217.2 0, 124.74 228.67 0, 125.13 227.98 0, 127.53 229.24 0, 125.57 232.7 0, 141.8 241.24 0, 138.6 246.89 0, 161.67 259.02 0, 168.11 247.65 0, 161.33 244.08 0, 186.32 199.99 0))",0.0 +157,AOI_5_Khartoum_img1301,27,"POLYGON ((539.59 157.43 0,516.65 152.33 0,509.91 180.48 0,532.85 185.58 0,539.59 157.43 0))","POLYGON ((32.561005294000061 15.534446132 0,32.56098709200009 15.534370136000128 0,32.560925157000035 15.534383906000114 0,32.560943359000113 15.534459903 0,32.561005294000061 15.534446132 0))","POLYGON Z ((539.59 157.43 0, 516.65 152.33 0, 509.91 180.48 0, 532.85 185.58 0, 539.59 157.43 0))",0.0 +158,AOI_5_Khartoum_img1301,28,"POLYGON ((464.97 130.74 0,442.63 125.32 0,439.46 137.44 0,432.01 135.63 0,429.33 145.88 0,422.84 144.3 0,415.32 173.05 0,421.81 174.63 0,432.1 177.12 0,430.92 181.64 0,448.16 185.83 0,447.16 189.67 0,467.14 194.52 0,471.63 177.32 0,478.34 178.95 0,482.07 164.68 0,455.39 158.2 0,459.54 142.31 0,461.8 142.86 0,464.97 130.74 0))","POLYGON ((32.560803806000052 15.534518192000077 0,32.560795253000073 15.534485481000042 0,32.560789161000059 15.534486959000024 0,32.560777942000115 15.534444049000111 0,32.560849994000115 15.534426562000052 0,32.560839922000113 15.534388038000095 0,32.560821807000018 15.534392435000051 0,32.560809666000054 15.534345998000077 0,32.560755727000085 15.534359089000073 0,32.560758440000029 15.534369465000045 0,32.560711880000085 15.534380765000057 0,32.560715069000032 15.534392963000116 0,32.560687288000068 15.534399705000036 0,32.560669767000036 15.534403958000086 0,32.560690064000049 15.534481589000036 0,32.560707586000049 15.534477336000075 0,32.560714819000083 15.534505002000138 0,32.560734939000078 15.534500119 0,32.560743493000054 15.534532831000144 0,32.560803806000052 15.534518192000077 0))","POLYGON Z ((464.97 130.74 0, 442.63 125.32 0, 439.46 137.44 0, 432.01 135.63 0, 429.33 145.88 0, 422.84 144.3 0, 415.32 173.05 0, 421.81 174.63 0, 432.1 177.12 0, 430.92 181.64 0, 448.16 185.83 0, 447.16 189.67 0, 467.14 194.52 0, 471.63 177.32 0, 478.34 178.95 0, 482.07 164.68 0, 455.39 158.2 0, 459.54 142.31 0, 461.8 142.86 0, 464.97 130.74 0))",0.7093663843535448 +159,AOI_5_Khartoum_img1301,29,"POLYGON ((650.0 184.89 0,650.0 104.12 0,623.93 95.61 0,621.79 101.69 0,601.6 159.11 0,598.46 168.06 0,595.23 177.23 0,627.11 187.64 0,630.33 178.47 0,639.77 181.55 0,650.0 184.89 0))","POLYGON ((32.561303400025558 15.534371998632215 0,32.561275786000095 15.534381013000017 0,32.561250299000079 15.534389332000091 0,32.561241593000069 15.53436457500012 0,32.56115552200005 15.53439267 0,32.561164229000063 15.534417427000113 0,32.561172730000081 15.534441602000127 0,32.561227244000108 15.53459662900015 0,32.561233021000085 15.534613058000046 0,32.561303400025558 15.53459008452268 0,32.561303400025558 15.534371998632215 0))","POLYGON Z ((650 184.89 0, 650 104.12 0, 623.9299999999999 95.61 0, 621.79 101.69 0, 601.6 159.11 0, 598.46 168.06 0, 595.23 177.23 0, 627.11 187.64 0, 630.33 178.47 0, 639.77 181.55 0, 650 184.89 0))",0.7289241902378097 +160,AOI_5_Khartoum_img1301,30,"POLYGON ((420.98 122.74 0,371.4 104.54 0,374.74 96.09 0,359.95 90.66 0,335.97 151.33 0,350.76 156.75 0,351.72 154.32 0,365.66 159.44 0,363.62 164.58 0,380.38 170.73 0,383.56 162.67 0,391.64 165.64 0,393.06 162.06 0,396.97 163.49 0,405.53 141.84 0,412.43 144.37 0,420.98 122.74 0))","POLYGON ((32.560685047000085 15.534539802000129 0,32.560661958000011 15.53448140900008 0,32.560643338000041 15.53448824400008 0,32.560620219000079 15.53442977499999 0,32.560609663000086 15.53443364900008 0,32.560605838000086 15.534423974000086 0,32.560584014000042 15.534431984000074 0,32.560575415000073 15.534410234000108 0,32.560530187000062 15.534426834000056 0,32.560535675000054 15.534440714000088 0,32.560498038000041 15.534454529000126 0,32.560495441000086 15.534447962000087 0,32.56045551100005 15.534462618000093 0,32.560520273000073 15.534626407000045 0,32.560560204000105 15.534611752000046 0,32.560551182000026 15.534588936000031 0,32.560685047000085 15.534539802000129 0))","POLYGON Z ((420.98 122.74 0, 371.4 104.54 0, 374.74 96.09 0, 359.95 90.66 0, 335.97 151.33 0, 350.76 156.75 0, 351.72 154.32 0, 365.66 159.44 0, 363.62 164.58 0, 380.38 170.73 0, 383.56 162.67 0, 391.64 165.64 0, 393.06 162.06 0, 396.97 163.49 0, 405.53 141.84 0, 412.43 144.37 0, 420.98 122.74 0))",0.7655201181939765 +161,AOI_5_Khartoum_img1301,31,"POLYGON ((101.59 87.83 0,96.25 95.33 0,89.41 90.81 0,35.54 166.46 0,54.39 178.93 0,61.38 183.55 0,65.85 186.5 0,76.74 171.21 0,72.27 168.26 0,87.35 147.08 0,86.64 146.61 0,101.74 125.41 0,100.3 124.46 0,113.11 106.47 0,118.45 98.97 0,101.59 87.83 0))","POLYGON ((32.559822688000096 15.53463407100006 0,32.559868224000098 15.534603969000036 0,32.559853803000102 15.534583718 0,32.559819216000072 15.534535147000028 0,32.559823088000051 15.534532587000101 0,32.559782324000054 15.53447534499999 0,32.559784243000031 15.534474076 0,32.559743528000105 15.534416900000064 0,32.559755587000069 15.534408929000088 0,32.559726194000085 15.53436765300008 0,32.559714135000021 15.534375625000111 0,32.559695264000069 15.534388099000029 0,32.55964435300011 15.534421753000087 0,32.559789813000052 15.53462601900002 0,32.559808266000068 15.534613819000118 0,32.559822688000096 15.53463407100006 0))","POLYGON Z ((101.59 87.83 0, 96.25 95.33 0, 89.41 90.81 0, 35.54 166.46 0, 54.39 178.93 0, 61.38 183.55 0, 65.84999999999999 186.5 0, 76.73999999999999 171.21 0, 72.27 168.26 0, 87.34999999999999 147.08 0, 86.64 146.61 0, 101.74 125.41 0, 100.3 124.46 0, 113.11 106.47 0, 118.45 98.97 0, 101.59 87.83 0))",0.46250234195396545 +162,AOI_5_Khartoum_img1301,32,"POLYGON ((604.05 80.7 0,569.73 72.8 0,567.46 81.99 0,561.74 80.67 0,562.57 77.31 0,545.99 73.5 0,539.24 100.71 0,528.84 142.66 0,535.35 144.16 0,533.2 152.83 0,554.83 157.81 0,556.98 149.13 0,585.47 155.69 0,595.86 113.74 0,599.09 100.71 0,604.05 80.7 0))","POLYGON ((32.561179339000034 15.534653308000083 0,32.561165948000067 15.534599281000052 0,32.561157231000053 15.534564111000144 0,32.56112915600005 15.534450842000023 0,32.561052235000027 15.534468539000088 0,32.561046430000047 15.534445119000067 0,32.560988034000069 15.53445855500007 0,32.560993839000083 15.534481975000091 0,32.560976281000066 15.534486016000102 0,32.561004356000069 15.534599285000134 0,32.561022567000116 15.534672760000031 0,32.561067334000036 15.534662460000112 0,32.561065085000067 15.534653387000088 0,32.561080535000023 15.534649834000048 0,32.561086680000095 15.534674628000072 0,32.561179339000034 15.534653308000083 0))","POLYGON Z ((604.05 80.7 0, 569.73 72.8 0, 567.46 81.98999999999999 0, 561.74 80.67 0, 562.5700000000001 77.31 0, 545.99 73.5 0, 539.24 100.71 0, 528.84 142.66 0, 535.35 144.16 0, 533.2 152.83 0, 554.83 157.81 0, 556.98 149.13 0, 585.47 155.69 0, 595.86 113.74 0, 599.09 100.71 0, 604.05 80.7 0))",0.47834545892893854 +163,AOI_5_Khartoum_img1301,33,"POLYGON ((316.93 78.52 0,320.37 68.36 0,298.4 61.44 0,292.08 80.06 0,285.61 78.02 0,278.76 98.21 0,278.64 98.17 0,270.89 95.73 0,267.42 105.95 0,275.17 108.39 0,270.05 123.48 0,276.65 125.55 0,298.62 132.47 0,302.65 120.59 0,322.39 126.81 0,327.49 111.79 0,336.67 84.74 0,316.93 78.52 0))","POLYGON ((32.560404101000088 15.534659195000113 0,32.560457400000097 15.534642407000085 0,32.560432620000071 15.534569380000118 0,32.560418857000087 15.534528817000064 0,32.56036555700009 15.534545605000057 0,32.560354672000081 15.53451352200006 0,32.560295346000011 15.534532208000014 0,32.560277547000091 15.534537815000027 0,32.560291369000041 15.534578549000109 0,32.560270438000074 15.534585142000088 0,32.560279802000032 15.534612738000062 0,32.560300732000066 15.534606144000092 0,32.560301049000032 15.53460604500011 0,32.560319544000052 15.534660548000073 0,32.560337026000113 15.534655041000082 0,32.560354081000114 15.534705304000106 0,32.560413406000109 15.53468661800005 0,32.560404101000088 15.534659195000113 0))","POLYGON Z ((316.93 78.52 0, 320.37 68.36 0, 298.4 61.44 0, 292.08 80.06 0, 285.61 78.02 0, 278.76 98.20999999999999 0, 278.64 98.17 0, 270.89 95.73 0, 267.42 105.95 0, 275.17 108.39 0, 270.05 123.48 0, 276.65 125.55 0, 298.62 132.47 0, 302.65 120.59 0, 322.39 126.81 0, 327.49 111.79 0, 336.67 84.73999999999999 0, 316.93 78.52 0))",0.4514723314550751 +164,AOI_5_Khartoum_img1301,34,"POLYGON ((76.98 68.32 0,62.19 58.34 0,55.72 67.25 0,70.51 77.22 0,76.98 68.32 0))","POLYGON ((32.559756253000053 15.534686748000018 0,32.559738780000025 15.534662699000057 0,32.559698849000057 15.534689629000098 0,32.559716323000089 15.534713678000175 0,32.559756253000053 15.534686748000018 0))","POLYGON Z ((76.98 68.31999999999999 0, 62.19 58.34 0, 55.72 67.25 0, 70.51000000000001 77.22 0, 76.98 68.31999999999999 0))",0.7167084982786652 +165,AOI_5_Khartoum_img1301,35,"POLYGON ((16.6 98.33 0,28.9 83.29 0,0.0 61.34 0,0.0 117.45 0,0.59 117.9 0,8.35 108.41 0,14.6 113.16 0,0.0 131.01 0,0.0 147.96 0,4.79 151.59 0,10.76 155.39 0,30.57 167.27 0,58.74 123.66 0,61.41 125.27 0,81.77 93.76 0,43.81 70.99 0,23.47 102.47 0,16.6 98.33 0))","POLYGON ((32.55959322000011 15.534605718 0,32.559611760000038 15.534594527000035 0,32.559666679000038 15.534679533000029 0,32.559769184000061 15.534618060000138 0,32.559714215000092 15.534532975000051 0,32.559706992000017 15.534537307000091 0,32.559630932000076 15.534419575000184 0,32.559577458000042 15.534451644000052 0,32.559561339000041 15.534461895000092 0,32.559548400025243 15.53447172125124 0,32.559548400025243 15.534517476487832 0,32.559587828000069 15.534565671000093 0,32.559570946000072 15.534578492000092 0,32.559549994000108 15.53455288099998 0,32.559548400025243 15.534554091610673 0,32.559548400025243 15.534705572765176 0,32.559626432000115 15.534646312000062 0,32.55959322000011 15.534605718 0))","POLYGON Z ((16.6 98.33 0, 28.9 83.29000000000001 0, 0 61.34 0, 0 117.45 0, 0.59 117.9 0, 8.35 108.41 0, 14.6 113.16 0, 0 131.01 0, 0 147.96 0, 4.79 151.59 0, 10.76 155.39 0, 30.57 167.27 0, 58.74 123.66 0, 61.41 125.27 0, 81.77 93.76000000000001 0, 43.81 70.98999999999999 0, 23.47 102.47 0, 16.6 98.33 0))",0.65335731817472 +166,AOI_5_Khartoum_img1301,36,"POLYGON ((500.77 80.05 0,503.87 74.08 0,500.33 72.38 0,513.9 46.24 0,474.85 27.43 0,445.99 83.05 0,430.68 75.67 0,417.12 101.8 0,413.7 108.4 0,435.5 118.91 0,438.93 112.31 0,471.47 127.98 0,481.11 132.63 0,492.77 110.15 0,483.14 105.51 0,492.27 87.9 0,495.81 89.61 0,498.35 84.72 0,505.78 88.31 0,508.21 83.64 0,500.77 80.05 0))","POLYGON ((32.560900478000079 15.534655054000105 0,32.560920557000046 15.534645381000066 0,32.560914017000066 15.534632776000034 0,32.560893938000071 15.534642449000073 0,32.560887092000065 15.534629259000045 0,32.560877540000057 15.534633861000044 0,32.560852867000072 15.534586317000114 0,32.560878878000068 15.534573787000042 0,32.560847390000049 15.534513112000138 0,32.560821379000053 15.534525642000057 0,32.56073350200009 15.53456797600013 0,32.560724253000082 15.534550154000131 0,32.560665385000085 15.534578513000048 0,32.560674634000087 15.534596335000133 0,32.560711247000022 15.534666888000055 0,32.560752577000102 15.53464697800012 0,32.560830506000059 15.534797142000061 0,32.560935921000073 15.534746359000085 0,32.560899291000105 15.534675777000082 0,32.560908845000036 15.534671175000096 0,32.560900478000079 15.534655054000105 0))","POLYGON Z ((500.77 80.05 0, 503.87 74.08 0, 500.33 72.38 0, 513.9 46.24 0, 474.85 27.43 0, 445.99 83.05 0, 430.68 75.67 0, 417.12 101.8 0, 413.7 108.4 0, 435.5 118.91 0, 438.93 112.31 0, 471.47 127.98 0, 481.11 132.63 0, 492.77 110.15 0, 483.14 105.51 0, 492.27 87.90000000000001 0, 495.81 89.61 0, 498.35 84.72 0, 505.78 88.31 0, 508.21 83.64 0, 500.77 80.05 0))",0.6624693079745699 +167,AOI_5_Khartoum_img1301,37,"POLYGON ((444.93 42.88 0,427.63 33.96 0,412.64 26.23 0,401.81 20.64 0,411.89 2.51 0,407.02 -0.0 0,395.94 -0.0 0,388.33 13.69 0,392.24 15.7 0,384.04 30.45 0,370.51 54.8 0,380.08 59.74 0,369.67 78.47 0,395.49 91.79 0,404.39 96.39 0,412.28 82.21 0,403.37 77.61 0,419.43 48.71 0,436.73 57.63 0,444.93 42.88 0))","POLYGON ((32.560749710000024 15.5347554130001 0,32.560727580000055 15.534715593000064 0,32.560680867000073 15.534739690000078 0,32.560637498000069 15.53466165000002 0,32.560661543000073 15.534649246000166 0,32.560640264000043 15.534610955000103 0,32.56061621800005 15.534623360000163 0,32.560546507000041 15.534659322 0,32.560574621000043 15.534709911000089 0,32.560548772000054 15.534723246000064 0,32.560585306000071 15.534788988000079 0,32.560607436000048 15.534828808000128 0,32.560596894000078 15.5348342470001 0,32.560617430121134 15.534871199940053 0,32.560647359665445 15.534871199940053 0,32.560660491000121 15.53486442600007 0,32.560633286000041 15.534815474000068 0,32.560662522000101 15.534800391000076 0,32.560702997000114 15.534779511000082 0,32.560749710000024 15.5347554130001 0))","POLYGON Z ((444.93 42.88 0, 427.63 33.96 0, 412.64 26.23 0, 401.81 20.64 0, 411.89 2.51 0, 407.02 -0 0, 395.94 -0 0, 388.33 13.69 0, 392.24 15.7 0, 384.04 30.45 0, 370.51 54.8 0, 380.08 59.74 0, 369.67 78.47 0, 395.49 91.79000000000001 0, 404.39 96.39 0, 412.28 82.20999999999999 0, 403.37 77.61 0, 419.43 48.71 0, 436.73 57.63 0, 444.93 42.88 0))",0.0 +168,AOI_5_Khartoum_img1301,38,"POLYGON ((650.0 10.5 0,650 0 0,642.84 -0.0 0,640.99 8.73 0,650.0 10.5 0))","POLYGON ((32.561303400025558 15.534842848503219 0,32.561279079000037 15.534847617 0,32.561284060042269 15.534871199940053 0,32.561303400025558 15.534871199940053 0,32.561303400025558 15.534842848503219 0))","POLYGON Z ((650 10.5 0, 650 0 0, 642.84 -0 0, 640.99 8.73 0, 650 10.5 0))",0.6259434262948192 +169,AOI_5_Khartoum_img1301,39,"POLYGON ((361.98 -0.0 0,322.27 -0.0 0,356.15 13.54 0,361.98 -0.0 0))","POLYGON ((32.56052573863834 15.534871199940053 0,32.560509999000061 15.534834642000057 0,32.560418526896022 15.534871199940053 0,32.56052573863834 15.534871199940053 0))","POLYGON Z ((361.98 -0 0, 322.27 -0 0, 356.15 13.54 0, 361.98 -0 0))",0.0 +170,AOI_5_Khartoum_img1301,40,"POLYGON ((287.31 -0.0 0,265.64 -0.0 0,266.23 0.19 0,285.14 6.26 0,287.31 -0.0 0))","POLYGON ((32.560324136778689 15.534871199940053 0,32.560318286000033 15.53485428900013 0,32.560267233000033 15.534870686000044 0,32.560265632752227 15.534871199940053 0,32.560324136778689 15.534871199940053 0))","POLYGON Z ((287.31 -0 0, 265.64 -0 0, 266.23 0.19 0, 285.14 6.26 0, 287.31 -0 0))",0.018676459251101306 +171,AOI_5_Khartoum_img463,-1,POLYGON EMPTY,POLYGON EMPTY,POLYGON EMPTY,0.0 diff --git a/docker/solaris/solaris/data/SN2_sample_preds.csv b/docker/solaris/solaris/data/SN2_sample_preds.csv new file mode 100644 index 00000000..124a522f --- /dev/null +++ b/docker/solaris/solaris/data/SN2_sample_preds.csv @@ -0,0 +1,146 @@ +ImageId,BuildingId,PolygonWKT_Pix,Confidence +AOI_2_Vegas_img5979,0,"POLYGON ((650 0 0, 650 5 0, 650 6 0, 650 7 0, 650 8 0, 650 9 0, 650 10 0, 650 11 0, 650 12 0, 650 13 0, 650 14 0, 650 15 0, 650 16 0, 650 17 0, 650 18 0, 650 19 0, 650 20 0, 650 21 0, 650 22 0, 650 23 0, 650 24 0, 650 25 0, 650 26 0, 650 27 0, 650 28 0, 650 29 0, 650 30 0, 650 31 0, 650 32 0, 650 33 0, 650 34 0, 650 35 0, 650 36 0, 650 37 0, 650 38 0, 650 39 0, 650 40 0, 650 41 0, 650 42 0, 650 43 0, 650 44 0, 650 45 0, 650 46 0, 650 47 0, 650 48 0, 650 49 0, 650 50 0, 650 51 0, 650 52 0, 650 53 0, 650 54 0, 650 55 0, 650 56 0, 650 57 0, 650 58 0, 650 59 0, 650 60 0, 650 61 0, 650 62 0, 650 63 0, 650 64 0, 650 65 0, 650 66 0, 650 67 0, 650 68 0, 650 69 0, 650 70 0, 650 71 0, 650 72 0, 650 73 0, 650 74 0, 650 75 0, 650 76 0, 650 77 0, 650 78 0, 650 79 0, 650 136 0, 650 141 0, 647 142 0, 538 144 0, 530 144 0, 466 142 0, 452 139 0, 450 138 0, 445 135 0, 437 129 0, 436 126 0, 453 39 0, 470 2 0, 471 0 0, 650 0 0))",7 +AOI_2_Vegas_img5979,1,"POLYGON ((290 600 0, 317 612 0, 321 614 0, 322 633 0, 317 639 0, 316 640 0, 315 641 0, 301 649 0, 300 649 0, 299 649 0, 298 649 0, 297 649 0, 296 649 0, 295 649 0, 294 649 0, 293 649 0, 292 649 0, 291 649 0, 290 649 0, 289 649 0, 288 649 0, 287 649 0, 286 649 0, 285 649 0, 284 649 0, 283 649 0, 282 649 0, 281 649 0, 280 649 0, 279 649 0, 278 649 0, 277 649 0, 276 649 0, 275 649 0, 274 649 0, 273 649 0, 272 649 0, 271 649 0, 270 649 0, 269 649 0, 268 649 0, 267 649 0, 266 649 0, 265 649 0, 264 649 0, 263 649 0, 262 649 0, 261 649 0, 260 649 0, 259 649 0, 258 649 0, 257 649 0, 256 649 0, 255 649 0, 254 649 0, 253 649 0, 252 649 0, 251 649 0, 250 649 0, 249 649 0, 248 649 0, 247 649 0, 246 649 0, 245 649 0, 244 649 0, 243 649 0, 242 649 0, 241 649 0, 240 649 0, 239 649 0, 238 649 0, 237 649 0, 236 649 0, 235 649 0, 234 649 0, 233 649 0, 232 649 0, 231 649 0, 230 649 0, 229 649 0, 228 649 0, 227 649 0, 226 649 0, 225 649 0, 224 649 0, 223 649 0, 222 649 0, 221 649 0, 220 649 0, 219 649 0, 218 649 0, 217 649 0, 216 649 0, 215 649 0, 127 649 0, 126 647 0, 125 628 0, 125 607 0, 126 605 0, 129 602 0, 130 601 0, 133 600 0, 290 600 0))",6 +AOI_2_Vegas_img5979,2,"POLYGON ((507 206 0, 621 210 0, 647 211 0, 650 212 0, 650 411 0, 650 416 0, 643 419 0, 640 420 0, 631 421 0, 568 426 0, 540 420 0, 538 419 0, 537 418 0, 461 216 0, 460 213 0, 461 211 0, 463 210 0, 474 207 0, 505 206 0, 507 206 0))",5 +AOI_2_Vegas_img5979,3,"POLYGON ((3 601 0, 35 602 0, 40 603 0, 42 604 0, 66 627 0, 67 628 0, 68 636 0, 67 637 0, 66 638 0, 65 639 0, 46 649 0, 45 649 0, 44 649 0, 43 649 0, 42 649 0, 41 649 0, 40 649 0, 39 649 0, 38 649 0, 37 649 0, 36 649 0, 35 649 0, 2 649 0, 1 647 0, 1 602 0, 2 601 0, 3 601 0))",4 +AOI_2_Vegas_img5979,4,"POLYGON ((170 479 0, 174 480 0, 176 481 0, 192 493 0, 195 496 0, 195 498 0, 195 500 0, 194 503 0, 192 507 0, 191 508 0, 178 515 0, 173 517 0, 170 518 0, 169 518 0, 168 518 0, 166 517 0, 152 507 0, 151 506 0, 150 505 0, 149 504 0, 148 503 0, 147 502 0, 146 499 0, 146 498 0, 146 497 0, 147 495 0, 148 494 0, 149 493 0, 158 484 0, 166 480 0, 168 479 0, 170 479 0))",3 +AOI_2_Vegas_img5979,5,"POLYGON ((226 478 0, 230 479 0, 233 481 0, 249 493 0, 250 494 0, 251 495 0, 251 497 0, 251 498 0, 251 499 0, 251 500 0, 251 501 0, 251 502 0, 250 505 0, 247 508 0, 236 516 0, 233 517 0, 225 519 0, 210 517 0, 208 516 0, 199 501 0, 198 498 0, 199 496 0, 214 484 0, 216 483 0, 222 480 0, 224 479 0, 226 478 0))",2 +AOI_2_Vegas_img5979,6,"POLYGON ((283 478 0, 289 479 0, 294 482 0, 307 493 0, 308 494 0, 308 498 0, 307 503 0, 296 511 0, 289 516 0, 284 517 0, 280 516 0, 274 514 0, 264 507 0, 263 506 0, 262 505 0, 261 504 0, 260 499 0, 260 498 0, 260 497 0, 260 496 0, 261 492 0, 269 484 0, 278 479 0, 282 478 0, 283 478 0))",1 +AOI_2_Vegas_img3457,0,"POLYGON ((36 34 0, 56 35 0, 62 36 0, 64 37 0, 68 45 0, 68 47 0, 68 51 0, 67 56 0, 66 59 0, 50 86 0, 49 87 0, 42 90 0, 38 91 0, 33 92 0, 2 95 0, 0 94 0, 0 85 0, 0 84 0, 0 83 0, 0 82 0, 0 81 0, 0 80 0, 0 79 0, 0 78 0, 0 77 0, 0 76 0, 0 75 0, 0 74 0, 0 73 0, 0 72 0, 0 71 0, 0 70 0, 0 69 0, 0 68 0, 0 67 0, 0 66 0, 0 65 0, 0 64 0, 2 53 0, 3 49 0, 5 46 0, 8 42 0, 12 37 0, 13 36 0, 15 35 0, 19 34 0, 36 34 0))",30 +AOI_2_Vegas_img3457,1,"POLYGON ((46 534 0, 52 535 0, 53 536 0, 54 537 0, 55 539 0, 56 541 0, 56 545 0, 56 554 0, 56 558 0, 53 582 0, 52 587 0, 50 590 0, 45 591 0, 33 592 0, 4 593 0, 2 593 0, 0 592 0, 0 583 0, 0 582 0, 0 581 0, 0 580 0, 0 579 0, 0 578 0, 0 577 0, 0 576 0, 0 575 0, 0 574 0, 0 573 0, 0 572 0, 0 571 0, 0 570 0, 0 569 0, 0 568 0, 0 567 0, 0 566 0, 0 565 0, 0 564 0, 0 563 0, 0 562 0, 0 561 0, 18 537 0, 20 536 0, 24 535 0, 32 534 0, 46 534 0))",29 +AOI_2_Vegas_img3457,2,"POLYGON ((49 177 0, 55 178 0, 60 179 0, 61 185 0, 61 189 0, 61 190 0, 61 191 0, 58 222 0, 57 227 0, 55 230 0, 50 234 0, 45 235 0, 31 236 0, 4 237 0, 0 236 0, 0 227 0, 0 226 0, 0 225 0, 0 224 0, 0 223 0, 0 222 0, 0 221 0, 0 220 0, 0 219 0, 0 218 0, 0 217 0, 0 216 0, 0 215 0, 0 214 0, 0 213 0, 0 212 0, 0 211 0, 0 210 0, 0 209 0, 0 206 0, 12 180 0, 13 179 0, 16 178 0, 20 177 0, 49 177 0))",28 +AOI_2_Vegas_img3457,3,"POLYGON ((345 242 0, 369 244 0, 375 245 0, 384 249 0, 385 250 0, 387 257 0, 387 261 0, 387 262 0, 386 274 0, 384 288 0, 383 293 0, 382 295 0, 377 302 0, 364 305 0, 359 306 0, 351 304 0, 349 303 0, 305 271 0, 301 268 0, 300 267 0, 299 262 0, 298 253 0, 298 249 0, 299 245 0, 303 244 0, 309 243 0, 345 242 0))",27 +AOI_2_Vegas_img3457,4,"POLYGON ((173 247 0, 179 248 0, 182 257 0, 182 261 0, 182 262 0, 182 266 0, 181 271 0, 180 274 0, 170 300 0, 168 302 0, 166 303 0, 164 304 0, 159 305 0, 145 307 0, 144 307 0, 143 307 0, 142 307 0, 109 302 0, 105 301 0, 104 296 0, 104 295 0, 104 286 0, 106 275 0, 107 271 0, 108 268 0, 111 263 0, 112 262 0, 114 261 0, 146 249 0, 149 248 0, 153 247 0, 173 247 0))",26 +AOI_2_Vegas_img3457,5,"POLYGON ((331 461 0, 337 462 0, 351 465 0, 363 468 0, 384 474 0, 385 475 0, 385 479 0, 385 480 0, 384 485 0, 383 487 0, 374 503 0, 372 505 0, 353 515 0, 350 516 0, 345 517 0, 344 517 0, 343 517 0, 342 517 0, 314 515 0, 310 514 0, 306 512 0, 301 508 0, 300 507 0, 299 506 0, 298 501 0, 298 500 0, 299 495 0, 300 491 0, 301 489 0, 323 464 0, 324 463 0, 326 462 0, 330 461 0, 331 461 0))",25 +AOI_2_Vegas_img3457,6,"POLYGON ((137 318 0, 143 319 0, 146 320 0, 148 321 0, 184 350 0, 188 358 0, 188 362 0, 187 367 0, 185 370 0, 181 374 0, 176 375 0, 165 376 0, 164 376 0, 163 376 0, 162 376 0, 161 376 0, 160 376 0, 159 376 0, 158 376 0, 157 376 0, 156 376 0, 155 376 0, 154 376 0, 153 376 0, 118 372 0, 114 371 0, 113 370 0, 102 348 0, 101 343 0, 100 338 0, 101 333 0, 102 329 0, 104 323 0, 105 322 0, 109 321 0, 114 320 0, 119 319 0, 131 318 0, 137 318 0))",24 +AOI_2_Vegas_img3457,7,"POLYGON ((139 176 0, 147 178 0, 150 179 0, 156 182 0, 157 183 0, 177 212 0, 179 215 0, 181 223 0, 181 225 0, 180 228 0, 179 229 0, 177 230 0, 174 231 0, 133 235 0, 132 235 0, 121 234 0, 112 230 0, 108 228 0, 107 225 0, 110 182 0, 111 180 0, 112 179 0, 114 178 0, 128 176 0, 139 176 0))",23 +AOI_2_Vegas_img3457,8,"POLYGON ((357 173 0, 361 174 0, 363 175 0, 363 177 0, 363 178 0, 363 179 0, 363 180 0, 363 181 0, 351 218 0, 350 221 0, 344 226 0, 341 227 0, 330 230 0, 300 230 0, 298 229 0, 297 228 0, 295 225 0, 294 222 0, 293 212 0, 294 209 0, 295 207 0, 296 205 0, 297 204 0, 332 175 0, 334 174 0, 337 173 0, 357 173 0))",22 +AOI_2_Vegas_img3457,9,"POLYGON ((142 389 0, 174 391 0, 178 393 0, 181 407 0, 181 408 0, 181 409 0, 180 416 0, 154 444 0, 153 445 0, 152 446 0, 123 445 0, 119 443 0, 110 436 0, 109 435 0, 108 433 0, 114 414 0, 121 394 0, 122 393 0, 123 392 0, 129 391 0, 142 389 0))",21 +AOI_2_Vegas_img3457,10,"POLYGON ((280 523 0, 317 530 0, 321 531 0, 333 537 0, 338 543 0, 340 546 0, 341 550 0, 341 552 0, 341 553 0, 341 554 0, 341 555 0, 341 556 0, 341 557 0, 340 560 0, 332 569 0, 331 570 0, 330 571 0, 329 572 0, 322 577 0, 304 589 0, 302 590 0, 300 591 0, 298 592 0, 295 593 0, 288 595 0, 283 595 0, 279 593 0, 275 588 0, 265 575 0, 262 571 0, 261 569 0, 249 542 0, 248 539 0, 248 536 0, 249 533 0, 250 531 0, 252 530 0, 263 526 0, 277 523 0, 280 523 0))",20 +AOI_2_Vegas_img3457,11,"POLYGON ((90 534 0, 127 540 0, 131 541 0, 134 542 0, 138 558 0, 139 571 0, 139 573 0, 139 575 0, 138 583 0, 137 588 0, 136 591 0, 133 592 0, 118 595 0, 113 595 0, 107 593 0, 102 591 0, 100 590 0, 72 565 0, 71 564 0, 70 561 0, 72 542 0, 74 538 0, 75 537 0, 77 536 0, 85 534 0, 90 534 0))",19 +AOI_2_Vegas_img3457,12,"POLYGON ((354 385 0, 369 388 0, 373 389 0, 380 393 0, 381 395 0, 381 410 0, 381 412 0, 378 427 0, 376 433 0, 371 439 0, 365 443 0, 362 444 0, 361 444 0, 360 444 0, 359 444 0, 339 440 0, 296 427 0, 292 425 0, 291 422 0, 290 419 0, 290 418 0, 290 417 0, 290 416 0, 291 402 0, 292 400 0, 293 399 0, 304 389 0, 306 388 0, 322 386 0, 353 385 0, 354 385 0))",18 +AOI_2_Vegas_img3457,13,"POLYGON ((222 538 0, 228 539 0, 230 540 0, 231 541 0, 232 542 0, 232 546 0, 232 547 0, 232 548 0, 232 549 0, 232 550 0, 232 551 0, 232 552 0, 232 556 0, 230 586 0, 229 591 0, 222 596 0, 217 597 0, 211 598 0, 205 598 0, 180 597 0, 176 596 0, 167 592 0, 165 591 0, 164 590 0, 163 585 0, 163 584 0, 163 577 0, 163 576 0, 163 575 0, 164 571 0, 165 568 0, 206 540 0, 208 539 0, 212 538 0, 222 538 0))",17 +AOI_2_Vegas_img3457,14,"POLYGON ((353 99 0, 361 100 0, 367 101 0, 368 102 0, 369 103 0, 370 104 0, 370 108 0, 370 109 0, 364 155 0, 363 160 0, 362 163 0, 361 164 0, 356 165 0, 348 166 0, 347 166 0, 343 165 0, 303 145 0, 302 144 0, 292 113 0, 291 108 0, 292 104 0, 294 103 0, 298 102 0, 351 99 0, 353 99 0))",16 +AOI_2_Vegas_img3457,15,"POLYGON ((33 247 0, 39 248 0, 49 250 0, 52 257 0, 53 282 0, 53 283 0, 53 287 0, 52 292 0, 50 301 0, 37 305 0, 32 306 0, 31 306 0, 28 306 0, 24 305 0, 4 291 0, 3 290 0, 0 286 0, 0 277 0, 0 276 0, 0 275 0, 0 274 0, 0 273 0, 0 272 0, 0 271 0, 0 270 0, 0 269 0, 0 268 0, 0 267 0, 0 266 0, 0 265 0, 0 264 0, 0 263 0, 0 262 0, 0 261 0, 0 260 0, 0 259 0, 0 250 0, 2 249 0, 22 247 0, 33 247 0))",15 +AOI_2_Vegas_img3457,16,"POLYGON ((5 99 0, 11 100 0, 49 109 0, 49 113 0, 49 114 0, 45 153 0, 44 158 0, 43 159 0, 38 160 0, 37 160 0, 36 160 0, 35 160 0, 34 160 0, 33 160 0, 32 160 0, 31 160 0, 30 160 0, 29 160 0, 28 160 0, 27 160 0, 26 160 0, 25 160 0, 24 160 0, 23 160 0, 22 160 0, 21 160 0, 20 160 0, 11 159 0, 7 158 0, 0 153 0, 0 144 0, 0 143 0, 0 142 0, 0 141 0, 0 140 0, 0 139 0, 0 138 0, 0 137 0, 0 109 0, 0 100 0, 2 99 0, 5 99 0))",14 +AOI_2_Vegas_img3457,17,"POLYGON ((38 326 0, 46 328 0, 48 329 0, 49 330 0, 51 333 0, 51 335 0, 51 336 0, 51 337 0, 49 371 0, 48 374 0, 46 376 0, 43 377 0, 42 377 0, 41 377 0, 40 377 0, 39 377 0, 38 377 0, 37 377 0, 36 377 0, 35 377 0, 34 377 0, 33 377 0, 32 377 0, 31 377 0, 30 377 0, 29 377 0, 28 377 0, 27 377 0, 26 377 0, 25 377 0, 24 377 0, 23 377 0, 22 377 0, 21 377 0, 2 376 0, 0 375 0, 0 353 0, 0 352 0, 1 351 0, 32 328 0, 34 327 0, 37 326 0, 38 326 0))",13 +AOI_2_Vegas_img3457,18,"POLYGON ((353 31 0, 365 33 0, 378 64 0, 378 68 0, 377 73 0, 376 75 0, 375 77 0, 361 83 0, 356 85 0, 351 86 0, 343 87 0, 332 88 0, 310 89 0, 297 88 0, 293 87 0, 292 84 0, 291 79 0, 292 75 0, 294 70 0, 296 65 0, 297 64 0, 311 50 0, 330 33 0, 333 32 0, 337 31 0, 353 31 0))",12 +AOI_2_Vegas_img3457,19,"POLYGON ((126 36 0, 161 37 0, 165 38 0, 166 39 0, 176 68 0, 176 70 0, 175 84 0, 174 87 0, 172 89 0, 159 96 0, 156 97 0, 152 98 0, 145 98 0, 143 97 0, 103 62 0, 102 61 0, 101 59 0, 100 56 0, 100 50 0, 101 48 0, 108 39 0, 109 38 0, 111 37 0, 116 36 0, 126 36 0))",11 +AOI_2_Vegas_img3457,20,"POLYGON ((353 315 0, 361 317 0, 362 318 0, 362 320 0, 362 321 0, 349 365 0, 348 368 0, 347 370 0, 346 371 0, 344 372 0, 341 373 0, 338 374 0, 337 374 0, 336 374 0, 335 374 0, 334 374 0, 313 372 0, 298 368 0, 296 367 0, 295 364 0, 294 361 0, 294 360 0, 294 359 0, 294 358 0, 294 357 0, 295 355 0, 319 318 0, 321 317 0, 323 316 0, 327 315 0, 353 315 0))",10 +AOI_2_Vegas_img3457,21,"POLYGON ((647 46 0, 650 47 0, 650 144 0, 650 149 0, 647 150 0, 642 151 0, 639 151 0, 604 150 0, 566 145 0, 562 143 0, 561 141 0, 558 133 0, 557 130 0, 557 129 0, 557 128 0, 557 127 0, 557 126 0, 558 105 0, 559 103 0, 560 101 0, 594 51 0, 596 49 0, 598 48 0, 600 47 0, 611 46 0, 647 46 0))",9 +AOI_2_Vegas_img3457,22,"POLYGON ((37 391 0, 44 392 0, 50 393 0, 52 394 0, 53 395 0, 55 420 0, 55 429 0, 55 433 0, 54 438 0, 51 441 0, 46 442 0, 12 446 0, 9 446 0, 5 445 0, 4 444 0, 0 432 0, 0 431 0, 0 422 0, 0 421 0, 0 420 0, 0 419 0, 0 418 0, 0 417 0, 0 416 0, 0 415 0, 0 414 0, 0 413 0, 0 412 0, 0 411 0, 0 410 0, 0 409 0, 0 408 0, 0 407 0, 0 403 0, 0 394 0, 2 393 0, 25 391 0, 37 391 0))",8 +AOI_2_Vegas_img3457,23,"POLYGON ((158 0 0, 158 1 0, 158 5 0, 157 10 0, 156 14 0, 150 18 0, 145 19 0, 134 20 0, 133 20 0, 132 20 0, 131 20 0, 130 20 0, 119 20 0, 115 19 0, 110 15 0, 109 10 0, 108 4 0, 108 3 0, 108 2 0, 109 0 0, 158 0 0))",7 +AOI_2_Vegas_img3457,24,"POLYGON ((576 60 0, 582 61 0, 583 69 0, 583 73 0, 582 78 0, 580 81 0, 577 84 0, 572 85 0, 571 85 0, 570 85 0, 569 85 0, 568 85 0, 557 85 0, 553 84 0, 549 75 0, 548 70 0, 548 66 0, 549 62 0, 550 61 0, 554 60 0, 576 60 0))",6 +AOI_2_Vegas_img3457,25,"POLYGON ((646 1 0, 648 2 0, 649 5 0, 649 23 0, 648 24 0, 647 25 0, 643 27 0, 575 41 0, 574 41 0, 560 36 0, 554 30 0, 553 28 0, 553 27 0, 553 26 0, 553 25 0, 555 2 0, 556 1 0, 646 1 0))",5 +AOI_2_Vegas_img3457,26,"POLYGON ((116 96 0, 132 97 0, 136 98 0, 143 100 0, 151 131 0, 151 133 0, 151 135 0, 150 150 0, 149 153 0, 148 155 0, 145 156 0, 120 159 0, 118 158 0, 113 154 0, 112 151 0, 109 139 0, 108 105 0, 108 101 0, 109 99 0, 111 98 0, 113 97 0, 116 96 0))",4 +AOI_2_Vegas_img3457,27,"POLYGON ((356 0 0, 356 2 0, 355 7 0, 353 8 0, 349 9 0, 344 10 0, 343 10 0, 339 9 0, 336 8 0, 335 3 0, 336 0 0, 356 0 0))",3 +AOI_2_Vegas_img3457,28,"POLYGON ((75 87 0, 81 88 0, 81 92 0, 81 93 0, 80 98 0, 75 99 0, 73 99 0, 69 98 0, 68 93 0, 69 89 0, 70 88 0, 74 87 0, 75 87 0))",2 +AOI_2_Vegas_img3457,29,"POLYGON ((46 0 0, 46 1 0, 46 3 0, 45 8 0, 44 11 0, 41 12 0, 25 9 0, 23 8 0, 22 7 0, 21 6 0, 20 5 0, 19 2 0, 20 0 0, 46 0 0))",1 +AOI_5_Khartoum_img1301,0,"POLYGON ((101 237 0, 105 238 0, 108 239 0, 113 241 0, 183 272 0, 185 273 0, 227 299 0, 233 327 0, 233 329 0, 232 332 0, 215 352 0, 214 353 0, 211 354 0, 210 354 0, 155 353 0, 153 352 0, 105 324 0, 104 323 0, 103 322 0, 96 307 0, 85 279 0, 84 276 0, 84 275 0, 93 245 0, 94 243 0, 97 239 0, 98 238 0, 100 237 0, 101 237 0))",32 +AOI_5_Khartoum_img1301,1,"POLYGON ((570 171 0, 578 173 0, 579 174 0, 580 175 0, 588 193 0, 588 195 0, 588 196 0, 585 211 0, 577 244 0, 576 247 0, 573 248 0, 572 248 0, 571 248 0, 570 248 0, 569 248 0, 560 248 0, 555 247 0, 547 244 0, 545 243 0, 544 242 0, 543 241 0, 531 216 0, 530 213 0, 529 210 0, 525 196 0, 525 192 0, 527 188 0, 529 187 0, 560 172 0, 567 171 0, 570 171 0))",31 +AOI_5_Khartoum_img1301,2,"POLYGON ((629 373 0, 635 374 0, 643 376 0, 650 378 0, 650 387 0, 650 388 0, 650 389 0, 650 390 0, 650 391 0, 650 420 0, 650 429 0, 634 435 0, 629 436 0, 614 437 0, 613 437 0, 612 437 0, 611 437 0, 610 437 0, 609 437 0, 597 435 0, 593 434 0, 563 418 0, 557 414 0, 556 409 0, 556 408 0, 556 407 0, 556 406 0, 556 405 0, 557 401 0, 558 397 0, 567 377 0, 573 375 0, 577 374 0, 599 373 0, 629 373 0))",30 +AOI_5_Khartoum_img1301,3,"POLYGON ((578 100 0, 593 103 0, 597 104 0, 604 107 0, 606 123 0, 606 125 0, 605 128 0, 598 137 0, 574 151 0, 571 152 0, 541 138 0, 539 137 0, 538 134 0, 538 133 0, 538 121 0, 540 111 0, 541 109 0, 544 106 0, 546 105 0, 578 100 0))",29 +AOI_5_Khartoum_img1301,4,"POLYGON ((200 212 0, 206 213 0, 215 216 0, 243 226 0, 244 227 0, 247 232 0, 247 236 0, 247 237 0, 247 238 0, 247 239 0, 247 240 0, 246 245 0, 240 262 0, 237 268 0, 235 271 0, 222 275 0, 217 276 0, 211 277 0, 207 277 0, 203 276 0, 197 274 0, 191 271 0, 190 270 0, 189 269 0, 178 249 0, 177 244 0, 177 242 0, 178 238 0, 188 218 0, 190 215 0, 191 214 0, 195 213 0, 199 212 0, 200 212 0))",28 +AOI_5_Khartoum_img1301,5,"POLYGON ((309 286 0, 315 287 0, 317 288 0, 326 296 0, 327 297 0, 328 298 0, 328 302 0, 327 307 0, 324 315 0, 321 322 0, 320 324 0, 318 326 0, 313 327 0, 312 327 0, 311 327 0, 310 327 0, 306 326 0, 299 323 0, 295 321 0, 294 316 0, 296 307 0, 297 303 0, 298 299 0, 303 288 0, 304 287 0, 308 286 0, 309 286 0))",27 +AOI_5_Khartoum_img1301,6,"POLYGON ((253 313 0, 275 314 0, 279 315 0, 285 318 0, 286 319 0, 287 320 0, 288 321 0, 289 323 0, 297 352 0, 297 353 0, 297 355 0, 295 370 0, 294 373 0, 293 374 0, 290 375 0, 283 375 0, 282 375 0, 281 375 0, 280 375 0, 279 375 0, 278 375 0, 232 369 0, 230 368 0, 229 365 0, 229 364 0, 229 363 0, 229 362 0, 229 361 0, 229 360 0, 229 359 0, 229 358 0, 229 357 0, 229 356 0, 229 355 0, 233 333 0, 234 331 0, 244 317 0, 246 315 0, 248 314 0, 250 313 0, 253 313 0))",26 +AOI_5_Khartoum_img1301,7,"POLYGON ((637 196 0, 643 197 0, 644 198 0, 645 199 0, 646 200 0, 650 207 0, 650 208 0, 650 217 0, 650 218 0, 650 257 0, 650 266 0, 647 267 0, 646 267 0, 645 267 0, 644 267 0, 643 267 0, 603 258 0, 599 257 0, 598 252 0, 597 247 0, 597 246 0, 597 245 0, 597 244 0, 597 243 0, 597 242 0, 597 241 0, 597 240 0, 597 239 0, 597 238 0, 602 203 0, 603 199 0, 604 198 0, 608 197 0, 631 196 0, 637 196 0))",25 +AOI_5_Khartoum_img1301,8,"POLYGON ((494 427 0, 506 428 0, 510 429 0, 526 434 0, 528 435 0, 529 437 0, 530 439 0, 530 441 0, 522 466 0, 521 469 0, 512 485 0, 510 488 0, 507 489 0, 502 489 0, 501 489 0, 481 482 0, 479 481 0, 478 478 0, 478 477 0, 478 471 0, 478 470 0, 488 430 0, 489 428 0, 491 427 0, 494 427 0))",24 +AOI_5_Khartoum_img1301,9,"POLYGON ((61 133 0, 67 134 0, 88 141 0, 88 145 0, 88 146 0, 88 147 0, 87 152 0, 86 154 0, 83 160 0, 81 164 0, 74 178 0, 71 183 0, 68 185 0, 63 186 0, 59 185 0, 53 182 0, 46 178 0, 41 175 0, 36 171 0, 35 170 0, 34 165 0, 34 164 0, 36 156 0, 48 138 0, 50 136 0, 55 134 0, 59 133 0, 61 133 0))",23 +AOI_5_Khartoum_img1301,10,"POLYGON ((450 121 0, 456 122 0, 465 125 0, 467 126 0, 468 127 0, 483 164 0, 483 166 0, 483 170 0, 482 175 0, 481 179 0, 469 192 0, 466 195 0, 461 196 0, 457 195 0, 442 186 0, 425 175 0, 422 173 0, 421 168 0, 420 161 0, 421 157 0, 426 148 0, 445 122 0, 449 121 0, 450 121 0))",22 +AOI_5_Khartoum_img1301,11,"POLYGON ((398 396 0, 461 406 0, 465 407 0, 478 416 0, 479 421 0, 479 423 0, 479 424 0, 479 425 0, 479 426 0, 479 427 0, 479 429 0, 478 432 0, 471 449 0, 458 462 0, 454 465 0, 451 466 0, 450 466 0, 449 466 0, 448 466 0, 447 466 0, 441 466 0, 440 466 0, 398 459 0, 392 456 0, 386 439 0, 385 436 0, 385 435 0, 385 431 0, 394 400 0, 395 398 0, 396 397 0, 398 396 0))",21 +AOI_5_Khartoum_img1301,12,"POLYGON ((298 55 0, 302 56 0, 338 69 0, 342 71 0, 345 73 0, 345 75 0, 345 76 0, 344 79 0, 324 110 0, 323 111 0, 320 112 0, 319 112 0, 318 112 0, 305 108 0, 300 106 0, 296 104 0, 287 99 0, 284 93 0, 283 90 0, 282 86 0, 282 85 0, 282 84 0, 291 58 0, 292 56 0, 294 55 0, 298 55 0))",20 +AOI_5_Khartoum_img1301,13,"POLYGON ((323 0 0, 336 7 0, 338 9 0, 338 10 0, 338 14 0, 337 19 0, 314 50 0, 312 52 0, 307 53 0, 306 53 0, 305 53 0, 301 52 0, 298 51 0, 281 41 0, 265 31 0, 234 11 0, 230 7 0, 229 2 0, 230 0 0, 323 0 0))",19 +AOI_5_Khartoum_img1301,14,"POLYGON ((650 0 0, 650 7 0, 650 8 0, 650 9 0, 650 10 0, 650 13 0, 647 14 0, 643 13 0, 642 12 0, 641 11 0, 640 6 0, 640 3 0, 641 0 0, 650 0 0))",18 +AOI_5_Khartoum_img1301,15,"POLYGON ((583 442 0, 589 443 0, 596 445 0, 631 462 0, 633 464 0, 634 473 0, 634 474 0, 634 478 0, 633 483 0, 631 491 0, 627 503 0, 624 509 0, 615 511 0, 610 512 0, 574 508 0, 570 507 0, 569 506 0, 562 493 0, 561 488 0, 561 487 0, 561 486 0, 561 485 0, 562 466 0, 563 462 0, 566 451 0, 569 446 0, 571 444 0, 575 443 0, 583 442 0))",17 +AOI_5_Khartoum_img1301,16,"POLYGON ((490 39 0, 496 40 0, 499 42 0, 500 55 0, 501 75 0, 501 79 0, 501 80 0, 500 85 0, 490 125 0, 487 128 0, 483 129 0, 478 130 0, 474 129 0, 444 116 0, 443 114 0, 442 109 0, 442 108 0, 443 104 0, 446 94 0, 466 49 0, 475 40 0, 479 39 0, 490 39 0))",16 +AOI_5_Khartoum_img1301,17,"POLYGON ((435 322 0, 441 323 0, 456 328 0, 457 329 0, 457 333 0, 457 334 0, 457 335 0, 452 362 0, 451 367 0, 443 386 0, 441 388 0, 436 389 0, 432 388 0, 429 387 0, 426 386 0, 415 382 0, 410 380 0, 408 379 0, 407 378 0, 406 373 0, 406 372 0, 406 371 0, 406 364 0, 407 360 0, 428 323 0, 432 322 0, 435 322 0))",15 +AOI_5_Khartoum_img1301,18,"POLYGON ((395 311 0, 401 312 0, 404 313 0, 407 315 0, 408 316 0, 409 317 0, 412 321 0, 413 323 0, 420 343 0, 420 347 0, 419 352 0, 417 357 0, 415 361 0, 413 363 0, 408 364 0, 392 365 0, 384 364 0, 380 363 0, 371 359 0, 370 358 0, 369 356 0, 368 354 0, 367 349 0, 372 324 0, 373 320 0, 376 314 0, 380 313 0, 394 311 0, 395 311 0))",14 +AOI_5_Khartoum_img1301,19,"POLYGON ((520 317 0, 530 318 0, 536 319 0, 537 320 0, 539 323 0, 553 347 0, 553 351 0, 552 356 0, 545 386 0, 544 387 0, 534 393 0, 527 396 0, 522 397 0, 518 396 0, 511 383 0, 510 378 0, 509 373 0, 508 368 0, 507 343 0, 507 342 0, 507 341 0, 507 340 0, 507 339 0, 507 338 0, 507 337 0, 508 333 0, 512 318 0, 516 317 0, 520 317 0))",13 +AOI_5_Khartoum_img1301,20,"POLYGON ((23 47 0, 29 48 0, 30 49 0, 31 50 0, 61 85 0, 62 87 0, 62 91 0, 61 96 0, 51 141 0, 48 144 0, 24 161 0, 19 162 0, 15 161 0, 12 160 0, 10 159 0, 0 142 0, 0 133 0, 0 132 0, 0 131 0, 0 130 0, 0 129 0, 0 128 0, 0 127 0, 0 126 0, 0 125 0, 0 124 0, 0 123 0, 0 118 0, 0 58 0, 0 49 0, 2 48 0, 10 47 0, 23 47 0))",12 +AOI_5_Khartoum_img1301,21,"POLYGON ((366 93 0, 370 94 0, 409 109 0, 416 113 0, 417 114 0, 418 115 0, 419 116 0, 422 120 0, 423 128 0, 423 130 0, 423 132 0, 422 135 0, 404 159 0, 396 167 0, 393 168 0, 390 169 0, 383 170 0, 382 170 0, 381 170 0, 380 170 0, 379 170 0, 378 170 0, 374 169 0, 370 167 0, 335 149 0, 334 148 0, 333 145 0, 336 129 0, 337 127 0, 358 97 0, 361 94 0, 363 93 0, 366 93 0))",11 +AOI_5_Khartoum_img1301,22,"POLYGON ((3 169 0, 7 170 0, 15 174 0, 30 182 0, 33 187 0, 33 189 0, 28 204 0, 12 220 0, 9 221 0, 8 221 0, 7 221 0, 6 221 0, 5 221 0, 1 219 0, 0 218 0, 0 216 0, 0 213 0, 0 212 0, 0 211 0, 0 210 0, 0 209 0, 0 208 0, 0 207 0, 0 206 0, 0 205 0, 0 204 0, 0 203 0, 0 202 0, 0 201 0, 0 200 0, 0 199 0, 0 198 0, 0 197 0, 0 196 0, 0 195 0, 0 194 0, 0 193 0, 0 175 0, 0 170 0, 2 169 0, 3 169 0))",10 +AOI_5_Khartoum_img1301,23,"POLYGON ((354 290 0, 360 291 0, 377 304 0, 377 328 0, 377 332 0, 376 337 0, 368 349 0, 367 350 0, 362 351 0, 345 348 0, 341 347 0, 329 339 0, 326 336 0, 325 331 0, 325 329 0, 326 325 0, 348 291 0, 352 290 0, 354 290 0))",9 +AOI_5_Khartoum_img1301,24,"POLYGON ((101 86 0, 107 87 0, 111 90 0, 112 91 0, 113 92 0, 114 93 0, 114 97 0, 113 102 0, 103 122 0, 90 146 0, 89 147 0, 84 148 0, 80 147 0, 65 134 0, 64 133 0, 63 128 0, 63 127 0, 63 126 0, 63 125 0, 63 124 0, 63 123 0, 63 122 0, 64 118 0, 73 98 0, 76 95 0, 79 93 0, 81 92 0, 96 87 0, 100 86 0, 101 86 0))",8 +AOI_5_Khartoum_img1301,25,"POLYGON ((419 176 0, 425 177 0, 429 180 0, 430 185 0, 430 189 0, 430 190 0, 429 207 0, 428 212 0, 427 214 0, 425 216 0, 420 217 0, 395 220 0, 393 220 0, 389 219 0, 385 217 0, 384 216 0, 383 211 0, 384 207 0, 388 201 0, 411 178 0, 413 177 0, 417 176 0, 419 176 0))",7 +AOI_5_Khartoum_img1301,26,"POLYGON ((280 218 0, 284 219 0, 318 235 0, 352 252 0, 354 254 0, 354 256 0, 354 258 0, 353 261 0, 350 267 0, 348 269 0, 346 270 0, 278 296 0, 275 297 0, 270 296 0, 264 293 0, 263 290 0, 262 277 0, 262 276 0, 262 275 0, 264 253 0, 267 237 0, 268 234 0, 269 232 0, 276 220 0, 277 219 0, 279 218 0, 280 218 0))",6 +AOI_5_Khartoum_img1301,27,"POLYGON ((627 100 0, 633 101 0, 637 102 0, 650 107 0, 650 137 0, 650 146 0, 637 176 0, 633 177 0, 628 178 0, 624 177 0, 609 171 0, 608 170 0, 607 165 0, 607 164 0, 607 159 0, 607 158 0, 607 151 0, 607 150 0, 608 126 0, 609 121 0, 610 117 0, 620 102 0, 621 101 0, 625 100 0, 627 100 0))",5 +AOI_5_Khartoum_img1301,28,"POLYGON ((538 459 0, 542 460 0, 544 461 0, 555 486 0, 555 488 0, 555 489 0, 555 490 0, 555 491 0, 555 492 0, 554 495 0, 531 524 0, 527 529 0, 524 530 0, 521 530 0, 517 529 0, 499 523 0, 497 522 0, 496 519 0, 496 518 0, 500 498 0, 501 496 0, 523 462 0, 524 461 0, 526 460 0, 528 459 0, 538 459 0))",4 +AOI_5_Khartoum_img1301,29,"POLYGON ((131 636 0, 137 638 0, 139 639 0, 140 640 0, 140 641 0, 140 642 0, 139 645 0, 138 646 0, 137 647 0, 136 648 0, 134 649 0, 125 649 0, 124 647 0, 123 645 0, 122 641 0, 122 639 0, 123 638 0, 124 637 0, 129 636 0, 131 636 0))",3 +AOI_5_Khartoum_img1301,30,"POLYGON ((65 60 0, 71 61 0, 77 66 0, 77 70 0, 76 75 0, 71 76 0, 70 76 0, 69 76 0, 68 76 0, 64 75 0, 62 74 0, 58 70 0, 57 65 0, 58 61 0, 62 60 0, 65 60 0))",2 +AOI_5_Khartoum_img1301,31,"POLYGON ((399 269 0, 405 270 0, 425 280 0, 426 314 0, 426 315 0, 426 319 0, 425 324 0, 420 325 0, 419 325 0, 418 325 0, 417 325 0, 416 325 0, 415 325 0, 411 324 0, 404 321 0, 377 305 0, 376 304 0, 375 299 0, 375 298 0, 375 297 0, 375 296 0, 375 295 0, 376 291 0, 380 280 0, 385 275 0, 395 270 0, 399 269 0))",1 +AOI_5_Khartoum_img1306,0,"POLYGON ((427 569 0, 433 570 0, 434 573 0, 436 582 0, 439 598 0, 439 602 0, 438 607 0, 437 608 0, 433 609 0, 428 610 0, 408 614 0, 403 615 0, 385 618 0, 378 618 0, 374 617 0, 369 613 0, 368 612 0, 367 611 0, 366 608 0, 364 599 0, 363 594 0, 363 591 0, 364 587 0, 380 576 0, 384 575 0, 388 574 0, 394 573 0, 426 569 0, 427 569 0))",40 +AOI_5_Khartoum_img1306,1,"POLYGON ((189 385 0, 195 386 0, 198 410 0, 198 414 0, 197 419 0, 183 447 0, 182 448 0, 177 449 0, 163 451 0, 162 451 0, 161 451 0, 160 451 0, 159 451 0, 155 450 0, 137 440 0, 134 438 0, 133 436 0, 131 431 0, 125 406 0, 124 401 0, 124 397 0, 125 393 0, 146 387 0, 150 386 0, 184 385 0, 189 385 0))",39 +AOI_5_Khartoum_img1306,2,"POLYGON ((561 83 0, 563 84 0, 564 85 0, 565 86 0, 568 90 0, 570 93 0, 576 103 0, 579 111 0, 579 112 0, 579 113 0, 578 116 0, 577 117 0, 576 118 0, 575 119 0, 574 120 0, 572 121 0, 562 125 0, 557 127 0, 552 127 0, 551 126 0, 541 106 0, 539 101 0, 539 98 0, 540 95 0, 542 93 0, 543 92 0, 548 89 0, 552 87 0, 554 86 0, 556 85 0, 558 84 0, 561 83 0))",38 +AOI_5_Khartoum_img1306,3,"POLYGON ((566 326 0, 572 327 0, 575 331 0, 576 333 0, 576 337 0, 574 365 0, 573 370 0, 571 373 0, 566 374 0, 540 378 0, 539 378 0, 538 378 0, 534 377 0, 530 376 0, 521 345 0, 520 340 0, 521 336 0, 522 334 0, 523 333 0, 525 332 0, 534 329 0, 541 327 0, 545 326 0, 566 326 0))",37 +AOI_5_Khartoum_img1306,4,"POLYGON ((449 119 0, 453 120 0, 460 122 0, 467 129 0, 468 134 0, 468 136 0, 468 137 0, 468 138 0, 467 141 0, 465 146 0, 461 150 0, 458 151 0, 450 153 0, 398 163 0, 350 171 0, 348 170 0, 339 164 0, 333 159 0, 331 157 0, 330 154 0, 329 150 0, 329 146 0, 330 144 0, 332 143 0, 342 139 0, 448 119 0, 449 119 0))",36 +AOI_5_Khartoum_img1306,5,"POLYGON ((550 463 0, 556 464 0, 583 474 0, 595 484 0, 596 485 0, 597 488 0, 598 493 0, 598 496 0, 598 500 0, 597 505 0, 595 507 0, 593 508 0, 588 509 0, 570 511 0, 562 511 0, 558 510 0, 535 499 0, 534 498 0, 533 496 0, 532 493 0, 530 486 0, 529 481 0, 529 471 0, 530 467 0, 532 466 0, 536 465 0, 547 463 0, 550 463 0))",35 +AOI_5_Khartoum_img1306,6,"POLYGON ((84 559 0, 112 561 0, 116 562 0, 117 563 0, 118 566 0, 130 613 0, 130 614 0, 130 616 0, 129 619 0, 125 625 0, 122 626 0, 119 627 0, 118 627 0, 117 627 0, 116 627 0, 115 627 0, 112 627 0, 77 624 0, 73 623 0, 66 620 0, 64 619 0, 63 616 0, 60 599 0, 57 580 0, 58 578 0, 61 574 0, 72 560 0, 74 559 0, 84 559 0))",34 +AOI_5_Khartoum_img1306,7,"POLYGON ((236 284 0, 240 285 0, 242 292 0, 246 332 0, 246 334 0, 239 355 0, 236 356 0, 207 360 0, 201 360 0, 188 359 0, 186 358 0, 185 357 0, 184 356 0, 182 353 0, 181 350 0, 181 349 0, 182 347 0, 184 343 0, 200 315 0, 230 285 0, 232 284 0, 236 284 0))",33 +AOI_5_Khartoum_img1306,8,"POLYGON ((500 494 0, 504 495 0, 505 496 0, 506 497 0, 511 507 0, 512 530 0, 512 535 0, 512 537 0, 510 543 0, 507 544 0, 477 546 0, 457 542 0, 455 541 0, 441 530 0, 440 527 0, 435 507 0, 435 505 0, 436 503 0, 438 502 0, 440 501 0, 499 494 0, 500 494 0))",32 +AOI_5_Khartoum_img1306,9,"POLYGON ((417 508 0, 419 509 0, 420 510 0, 422 518 0, 423 528 0, 424 544 0, 423 545 0, 422 546 0, 381 563 0, 380 563 0, 369 555 0, 368 554 0, 367 553 0, 366 552 0, 365 550 0, 364 547 0, 361 527 0, 361 519 0, 363 512 0, 364 510 0, 365 509 0, 367 508 0, 417 508 0))",31 +AOI_5_Khartoum_img1306,10,"POLYGON ((105 86 0, 111 87 0, 122 92 0, 128 103 0, 131 112 0, 131 125 0, 131 129 0, 130 134 0, 128 135 0, 123 136 0, 115 137 0, 69 138 0, 67 138 0, 63 137 0, 58 135 0, 57 132 0, 53 116 0, 52 111 0, 52 108 0, 53 98 0, 54 94 0, 58 93 0, 105 86 0))",30 +AOI_5_Khartoum_img1306,11,"POLYGON ((12 465 0, 18 466 0, 41 484 0, 42 486 0, 43 489 0, 44 496 0, 44 498 0, 44 502 0, 43 507 0, 31 514 0, 26 515 0, 19 516 0, 9 517 0, 5 517 0, 1 516 0, 0 514 0, 0 476 0, 0 467 0, 4 466 0, 10 465 0, 12 465 0))",29 +AOI_5_Khartoum_img1306,12,"POLYGON ((237 47 0, 241 48 0, 242 50 0, 242 52 0, 241 55 0, 240 57 0, 238 60 0, 235 63 0, 233 64 0, 230 65 0, 220 68 0, 199 71 0, 188 71 0, 186 70 0, 185 69 0, 184 68 0, 183 65 0, 182 62 0, 182 61 0, 182 60 0, 182 59 0, 183 57 0, 184 56 0, 185 55 0, 187 54 0, 190 53 0, 196 52 0, 236 47 0, 237 47 0))",28 +AOI_5_Khartoum_img1306,13,"POLYGON ((328 354 0, 332 355 0, 342 367 0, 343 371 0, 344 400 0, 344 404 0, 344 406 0, 343 409 0, 342 410 0, 339 411 0, 304 420 0, 285 418 0, 281 416 0, 280 415 0, 279 414 0, 278 412 0, 277 409 0, 276 406 0, 275 403 0, 274 397 0, 275 395 0, 276 393 0, 297 367 0, 298 366 0, 300 365 0, 302 364 0, 319 356 0, 324 354 0, 328 354 0))",27 +AOI_5_Khartoum_img1306,14,"POLYGON ((490 434 0, 496 435 0, 497 438 0, 498 443 0, 500 456 0, 502 471 0, 502 472 0, 502 476 0, 501 481 0, 445 501 0, 440 502 0, 434 502 0, 430 501 0, 429 496 0, 429 478 0, 430 474 0, 442 442 0, 443 441 0, 447 440 0, 478 434 0, 490 434 0))",26 +AOI_5_Khartoum_img1306,15,"POLYGON ((606 528 0, 612 529 0, 620 533 0, 621 537 0, 621 541 0, 621 542 0, 621 543 0, 621 553 0, 621 557 0, 620 562 0, 619 563 0, 614 564 0, 581 569 0, 574 570 0, 555 569 0, 551 568 0, 549 564 0, 548 559 0, 548 543 0, 549 539 0, 550 538 0, 554 536 0, 557 535 0, 561 534 0, 565 533 0, 587 529 0, 605 528 0, 606 528 0))",25 +AOI_5_Khartoum_img1306,16,"POLYGON ((265 42 0, 271 43 0, 272 45 0, 273 51 0, 273 54 0, 273 58 0, 272 63 0, 267 64 0, 263 63 0, 255 61 0, 254 60 0, 253 58 0, 252 53 0, 252 52 0, 252 51 0, 253 47 0, 254 44 0, 257 43 0, 261 42 0, 265 42 0))",24 +AOI_5_Khartoum_img1306,17,"POLYGON ((222 622 0, 228 623 0, 229 627 0, 229 632 0, 229 636 0, 228 641 0, 227 642 0, 222 643 0, 219 643 0, 215 642 0, 214 639 0, 213 634 0, 213 633 0, 213 632 0, 214 628 0, 215 626 0, 218 623 0, 222 622 0))",23 +AOI_5_Khartoum_img1306,18,"POLYGON ((305 619 0, 353 636 0, 355 637 0, 356 638 0, 356 639 0, 356 640 0, 356 641 0, 356 642 0, 356 643 0, 356 647 0, 355 648 0, 354 649 0, 353 649 0, 352 649 0, 351 649 0, 350 649 0, 349 649 0, 348 649 0, 347 649 0, 346 649 0, 345 649 0, 344 649 0, 343 649 0, 342 649 0, 341 649 0, 340 649 0, 339 649 0, 338 649 0, 337 649 0, 336 649 0, 335 649 0, 334 649 0, 333 649 0, 332 649 0, 331 649 0, 330 649 0, 329 649 0, 328 649 0, 327 649 0, 326 649 0, 325 649 0, 324 649 0, 323 649 0, 322 649 0, 321 649 0, 320 649 0, 319 649 0, 318 649 0, 317 649 0, 316 649 0, 315 649 0, 310 649 0, 309 649 0, 302 649 0, 299 647 0, 298 645 0, 297 643 0, 296 640 0, 295 637 0, 295 636 0, 295 633 0, 296 622 0, 297 621 0, 298 620 0, 305 619 0))",22 +AOI_5_Khartoum_img1306,19,"POLYGON ((79 331 0, 83 332 0, 86 333 0, 93 339 0, 94 340 0, 105 355 0, 105 359 0, 105 361 0, 103 429 0, 102 432 0, 98 436 0, 95 437 0, 74 441 0, 51 444 0, 34 444 0, 2 431 0, 0 430 0, 0 425 0, 0 424 0, 0 423 0, 0 422 0, 0 421 0, 0 420 0, 0 419 0, 0 418 0, 0 417 0, 0 416 0, 0 415 0, 0 414 0, 0 413 0, 0 412 0, 0 411 0, 0 410 0, 0 409 0, 0 408 0, 0 407 0, 0 406 0, 0 349 0, 0 344 0, 2 343 0, 41 333 0, 77 331 0, 79 331 0))",21 +AOI_5_Khartoum_img1306,20,"POLYGON ((39 162 0, 45 163 0, 46 164 0, 47 165 0, 48 166 0, 49 167 0, 52 194 0, 52 196 0, 52 200 0, 51 205 0, 47 207 0, 13 217 0, 9 218 0, 4 219 0, 0 218 0, 0 210 0, 0 201 0, 0 178 0, 0 169 0, 2 168 0, 20 164 0, 27 163 0, 35 162 0, 39 162 0))",20 +AOI_5_Khartoum_img1306,21,"POLYGON ((33 0 0, 51 4 0, 55 5 0, 57 6 0, 58 7 0, 61 27 0, 61 29 0, 60 32 0, 44 59 0, 41 60 0, 38 61 0, 32 62 0, 24 63 0, 20 61 0, 19 60 0, 18 59 0, 17 58 0, 0 32 0, 0 29 0, 0 27 0, 0 26 0, 0 25 0, 0 24 0, 0 23 0, 0 22 0, 0 21 0, 0 20 0, 0 19 0, 0 18 0, 0 17 0, 0 16 0, 0 15 0, 0 14 0, 0 13 0, 0 12 0, 0 11 0, 0 10 0, 0 9 0, 0 8 0, 0 7 0, 0 6 0, 0 5 0, 0 4 0, 0 3 0, 0 2 0, 0 1 0, 0 0 0, 33 0 0))",19 +AOI_5_Khartoum_img1306,22,"POLYGON ((32 526 0, 46 528 0, 52 529 0, 62 551 0, 62 555 0, 62 556 0, 62 557 0, 61 562 0, 60 563 0, 55 566 0, 31 574 0, 27 575 0, 22 576 0, 10 577 0, 9 577 0, 5 576 0, 1 574 0, 0 573 0, 0 545 0, 0 536 0, 2 535 0, 25 527 0, 29 526 0, 32 526 0))",18 +AOI_5_Khartoum_img1306,23,"POLYGON ((448 27 0, 450 28 0, 453 31 0, 454 32 0, 458 50 0, 458 52 0, 455 55 0, 454 56 0, 453 57 0, 449 59 0, 440 61 0, 430 63 0, 382 71 0, 369 73 0, 349 76 0, 335 77 0, 333 76 0, 331 73 0, 330 71 0, 328 65 0, 327 61 0, 327 60 0, 327 56 0, 328 53 0, 329 52 0, 334 48 0, 342 45 0, 371 39 0, 409 32 0, 442 27 0, 448 27 0))",17 +AOI_5_Khartoum_img1306,24,"POLYGON ((38 567 0, 44 568 0, 55 571 0, 58 590 0, 59 598 0, 59 602 0, 58 607 0, 56 610 0, 46 618 0, 35 621 0, 30 622 0, 25 623 0, 4 626 0, 2 626 0, 0 625 0, 0 616 0, 0 615 0, 0 614 0, 0 613 0, 0 612 0, 0 611 0, 0 610 0, 0 609 0, 0 608 0, 0 607 0, 0 606 0, 0 605 0, 0 604 0, 0 603 0, 0 591 0, 0 582 0, 2 581 0, 33 568 0, 37 567 0, 38 567 0))",16 +AOI_5_Khartoum_img1306,25,"POLYGON ((394 343 0, 398 344 0, 406 381 0, 406 383 0, 406 385 0, 405 388 0, 402 391 0, 400 392 0, 397 393 0, 394 394 0, 354 397 0, 349 397 0, 347 396 0, 346 395 0, 345 392 0, 344 389 0, 343 385 0, 341 377 0, 341 368 0, 342 366 0, 344 364 0, 346 363 0, 383 345 0, 389 343 0, 394 343 0))",15 +AOI_5_Khartoum_img1306,26,"POLYGON ((277 612 0, 279 613 0, 281 614 0, 282 615 0, 284 622 0, 284 623 0, 284 624 0, 284 625 0, 284 626 0, 284 627 0, 284 628 0, 284 632 0, 283 633 0, 282 634 0, 278 636 0, 269 636 0, 268 634 0, 267 620 0, 267 615 0, 268 614 0, 269 613 0, 273 612 0, 277 612 0))",14 +AOI_5_Khartoum_img1306,27,"POLYGON ((111 630 0, 142 634 0, 148 635 0, 148 638 0, 148 642 0, 147 647 0, 144 650 0, 135 650 0, 123 650 0, 122 650 0, 121 650 0, 120 650 0, 119 650 0, 118 650 0, 117 650 0, 116 650 0, 115 650 0, 114 650 0, 96 650 0, 92 650 0, 67 650 0, 66 647 0, 67 643 0, 69 641 0, 77 635 0, 80 634 0, 84 633 0, 88 632 0, 103 630 0, 111 630 0))",13 +AOI_5_Khartoum_img1306,28,"POLYGON ((519 623 0, 525 624 0, 526 625 0, 527 626 0, 531 642 0, 531 646 0, 530 650 0, 520 650 0, 519 650 0, 518 650 0, 517 650 0, 516 650 0, 515 650 0, 514 650 0, 513 650 0, 512 650 0, 511 650 0, 510 650 0, 509 650 0, 508 650 0, 507 650 0, 506 650 0, 505 650 0, 504 650 0, 503 650 0, 502 650 0, 501 650 0, 500 650 0, 499 650 0, 498 650 0, 497 650 0, 496 650 0, 471 650 0, 470 647 0, 470 644 0, 471 640 0, 472 638 0, 473 637 0, 487 629 0, 502 625 0, 506 624 0, 518 623 0, 519 623 0))",12 +AOI_5_Khartoum_img1306,29,"POLYGON ((81 153 0, 87 154 0, 102 171 0, 102 175 0, 102 179 0, 101 184 0, 89 198 0, 88 199 0, 85 200 0, 80 201 0, 75 202 0, 74 202 0, 70 201 0, 67 200 0, 60 194 0, 58 191 0, 57 186 0, 55 172 0, 55 166 0, 56 162 0, 64 156 0, 68 155 0, 80 153 0, 81 153 0))",11 +AOI_5_Khartoum_img1306,30,"POLYGON ((71 445 0, 77 446 0, 81 447 0, 91 451 0, 98 458 0, 99 459 0, 100 460 0, 101 462 0, 101 466 0, 101 467 0, 101 468 0, 101 469 0, 101 470 0, 100 475 0, 90 484 0, 61 510 0, 56 511 0, 52 510 0, 50 507 0, 49 505 0, 48 503 0, 44 494 0, 43 489 0, 42 458 0, 42 457 0, 43 453 0, 44 452 0, 46 451 0, 62 446 0, 66 445 0, 71 445 0))",10 +AOI_5_Khartoum_img1306,31,"POLYGON ((558 587 0, 599 601 0, 617 610 0, 649 627 0, 649 647 0, 648 648 0, 647 649 0, 646 649 0, 645 649 0, 644 649 0, 643 649 0, 642 649 0, 641 649 0, 640 649 0, 617 649 0, 578 649 0, 577 649 0, 576 649 0, 575 649 0, 574 649 0, 573 649 0, 572 649 0, 571 649 0, 570 649 0, 568 649 0, 566 648 0, 565 646 0, 564 644 0, 563 642 0, 561 634 0, 554 603 0, 553 590 0, 553 589 0, 554 588 0, 557 587 0, 558 587 0))",9 +AOI_5_Khartoum_img1306,32,"POLYGON ((253 97 0, 274 99 0, 276 100 0, 277 101 0, 278 102 0, 279 105 0, 279 107 0, 276 110 0, 275 111 0, 274 112 0, 268 113 0, 253 115 0, 252 114 0, 251 113 0, 250 112 0, 249 110 0, 245 101 0, 245 98 0, 246 97 0, 253 97 0))",8 +AOI_5_Khartoum_img1306,33,"POLYGON ((387 630 0, 391 631 0, 392 634 0, 393 638 0, 393 643 0, 393 645 0, 392 648 0, 390 649 0, 387 650 0, 374 650 0, 373 647 0, 372 642 0, 372 641 0, 372 638 0, 373 636 0, 376 633 0, 378 632 0, 381 631 0, 384 630 0, 387 630 0))",7 +AOI_5_Khartoum_img1306,34,"POLYGON ((596 16 0, 600 17 0, 600 19 0, 600 20 0, 600 21 0, 599 24 0, 597 25 0, 594 26 0, 593 26 0, 586 26 0, 584 25 0, 583 24 0, 582 21 0, 583 19 0, 585 18 0, 588 17 0, 596 16 0))",6 +AOI_5_Khartoum_img1306,35,"POLYGON ((532 256 0, 536 257 0, 543 261 0, 570 280 0, 571 282 0, 571 288 0, 571 290 0, 570 293 0, 556 310 0, 554 311 0, 552 312 0, 549 313 0, 546 314 0, 507 323 0, 503 323 0, 501 322 0, 496 318 0, 495 317 0, 493 312 0, 492 309 0, 491 305 0, 486 277 0, 486 275 0, 487 272 0, 491 261 0, 492 259 0, 494 258 0, 499 256 0, 532 256 0))",5 +AOI_5_Khartoum_img1306,36,"POLYGON ((617 349 0, 621 350 0, 636 358 0, 650 366 0, 650 371 0, 650 372 0, 650 373 0, 637 403 0, 634 404 0, 631 405 0, 620 408 0, 604 410 0, 602 410 0, 600 409 0, 593 404 0, 592 401 0, 588 375 0, 588 374 0, 589 372 0, 602 352 0, 604 351 0, 614 349 0, 617 349 0))",4 +AOI_5_Khartoum_img1306,37,"POLYGON ((2 110 0, 6 111 0, 9 113 0, 11 118 0, 15 142 0, 16 151 0, 16 153 0, 16 154 0, 16 155 0, 16 156 0, 16 157 0, 16 158 0, 16 159 0, 16 160 0, 16 161 0, 16 162 0, 15 165 0, 13 167 0, 11 168 0, 8 169 0, 2 169 0, 0 168 0, 0 116 0, 0 111 0, 2 110 0))",3 +AOI_5_Khartoum_img1306,38,"POLYGON ((310 424 0, 314 425 0, 315 426 0, 316 427 0, 319 437 0, 319 439 0, 319 441 0, 318 444 0, 317 445 0, 314 446 0, 313 446 0, 312 446 0, 309 446 0, 307 445 0, 306 444 0, 305 442 0, 304 439 0, 303 436 0, 303 435 0, 303 434 0, 303 433 0, 303 432 0, 304 429 0, 305 427 0, 308 425 0, 310 424 0))",2 +AOI_5_Khartoum_img1306,39,"POLYGON ((102 0 0, 113 19 0, 113 21 0, 112 24 0, 110 25 0, 107 26 0, 100 28 0, 72 32 0, 65 32 0, 63 31 0, 62 30 0, 61 28 0, 59 23 0, 58 20 0, 57 12 0, 56 2 0, 57 0 0, 102 0 0))",1 +AOI_5_Khartoum_img130,0,"POLYGON ((124 288 0, 130 289 0, 131 290 0, 139 340 0, 139 348 0, 139 352 0, 138 357 0, 127 363 0, 122 364 0, 121 364 0, 120 364 0, 119 364 0, 115 363 0, 112 362 0, 103 358 0, 97 355 0, 81 343 0, 80 342 0, 79 337 0, 76 317 0, 75 306 0, 75 305 0, 76 298 0, 77 294 0, 81 293 0, 118 288 0, 124 288 0))",35 +AOI_5_Khartoum_img130,1,"POLYGON ((161 182 0, 165 183 0, 169 184 0, 188 216 0, 189 219 0, 192 239 0, 192 241 0, 192 243 0, 190 249 0, 189 250 0, 186 251 0, 154 257 0, 142 259 0, 141 259 0, 139 258 0, 138 257 0, 137 256 0, 136 255 0, 132 245 0, 131 242 0, 130 237 0, 129 230 0, 128 222 0, 128 219 0, 129 192 0, 130 190 0, 132 189 0, 154 183 0, 158 182 0, 161 182 0))",34 +AOI_5_Khartoum_img130,2,"POLYGON ((561 432 0, 567 433 0, 567 437 0, 567 438 0, 565 450 0, 564 455 0, 563 460 0, 553 476 0, 550 479 0, 532 496 0, 527 497 0, 517 498 0, 513 497 0, 512 496 0, 511 491 0, 510 484 0, 516 451 0, 517 447 0, 527 435 0, 531 434 0, 546 432 0, 561 432 0))",33 +AOI_5_Khartoum_img130,3,"POLYGON ((268 271 0, 272 272 0, 273 273 0, 274 275 0, 278 301 0, 278 304 0, 278 306 0, 277 309 0, 276 311 0, 255 346 0, 253 348 0, 251 349 0, 248 350 0, 245 351 0, 242 351 0, 236 348 0, 233 346 0, 232 345 0, 231 344 0, 228 339 0, 227 337 0, 226 334 0, 221 295 0, 221 294 0, 221 293 0, 222 291 0, 223 289 0, 224 288 0, 225 287 0, 227 286 0, 237 281 0, 239 280 0, 241 279 0, 247 276 0, 249 275 0, 252 274 0, 264 271 0, 268 271 0))",32 +AOI_5_Khartoum_img130,4,"POLYGON ((250 375 0, 256 376 0, 291 393 0, 292 394 0, 293 397 0, 294 402 0, 297 427 0, 297 431 0, 297 435 0, 296 440 0, 295 441 0, 293 442 0, 288 443 0, 283 444 0, 252 449 0, 250 449 0, 242 447 0, 238 431 0, 231 400 0, 230 395 0, 229 389 0, 229 384 0, 230 380 0, 233 378 0, 237 377 0, 241 376 0, 245 375 0, 250 375 0))",31 +AOI_5_Khartoum_img130,5,"POLYGON ((316 160 0, 322 161 0, 323 162 0, 324 163 0, 325 166 0, 327 174 0, 327 178 0, 327 179 0, 327 180 0, 327 181 0, 327 182 0, 327 183 0, 327 184 0, 327 185 0, 327 186 0, 326 191 0, 314 208 0, 309 209 0, 297 209 0, 279 208 0, 275 207 0, 274 206 0, 273 201 0, 271 186 0, 271 185 0, 274 171 0, 275 167 0, 283 164 0, 287 163 0, 291 162 0, 312 160 0, 316 160 0))",30 +AOI_5_Khartoum_img130,6,"POLYGON ((391 370 0, 422 373 0, 426 374 0, 429 375 0, 430 376 0, 431 377 0, 432 378 0, 438 408 0, 438 415 0, 438 417 0, 437 420 0, 436 421 0, 434 422 0, 431 423 0, 428 424 0, 425 425 0, 404 429 0, 395 430 0, 394 430 0, 387 430 0, 384 429 0, 382 428 0, 381 425 0, 378 408 0, 375 389 0, 375 381 0, 379 373 0, 381 372 0, 390 370 0, 391 370 0))",29 +AOI_5_Khartoum_img130,7,"POLYGON ((644 264 0, 650 265 0, 650 319 0, 650 328 0, 647 329 0, 646 329 0, 645 329 0, 644 329 0, 640 328 0, 637 327 0, 625 313 0, 623 307 0, 616 279 0, 615 274 0, 615 273 0, 616 269 0, 620 268 0, 624 267 0, 628 266 0, 633 265 0, 644 264 0))",28 +AOI_5_Khartoum_img130,8,"POLYGON ((560 587 0, 575 590 0, 579 591 0, 580 592 0, 585 597 0, 586 598 0, 587 599 0, 588 600 0, 591 605 0, 597 638 0, 597 640 0, 596 643 0, 593 644 0, 558 650 0, 544 650 0, 538 636 0, 537 633 0, 531 612 0, 531 608 0, 535 592 0, 536 590 0, 537 589 0, 539 588 0, 559 587 0, 560 587 0))",27 +AOI_5_Khartoum_img130,9,"POLYGON ((500 136 0, 508 140 0, 519 147 0, 520 151 0, 527 182 0, 527 192 0, 526 193 0, 525 194 0, 524 195 0, 513 198 0, 508 199 0, 503 199 0, 478 193 0, 476 192 0, 475 191 0, 451 163 0, 450 161 0, 443 142 0, 445 139 0, 446 138 0, 494 136 0, 500 136 0))",26 +AOI_5_Khartoum_img130,10,"POLYGON ((190 285 0, 204 286 0, 208 287 0, 213 312 0, 213 318 0, 213 320 0, 212 323 0, 210 325 0, 194 332 0, 191 333 0, 188 334 0, 166 338 0, 162 338 0, 160 337 0, 159 336 0, 156 330 0, 155 327 0, 154 323 0, 150 298 0, 150 295 0, 151 293 0, 153 291 0, 155 290 0, 168 286 0, 173 285 0, 190 285 0))",25 +AOI_5_Khartoum_img130,11,"POLYGON ((351 367 0, 356 368 0, 360 369 0, 367 371 0, 369 374 0, 370 377 0, 373 387 0, 373 398 0, 373 400 0, 364 428 0, 362 434 0, 359 435 0, 315 441 0, 314 441 0, 313 441 0, 311 440 0, 310 439 0, 309 438 0, 308 437 0, 307 434 0, 302 407 0, 300 395 0, 299 377 0, 299 376 0, 300 374 0, 302 373 0, 348 367 0, 351 367 0))",24 +AOI_5_Khartoum_img130,12,"POLYGON ((167 113 0, 173 114 0, 175 115 0, 176 116 0, 177 118 0, 177 122 0, 177 123 0, 177 124 0, 176 129 0, 170 140 0, 155 158 0, 154 159 0, 150 160 0, 145 161 0, 132 163 0, 129 163 0, 122 162 0, 118 161 0, 117 156 0, 115 133 0, 115 130 0, 116 126 0, 118 124 0, 120 123 0, 122 122 0, 126 121 0, 166 113 0, 167 113 0))",23 +AOI_5_Khartoum_img130,13,"POLYGON ((61 211 0, 65 212 0, 66 213 0, 68 227 0, 70 247 0, 70 256 0, 70 258 0, 69 261 0, 67 263 0, 49 274 0, 47 275 0, 43 277 0, 20 285 0, 17 286 0, 9 288 0, 2 288 0, 0 287 0, 0 230 0, 1 228 0, 7 221 0, 8 220 0, 10 219 0, 49 211 0, 61 211 0))",22 +AOI_5_Khartoum_img130,14,"POLYGON ((66 397 0, 70 398 0, 85 439 0, 86 442 0, 86 444 0, 86 446 0, 85 449 0, 80 461 0, 78 463 0, 76 464 0, 74 465 0, 72 466 0, 70 467 0, 53 473 0, 44 476 0, 41 477 0, 24 479 0, 23 479 0, 21 478 0, 20 477 0, 19 476 0, 18 475 0, 17 474 0, 16 472 0, 15 469 0, 12 452 0, 11 446 0, 11 445 0, 27 413 0, 44 403 0, 46 402 0, 53 399 0, 56 398 0, 64 397 0, 66 397 0))",21 +AOI_5_Khartoum_img130,15,"POLYGON ((332 172 0, 387 176 0, 391 177 0, 393 178 0, 394 179 0, 396 193 0, 396 195 0, 396 196 0, 396 197 0, 396 198 0, 395 201 0, 393 204 0, 392 205 0, 391 206 0, 374 216 0, 371 217 0, 343 221 0, 342 221 0, 340 220 0, 339 218 0, 334 205 0, 328 188 0, 327 185 0, 326 178 0, 326 177 0, 328 173 0, 330 172 0, 332 172 0))",20 +AOI_5_Khartoum_img130,16,"POLYGON ((585 339 0, 591 340 0, 624 350 0, 631 360 0, 632 363 0, 632 367 0, 632 368 0, 631 374 0, 630 379 0, 629 380 0, 604 394 0, 599 395 0, 596 395 0, 592 394 0, 591 393 0, 590 392 0, 589 391 0, 581 382 0, 580 380 0, 579 378 0, 578 375 0, 574 360 0, 573 355 0, 573 353 0, 574 349 0, 578 341 0, 581 340 0, 585 339 0))",19 +AOI_5_Khartoum_img130,17,"POLYGON ((647 374 0, 650 375 0, 650 384 0, 650 385 0, 650 386 0, 650 387 0, 650 388 0, 650 389 0, 650 390 0, 650 391 0, 650 392 0, 650 393 0, 650 394 0, 650 395 0, 650 396 0, 650 397 0, 650 398 0, 650 399 0, 650 401 0, 650 405 0, 649 410 0, 646 413 0, 641 414 0, 624 417 0, 617 418 0, 613 418 0, 609 417 0, 608 412 0, 607 406 0, 607 405 0, 607 404 0, 607 403 0, 608 399 0, 609 398 0, 622 388 0, 642 375 0, 646 374 0, 647 374 0))",18 +AOI_5_Khartoum_img130,18,"POLYGON ((364 454 0, 370 455 0, 373 458 0, 374 459 0, 377 473 0, 378 481 0, 378 485 0, 377 490 0, 354 511 0, 350 514 0, 335 517 0, 330 518 0, 326 517 0, 324 516 0, 323 515 0, 319 497 0, 318 492 0, 318 491 0, 319 487 0, 320 483 0, 334 466 0, 335 465 0, 349 456 0, 353 455 0, 361 454 0, 364 454 0))",17 +AOI_5_Khartoum_img130,19,"POLYGON ((641 59 0, 645 60 0, 650 62 0, 650 67 0, 650 68 0, 650 69 0, 650 70 0, 650 71 0, 650 72 0, 650 73 0, 650 74 0, 650 75 0, 650 76 0, 650 77 0, 650 78 0, 647 79 0, 640 81 0, 635 82 0, 598 88 0, 597 88 0, 595 87 0, 594 86 0, 593 83 0, 591 73 0, 591 65 0, 592 63 0, 593 62 0, 595 61 0, 641 59 0))",16 +AOI_5_Khartoum_img130,20,"POLYGON ((613 425 0, 628 428 0, 632 429 0, 634 430 0, 636 432 0, 636 441 0, 636 443 0, 635 446 0, 609 479 0, 608 480 0, 605 481 0, 602 482 0, 600 482 0, 598 481 0, 588 474 0, 587 473 0, 585 468 0, 584 465 0, 583 462 0, 583 461 0, 583 460 0, 596 430 0, 597 428 0, 599 427 0, 601 426 0, 612 425 0, 613 425 0))",15 +AOI_5_Khartoum_img130,21,"POLYGON ((639 0 0, 647 1 0, 650 2 0, 650 49 0, 650 54 0, 647 55 0, 638 56 0, 634 56 0, 630 55 0, 628 54 0, 623 50 0, 622 49 0, 621 48 0, 620 47 0, 619 46 0, 618 45 0, 617 42 0, 631 2 0, 632 0 0, 639 0 0))",14 +AOI_5_Khartoum_img130,22,"POLYGON ((150 397 0, 156 398 0, 164 400 0, 165 401 0, 166 402 0, 167 404 0, 168 406 0, 174 433 0, 176 448 0, 176 456 0, 176 460 0, 175 465 0, 167 476 0, 166 477 0, 164 478 0, 160 479 0, 151 481 0, 146 482 0, 132 484 0, 125 484 0, 121 483 0, 118 481 0, 97 456 0, 96 454 0, 95 449 0, 95 446 0, 96 442 0, 107 407 0, 116 400 0, 120 399 0, 149 397 0, 150 397 0))",13 +AOI_5_Khartoum_img130,23,"POLYGON ((329 250 0, 337 251 0, 343 252 0, 344 275 0, 344 279 0, 343 284 0, 339 295 0, 336 299 0, 332 302 0, 315 306 0, 310 307 0, 305 308 0, 301 308 0, 297 307 0, 289 303 0, 288 302 0, 287 301 0, 286 299 0, 285 294 0, 285 293 0, 286 289 0, 324 251 0, 328 250 0, 329 250 0))",12 +AOI_5_Khartoum_img130,24,"POLYGON ((527 613 0, 531 614 0, 532 616 0, 533 619 0, 537 642 0, 537 644 0, 537 645 0, 537 647 0, 536 650 0, 531 650 0, 530 650 0, 529 650 0, 528 650 0, 527 650 0, 526 650 0, 525 650 0, 524 650 0, 523 650 0, 522 650 0, 521 650 0, 520 650 0, 519 650 0, 518 650 0, 517 650 0, 516 650 0, 515 650 0, 514 650 0, 513 650 0, 512 650 0, 511 650 0, 510 650 0, 509 650 0, 508 650 0, 507 650 0, 506 650 0, 505 650 0, 504 650 0, 503 650 0, 502 650 0, 481 650 0, 480 647 0, 476 628 0, 476 625 0, 477 623 0, 479 621 0, 481 620 0, 484 619 0, 525 613 0, 527 613 0))",11 +AOI_5_Khartoum_img130,25,"POLYGON ((60 0 0, 67 4 0, 68 5 0, 69 6 0, 70 7 0, 71 9 0, 71 13 0, 71 14 0, 71 15 0, 71 16 0, 71 17 0, 71 18 0, 71 19 0, 71 20 0, 71 24 0, 70 29 0, 69 30 0, 64 32 0, 21 48 0, 16 49 0, 12 48 0, 2 45 0, 0 44 0, 0 14 0, 0 5 0, 0 4 0, 0 3 0, 0 0 0, 60 0 0))",10 +AOI_5_Khartoum_img130,26,"POLYGON ((533 209 0, 539 210 0, 542 212 0, 546 220 0, 551 245 0, 551 249 0, 551 250 0, 551 251 0, 550 256 0, 548 261 0, 543 262 0, 528 263 0, 506 263 0, 502 262 0, 496 260 0, 495 259 0, 494 254 0, 494 253 0, 495 249 0, 522 213 0, 523 212 0, 529 210 0, 533 209 0))",9 +AOI_5_Khartoum_img130,27,"POLYGON ((386 248 0, 392 249 0, 407 292 0, 407 296 0, 406 301 0, 404 302 0, 399 303 0, 378 307 0, 365 309 0, 363 309 0, 359 308 0, 350 272 0, 349 267 0, 349 265 0, 350 261 0, 352 259 0, 379 249 0, 383 248 0, 386 248 0))",8 +AOI_5_Khartoum_img130,28,"POLYGON ((606 211 0, 610 212 0, 613 219 0, 613 221 0, 613 222 0, 613 223 0, 612 226 0, 599 242 0, 588 255 0, 587 256 0, 584 257 0, 579 256 0, 573 253 0, 553 242 0, 551 236 0, 550 233 0, 550 232 0, 551 224 0, 554 218 0, 556 217 0, 604 211 0, 606 211 0))",7 +AOI_5_Khartoum_img130,29,"POLYGON ((290 35 0, 296 36 0, 298 43 0, 299 50 0, 299 52 0, 299 56 0, 298 61 0, 296 62 0, 293 63 0, 288 64 0, 284 63 0, 283 62 0, 281 56 0, 280 51 0, 280 50 0, 280 49 0, 281 45 0, 283 39 0, 285 36 0, 289 35 0, 290 35 0))",6 +AOI_5_Khartoum_img130,30,"POLYGON ((16 518 0, 20 519 0, 21 520 0, 22 522 0, 22 524 0, 22 525 0, 22 526 0, 22 527 0, 22 528 0, 22 529 0, 22 530 0, 22 531 0, 22 532 0, 21 579 0, 20 582 0, 18 583 0, 15 584 0, 14 584 0, 11 583 0, 6 581 0, 4 580 0, 3 579 0, 0 575 0, 0 570 0, 0 569 0, 0 568 0, 0 567 0, 0 566 0, 0 565 0, 0 564 0, 0 563 0, 0 560 0, 0 526 0, 0 521 0, 2 520 0, 16 518 0))",5 +AOI_5_Khartoum_img130,31,"POLYGON ((572 0 0, 578 1 0, 579 2 0, 580 3 0, 580 7 0, 580 8 0, 572 49 0, 571 54 0, 567 58 0, 553 61 0, 548 62 0, 547 62 0, 546 62 0, 545 62 0, 513 60 0, 509 59 0, 508 58 0, 507 56 0, 505 51 0, 504 46 0, 504 45 0, 505 41 0, 520 11 0, 521 10 0, 541 5 0, 545 4 0, 564 1 0, 571 0 0, 572 0 0))",4 +AOI_5_Khartoum_img130,32,"POLYGON ((2 40 0, 8 41 0, 10 42 0, 41 84 0, 41 85 0, 41 89 0, 40 94 0, 39 97 0, 34 98 0, 33 98 0, 25 98 0, 24 98 0, 17 97 0, 13 96 0, 10 95 0, 0 86 0, 0 50 0, 0 41 0, 2 40 0))",3 +AOI_5_Khartoum_img130,33,"POLYGON ((441 628 0, 447 629 0, 450 637 0, 451 643 0, 451 647 0, 450 650 0, 415 650 0, 411 650 0, 410 647 0, 409 642 0, 409 639 0, 410 635 0, 414 634 0, 430 630 0, 434 629 0, 440 628 0, 441 628 0))",2 +AOI_5_Khartoum_img130,34,"POLYGON ((626 0 0, 627 10 0, 627 12 0, 627 14 0, 626 17 0, 625 18 0, 622 19 0, 619 19 0, 617 18 0, 616 16 0, 615 13 0, 614 8 0, 614 2 0, 615 0 0, 626 0 0))",1 +AOI_5_Khartoum_img463,-1,POLYGON EMPTY,1 diff --git a/docker/solaris/solaris/data/SN2_sample_truth.csv b/docker/solaris/solaris/data/SN2_sample_truth.csv new file mode 100644 index 00000000..3cd41d1d --- /dev/null +++ b/docker/solaris/solaris/data/SN2_sample_truth.csv @@ -0,0 +1,173 @@ +ImageId,BuildingId,PolygonWKT_Pix,PolygonWKT_Geo +AOI_2_Vegas_img3457,1,"POLYGON ((230.11 542.07 0,204.78 541.94 0,204.61 564.45 0,177.46 564.32 0,177.42 568.76 0,164.43 568.69 0,164.2 597.85 0,178.24 597.92 0,178.12 613.19 0,200.64 613.3 0,200.75 599.42 0,229.66 599.56 0,230.11 542.07 0))","POLYGON ((-115.21739131 36.181239121000033 0,-115.217392504999964 36.181083877000049 0,-115.217470583999955 36.181084268000063 0,-115.217470871999978 36.181046782000067 0,-115.217531680999969 36.181047088000071 0,-115.217531363999967 36.181088314000078 0,-115.217569252999965 36.181088504000058 0,-115.21756864799994 36.181167229000039 0,-115.217533557999957 36.181167053000024 0,-115.217533464999974 36.181179047000057 0,-115.217460161999952 36.181178679000027 0,-115.217459693999956 36.181239464000043 0,-115.21739131 36.181239121000033 0))" +AOI_2_Vegas_img3457,2,"POLYGON ((101.94 537.25 0,72.09 537.8 0,72.95 567.68 0,97.98 567.21 0,98.71 592.53 0,137.68 591.8 0,136.25 541.85 0,102.09 542.49 0,101.94 537.25 0))","POLYGON ((-115.21773737 36.181252136000069 0,-115.217736963999982 36.18123797700008 0,-115.21764473799999 36.18123969800007 0,-115.217640873999983 36.181104831000027 0,-115.217746083999941 36.18110286700005 0,-115.217748041999982 36.181171229000029 0,-115.217815634999965 36.181169968000063 0,-115.217817944999979 36.181250633000047 0,-115.21773737 36.181252136000069 0))" +AOI_2_Vegas_img3457,3,"POLYGON ((51.41 536.38 0,19.18 537.57 0,20.07 553.14 0,10.38 553.5 0,10.82 561.39 0,0.0 561.79 0,0.0 592.28 0,54.47 590.27 0,51.41 536.38 0))","POLYGON ((-115.217873800999939 36.181254469000066 0,-115.217865543999949 36.181108972000061 0,-115.218012599821591 36.181103536019876 0,-115.218012599821591 36.181185860899824 0,-115.21798337499996 36.181186942000068 0,-115.217984584999954 36.18120826300003 0,-115.217958421999981 36.181209231000025 0,-115.217960805999951 36.181251253000028 0,-115.217873800999939 36.181254469000066 0))" +AOI_2_Vegas_img3457,4,"POLYGON ((282.81 520.66 0,245.8 534.0 0,280.51 596.7 0,340.87 574.93 0,332.63 560.04 0,339.82 557.45 0,330.14 539.98 0,322.96 542.57 0,315.9 529.81 0,292.54 538.24 0,282.81 520.66 0))","POLYGON ((-115.217249008999943 36.181296929000041 0,-115.217222733999961 36.181249464000075 0,-115.217159678999963 36.181272204000038 0,-115.217140614999948 36.181237764000059 0,-115.217121214999963 36.181244760000027 0,-115.217095097999959 36.181197581000049 0,-115.217114497999944 36.181190584000035 0,-115.21709224599999 36.181150388000049 0,-115.217255232999946 36.181091608000031 0,-115.21734894 36.181260890000033 0,-115.217249008999943 36.181296929000041 0))" +AOI_2_Vegas_img3457,5,"POLYGON ((382.07 477.73 0,324.11 459.67 0,311.11 486.86 0,305.0 484.95 0,294.39 507.15 0,323.59 516.24 0,327.26 508.57 0,350.92 515.94 0,356.67 503.9 0,367.88 507.39 0,382.07 477.73 0))","POLYGON ((-115.216981011999962 36.181412830000056 0,-115.217019310999945 36.181332737000048 0,-115.21704957899999 36.181342166000036 0,-115.217065118999983 36.18130966800004 0,-115.217129006999983 36.181329572000038 0,-115.217138914999964 36.18130885000005 0,-115.217217740999956 36.181333407000068 0,-115.217189090999966 36.181393323000066 0,-115.217172606999952 36.181388187000039 0,-115.21713750899994 36.181461584000033 0,-115.216981011999962 36.181412830000056 0))" +AOI_2_Vegas_img3457,6,"POLYGON ((159.33 438.89 0,159.33 426.1 0,183.28 426.1 0,183.28 393.42 0,120.95 393.42 0,120.95 414.17 0,108.98 414.17 0,108.98 438.89 0,159.33 438.89 0))","POLYGON ((-115.217582400999959 36.181517688000042 0,-115.217718353999942 36.181517688000042 0,-115.217718353999942 36.181584450000059 0,-115.21768603 36.181584450000059 0,-115.21768603 36.181640468000069 0,-115.217517752999981 36.181640468000069 0,-115.217517752999981 36.181552220000071 0,-115.217582400999959 36.181552220000071 0,-115.217582400999959 36.181517688000042 0))" +AOI_2_Vegas_img3457,7,"POLYGON ((52.89 409.31 0,70.25 408.77 0,69.51 393.3 0,52.15 393.84 0,0.0 395.46 0,0.0 420.85 0,5.35 420.68 0,6.62 447.2 0,54.62 445.71 0,52.89 409.31 0))","POLYGON ((-115.217869804999964 36.181597575000069 0,-115.217865120999988 36.181499271000064 0,-115.217994738999948 36.181495248000033 0,-115.21799815199995 36.181566861000078 0,-115.218012599821591 36.181566412621173 0,-115.218012599821591 36.18163495942656 0,-115.217871794999951 36.181639331000042 0,-115.217824922999966 36.18164078600006 0,-115.217822932999979 36.181599030000029 0,-115.217869804999964 36.181597575000069 0))" +AOI_2_Vegas_img3457,8,"POLYGON ((366.29 388.17 0,295.58 390.42 0,296.76 414.71 0,325.41 413.8 0,326.81 442.4 0,368.87 441.07 0,366.29 388.17 0))","POLYGON ((-115.21702363 36.181654639000044 0,-115.217016663999971 36.181511821000072 0,-115.217130214999941 36.181508213000029 0,-115.21713398199995 36.181585440000049 0,-115.217211341999985 36.181582982000066 0,-115.217214540999976 36.181648573000075 0,-115.21702363 36.181654639000044 0))" +AOI_2_Vegas_img3457,9,"POLYGON ((50.7 376.93 0,50.7 344.61 0,59.07 344.61 0,59.07 329.68 0,50.7 329.68 0,5.37 329.68 0,5.37 353.49 0,0.0 353.49 0,0.0 376.93 0,50.7 376.93 0))","POLYGON ((-115.217875697999943 36.181684976000042 0,-115.218012599821591 36.181684976000042 0,-115.218012599821591 36.181748283000047 0,-115.217998102999957 36.181748283000047 0,-115.217998102999957 36.181812551000064 0,-115.217875697999943 36.181812551000064 0,-115.21785311799999 36.181812551000064 0,-115.21785311799999 36.181772264000074 0,-115.217875697999943 36.181772264000074 0,-115.217875697999943 36.181684976000042 0))" +AOI_2_Vegas_img3457,10,"POLYGON ((149.12 322.04 0,98.33 323.53 0,99.65 353.05 0,111.2 352.71 0,112.24 375.95 0,184.95 373.83 0,183.93 351.13 0,150.47 352.11 0,149.12 322.04 0))","POLYGON ((-115.217609970999945 36.18183317900008 0,-115.21760633699995 36.18175201300005 0,-115.217515980999963 36.181754648000037 0,-115.217513235999945 36.181693353000071 0,-115.217709555999988 36.181687625000052 0,-115.217712365999944 36.181750371000078 0,-115.217743542999983 36.181749461000038 0,-115.217747112999973 36.181829177000054 0,-115.217609970999945 36.18183317900008 0))" +AOI_2_Vegas_img3457,11,"POLYGON ((324.42 373.42 0,324.23 368.6 0,366.64 367.49 0,364.64 317.43 0,316.98 318.67 0,317.9 341.53 0,307.94 341.79 0,308.25 349.6 0,296.07 349.91 0,297.03 374.13 0,324.42 373.42 0))","POLYGON ((-115.217136668999956 36.181694463000042 0,-115.217210610999985 36.18169253800005 0,-115.217213223999977 36.18175793100005 0,-115.21718032 36.181758788000025 0,-115.217181162999964 36.181779856000048 0,-115.217154278999942 36.181780556000035 0,-115.217156745999944 36.181842278000033 0,-115.217028072999938 36.181845628000076 0,-115.217022670999938 36.181710464000048 0,-115.21713719 36.181707482000036 0,-115.217136668999956 36.181694463000042 0))" +AOI_2_Vegas_img3457,12,"POLYGON ((62.56 252.91 0,0.0 251.95 0,0.0 302.69 0,49.53 303.45 0,50.14 277.44 0,61.97 277.62 0,62.56 252.91 0))","POLYGON ((-115.217843696999978 36.182019841000056 0,-115.21784527599999 36.181953126000053 0,-115.21787722 36.18195361800008 0,-115.21787888199998 36.181883386000038 0,-115.218012599821591 36.181885447936899 0,-115.218012599821591 36.182022445894503 0,-115.217843696999978 36.182019841000056 0))" +AOI_2_Vegas_img3457,13,"POLYGON ((168.31 303.79 0,167.72 274.94 0,179.1 274.79 0,178.61 250.88 0,146.31 251.31 0,146.52 261.27 0,107.49 261.79 0,108.36 304.59 0,168.31 303.79 0))","POLYGON ((-115.21755817199994 36.181882463000079 0,-115.217720016999976 36.181880303000071 0,-115.217722383999956 36.181995872000073 0,-115.217617000999951 36.18199727800004 0,-115.217617551999979 36.182024173000059 0,-115.217530348999958 36.182025336000038 0,-115.217529026999955 36.181960762000074 0,-115.217559767999944 36.181960351000043 0,-115.21755817199994 36.181882463000079 0))" +AOI_2_Vegas_img3457,14,"POLYGON ((401.28 242.41 0,391.84 241.91 0,390.77 254.9 0,400.2 255.41 0,401.28 242.41 0))","POLYGON ((-115.216929151999977 36.182048185000042 0,-115.21693205 36.182013106000056 0,-115.216957526999977 36.182014477000052 0,-115.216954628999986 36.182049556000038 0,-115.216929151999977 36.182048185000042 0))" +AOI_2_Vegas_img3457,15,"POLYGON ((380.48 231.74 0,345.72 231.93 0,345.85 247.73 0,301.94 247.96 0,297.71 247.98 0,297.92 274.37 0,302.16 274.35 0,327.49 274.21 0,327.65 293.42 0,380.99 293.14 0,380.48 231.74 0))","POLYGON ((-115.216985290999958 36.182076990000041 0,-115.21698393 36.181911223000043 0,-115.217127948999973 36.181910453000057 0,-115.217128373999969 36.181962331000079 0,-115.21719677599998 36.181961966000074 0,-115.217208208999978 36.181961905000037 0,-115.217208792999941 36.182033145000048 0,-115.21719736099999 36.182033206000028 0,-115.21707880299999 36.182033840000031 0,-115.21707915199994 36.182076488000064 0,-115.216985290999958 36.182076990000041 0))" +AOI_2_Vegas_img3457,16,"POLYGON ((56.03 235.52 0,55.02 180.53 0,15.03 181.01 0,15.26 193.27 0,9.07 193.35 0,9.35 208.39 0,0.0 208.5 0,0.0 236.19 0,56.03 235.52 0))","POLYGON ((-115.21786132699998 36.182066807000069 0,-115.218012599821591 36.182064997877973 0,-115.218012599821591 36.182139748220848 0,-115.217987362999963 36.182140050000044 0,-115.217988108999975 36.182180660000029 0,-115.217971400999943 36.182180860000074 0,-115.217972007999947 36.182213969000031 0,-115.21786405099999 36.182215260000078 0,-115.21786132699998 36.182066807000069 0))" +AOI_2_Vegas_img3457,17,"POLYGON ((177.9 202.26 0,156.18 202.15 0,156.36 179.56 0,109.25 179.31 0,109.0 209.97 0,100.12 209.92 0,99.91 234.68 0,177.63 235.09 0,177.9 202.26 0))","POLYGON ((-115.217532275999986 36.182156590000034 0,-115.217533002999971 36.182067955000036 0,-115.217742834999967 36.182069076000062 0,-115.217742286999965 36.182135911000046 0,-115.217718308999963 36.182135782000046 0,-115.21771763 36.182218555000077 0,-115.217590421999944 36.182217875000049 0,-115.217590920999953 36.182156903000077 0,-115.217532275999986 36.182156590000034 0))" +AOI_2_Vegas_img3457,18,"POLYGON ((329.54 232.05 0,329.46 227.66 0,364.95 227.26 0,364.08 177.32 0,320.85 177.81 0,321.31 204.44 0,293.18 204.76 0,293.66 232.46 0,329.54 232.05 0))","POLYGON ((-115.217122847999974 36.182076173000041 0,-115.21721972099999 36.182075071000042 0,-115.21722102699999 36.182149842000058 0,-115.217145057999971 36.182150706000073 0,-115.217146312999944 36.182222614000068 0,-115.217029577999938 36.182223941000075 0,-115.217027224999981 36.182089111000039 0,-115.217123054999945 36.182088021000027 0,-115.217122847999974 36.182076173000041 0))" +AOI_2_Vegas_img3457,19,"POLYGON ((106.12 183.46 0,106.12 174.22 0,92.04 174.22 0,92.04 183.46 0,106.12 183.46 0))","POLYGON ((-115.21772607299999 36.18220735400007 0,-115.217764100999943 36.18220735400007 0,-115.217764100999943 36.182232293000027 0,-115.21772607299999 36.182232293000027 0,-115.21772607299999 36.18220735400007 0))" +AOI_2_Vegas_img3457,20,"POLYGON ((148.67 159.67 0,147.83 143.73 0,181.55 142.58 0,179.67 106.78 0,107.84 109.24 0,109.8 146.6 0,86.83 147.39 0,87.58 161.76 0,148.67 159.67 0))","POLYGON ((-115.21761119699994 36.182271588000049 0,-115.217776120999986 36.182265947000076 0,-115.217778157999987 36.182304760000079 0,-115.217716137999957 36.182306881000045 0,-115.217721433999941 36.182407758000068 0,-115.21752749699999 36.182414391000066 0,-115.21752242299999 36.182317730000079 0,-115.217613455999981 36.182314617000031 0,-115.21761119699994 36.182271588000049 0))" +AOI_2_Vegas_img3457,21,"POLYGON ((372.49 104.12 0,289.43 104.56 0,289.61 125.9 0,300.24 125.84 0,300.47 153.54 0,362.23 153.22 0,362.0 125.92 0,372.66 125.87 0,372.49 104.12 0))","POLYGON ((-115.217006881999964 36.182421569000041 0,-115.217006406999985 36.182362861000058 0,-115.217035187999954 36.182362709000074 0,-115.217034590999958 36.182289015000038 0,-115.217201341999953 36.182288135000078 0,-115.217201947999968 36.182362928000032 0,-115.217230663999942 36.182362776000048 0,-115.21723113 36.182420386000047 0,-115.217006881999964 36.182421569000041 0))" +AOI_2_Vegas_img3457,22,"POLYGON ((9.42 102.04 0,0.0 101.92 0,0.0 161.97 0,57.54 162.7 0,58.56 110.44 0,9.27 109.81 0,9.42 102.04 0))","POLYGON ((-115.217987170999947 36.182427184000062 0,-115.217987582999967 36.182406224000033 0,-115.21785447799999 36.182404518000055 0,-115.217857253999966 36.182263398000032 0,-115.218012599821591 36.182265387663136 0,-115.218012599821591 36.18242750942143 0,-115.217987170999947 36.182427184000062 0))" +AOI_2_Vegas_img3457,23,"POLYGON ((79.4 99.18 0,79.4 89.59 0,70.16 89.59 0,70.16 99.18 0,79.4 99.18 0))","POLYGON ((-115.217798214999959 36.18243491800007 0,-115.217823170999964 36.18243491800007 0,-115.217823170999964 36.182460817000049 0,-115.217798214999959 36.182460817000049 0,-115.217798214999959 36.18243491800007 0))" +AOI_2_Vegas_img3457,24,"POLYGON ((583.77 61.96 0,546.46 62.58 0,546.97 82.56 0,553.12 82.46 0,553.33 90.94 0,584.49 90.43 0,583.77 61.96 0))","POLYGON ((-115.216436415999965 36.182535396000048 0,-115.21643447299999 36.18245853600007 0,-115.216518605999966 36.182457149000072 0,-115.216519184999981 36.182480067000029 0,-115.216535786999941 36.182479793000027 0,-115.216537150999955 36.182533737000028 0,-115.216436415999965 36.182535396000048 0))" +AOI_2_Vegas_img3457,25,"POLYGON ((650.0 147.27 0,650.0 59.74 0,601.57 59.74 0,601.57 111.46 0,568.47 111.46 0,568.47 147.27 0,650.0 147.27 0))","POLYGON ((-115.216257599818093 36.182305062000069 0,-115.216477730999941 36.182305062000069 0,-115.216477730999941 36.182401751000043 0,-115.216388363999954 36.182401751000043 0,-115.216388363999954 36.182541411000045 0,-115.216257599818093 36.182541411000045 0,-115.216257599818093 36.182305062000069 0))" +AOI_2_Vegas_img3457,26,"POLYGON ((68.4 38.78 0,3.9 38.07 0,3.4 67.46 0,0.0 67.43 0,0.0 91.27 0,49.19 91.81 0,49.69 62.72 0,67.99 62.92 0,68.4 38.78 0))","POLYGON ((-115.217827923999948 36.182597983000051 0,-115.217829030999951 36.182532822000042 0,-115.21787844499994 36.182533368000065 0,-115.21787977799994 36.182454807000056 0,-115.218012599821591 36.182456275648939 0,-115.218012599821591 36.182520647445202 0,-115.218003428999964 36.182520546000035 0,-115.218002080999952 36.182599908000043 0,-115.217827923999948 36.182597983000051 0))" +AOI_2_Vegas_img3457,27,"POLYGON ((385.87 43.57 0,363.07 43.25 0,363.29 33.05 0,326.62 32.53 0,326.2 51.91 0,307.39 51.65 0,307.11 64.21 0,294.16 64.02 0,293.66 87.08 0,384.9 88.37 0,385.87 43.57 0))","POLYGON ((-115.216970752999941 36.182585054000072 0,-115.21697336699998 36.182464111000058 0,-115.21721972099999 36.182467579000047 0,-115.217218376999938 36.182529837000061 0,-115.21718339 36.182529344000045 0,-115.217182656999967 36.182563256000037 0,-115.21713185599998 36.182562541000038 0,-115.217130724999947 36.182614871000055 0,-115.217031708999968 36.182613477000075 0,-115.217032304999975 36.182585920000065 0,-115.216970752999941 36.182585054000072 0))" +AOI_2_Vegas_img3457,28,"POLYGON ((130.9 29.9 0,113.29 29.9 0,113.29 39.85 0,106.69 39.85 0,106.69 72.18 0,144.54 72.18 0,144.54 91.36 0,179.76 91.36 0,179.76 67.56 0,166.55 67.56 0,166.55 39.49 0,130.9 39.49 0,130.9 29.9 0))","POLYGON ((-115.217659171999969 36.182621963000031 0,-115.217659171999969 36.182596064000052 0,-115.217562910999959 36.182596064000052 0,-115.217562910999959 36.182520287000045 0,-115.217527259999898 36.182520287000045 0,-115.217527259999898 36.182456020000075 0,-115.217622330999973 36.182456020000075 0,-115.217622330999973 36.182507818000033 0,-115.217724533999956 36.182507818000033 0,-115.217724533999956 36.182595106000065 0,-115.21770670799998 36.182595106000065 0,-115.21770670799998 36.182621963000031 0,-115.217659171999969 36.182621963000031 0))" +AOI_2_Vegas_img3457,29,"POLYGON ((337.84 24.97 0,337.84 16.09 0,327.27 16.09 0,327.27 24.97 0,337.84 24.97 0))","POLYGON ((-115.217100443999982 36.182635288000029 0,-115.217128964999972 36.182635288000029 0,-115.217128964999972 36.182659268000066 0,-115.217100443999982 36.182659268000066 0,-115.217100443999982 36.182635288000029 0))" +AOI_2_Vegas_img3457,30,"POLYGON ((67.97 12.23 0,67.6 -0.0 0,51.97 -0.0 0,52.36 12.55 0,67.97 12.23 0))","POLYGON ((-115.217829072999962 36.182669667000027 0,-115.217871234999961 36.182668827000043 0,-115.217872271975864 36.18270269983821 0,-115.21783008340249 36.18270269983821 0,-115.217829072999962 36.182669667000027 0))" +AOI_2_Vegas_img3457,31,"POLYGON ((44.44 -0.0 0,11.65 -0.0 0,11.85 14.04 0,44.63 13.74 0,44.44 -0.0 0))","POLYGON ((-115.217892623097768 36.18270269983821 0,-115.21789209799999 36.182665607000047 0,-115.217980600999965 36.182664790000047 0,-115.217981137925307 36.18270269983821 0,-115.217892623097768 36.18270269983821 0))" +AOI_2_Vegas_img3457,32,"POLYGON ((169.61 -0.0 0,110.63 -0.0 0,110.99 18.35 0,157.19 17.75 0,156.88 1.63 0,156.87 1.4 0,169.63 1.23 0,169.61 -0.0 0))","POLYGON ((-115.217554658840029 36.18270269983821 0,-115.217554592999988 36.182699371000069 0,-115.217589047999979 36.182698927000047 0,-115.21758903599999 36.182698286000061 0,-115.217588174999946 36.182654766000041 0,-115.217712924999944 36.18265315900004 0,-115.217713904619245 36.18270269983821 0,-115.217554658840029 36.18270269983821 0))" +AOI_2_Vegas_img3457,33,"POLYGON ((355.98 -0.0 0,317.43 -0.0 0,317.43 8.21 0,355.98 8.21 0,355.98 -0.0 0))","POLYGON ((-115.21705144399999 36.18270269983821 0,-115.21705144399999 36.182680523000045 0,-115.21715554799999 36.182680523000045 0,-115.21715554799999 36.18270269983821 0,-115.21705144399999 36.18270269983821 0))" +AOI_2_Vegas_img3457,34,"POLYGON ((650.0 31.29 0,650 0 0,566.98 -0.0 0,567.34 31.9 0,650.0 31.29 0))","POLYGON ((-115.216257599818093 36.182618216998584 0,-115.216480781999962 36.182616579000069 0,-115.21648175195908 36.18270269983821 0,-115.216257599818093 36.18270269983821 0,-115.216257599818093 36.182618216998584 0))" +AOI_2_Vegas_img5979,1,"POLYGON ((650.0 132.22 0,650 0 0,482.84 -0.0 0,482.84 132.22 0,650.0 132.22 0))","POLYGON ((-115.160097599706134 36.171815699000035 0,-115.160548934999952 36.171815699000035 0,-115.160548934999952 36.172172699817217 0,-115.160097599706134 36.172172699817217 0,-115.160097599706134 36.171815699000035 0))" +AOI_2_Vegas_img5979,2,"POLYGON ((650.0 419.12 0,650.0 220.42 0,545.53 220.42 0,545.53 288.64 0,536.02 288.64 0,536.02 300.16 0,530.21 300.16 0,530.21 327.44 0,545.53 327.44 0,545.53 419.12 0,650.0 419.12 0))","POLYGON ((-115.160097599706134 36.171041089000028 0,-115.160379674999945 36.171041089000028 0,-115.160379674999945 36.171288599000036 0,-115.160421031999988 36.171288599000036 0,-115.160421031999988 36.171362277000071 0,-115.160405344999958 36.171362277000071 0,-115.160405344999958 36.171393360000025 0,-115.160379674999945 36.171393360000025 0,-115.160379674999945 36.171577553000077 0,-115.160097599706134 36.171577553000077 0,-115.160097599706134 36.171041089000028 0))" +AOI_2_Vegas_img5979,3,"POLYGON ((40.43 609.78 0,0.0 609.66 0,0 650 0,40.23 650.0 0,40.43 609.78 0))","POLYGON ((-115.161743448999971 36.17052628700003 0,-115.161743970522906 36.170417699813719 0,-115.161852599709633 36.170417699813719 0,-115.161852599709633 36.17052662851922 0,-115.161743448999971 36.17052628700003 0))" +AOI_2_Vegas_img5979,4,"POLYGON ((129.76 650.0 0,300.27 650.0 0,300.27 638.97 0,310.73 632.15 0,300.27 625.3 0,300.27 610.2 0,295.41 605.98 0,209.05 605.98 0,129.76 605.98 0,129.76 650.0 0))","POLYGON ((-115.161502248999966 36.170417699813719 0,-115.161502248999966 36.17053654800003 0,-115.161288153999976 36.17053654800003 0,-115.16105499 36.17053654800003 0,-115.161041863999969 36.170525166000061 0,-115.161041863999969 36.170484385000066 0,-115.161013632999982 36.170465896000053 0,-115.161041863999969 36.170447476000049 0,-115.161041863999969 36.170417699813719 0,-115.161502248999966 36.170417699813719 0))" +AOI_2_Vegas_img5979,5,"POLYGON ((252.81 501.09 0,280.83 524.28 0,309.51 501.69 0,281.5 478.51 0,252.81 501.09 0))","POLYGON ((-115.161170003999985 36.170819754000036 0,-115.16109255799995 36.170880731000068 0,-115.161016917999973 36.170818127000075 0,-115.16109436499994 36.170757149000053 0,-115.161170003999985 36.170819754000036 0))" +AOI_2_Vegas_img5979,6,"POLYGON ((195.56 502.36 0,224.14 524.85 0,252.81 501.09 0,224.23 478.61 0,195.56 502.36 0))","POLYGON ((-115.161324596999975 36.170816319000039 0,-115.16124717 36.170880458000056 0,-115.161170003999985 36.170819754000036 0,-115.16124743099999 36.170755614000029 0,-115.161324596999975 36.170816319000039 0))" +AOI_2_Vegas_img5979,7,"POLYGON ((167.09 478.51 0,140.74 499.01 0,169.21 522.86 0,195.56 502.36 0,167.09 478.51 0))","POLYGON ((-115.161401446999946 36.170880713000031 0,-115.161324596999975 36.170816319000039 0,-115.161395742999957 36.170760987000051 0,-115.161472592999985 36.170825381000043 0,-115.161401446999946 36.170880713000031 0))" +AOI_2_Vegas_img5979,8,"POLYGON ((275.65 116.75 0,275.65 121.9 0,285.77 121.9 0,285.77 116.75 0,275.65 116.75 0))","POLYGON ((-115.161108343999956 36.171857469000031 0,-115.16108101 36.171857469000031 0,-115.16108101 36.171843558000035 0,-115.161108343999956 36.171843558000035 0,-115.161108343999956 36.171857469000031 0))" +AOI_5_Khartoum_img130,1,"POLYGON ((227.83 650.0 0,249.16 650.0 0,248.72 647.08 0,227.83 650.0 0))","POLYGON ((32.505758537123519 15.519076199937246 0,32.505814932000142 15.519084092000131 0,32.505816121577858 15.519076199937246 0,32.505758537123519 15.519076199937246 0))" +AOI_5_Khartoum_img130,2,"POLYGON ((312.89 643.21 0,262.19 649.94 0,262.2 650.0 0,313.86 650.0 0,312.89 643.21 0))","POLYGON ((32.505988199000058 15.519094523000126 0,32.505990818706096 15.519076199937246 0,32.505851346163126 15.519076199937246 0,32.505851324000062 15.519076354999989 0,32.505988199000058 15.519094523000126 0))" +AOI_5_Khartoum_img130,3,"POLYGON ((643.01 650.0 0,650 650 0,650.0 648.87 0,643.01 650.0 0))","POLYGON ((32.506879519596467 15.519076199937246 0,32.506898400015778 15.519079244935215 0,32.506898400015778 15.519076199937246 0,32.506879519596467 15.519076199937246 0))" +AOI_5_Khartoum_img130,4,"POLYGON ((418.81 650.0 0,455.82 650.0 0,453.12 627.27 0,416.59 631.3 0,418.81 650.0 0))","POLYGON ((32.506274185670868 15.519076199937246 0,32.506268188000114 15.519126688000076 0,32.506366825000072 15.519137566999989 0,32.506374114377337 15.519076199937246 0,32.506274185670868 15.519076199937246 0))" +AOI_5_Khartoum_img130,5,"POLYGON ((458.28 650.0 0,478.05 650.0 0,474.38 623.6 0,454.96 626.1 0,458.28 650.0 0))","POLYGON ((32.50638075850712 15.519076199937246 0,32.506371795000057 15.519140718000129 0,32.506424224000078 15.519147480000075 0,32.506434126255328 15.519076199937246 0,32.50638075850712 15.519076199937246 0))" +AOI_5_Khartoum_img130,6,"POLYGON ((490.78 650.0 0,541.71 650.0 0,537.3 614.94 0,487.11 620.8 0,490.78 650.0 0))","POLYGON ((32.506468518218028 15.519076199937246 0,32.506458602000066 15.51915502900005 0,32.506594120000038 15.519170856000112 0,32.506606027267551 15.519076199937246 0,32.506468518218028 15.519076199937246 0))" +AOI_5_Khartoum_img130,7,"POLYGON ((326.56 650.0 0,384.12 650.0 0,379.49 614.09 0,322.8 620.87 0,326.56 650.0 0))","POLYGON ((32.506025099951302 15.519076199937246 0,32.506014961000055 15.519154845000038 0,32.50616802600009 15.519173167000076 0,32.506180527906395 15.519076199937246 0,32.506025099951302 15.519076199937246 0))" +AOI_5_Khartoum_img130,8,"POLYGON ((442.51 624.52 0,440.78 607.0 0,417.15 609.17 0,418.87 626.68 0,442.51 624.52 0))","POLYGON ((32.50633817300006 15.519145002000103 0,32.506274356000112 15.519139162000108 0,32.506269695000078 15.519186450000133 0,32.506333512000062 15.519192290000076 0,32.50633817300006 15.519145002000103 0))" +AOI_5_Khartoum_img130,9,"POLYGON ((475.15 619.54 0,473.4 603.7 0,449.89 606.11 0,451.65 621.96 0,475.15 619.54 0))","POLYGON ((32.506426312000038 15.519158439 0,32.506362851000084 15.519151912000039 0,32.506358111000083 15.519194694000131 0,32.5064215720001 15.519201222000042 0,32.506426312000038 15.519158439 0))" +AOI_5_Khartoum_img130,10,"POLYGON ((583.78 589.25 0,552.38 592.37 0,553.71 604.81 0,546.36 605.54 0,550.93 648.29 0,601.2 643.29 0,596.62 600.54 0,585.11 601.68 0,583.78 589.25 0))","POLYGON ((32.50671960800004 15.519240220000098 0,32.506723203000028 15.519206654000085 0,32.506754276000073 15.519209743000019 0,32.506766635000055 15.519094320000015 0,32.506630920000092 15.519080828000074 0,32.506618560000042 15.51919625200002 0,32.506638412000015 15.519198225000121 0,32.506634818000059 15.51923179000004 0,32.50671960800004 15.519240220000098 0))" +AOI_5_Khartoum_img130,11,"POLYGON ((530.4 604.28 0,528.57 585.69 0,495.12 588.74 0,496.95 607.33 0,530.4 604.28 0))","POLYGON ((32.506575484000088 15.519199648 0,32.506485166000076 15.519191403000089 0,32.506480231000111 15.519241596 0,32.506570549000052 15.519249840000123 0,32.506575484000088 15.519199648 0))" +AOI_5_Khartoum_img130,12,"POLYGON ((635.8 608.73 0,631.73 574.81 0,599.78 578.38 0,603.85 612.3 0,635.8 608.73 0))","POLYGON ((32.506860072000073 15.519187620000071 0,32.50677379400004 15.519178001000077 0,32.506762797000128 15.519269582000108 0,32.506849075000034 15.519279201000115 0,32.506860072000073 15.519187620000071 0))" +AOI_5_Khartoum_img130,13,"POLYGON ((650.0 595.78 0,650.0 557.43 0,642.68 558.7 0,649.16 595.92 0,650.0 595.78 0))","POLYGON ((32.506898400015778 15.519222597241322 0,32.506896142000123 15.519222204000119 0,32.506878642000046 15.519322704999988 0,32.506898400015778 15.519326145280065 0,32.506898400015778 15.519222597241322 0))" +AOI_5_Khartoum_img130,14,"POLYGON ((6.07 512.42 0,0.0 513.15 0,0.0 590.42 0,20.58 587.96 0,16.35 555.14 0,11.64 555.7 0,6.07 512.42 0))","POLYGON ((32.505159779000067 15.519447660000099 0,32.505174821000054 15.519330802000029 0,32.505187548000066 15.519332323000066 0,32.50519895700009 15.51924369500005 0,32.505143400015463 15.519237054876516 0,32.505143400015463 15.51944570258228 0,32.505159779000067 15.519447660000099 0))" +AOI_5_Khartoum_img130,15,"POLYGON ((256.52 548.23 0,244.99 476.11 0,194.36 483.63 0,205.9 555.75 0,256.52 548.23 0))","POLYGON ((32.505836013000113 15.519350967000058 0,32.50569933000002 15.519330667000075 0,32.5056681780001 15.519525411000048 0,32.505804860000062 15.519545711000083 0,32.505836013000113 15.519350967000058 0))" +AOI_5_Khartoum_img130,16,"POLYGON ((189.65 471.87 0,188.32 464.46 0,177.51 466.28 0,178.84 473.69 0,189.65 471.87 0))","POLYGON ((32.505655466000043 15.51955714100014 0,32.505626278000094 15.519552250000107 0,32.505622667000047 15.519572256000112 0,32.505651856000036 15.519577147000042 0,32.505655466000043 15.51955714100014 0))" +AOI_5_Khartoum_img130,17,"POLYGON ((349.99 452.4 0,322.36 455.52 0,315.73 456.26 0,319.34 486.01 0,325.97 485.26 0,329.93 517.92 0,356.44 514.93 0,355.96 510.98 0,361.44 507.28 0,363.38 500.15 0,369.66 493.43 0,381.42 492.11 0,379.85 479.2 0,377.05 456.12 0,350.8 459.08 0,349.99 452.4 0))","POLYGON ((32.506088364000071 15.519609710000038 0,32.506090550000117 15.519591687000117 0,32.506161441000053 15.519599671000012 0,32.506168998000106 15.519537372000057 0,32.50617322800008 15.519502507000052 0,32.506141491000044 15.519498933000069 0,32.506124523000061 15.51948079400014 0,32.506119294000094 15.519461557000142 0,32.50610448500003 15.519451556000137 0,32.506105781000052 15.519440877000017 0,32.506034205000056 15.519432816000077 0,32.506023509000023 15.519520987000019 0,32.50600561100002 15.519518971000016 0,32.505995867000038 15.519599293000095 0,32.506013766000038 15.519601308000082 0,32.506088364000071 15.519609710000038 0))" +AOI_5_Khartoum_img130,18,"POLYGON ((542.01 498.92 0,539.74 481.49 0,561.16 478.9 0,559.02 462.53 0,574.52 460.66 0,570.66 431.06 0,505.43 438.95 0,509.29 468.54 0,511.42 484.92 0,513.69 502.35 0,542.01 498.92 0))","POLYGON ((32.506606821000069 15.519484105000119 0,32.506530364000056 15.519474859000107 0,32.506524233000071 15.519521927000069 0,32.506518475000099 15.519566130000126 0,32.506508068000088 15.519646031000079 0,32.506684188000065 15.519667330000088 0,32.506694596000074 15.51958742800009 0,32.506652761000048 15.51958237 0,32.506658519000048 15.519538166000141 0,32.506600689000088 15.519531173000068 0,32.506606821000069 15.519484105000119 0))" +AOI_5_Khartoum_img130,19,"POLYGON ((635.87 428.05 0,597.26 432.94 0,603.07 478.73 0,612.27 477.56 0,613.95 490.85 0,650.0 486.28 0,650.0 435.45 0,637.02 437.1 0,635.87 428.05 0))","POLYGON ((32.506860244000045 15.519675476000085 0,32.50686334400006 15.519651031 0,32.506898400015778 15.519655477098608 0,32.506898400015778 15.519518250458857 0,32.50680106900009 15.519505906000139 0,32.506796519000027 15.51954177800013 0,32.506771678000071 15.519538627000079 0,32.506755999000092 15.519662255000057 0,32.506860244000045 15.519675476000085 0))" +AOI_5_Khartoum_img130,20,"POLYGON ((80.25 471.74 0,94.85 468.52 0,89.04 401.34 0,43.87 407.8 0,45.79 430.01 0,37.5 432.43 0,38.17 440.48 0,38.34 442.57 0,32.45 443.24 0,35.83 482.23 0,80.25 471.74 0))","POLYGON ((32.505360085000106 15.519557511000096 0,32.505240131000043 15.519529176000061 0,32.505231025000043 15.519634458000095 0,32.505246912000068 15.519636268000102 0,32.505246446000093 15.519641899000073 0,32.505244645000097 15.519663649000037 0,32.505267034000084 15.519670175000041 0,32.505261848000075 15.519730142000064 0,32.505383816000069 15.519747569000119 0,32.505399503000049 15.519566190000106 0,32.505360085000106 15.519557511000096 0))" +AOI_5_Khartoum_img130,21,"POLYGON ((132.81 467.69 0,173.49 462.16 0,164.51 400.84 0,123.83 406.37 0,127.43 430.92 0,112.19 432.99 0,117.57 469.76 0,132.81 467.69 0))","POLYGON ((32.505501987000066 15.519568432000098 0,32.505460846000069 15.519562839000109 0,32.505446308000067 15.519662129000029 0,32.505487449000029 15.51966772200017 0,32.505477743000057 15.519734009000111 0,32.505587570000053 15.519748938000138 0,32.505611813000044 15.519583361000112 0,32.505501987000066 15.519568432000098 0))" +AOI_5_Khartoum_img130,22,"POLYGON ((370.52 437.32 0,364.0 384.91 0,310.74 391.06 0,317.26 443.47 0,370.52 437.32 0))","POLYGON ((32.506143798000089 15.519650447000032 0,32.506000001000096 15.519633844000044 0,32.505982405000069 15.519775331000124 0,32.506126201000065 15.519791934000125 0,32.506143798000089 15.519650447000032 0))" +AOI_5_Khartoum_img130,23,"POLYGON ((261.49 380.06 0,237.68 382.58 0,245.33 449.77 0,269.15 447.26 0,279.22 446.19 0,302.49 443.73 0,297.35 398.65 0,274.09 401.11 0,272.91 390.79 0,262.84 391.85 0,261.49 380.06 0))","POLYGON ((32.505849435000115 15.519805033000106 0,32.505853062000035 15.519773194000052 0,32.50588025600009 15.519776069999981 0,32.505883431000015 15.51974819800011 0,32.505946246000086 15.519754841000102 0,32.505960110000039 15.519633128000127 0,32.505897295000068 15.519626485000085 0,32.505870100000024 15.519623610000044 0,32.505805796000061 15.519616809000123 0,32.505785130000099 15.519798233000087 0,32.505849435000115 15.519805033000106 0))" +AOI_5_Khartoum_img130,24,"POLYGON ((394.93 361.36 0,373.1 363.72 0,375.22 381.99 0,380.76 429.63 0,439.05 423.34 0,433.51 375.7 0,397.06 379.63 0,394.93 361.36 0))","POLYGON ((32.506209721000104 15.519855531000118 0,32.506215456000085 15.519806197000017 0,32.506313871000032 15.519816818000089 0,32.506328823000054 15.519688192000075 0,32.506171456000047 15.519671209 0,32.506156505000028 15.519799836000042 0,32.506150770000083 15.519849169 0,32.506209721000104 15.519855531000118 0))" +AOI_5_Khartoum_img130,25,"POLYGON ((615.41 384.53 0,642.29 381.13 0,637.93 346.68 0,589.11 352.84 0,593.31 387.32 0,605.54 385.77 0,607.15 396.54 0,616.77 395.32 0,615.41 384.53 0))","POLYGON ((32.50680500900009 15.519792975000062 0,32.506808691000089 15.519763831000091 0,32.506782697000141 15.519760547000097 0,32.506778369000081 15.519789608000043 0,32.506745346000095 15.519785436000083 0,32.506734001000055 15.51987851900004 0,32.506865823000069 15.51989517600002 0,32.506877577000076 15.519802144000078 0,32.50680500900009 15.519792975000062 0))" +AOI_5_Khartoum_img130,26,"POLYGON ((170.36 359.91 0,168.94 341.85 0,151.6 343.11 0,153.02 361.17 0,170.36 359.91 0))","POLYGON ((32.505603359000062 15.519859452000093 0,32.505556551000069 15.519856048000058 0,32.505552733000108 15.519904797000112 0,32.505599540000041 15.519908201000147 0,32.505603359000062 15.519859452000093 0))" +AOI_5_Khartoum_img130,27,"POLYGON ((444.63 367.06 0,440.37 339.53 0,406.47 344.41 0,410.74 371.93 0,444.63 367.06 0))","POLYGON ((32.506343906000048 15.519840146000034 0,32.506252399000068 15.519826978000095 0,32.506240881000032 15.519901289000126 0,32.506332388000047 15.519914457000104 0,32.506343906000048 15.519840146000034 0))" +AOI_5_Khartoum_img130,28,"POLYGON ((402.74 319.57 0,400.09 303.91 0,358.28 310.48 0,360.93 326.14 0,402.74 319.57 0))","POLYGON ((32.506230807000101 15.519968372000044 0,32.506117909000096 15.51995062400009 0,32.506110751000037 15.519992894000076 0,32.506223649000077 15.520010643000033 0,32.506230807000101 15.519968372000044 0))" +AOI_5_Khartoum_img130,29,"POLYGON ((144.43 341.39 0,136.8 294.96 0,83.46 303.11 0,91.09 349.53 0,104.53 347.48 0,108.86 373.86 0,148.77 367.77 0,144.43 341.39 0))","POLYGON ((32.505533371000098 15.519909450000027 0,32.505545081000037 15.519838228000095 0,32.505437334000092 15.519821781000157 0,32.505425625000086 15.519893003000062 0,32.505389337000054 15.519887465000071 0,32.505368729000061 15.520012809000034 0,32.50551276300007 15.520034795000146 0,32.505533371000098 15.519909450000027 0))" +AOI_5_Khartoum_img130,30,"POLYGON ((190.87 284.89 0,149.74 290.13 0,156.15 336.85 0,197.28 331.61 0,196.75 327.75 0,214.44 325.5 0,209.16 286.96 0,191.46 289.21 0,190.87 284.89 0))","POLYGON ((32.50565874600003 15.520061991000066 0,32.505660346000056 15.520050322000102 0,32.505708121000097 15.52005640699999 0,32.505722397000092 15.519952349000041 0,32.505674621000033 15.519946265000016 0,32.505676049000087 15.519935860000052 0,32.505565005000037 15.519921718000074 0,32.505547702000051 15.520047848000097 0,32.50565874600003 15.520061991000066 0))" +AOI_5_Khartoum_img130,31,"POLYGON ((573.35 333.62 0,566.51 280.23 0,523.85 285.69 0,525.33 297.93 0,509.84 315.27 0,511.36 327.11 0,505.23 327.9 0,507.05 342.11 0,573.35 333.62 0))","POLYGON ((32.506691434000068 15.519930429000089 0,32.506512441000069 15.519907500000095 0,32.506507525000103 15.519945874000053 0,32.506524075000058 15.519947994000049 0,32.506519980000107 15.519979965000118 0,32.50656179800005 15.520026781000116 0,32.506557802000046 15.520059834000067 0,32.506672968000018 15.520074587000133 0,32.506691434000068 15.519930429000089 0))" +AOI_5_Khartoum_img130,32,"POLYGON ((627.75 277.82 0,579.11 283.75 0,585.98 340.15 0,634.66 334.51 0,650.0 332.88 0,650.0 321.25 0,648.13 305.87 0,631.42 307.9 0,627.75 277.82 0))","POLYGON ((32.506838327000011 15.520081076000057 0,32.506848226000052 15.519999859000032 0,32.506893339000037 15.520005357000148 0,32.506898400015778 15.519963832860016 0,32.506898400015778 15.519932410483392 0,32.506856981000034 15.519928023000064 0,32.506725553000059 15.519912783000056 0,32.506706994000055 15.520065069000077 0,32.506838327000011 15.520081076000057 0))" +AOI_5_Khartoum_img130,33,"POLYGON ((154.0 284.41 0,152.43 274.16 0,142.92 275.52 0,144.5 285.77 0,154.0 284.41 0))","POLYGON ((32.505559210000058 15.520063283000116 0,32.50553354000008 15.520059619000042 0,32.505529283000058 15.520087307000079 0,32.505554952000104 15.520090971000101 0,32.505559210000058 15.520063283000116 0))" +AOI_5_Khartoum_img130,34,"POLYGON ((262.02 328.0 0,266.82 325.96 0,270.64 321.76 0,273.15 316.68 0,274.01 311.97 0,284.03 310.65 0,279.78 280.73 0,278.58 272.27 0,254.56 275.43 0,255.76 283.89 0,234.62 286.67 0,224.83 287.96 0,233.52 349.26 0,243.31 347.97 0,243.74 350.97 0,264.88 348.19 0,262.02 328.0 0))","POLYGON ((32.505850841000075 15.519945601000106 0,32.505858574000072 15.519891097000114 0,32.505801490000074 15.519883579000142 0,32.505800342000036 15.51989167000003 0,32.505773908000059 15.519888189000076 0,32.505750429000038 15.520053697000119 0,32.505776862000076 15.520057178000096 0,32.505833946000088 15.520064697000061 0,32.505830705000072 15.520087541000127 0,32.505895564000106 15.520096084000064 0,32.505898806000054 15.520073239000094 0,32.505910269000076 15.519992436000088 0,32.505883218000086 15.519988873000155 0,32.505880915000091 15.519976176000061 0,32.505874141000056 15.51996243500013 0,32.505863802000114 15.519951099000068 0,32.505850841000075 15.519945601000106 0))" +AOI_5_Khartoum_img130,35,"POLYGON ((214.01 276.98 0,212.29 265.18 0,173.87 270.37 0,175.58 282.17 0,214.01 276.98 0))","POLYGON ((32.505721224000069 15.520083344000117 0,32.505617475000058 15.520069339000051 0,32.505612842000104 15.52010120700011 0,32.505716591000116 15.520115212000112 0,32.505721224000069 15.520083344000117 0))" +AOI_5_Khartoum_img130,36,"POLYGON ((333.19 299.31 0,340.79 294.29 0,345.74 282.65 0,340.7 249.5 0,288.41 256.88 0,293.45 290.03 0,295.65 304.53 0,298.61 304.11 0,299.48 309.8 0,317.46 307.27 0,333.19 299.31 0))","POLYGON ((32.506043020000043 15.520023062000057 0,32.506000537000041 15.520001582000088 0,32.505951996000086 15.519994732000109 0,32.505949660000098 15.52001010300004 0,32.505941657000079 15.520008974000046 0,32.505935706000045 15.520048125000137 0,32.505922102000078 15.520137616000094 0,32.506063284000028 15.520157540000081 0,32.50607688700007 15.520068049000074 0,32.506063522000048 15.520036621000086 0,32.506043020000043 15.520023062000057 0))" +AOI_5_Khartoum_img130,37,"POLYGON ((380.32 228.03 0,349.13 233.24 0,346.65 233.66 0,342.81 234.3 0,334.24 235.73 0,335.94 245.2 0,344.51 243.77 0,351.15 280.62 0,354.99 279.97 0,359.05 302.51 0,361.91 304.2 0,399.45 297.93 0,391.08 251.49 0,384.74 252.55 0,380.32 228.03 0))","POLYGON ((32.506170273000059 15.520215530000049 0,32.506182198000033 15.520149314000127 0,32.506199326000058 15.520152178000115 0,32.506221905000061 15.520026793000081 0,32.50612056500006 15.52000985000007 0,32.5061128400001 15.520014430000096 0,32.506101883000056 15.520075274000071 0,32.506091503000093 15.52007353800016 0,32.506073589000074 15.520173016000134 0,32.506050450000039 15.520169147000118 0,32.506045843000031 15.520194727 0,32.506068983000063 15.52019859600013 0,32.506079363000026 15.520200331000126 0,32.50608606100009 15.520201451000066 0,32.506170273000059 15.520215530000049 0))" +AOI_5_Khartoum_img130,38,"POLYGON ((0.87 252.82 0,0.0 245.59 0,0.0 252.92 0,0.87 252.82 0))","POLYGON ((32.505145745000064 15.520148575000054 0,32.505143400015463 15.520148313694859 0,32.505143400015463 15.520168114550797 0,32.505145745000064 15.520148575000054 0))" +AOI_5_Khartoum_img130,39,"POLYGON ((553.42 261.76 0,546.66 213.41 0,490.11 221.32 0,496.66 269.7 0,553.42 261.76 0))","POLYGON ((32.506637632000071 15.520124437000016 0,32.506484395000037 15.520103021000111 0,32.506466693000057 15.520233648000099 0,32.506619388000033 15.520254988000103 0,32.506637632000071 15.520124437000016 0))" +AOI_5_Khartoum_img130,40,"POLYGON ((606.63 247.91 0,611.25 247.28 0,615.17 244.78 0,613.5 229.75 0,627.9 228.26 0,624.69 199.4 0,583.35 203.66 0,584.8 216.72 0,565.01 218.76 0,568.44 249.61 0,582.63 248.14 0,584.2 262.24 0,589.8 261.66 0,599.8 260.63 0,598.48 248.69 0,603.33 248.3 0,606.63 247.91 0))","POLYGON ((32.506781293000074 15.520161831000113 0,32.506772381000069 15.520160801000062 0,32.506759285000065 15.520159743000022 0,32.506762871000056 15.520127498000051 0,32.506735865000046 15.520124710000077 0,32.506720742000027 15.520123148000094 0,32.506716508000054 15.520161218000053 0,32.506678189000048 15.520157263000053 0,32.506668929000078 15.520240535000044 0,32.506722371000052 15.520246052000054 0,32.506718451000026 15.520281307000083 0,32.506830051000051 15.520292828000096 0,32.506838717000065 15.52021489400005 0,32.506799844000106 15.520210881000077 0,32.506804358000153 15.520170288 0,32.506793772000023 15.520163549000184 0,32.506781293000074 15.520161831000113 0))" +AOI_5_Khartoum_img130,41,"POLYGON ((548.1 201.82 0,546.95 191.48 0,538.2 192.37 0,539.35 202.72 0,548.1 201.82 0))","POLYGON ((32.506623260000083 15.520286275000057 0,32.506599634000082 15.520283849000029 0,32.506596544000033 15.520311788000104 0,32.506620170000105 15.520314214000145 0,32.506623260000083 15.520286275000057 0))" +AOI_5_Khartoum_img130,42,"POLYGON ((178.66 182.32 0,137.7 189.32 0,142.4 214.88 0,137.45 215.72 0,138.76 222.81 0,145.92 261.71 0,202.72 252.0 0,195.56 213.11 0,186.83 214.6 0,185.53 207.51 0,183.37 207.88 0,178.66 182.32 0))","POLYGON ((32.50562578800006 15.520338944000068 0,32.505638491000084 15.520269930000051 0,32.505644328000059 15.520270928000084 0,32.505647851000056 15.520251786000074 0,32.505671423000038 15.520255813000071 0,32.505690752000056 15.52015079700003 0,32.505537372000063 15.52012458900006 0,32.505518044000077 15.520229605000065 0,32.505514520000084 15.520248747000075 0,32.505527890000089 15.520251032 0,32.505515188000061 15.520320046000077 0,32.50562578800006 15.520338944000068 0))" +AOI_5_Khartoum_img130,43,"POLYGON ((547.8 189.15 0,547.01 179.65 0,534.94 180.58 0,535.73 190.08 0,547.8 189.15 0))","POLYGON ((32.506622451000034 15.520320491000071 0,32.506589879000103 15.520317980000099 0,32.506587750000037 15.520343621000054 0,32.50662032200011 15.520346132000062 0,32.506622451000034 15.520320491000071 0))" +AOI_5_Khartoum_img130,44,"POLYGON ((324.42 185.84 0,320.27 158.81 0,268.51 166.19 0,272.65 193.21 0,274.71 206.61 0,293.29 203.96 0,294.24 210.11 0,327.42 205.38 0,324.42 185.84 0))","POLYGON ((32.506019322000071 15.52032944399999 0,32.506027424000031 15.52027668100005 0,32.505937844000144 15.520263910000127 0,32.505935296000054 15.520280502000089 0,32.505885121000084 15.520273349000128 0,32.505879566000047 15.520309521000138 0,32.505868364000072 15.520382477000048 0,32.506008119000072 15.520402401000124 0,32.506019322000071 15.52032944399999 0))" +AOI_5_Khartoum_img130,45,"POLYGON ((408.06 151.96 0,338.49 159.91 0,339.28 166.28 0,332.05 167.1 0,339.89 230.77 0,413.04 222.4 0,407.75 179.46 0,411.81 179.0 0,409.25 158.28 0,408.85 158.32 0,408.06 151.96 0))","POLYGON ((32.506245168000092 15.520420905000114 0,32.506247283000093 15.520403730000076 0,32.506248386000046 15.520403856000078 0,32.506255275000079 15.520347901000049 0,32.506244321000096 15.520346648000125 0,32.506258597000098 15.520230709000046 0,32.506061098000053 15.520208132000102 0,32.506039932000128 15.520380026000096 0,32.506059443000012 15.520382257000076 0,32.506057328000068 15.520399431000033 0,32.506245168000092 15.520420905000114 0))" +AOI_5_Khartoum_img130,46,"POLYGON ((479.09 134.8 0,451.29 138.87 0,460.2 195.36 0,480.2 192.43 0,481.67 201.76 0,527.08 195.11 0,525.61 185.78 0,524.19 176.75 0,529.31 176.0 0,524.59 146.08 0,519.47 146.83 0,501.87 149.41 0,499.65 135.36 0,479.64 138.29 0,479.09 134.8 0))","POLYGON ((32.506436932000078 15.520467232000106 0,32.50643841800008 15.520457809000064 0,32.506492456000068 15.520465723000022 0,32.50649844000003 15.520427789999985 0,32.506545960000068 15.520434750000099 0,32.506559798000104 15.520436776000071 0,32.506572541000025 15.520356005000059 0,32.506558702000092 15.520353979000109 0,32.506562550000091 15.520329584000072 0,32.506566523000039 15.520304405000035 0,32.506443906000072 15.520286446000036 0,32.506439934000085 15.520311626000103 0,32.506385941000083 15.520303718000033 0,32.506361881000068 15.520456239000019 0,32.506436932000078 15.520467232000106 0))" +AOI_5_Khartoum_img130,47,"POLYGON ((161.59 159.84 0,159.14 145.1 0,177.1 142.31 0,175.47 132.54 0,182.73 131.42 0,179.55 112.36 0,189.56 110.81 0,183.46 74.26 0,116.11 84.7 0,122.21 121.24 0,129.48 164.81 0,161.59 159.84 0))","POLYGON ((32.505579706000084 15.520399645000015 0,32.505493003000069 15.520386213000119 0,32.505473375000065 15.520503840000107 0,32.505456908000042 15.520602518000087 0,32.50563873300009 15.520630686000073 0,32.505655200000049 15.520532008000092 0,32.505628193000049 15.520527824000116 0,32.505636779000056 15.520476366000066 0,32.505617178000065 15.520473329000088 0,32.505621580000053 15.520446950000091 0,32.505573067000064 15.520439434000103 0,32.505579706000084 15.520399645000015 0))" +AOI_5_Khartoum_img130,48,"POLYGON ((578.23 88.8 0,575.94 67.93 0,567.0 68.84 0,569.29 89.71 0,578.23 88.8 0))","POLYGON ((32.506704615000054 15.520591446000045 0,32.506680490000115 15.520588989000066 0,32.506674311000083 15.520645326000082 0,32.506698436000086 15.52064778300006 0,32.506704615000054 15.520591446000045 0))" +AOI_5_Khartoum_img130,49,"POLYGON ((0.0 49.55 0,0.0 89.38 0,16.73 87.57 0,12.14 48.23 0,0.0 49.55 0))","POLYGON ((32.505143400015463 15.520697421738316 0,32.505176177000081 15.52070097600008 0,32.505188583000105 15.520594765000112 0,32.505143400015463 15.520589865493031 0,32.505143400015463 15.520697421738316 0))" +AOI_5_Khartoum_img130,50,"POLYGON ((61.97 14.78 0,25.9 18.86 0,33.29 79.59 0,69.36 75.51 0,92.47 72.9 0,87.56 32.59 0,64.45 35.21 0,61.97 14.78 0))","POLYGON ((32.505310710000046 15.520791284000035 0,32.50531742400009 15.520736142999976 0,32.505379812000086 15.520743196000092 0,32.505393063000113 15.520634364000097 0,32.505330675000117 15.520627311000082 0,32.505233284000042 15.520616302000125 0,32.505213319000077 15.520780275000078 0,32.505310710000046 15.520791284000035 0))" +AOI_5_Khartoum_img130,51,"POLYGON ((579.72 28.37 0,577.57 13.74 0,512.96 22.56 0,515.11 37.19 0,520.72 36.43 0,524.53 62.29 0,539.08 60.31 0,539.91 65.96 0,584.36 59.89 0,583.53 54.24 0,579.72 28.37 0))","POLYGON ((32.506708652000057 15.52075459200011 0,32.50671892200009 15.520684755000055 0,32.506721167000066 15.520669485000049 0,32.506601156000059 15.520653101000079 0,32.506598911000012 15.520668371 0,32.506559625000101 15.520663008000064 0,32.506549355000082 15.52073284500003 0,32.506534195000043 15.520730775000086 0,32.506528386000099 15.520770284000015 0,32.506702843000021 15.520794101 0,32.506708652000057 15.52075459200011 0))" +AOI_5_Khartoum_img130,52,"POLYGON ((85.78 29.36 0,83.93 3.15 0,64.76 4.4 0,66.61 30.61 0,85.78 29.36 0))","POLYGON ((32.505375013000076 15.520751939000089 0,32.505323241000035 15.520748549000132 0,32.50531825000008 15.52081931400004 0,32.505370021000026 15.520822704000112 0,32.505375013000076 15.520751939000089 0))" +AOI_5_Khartoum_img130,53,"POLYGON ((650.0 77.73 0,650 0 0,609.69 -0.0 0,586.11 2.81 0,596.81 83.01 0,632.63 80.2 0,636.14 104.78 0,650.0 102.8 0,650.0 96.42 0,647.38 78.09 0,650.0 77.73 0))","POLYGON ((32.506898400015778 15.520621331813562 0,32.506891331000084 15.520620350000067 0,32.506898400015778 15.520570867635795 0,32.506898400015778 15.520553632296748 0,32.50686098900011 15.520548288000072 0,32.506851507000071 15.520614661000087 0,32.506754792000052 15.52060707600009 0,32.506725888000069 15.520823612000129 0,32.506789575501678 15.520831199937557 0,32.506898400015778 15.520831199937557 0,32.506898400015778 15.520621331813562 0))" +AOI_5_Khartoum_img130,54,"POLYGON ((280.64 21.78 0,281.73 21.68 0,279.75 1.37 0,285.6 0.84 0,285.52 -0.0 0,209.13 -0.0 0,202.41 0.61 0,205.14 28.62 0,196.4 29.41 0,199.48 61.0 0,233.49 57.92 0,232.91 51.97 0,273.65 48.28 0,273.36 45.32 0,280.08 44.71 0,281.74 61.69 0,284.51 61.43 0,296.32 60.37 0,293.8 34.59 0,281.99 35.66 0,280.64 21.78 0))","POLYGON ((32.505901127000051 15.520772394000092 0,32.505904781000019 15.520734926000038 0,32.505936670000018 15.520737813000057 0,32.505943456000089 15.520668214000107 0,32.505911568000087 15.520665327000076 0,32.505904085000061 15.520664649000038 0,32.505899614000079 15.520710489000063 0,32.505881474000027 15.520708847000034 0,32.505882254 15.520700848000057 0,32.505772254000107 15.520690890000095 0,32.505773822000073 15.520674807999976 0,32.505682009000061 15.520666496000073 0,32.50567369000008 15.520751803000104 0,32.505697270000034 15.52075393800008 0,32.505689896000064 15.520829555999986 0,32.50570805494521 15.520831199937557 0,32.505914291058474 15.520831199937557 0,32.505914512000068 15.520828934000154 0,32.505898720000069 15.520827504000085 0,32.505904068000049 15.52077266000002 0,32.505901127000051 15.520772394000092 0))" +AOI_5_Khartoum_img130,55,"POLYGON ((575.41 3.04 0,574.96 -0.0 0,507.87 -0.0 0,509.61 11.94 0,575.41 3.04 0))","POLYGON ((32.506696999000063 15.520823005000098 0,32.506519343000029 15.52079895500008 0,32.506514641427877 15.520831199937557 0,32.506695804072599 15.520831199937557 0,32.506696999000063 15.520823005000098 0))" +AOI_5_Khartoum_img130,56,"POLYGON ((22.28 252.28 0,0.0 254.86 0,0.0 287.08 0,0.19 288.48 0,74.26 278.97 0,72.15 262.11 0,67.93 229.1 0,20.17 234.72 0,22.28 252.28 0))","POLYGON ((32.505203558000083 15.520150050000073 0,32.505197869000021 15.520197459000061 0,32.505326823000068 15.520212630000072 0,32.505338201000029 15.520123500000032 0,32.505343890000091 15.520077987 0,32.505143903000068 15.520052299000081 0,32.505143400015463 15.520056081510267 0,32.505143400015463 15.520143085152903 0,32.505203558000083 15.520150050000073 0))" +AOI_5_Khartoum_img1306,1,"POLYGON ((231.95 623.11 0,218.53 625.09 0,213.96 625.77 0,217.23 646.31 0,221.8 645.64 0,222.5 650.0 0,273.24 650.0 0,271.38 638.31 0,235.22 643.65 0,231.95 623.11 0))","POLYGON ((32.560174652000114 15.541963800000048 0,32.560183484000078 15.541908332000064 0,32.560281133000075 15.541922765000082 0,32.560286158723564 15.541891199941301 0,32.560149146802658 15.541891199941301 0,32.560147271000069 15.541902980000042 0,32.560134927000043 15.541901156000099 0,32.56012609400004 15.541956623000116 0,32.56013843900007 15.541958447000022 0,32.560174652000114 15.541963800000048 0))" +AOI_5_Khartoum_img1306,2,"POLYGON ((307.33 620.22 0,297.41 621.77 0,301.72 650.0 0,311.89 650.0 0,307.33 620.22 0))","POLYGON ((32.560378198 15.541971596000078 0,32.560390490074617 15.541891199941301 0,32.560363050688828 15.541891199941301 0,32.560351398000101 15.541967409000151 0,32.560378198 15.541971596000078 0))" +AOI_5_Khartoum_img1306,3,"POLYGON ((331.18 650.0 0,353.92 650.0 0,348.75 622.23 0,326.65 625.68 0,331.18 650.0 0))","POLYGON ((32.560442576077932 15.541891199941301 0,32.56043036800002 15.541956858000017 0,32.560490035000093 15.541966180000045 0,32.560503977192859 15.541891199941301 0,32.560442576077932 15.541891199941301 0))" +AOI_5_Khartoum_img1306,4,"POLYGON ((281.17 632.19 0,279.27 613.05 0,267.64 614.13 0,269.54 633.26 0,281.17 632.19 0))","POLYGON ((32.560307563000066 15.54193929800012 0,32.56027614900006 15.541936400000115 0,32.560271015000083 15.541988060000017 0,32.560302429000046 15.541990958000099 0,32.560307563000066 15.54193929800012 0))" +AOI_5_Khartoum_img1306,5,"POLYGON ((64.85 632.31 0,55.67 576.54 0,0.0 584.93 0,0.0 636.26 0,16.89 633.68 0,19.15 647.44 0,43.91 643.66 0,42.6 635.71 0,47.01 635.03 0,50.38 634.52 0,64.85 632.31 0))","POLYGON ((32.559723487000085 15.541938965000115 0,32.559684417000035 15.54193299700011 0,32.559675335000087 15.541931611000065 0,32.559663427000068 15.541929791000076 0,32.559666961000048 15.541908318000038 0,32.559600106000048 15.541898107000181 0,32.559593993000078 15.541935261000088 0,32.559548400025243 15.541928296858449 0,32.559548400025243 15.542066890148424 0,32.55969871000012 15.542089534000109 0,32.559723487000085 15.541938965000115 0))" +AOI_5_Khartoum_img1306,6,"POLYGON ((399.51 618.17 0,450.43 611.69 0,445.5 575.73 0,394.58 582.21 0,395.92 591.98 0,379.68 594.05 0,383.27 620.24 0,399.51 618.17 0))","POLYGON ((32.560627083000085 15.541977136000117 0,32.560583235000031 15.541971556000087 0,32.560573542000043 15.542042269000039 0,32.560617388000011 15.54204784800009 0,32.560613773000057 15.542074227000063 0,32.560751238000087 15.542091719000057 0,32.560764548000087 15.541994628000099 0,32.560627083000085 15.541977136000117 0))" +AOI_5_Khartoum_img1306,7,"POLYGON ((143.79 624.11 0,133.58 566.78 0,65.72 578.01 0,75.93 635.34 0,143.79 624.11 0))","POLYGON ((32.559936637000092 15.541961107000137 0,32.559753418000106 15.541930780000094 0,32.559725846000092 15.542085576000121 0,32.55990906600011 15.542115902000122 0,32.559936637000092 15.541961107000137 0))" +AOI_5_Khartoum_img1306,8,"POLYGON ((504.93 614.34 0,524.9 611.86 0,516.94 552.21 0,448.84 560.64 0,456.8 620.29 0,382.06 629.55 0,373.51 630.6 0,376.09 650.0 0,534.77 650.0 0,531.4 624.77 0,506.73 627.82 0,504.93 614.34 0))","POLYGON ((32.560911703000045 15.541987495000114 0,32.56091655900007 15.541951085000113 0,32.560983191000069 15.541959333 0,32.560992277643727 15.541891199941301 0,32.560563847749656 15.541891199941301 0,32.560556864000041 15.54194356799999 0,32.560579957000066 15.541946427000049 0,32.560781751000064 15.541971407000133 0,32.560760270000067 15.542132479000029 0,32.560944143000043 15.542155241000154 0,32.560965625000037 15.541994169000105 0,32.560911703000045 15.541987495000114 0))" +AOI_5_Khartoum_img1306,9,"POLYGON ((85.5 565.78 0,82.64 548.18 0,71.38 549.88 0,74.24 567.48 0,85.5 565.78 0))","POLYGON ((32.559779245000101 15.542118588000115 0,32.559748850000105 15.542114002000023 0,32.55974112500008 15.542161517000139 0,32.55977152 15.542166104000083 0,32.559779245000101 15.542118588000115 0))" +AOI_5_Khartoum_img1306,10,"POLYGON ((116.89 556.06 0,113.36 530.19 0,98.48 532.07 0,102.0 557.94 0,116.89 556.06 0))","POLYGON ((32.559863992000025 15.542144847000049 0,32.559823810000054 15.542139770000073 0,32.559814303000095 15.542209612000075 0,32.559854485000102 15.542214689000089 0,32.559863992000025 15.542144847000049 0))" +AOI_5_Khartoum_img1306,11,"POLYGON ((96.89 545.12 0,94.3 529.95 0,79.56 532.28 0,82.14 547.45 0,96.89 545.12 0))","POLYGON ((32.559809993000044 15.542174376000094 0,32.559770182000058 15.542168079000117 0,32.559763202000056 15.54220903900007 0,32.559803014000074 15.542215336000062 0,32.559809993000044 15.542174376000094 0))" +AOI_5_Khartoum_img1306,12,"POLYGON ((43.87 529.32 0,48.27 528.65 0,52.7 527.98 0,50.51 514.68 0,46.08 515.35 0,34.94 517.05 0,35.16 518.42 0,20.11 520.71 0,22.64 536.08 0,0.0 539.54 0,0.0 572.84 0,50.34 565.15 0,49.45 559.73 0,53.29 559.14 0,58.24 558.39 0,56.67 548.84 0,51.72 549.59 0,47.32 550.27 0,43.87 529.32 0))","POLYGON ((32.55966685100001 15.542217025000038 0,32.55967615600003 15.542160481000034 0,32.559688031000064 15.542162295000047 0,32.559701400000044 15.542164337000029 0,32.55970564200004 15.54213855300002 0,32.559692274000099 15.54213651100005 0,32.559681917000027 15.542134929000065 0,32.55968432500007 15.542120293000087 0,32.559548400025243 15.542099531776843 0,32.559548400025243 15.542189451260317 0,32.559609530000046 15.54219878800002 0,32.559602704000042 15.542240270000102 0,32.559643341000026 15.542246477000026 0,32.559642735000111 15.542250152000099 0,32.55967281800006 15.542254747000117 0,32.559684778000118 15.542256574000062 0,32.559690686000096 15.542220665000054 0,32.55967872600008 15.542218839000038 0,32.55966685100001 15.542217025000038 0))" +AOI_5_Khartoum_img1306,13,"POLYGON ((404.67 511.82 0,374.48 516.04 0,375.6 523.46 0,380.95 559.01 0,440.95 550.63 0,435.6 515.08 0,405.79 519.24 0,404.67 511.82 0))","POLYGON ((32.56064102000007 15.542264296000047 0,32.560644039000081 15.542244245000067 0,32.560724522000037 15.542255492000091 0,32.56073897400006 15.542159500000079 0,32.560576971000081 15.542136862000124 0,32.560562520000083 15.54223285400011 0,32.560559502000039 15.542252905000066 0,32.56064102000007 15.542264296000047 0))" +AOI_5_Khartoum_img1306,14,"POLYGON ((33.64 509.17 0,44.78 507.46 0,36.55 457.43 0,0.0 463.01 0,0.0 514.3 0,33.64 509.17 0))","POLYGON ((32.559639231000091 15.542271451000069 0,32.559548400025243 15.542257577675533 0,32.559548400025243 15.542396060607647 0,32.559647084000098 15.542411133 0,32.559669313000015 15.542276046000113 0,32.559639231000091 15.542271451000069 0))" +AOI_5_Khartoum_img1306,15,"POLYGON ((71.89 522.8 0,69.73 509.68 0,68.94 504.88 0,100.76 500.02 0,91.81 445.63 0,43.6 452.99 0,44.14 456.27 0,52.38 506.3 0,53.67 514.19 0,55.5 525.3 0,71.89 522.8 0))","POLYGON ((32.559742514000064 15.542234641000102 0,32.559698258000033 15.542227881000024 0,32.559693322000065 15.542257879000044 0,32.559689817000049 15.542279178000065 0,32.55966758800006 15.542414265000117 0,32.559666130000068 15.542423123000091 0,32.559796289000069 15.542443004000086 0,32.559820453000086 15.54229615600007 0,32.559734550000044 15.542283035000091 0,32.559736683000025 15.542270076000023 0,32.559742514000064 15.542234641000102 0))" +AOI_5_Khartoum_img1306,16,"POLYGON ((122.53 451.76 0,121.37 440.86 0,111.07 441.87 0,112.23 452.78 0,122.53 451.76 0))","POLYGON ((32.559879234000093 15.542426447000066 0,32.559851426000094 15.542423699999986 0,32.559848291000058 15.542453141000063 0,32.559876100000068 15.542455889000033 0,32.559879234000093 15.542426447000066 0))" +AOI_5_Khartoum_img1306,17,"POLYGON ((456.67 487.95 0,504.45 481.21 0,497.4 434.88 0,430.14 444.38 0,437.19 490.71 0,438.91 501.97 0,443.0 536.1 0,444.3 546.97 0,498.21 540.97 0,496.91 530.11 0,512.4 528.38 0,508.31 494.25 0,458.52 499.79 0,456.67 487.95 0))","POLYGON ((32.560781407000029 15.542328723000045 0,32.560786415000095 15.542296776000072 0,32.560920847000055 15.542311731000041 0,32.56093189200007 15.542219566000069 0,32.560890061000109 15.542214913000169 0,32.560893577000051 15.542185574000047 0,32.5607480090001 15.542169381000072 0,32.560744492000019 15.542198719000087 0,32.560733446000071 15.542290884000185 0,32.560728819000055 15.542321296000107 0,32.560709786000103 15.542446377000042 0,32.560891374000065 15.54247202300016 0,32.56091040600009 15.542346942000098 0,32.560781407000029 15.542328723000045 0))" +AOI_5_Khartoum_img1306,18,"POLYGON ((68.46 325.05 0,0.0 335.5 0,0.0 451.93 0,48.67 444.49 0,109.75 435.16 0,94.05 339.8 0,71.46 343.26 0,68.46 325.05 0))","POLYGON ((32.559733251000097 15.542768575000107 0,32.559741342000102 15.542719410000101 0,32.55980234400004 15.542728728000085 0,32.55984471200005 15.542471257000065 0,32.559679803000087 15.542446069000063 0,32.559548400025243 15.54242599906927 0,32.559548400025243 15.542740341026278 0,32.559733251000097 15.542768575000107 0))" +AOI_5_Khartoum_img1306,19,"POLYGON ((90.41 336.71 0,87.4 322.97 0,95.08 321.41 0,92.71 310.58 0,73.7 314.45 0,76.07 325.28 0,79.08 339.01 0,90.41 336.71 0))","POLYGON ((32.559792510000122 15.542737092000046 0,32.559761912000077 15.542730869000074 0,32.559753788000059 15.542767945000097 0,32.559747379000058 15.542797192000039 0,32.559798718000046 15.542807634000058 0,32.559805126000057 15.542778387000077 0,32.5597843850001 15.54277416900006 0,32.559792510000122 15.542737092000046 0))" +AOI_5_Khartoum_img1306,20,"POLYGON ((471.72 282.72 0,469.69 268.58 0,468.29 258.75 0,451.83 260.94 0,453.23 270.76 0,246.62 298.22 0,245.09 287.53 0,231.56 289.32 0,233.09 300.02 0,157.15 310.11 0,156.56 305.99 0,119.21 310.96 0,119.8 315.07 0,123.19 338.73 0,160.48 333.78 0,162.07 344.89 0,169.74 343.87 0,171.5 356.22 0,163.84 357.23 0,135.65 360.98 0,140.83 397.11 0,131.72 398.32 0,135.58 425.26 0,137.95 441.84 0,158.31 439.14 0,159.91 450.31 0,208.85 443.8 0,207.25 432.63 0,206.29 425.91 0,200.61 386.26 0,208.88 385.16 0,206.21 366.5 0,216.58 365.12 0,272.77 357.65 0,274.31 368.38 0,218.11 375.85 0,219.25 383.79 0,230.52 382.29 0,233.7 404.5 0,247.77 402.63 0,250.27 420.07 0,277.65 416.43 0,279.04 426.18 0,346.37 417.23 0,344.35 403.14 0,412.86 394.04 0,412.07 388.52 0,485.47 378.76 0,483.01 361.61 0,457.12 365.05 0,456.37 359.77 0,418.55 364.8 0,416.9 353.28 0,454.72 348.26 0,453.72 341.24 0,479.6 337.8 0,477.99 326.49 0,471.72 282.72 0))","POLYGON ((32.560822042000048 15.542882843000086 0,32.560838961000066 15.542764667000039 0,32.560843330000118 15.54273415000007 0,32.560773433000044 15.542724861000103 0,32.560776146000094 15.542705911000091 0,32.560674034000087 15.542692342000079 0,32.560678486000072 15.542661247000135 0,32.560780598000044 15.542674817000011 0,32.560782637000067 15.542660571000118 0,32.56085253400007 15.542669860000085 0,32.560859166000114 15.542623543000049 0,32.560660996000031 15.542597209000089 0,32.560663132000066 15.542582291000073 0,32.560478147000069 15.542557709000048 0,32.560483592000061 15.542519678000113 0,32.560301817000081 15.542495523000049 0,32.560298049000025 15.542521840000134 0,32.560224133000162 15.542512018000066 0,32.560217391000045 15.542559110000099 0,32.560179382000086 15.542554060000054 0,32.560170796000072 15.542614022000105 0,32.560140376000092 15.542609979000073 0,32.560137308000044 15.542631403000078 0,32.560289028000042 15.542651565000018 0,32.560284881000058 15.542680539000026 0,32.56013316100006 15.542660377000113 0,32.560105171000046 15.5426566570001 0,32.560112386000078 15.542606259000047 0,32.560090054000064 15.542603292000084 0,32.560105381000028 15.542496237000098 0,32.560107980000076 15.542478086000139 0,32.560112296000042 15.542447934000053 0,32.55998016200008 15.542430376000102 0,32.55997584600005 15.542460528000074 0,32.559920871000038 15.542453222000102 0,32.559914459000076 15.542498003000045 0,32.559904049000075 15.54257072400004 0,32.55992863500007 15.542573991000047 0,32.559914667000029 15.542671556000132 0,32.559990763000052 15.542681668000034 0,32.560011456000034 15.542684418000063 0,32.560006685000126 15.542717744000051 0,32.559985992000087 15.542714994000148 0,32.559981697000055 15.542744997000115 0,32.559881014000027 15.542731617000074 0,32.559871868000094 15.542795505000123 0,32.559870277000101 15.54280661900005 0,32.559971108000106 15.542820019000066 0,32.559972699000056 15.542808904 0,32.560177733000117 15.542836151000083 0,32.560173600000056 15.542865025000017 0,32.560210140000059 15.542869881000069 0,32.560214274000018 15.54284100600009 0,32.560772125000064 15.542915137000065 0,32.560768329000034 15.542941657000069 0,32.560812777000031 15.542947563000046 0,32.560816573000068 15.542921044 0,32.560822042000048 15.542882843000086 0))" +AOI_5_Khartoum_img1306,21,"POLYGON ((612.75 244.25 0,499.14 259.25 0,501.11 273.04 0,494.15 273.96 0,500.58 319.11 0,507.53 318.2 0,562.74 310.9 0,564.45 322.91 0,518.49 328.98 0,525.92 381.17 0,570.75 375.25 0,571.8 382.64 0,587.32 380.59 0,587.68 383.14 0,589.1 393.09 0,528.75 401.06 0,534.8 443.61 0,527.26 444.6 0,528.6 453.98 0,536.14 452.99 0,537.72 464.08 0,525.37 465.71 0,530.3 500.34 0,530.75 503.51 0,539.26 502.39 0,560.26 650.0 0,650 650 0,650.0 626.45 0,636.15 628.28 0,634.68 617.95 0,650.0 615.93 0,650.0 510.14 0,646.42 485.01 0,650.0 484.53 0,650.0 461.11 0,635.85 462.98 0,632.64 440.41 0,634.63 440.15 0,633.09 429.26 0,650.0 427.03 0,650.0 396.4 0,649.25 391.14 0,650.0 391.04 0,650.0 348.32 0,646.32 322.47 0,622.59 325.6 0,621.4 317.21 0,593.9 320.84 0,592.74 312.68 0,612.0 310.14 0,611.66 307.75 0,611.76 307.74 0,610.11 296.14 0,618.25 295.06 0,641.98 291.93 0,637.68 261.71 0,615.65 264.62 0,612.75 244.25 0))","POLYGON ((32.561202819000115 15.542986733000136 0,32.561210646000042 15.542931721000093 0,32.561270132000047 15.542939577000066 0,32.561281740000055 15.542857987000033 0,32.561217671000065 15.542849526000076 0,32.561195707000046 15.542846626 0,32.56120016200002 15.542815307000076 0,32.561199879000078 15.542815269000055 0,32.561200796000044 15.542808819000092 0,32.561148790000075 15.542801951000143 0,32.561151925000104 15.542779922000053 0,32.561226179000066 15.542789727000144 0,32.561229401000077 15.54276708000002 0,32.561293470000066 15.542775541000017 0,32.561303400025558 15.542705748243776 0,32.561303400025558 15.54259038864968 0,32.561301381000114 15.542590122000098 0,32.561303400025558 15.542575930939897 0,32.561303400025558 15.542493224901211 0,32.561257734000066 15.542487195000023 0,32.561261914000063 15.54245780500009 0,32.5612565220001 15.542457093000115 0,32.5612651920001 15.54239615300007 0,32.561303400025558 15.542401198776028 0,32.561303400025558 15.54233795909229 0,32.561293744000068 15.542336684000103 0,32.561303400025558 15.542268816303114 0,32.561303400025558 15.541983186729858 0,32.561262027000097 15.54197772300007 0,32.561265994000074 15.541949837000136 0,32.561303400025558 15.541954776847133 0,32.561303400025558 15.541891199941301 0,32.561061111597326 15.541891199941301 0,32.561004408000031 15.542289751000069 0,32.560981424000019 15.542286715000047 0,32.560980205000078 15.542295278000037 0,32.56096690300005 15.54238878000006 0,32.561000232000076 15.542393180999982 0,32.560995971000068 15.542423131000088 0,32.560975610000071 15.542420443000012 0,32.560972006000036 15.542445772000097 0,32.56099236700004 15.542448461000115 0,32.560976023 15.542563342000117 0,32.561138965000055 15.542584860000105 0,32.56113514500003 15.542611709 0,32.561134164000045 15.54261860300006 0,32.561092269000021 15.542613070000142 0,32.561089430000052 15.542633026000097 0,32.560968383000038 15.542617040000048 0,32.560948334000081 15.542757952000025 0,32.561072408000015 15.542774337000063 0,32.561067796000017 15.542806757000118 0,32.560918732000047 15.542787072000102 0,32.560899953000032 15.542784592000119 0,32.560882608000057 15.542906504000022 0,32.560901387000072 15.542908984000082 0,32.560896089000025 15.542946226000046 0,32.561202819000115 15.542986733000136 0))" +AOI_5_Khartoum_img1306,22,"POLYGON ((30.61 236.48 0,29.12 221.6 0,2.71 224.05 0,4.2 238.92 0,10.33 238.36 0,11.28 247.95 0,31.56 246.08 0,30.61 236.48 0))","POLYGON ((32.559631035000073 15.543007698000086 0,32.559633616000049 15.542981790000121 0,32.559578862000045 15.542976726000129 0,32.559576281000027 15.5430026330001 0,32.559559731000078 15.543001103000098 0,32.559555728000092 15.543041274000068 0,32.559627032000051 15.543047868000089 0,32.559631035000073 15.543007698000086 0))" +AOI_5_Khartoum_img1306,23,"POLYGON ((465.56 149.51 0,461.1 122.32 0,460.28 117.33 0,442.81 119.99 0,443.63 124.98 0,338.56 140.98 0,343.02 168.17 0,465.56 149.51 0))","POLYGON ((32.560805408000107 15.543242514000086 0,32.560474557000099 15.543192138000084 0,32.560462513000047 15.543265559000167 0,32.560746203000051 15.543308755000059 0,32.560743993000088 15.543322230000101 0,32.560791152000043 15.543329411000164 0,32.560793363000101 15.543315936000123 0,32.560805408000107 15.543242514000086 0))" +AOI_5_Khartoum_img1306,24,"POLYGON ((620.65 139.12 0,616.11 110.73 0,594.14 113.99 0,598.67 142.38 0,620.65 139.12 0))","POLYGON ((32.561224144000029 15.543270568000048 0,32.561164819000098 15.543261775000143 0,32.561152580000055 15.54333842200011 0,32.561211904000061 15.543347216 0,32.561224144000029 15.543270568000048 0))" +AOI_5_Khartoum_img1306,25,"POLYGON ((15.24 108.95 0,0.0 110.31 0,0.0 176.6 0,1.56 192.77 0,4.41 222.25 0,26.0 220.32 0,26.06 221.0 0,32.56 220.42 0,31.69 211.47 0,58.61 209.05 0,55.73 154.88 0,55.54 152.89 0,61.16 152.39 0,58.08 120.52 0,16.72 124.23 0,15.24 108.95 0))","POLYGON ((32.559589554000112 15.543352045000127 0,32.559593538000136 15.54331078000013 0,32.559705221000051 15.543320789000122 0,32.559713528000039 15.543234752000032 0,32.559698359000031 15.543233393 0,32.559698877000102 15.543228030000051 0,32.559706655000056 15.543081758000092 0,32.559633968000099 15.543075244000065 0,32.559636302000051 15.543051074000047 0,32.559618766000028 15.543049502000082 0,32.559618588000042 15.543051340000051 0,32.559560302000108 15.543046116000054 0,32.559552617000115 15.543125712000119 0,32.559548400025243 15.543169389249563 0,32.559548400025243 15.543348356732697 0,32.559589554000112 15.543352045000127 0))" +AOI_5_Khartoum_img1306,26,"POLYGON ((93.32 144.58 0,137.19 139.91 0,136.27 131.94 0,131.57 90.99 0,59.16 98.7 0,63.86 139.65 0,71.95 210.2 0,100.5 207.16 0,144.8 202.45 0,139.64 157.5 0,95.34 162.22 0,93.32 144.58 0))","POLYGON ((32.559800364000061 15.543255833000039 0,32.559805828000059 15.543208208000097 0,32.559925429000074 15.543220944000089 0,32.559939350000036 15.543099597000026 0,32.559819749000091 15.543086861000072 0,32.559742667000023 15.543078653000135 0,32.559720815000034 15.543269134000115 0,32.559708128000103 15.543379717000104 0,32.559903649000056 15.54340053700013 0,32.559916336000093 15.543289954000116 0,32.559918804000077 15.543268445000079 0,32.559800364000061 15.543255833000039 0))" +AOI_5_Khartoum_img1306,27,"POLYGON ((578.11 117.5 0,562.79 86.46 0,539.69 97.04 0,555.01 128.09 0,578.11 117.5 0))","POLYGON ((32.561109303000016 15.543328946000017 0,32.561046937000086 15.543300369000049 0,32.561005560000055 15.543384184000047 0,32.561067928000057 15.543412761000129 0,32.561109303000016 15.543328946000017 0))" +AOI_5_Khartoum_img1306,28,"POLYGON ((236.43 62.69 0,234.26 48.74 0,184.96 55.86 0,187.13 69.81 0,236.43 62.69 0))","POLYGON ((32.560186759000054 15.543476934000152 0,32.560053658000086 15.543457700000015 0,32.560047793000038 15.543495368000103 0,32.560180893000101 15.54351460300013 0,32.560186759000054 15.543476934000152 0))" +AOI_5_Khartoum_img1306,29,"POLYGON ((259.52 110.85 0,278.27 108.45 0,269.12 42.29 0,250.37 44.7 0,257.43 95.72 0,242.3 97.67 0,244.39 112.8 0,259.52 110.85 0))","POLYGON ((32.560249105000054 15.543346892000109 0,32.560208253000077 15.543341644000066 0,32.560202599000029 15.543382502 0,32.560243450000016 15.543387748999976 0,32.560224386000094 15.543525502 0,32.560275015000045 15.543532005000058 0,32.560299733000072 15.543353396000102 0,32.560249105000054 15.543346892000109 0))" +AOI_5_Khartoum_img1306,30,"POLYGON ((445.6 29.06 0,323.34 45.35 0,325.71 61.84 0,332.35 60.96 0,334.33 74.78 0,376.02 69.23 0,375.74 67.28 0,440.53 58.65 0,454.18 56.83 0,452.47 44.96 0,447.97 45.56 0,445.6 29.06 0))","POLYGON ((32.560751515000078 15.543567727000065 0,32.560757906000077 15.543523195000024 0,32.560770081000065 15.543524816000135 0,32.560774680000094 15.543492765000044 0,32.560737825000025 15.543487857000125 0,32.560562896000086 15.543464557000016 0,32.560563653000038 15.543459279000064 0,32.560451094000022 15.543444287000117 0,32.560445737000066 15.543481617000101 0,32.560427816000072 15.543479230000022 0,32.560421425000072 15.543523762000063 0,32.560751515000078 15.543567727000065 0))" +AOI_5_Khartoum_img1306,31,"POLYGON ((565.45 38.54 0,560.34 8.96 0,484.28 21.18 0,489.4 50.75 0,565.45 38.54 0))","POLYGON ((32.561075124000084 15.543542150000084 0,32.560869768000011 15.543509175000118 0,32.560855955000058 15.543589026000035 0,32.561061311000095 15.543622 0,32.561075124000084 15.543542150000084 0))" +AOI_5_Khartoum_img1306,32,"POLYGON ((105.67 -0.0 0,0 0 0,0.0 39.91 0,3.22 39.38 0,7.08 38.74 0,11.78 65.1 0,50.24 58.74 0,46.27 36.45 0,110.28 25.86 0,105.67 -0.0 0))","POLYGON ((32.559833703199736 15.543646199941612 0,32.559846144000055 15.543576370000038 0,32.559673321000055 15.543547791000035 0,32.559684046000022 15.543487593000041 0,32.559580201000053 15.54347042100008 0,32.559567519000097 15.543541598000049 0,32.559557101000095 15.543539875000031 0,32.559548400025243 15.543538436219842 0,32.559548400025243 15.543646199941612 0,32.559833703199736 15.543646199941612 0))" +AOI_5_Khartoum_img1306,33,"POLYGON ((583.37 23.65 0,598.17 22.94 0,597.67 12.36 0,582.86 13.06 0,583.37 23.65 0))","POLYGON ((32.561123486000056 15.54358235600005 0,32.561122125000047 15.543610935000025 0,32.561162100000033 15.543612839000055 0,32.561163460000081 15.543584259000072 0,32.561123486000056 15.54358235600005 0))" +AOI_5_Khartoum_img1301,1,"POLYGON ((134.59 647.52 0,132.74 635.82 0,123.11 637.23 0,124.96 648.93 0,134.59 647.52 0))","POLYGON ((32.559911783000103 15.53312289300003 0,32.559885786000066 15.533119081000107 0,32.559880795000019 15.533150683000049 0,32.559906792000056 15.533154495000085 0,32.559911783000103 15.53312289300003 0))" +AOI_5_Khartoum_img1301,2,"POLYGON ((90.19 628.48 0,59.2 631.12 0,60.93 650.0 0,111.14 650.0 0,110.38 641.7 0,91.55 643.3 0,90.19 628.48 0))","POLYGON ((32.559791914000101 15.533174295000054 0,32.559795584000014 15.533134286000109 0,32.559846416000035 15.533138614000093 0,32.559848471492401 15.533116199939741 0,32.559712914923942 15.533116199939741 0,32.559708240000063 15.533167171000109 0,32.559791914000101 15.533174295000054 0))" +AOI_5_Khartoum_img1301,3,"POLYGON ((110.61 554.25 0,101.45 550.72 0,97.76 559.62 0,106.91 563.15 0,110.61 554.25 0))","POLYGON ((32.5598470360001 15.533374738000141 0,32.559837053000074 15.533350694000049 0,32.559812343000019 15.533360217000064 0,32.559822326000109 15.53338426100008 0,32.5598470360001 15.533374738000141 0))" +AOI_5_Khartoum_img1301,4,"POLYGON ((4.46 528.39 0,2.49 528.55 0,0.58 529.05 0,0.0 529.31 0,0.0 549.35 0,0.58 549.61 0,2.49 550.1 0,4.46 550.27 0,6.43 550.1 0,8.35 549.61 0,10.14 548.8 0,11.76 547.71 0,13.16 546.36 0,14.3 544.8 0,15.13 543.07 0,15.64 541.23 0,15.82 539.33 0,15.64 537.43 0,15.13 535.59 0,14.3 533.86 0,13.16 532.3 0,11.76 530.95 0,10.14 529.85 0,8.34 529.05 0,6.43 528.55 0,4.46 528.39 0))","POLYGON ((32.559560445000038 15.533444552000118 0,32.55956576905934 15.533444103257006 0,32.559570931356674 15.533442770586618 0,32.559575775038276 15.533440594481423 0,32.559580152931311 15.533437641061282 0,32.559583932015713 15.533434000064302 0,32.559586997465921 15.53342978212034 0,32.559589256139787 15.533425115389424 0,32.55959063940864 15.533420141667822 0,32.559591105242575 15.533415012079498 0,32.559590639487432 15.533409882484406 0,32.55958925629497 15.533404908742716 0,32.559586997692776 15.53340024197904 0,32.559583932307348 15.533396023990676 0,32.559580153278858 15.533392382939093 0,32.559575775431185 15.533389429455838 0,32.559570931783014 15.533387253281026 0,32.559565769506143 15.533385920536658 0,32.559560445453741 15.533385471717519 0,32.559555121394446 15.533385920460745 0,32.559549959097097 15.533387253131515 0,32.559548400025243 15.533387953570875 0,32.559548400025243 15.533442070162417 0,32.559549958670758 15.533442770437107 0,32.559555120947635 15.533444103181106 0,32.559560445000038 15.533444552000118 0))" +AOI_5_Khartoum_img1301,5,"POLYGON ((479.12 481.1 0,442.5 472.15 0,434.89 501.04 0,471.51 510.0 0,479.12 481.1 0))","POLYGON ((32.56084202400006 15.53357222 0,32.560821468000036 15.533494199000106 0,32.560722593000051 15.533518382000056 0,32.560743149000082 15.533596402000022 0,32.56084202400006 15.53357222 0))" +AOI_5_Khartoum_img1301,6,"POLYGON ((432.39 491.17 0,437.17 470.55 0,400.53 462.65 0,395.74 483.27 0,415.84 487.61 0,413.87 496.09 0,430.42 499.66 0,432.39 491.17 0))","POLYGON ((32.560715842000072 15.533545033000124 0,32.560710522000065 15.533522122000095 0,32.560665843000017 15.533531752000114 0,32.560671162000062 15.533554663000105 0,32.560616894000063 15.533566358000098 0,32.560629824000074 15.533622050000073 0,32.560728771000086 15.533600726000014 0,32.560715842000072 15.533545033000124 0))" +AOI_5_Khartoum_img1301,7,"POLYGON ((637.17 548.43 0,642.66 529.49 0,645.93 518.21 0,646.95 514.68 0,650.0 504.15 0,650.0 466.58 0,613.11 456.66 0,614.15 453.05 0,590.04 446.57 0,580.72 478.76 0,584.71 479.83 0,579.84 496.64 0,566.86 541.47 0,586.98 546.88 0,596.68 549.49 0,633.96 559.51 0,637.17 548.43 0))","POLYGON ((32.561268768000055 15.533390433000037 0,32.561260101000109 15.533360512000035 0,32.561159427000057 15.533387581000113 0,32.561133233000064 15.533394624 0,32.561078916000056 15.533409228000028 0,32.561113977000041 15.533530277000171 0,32.561127120000087 15.533575653000078 0,32.561116332000026 15.533578554000083 0,32.561141507000059 15.533665471000139 0,32.561206613000088 15.533647966000046 0,32.561203791000068 15.533638223000072 0,32.561303400025558 15.533611440335612 0,32.561303400025558 15.533510001653005 0,32.561295162000093 15.533481560000135 0,32.561292403000031 15.533472035000072 0,32.561283580000065 15.533441574000053 0,32.561268768000055 15.533390433000037 0))" +AOI_5_Khartoum_img1301,8,"POLYGON ((551.57 503.04 0,520.77 493.32 0,533.98 454.47 0,539.35 438.69 0,502.29 426.99 0,496.92 442.78 0,473.08 512.89 0,477.64 514.33 0,487.92 517.58 0,487.5 518.81 0,509.72 525.83 0,540.52 535.55 0,551.57 503.04 0))","POLYGON ((32.561037628000079 15.533512982000056 0,32.561007791000115 15.533425225000027 0,32.560924642000025 15.533451469000111 0,32.560864660000078 15.533470401000066 0,32.560865794000094 15.533473738000025 0,32.560838024000098 15.533482503000146 0,32.560825722000061 15.533486386000112 0,32.560890092000065 15.533675705000036 0,32.560904582000035 15.533718321000096 0,32.561004636000085 15.533686742000084 0,32.560990146000051 15.533644125000043 0,32.560954479000017 15.533539225000059 0,32.561037628000079 15.533512982000056 0))" +AOI_5_Khartoum_img1301,9,"POLYGON ((427.18 410.01 0,430.6 401.81 0,410.4 393.98 0,389.28 444.52 0,409.47 452.36 0,406.21 460.17 0,421.8 466.22 0,425.06 458.41 0,454.8 469.95 0,464.84 445.93 0,472.66 448.96 0,479.99 431.41 0,472.17 428.38 0,472.5 427.59 0,473.54 425.09 0,467.85 422.88 0,470.26 417.12 0,440.29 405.49 0,436.84 413.76 0,427.18 410.01 0))","POLYGON ((32.560701774000052 15.533764179000153 0,32.560727859000067 15.533754059000012 0,32.560737184000089 15.533776372000013 0,32.56081809100003 15.533744982 0,32.560811587000067 15.533729421000036 0,32.56082697100004 15.533723452000078 0,32.560824149000048 15.533716701000092 0,32.560823262000056 15.533714576000087 0,32.560844372000098 15.533706386000041 0,32.56082456900004 15.533659002000102 0,32.560803457000063 15.533667192999987 0,32.560776354000048 15.533602346000134 0,32.56069606300013 15.53363349600005 0,32.560687249000061 15.533612406000053 0,32.560645164000093 15.533628733000093 0,32.560653980000048 15.533649824000145 0,32.560599444000061 15.533670983000038 0,32.560656487000102 15.533807466000079 0,32.56071102300006 15.533786307000137 0,32.560701774000052 15.533764179000153 0))" +AOI_5_Khartoum_img1301,10,"POLYGON ((650.0 425.27 0,650.0 384.01 0,630.74 380.77 0,629.27 388.91 0,591.35 382.53 0,583.88 423.75 0,590.99 424.94 0,589.15 435.14 0,634.5 442.77 0,635.91 434.97 0,644.87 436.47 0,646.99 424.76 0,650.0 425.27 0))","POLYGON ((32.561303400025558 15.53372297579989 0,32.561295273000056 15.533724342 0,32.561289547000051 15.533692722 0,32.561265367000026 15.533696788000064 0,32.56126155100003 15.533675721000122 0,32.561139096000026 15.533696309000133 0,32.56114408400002 15.533723854000117 0,32.561124883000083 15.533727082000048 0,32.561145037000095 15.533838365000085 0,32.561247417000061 15.533821153000121 0,32.561251397000021 15.533843125000054 0,32.561303400025558 15.533834382159755 0,32.561303400025558 15.53372297579989 0))" +AOI_5_Khartoum_img1301,11,"POLYGON ((650.0 372.07 0,650.0 358.49 0,608.42 352.63 0,606.4 365.92 0,650.0 372.07 0))","POLYGON ((32.561303400025558 15.533866622498707 0,32.56118568300009 15.533883220000124 0,32.561191134000097 15.53391910900004 0,32.561303400025558 15.533903280062058 0,32.561303400025558 15.533866622498707 0))" +AOI_5_Khartoum_img1301,12,"POLYGON ((555.6 414.78 0,569.52 368.96 0,558.38 365.82 0,559.66 361.6 0,517.81 349.8 0,516.53 354.02 0,502.61 399.83 0,509.99 401.91 0,506.31 414.03 0,551.92 426.89 0,555.6 414.78 0))","POLYGON ((32.561048516000014 15.533751307000037 0,32.561038576000072 15.533718588000131 0,32.560915444000052 15.533753310000135 0,32.560925384000029 15.533786030000032 0,32.560905456000036 15.533791649000099 0,32.560943032000118 15.533915346000112 0,32.560946496000071 15.53392674600007 0,32.561059483000051 15.533894886000073 0,32.561056020000031 15.533883485 0,32.56108609300005 15.533875005000041 0,32.561048516000014 15.533751307000037 0))" +AOI_5_Khartoum_img1301,13,"POLYGON ((151.55 354.3 0,144.48 351.29 0,149.04 341.34 0,139.44 337.26 0,130.11 357.61 0,139.71 361.7 0,146.78 364.71 0,151.55 354.3 0))","POLYGON ((32.559957589000128 15.533914590000062 0,32.559944710000011 15.533886496000058 0,32.559925615000061 15.533894622000053 0,32.559899706000067 15.533905646000054 0,32.559924901000038 15.533960607000068 0,32.559950808000032 15.533949582000012 0,32.559938493000047 15.533922717000076 0,32.559957589000128 15.533914590000062 0))" +AOI_5_Khartoum_img1301,14,"POLYGON ((460.79 333.21 0,432.9 325.88 0,426.22 349.48 0,421.75 348.3 0,417.18 364.46 0,412.07 363.12 0,408.11 377.1 0,413.22 378.45 0,412.07 382.51 0,416.54 383.68 0,444.43 391.01 0,460.79 333.21 0))","POLYGON ((32.560792535000111 15.533971534000033 0,32.560748357000094 15.533815466000085 0,32.560673060000042 15.533835252000118 0,32.560660993000042 15.533838423000043 0,32.560664100000096 15.53384939700007 0,32.560650296000048 15.533853025000058 0,32.560660985000069 15.533890787000129 0,32.560674789000117 15.53388715900009 0,32.56068713600007 15.53393078 0,32.560699203000077 15.533927609000111 0,32.560717237000105 15.533991320000078 0,32.560792535000111 15.533971534000033 0))" +AOI_5_Khartoum_img1301,15,"POLYGON ((420.22 324.52 0,384.06 315.33 0,371.98 359.46 0,408.14 368.65 0,420.22 324.52 0))","POLYGON ((32.56068298800006 15.533994999000056 0,32.560650371000072 15.533875844000049 0,32.560552739000094 15.533900652000074 0,32.560585356000075 15.534019808000021 0,32.56068298800006 15.533994999000056 0))" +AOI_5_Khartoum_img1301,16,"POLYGON ((307.04 376.6 0,292.76 370.32 0,307.98 338.24 0,252.9 313.98 0,225.86 370.97 0,280.94 395.23 0,286.37 383.8 0,300.65 390.09 0,307.04 376.6 0))","POLYGON ((32.560377420000059 15.53385436800006 0,32.560360148000036 15.533817966000083 0,32.560321592000044 15.533834947000051 0,32.560306941000064 15.533804069000027 0,32.560158221000073 15.533869572000134 0,32.560231236000092 15.5340234590001 0,32.56037995700008 15.533957956000094 0,32.560338864000059 15.533871349000092 0,32.560377420000059 15.53385436800006 0))" +AOI_5_Khartoum_img1301,17,"POLYGON ((382.84 304.94 0,358.95 295.34 0,338.58 342.4 0,362.47 352.0 0,382.84 304.94 0))","POLYGON ((32.560582069000112 15.534047853000134 0,32.560527070000042 15.533920804000083 0,32.560462563000087 15.533946727000153 0,32.560517563000083 15.534073774000092 0,32.560582069000112 15.534047853000134 0))" +AOI_5_Khartoum_img1301,18,"POLYGON ((420.39 289.73 0,396.56 280.24 0,385.85 305.21 0,409.69 314.7 0,420.39 289.73 0))","POLYGON ((32.560683456000056 15.53408893 0,32.560654554000074 15.534021513000033 0,32.560590203000103 15.534047122000096 0,32.560619104000061 15.534114539000074 0,32.560683456000056 15.53408893 0))" +AOI_5_Khartoum_img1301,19,"POLYGON ((224.04 352.87 0,243.27 319.37 0,247.39 312.19 0,214.39 293.59 0,181.29 275.95 0,174.49 287.8 0,157.49 317.4 0,180.16 329.48 0,175.58 337.45 0,173.26 341.49 0,177.16 343.57 0,175.0 347.33 0,192.56 356.69 0,192.95 356.02 0,196.43 357.88 0,198.21 354.79 0,203.16 357.42 0,205.48 353.39 0,219.46 360.84 0,224.04 352.87 0))","POLYGON ((32.56015329800006 15.533918464000102 0,32.560140935000092 15.533896930000052 0,32.56010318900011 15.533917048000033 0,32.560096934000065 15.533906154000054 0,32.560083568000046 15.533913278000124 0,32.560078774000019 15.533904929000093 0,32.560069358000099 15.533909948000085 0,32.560068317000038 15.533908136000132 0,32.560020899000037 15.533933408000097 0,32.56002673400009 15.533943570000112 0,32.560016210000065 15.533949179000032 0,32.560022464000042 15.533960073000038 0,32.560034827000081 15.533981606000044 0,32.559973633000034 15.534014222000105 0,32.56001952400004 15.534094147000078 0,32.560037893000064 15.534126141000037 0,32.560127261000083 15.5340785090001 0,32.560216350000118 15.534028278000058 0,32.560205220000043 15.534008894000102 0,32.56015329800006 15.533918464000102 0))" +AOI_5_Khartoum_img1301,20,"POLYGON ((138.62 331.45 0,131.53 328.1 0,132.11 326.99 0,156.87 289.46 0,152.75 286.94 0,163.46 270.71 0,110.29 238.14 0,99.58 254.37 0,103.04 256.49 0,87.34 280.28 0,104.33 290.68 0,100.5 296.48 0,103.81 317.26 0,114.86 331.7 0,126.63 337.43 0,133.84 340.84 0,138.62 331.45 0))","POLYGON ((32.559922677000024 15.533976280000028 0,32.559909765000086 15.533950932000073 0,32.559890306000092 15.533960134000125 0,32.559858522000113 15.533975610000065 0,32.559828681000091 15.53401461000013 0,32.559819755000071 15.534070696000043 0,32.559830086000062 15.534086354000062 0,32.559784213000093 15.534114453000102 0,32.559826599000083 15.53417868800004 0,32.559817264000095 15.534184406000024 0,32.559846175000089 15.53422821900002 0,32.559989733000094 15.534140285000062 0,32.559960822000029 15.534096471000099 0,32.55997195500003 15.53408965099999 0,32.559905094000051 15.533988325000088 0,32.559903526000049 15.533985337000081 0,32.559922677000024 15.533976280000028 0))" +AOI_5_Khartoum_img1301,21,"POLYGON ((360.62 253.29 0,323.04 238.05 0,302.44 229.69 0,292.25 253.01 0,289.14 260.12 0,274.87 254.34 0,257.97 293.01 0,272.23 298.8 0,288.78 305.51 0,304.03 270.62 0,308.79 259.72 0,312.86 261.37 0,318.22 263.54 0,293.83 319.36 0,322.2 330.87 0,346.59 275.05 0,350.43 276.61 0,360.62 253.29 0))","POLYGON ((32.560522065000036 15.534187304000131 0,32.560494556000116 15.534124355000039 0,32.560484202000033 15.534128555000127 0,32.560418338000026 15.533977843000052 0,32.560341731000101 15.534008920000057 0,32.560407595000115 15.534159633000044 0,32.560393110000092 15.534165509000049 0,32.560382144000087 15.534169957000124 0,32.560369281000085 15.534140523000065 0,32.56032811300004 15.534046321000108 0,32.56028343400007 15.534064446000089 0,32.560244926000045 15.53408006700009 0,32.56029056200002 15.534184490000051 0,32.560329069000048 15.534168869000075 0,32.560337465000039 15.534188083000133 0,32.56036497500002 15.534251031000082 0,32.56042062000008 15.534228457000049 0,32.560522065000036 15.534187304000131 0))" +AOI_5_Khartoum_img1301,22,"POLYGON ((650.0 257.03 0,650.0 202.88 0,604.19 198.25 0,598.35 251.81 0,650.0 257.03 0))","POLYGON ((32.561303400025558 15.534177214264373 0,32.561163951000069 15.534191318000095 0,32.561179708000054 15.534335938000044 0,32.561303400025558 15.534323427911589 0,32.561303400025558 15.534177214264373 0))" +AOI_5_Khartoum_img1301,23,"POLYGON ((582.15 192.7 0,535.31 187.28 0,531.77 215.68 0,527.4 215.17 0,524.45 238.78 0,528.82 239.29 0,528.36 243.01 0,575.19 248.43 0,583.28 249.37 0,585.81 229.16 0,577.71 228.22 0,582.15 192.7 0))","POLYGON ((32.561120193000114 15.534350900000032 0,32.561108219000104 15.534255007000013 0,32.561130084000077 15.534252473000089 0,32.56112326900007 15.534197897000139 0,32.561101405000059 15.534200431000048 0,32.560974959000099 15.534215086000138 0,32.560976213000053 15.534225127000111 0,32.560964410000089 15.534226495000048 0,32.560972370000044 15.53429024300007 0,32.560984172000083 15.534288875000055 0,32.560993748000051 15.534365555 0,32.561120193000114 15.534350900000032 0))" +AOI_5_Khartoum_img1301,24,"POLYGON ((287.31 220.15 0,227.43 183.9 0,186.76 268.88 0,250.45 297.17 0,287.31 220.15 0))","POLYGON ((32.560324124000047 15.534276792000036 0,32.560224605 15.534068838000161 0,32.560052662000018 15.534145221000147 0,32.560162469000055 15.534374678000132 0,32.560324124000047 15.534276792000036 0))" +AOI_5_Khartoum_img1301,25,"POLYGON ((3.65 171.54 0,0.0 177.03 0,0.0 234.58 0,0.59 234.94 0,4.56 237.39 0,12.83 224.97 0,20.99 230.01 0,30.01 216.46 0,34.3 219.11 0,48.76 197.38 0,36.3 189.69 0,21.85 211.42 0,17.87 208.96 0,32.98 186.25 0,13.52 174.24 0,11.92 176.64 0,3.65 171.54 0))","POLYGON ((32.559558267000106 15.534408050000076 0,32.559580589000085 15.534394263000101 0,32.559584913000023 15.534400762000059 0,32.559637450000025 15.534368315000121 0,32.55959665700005 15.534307002000018 0,32.559607383000085 15.534300376999957 0,32.55964641500006 15.534359040000066 0,32.559680043000064 15.534338270000077 0,32.55964101200005 15.53427960700003 0,32.559629429000069 15.534286760000031 0,32.559605081000022 15.534250165000115 0,32.559583035000074 15.534263781000075 0,32.559560715000053 15.534230234000033 0,32.559549988000057 15.534236860000124 0,32.559548400025243 15.534237840768091 0,32.559548400025243 15.534393220742379 0,32.559558267000106 15.534408050000076 0))" +AOI_5_Khartoum_img1301,26,"POLYGON ((186.32 199.99 0,170.04 191.43 0,172.16 187.68 0,131.74 166.41 0,104.62 214.24 0,102.95 217.2 0,124.74 228.67 0,125.13 227.98 0,127.53 229.24 0,125.57 232.7 0,141.8 241.24 0,138.6 246.89 0,161.67 259.02 0,168.11 247.65 0,161.33 244.08 0,186.32 199.99 0))","POLYGON ((32.560051451000056 15.534331219000032 0,32.559983986000084 15.534212192000037 0,32.560002309000097 15.534202551000082 0,32.55998490000006 15.534171836000127 0,32.559922622000101 15.53420460400009 0,32.559931258000042 15.534219842000127 0,32.559887438000018 15.534242898000091 0,32.559892736000073 15.534252244000127 0,32.559886243000072 15.534255660000039 0,32.559885189000056 15.534253799000066 0,32.55982635400008 15.534284756000057 0,32.559830884000078 15.534292747000098 0,32.559904089000042 15.534421902000016 0,32.560013236000039 15.534364474000053 0,32.560007496000011 15.5343543450001 0,32.560051451000056 15.534331219000032 0))" +AOI_5_Khartoum_img1301,27,"POLYGON ((539.59 157.43 0,516.65 152.33 0,509.91 180.48 0,532.85 185.58 0,539.59 157.43 0))","POLYGON ((32.561005294000061 15.534446132 0,32.56098709200009 15.534370136000128 0,32.560925157000035 15.534383906000114 0,32.560943359000113 15.534459903 0,32.561005294000061 15.534446132 0))" +AOI_5_Khartoum_img1301,28,"POLYGON ((464.97 130.74 0,442.63 125.32 0,439.46 137.44 0,432.01 135.63 0,429.33 145.88 0,422.84 144.3 0,415.32 173.05 0,421.81 174.63 0,432.1 177.12 0,430.92 181.64 0,448.16 185.83 0,447.16 189.67 0,467.14 194.52 0,471.63 177.32 0,478.34 178.95 0,482.07 164.68 0,455.39 158.2 0,459.54 142.31 0,461.8 142.86 0,464.97 130.74 0))","POLYGON ((32.560803806000052 15.534518192000077 0,32.560795253000073 15.534485481000042 0,32.560789161000059 15.534486959000024 0,32.560777942000115 15.534444049000111 0,32.560849994000115 15.534426562000052 0,32.560839922000113 15.534388038000095 0,32.560821807000018 15.534392435000051 0,32.560809666000054 15.534345998000077 0,32.560755727000085 15.534359089000073 0,32.560758440000029 15.534369465000045 0,32.560711880000085 15.534380765000057 0,32.560715069000032 15.534392963000116 0,32.560687288000068 15.534399705000036 0,32.560669767000036 15.534403958000086 0,32.560690064000049 15.534481589000036 0,32.560707586000049 15.534477336000075 0,32.560714819000083 15.534505002000138 0,32.560734939000078 15.534500119 0,32.560743493000054 15.534532831000144 0,32.560803806000052 15.534518192000077 0))" +AOI_5_Khartoum_img1301,29,"POLYGON ((650.0 184.89 0,650.0 104.12 0,623.93 95.61 0,621.79 101.69 0,601.6 159.11 0,598.46 168.06 0,595.23 177.23 0,627.11 187.64 0,630.33 178.47 0,639.77 181.55 0,650.0 184.89 0))","POLYGON ((32.561303400025558 15.534371998632215 0,32.561275786000095 15.534381013000017 0,32.561250299000079 15.534389332000091 0,32.561241593000069 15.53436457500012 0,32.56115552200005 15.53439267 0,32.561164229000063 15.534417427000113 0,32.561172730000081 15.534441602000127 0,32.561227244000108 15.53459662900015 0,32.561233021000085 15.534613058000046 0,32.561303400025558 15.53459008452268 0,32.561303400025558 15.534371998632215 0))" +AOI_5_Khartoum_img1301,30,"POLYGON ((420.98 122.74 0,371.4 104.54 0,374.74 96.09 0,359.95 90.66 0,335.97 151.33 0,350.76 156.75 0,351.72 154.32 0,365.66 159.44 0,363.62 164.58 0,380.38 170.73 0,383.56 162.67 0,391.64 165.64 0,393.06 162.06 0,396.97 163.49 0,405.53 141.84 0,412.43 144.37 0,420.98 122.74 0))","POLYGON ((32.560685047000085 15.534539802000129 0,32.560661958000011 15.53448140900008 0,32.560643338000041 15.53448824400008 0,32.560620219000079 15.53442977499999 0,32.560609663000086 15.53443364900008 0,32.560605838000086 15.534423974000086 0,32.560584014000042 15.534431984000074 0,32.560575415000073 15.534410234000108 0,32.560530187000062 15.534426834000056 0,32.560535675000054 15.534440714000088 0,32.560498038000041 15.534454529000126 0,32.560495441000086 15.534447962000087 0,32.56045551100005 15.534462618000093 0,32.560520273000073 15.534626407000045 0,32.560560204000105 15.534611752000046 0,32.560551182000026 15.534588936000031 0,32.560685047000085 15.534539802000129 0))" +AOI_5_Khartoum_img1301,31,"POLYGON ((101.59 87.83 0,96.25 95.33 0,89.41 90.81 0,35.54 166.46 0,54.39 178.93 0,61.38 183.55 0,65.85 186.5 0,76.74 171.21 0,72.27 168.26 0,87.35 147.08 0,86.64 146.61 0,101.74 125.41 0,100.3 124.46 0,113.11 106.47 0,118.45 98.97 0,101.59 87.83 0))","POLYGON ((32.559822688000096 15.53463407100006 0,32.559868224000098 15.534603969000036 0,32.559853803000102 15.534583718 0,32.559819216000072 15.534535147000028 0,32.559823088000051 15.534532587000101 0,32.559782324000054 15.53447534499999 0,32.559784243000031 15.534474076 0,32.559743528000105 15.534416900000064 0,32.559755587000069 15.534408929000088 0,32.559726194000085 15.53436765300008 0,32.559714135000021 15.534375625000111 0,32.559695264000069 15.534388099000029 0,32.55964435300011 15.534421753000087 0,32.559789813000052 15.53462601900002 0,32.559808266000068 15.534613819000118 0,32.559822688000096 15.53463407100006 0))" +AOI_5_Khartoum_img1301,32,"POLYGON ((604.05 80.7 0,569.73 72.8 0,567.46 81.99 0,561.74 80.67 0,562.57 77.31 0,545.99 73.5 0,539.24 100.71 0,528.84 142.66 0,535.35 144.16 0,533.2 152.83 0,554.83 157.81 0,556.98 149.13 0,585.47 155.69 0,595.86 113.74 0,599.09 100.71 0,604.05 80.7 0))","POLYGON ((32.561179339000034 15.534653308000083 0,32.561165948000067 15.534599281000052 0,32.561157231000053 15.534564111000144 0,32.56112915600005 15.534450842000023 0,32.561052235000027 15.534468539000088 0,32.561046430000047 15.534445119000067 0,32.560988034000069 15.53445855500007 0,32.560993839000083 15.534481975000091 0,32.560976281000066 15.534486016000102 0,32.561004356000069 15.534599285000134 0,32.561022567000116 15.534672760000031 0,32.561067334000036 15.534662460000112 0,32.561065085000067 15.534653387000088 0,32.561080535000023 15.534649834000048 0,32.561086680000095 15.534674628000072 0,32.561179339000034 15.534653308000083 0))" +AOI_5_Khartoum_img1301,33,"POLYGON ((316.93 78.52 0,320.37 68.36 0,298.4 61.44 0,292.08 80.06 0,285.61 78.02 0,278.76 98.21 0,278.64 98.17 0,270.89 95.73 0,267.42 105.95 0,275.17 108.39 0,270.05 123.48 0,276.65 125.55 0,298.62 132.47 0,302.65 120.59 0,322.39 126.81 0,327.49 111.79 0,336.67 84.74 0,316.93 78.52 0))","POLYGON ((32.560404101000088 15.534659195000113 0,32.560457400000097 15.534642407000085 0,32.560432620000071 15.534569380000118 0,32.560418857000087 15.534528817000064 0,32.56036555700009 15.534545605000057 0,32.560354672000081 15.53451352200006 0,32.560295346000011 15.534532208000014 0,32.560277547000091 15.534537815000027 0,32.560291369000041 15.534578549000109 0,32.560270438000074 15.534585142000088 0,32.560279802000032 15.534612738000062 0,32.560300732000066 15.534606144000092 0,32.560301049000032 15.53460604500011 0,32.560319544000052 15.534660548000073 0,32.560337026000113 15.534655041000082 0,32.560354081000114 15.534705304000106 0,32.560413406000109 15.53468661800005 0,32.560404101000088 15.534659195000113 0))" +AOI_5_Khartoum_img1301,34,"POLYGON ((76.98 68.32 0,62.19 58.34 0,55.72 67.25 0,70.51 77.22 0,76.98 68.32 0))","POLYGON ((32.559756253000053 15.534686748000018 0,32.559738780000025 15.534662699000057 0,32.559698849000057 15.534689629000098 0,32.559716323000089 15.534713678000175 0,32.559756253000053 15.534686748000018 0))" +AOI_5_Khartoum_img1301,35,"POLYGON ((16.6 98.33 0,28.9 83.29 0,0.0 61.34 0,0.0 117.45 0,0.59 117.9 0,8.35 108.41 0,14.6 113.16 0,0.0 131.01 0,0.0 147.96 0,4.79 151.59 0,10.76 155.39 0,30.57 167.27 0,58.74 123.66 0,61.41 125.27 0,81.77 93.76 0,43.81 70.99 0,23.47 102.47 0,16.6 98.33 0))","POLYGON ((32.55959322000011 15.534605718 0,32.559611760000038 15.534594527000035 0,32.559666679000038 15.534679533000029 0,32.559769184000061 15.534618060000138 0,32.559714215000092 15.534532975000051 0,32.559706992000017 15.534537307000091 0,32.559630932000076 15.534419575000184 0,32.559577458000042 15.534451644000052 0,32.559561339000041 15.534461895000092 0,32.559548400025243 15.53447172125124 0,32.559548400025243 15.534517476487832 0,32.559587828000069 15.534565671000093 0,32.559570946000072 15.534578492000092 0,32.559549994000108 15.53455288099998 0,32.559548400025243 15.534554091610673 0,32.559548400025243 15.534705572765176 0,32.559626432000115 15.534646312000062 0,32.55959322000011 15.534605718 0))" +AOI_5_Khartoum_img1301,36,"POLYGON ((500.77 80.05 0,503.87 74.08 0,500.33 72.38 0,513.9 46.24 0,474.85 27.43 0,445.99 83.05 0,430.68 75.67 0,417.12 101.8 0,413.7 108.4 0,435.5 118.91 0,438.93 112.31 0,471.47 127.98 0,481.11 132.63 0,492.77 110.15 0,483.14 105.51 0,492.27 87.9 0,495.81 89.61 0,498.35 84.72 0,505.78 88.31 0,508.21 83.64 0,500.77 80.05 0))","POLYGON ((32.560900478000079 15.534655054000105 0,32.560920557000046 15.534645381000066 0,32.560914017000066 15.534632776000034 0,32.560893938000071 15.534642449000073 0,32.560887092000065 15.534629259000045 0,32.560877540000057 15.534633861000044 0,32.560852867000072 15.534586317000114 0,32.560878878000068 15.534573787000042 0,32.560847390000049 15.534513112000138 0,32.560821379000053 15.534525642000057 0,32.56073350200009 15.53456797600013 0,32.560724253000082 15.534550154000131 0,32.560665385000085 15.534578513000048 0,32.560674634000087 15.534596335000133 0,32.560711247000022 15.534666888000055 0,32.560752577000102 15.53464697800012 0,32.560830506000059 15.534797142000061 0,32.560935921000073 15.534746359000085 0,32.560899291000105 15.534675777000082 0,32.560908845000036 15.534671175000096 0,32.560900478000079 15.534655054000105 0))" +AOI_5_Khartoum_img1301,37,"POLYGON ((444.93 42.88 0,427.63 33.96 0,412.64 26.23 0,401.81 20.64 0,411.89 2.51 0,407.02 -0.0 0,395.94 -0.0 0,388.33 13.69 0,392.24 15.7 0,384.04 30.45 0,370.51 54.8 0,380.08 59.74 0,369.67 78.47 0,395.49 91.79 0,404.39 96.39 0,412.28 82.21 0,403.37 77.61 0,419.43 48.71 0,436.73 57.63 0,444.93 42.88 0))","POLYGON ((32.560749710000024 15.5347554130001 0,32.560727580000055 15.534715593000064 0,32.560680867000073 15.534739690000078 0,32.560637498000069 15.53466165000002 0,32.560661543000073 15.534649246000166 0,32.560640264000043 15.534610955000103 0,32.56061621800005 15.534623360000163 0,32.560546507000041 15.534659322 0,32.560574621000043 15.534709911000089 0,32.560548772000054 15.534723246000064 0,32.560585306000071 15.534788988000079 0,32.560607436000048 15.534828808000128 0,32.560596894000078 15.5348342470001 0,32.560617430121134 15.534871199940053 0,32.560647359665445 15.534871199940053 0,32.560660491000121 15.53486442600007 0,32.560633286000041 15.534815474000068 0,32.560662522000101 15.534800391000076 0,32.560702997000114 15.534779511000082 0,32.560749710000024 15.5347554130001 0))" +AOI_5_Khartoum_img1301,38,"POLYGON ((650.0 10.5 0,650 0 0,642.84 -0.0 0,640.99 8.73 0,650.0 10.5 0))","POLYGON ((32.561303400025558 15.534842848503219 0,32.561279079000037 15.534847617 0,32.561284060042269 15.534871199940053 0,32.561303400025558 15.534871199940053 0,32.561303400025558 15.534842848503219 0))" +AOI_5_Khartoum_img1301,39,"POLYGON ((361.98 -0.0 0,322.27 -0.0 0,356.15 13.54 0,361.98 -0.0 0))","POLYGON ((32.56052573863834 15.534871199940053 0,32.560509999000061 15.534834642000057 0,32.560418526896022 15.534871199940053 0,32.56052573863834 15.534871199940053 0))" +AOI_5_Khartoum_img1301,40,"POLYGON ((287.31 -0.0 0,265.64 -0.0 0,266.23 0.19 0,285.14 6.26 0,287.31 -0.0 0))","POLYGON ((32.560324136778689 15.534871199940053 0,32.560318286000033 15.53485428900013 0,32.560267233000033 15.534870686000044 0,32.560265632752227 15.534871199940053 0,32.560324136778689 15.534871199940053 0))" +AOI_5_Khartoum_img463,-1,POLYGON EMPTY,POLYGON EMPTY diff --git a/docker/solaris/solaris/data/SN2_test_results.csv b/docker/solaris/solaris/data/SN2_test_results.csv new file mode 100644 index 00000000..8fa869d4 --- /dev/null +++ b/docker/solaris/solaris/data/SN2_test_results.csv @@ -0,0 +1,3 @@ +F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos +0.8860759493670887,7,2,0.9459459459459459,0.8333333333333334,35 +0.4444444444444444,75,55,0.48598130841121495,0.4094488188976378,52 diff --git a/docker/solaris/solaris/data/SN2_test_results_full.csv b/docker/solaris/solaris/data/SN2_test_results_full.csv new file mode 100644 index 00000000..495c4e61 --- /dev/null +++ b/docker/solaris/solaris/data/SN2_test_results_full.csv @@ -0,0 +1,7 @@ +F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos,imageID,iou_field,AOI +0.4943820224719101,32,13,0.6285714285714286,0.4074074074074074,22,AOI_5_Khartoum_img130,iou_score,AOI_5_Khartoum +0.8749999999999999,6,2,0.9333333333333333,0.8235294117647058,28,AOI_2_Vegas_img3457,iou_score,AOI_2_Vegas +0.9333333333333333,1,0,1.0,0.875,7,AOI_2_Vegas_img5979,iou_score,AOI_2_Vegas +0.47222222222222215,23,15,0.53125,0.425,17,AOI_5_Khartoum_img1301,iou_score,AOI_5_Khartoum +0.35616438356164376,20,27,0.325,0.3939393939393939,13,AOI_5_Khartoum_img1306,iou_score,AOI_5_Khartoum +0.0,0,0,0.0,0.0,0,AOI_5_Khartoum_img463,iou_score,AOI_5_Khartoum diff --git a/docker/solaris/solaris/data/__init__.py b/docker/solaris/solaris/data/__init__.py new file mode 100644 index 00000000..2adbf1fa --- /dev/null +++ b/docker/solaris/solaris/data/__init__.py @@ -0,0 +1,41 @@ +import os +import pandas as pd +import geopandas as gpd +from osgeo import gdal +import rasterio + +# from . import coco + +# define the current directory as `data_dir` +data_dir = os.path.abspath(os.path.dirname(__file__)) + + +def load_geojson(gj_fname): + """Load a geojson into a gdf using GeoPandas.""" + return gpd.read_file(os.path.join(data_dir, gj_fname)) + + +def gt_gdf(): + """Load in a ground truth GDF example.""" + return load_geojson('gt.geojson') + + +def pred_gdf(): + """Load in an example prediction GDF.""" + return load_geojson('pred.geojson') + + +def sample_load_rasterio(): + return rasterio.open(os.path.join(data_dir, 'sample_geotiff.tif')) + + +def sample_load_gdal(): + return gdal.Open(os.path.join(data_dir, 'sample_geotiff.tif')) + + +def sample_load_geojson(): + return gpd.read_file(os.path.join(data_dir, 'sample.geojson')) + + +def sample_load_csv(): + return pd.read_file(os.path.join(data_dir, 'sample.csv')) diff --git a/docker/solaris/solaris/data/aff_gdf_result.csv b/docker/solaris/solaris/data/aff_gdf_result.csv new file mode 100644 index 00000000..b5e32acd --- /dev/null +++ b/docker/solaris/solaris/data/aff_gdf_result.csv @@ -0,0 +1,152 @@ +ImageId,BuildingId,geometry,PolygonWKT_Geo +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,0,"POLYGON ((733667 3724691, 733683 3724694, 733684 3724689, 733668 3724689, 733667 3724691))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,1,"POLYGON ((733803 3724693, 733810 3724693, 733817 3724694, 733819 3724689, 733802 3724689, 733803 3724693))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,2,"POLYGON ((733701 3724700, 733705 3724692, 733700 3724689, 733688 3724689, 733687 3724692, 733701 3724700))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,3,"POLYGON ((733791 3724701, 733796 3724701, 733796 3724695, 733800 3724695, 733801 3724693, 733801 3724689, 733787 3724689, 733787 3724689, 733786 3724691, 733787 3724694, 733786 3724697, 733787 3724698, 733788 3724698, 733791 3724700, 733791 3724701))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,4,"POLYGON ((733878 3724698, 733880 3724697, 733883 3724697, 733886 3724699, 733887 3724697, 733889 3724696, 733889 3724690, 733881 3724690, 733878 3724691, 733878 3724698))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,5,"POLYGON ((733862 3724700, 733862 3724702, 733868 3724702, 733868 3724699, 733875 3724700, 733876 3724690, 733861 3724690, 733858 3724691, 733858 3724695, 733860 3724697, 733862 3724700))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,6,"POLYGON ((734007 3725041, 733950 3725039, 733948 3725139, 734006 3725139, 734007 3725041))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,7,"POLYGON ((733884 3724943, 733894 3724943, 733897 3724930, 733890 3724929, 733887 3724931, 733884 3724943))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,8,"POLYGON ((733877 3724958, 733893 3724958, 733894 3724947, 733879 3724946, 733877 3724958))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,9,"POLYGON ((733634 3724946, 733633 3724960, 733637 3724961, 733638 3724960, 733640 3724960, 733640 3724954, 733640 3724947, 733634 3724946))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,10,"POLYGON ((733774 3724959, 733783 3724959, 733783 3724957, 733780 3724955, 733781 3724953, 733781 3724950, 733774 3724950, 733774 3724959))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,11,"POLYGON ((733601 3724959, 733607 3724959, 733607 3724950, 733601 3724950, 733601 3724959))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,12,"POLYGON ((733629 3724959, 733629 3724958, 733629 3724955, 733629 3724953, 733630 3724951, 733629 3724950, 733626 3724950, 733625 3724959, 733629 3724959))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,13,"POLYGON ((733665 3724963, 733671 3724963, 733673 3724947, 733663 3724947, 733663 3724951, 733662 3724954, 733664 3724958, 733665 3724960, 733665 3724963))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,14,"POLYGON ((733687 3724962, 733688 3724953, 733678 3724952, 733677 3724961, 733681 3724961, 733681 3724963, 733686 3724963, 733686 3724962, 733687 3724962))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,15,"POLYGON ((733751 3724962, 733751 3724955, 733746 3724956, 733746 3724954, 733742 3724954, 733742 3724962, 733751 3724962))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,16,"POLYGON ((733615 3724963, 733624 3724963, 733624 3724953, 733619 3724953, 733619 3724957, 733612 3724957, 733612 3724961, 733615 3724961, 733615 3724963))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,17,"POLYGON ((733846 3724963, 733846 3724955, 733836 3724955, 733836 3724962, 733846 3724963))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,18,"POLYGON ((733831 3724963, 733831 3724957, 733821 3724956, 733821 3724962, 733831 3724963))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,19,"POLYGON ((733815 3724963, 733815 3724956, 733804 3724956, 733803 3724963, 733815 3724963))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,20,"POLYGON ((733767 3724965, 733768 3724958, 733767 3724958, 733767 3724956, 733755 3724955, 733755 3724964, 733767 3724965))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,21,"POLYGON ((733888 3724973, 733888 3724971, 733886 3724970, 733887 3724961, 733879 3724960, 733876 3724972, 733888 3724973))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,22,"POLYGON ((733878 3724986, 733885 3724988, 733885 3724976, 733877 3724976, 733878 3724986))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,23,"POLYGON ((733878 3725002, 733886 3725003, 733887 3724992, 733886 3724991, 733885 3724990, 733880 3724990, 733878 3725002))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,24,"POLYGON ((733759 3725000, 733770 3725000, 733770 3724996, 733767 3724996, 733767 3724993, 733761 3724993, 733761 3724995, 733756 3724995, 733756 3724998, 733759 3725000))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,25,"POLYGON ((733797 3725004, 733798 3724998, 733797 3724994, 733787 3724995, 733786 3725004, 733797 3725004))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,26,"POLYGON ((733633 3725003, 733643 3725003, 733642 3725005, 733646 3725005, 733646 3725003, 733647 3725003, 733648 3724994, 733634 3724993, 733633 3725003))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,27,"POLYGON ((733803 3725005, 733812 3725005, 733812 3725002, 733814 3725002, 733814 3724993, 733806 3724993, 733806 3724996, 733801 3724996, 733801 3724999, 733803 3724999, 733803 3725005))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,28,"POLYGON ((733681 3725002, 733682 3725007, 733694 3725006, 733694 3725002, 733694 3724992, 733682 3724992, 733681 3725002))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,29,"POLYGON ((733818 3725004, 733822 3725005, 733822 3725002, 733828 3725002, 733828 3724996, 733825 3724996, 733825 3724995, 733823 3724995, 733823 3724996, 733818 3724996, 733818 3725004))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,30,"POLYGON ((733835 3725003, 733846 3725004, 733847 3724996, 733834 3724995, 733833 3725001, 733835 3725001, 733835 3725003))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,31,"POLYGON ((733755 3724997, 733743 3724996, 733742 3725004, 733745 3725003, 733746 3725000, 733747 3725000, 733748 3725000, 733750 3725000, 733752 3724999, 733754 3724999, 733755 3724997))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,32,"POLYGON ((733706 3725006, 733706 3724999, 733703 3724998, 733703 3724995, 733697 3724995, 733696 3725006, 733706 3725006))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,33,"POLYGON ((733723 3725006, 733724 3724997, 733713 3724997, 733713 3725005, 733723 3725006))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,34,"POLYGON ((733731 3725007, 733731 3725005, 733737 3725005, 733738 3725002, 733741 3725002, 733741 3724998, 733739 3724998, 733740 3724996, 733727 3724996, 733727 3725007, 733731 3725007))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,35,"POLYGON ((733668 3725012, 733670 3725012, 733670 3725006, 733675 3725006, 733676 3725004, 733680 3725003, 733679 3724994, 733667 3724995, 733668 3725012))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,36,"POLYGON ((733660 3725013, 733661 3725009, 733659 3725009, 733661 3725000, 733660 3725000, 733659 3724999, 733659 3724997, 733661 3724997, 733661 3724995, 733652 3724996, 733651 3725013, 733660 3725013))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,37,"POLYGON ((733877 3725019, 733886 3725019, 733887 3725009, 733889 3725009, 733889 3725006, 733878 3725006, 733877 3725019))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,38,"POLYGON ((733700 3725028, 733700 3725022, 733695 3725022, 733695 3725028, 733700 3725028))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,39,"POLYGON ((733875 3725035, 733882 3725036, 733883 3725026, 733886 3725026, 733886 3725022, 733882 3725022, 733882 3725023, 733876 3725023, 733875 3725035))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,40,"POLYGON ((733814 3725042, 733814 3725035, 733808 3725034, 733807 3725041, 733814 3725042))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,41,"POLYGON ((733875 3725050, 733883 3725051, 733885 3725038, 733876 3725037, 733875 3725050))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,42,"POLYGON ((733755 3725065, 733766 3725065, 733766 3725047, 733761 3725047, 733762 3725042, 733756 3725042, 733756 3725048, 733755 3725065))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,43,"POLYGON ((733607 3725062, 733607 3725061, 733607 3725060, 733607 3725058, 733608 3725057, 733608 3725056, 733609 3725055, 733609 3725053, 733602 3725053, 733601 3725054, 733601 3725062, 733607 3725062))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,44,"POLYGON ((733710 3725064, 733722 3725065, 733722 3725054, 733710 3725053, 733710 3725064))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,45,"POLYGON ((733832 3725063, 733844 3725063, 733844 3725058, 733839 3725058, 733838 3725055, 733834 3725055, 733834 3725059, 733832 3725059, 733832 3725063))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,46,"POLYGON ((733882 3725065, 733882 3725061, 733886 3725062, 733887 3725056, 733883 3725055, 733883 3725054, 733875 3725053, 733874 3725065, 733882 3725065))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,47,"POLYGON ((733771 3725065, 733782 3725066, 733782 3725058, 733774 3725058, 733773 3725055, 733770 3725055, 733771 3725065))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,48,"POLYGON ((733642 3725068, 733644 3725066, 733644 3725053, 733636 3725053, 733636 3725059, 733633 3725059, 733632 3725064, 733636 3725064, 733636 3725068, 733642 3725068))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,49,"POLYGON ((733724 3725065, 733734 3725065, 733734 3725056, 733724 3725057, 733724 3725065))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,50,"POLYGON ((733664 3725065, 733671 3725065, 733672 3725058, 733664 3725057, 733664 3725065))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,51,"POLYGON ((733661 3725066, 733661 3725057, 733648 3725057, 733648 3725064, 733653 3725064, 733653 3725066, 733661 3725066))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,52,"POLYGON ((733694 3725058, 733694 3725066, 733705 3725066, 733706 3725061, 733704 3725061, 733704 3725057, 733694 3725058))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,53,"POLYGON ((733786 3725066, 733797 3725066, 733797 3725061, 733795 3725062, 733796 3725057, 733786 3725057, 733786 3725066))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,54,"POLYGON ((733687 3725066, 733691 3725064, 733691 3725058, 733682 3725059, 733682 3725063, 733682 3725066, 733687 3725066))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,55,"POLYGON ((733816 3725067, 733817 3725065, 733819 3725063, 733822 3725061, 733827 3725064, 733829 3725064, 733829 3725058, 733816 3725057, 733816 3725067))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,56,"POLYGON ((733752 3725066, 733752 3725061, 733750 3725061, 733750 3725058, 733744 3725058, 733744 3725059, 733741 3725059, 733741 3725066, 733741 3725066, 733741 3725067, 733743 3725067, 733743 3725066, 733744 3725066, 733744 3725067, 733746 3725067, 733746 3725066, 733746 3725066, 733746 3725067, 733748 3725067, 733748 3725066, 733752 3725066))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,57,"POLYGON ((733812 3725066, 733812 3725058, 733801 3725057, 733801 3725066, 733804 3725066, 733804 3725067, 733807 3725067, 733807 3725066, 733812 3725066))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,58,"POLYGON ((733873 3725080, 733882 3725081, 733882 3725074, 733885 3725074, 733885 3725068, 733874 3725068, 733873 3725080))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,59,"POLYGON ((733874 3725095, 733882 3725096, 733882 3725083, 733875 3725082, 733874 3725095))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,60,"POLYGON ((733621 3725097, 733621 3725105, 733622 3725105, 733623 3725106, 733624 3725107, 733623 3725109, 733624 3725109, 733626 3725109, 733628 3725110, 733628 3725108, 733629 3725107, 733629 3725107, 733630 3725107, 733631 3725105, 733631 3725103, 733631 3725101, 733631 3725099, 733631 3725098, 733631 3725097, 733629 3725096, 733629 3725095, 733630 3725094, 733628 3725093, 733627 3725093, 733626 3725094, 733625 3725094, 733624 3725095, 733624 3725096, 733622 3725097, 733621 3725097))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,61,"POLYGON ((733896 3725106, 733897 3725100, 733888 3725099, 733887 3725105, 733896 3725106))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,62,"POLYGON ((733712 3725107, 733722 3725107, 733722 3725099, 733720 3725099, 733719 3725098, 733716 3725098, 733716 3725099, 733712 3725099, 733712 3725107))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,63,"POLYGON ((733835 3725114, 733835 3725109, 733840 3725109, 733841 3725104, 733845 3725102, 733845 3725092, 733839 3725091, 733839 3725100, 733831 3725099, 733831 3725114, 733835 3725114))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,64,"POLYGON ((733741 3725107, 733746 3725108, 733746 3725103, 733750 3725103, 733750 3725102, 733752 3725100, 733754 3725100, 733761 3725101, 733763 3725104, 733768 3725105, 733769 3725099, 733746 3725098, 733744 3725098, 733742 3725098, 733741 3725100, 733741 3725107))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,65,"POLYGON ((733786 3725108, 733793 3725107, 733793 3725105, 733788 3725105, 733788 3725102, 733790 3725101, 733792 3725099, 733786 3725099, 733786 3725108))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,66,"POLYGON ((733633 3725110, 733656 3725111, 733656 3725099, 733644 3725099, 733639 3725095, 733635 3725096, 733633 3725099, 733633 3725110))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,67,"POLYGON ((733725 3725109, 733737 3725109, 733738 3725099, 733734 3725099, 733734 3725097, 733725 3725098, 733725 3725105, 733725 3725109))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,68,"POLYGON ((733802 3725109, 733803 3725105, 733811 3725105, 733813 3725099, 733802 3725099, 733802 3725109))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,69,"POLYGON ((733680 3725106, 733685 3725106, 733686 3725110, 733692 3725110, 733693 3725099, 733680 3725098, 733680 3725106))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,70,"POLYGON ((733872 3725111, 733881 3725112, 733881 3725098, 733873 3725098, 733872 3725111))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,71,"POLYGON ((733673 3725113, 733673 3725108, 733676 3725108, 733676 3725101, 733678 3725101, 733678 3725099, 733675 3725099, 733675 3725097, 733666 3725097, 733665 3725099, 733658 3725100, 733658 3725108, 733665 3725108, 733665 3725113, 733673 3725113))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,72,"POLYGON ((733770 3725112, 733776 3725112, 733776 3725108, 733783 3725108, 733784 3725100, 733771 3725100, 733770 3725112))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,73,"POLYGON ((733695 3725112, 733708 3725113, 733708 3725110, 733704 3725110, 733704 3725105, 733707 3725105, 733707 3725100, 733695 3725100, 733695 3725112))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,74,"POLYGON ((733881 3725139, 733882 3725131, 733877 3725130, 733875 3725130, 733875 3725132, 733875 3725133, 733876 3725134, 733875 3725135, 733875 3725136, 733874 3725137, 733873 3725138, 733872 3725139, 733872 3725139, 733881 3725139))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,75,"POLYGON ((734017 3724710, 734024 3724709, 734024 3724708, 734026 3724706, 734025 3724692, 734022 3724693, 734021 3724693, 734020 3724692, 734016 3724692, 734017 3724710))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,76,"POLYGON ((734036 3724732, 734040 3724732, 734041 3724725, 734040 3724722, 734039 3724719, 734042 3724719, 734041 3724712, 734030 3724713, 734030 3724722, 734031 3724723, 734033 3724723, 734035 3724723, 734037 3724726, 734037 3724730, 734036 3724732))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,77,"POLYGON ((733993 3724758, 733998 3724758, 733998 3724752, 733996 3724754, 733993 3724754, 733993 3724758))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,78,"POLYGON ((733995 3724788, 733996 3724769, 733986 3724769, 733986 3724786, 733989 3724786, 733989 3724788, 733995 3724788))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,79,"POLYGON ((734021 3724786, 734024 3724775, 734001 3724776, 734001 3724786, 734021 3724786))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,80,"POLYGON ((734003 3724830, 734013 3724830, 734015 3724827, 734016 3724822, 734014 3724821, 734014 3724819, 734015 3724812, 734010 3724812, 734008 3724821, 734008 3724826, 734002 3724826, 734003 3724830))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,81,"POLYGON ((733983 3724832, 733984 3724831, 733988 3724830, 733997 3724830, 733997 3724822, 733996 3724821, 733996 3724819, 733994 3724817, 733989 3724818, 733988 3724821, 733988 3724826, 733983 3724826, 733983 3724832))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,82,"POLYGON ((734024 3724828, 734032 3724828, 734033 3724830, 734035 3724830, 734035 3724827, 734032 3724826, 734029 3724821, 734026 3724821, 734024 3724823, 734024 3724828))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,83,"POLYGON ((733963 3724834, 733967 3724831, 733969 3724831, 733976 3724832, 733977 3724828, 733976 3724824, 733964 3724826, 733963 3724834))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,84,"POLYGON ((733949 3725027, 734007 3725027, 734007 3724908, 733950 3724907, 733949 3725027))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,85,"POLYGON ((733890 3724713, 733899 3724714, 733903 3724712, 733905 3724691, 733903 3724689, 733894 3724689, 733894 3724698, 733892 3724699, 733890 3724713))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,86,"POLYGON ((733618 3724718, 733620 3724715, 733621 3724715, 733621 3724714, 733624 3724710, 733622 3724709, 733623 3724707, 733617 3724703, 733615 3724706, 733614 3724705, 733613 3724708, 733610 3724713, 733618 3724718))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,87,"POLYGON ((733633 3724724, 733637 3724718, 733637 3724716, 733640 3724710, 733634 3724707, 733626 3724720, 733633 3724724))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,88,"POLYGON ((733642 3724718, 733644 3724720, 733646 3724720, 733647 3724720, 733648 3724720, 733651 3724723, 733655 3724716, 733646 3724710, 733642 3724718))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,89,"POLYGON ((733938 3724727, 733945 3724725, 733947 3724727, 733948 3724722, 733952 3724721, 733952 3724718, 733949 3724717, 733949 3724715, 733939 3724714, 733938 3724727))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,90,"POLYGON ((733654 3724726, 733663 3724731, 733670 3724720, 733660 3724715, 733654 3724726))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,91,"POLYGON ((733887 3724733, 733900 3724733, 733900 3724730, 733902 3724726, 733901 3724720, 733896 3724720, 733895 3724724, 733886 3724723, 733886 3724725, 733888 3724726, 733889 3724729, 733887 3724733))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,92,"POLYGON ((733666 3724733, 733674 3724738, 733681 3724729, 733681 3724727, 733676 3724724, 733674 3724723, 733671 3724723, 733667 3724728, 733666 3724733))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,93,"POLYGON ((733786 3724741, 733802 3724742, 733803 3724733, 733801 3724731, 733799 3724731, 733794 3724731, 733790 3724730, 733786 3724730, 733786 3724741))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,94,"POLYGON ((733803 3724741, 733815 3724742, 733817 3724740, 733819 3724739, 733819 3724732, 733810 3724731, 733808 3724732, 733806 3724732, 733805 3724730, 733803 3724741))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,95,"POLYGON ((733773 3724743, 733782 3724744, 733783 3724731, 733776 3724730, 733774 3724731, 733773 3724743))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,96,"POLYGON ((733827 3724741, 733834 3724743, 733839 3724742, 733840 3724733, 733839 3724733, 733828 3724732, 733827 3724741))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,97,"POLYGON ((733855 3724743, 733860 3724744, 733861 3724745, 733867 3724747, 733871 3724746, 733872 3724744, 733871 3724741, 733872 3724739, 733871 3724737, 733868 3724735, 733865 3724736, 733857 3724735, 733855 3724743))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,98,"POLYGON ((733691 3724747, 733694 3724750, 733696 3724750, 733696 3724748, 733698 3724746, 733702 3724747, 733704 3724745, 733699 3724741, 733699 3724740, 733696 3724738, 733691 3724747))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,99,"POLYGON ((733887 3724756, 733896 3724758, 733898 3724752, 733901 3724747, 733900 3724741, 733898 3724739, 733893 3724739, 733891 3724745, 733891 3724750, 733887 3724752, 733887 3724756))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,100,"POLYGON ((733716 3724752, 733708 3724746, 733705 3724754, 733708 3724755, 733710 3724756, 733713 3724757, 733716 3724755, 733717 3724753, 733716 3724752))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,101,"POLYGON ((733614 3724744, 733609 3724746, 733604 3724753, 733609 3724757, 733619 3724760, 733622 3724750, 733614 3724744))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,102,"POLYGON ((733940 3724761, 733952 3724761, 733952 3724749, 733942 3724749, 733942 3724756, 733938 3724757, 733940 3724761))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,103,"POLYGON ((733635 3724767, 733640 3724758, 733636 3724756, 733633 3724751, 733631 3724753, 733627 3724757, 733627 3724761, 733630 3724764, 733635 3724767))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,104,"POLYGON ((733731 3724767, 733733 3724766, 733734 3724767, 733736 3724763, 733733 3724762, 733729 3724759, 733726 3724763, 733731 3724767))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,105,"POLYGON ((733743 3724769, 733740 3724773, 733738 3724775, 733734 3724776, 733734 3724779, 733739 3724782, 733741 3724779, 733746 3724776, 733746 3724772, 733743 3724769))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,106,"POLYGON ((733890 3724782, 733897 3724782, 733898 3724772, 733893 3724772, 733894 3724775, 733893 3724777, 733892 3724778, 733890 3724781, 733890 3724782))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,107,"POLYGON ((733945 3724792, 733953 3724792, 733954 3724781, 733953 3724769, 733944 3724769, 733945 3724792))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,108,"POLYGON ((733692 3724787, 733688 3724789, 733684 3724788, 733680 3724784, 733679 3724782, 733679 3724778, 733673 3724784, 733688 3724793, 733692 3724787))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,109,"POLYGON ((733851 3724791, 733868 3724791, 733869 3724787, 733867 3724786, 733865 3724786, 733863 3724785, 733860 3724784, 733858 3724782, 733855 3724782, 733851 3724781, 733851 3724791))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,110,"POLYGON ((733822 3724802, 733827 3724797, 733823 3724792, 733825 3724790, 733812 3724777, 733804 3724784, 733822 3724802))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,111,"POLYGON ((733742 3724797, 733746 3724801, 733750 3724797, 733756 3724792, 733758 3724794, 733762 3724792, 733754 3724783, 733752 3724784, 733753 3724786, 733753 3724789, 733752 3724791, 733750 3724794, 733748 3724795, 733742 3724797))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,112,"POLYGON ((733603 3724809, 733607 3724808, 733611 3724809, 733615 3724800, 733606 3724791, 733603 3724794, 733601 3724798, 733601 3724803, 733602 3724803, 733601 3724805, 733601 3724808, 733603 3724809))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,113,"POLYGON ((733760 3724809, 733763 3724806, 733766 3724806, 733770 3724810, 733774 3724807, 733772 3724802, 733768 3724797, 733765 3724797, 733760 3724800, 733758 3724803, 733758 3724805, 733758 3724808, 733760 3724809))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,114,"POLYGON ((733619 3724812, 733626 3724815, 733628 3724809, 733631 3724807, 733632 3724804, 733634 3724800, 733630 3724799, 733622 3724796, 733620 3724801, 733622 3724802, 733621 3724804, 733619 3724809, 733619 3724812))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,115,"POLYGON ((733694 3724818, 733702 3724812, 733692 3724797, 733684 3724803, 733694 3724818))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,116,"POLYGON ((733709 3724819, 733712 3724817, 733709 3724813, 733706 3724815, 733709 3724819))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,117,"POLYGON ((733882 3724826, 733890 3724827, 733893 3724809, 733890 3724806, 733886 3724808, 733882 3724826))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,118,"POLYGON ((733655 3724823, 733658 3724819, 733660 3724819, 733661 3724817, 733659 3724815, 733657 3724813, 733655 3724815, 733656 3724817, 733653 3724821, 733655 3724823))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,119,"POLYGON ((733783 3724821, 733780 3724818, 733781 3724815, 733782 3724814, 733778 3724809, 733773 3724814, 733775 3724816, 733774 3724819, 733771 3724824, 733774 3724828, 733783 3724821))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,120,"POLYGON ((733839 3724827, 733844 3724832, 733846 3724832, 733848 3724832, 733850 3724830, 733851 3724823, 733850 3724820, 733844 3724815, 733837 3724822, 733841 3724825, 733839 3724827))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,121,"POLYGON ((733709 3724825, 733705 3724820, 733697 3724826, 733699 3724827, 733696 3724830, 733700 3724834, 733702 3724834, 733707 3724831, 733708 3724829, 733706 3724827, 733709 3724825))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,122,"POLYGON ((733946 3724842, 733956 3724843, 733958 3724827, 733952 3724826, 733951 3724829, 733947 3724829, 733946 3724842))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,123,"POLYGON ((733637 3724838, 733644 3724842, 733646 3724840, 733646 3724838, 733647 3724836, 733647 3724834, 733644 3724832, 733643 3724830, 733637 3724838))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,124,"POLYGON ((733792 3724845, 733800 3724839, 733793 3724830, 733790 3724832, 733787 3724834, 733790 3724838, 733789 3724840, 733792 3724845))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,125,"POLYGON ((733878 3724848, 733892 3724849, 733891 3724845, 733891 3724843, 733891 3724839, 733890 3724835, 733891 3724830, 733883 3724831, 733881 3724843, 733878 3724844, 733878 3724848))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,126,"POLYGON ((733607 3724853, 733613 3724844, 733604 3724837, 733601 3724840, 733601 3724841, 733601 3724845, 733605 3724847, 733605 3724851, 733607 3724853))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,127,"POLYGON ((733715 3724850, 733721 3724856, 733722 3724856, 733723 3724857, 733728 3724859, 733732 3724856, 733733 3724855, 733727 3724845, 733725 3724846, 733723 3724842, 733716 3724848, 733715 3724850))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,128,"POLYGON ((733767 3724853, 733773 3724854, 733773 3724849, 733768 3724848, 733767 3724853))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,129,"POLYGON ((733632 3724859, 733639 3724848, 733630 3724842, 733632 3724845, 733631 3724850, 733629 3724853, 733626 3724853, 733625 3724853, 733623 3724856, 733619 3724853, 733615 3724860, 733622 3724865, 733625 3724861, 733628 3724863, 733629 3724861, 733630 3724862, 733632 3724859))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,130,"POLYGON ((733802 3724862, 733810 3724862, 733810 3724857, 733809 3724854, 733799 3724855, 733799 3724861, 733801 3724861, 733802 3724862))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,131,"POLYGON ((733941 3724867, 733951 3724868, 733952 3724853, 733943 3724852, 733941 3724867))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,132,"POLYGON ((733786 3724868, 733794 3724868, 733793 3724856, 733785 3724857, 733781 3724857, 733781 3724865, 733785 3724865, 733786 3724868))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,133,"POLYGON ((733742 3724868, 733754 3724868, 733755 3724868, 733755 3724860, 733754 3724859, 733742 3724859, 733742 3724868))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,134,"POLYGON ((733763 3724869, 733775 3724868, 733775 3724860, 733763 3724861, 733763 3724869))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,135,"POLYGON ((733662 3724862, 733660 3724870, 733666 3724871, 733667 3724869, 733670 3724870, 733671 3724863, 733669 3724862, 733668 3724862, 733666 3724860, 733664 3724860, 733662 3724862, 733662 3724862))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,136,"POLYGON ((733942 3724890, 733951 3724891, 733950 3724888, 733950 3724887, 733951 3724885, 733952 3724883, 733953 3724881, 733952 3724878, 733951 3724876, 733942 3724876, 733942 3724890))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,137,"POLYGON ((733670 3724907, 733677 3724908, 733679 3724895, 733671 3724895, 733670 3724907))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,138,"POLYGON ((733722 3724907, 733731 3724907, 733731 3724902, 733733 3724901, 733732 3724899, 733729 3724899, 733725 3724900, 733722 3724898, 733722 3724907))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,139,"POLYGON ((733658 3724910, 733666 3724910, 733667 3724898, 733665 3724898, 733665 3724896, 733659 3724896, 733658 3724910))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,140,"POLYGON ((733768 3724908, 733777 3724909, 733778 3724898, 733773 3724897, 733772 3724899, 733769 3724899, 733768 3724908))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,141,"POLYGON ((733637 3724912, 733641 3724911, 733641 3724909, 733643 3724909, 733643 3724907, 733647 3724907, 733648 3724905, 733648 3724902, 733645 3724902, 733646 3724896, 733640 3724895, 733640 3724899, 733637 3724901, 733635 3724905, 733635 3724909, 733637 3724909, 733637 3724912))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,142,"POLYGON ((733700 3724908, 733702 3724907, 733702 3724901, 733693 3724900, 733692 3724906, 733697 3724908, 733700 3724908))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,143,"POLYGON ((733819 3724910, 733819 3724899, 733812 3724898, 733812 3724911, 733814 3724909, 733817 3724909, 733819 3724910))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,144,"POLYGON ((733797 3724914, 733807 3724914, 733806 3724900, 733803 3724899, 733796 3724899, 733797 3724914))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,145,"POLYGON ((733706 3724916, 733713 3724917, 733711 3724914, 733712 3724910, 733715 3724907, 733716 3724904, 733712 3724903, 733713 3724900, 733708 3724899, 733707 3724905, 733706 3724908, 733706 3724916))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,146,"POLYGON ((733751 3724918, 733756 3724918, 733755 3724916, 733756 3724914, 733759 3724912, 733762 3724912, 733763 3724910, 733762 3724906, 733762 3724899, 733752 3724898, 733751 3724918))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,147,"POLYGON ((733845 3724917, 733854 3724921, 733859 3724908, 733858 3724908, 733860 3724904, 733852 3724901, 733848 3724909, 733846 3724908, 733844 3724913, 733846 3724914, 733845 3724917))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,148,"POLYGON ((733613 3724916, 733614 3724911, 733613 3724909, 733614 3724907, 733612 3724906, 733604 3724906, 733602 3724908, 733603 3724916, 733613 3724916))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,149,"POLYGON ((733791 3724929, 733801 3724929, 733801 3724921, 733791 3724921, 733791 3724929))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,150,"POLYGON ((733712 3724936, 733722 3724936, 733722 3724930, 733712 3724930, 733712 3724936))",1 diff --git a/docker/solaris/solaris/data/cli_test/__init__.py b/docker/solaris/solaris/data/cli_test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/data/cli_test/expected/__init__.py b/docker/solaris/solaris/data/cli_test/expected/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/data/cli_test/expected/gj_to_px_result.geojson b/docker/solaris/solaris/data/cli_test/expected/gj_to_px_result.geojson new file mode 100644 index 00000000..5867abc9 --- /dev/null +++ b/docker/solaris/solaris/data/cli_test/expected/gj_to_px_result.geojson @@ -0,0 +1,49 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102923, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 371.9610521071786, "origlen": 0, "partialDec": 0.40890572019922689, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 0.0, 3.0 ], [ 8.0, 8.0 ], [ 6.0, 21.0 ], [ 5.0, 29.0 ], [ 19.0, 38.0 ], [ 18.0, 58.0 ], [ 0.0, 54.0 ], [ 0.0, 3.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135943, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 293.80215091143663, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 27.0, 226.0 ], [ 34.0, 226.0 ], [ 35.0, 221.0 ], [ 45.0, 221.0 ], [ 44.0, 229.0 ], [ 56.0, 230.0 ], [ 55.0, 262.0 ], [ 47.0, 267.0 ], [ 26.0, 266.0 ], [ 27.0, 226.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134696, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 160.8812060779413, "origlen": 0, "partialDec": 0.33276744108985457, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 60.0, 885.0 ], [ 74.0, 900.0 ], [ 52.0, 900.0 ], [ 48.0, 896.0 ], [ 60.0, 885.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102932, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 250.90410248692208, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 66.0, 443.0 ], [ 86.0, 444.0 ], [ 84.0, 494.0 ], [ 64.0, 493.0 ], [ 66.0, 443.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 87.0, 73.0 ], [ 107.0, 84.0 ], [ 98.0, 100.0 ], [ 91.0, 99.0 ], [ 53.0, 79.0 ], [ 60.0, 67.0 ], [ 80.0, 78.0 ], [ 87.0, 73.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 87.0, 503.0 ], [ 91.0, 512.0 ], [ 93.0, 551.0 ], [ 71.0, 553.0 ], [ 71.0, 545.0 ], [ 70.0, 526.0 ], [ 70.0, 502.0 ], [ 87.0, 503.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102938, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 241.49355060420999, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 89.0, 562.0 ], [ 92.0, 580.0 ], [ 95.0, 593.0 ], [ 93.0, 607.0 ], [ 85.0, 611.0 ], [ 73.0, 611.0 ], [ 72.0, 606.0 ], [ 73.0, 588.0 ], [ 72.0, 562.0 ], [ 89.0, 562.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117300, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 56.69138110426168, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 73.0, 653.0 ], [ 74.0, 641.0 ], [ 74.0, 635.0 ], [ 90.0, 635.0 ], [ 96.0, 636.0 ], [ 95.0, 642.0 ], [ 90.0, 643.0 ], [ 86.0, 643.0 ], [ 83.0, 644.0 ], [ 79.0, 646.0 ], [ 77.0, 653.0 ], [ 73.0, 653.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102940, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 247.85698155347222, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 104.0, 379.0 ], [ 96.0, 433.0 ], [ 84.0, 427.0 ], [ 72.0, 424.0 ], [ 74.0, 415.0 ], [ 83.0, 416.0 ], [ 84.0, 403.0 ], [ 81.0, 393.0 ], [ 81.0, 388.0 ], [ 85.0, 379.0 ], [ 104.0, 379.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 106.0, 314.0 ], [ 127.0, 321.0 ], [ 111.0, 370.0 ], [ 90.0, 363.0 ], [ 96.0, 344.0 ], [ 107.0, 336.0 ], [ 113.0, 326.0 ], [ 114.0, 319.0 ], [ 106.0, 314.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102924, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.59457935772951, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 130.0, 257.0 ], [ 162.0, 274.0 ], [ 155.0, 289.0 ], [ 125.0, 273.0 ], [ 124.0, 268.0 ], [ 130.0, 257.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102920, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 226.41975901786444, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 134.0, 98.0 ], [ 150.0, 108.0 ], [ 153.0, 114.0 ], [ 158.0, 117.0 ], [ 162.0, 128.0 ], [ 171.0, 133.0 ], [ 166.0, 145.0 ], [ 156.0, 141.0 ], [ 152.0, 135.0 ], [ 141.0, 129.0 ], [ 134.0, 122.0 ], [ 125.0, 113.0 ], [ 134.0, 98.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134694, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 98.143961268411516, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 214.0, 866.0 ], [ 232.0, 882.0 ], [ 216.0, 899.0 ], [ 211.0, 890.0 ], [ 214.0, 885.0 ], [ 213.0, 878.0 ], [ 203.0, 874.0 ], [ 214.0, 866.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102925, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 235.62354171707437, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 231.0, 330.0 ], [ 248.0, 357.0 ], [ 242.0, 364.0 ], [ 226.0, 373.0 ], [ 220.0, 363.0 ], [ 226.0, 359.0 ], [ 222.0, 354.0 ], [ 211.0, 359.0 ], [ 203.0, 347.0 ], [ 231.0, 330.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117299, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 17.925629600560526, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 226.0, 126.0 ], [ 228.0, 118.0 ], [ 236.0, 119.0 ], [ 235.0, 128.0 ], [ 226.0, 126.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 238.0, 153.0 ], [ 243.0, 157.0 ], [ 246.0, 154.0 ], [ 253.0, 155.0 ], [ 252.0, 160.0 ], [ 273.0, 171.0 ], [ 271.0, 182.0 ], [ 268.0, 190.0 ], [ 264.0, 198.0 ], [ 260.0, 201.0 ], [ 252.0, 199.0 ], [ 229.0, 188.0 ], [ 222.0, 183.0 ], [ 238.0, 153.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93146, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 85.499318876340681, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 392.0, 671.0 ], [ 417.0, 671.0 ], [ 419.0, 684.0 ], [ 393.0, 685.0 ], [ 392.0, 671.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93019, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 223.11504089232326, "origlen": 0, "partialDec": 0.71792570273177214, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 416.0, 871.0 ], [ 423.0, 879.0 ], [ 426.0, 893.0 ], [ 418.0, 900.0 ], [ 386.0, 900.0 ], [ 416.0, 871.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86007, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 288.75968082249199, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 407.0, 293.0 ], [ 428.0, 295.0 ], [ 425.0, 345.0 ], [ 402.0, 344.0 ], [ 404.0, 304.0 ], [ 407.0, 304.0 ], [ 407.0, 293.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86008, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 231.57048190271922, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 432.0, 226.0 ], [ 431.0, 245.0 ], [ 427.0, 245.0 ], [ 425.0, 259.0 ], [ 429.0, 259.0 ], [ 428.0, 276.0 ], [ 414.0, 275.0 ], [ 414.0, 264.0 ], [ 411.0, 256.0 ], [ 406.0, 249.0 ], [ 407.0, 243.0 ], [ 410.0, 239.0 ], [ 410.0, 233.0 ], [ 407.0, 231.0 ], [ 407.0, 225.0 ], [ 432.0, 226.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86009, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 259.07434426271021, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 412.0, 165.0 ], [ 433.0, 166.0 ], [ 431.0, 217.0 ], [ 410.0, 216.0 ], [ 412.0, 165.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86013, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 168.5733291901596, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 437.0, 115.0 ], [ 435.0, 146.0 ], [ 428.0, 156.0 ], [ 420.0, 155.0 ], [ 419.0, 154.0 ], [ 419.0, 150.0 ], [ 421.0, 143.0 ], [ 420.0, 136.0 ], [ 416.0, 131.0 ], [ 417.0, 115.0 ], [ 437.0, 115.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 459.0, 48.0 ], [ 456.0, 70.0 ], [ 451.0, 69.0 ], [ 447.0, 97.0 ], [ 426.0, 93.0 ], [ 434.0, 44.0 ], [ 459.0, 48.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86014, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 269.2410676574849, "origlen": 0, "partialDec": 0.67827110406115843, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 484.0, 0.0 ], [ 480.0, 10.0 ], [ 477.0, 9.0 ], [ 465.0, 36.0 ], [ 446.0, 28.0 ], [ 459.0, 0.0 ], [ 484.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93018, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 189.51651421072748, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 446.0, 842.0 ], [ 481.0, 828.0 ], [ 489.0, 847.0 ], [ 454.0, 861.0 ], [ 446.0, 842.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86006, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 300.47300947479948, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 482.0, 358.0 ], [ 496.0, 360.0 ], [ 494.0, 372.0 ], [ 528.0, 377.0 ], [ 525.0, 397.0 ], [ 492.0, 392.0 ], [ 491.0, 396.0 ], [ 477.0, 394.0 ], [ 482.0, 358.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134680, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 101.16730006427393, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 536.0, 150.0 ], [ 536.0, 157.0 ], [ 537.0, 163.0 ], [ 536.0, 166.0 ], [ 539.0, 179.0 ], [ 534.0, 182.0 ], [ 531.0, 184.0 ], [ 524.0, 189.0 ], [ 524.0, 192.0 ], [ 522.0, 192.0 ], [ 522.0, 182.0 ], [ 526.0, 179.0 ], [ 530.0, 172.0 ], [ 529.0, 168.0 ], [ 525.0, 165.0 ], [ 525.0, 150.0 ], [ 536.0, 150.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86011, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.33402454110015, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 523.0, 198.0 ], [ 545.0, 199.0 ], [ 542.0, 250.0 ], [ 523.0, 248.0 ], [ 520.0, 239.0 ], [ 519.0, 234.0 ], [ 527.0, 229.0 ], [ 529.0, 221.0 ], [ 523.0, 212.0 ], [ 523.0, 198.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 527.0, 262.0 ], [ 545.0, 262.0 ], [ 544.0, 293.0 ], [ 552.0, 293.0 ], [ 552.0, 315.0 ], [ 518.0, 314.0 ], [ 518.0, 301.0 ], [ 526.0, 301.0 ], [ 527.0, 262.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86015, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 251.33309650398371, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 568.0, 79.0 ], [ 574.0, 77.0 ], [ 574.0, 84.0 ], [ 577.0, 88.0 ], [ 584.0, 90.0 ], [ 588.0, 91.0 ], [ 554.0, 130.0 ], [ 549.0, 126.0 ], [ 546.0, 116.0 ], [ 548.0, 102.0 ], [ 568.0, 79.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86005, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.65732906731142, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 545.0, 404.0 ], [ 546.0, 384.0 ], [ 561.0, 384.0 ], [ 562.0, 379.0 ], [ 573.0, 379.0 ], [ 573.0, 384.0 ], [ 594.0, 385.0 ], [ 594.0, 405.0 ], [ 545.0, 404.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134689, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 28.431725003743587, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 595.0, 341.0 ], [ 593.0, 346.0 ], [ 596.0, 351.0 ], [ 594.0, 356.0 ], [ 595.0, 359.0 ], [ 597.0, 362.0 ], [ 591.0, 362.0 ], [ 591.0, 356.0 ], [ 589.0, 356.0 ], [ 589.0, 341.0 ], [ 595.0, 341.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 643.0, 605.0 ], [ 650.0, 639.0 ], [ 631.0, 644.0 ], [ 626.0, 622.0 ], [ 614.0, 624.0 ], [ 611.0, 612.0 ], [ 643.0, 605.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134690, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 75.233357615263444, "origlen": 0, "partialDec": 0.5426079647770381, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 664.0, 0.0 ], [ 666.0, 5.0 ], [ 663.0, 5.0 ], [ 657.0, 4.0 ], [ 655.0, 1.0 ], [ 653.0, 4.0 ], [ 654.0, 9.0 ], [ 655.0, 13.0 ], [ 652.0, 16.0 ], [ 649.0, 19.0 ], [ 649.0, 14.0 ], [ 647.0, 10.0 ], [ 646.0, 4.0 ], [ 644.0, 1.0 ], [ 644.0, 0.0 ], [ 664.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85996, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 147.78075500471959, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 720.0, 599.0 ], [ 720.0, 607.0 ], [ 724.0, 611.0 ], [ 725.0, 616.0 ], [ 723.0, 620.0 ], [ 718.0, 620.0 ], [ 714.0, 616.0 ], [ 703.0, 615.0 ], [ 698.0, 620.0 ], [ 691.0, 620.0 ], [ 691.0, 599.0 ], [ 720.0, 599.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86607, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 109.09963485201257, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 766.0, 220.0 ], [ 767.0, 204.0 ], [ 775.0, 199.0 ], [ 780.0, 196.0 ], [ 780.0, 206.0 ], [ 793.0, 207.0 ], [ 793.0, 220.0 ], [ 766.0, 220.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 794.0, 800.0 ], [ 808.0, 800.0 ], [ 811.0, 810.0 ], [ 808.0, 813.0 ], [ 807.0, 820.0 ], [ 768.0, 823.0 ], [ 760.0, 819.0 ], [ 761.0, 806.0 ], [ 775.0, 796.0 ], [ 783.0, 796.0 ], [ 791.0, 796.0 ], [ 794.0, 800.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86606, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 298.78711714810817, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 819.0, 146.0 ], [ 802.0, 147.0 ], [ 803.0, 163.0 ], [ 778.0, 164.0 ], [ 777.0, 128.0 ], [ 818.0, 127.0 ], [ 819.0, 146.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86604, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 243.05210583905563, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 795.0, 2.0 ], [ 817.0, 1.0 ], [ 818.0, 48.0 ], [ 805.0, 48.0 ], [ 796.0, 37.0 ], [ 795.0, 2.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 816.0, 58.0 ], [ 818.0, 105.0 ], [ 796.0, 106.0 ], [ 795.0, 62.0 ], [ 804.0, 62.0 ], [ 806.0, 59.0 ], [ 816.0, 58.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92641, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 127.89418152890968, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 821.0, 800.0 ], [ 839.0, 798.0 ], [ 842.0, 807.0 ], [ 860.0, 807.0 ], [ 864.0, 813.0 ], [ 836.0, 819.0 ], [ 830.0, 813.0 ], [ 822.0, 813.0 ], [ 821.0, 800.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86004, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 245.87085671707746, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 878.0, 363.0 ], [ 838.0, 366.0 ], [ 837.0, 350.0 ], [ 850.0, 349.0 ], [ 850.0, 344.0 ], [ 855.0, 343.0 ], [ 854.0, 338.0 ], [ 866.0, 337.0 ], [ 866.0, 343.0 ], [ 884.0, 342.0 ], [ 885.0, 357.0 ], [ 878.0, 363.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92640, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 140.04971670918377, "origlen": 0, "partialDec": 0.38635210049961444, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 887.0, 821.0 ], [ 886.0, 802.0 ], [ 900.0, 802.0 ], [ 900.0, 819.0 ], [ 895.0, 817.0 ], [ 889.0, 819.0 ], [ 887.0, 821.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_b_inner_mask.tif b/docker/solaris/solaris/data/cli_test/expected/sample_b_inner_mask.tif new file mode 100644 index 00000000..fef5c87e Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_b_inner_mask.tif differ diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_b_outer10_mask.tif b/docker/solaris/solaris/data/cli_test/expected/sample_b_outer10_mask.tif new file mode 100644 index 00000000..761c78da Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_b_outer10_mask.tif differ diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_c_mask.tif b/docker/solaris/solaris/data/cli_test/expected/sample_c_mask.tif new file mode 100644 index 00000000..63a43896 Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_c_mask.tif differ diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_fbc_mask.tif b/docker/solaris/solaris/data/cli_test/expected/sample_fbc_mask.tif new file mode 100644 index 00000000..689f8266 Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_fbc_mask.tif differ diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_fp_mask.tif b/docker/solaris/solaris/data/cli_test/expected/sample_fp_mask.tif new file mode 100644 index 00000000..36b12058 Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_fp_mask.tif differ diff --git a/docker/solaris/solaris/data/cli_test/expected/sample_graph.pkl b/docker/solaris/solaris/data/cli_test/expected/sample_graph.pkl new file mode 100644 index 00000000..7d597534 Binary files /dev/null and b/docker/solaris/solaris/data/cli_test/expected/sample_graph.pkl differ diff --git a/docker/solaris/solaris/data/cli_test/result/__init__.py b/docker/solaris/solaris/data/cli_test/result/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/data/coco.py b/docker/solaris/solaris/data/coco.py new file mode 100644 index 00000000..ca378e09 --- /dev/null +++ b/docker/solaris/solaris/data/coco.py @@ -0,0 +1,565 @@ +from ..utils.core import _check_df_load, _check_geom, get_files_recursively +from ..utils.geo import bbox_corners_to_coco, polygon_to_coco, split_multi_geometries +from ..utils.log import _get_logging_level +from ..vector.polygon import geojson_to_px_gdf, remove_multipolygons +import numpy as np +import rasterio +from tqdm.auto import tqdm +import json +import os +import pandas as pd +import geopandas as gpd +import logging + + +def geojson2coco(image_src, label_src, output_path=None, image_ext='.tif', + matching_re=None, category_attribute=None, score_attribute=None, + preset_categories=None, include_other=True, info_dict=None, + license_dict=None, recursive=False, override_crs=False, + explode_all_multipolygons=False, remove_all_multipolygons=False, + verbose=0): + """Generate COCO-formatted labels from one or multiple geojsons and images. + + This function ingests optionally georegistered polygon labels in geojson + format alongside image(s) and generates .json files per the + `COCO dataset specification`_ . Some models, like + many Mask R-CNN implementations, require labels to be in this format. The + function assumes you're providing image file(s) and geojson file(s) to + create the dataset. If the number of images and geojsons are both > 1 (e.g. + with a SpaceNet dataset), you must provide a regex pattern to extract + matching substrings to match images to label files. + + .. _COCO dataset specification: http://cocodataset.org/ + + Arguments + --------- + image_src : :class:`str` or :class:`list` or :class:`dict` + Source image(s) to use in the dataset. This can be:: + + 1. a string path to an image, + 2. the path to a directory containing a bunch of images, + 3. a list of image paths, + 4. a dictionary corresponding to COCO-formatted image records, or + 5. a string path to a COCO JSON containing image records. + + If a directory, the `recursive` flag will be used to determine whether + or not to descend into sub-directories. + label_src : :class:`str` or :class:`list` + Source labels to use in the dataset. This can be a string path to a + geojson, the path to a directory containing multiple geojsons, or a + list of geojson file paths. If a directory, the `recursive` flag will + determine whether or not to descend into sub-directories. + output_path : str, optional + The path to save the JSON-formatted COCO records to. If not provided, + the records will only be returned as a dict, and not saved to file. + image_ext : str, optional + The string to use to identify images when searching directories. Only + has an effect if `image_src` is a directory path. Defaults to + ``".tif"``. + matching_re : str, optional + A regular expression pattern to match filenames between `image_src` + and `label_src` if both are directories of multiple files. This has + no effect if those arguments do not both correspond to directories or + lists of files. Will raise a ``ValueError`` if multiple files are + provided for both `image_src` and `label_src` but no `matching_re` is + provided. + category_attribute : str, optional + The name of an attribute in the geojson that specifies which category + a given instance corresponds to. If not provided, it's assumed that + only one class of object is present in the dataset, which will be + termed ``"other"`` in the output json. + score_attribute : str, optional + The name of an attribute in the geojson that specifies the prediction + confidence of a model + preset_categories : :class:`list` of :class:`dict`s, optional + A pre-set list of categories to use for labels. These categories should + be formatted per + `the COCO category specification`_. + example: + [{'id': 1, 'name': 'Fighter Jet', 'supercategory': 'plane'}, + {'id': 2, 'name': 'Military Bomber', 'supercategory': 'plane'}, ... ] + include_other : bool, optional + If set to ``True``, and `preset_categories` is provided, objects that + don't fall into the specified categories will not be removed from the + dataset. They will instead be passed into a category named ``"other"`` + with its own associated category ``id``. If ``False``, objects whose + categories don't match a category from `preset_categories` will be + dropped. + info_dict : dict, optional + A dictonary with the following key-value pairs:: + + - ``"year"``: :class:`int` year of creation + - ``"version"``: :class:`str` version of the dataset + - ``"description"``: :class:`str` string description of the dataset + - ``"contributor"``: :class:`str` who contributed the dataset + - ``"url"``: :class:`str` URL where the dataset can be found + - ``"date_created"``: :class:`datetime.datetime` when the dataset + was created + + license_dict : dict, optional + A dictionary containing the licensing information for the dataset, with + the following key-value pairs:: + + - ``"name": :class:`str` the name of the license. + - ``"url": :class:`str` a link to the dataset's license. + + *Note*: This implementation assumes that all of the data uses one + license. If multiple licenses are provided, the image records will not + be assigned a license ID. + recursive : bool, optional + If `image_src` and/or `label_src` are directories, setting this flag + to ``True`` will induce solaris to descend into subdirectories to find + files. By default, solaris does not traverse the directory tree. + explode_all_multipolygons : bool, optional + Explode the multipolygons into individual geometries using sol.utils.geo.split_multi_geometries. + Be sure to inspect which geometries are multigeometries, each individual geometries within these + may represent artifacts rather than true labels. + remove_all_multipolygons : bool, optional + Filters MultiPolygons and GeometryCollections out of each tile geodataframe. Alternatively you + can edit each polygon manually to be a polygon before converting to COCO format. + verbose : int, optional + Verbose text output. By default, none is provided; if ``True`` or + ``1``, information-level outputs are provided; if ``2``, extremely + verbose text is output. + + Returns + ------- + coco_dataset : dict + A dictionary following the `COCO dataset specification`_ . Depending + on arguments provided, it may or may not include license and info + metadata. + """ + + # first, convert both image_src and label_src to lists of filenames + logger = logging.getLogger(__name__) + logger.setLevel(_get_logging_level(int(verbose))) + logger.debug('Preparing image filename: image ID dict.') + # pdb.set_trace() + if isinstance(image_src, str): + if image_src.endswith('json'): + logger.debug('COCO json provided. Extracting fname:id dict.') + with open(image_src, 'r') as f: + image_ref = json.load(f) + image_ref = {image['file_name']: image['id'] + for image in image_ref['images']} + else: + image_list = _get_fname_list(image_src, recursive=recursive, + extension=image_ext) + image_ref = dict(zip(image_list, + list(range(1, len(image_list) + 1)) + )) + elif isinstance(image_src, dict): + logger.debug('image COCO dict provided. Extracting fname:id dict.') + if 'images' in image_src.keys(): + image_ref = image_src['images'] + else: + image_ref = image_src + image_ref = {image['file_name']: image['id'] + for image in image_ref} + else: + logger.debug('Non-COCO formatted image set provided. Generating ' + 'image fname:id dict with arbitrary ID integers.') + image_list = _get_fname_list(image_src, recursive=recursive, + extension=image_ext) + image_ref = dict(zip(image_list, list(range(1, len(image_list) + 1)))) + + logger.debug('Preparing label filename list.') + label_list = _get_fname_list(label_src, recursive=recursive, + extension='json') + + logger.debug('Checking if images and vector labels must be matched.') + do_matches = len(image_ref) > 1 and len(label_list) > 1 + if do_matches: + logger.info('Matching images to label files.') + im_names = pd.DataFrame({'image_fname': list(image_ref.keys())}) + label_names = pd.DataFrame({'label_fname': label_list}) + logger.debug('Getting substrings for matching from image fnames.') + if matching_re is not None: + im_names['match_substr'] = im_names['image_fname'].str.extract( + matching_re) + logger.debug('Getting substrings for matching from label fnames.') + label_names['match_substr'] = label_names[ + 'label_fname'].str.extract(matching_re) + else: + logger.debug('matching_re is none, getting full filenames ' + 'without extensions for matching.') + im_names['match_substr'] = im_names['image_fname'].apply( + lambda x: os.path.splitext(os.path.split(x)[1])[0]) + im_names['match_substr'] = im_names['match_substr'].astype( + str) + label_names['match_substr'] = label_names['label_fname'].apply( + lambda x: os.path.splitext(os.path.split(x)[1])[0]) + label_names['match_substr'] = label_names['match_substr'].astype( + str) + match_df = im_names.merge(label_names, on='match_substr', how='inner') + + logger.info('Loading labels.') + label_df = pd.DataFrame({'label_fname': [], + 'category_str': [], + 'geometry': []}) + for gj in tqdm(label_list): + logger.debug('Reading in {}'.format(gj)) + curr_gdf = gpd.read_file(gj) + + if remove_all_multipolygons is True and explode_all_multipolygons is True: + raise ValueError("Only one of remove_all_multipolygons or explode_all_multipolygons can be set to True.") + if remove_all_multipolygons is True and explode_all_multipolygons is False: + curr_gdf = remove_multipolygons(curr_gdf) + elif explode_all_multipolygons is True: + curr_gdf = split_multi_geometries(curr_gdf) + + curr_gdf['label_fname'] = gj + curr_gdf['image_fname'] = '' + curr_gdf['image_id'] = np.nan + if category_attribute is None: + logger.debug('No category attribute provided. Creating a default ' + '"other" category.') + curr_gdf['category_str'] = 'other' # add arbitrary value + tmp_category_attribute = 'category_str' + else: + tmp_category_attribute = category_attribute + if do_matches: # multiple images: multiple labels + logger.debug('do_matches is True, finding matching image') + logger.debug('Converting to pixel coordinates.') + if len(curr_gdf) > 0: # if there are geoms, reproj to px coords + curr_gdf = geojson_to_px_gdf( + curr_gdf, + override_crs=override_crs, + im_path=match_df.loc[match_df['label_fname'] == gj, + 'image_fname'].values[0]) + curr_gdf['image_id'] = image_ref[match_df.loc[ + match_df['label_fname'] == gj, 'image_fname'].values[0]] + # handle case with multiple images, one big geojson + elif len(image_ref) > 1 and len(label_list) == 1: + logger.debug('do_matches is False. Many images:1 label detected.') + raise NotImplementedError('one label file: many images ' + 'not implemented yet.') + elif len(image_ref) == 1 and len(label_list) == 1: + logger.debug('do_matches is False. 1 image:1 label detected.') + logger.debug('Converting to pixel coordinates.') + # match the two images + curr_gdf = geojson_to_px_gdf(curr_gdf, + override_crs=override_crs, + im_path=list(image_ref.keys())[0]) + curr_gdf['image_id'] = list(image_ref.values())[0] + curr_gdf = curr_gdf.rename( + columns={tmp_category_attribute: 'category_str'}) + if score_attribute is not None: + curr_gdf = curr_gdf[['image_id', 'label_fname', 'category_str', + score_attribute, 'geometry']] + else: + curr_gdf = curr_gdf[['image_id', 'label_fname', 'category_str', + 'geometry']] + label_df = pd.concat([label_df, curr_gdf], axis='index', + ignore_index=True, sort=False) + + logger.info('Finished loading labels.') + logger.info('Generating COCO-formatted annotations.') + coco_dataset = df_to_coco_annos(label_df, + geom_col='geometry', + image_id_col='image_id', + category_col='category_str', + score_col=score_attribute, + preset_categories=preset_categories, + include_other=include_other, + verbose=verbose) + + logger.info('Generating COCO-formatted image and license records.') + if license_dict is not None: + logger.debug('Getting license ID.') + if len(license_dict) == 1: + logger.debug('Only one license present; assuming it applies to ' + 'all images.') + license_id = 1 + else: + logger.debug('Zero or multiple licenses present. Not trying to ' + 'match to images.') + license_id = None + logger.info('Adding licenses to dataset.') + coco_licenses = [] + license_idx = 1 + for license_name, license_url in license_dict.items(): + coco_licenses.append({'name': license_name, + 'url': license_url, + 'id': license_idx}) + license_idx += 1 + coco_dataset['licenses'] = coco_licenses + else: + logger.debug('No license information provided, skipping for image ' + 'COCO records.') + license_id = None + coco_image_records = make_coco_image_dict(image_ref, license_id) + coco_dataset['images'] = coco_image_records + + logger.info('Adding any additional information provided as arguments.') + if info_dict is not None: + coco_dataset['info'] = info_dict + + if output_path is not None: + with open(output_path, 'w') as outfile: + json.dump(coco_dataset, outfile) + + return coco_dataset + + +def df_to_coco_annos(df, output_path=None, geom_col='geometry', + image_id_col=None, category_col=None, score_col=None, + preset_categories=None, supercategory_col=None, + include_other=True, starting_id=1, verbose=0): + """Extract COCO-formatted annotations from a pandas ``DataFrame``. + + This function assumes that *annotations are already in pixel coordinates.* + If this is not the case, you can transform them using + :func:`solaris.vector.polygon.geojson_to_px_gdf`. + + Note that this function generates annotations formatted per the COCO object + detection specification. For additional information, see + `the COCO dataset specification`_. + + .. _the COCO dataset specification: http://cocodataset.org/#format-data + + Arguments + --------- + df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` containing geometries to store as annos. + image_id_col : str, optional + The column containing image IDs. If not provided, it's assumed that + all are in the same image, which will be assigned the ID of ``1``. + geom_col : str, optional + The name of the column in `df` that contains geometries. The geometries + should either be shapely :class:`shapely.geometry.Polygon` s or WKT + strings. Defaults to ``"geometry"``. + category_col : str, optional + The name of the column that specifies categories for each object. If + not provided, all objects will be placed in a single category named + ``"other"``. + score_col : str, optional + The name of the column that specifies the ouptut confidence of a model. + If not provided, will not be output. + preset_categories : :class:`list` of :class:`dict`s, optional + A pre-set list of categories to use for labels. These categories should + be formatted per + `the COCO category specification`_. + starting_id : int, optional + The number to start numbering annotation IDs at. Defaults to ``1``. + verbose : int, optional + Verbose text output. By default, none is provided; if ``True`` or + ``1``, information-level outputs are provided; if ``2``, extremely + verbose text is output. + + .. _the COCO category specification: http://cocodataset.org/#format-data + + Returns + ------- + output_dict : dict + A dictionary containing COCO-formatted annotation and category entries + per the `COCO dataset specification`_ + """ + logger = logging.getLogger(__name__) + logger.setLevel(_get_logging_level(int(verbose))) + logger.debug('Checking that df is loaded.') + df = _check_df_load(df) + temp_df = df.copy() # for manipulation + if preset_categories is not None and category_col is None: + logger.debug('preset_categories has a value, category_col is None.') + raise ValueError('category_col must be specified if using' + ' preset_categories.') + elif preset_categories is not None and category_col is not None: + logger.debug('Both preset_categories and category_col have values.') + logger.debug('Getting list of category names.') + category_dict = _coco_category_name_id_dict_from_list( + preset_categories) + category_names = list(category_dict.keys()) + if not include_other: + logger.info('Filtering out objects not contained in ' + ' preset_categories') + temp_df = temp_df.loc[temp_df[category_col].isin(category_names), + :] + else: + logger.info('Setting category to "other" for objects outside of ' + 'preset category list.') + temp_df.loc[~temp_df[category_col].isin(category_names), + category_col] = 'other' + if 'other' not in category_dict.keys(): + logger.debug('Adding "other" to category_dict.') + other_id = np.array(list(category_dict.values())).max() + 1 + category_dict['other'] = other_id + preset_categories.append({'id': other_id, + 'name': 'other', + 'supercategory': 'other'}) + elif preset_categories is None and category_col is not None: + logger.debug('No preset_categories, have category_col.') + logger.info(f'Collecting unique category names from {category_col}.') + category_names = list(temp_df[category_col].unique()) + logger.info('Generating category ID numbers arbitrarily.') + category_dict = {k: v for k, v in zip(category_names, + range(1, len(category_names)+1))} + else: + logger.debug('No category column or preset categories.') + logger.info('Setting category to "other" for all objects.') + category_col = 'category_col' + temp_df[category_col] = 'other' + category_names = ['other'] + category_dict = {'other': 1} + + if image_id_col is None: + temp_df['image_id'] = 1 + else: + temp_df.rename(columns={image_id_col: 'image_id'}) + logger.debug('Checking geometries.') + temp_df[geom_col] = temp_df[geom_col].apply(_check_geom) + logger.info('Getting area of geometries.') + temp_df['area'] = temp_df[geom_col].apply(lambda x: x.area) + logger.info('Getting geometry bounding boxes.') + temp_df['bbox'] = temp_df[geom_col].apply( + lambda x: bbox_corners_to_coco(x.bounds)) + temp_df['category_id'] = temp_df[category_col].map(category_dict) + temp_df['annotation_id'] = list(range(starting_id, + starting_id + len(temp_df))) + if score_col is not None: + temp_df['score'] = df[score_col] + + def _row_to_coco(row, geom_col, category_id_col, image_id_col, score_col): + "get a single annotation record from a row of temp_df." + if score_col is None: + + return {'id': row['annotation_id'], + 'image_id': int(row[image_id_col]), + 'category_id': int(row[category_id_col]), + 'segmentation': [polygon_to_coco(row[geom_col])], + 'area': row['area'], + 'bbox': row['bbox'], + 'iscrowd': 0} + else: + return {'id': row['annotation_id'], + 'image_id': int(row[image_id_col]), + 'category_id': int(row[category_id_col]), + 'segmentation': [polygon_to_coco(row[geom_col])], + 'score': float(row[score_col]), + 'area': row['area'], + 'bbox': row['bbox'], + 'iscrowd': 0} + + coco_annotations = temp_df.apply(_row_to_coco, axis=1, geom_col=geom_col, + category_id_col='category_id', + image_id_col=image_id_col, + score_col=score_col).tolist() + coco_categories = coco_categories_dict_from_df( + temp_df, category_id_col='category_id', + category_name_col=category_col, + supercategory_col=supercategory_col) + + output_dict = {'annotations': coco_annotations, + 'categories': coco_categories} + + if output_path is not None: + with open(output_path, 'w') as outfile: + json.dump(output_dict, outfile) + + return output_dict + + +def coco_categories_dict_from_df(df, category_id_col, category_name_col, + supercategory_col=None): + """Extract category IDs, category names, and supercat names from df. + + Arguments + --------- + df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` of records to filter for category info. + category_id_col : str + The name for the column in `df` that contains category IDs. + category_name_col : str + The name for the column in `df` that contains category names. + supercategory_col : str, optional + The name for the column in `df` that contains supercategory names, + if one exists. If not provided, supercategory will be left out of the + output. + + Returns + ------- + :class:`list` of :class:`dict` s + A :class:`list` of :class:`dict` s that contain category records per + the `COCO dataset specification`_ . + """ + cols_to_keep = [category_id_col, category_name_col] + rename_dict = {category_id_col: 'id', + category_name_col: 'name'} + if supercategory_col is not None: + cols_to_keep.append(supercategory_col) + rename_dict[supercategory_col] = 'supercategory' + coco_cat_df = df[cols_to_keep] + coco_cat_df = coco_cat_df.rename(columns=rename_dict) + coco_cat_df = coco_cat_df.drop_duplicates() + + return coco_cat_df.to_dict(orient='records') + + +def make_coco_image_dict(image_ref, license_id=None): + """Take a dict of ``image_fname: image_id`` pairs and make a coco dict. + + Note that this creates a relatively limited version of the standard + `COCO image record format`_ record, which only contains the following + keys:: + + * id ``(int)`` + * width ``(int)`` + * height ``(int)`` + * file_name ``(str)`` + * license ``(int)``, optional + + .. _COCO image record format: http://cocodataset.org/#format-data + + Arguments + --------- + image_ref : dict + A dictionary of ``image_fname: image_id`` key-value pairs. + license_id : int, optional + The license ID number for the relevant license. If not provided, no + license information will be included in the output. + + Returns + ------- + coco_images : list + A list of COCO-formatted image records ready for export to json. + """ + + image_records = [] + for image_fname, image_id in image_ref.items(): + with rasterio.open(image_fname) as f: + width = f.width + height = f.height + im_record = {'id': image_id, + 'file_name': os.path.split(image_fname)[1], + 'width': width, + 'height': height} + if license_id is not None: + im_record['license'] = license_id + image_records.append(im_record) + + return image_records + + +def _coco_category_name_id_dict_from_list(category_list): + """Extract ``{category_name: category_id}`` from a list.""" + # check if this is a full annotation json or just the categories + category_dict = {category['name']: category['id'] + for category in category_list} + return category_dict + + +def _get_fname_list(p, recursive=False, extension='.tif'): + """Get a list of filenames from p, which can be a dir, fname, or list.""" + if isinstance(p, list): + return p + elif isinstance(p, str): + if os.path.isdir(p): + return get_files_recursively(p, traverse_subdirs=recursive, + extension=extension) + elif os.path.isfile(p): + return [p] + else: + raise ValueError("If a string is provided, it must be a valid" + " path.") + else: + raise ValueError("{} is not a string or list.".format(p)) diff --git a/docker/solaris/solaris/data/coco_sample_1.json b/docker/solaris/solaris/data/coco_sample_1.json new file mode 100644 index 00000000..59670ad3 --- /dev/null +++ b/docker/solaris/solaris/data/coco_sample_1.json @@ -0,0 +1 @@ +{"annotations": [{"id": 1, "image_id": 1, "category_id": 1, "segmentation": [60.03418597159907, 74.87320505268872, 73.8337494416628, 90.0, 51.516283753560856, 90.0, 47.80893106292933, 85.93607368506491, 60.03418597159907, 74.87320505268872], "area": 214.14410906402435, "bbox": [47.80893106292933, 74.87320505268872, 26.02481837873347, 15.126794947311282], "iscrowd": 0}, {"id": 2, "image_id": 2, "category_id": 1, "segmentation": [90.0, 11.015026673674583, 70.7970443549566, 13.249627484939992, 70.8928169994615, 4.990449592471123, 70.69254911504686, 0.0, 90.0, 0.0, 90.0, 11.015026673674583], "area": 232.6028019573394, "bbox": [70.69254911504686, 0.0, 19.30745088495314, 13.249627484939992], "iscrowd": 0}, {"id": 3, "image_id": 2, "category_id": 1, "segmentation": [89.06576380180195, 21.638346442952752, 90.0, 28.386366279795766, 90.0, 68.61032488476485, 85.23654213640839, 70.96199104283005, 73.38412117748521, 70.6515495320782, 71.78515014378354, 65.98500318173319, 72.83866719854996, 48.2692635813728, 72.19266184815206, 21.76100580766797, 89.06576380180195, 21.638346442952752], "area": 853.8212747899074, "bbox": [71.78515014378354, 21.638346442952752, 18.21484985621646, 49.3236445998773], "iscrowd": 0}], "categories": [{"id": 1, "name": "other"}], "licenses": [{"name": "CC-BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "id": 1}], "images": [{"id": 1, "file_name": "sample_geotiff_733601_3724734.tif", "width": 90, "height": 90, "license": 1}, {"id": 2, "file_name": "sample_geotiff_733601_3724869.tif", "width": 90, "height": 90, "license": 1}]} \ No newline at end of file diff --git a/docker/solaris/solaris/data/coco_sample_2.json b/docker/solaris/solaris/data/coco_sample_2.json new file mode 100644 index 00000000..86026633 --- /dev/null +++ b/docker/solaris/solaris/data/coco_sample_2.json @@ -0,0 +1 @@ +{"annotations": [{"id": 1, "image_id": 1, "category_id": 1, "segmentation": [0.0, 2.845103836618364, 7.787239895900711, 7.813573766499758, 6.348949391860515, 21.166115891188383, 5.487595358863473, 29.24418894201517, 19.3797596283257, 37.85056554712355, 18.118415302364156, 57.70217224024236, 0.0, 54.131107677705586, 0.0, 2.845103836618364], "area": 608.3880075917921, "bbox": [0.0, 2.845103836618364, 19.3797596283257, 54.857068403624], "iscrowd": 0}, {"id": 2, "image_id": 1, "category_id": 2, "segmentation": [27.38481539185159, 226.1645903000608, 34.46586190746166, 226.48033855389804, 34.72251786501147, 221.01391235832125, 44.8147500208579, 221.47823364380747, 44.453276831656694, 229.49973394535482, 56.44128756551072, 230.05102432798594, 54.999366192379966, 261.5376432267949, 46.934077847748995, 267.3053462980315, 25.54191842698492, 266.33953956048936, 27.38481539185159, 226.1645903000608], "area": 1175.2086036457465, "bbox": [25.54191842698492, 221.01391235832125, 30.8993691385258, 46.29143393971026], "iscrowd": 0}, {"id": 3, "image_id": 1, "category_id": 1, "segmentation": [60.03418597159907, 884.8732050526887, 73.8337494416628, 900.0, 51.516283753560856, 900.0, 47.80893106292933, 895.9360736850649, 60.03418597159907, 884.8732050526887], "area": 214.14410906402435, "bbox": [47.80893106292933, 884.8732050526887, 26.02481837873347, 15.126794947311282], "iscrowd": 0}, {"id": 4, "image_id": 1, "category_id": 2, "segmentation": [65.83512698789127, 443.34588148258626, 86.05315328529105, 444.11831593420357, 84.12356285331771, 493.6842159954831, 63.905484846793115, 492.9117766721174, 65.83512698789127, 443.34588148258626], "area": 1003.6164099476883, "bbox": [63.905484846793115, 443.34588148258626, 22.14766843849793, 50.338334512896836], "iscrowd": 0}, {"id": 5, "image_id": 1, "category_id": 2, "segmentation": [87.2731370574329, 72.93001714255661, 106.98580074869096, 84.21334314905107, 97.70029512513429, 100.08772260416299, 91.15104462415911, 98.73803176078945, 53.36824434134178, 78.81699287053198, 59.59959887806326, 67.12329848110676, 79.5907216486521, 77.6452063396573, 87.2731370574329, 72.93001714255661], "area": 832.0140045611614, "bbox": [53.36824434134178, 67.12329848110676, 53.617556407349184, 32.96442412305623], "iscrowd": 0}, {"id": 6, "image_id": 1, "category_id": 2, "segmentation": [87.33356586564332, 502.7506626434624, 90.79576571006328, 511.5002211164683, 93.23485574219376, 550.6385944513604, 70.7970443549566, 553.24962748494, 70.8928169994615, 544.9904495924711, 70.136441974435, 526.1424378501251, 69.54070870671421, 501.6971649955958, 87.33356586564332, 502.7506626434624], "area": 1055.98129961667, "bbox": [69.54070870671421, 501.6971649955958, 23.694147035479546, 51.55246248934418], "iscrowd": 0}, {"id": 7, "image_id": 1, "category_id": 2, "segmentation": [89.06576380180195, 561.6383464429528, 91.58933665603399, 579.8661990063265, 94.85523002082482, 592.7489638356492, 93.41836131247692, 606.9227179316804, 85.23654213640839, 610.96199104283, 73.38412117748521, 610.6515495320782, 71.78515014378354, 605.9850031817332, 72.83866719854996, 588.2692635813728, 72.19266184815206, 561.761005807668, 89.06576380180195, 561.6383464429528], "area": 965.97420241684, "bbox": [71.78515014378354, 561.6383464429528, 23.07007987704128, 49.3236445998773], "iscrowd": 0}, {"id": 8, "image_id": 1, "category_id": 2, "segmentation": [73.42513769492507, 652.7116207033396, 73.87162248673849, 640.5596289457753, 73.96795534505509, 634.6088027460501, 89.67092586541548, 635.2249299148098, 95.65740334033035, 635.5673428568989, 95.31320596928708, 642.0125183537602, 90.37084288243204, 643.375933191739, 86.37565372907557, 643.2291440004483, 83.40025364165194, 643.7899634344503, 78.97129776002839, 645.6513671381399, 77.39582740026526, 652.6148558100685, 73.42513769492507, 652.7116207033396], "area": 226.76552441704672, "bbox": [73.42513769492507, 634.6088027460501, 22.232265645405278, 18.102817957289517], "iscrowd": 0}, {"id": 9, "image_id": 1, "category_id": 2, "segmentation": [104.26538560027257, 379.3509592106566, 95.71053624199703, 432.8294323347509, 83.6166091109626, 427.17569901794195, 71.57171053020284, 424.2952412031591, 74.16042645578273, 415.48699966818094, 82.98187345149927, 415.6049499800429, 84.05773156136274, 402.6163706937805, 81.36960026691668, 392.8713309513405, 80.55370765388943, 388.34107194375247, 84.53470388124697, 378.76643434073776, 104.26538560027257, 379.3509592106566], "area": 991.4279262138889, "bbox": [71.57171053020284, 378.76643434073776, 32.69367507006973, 54.06299799401313], "iscrowd": 0}, {"id": 10, "image_id": 1, "category_id": 2, "segmentation": [105.87982941744849, 313.81173481605947, 127.49495613621548, 320.8758743349463, 111.21164846420288, 370.3255268894136, 89.93423808692023, 363.40850543417037, 96.1564225088805, 344.47919271234423, 106.8362458597403, 336.29499020427465, 112.88286360329948, 326.15949539188296, 113.55506858509034, 319.46216831356287, 105.87982941744849, 313.81173481605947], "area": 946.9628953122234, "bbox": [89.93423808692023, 313.81173481605947, 37.56071804929525, 56.513792073354125], "iscrowd": 0}, {"id": 11, "image_id": 1, "category_id": 2, "segmentation": [129.5470552511979, 257.30139927752316, 162.33337076054886, 274.0591311287135, 154.6747992991004, 288.89506167545915, 124.72422339068726, 273.46653464064, 123.76762919081375, 268.4957895213738, 129.5470552511979, 257.30139927752316], "area": 606.3783174309181, "bbox": [123.76762919081375, 257.30139927752316, 38.56574156973511, 31.593662397935987], "iscrowd": 0}, {"id": 12, "image_id": 1, "category_id": 2, "segmentation": [133.95478952932172, 97.67253606952727, 150.39837071765214, 108.12547634728253, 153.35539799532853, 113.66894138418138, 158.35767206153832, 117.05394644103944, 162.1479135560803, 127.83751212060452, 171.07018301589414, 132.85823319572955, 166.4350645239465, 144.5352517813444, 156.12809146079235, 140.59148615878075, 151.76094630081207, 135.0823942553252, 140.6975575806573, 129.0484553426504, 133.83045004075393, 121.51388919819146, 124.8308775019832, 113.32103253901005, 133.95478952932172, 97.67253606952727], "area": 905.6790360714577, "bbox": [124.8308775019832, 97.67253606952727, 46.23930551391095, 46.862715711817145], "iscrowd": 0}, {"id": 13, "image_id": 1, "category_id": 2, "segmentation": [213.84757142001763, 865.6317731337622, 231.50301338662393, 882.3587670447305, 216.42948372568935, 899.1955095911399, 210.5498044961132, 889.5282784951851, 214.30827019084245, 884.5313852354884, 213.079774370417, 877.5474507408217, 203.49479921744205, 874.2963477959856, 213.84757142001763, 865.6317731337622], "area": 392.57584507364606, "bbox": [203.49479921744205, 865.6317731337622, 28.008214169181883, 33.56373645737767], "iscrowd": 0}, {"id": 14, "image_id": 1, "category_id": 2, "segmentation": [231.21248426428065, 330.48871645797044, 248.18980536190793, 356.7320148414001, 241.67447824659757, 364.39305018913, 225.53970709559508, 372.71031646989286, 220.28613743791357, 362.82807022240013, 226.06819452391937, 359.3577359961346, 222.16971050621942, 354.03699261229485, 211.39998442842625, 359.29360383190215, 203.3048722210806, 347.08347506821156, 231.21248426428065, 330.48871645797044], "area": 942.4941668682975, "bbox": [203.3048722210806, 330.48871645797044, 44.88493314082734, 42.22160001192242], "iscrowd": 0}, {"id": 15, "image_id": 1, "category_id": 2, "segmentation": [226.15747217368335, 126.1882931953296, 227.64633786818013, 117.96173016168177, 236.09353622514755, 119.48706156853586, 234.58611978427507, 127.7140777958557, 226.15747217368335, 126.1882931953296], "area": 71.7025184022421, "bbox": [226.15747217368335, 117.96173016168177, 9.9360640514642, 9.75234763417393], "iscrowd": 0}, {"id": 16, "image_id": 1, "category_id": 2, "segmentation": [237.77231920976192, 153.3169838031754, 243.4796773325652, 156.68477841839194, 245.5267063616775, 153.83819461707026, 253.28920675627887, 154.69214213639498, 252.36845179693773, 160.33013889566064, 273.4036889746785, 171.02616648748517, 271.2190176327713, 181.93320011347532, 268.2431232582312, 190.08504440169781, 263.83925927826203, 197.53924131486565, 260.0654399082996, 201.13819517660886, 252.25840627076104, 199.2199343442917, 229.10111278295517, 187.5324132423848, 221.94777155457996, 183.48959933500737, 237.77231920976192, 153.3169838031754], "area": 1507.8715455558022, "bbox": [221.94777155457996, 153.3169838031754, 51.45591742009856, 47.82121137343347], "iscrowd": 0}, {"id": 17, "image_id": 1, "category_id": 2, "segmentation": [392.3872257217299, 671.1492497138679, 417.30380885861814, 671.2074872627854, 418.62818518141285, 684.4039134653285, 393.0598451150581, 685.027445490472, 392.3872257217299, 671.1492497138679], "area": 341.9972755053627, "bbox": [392.3872257217299, 671.1492497138679, 26.240959459682927, 13.878195776604116], "iscrowd": 0}, {"id": 18, "image_id": 1, "category_id": 1, "segmentation": [415.6500815402251, 870.6108930064365, 423.3889202498831, 878.856587799266, 425.6205423306674, 893.4736175602302, 417.6680606456939, 900.0, 385.6889950442128, 900.0, 415.6500815402251, 870.6108930064365], "area": 640.7200900905971, "bbox": [385.6889950442128, 870.6108930064365, 39.93154728645459, 29.389106993563473], "iscrowd": 0}, {"id": 19, "image_id": 1, "category_id": 2, "segmentation": [407.2164936910849, 293.369396366179, 427.77757996553555, 294.5104229282588, 424.95420863106847, 345.4521803893149, 401.8091458447743, 344.1744022862986, 404.039300782606, 303.9233255367726, 406.6232181608211, 304.0600705072284, 407.2164936910849, 293.369396366179], "area": 1155.038723289968, "bbox": [401.8091458447743, 293.369396366179, 25.96843412076123, 52.0827840231359], "iscrowd": 0}, {"id": 20, "image_id": 1, "category_id": 2, "segmentation": [432.1206162075978, 225.95247913245112, 430.60763758723624, 245.3663629340008, 426.6382694914937, 244.7529080240056, 425.1836831646506, 258.9493404906243, 429.14439756423235, 259.2078733071685, 428.2809057792183, 275.56508298031986, 414.204479301814, 274.6432346571237, 414.4758333056234, 263.69406074192375, 411.21745124668814, 255.69423871394247, 405.99594805110246, 248.6523243561387, 406.9461998385377, 242.70285607129335, 410.4586205475498, 239.0436596525833, 410.1156435646117, 232.59303102549165, 406.6664985958487, 231.23442135937512, 407.21412571519613, 224.76207193825394, 432.1206162075978, 225.95247913245112], "area": 926.2819276108769, "bbox": [405.99594805110246, 224.76207193825394, 26.124668156495318, 50.80301104206592], "iscrowd": 0}, {"id": 21, "image_id": 1, "category_id": 2, "segmentation": [412.0414752406068, 165.4036012943834, 432.5184705699794, 166.1471375450492, 430.66934231179766, 216.68781219702214, 410.19229355221614, 215.94427104014903, 412.0414752406068, 165.4036012943834], "area": 1036.2973770508409, "bbox": [410.19229355221614, 165.4036012943834, 22.326177017763257, 51.28421090263873], "iscrowd": 0}, {"id": 22, "image_id": 1, "category_id": 2, "segmentation": [436.6716912172269, 114.92877714522183, 435.17816135426983, 145.7952524824068, 428.3110607606359, 155.7733131237328, 420.42894698819146, 155.34407423250377, 418.76928842114285, 153.5201010480523, 419.0462323431857, 149.65126746241003, 420.8805788680911, 143.41388408094645, 419.8326982872095, 136.22578839305788, 416.34996173810214, 131.20568466931581, 416.69879815378226, 115.06078892573714, 436.6716912172269, 114.92877714522183], "area": 674.2933167606384, "bbox": [416.34996173810214, 114.92877714522183, 20.32172947912477, 40.844535978510976], "iscrowd": 0}, {"id": 23, "image_id": 1, "category_id": 2, "segmentation": [459.1644711194094, 47.61499526724219, 455.6476237687748, 70.11859888583422, 450.8766771061346, 69.36933278851211, 446.62103112763725, 96.59647608082741, 426.43874416314065, 93.47081579640508, 434.2112922635861, 43.74008092097938, 459.1644711194094, 47.61499526724219], "area": 1137.873532469484, "bbox": [426.43874416314065, 43.74008092097938, 32.72572695626877, 52.856395159848034], "iscrowd": 0}, {"id": 24, "image_id": 1, "category_id": 1, "segmentation": [484.2024364131503, 0.0, 479.75414649397135, 9.601744243875146, 477.463500038255, 8.547827863134444, 464.7478086431511, 36.00354308541864, 446.46081846160814, 27.615649731829762, 459.25223012291826, 0.0, 484.2024364131503, 0.0], "area": 730.4737448745893, "bbox": [446.46081846160814, 0.0, 37.74161795154214, 36.00354308541864], "iscrowd": 0}, {"id": 25, "image_id": 1, "category_id": 2, "segmentation": [446.38990870770067, 842.2273999303579, 481.3434248256963, 828.2793587576598, 488.85468638362363, 846.9848243454471, 453.91915302863345, 860.910242264159, 446.38990870770067, 842.2273999303579], "area": 758.0660568429099, "bbox": [446.38990870770067, 828.2793587576598, 42.46477767592296, 32.63088350649923], "iscrowd": 0}, {"id": 26, "image_id": 1, "category_id": 2, "segmentation": [482.2356772432104, 357.92745217029005, 495.83988205646165, 360.03715515416116, 493.9617549048271, 372.0909278737381, 527.8789904229343, 377.3452287474647, 524.7309722411446, 397.46488589048386, 492.0437040710822, 392.40253333747387, 491.48141598375514, 395.98978219833225, 476.6471859868616, 393.6881181783974, 482.2356772432104, 357.92745217029005], "area": 1201.892037899198, "bbox": [476.6471859868616, 357.92745217029005, 51.23180443607271, 39.5374337201938], "iscrowd": 0}, {"id": 27, "image_id": 1, "category_id": 2, "segmentation": [536.0753469388001, 150.17036613915116, 535.8976141829044, 157.3439671061933, 537.3988791736774, 162.56776288338006, 536.2554783222731, 165.92503716237843, 539.3879669941962, 178.65563245117664, 533.924685027916, 182.25146871525794, 530.728569818195, 184.41585112269968, 524.4869354791008, 188.82972381450236, 524.3672584414016, 192.2951983232051, 522.0256408345886, 192.1969511229545, 522.3906255022157, 182.04453698452562, 525.8205245367717, 178.80905124824494, 530.0610870544333, 171.51414068136364, 528.5654665878974, 168.04368018638343, 524.6151287727989, 165.1658039363101, 525.0040782545693, 149.90775556955487, 536.0753469388001, 150.17036613915116], "area": 404.6692002570957, "bbox": [522.0256408345886, 149.90775556955487, 17.36232615960762, 42.38744275365025], "iscrowd": 0}, {"id": 28, "image_id": 1, "category_id": 2, "segmentation": [523.4165055262856, 198.2224921071902, 544.9352911333553, 199.05147654097527, 542.4260684649926, 249.9190295347944, 522.5057537995744, 248.40736349392682, 520.1315930527635, 239.36497628502548, 518.9120450657792, 233.5128228161484, 526.6003856104799, 229.04146504867822, 528.8170812996104, 220.97468123119324, 522.6861530637834, 212.42346664890647, 523.4165055262856, 198.2224921071902], "area": 1021.3360981644006, "bbox": [518.9120450657792, 198.2224921071902, 26.02324606757611, 51.6965374276042], "iscrowd": 0}, {"id": 29, "image_id": 1, "category_id": 2, "segmentation": [526.6318989666179, 261.5354417562485, 545.283115554601, 262.0126638803631, 544.4942456730641, 292.8397702910006, 552.0921549452469, 293.03174194227904, 551.5256321858615, 314.70872772019356, 517.8084108987823, 313.8443781072274, 518.1340601162519, 301.317967700772, 525.6015503380913, 301.4909356869757, 526.6318989666179, 261.5354417562485], "area": 1238.4757250715768, "bbox": [517.8084108987823, 261.5354417562485, 34.28374404646456, 53.17328596394509], "iscrowd": 0}, {"id": 30, "image_id": 1, "category_id": 2, "segmentation": [567.6196071440354, 79.48379767127335, 573.6413344931789, 77.47242644708604, 573.9580853967927, 84.36761443130672, 576.7988056694157, 88.18258353415877, 583.9389728535898, 90.16137413866818, 588.2799405395053, 90.78792378865182, 553.5269071373623, 129.635154761374, 549.2492460440844, 126.27696036919951, 545.6672062769067, 115.66594129707664, 547.9324978117365, 101.98241242859513, 567.6196071440354, 79.48379767127335], "area": 1005.3323860159348, "bbox": [545.6672062769067, 77.47242644708604, 42.61273426259868, 52.16272831428796], "iscrowd": 0}, {"id": 31, "image_id": 1, "category_id": 2, "segmentation": [545.0001820274629, 404.13968645595014, 545.5456960839219, 383.8837510570884, 561.4482773041818, 384.3170414939523, 561.6076893680729, 378.67540270090103, 573.1816837256774, 378.9923253301531, 573.0370411262847, 384.47823309339583, 594.233580631204, 385.0264944685623, 593.6913648874033, 405.41553003899753, 545.0001820274629, 404.13968645595014], "area": 1054.6293162692457, "bbox": [545.0001820274629, 378.67540270090103, 49.23339860374108, 26.7401273380965], "iscrowd": 0}, {"id": 32, "image_id": 1, "category_id": 2, "segmentation": [594.5955353775062, 341.2696067793295, 592.6359737580642, 346.1783115705475, 595.5670321371872, 351.4116119751707, 594.498773291707, 355.56610705144703, 595.03770820098, 359.39284333679825, 596.8756023284514, 362.4332278929651, 590.6566405876074, 362.45178297907114, 590.6322764432989, 356.1265549827367, 588.5902138527017, 356.1319851242006, 588.5435697012581, 341.2840873301029, 594.5955353775062, 341.2696067793295], "area": 113.72690001497435, "bbox": [588.5435697012581, 341.2696067793295, 8.332032627193257, 21.18217619974166], "iscrowd": 0}, {"id": 33, "image_id": 1, "category_id": 2, "segmentation": [642.5548559394665, 605.4954561004415, 650.1750435382128, 639.3135964740068, 630.8239523877855, 643.6478302329779, 625.8465431011282, 621.5068183001131, 613.9918430529069, 624.1488145207986, 611.3485644147731, 612.4494963856414, 642.5548559394665, 605.4954561004415], "area": 833.1100217257011, "bbox": [611.3485644147731, 605.4954561004415, 38.82647912343964, 38.15237413253635], "iscrowd": 0}, {"id": 34, "image_id": 1, "category_id": 1, "segmentation": [664.1072873058729, 0.0, 665.963440204272, 5.102927703410387, 663.4489023594651, 4.764764592982829, 657.1336445596535, 3.8756691990420222, 655.4026131848805, 1.4097766196355224, 653.3671769341454, 3.967581197619438, 653.9054305306636, 8.526797778904438, 655.4832516363822, 13.08284202683717, 651.7876941068098, 16.08068347070366, 648.9221955472603, 18.858505848795176, 649.2320157396607, 14.056635465472937, 646.6069010677747, 9.94786886498332, 646.0617618362885, 4.3456138940528035, 644.300418858882, 0.6374908359721303, 644.1158141889609, 0.0, 664.1072873058729, 0.0], "area": 163.2888762358447, "bbox": [644.1158141889609, 0.0, 21.847626015311107, 18.858505848795176], "iscrowd": 0}, {"id": 35, "image_id": 1, "category_id": 2, "segmentation": [719.5465582867619, 598.6448629098013, 720.3695895529818, 606.5043138191104, 724.4469213041011, 610.7773993844166, 725.2147377748042, 616.3742183251306, 723.0049092427362, 620.1570535134524, 718.3882041969337, 619.6482330150902, 713.9281129532028, 616.4276928380132, 703.0521553403232, 615.0506034400314, 697.5230599956121, 619.7578771309927, 691.4791235984303, 620.1051252679899, 691.3655090769753, 598.7110763099045, 719.5465582867619, 598.6448629098013], "area": 591.1230200188784, "bbox": [691.3655090769753, 598.6448629098013, 33.84922869782895, 21.512190603651106], "iscrowd": 0}, {"id": 36, "image_id": 1, "category_id": 2, "segmentation": [766.4948697979562, 219.65857510454953, 766.9394415735733, 203.64448958076537, 774.5338656448293, 199.1309275366366, 779.6678650518879, 196.49748022854328, 780.0790439015254, 206.49778971262276, 792.9675920906011, 206.67150652222335, 792.7963749796618, 220.19298242591321, 766.4948697979562, 219.65857510454953], "area": 436.3985394080503, "bbox": [766.4948697979562, 196.49748022854328, 26.472722292644903, 23.695502197369933], "iscrowd": 0}, {"id": 37, "image_id": 1, "category_id": 2, "segmentation": [794.3443439039402, 800.0892576370388, 808.1741715462413, 800.0180635405704, 811.0787292337045, 810.2460591299459, 808.0733455095906, 813.3824441283941, 807.4013454001397, 820.0798055976629, 768.3534275970887, 822.9638589080423, 759.5865474676248, 819.0049897767603, 760.6663332274184, 806.1938135968521, 774.9641209535766, 796.3894291333854, 783.0687794568948, 796.0362568320706, 791.1689595563803, 796.2602832280099, 794.3443439039402, 800.0892576370388], "area": 1091.069005525542, "bbox": [759.5865474676248, 796.0362568320706, 51.49218176607974, 26.927602075971663], "iscrowd": 0}, {"id": 38, "image_id": 1, "category_id": 2, "segmentation": [818.6449922658503, 145.89386002346873, 802.3041800016072, 146.51470147818327, 802.9269792409614, 162.90226284973323, 777.9049582753796, 163.82376996334642, 776.5785818777513, 128.4980932334438, 817.9413526556455, 126.95574354380369, 818.6449922658503, 145.89386002346873], "area": 1195.1484685924327, "bbox": [776.5785818777513, 126.95574354380369, 42.06641038809903, 36.86802641954273], "iscrowd": 0}, {"id": 39, "image_id": 1, "category_id": 2, "segmentation": [794.6951001100242, 2.116394373588264, 816.6974766298663, 1.4683247059583664, 818.0185485305265, 47.98086807690561, 804.6792487849016, 48.35088091529906, 795.6887790956534, 36.73992804996669, 794.6951001100242, 2.116394373588264], "area": 972.2084233562225, "bbox": [794.6951001100242, 1.4683247059583664, 23.323448420502245, 46.88255620934069], "iscrowd": 0}, {"id": 40, "image_id": 1, "category_id": 2, "segmentation": [816.4727658838965, 58.42847666423768, 817.5045415207278, 105.2588225658983, 796.3883123914711, 105.70768600795418, 795.4426827779971, 62.4043871788308, 803.5142542636022, 62.22955618426204, 806.4344622618519, 58.65132196247578, 816.4727658838965, 58.42847666423768], "area": 955.3143319089635, "bbox": [795.4426827779971, 58.42847666423768, 22.061858742730692, 47.2792093437165], "iscrowd": 0}, {"id": 41, "image_id": 1, "category_id": 2, "segmentation": [820.6129307732917, 800.003008636646, 838.7254208496306, 798.1625695805997, 841.7378241324332, 806.7232382101938, 859.6126089137979, 806.553273351863, 864.2543455683626, 813.4094758052379, 835.6097001582384, 818.8363996865228, 830.4454396180809, 813.3912856318057, 821.766802502796, 813.04821888078, 820.6129307732917, 800.003008636646], "area": 511.57672611563873, "bbox": [820.6129307732917, 798.1625695805997, 43.64141479507089, 20.673830105923116], "iscrowd": 0}, {"id": 42, "image_id": 1, "category_id": 2, "segmentation": [877.9025507906917, 363.2765975808725, 838.2836305517703, 366.3079837486148, 837.0310469446704, 349.9801886640489, 850.4099756779615, 348.9433259088546, 850.0006144659128, 343.5819130791351, 854.7069255011156, 343.20067395456135, 854.3236986373086, 338.14936562720686, 865.5350079541095, 337.29858210776, 865.9971866649576, 343.30238648783416, 884.2128435778432, 341.9032686809078, 885.3848932385445, 357.21201885771006, 877.9025507906917, 363.2765975808725], "area": 983.4834268683098, "bbox": [837.0310469446704, 337.29858210776, 48.353846293874085, 29.009401640854776], "iscrowd": 0}, {"id": 43, "image_id": 1, "category_id": 1, "segmentation": [886.5125979208387, 820.9232407584786, 886.2984008654021, 802.261728647165, 900.0, 802.1414167098701, 900.0, 818.7495461180806, 894.7975760672707, 816.548164521344, 888.9884018914308, 818.9095647959039, 886.5125979208387, 820.9232407584786], "area": 216.4340088998764, "bbox": [886.2984008654021, 802.1414167098701, 13.701599134597927, 18.78182404860854], "iscrowd": 0}], "categories": [{"id": 1, "name": 1.0}, {"id": 2, "name": 0.0}], "images": [{"id": 1, "file_name": "sample_geotiff.tif", "width": 900, "height": 900}]} \ No newline at end of file diff --git a/docker/solaris/solaris/data/coco_sample_3.json b/docker/solaris/solaris/data/coco_sample_3.json new file mode 100644 index 00000000..bb535354 --- /dev/null +++ b/docker/solaris/solaris/data/coco_sample_3.json @@ -0,0 +1 @@ +{"annotations": [{"id": 1, "image_id": 33, "category_id": 1, "segmentation": [46.4948697979562, 39.65857510454953, 46.939441573573276, 23.644489580765367, 54.53386564482935, 19.13092753663659, 59.66786505188793, 16.49748022854328, 60.07904390152544, 26.49778971262276, 72.9675920906011, 26.671506522223353, 72.79637497966178, 40.192982425913215, 46.4948697979562, 39.65857510454953], "area": 436.3985394080503, "bbox": [46.4948697979562, 16.49748022854328, 26.472722292644903, 23.695502197369933], "iscrowd": 0}, {"id": 2, "image_id": 81, "category_id": 1, "segmentation": [0.0, 74.49084545299411, 16.985800748690963, 84.21334314905107, 13.600973336491734, 90.0, 0.0, 90.0, 0.0, 74.49084545299411], "area": 171.06978722664007, "bbox": [0.0, 74.49084545299411, 16.985800748690963, 15.509154547005892], "iscrowd": 0}, {"id": 3, "image_id": 46, "category_id": 1, "segmentation": [9.164471119409427, 47.61499526724219, 5.6476237687747926, 70.11859888583422, 0.8766771061345935, 69.36933278851211, 0.0, 74.9782140981406, 0.0, 46.19186834525317, 9.164471119409427, 47.61499526724219], "area": 173.1982290406861, "bbox": [0.0, 46.19186834525317, 9.164471119409427, 28.786345752887428], "iscrowd": 0}, {"id": 4, "image_id": 46, "category_id": 1, "segmentation": [34.20243641315028, 0.0, 29.754146493971348, 9.601744243875146, 27.463500038255006, 8.547827863134444, 14.747808643151075, 36.00354308541864, 0.0, 29.23900475166738, 0.0, 19.97483570035547, 9.252230122918263, 0.0, 34.20243641315028, 0.0], "area": 714.0799568413281, "bbox": [0.0, 0.0, 34.20243641315028, 36.00354308541864], "iscrowd": 0}, {"id": 5, "image_id": 67, "category_id": 1, "segmentation": [46.157472173683345, 36.18829319532961, 47.646337868180126, 27.96173016168177, 56.093536225147545, 29.487061568535864, 54.58611978427507, 37.7140777958557, 46.157472173683345, 36.18829319532961], "area": 71.7025184022421, "bbox": [46.157472173683345, 27.96173016168177, 9.9360640514642, 9.75234763417393], "iscrowd": 0}, {"id": 6, "image_id": 67, "category_id": 1, "segmentation": [57.77231920976192, 63.31698380317539, 63.47967733256519, 66.68477841839194, 65.5267063616775, 63.83819461707026, 73.28920675627887, 64.69214213639498, 72.36845179693773, 70.33013889566064, 90.0, 79.29545383248478, 90.0, 90.0, 43.777952001430094, 90.0, 57.77231920976192, 63.31698380317539], "area": 826.8591676400929, "bbox": [43.777952001430094, 63.31698380317539, 46.222047998569906, 26.68301619682461], "iscrowd": 0}, {"id": 7, "image_id": 90, "category_id": 1, "segmentation": [0.0, 2.845103836618364, 7.787239895900711, 7.813573766499758, 6.348949391860515, 21.166115891188383, 5.487595358863473, 29.24418894201517, 19.3797596283257, 37.85056554712355, 18.118415302364156, 57.70217224024236, 0.0, 54.131107677705586, 0.0, 2.845103836618364], "area": 608.3880075917921, "bbox": [0.0, 2.845103836618364, 19.3797596283257, 54.857068403624], "iscrowd": 0}, {"id": 8, "image_id": 90, "category_id": 1, "segmentation": [87.2731370574329, 72.93001714255661, 90.0, 74.49084545299411, 90.0, 90.0, 74.57824886287563, 90.0, 53.36824434134178, 78.81699287053198, 59.59959887806326, 67.12329848110676, 79.5907216486521, 77.6452063396573, 87.2731370574329, 72.93001714255661], "area": 497.127916376848, "bbox": [53.36824434134178, 67.12329848110676, 36.63175565865822, 22.876701518893242], "iscrowd": 0}, {"id": 9, "image_id": 79, "category_id": 1, "segmentation": [27.38481539185159, 46.16459030006081, 34.46586190746166, 46.48033855389804, 34.72251786501147, 41.01391235832125, 44.8147500208579, 41.47823364380747, 44.453276831656694, 49.49973394535482, 56.44128756551072, 50.05102432798594, 54.999366192379966, 81.5376432267949, 46.934077847748995, 87.30534629803151, 25.54191842698492, 86.33953956048936, 27.38481539185159, 46.16459030006081], "area": 1175.2086036457465, "bbox": [25.54191842698492, 41.01391235832125, 30.8993691385258, 46.29143393971026], "iscrowd": 0}, {"id": 10, "image_id": 69, "category_id": 1, "segmentation": [73.41650552628562, 18.22249210719019, 90.0, 18.86135059222579, 90.0, 69.73492575064301, 72.50575379957445, 68.40736349392682, 70.13159305276349, 59.36497628502548, 68.91204506577924, 53.5128228161484, 76.60038561047986, 49.04146504867822, 78.81708129961044, 40.974681231193244, 72.68615306378342, 32.42346664890647, 73.41650552628562, 18.22249210719019], "area": 833.8630841664508, "bbox": [68.91204506577924, 18.22249210719019, 21.08795493422076, 51.51243364345282], "iscrowd": 0}, {"id": 11, "image_id": 69, "category_id": 1, "segmentation": [87.3454215482343, 0.0, 83.92468502791598, 2.2514687152579427, 80.72856981819496, 4.415851122699678, 74.4869354791008, 8.829723814502358, 74.36725844140165, 12.295198323205113, 72.02564083458856, 12.196951122954488, 72.3906255022157, 2.044536984525621, 74.55801426572725, 0.0, 87.3454215482343, 0.0], "area": 80.96771207725746, "bbox": [72.02564083458856, 0.0, 15.319780713645741, 12.295198323205113], "iscrowd": 0}, {"id": 12, "image_id": 69, "category_id": 1, "segmentation": [76.63189896661788, 81.53544175624847, 90.0, 81.8774866592139, 90.0, 90.0, 76.41361994855106, 90.0, 76.63189896661788, 81.53544175624847], "area": 111.79264212592807, "bbox": [76.41361994855106, 81.53544175624847, 13.586380051448941, 8.464558243751526], "iscrowd": 0}, {"id": 13, "image_id": 91, "category_id": 1, "segmentation": [39.54705525119789, 77.30139927752316, 64.39172596856952, 90.0, 34.05710672074929, 90.0, 33.76762919081375, 88.49578952137381, 39.54705525119789, 77.30139927752316], "area": 198.57060778691468, "bbox": [33.76762919081375, 77.30139927752316, 30.624096777755767, 12.69860072247684], "iscrowd": 0}, {"id": 14, "image_id": 30, "category_id": 1, "segmentation": [74.69510011002421, 2.116394373588264, 90.0, 1.6655957344919443, 90.0, 48.203290989622474, 84.67924878490157, 48.35088091529906, 75.68877909565344, 36.73992804996669, 74.69510011002421, 2.116394373588264], "area": 629.7373986919162, "bbox": [74.69510011002421, 1.6655957344919443, 15.304899889975786, 46.685285180807114], "iscrowd": 0}, {"id": 15, "image_id": 30, "category_id": 1, "segmentation": [76.04529803805053, 90.0, 75.4426827779971, 62.4043871788308, 83.5142542636022, 62.22955618426204, 86.43446226185188, 58.65132196247578, 90.0, 58.572168815881014, 90.0, 90.0, 76.04529803805053, 90.0], "area": 413.36684590433003, "bbox": [75.4426827779971, 58.572168815881014, 14.557317222002894, 31.427831184118986], "iscrowd": 0}, {"id": 16, "image_id": 26, "category_id": 1, "segmentation": [32.38722572172992, 41.1492497138679, 57.30380885861814, 41.207487262785435, 58.628185181412846, 54.403913465328515, 33.059845115058124, 55.02744549047202, 32.38722572172992, 41.1492497138679], "area": 341.9972755053627, "bbox": [32.38722572172992, 41.1492497138679, 26.240959459682927, 13.878195776604116], "iscrowd": 0}, {"id": 17, "image_id": 47, "category_id": 1, "segmentation": [15.87982941744849, 43.81173481605947, 37.49495613621548, 50.875874334946275, 24.611747808288783, 90.0, 1.054635310312733, 90.0, 6.156422508880496, 74.47919271234423, 16.836245859740302, 66.29499020427465, 22.882863603299484, 56.159495391882956, 23.55506858509034, 49.46216831356287, 15.87982941744849, 43.81173481605947], "area": 785.2061057805855, "bbox": [1.054635310312733, 43.81173481605947, 36.440320825902745, 46.18826518394053], "iscrowd": 0}, {"id": 18, "image_id": 47, "category_id": 1, "segmentation": [64.39172596856952, 0.0, 72.33337076054886, 4.059131128713489, 64.67479929910041, 18.895061675459146, 34.72422339068726, 3.4665346406400204, 34.05710672074929, 0.0, 64.39172596856952, 0.0], "area": 407.8077096433108, "bbox": [34.05710672074929, 0.0, 38.27626403979957, 18.895061675459146], "iscrowd": 0}, {"id": 19, "image_id": 82, "category_id": 1, "segmentation": [32.235677243210375, 87.92745217029005, 45.60029082908295, 90.0, 31.911790226586163, 90.0, 32.235677243210375, 87.92745217029005], "area": 14.185036107844011, "bbox": [31.911790226586163, 87.92745217029005, 13.688500602496788, 2.072547829709947], "iscrowd": 0}, {"id": 20, "image_id": 82, "category_id": 1, "segmentation": [90.0, 44.413265183568, 67.80841089878231, 43.844378107227385, 68.13406011625193, 31.317967700771987, 75.60155033809133, 31.490935686975718, 76.41361994855106, 0.0, 90.0, 0.0, 90.0, 44.413265183568], "area": 719.7515460434106, "bbox": [67.80841089878231, 0.0, 22.191589101217687, 44.413265183568], "iscrowd": 0}, {"id": 21, "image_id": 43, "category_id": 1, "segmentation": [45.60029082908295, 0.0, 45.83988205646165, 0.037155154161155224, 43.9617549048271, 12.09092787373811, 77.8789904229343, 17.345228747464716, 74.73097224114463, 37.464885890483856, 42.043704071082175, 32.40253333747387, 41.48141598375514, 35.98978219833225, 26.647185986861587, 33.68811817839742, 31.911790226586163, 0.0, 45.60029082908295, 0.0], "area": 1187.7070017918181, "bbox": [26.647185986861587, 0.0, 51.23180443607271, 37.464885890483856], "iscrowd": 0}, {"id": 22, "image_id": 83, "category_id": 1, "segmentation": [14.265385600272566, 19.350959210656583, 5.710536241997033, 72.82943233475089, 0.0, 70.15984050929546, 0.0, 18.92834468651563, 14.265385600272566, 19.350959210656583], "area": 529.5328788236836, "bbox": [0.0, 18.92834468651563, 14.265385600272566, 53.90108764823526], "iscrowd": 0}, {"id": 23, "image_id": 83, "category_id": 1, "segmentation": [24.611747808288783, 0.0, 21.21164846420288, 10.325526889413595, 0.0, 3.4298838144168258, 0.0, 3.208442605100572, 1.054635310312733, 0.0, 24.611747808288783, 0.0], "area": 161.74950833598504, "bbox": [0.0, 0.0, 24.611747808288783, 10.325526889413595], "iscrowd": 0}, {"id": 24, "image_id": 5, "category_id": 1, "segmentation": [12.554855939466506, 65.49545610044152, 18.076425708597526, 90.0, 0.0, 90.0, 0.0, 68.29319238103926, 12.554855939466506, 65.49545610044152], "area": 357.7402049426008, "bbox": [0.0, 65.49545610044152, 18.076425708597526, 24.504543899558485], "iscrowd": 0}, {"id": 25, "image_id": 5, "category_id": 1, "segmentation": [89.54655828676187, 58.644862909801304, 90.0, 62.97495707683265, 90.0, 79.82587372139096, 88.38820419693366, 79.64823301509023, 83.9281129532028, 76.42769283801317, 73.05215534032322, 75.05060344003141, 67.52305999561213, 79.75787713099271, 61.479123598430306, 80.10512526798993, 61.365509076975286, 58.711076309904456, 89.54655828676187, 58.644862909801304], "area": 538.0936511766251, "bbox": [61.365509076975286, 58.644862909801304, 28.634490923024714, 21.46026235818863], "iscrowd": 0}, {"id": 26, "image_id": 89, "category_id": 1, "segmentation": [65.83512698789127, 83.34588148258626, 86.05315328529105, 84.11831593420357, 85.82418051804416, 90.0, 65.57607653690502, 90.0, 65.83512698789127, 83.34588148258626], "area": 126.91309660629496, "bbox": [65.57607653690502, 83.34588148258626, 20.477076748386025, 6.654118517413735], "iscrowd": 0}, {"id": 27, "image_id": 89, "category_id": 1, "segmentation": [90.0, 70.15984050929546, 83.6166091109626, 67.17569901794195, 71.57171053020284, 64.2952412031591, 74.16042645578273, 55.48699966818094, 82.98187345149927, 55.604949980042875, 84.05773156136274, 42.61637069378048, 81.36960026691668, 32.8713309513405, 80.55370765388943, 28.341071943752468, 84.53470388124697, 18.76643434073776, 90.0, 18.92834468651563, 90.0, 70.15984050929546], "area": 461.8950473926462, "bbox": [71.57171053020284, 18.76643434073776, 18.428289469797164, 51.3934061685577], "iscrowd": 0}, {"id": 28, "image_id": 89, "category_id": 1, "segmentation": [90.0, 3.4298838144168258, 89.93423808692023, 3.4085054341703653, 90.0, 3.208442605100572, 90.0, 3.4298838144168258], "area": 0.007281198779667139, "bbox": [89.93423808692023, 3.208442605100572, 0.06576191307976842, 0.22144120931625366], "iscrowd": 0}, {"id": 29, "image_id": 34, "category_id": 1, "segmentation": [18.076425708597526, 0.0, 20.175043538212776, 9.313596474006772, 0.823952387785539, 13.647830232977867, 0.0, 9.982642461545765, 0.0, 0.0, 18.076425708597526, 0.0], "area": 222.12668296683773, "bbox": [0.0, 0.0, 20.175043538212776, 13.647830232977867], "iscrowd": 0}, {"id": 30, "image_id": 45, "category_id": 1, "segmentation": [85.82418051804416, 0.0, 84.12356285331771, 43.6842159954831, 63.905484846793115, 42.91177667211741, 65.57607653690502, 0.0, 85.82418051804416, 0.0], "area": 876.703313340623, "bbox": [63.905484846793115, 0.0, 21.918695671251044, 43.6842159954831], "iscrowd": 0}, {"id": 31, "image_id": 45, "category_id": 1, "segmentation": [87.33356586564332, 52.75066264346242, 90.0, 59.48918798007071, 90.0, 90.0, 70.69254911504686, 90.0, 70.136441974435, 76.14243785012513, 69.54070870671421, 51.69716499559581, 87.33356586564332, 52.75066264346242], "area": 743.0935065030983, "bbox": [69.54070870671421, 51.69716499559581, 20.459291293285787, 38.30283500440419], "iscrowd": 0}, {"id": 32, "image_id": 70, "category_id": 1, "segmentation": [0.0, 59.48918798007071, 0.7957657100632787, 61.50022111646831, 2.5718621767591685, 90.0, 0.0, 90.0, 0.0, 59.48918798007071], "area": 48.788480674100526, "bbox": [0.0, 59.48918798007071, 2.5718621767591685, 30.51081201992929], "iscrowd": 0}, {"id": 33, "image_id": 78, "category_id": 1, "segmentation": [0.0, 79.29545383248478, 3.4036889746785164, 81.02616648748517, 1.6062362873926759, 90.0, 0.0, 90.0, 0.0, 79.29545383248478], "area": 25.424521397065256, "bbox": [0.0, 79.29545383248478, 3.4036889746785164, 10.704546167515218], "iscrowd": 0}, {"id": 34, "image_id": 56, "category_id": 1, "segmentation": [27.6196071440354, 79.48379767127335, 33.641334493178874, 77.47242644708604, 33.95808539679274, 84.36761443130672, 36.79880566941574, 88.18258353415877, 43.35667863464914, 90.0, 18.417546162148938, 90.0, 27.6196071440354, 79.48379767127335], "area": 137.1837220083038, "bbox": [18.417546162148938, 77.47242644708604, 24.939132472500205, 12.527573552913964], "iscrowd": 0}, {"id": 35, "image_id": 77, "category_id": 1, "segmentation": [0.0, 30.786809466779232, 31.343424825696275, 18.279358757659793, 38.85468638362363, 36.98482434544712, 3.9191530286334455, 50.910242264159024, 0.0, 41.18537280894816, 0.0, 30.786809466779232], "area": 739.296175358155, "bbox": [0.0, 18.279358757659793, 38.85468638362363, 32.63088350649923], "iscrowd": 0}, {"id": 36, "image_id": 62, "category_id": 1, "segmentation": [60.03418597159907, 74.87320505268872, 73.8337494416628, 90.0, 51.516283753560856, 90.0, 47.80893106292933, 85.93607368506491, 60.03418597159907, 74.87320505268872], "area": 214.14410906402435, "bbox": [47.80893106292933, 74.87320505268872, 26.02481837873347, 15.126794947311282], "iscrowd": 0}, {"id": 37, "image_id": 18, "category_id": 1, "segmentation": [7.1683560609817505, 0.0, 7.504541520727798, 15.2588225658983, 0.0, 15.418345098383725, 0.0, 0.0, 7.1683560609817505, 0.0], "area": 112.54414209771639, "bbox": [0.0, 0.0, 7.504541520727798, 15.418345098383725], "iscrowd": 0}, {"id": 38, "image_id": 18, "category_id": 1, "segmentation": [8.644992265850306, 55.89386002346873, 0.0, 56.222311845980585, 0.0, 37.251863522455096, 7.94135265564546, 36.95574354380369, 8.644992265850306, 55.89386002346873], "area": 157.30100119082695, "bbox": [0.0, 36.95574354380369, 8.644992265850306, 19.266568302176893], "iscrowd": 0}, {"id": 39, "image_id": 74, "category_id": 1, "segmentation": [5.078718527685851, 0.0, 4.4942456730641425, 22.839770291000605, 12.092154945246875, 23.03174194227904, 11.525632185861468, 44.708727720193565, 0.0, 44.413265183568, 0.0, 0.0, 5.078718527685851, 0.0], "area": 365.1927326447826, "bbox": [0.0, 0.0, 12.092154945246875, 44.708727720193565], "iscrowd": 0}, {"id": 40, "image_id": 74, "category_id": 1, "segmentation": [54.59553537750617, 71.26960677932948, 52.63597375806421, 76.17831157054752, 55.5670321371872, 81.41161197517067, 54.49877329170704, 85.56610705144703, 55.03770820097998, 89.39284333679825, 55.404730742098764, 90.0, 50.64719656528905, 90.0, 50.632276443298906, 86.1265549827367, 48.59021385270171, 86.13198512420058, 48.54356970125809, 71.28408733010292, 54.59553537750617, 71.26960677932948], "area": 100.3149576954687, "bbox": [48.54356970125809, 71.26960677932948, 7.023462435929105, 18.73039322067052], "iscrowd": 0}, {"id": 41, "image_id": 37, "category_id": 1, "segmentation": [52.041475240606815, 75.4036012943834, 72.5184705699794, 76.14713754504919, 72.01163682481274, 90.0, 51.50742230191827, 90.0, 52.041475240606815, 75.4036012943834], "area": 291.66476968636124, "bbox": [51.50742230191827, 75.4036012943834, 21.01104826806113, 14.596398705616593], "iscrowd": 0}, {"id": 42, "image_id": 37, "category_id": 1, "segmentation": [76.67169121722691, 24.92877714522183, 75.17816135426983, 55.795252482406795, 68.31106076063588, 65.7733131237328, 60.428946988191456, 65.34407423250377, 58.769288421142846, 63.52010104805231, 59.04623234318569, 59.65126746241003, 60.880578868091106, 53.413884080946445, 59.832698287209496, 46.22578839305788, 56.34996173810214, 41.205684669315815, 56.69879815378226, 25.060788925737143, 76.67169121722691, 24.92877714522183], "area": 674.2933167606384, "bbox": [56.34996173810214, 24.92877714522183, 20.32172947912477, 40.844535978510976], "iscrowd": 0}, {"id": 43, "image_id": 37, "category_id": 1, "segmentation": [87.65207087364979, 0.0, 86.62103112763725, 6.596476080827415, 66.43874416314065, 3.470815796405077, 66.98120714491233, 0.0, 87.65207087364979, 0.0], "area": 104.04970677925387, "bbox": [66.43874416314065, 0.0, 21.213326710509136, 6.596476080827415], "iscrowd": 0}, {"id": 44, "image_id": 60, "category_id": 1, "segmentation": [0.0, 18.86135059222579, 4.935291133355349, 19.051476540975273, 2.426068464992568, 69.91902953479439, 0.0, 69.73492575064301, 0.0, 18.86135059222579], "area": 187.47301399979517, "bbox": [0.0, 18.86135059222579, 4.935291133355349, 51.0576789425686], "iscrowd": 0}, {"id": 45, "image_id": 60, "category_id": 1, "segmentation": [0.0, 81.8774866592139, 5.283115554600954, 82.0126638803631, 5.078718527685851, 90.0, 0.0, 90.0, 0.0, 81.8774866592139], "area": 41.73880425540756, "bbox": [0.0, 81.8774866592139, 5.283115554600954, 8.1225133407861], "iscrowd": 0}, {"id": 46, "image_id": 10, "category_id": 1, "segmentation": [90.0, 1.3718089498579502, 88.07334550959058, 3.382444128394127, 87.40134540013969, 10.079805597662926, 48.35342759708874, 12.963858908042312, 39.58654746762477, 9.00498977676034, 40.34553006151691, 0.0, 90.0, 0.0, 90.0, 1.3718089498579502], "area": 551.8885931671214, "bbox": [39.58654746762477, 0.0, 50.41345253237523, 12.963858908042312], "iscrowd": 0}, {"id": 47, "image_id": 50, "category_id": 1, "segmentation": [5.00018202746287, 44.13968645595014, 5.545696083921939, 23.883751057088375, 21.44827730418183, 24.317041493952274, 21.607689368072897, 18.67540270090103, 33.18168372567743, 18.992325330153108, 33.03704112628475, 24.47823309339583, 54.23358063120395, 25.026494468562305, 53.691364887403324, 45.41553003899753, 5.00018202746287, 44.13968645595014], "area": 1054.6293162692457, "bbox": [5.00018202746287, 18.67540270090103, 49.23339860374108, 26.7401273380965], "iscrowd": 0}, {"id": 48, "image_id": 50, "category_id": 1, "segmentation": [55.404730742098764, 0.0, 56.87560232845135, 2.4332278929650784, 50.6566405876074, 2.4517829790711403, 50.64719656528905, 0.0, 55.404730742098764, 0.0], "area": 13.411942319545592, "bbox": [50.64719656528905, 0.0, 6.2284057631623, 2.4517829790711403], "iscrowd": 0}, {"id": 49, "image_id": 42, "category_id": 1, "segmentation": [74.34434390394017, 80.08925763703883, 88.17417154624127, 80.01806354057044, 90.0, 86.447464290075, 90.0, 90.0, 40.34553006151691, 90.0, 40.66633322741836, 86.19381359685212, 54.96412095357664, 76.38942913338542, 63.068779456894845, 76.03625683207065, 71.16895955638029, 76.26028322800994, 74.34434390394017, 80.08925763703883], "area": 536.524395087574, "bbox": [40.34553006151691, 76.03625683207065, 49.65446993848309, 13.963743167929351], "iscrowd": 0}, {"id": 50, "image_id": 27, "category_id": 1, "segmentation": [90.0, 9.982642461545765, 87.7558524676133, 0.0, 90.0, 0.0, 90.0, 9.982642461545765], "area": 11.201261223388338, "bbox": [87.7558524676133, 0.0, 2.2441475323867053, 9.982642461545765], "iscrowd": 0}, {"id": 51, "image_id": 58, "category_id": 1, "segmentation": [1.0088530853390694, 0.0, 1.0787292337045074, 0.24605912994593382, 0.0, 1.3718089498579502, 0.0, 0.0, 1.0088530853390694, 0.0], "area": 0.8640239648455269, "bbox": [0.0, 0.0, 1.0787292337045074, 1.3718089498579502], "iscrowd": 0}, {"id": 52, "image_id": 58, "category_id": 1, "segmentation": [51.94608688936569, 0.0, 54.25434556836262, 3.4094758052378893, 25.60970015823841, 8.836399686522782, 20.445439618080854, 3.3912856318056583, 11.766802502796054, 3.048218880780041, 11.497182195773348, 0.0, 51.94608688936569, 0.0], "area": 231.38187322206525, "bbox": [11.497182195773348, 0.0, 42.757163372589275, 8.836399686522782], "iscrowd": 0}, {"id": 53, "image_id": 58, "category_id": 1, "segmentation": [76.51259792083874, 10.923240758478642, 76.38722083508037, 0.0, 90.0, 0.0, 90.0, 8.749546118080616, 84.79757606727071, 6.548164521344006, 78.98840189143084, 8.909564795903862, 76.51259792083874, 10.923240758478642], "area": 109.92674037312283, "bbox": [76.38722083508037, 0.0, 13.61277916491963, 10.923240758478642], "iscrowd": 0}, {"id": 54, "image_id": 2, "category_id": 1, "segmentation": [90.0, 8.13114173244685, 74.57824886287563, 0.0, 90.0, 0.0, 90.0, 8.13114173244685], "area": 62.698222129240825, "bbox": [74.57824886287563, 0.0, 15.421751137124375, 8.13114173244685], "iscrowd": 0}, {"id": 55, "image_id": 100, "category_id": 1, "segmentation": [0.0, 86.447464290075, 1.0088530853390694, 90.0, 0.0, 90.0, 0.0, 86.447464290075], "area": 1.7919933058675266, "bbox": [0.0, 86.447464290075, 1.0088530853390694, 3.552535709924996], "iscrowd": 0}, {"id": 56, "image_id": 100, "category_id": 1, "segmentation": [10.612930773291737, 80.00300863664597, 28.72542084963061, 78.16256958059967, 31.73782413243316, 86.72323821019381, 49.612608913797885, 86.55327335186303, 51.94608688936569, 90.0, 11.497182195773348, 90.0, 10.612930773291737, 80.00300863664597], "area": 280.1948528932672, "bbox": [10.612930773291737, 78.16256958059967, 41.33315611607395, 11.837430419400334], "iscrowd": 0}, {"id": 57, "image_id": 100, "category_id": 1, "segmentation": [76.38722083508037, 90.0, 76.29840086540207, 82.261728647165, 90.0, 82.1414167098701, 90.0, 90.0, 76.38722083508037, 90.0], "area": 106.50726852578815, "bbox": [76.29840086540207, 82.1414167098701, 13.701599134597927, 7.8585832901299], "iscrowd": 0}, {"id": 58, "image_id": 16, "category_id": 1, "segmentation": [87.7558524676133, 90.0, 85.84654310112819, 81.50681830011308, 73.99184305290692, 84.14881452079862, 71.34856441477314, 72.4494963856414, 90.0, 68.29319238103926, 90.0, 90.0, 87.7558524676133, 90.0], "area": 242.04187258985382, "bbox": [71.34856441477314, 68.29319238103926, 18.651435585226864, 21.706807618960738], "iscrowd": 0}, {"id": 59, "image_id": 20, "category_id": 1, "segmentation": [13.600973336491734, 0.0, 7.700295125134289, 10.087722604162991, 1.1510446241591126, 8.738031760789454, 0.0, 8.13114173244685, 0.0, 0.0, 13.600973336491734, 0.0], "area": 101.11807882094999, "bbox": [0.0, 0.0, 13.600973336491734, 10.087722604162991], "iscrowd": 0}, {"id": 60, "image_id": 20, "category_id": 1, "segmentation": [43.954789529321715, 7.672536069527268, 60.39837071765214, 18.12547634728253, 63.35539799532853, 23.66894138418138, 68.35767206153832, 27.053946441039443, 72.14791355608031, 37.837512120604515, 81.07018301589414, 42.858233195729554, 76.4350645239465, 54.535251781344414, 66.12809146079235, 50.591486158780754, 61.760946300812066, 45.0823942553252, 50.6975575806573, 39.04845534265041, 43.83045004075393, 31.513889198191464, 34.830877501983196, 23.321032539010048, 43.954789529321715, 7.672536069527268], "area": 905.6790360714577, "bbox": [34.830877501983196, 7.672536069527268, 46.23930551391095, 46.862715711817145], "iscrowd": 0}, {"id": 61, "image_id": 32, "category_id": 1, "segmentation": [86.07534693880007, 60.170366139151156, 85.89761418290436, 67.3439671061933, 87.39887917367741, 72.56776288338006, 86.25547832227312, 75.92503716237843, 89.38796699419618, 88.65563245117664, 87.3454215482343, 90.0, 74.55801426572725, 90.0, 75.8205245367717, 88.80905124824494, 80.0610870544333, 81.51414068136364, 78.56546658789739, 78.04368018638343, 74.61512877279893, 75.16580393631011, 75.00407825456932, 59.907755569554865, 86.07534693880007, 60.170366139151156], "area": 323.70148817980674, "bbox": [74.55801426572725, 59.907755569554865, 14.829952728468925, 30.092244430445135], "iscrowd": 0}, {"id": 62, "image_id": 4, "category_id": 1, "segmentation": [51.21248426428065, 60.48871645797044, 68.18980536190793, 86.73201484140009, 65.41054755286314, 90.0, 44.99809380946681, 90.0, 46.068194523919374, 89.35773599613458, 42.16971050621942, 84.03699261229485, 31.39998442842625, 89.29360383190215, 23.304872221080586, 77.08347506821156, 51.21248426428065, 60.48871645797044], "area": 762.1552167288792, "bbox": [23.304872221080586, 60.48871645797044, 44.88493314082734, 29.51128354202956], "iscrowd": 0}, {"id": 63, "image_id": 35, "category_id": 1, "segmentation": [90.0, 5.272445622831583, 88.24312325823121, 10.085044401697814, 83.83925927826203, 17.53924131486565, 80.0654399082996, 21.13819517660886, 72.25840627076104, 19.219934344291687, 49.10111278295517, 7.532413242384791, 41.94777155457996, 3.4895993350073695, 43.777952001430094, 0.0, 90.0, 0.0, 90.0, 5.272445622831583], "area": 650.8216663385698, "bbox": [41.94777155457996, 0.0, 48.05222844542004, 21.13819517660886], "iscrowd": 0}, {"id": 64, "image_id": 68, "category_id": 1, "segmentation": [90.0, 56.222311845980585, 82.30418000160716, 56.51470147818327, 82.92697924096137, 72.90226284973323, 57.90495827537961, 73.82376996334642, 56.578581877751276, 38.4980932334438, 90.0, 37.251863522455096, 90.0, 56.222311845980585], "area": 1037.847467401222, "bbox": [56.578581877751276, 37.251863522455096, 33.421418122248724, 36.571906440891325], "iscrowd": 0}, {"id": 65, "image_id": 68, "category_id": 1, "segmentation": [90.0, 15.418345098383725, 76.38831239147112, 15.70768600795418, 76.04529803805053, 0.0, 90.0, 0.0, 90.0, 15.418345098383725], "area": 214.53288683628807, "bbox": [76.04529803805053, 0.0, 13.954701961949468, 15.70768600795418], "iscrowd": 0}, {"id": 66, "image_id": 85, "category_id": 1, "segmentation": [55.650081540225074, 60.61089300643653, 63.388920249883085, 68.85658779926598, 65.62054233066738, 83.4736175602302, 57.66806064569391, 90.0, 25.68899504421279, 90.0, 55.650081540225074, 60.61089300643653], "area": 640.7200900905971, "bbox": [25.68899504421279, 60.61089300643653, 39.93154728645459, 29.389106993563473], "iscrowd": 0}, {"id": 67, "image_id": 85, "category_id": 1, "segmentation": [86.38990870770067, 32.22739993035793, 90.0, 30.786809466779232, 90.0, 41.18537280894816, 86.38990870770067, 32.22739993035793], "area": 18.769881486993533, "bbox": [86.38990870770067, 30.786809466779232, 3.6100912922993302, 10.398563342168927], "iscrowd": 0}, {"id": 68, "image_id": 24, "category_id": 1, "segmentation": [65.41054755286314, 0.0, 61.67447824659757, 4.393050189130008, 45.53970709559508, 12.71031646989286, 40.28613743791357, 2.828070222400129, 44.99809380946681, 0.0, 65.41054755286314, 0.0], "area": 180.3389501399136, "bbox": [40.28613743791357, 0.0, 25.12441011494957, 12.71031646989286], "iscrowd": 0}, {"id": 69, "image_id": 21, "category_id": 1, "segmentation": [90.0, 11.015026673674583, 70.7970443549566, 13.249627484939992, 70.8928169994615, 4.990449592471123, 70.69254911504686, 0.0, 90.0, 0.0, 90.0, 11.015026673674583], "area": 232.6028019573394, "bbox": [70.69254911504686, 0.0, 19.30745088495314, 13.249627484939992], "iscrowd": 0}, {"id": 70, "image_id": 21, "category_id": 1, "segmentation": [89.06576380180195, 21.638346442952752, 90.0, 28.386366279795766, 90.0, 68.61032488476485, 85.23654213640839, 70.96199104283005, 73.38412117748521, 70.6515495320782, 71.78515014378354, 65.98500318173319, 72.83866719854996, 48.2692635813728, 72.19266184815206, 21.76100580766797, 89.06576380180195, 21.638346442952752], "area": 853.8212747899074, "bbox": [71.78515014378354, 21.638346442952752, 18.21484985621646, 49.3236445998773], "iscrowd": 0}, {"id": 71, "image_id": 36, "category_id": 1, "segmentation": [43.35667863464914, 0.0, 43.9389728535898, 0.1613741386681795, 48.27994053950533, 0.787923788651824, 13.526907137362286, 39.635154761374, 9.24924604408443, 36.276960369199514, 5.667206276906654, 25.665941297076643, 7.932497811736539, 11.982412428595126, 18.417546162148938, 0.0, 43.35667863464914, 0.0], "area": 868.1486640068417, "bbox": [5.667206276906654, 0.0, 42.61273426259868, 39.635154761374], "iscrowd": 0}, {"id": 72, "image_id": 59, "category_id": 1, "segmentation": [72.12061620759778, 45.95247913245112, 70.60763758723624, 65.36636293400079, 66.6382694914937, 64.75290802400559, 65.18368316465057, 78.94934049062431, 69.14439756423235, 79.20787330716848, 68.57468470185995, 90.0, 54.319552852073684, 90.0, 54.47583330562338, 83.69406074192375, 51.21745124668814, 75.69423871394247, 45.99594805110246, 68.6523243561387, 46.94619983853772, 62.702856071293354, 50.4586205475498, 59.0436596525833, 50.11564356461167, 52.593031025491655, 46.66649859584868, 51.23442135937512, 47.21412571519613, 44.76207193825394, 72.12061620759778, 45.95247913245112], "area": 853.8833159766914, "bbox": [45.99594805110246, 44.76207193825394, 26.124668156495318, 45.23792806174606], "iscrowd": 0}, {"id": 73, "image_id": 59, "category_id": 1, "segmentation": [72.01163682481274, 0.0, 70.66934231179766, 36.68781219702214, 50.19229355221614, 35.94427104014903, 51.50742230191827, 0.0, 72.01163682481274, 0.0], "area": 744.6326073660714, "bbox": [50.19229355221614, 0.0, 21.819343272596598, 36.68781219702214], "iscrowd": 0}, {"id": 74, "image_id": 40, "category_id": 1, "segmentation": [2.5718621767591685, 0.0, 3.2348557421937585, 10.638594451360404, 0.0, 11.015026673674583, 0.0, 0.0, 2.5718621767591685, 0.0], "area": 31.496510484543712, "bbox": [0.0, 0.0, 3.2348557421937585, 11.015026673674583], "iscrowd": 0}, {"id": 75, "image_id": 40, "category_id": 1, "segmentation": [0.0, 28.386366279795766, 1.5893366560339928, 39.8661990063265, 4.85523002082482, 52.74896383564919, 3.418361312476918, 66.92271793168038, 0.0, 68.61032488476485, 0.0, 28.386366279795766], "area": 112.15292762532019, "bbox": [0.0, 28.386366279795766, 4.85523002082482, 40.223958604969084], "iscrowd": 0}, {"id": 76, "image_id": 8, "category_id": 1, "segmentation": [0.0, 5.243752209469676, 5.657403340330347, 5.567342856898904, 5.3132059692870826, 12.012518353760242, 0.37084288243204355, 13.375933191739023, 0.0, 13.362307872623205, 0.0, 5.243752209469676], "area": 40.14142440463303, "bbox": [0.0, 5.243752209469676, 5.657403340330347, 8.132180982269347], "iscrowd": 0}, {"id": 77, "image_id": 73, "category_id": 1, "segmentation": [47.21649369108491, 23.36939636617899, 67.77757996553555, 24.510422928258777, 64.95420863106847, 75.45218038931489, 41.80914584477432, 74.17440228629857, 44.039300782606006, 33.92332553677261, 46.623218160821125, 34.060070507228374, 47.21649369108491, 23.36939636617899], "area": 1155.038723289968, "bbox": [41.80914584477432, 23.36939636617899, 25.96843412076123, 52.0827840231359], "iscrowd": 0}, {"id": 78, "image_id": 73, "category_id": 1, "segmentation": [68.57468470185995, 0.0, 68.2809057792183, 5.565082980319858, 54.20447930181399, 4.643234657123685, 54.319552852073684, 0.0, 68.57468470185995, 0.0], "area": 72.39861163357469, "bbox": [54.20447930181399, 0.0, 14.370205400045961, 5.565082980319858], "iscrowd": 0}, {"id": 79, "image_id": 11, "category_id": 1, "segmentation": [73.42513769492507, 22.711620703339577, 73.87162248673849, 10.55962894577533, 73.96795534505509, 4.60880274605006, 89.67092586541548, 5.224929914809763, 90.0, 5.243752209469676, 90.0, 13.362307872623205, 86.37565372907557, 13.229144000448287, 83.40025364165194, 13.789963434450328, 78.97129776002839, 15.651367138139904, 77.39582740026526, 22.614855810068548, 73.42513769492507, 22.711620703339577], "area": 186.62410001203688, "bbox": [73.42513769492507, 4.60880274605006, 16.57486230507493, 18.102817957289517], "iscrowd": 0}, {"id": 80, "image_id": 86, "category_id": 1, "segmentation": [6.4727658838965, 58.42847666423768, 7.1683560609817505, 90.0, 0.0, 90.0, 0.0, 58.572168815881014, 6.4727658838965, 58.42847666423768], "area": 214.87045707588126, "bbox": [0.0, 58.42847666423768, 7.1683560609817505, 31.571523335762322], "iscrowd": 0}, {"id": 81, "image_id": 86, "category_id": 1, "segmentation": [0.0, 1.6655957344919443, 6.697476629866287, 1.4683247059583664, 8.01854853052646, 47.98086807690561, 0.0, 48.203290989622474, 0.0, 1.6655957344919443], "area": 342.47102466864317, "bbox": [0.0, 1.4683247059583664, 8.01854853052646, 46.73496628366411], "iscrowd": 0}, {"id": 82, "image_id": 9, "category_id": 1, "segmentation": [33.84757142001763, 55.63177313376218, 51.503013386623934, 72.35876704473048, 36.42948372568935, 89.19550959113985, 30.54980449611321, 79.52827849518508, 34.30827019084245, 74.53138523548841, 33.079774370417, 67.54745074082166, 23.49479921744205, 64.29634779598564, 33.84757142001763, 55.63177313376218], "area": 392.57584507364606, "bbox": [23.49479921744205, 55.63177313376218, 28.008214169181883, 33.56373645737767], "iscrowd": 0}, {"id": 83, "image_id": 57, "category_id": 1, "segmentation": [90.0, 74.9782140981406, 87.65207087364979, 90.0, 66.98120714491233, 90.0, 74.21129226358607, 43.74008092097938, 90.0, 46.19186834525317, 90.0, 74.9782140981406], "area": 860.625596649731, "bbox": [66.98120714491233, 43.74008092097938, 23.018792855087668, 46.25991907902062], "iscrowd": 0}, {"id": 84, "image_id": 57, "category_id": 1, "segmentation": [90.0, 29.23900475166738, 86.46081846160814, 27.615649731829762, 90.0, 19.97483570035547, 90.0, 29.23900475166738], "area": 16.393788037472167, "bbox": [86.46081846160814, 19.97483570035547, 3.5391815383918583, 9.26416905131191], "iscrowd": 0}, {"id": 85, "image_id": 97, "category_id": 1, "segmentation": [0.0, 62.97495707683265, 0.369589552981779, 66.5043138191104, 4.446921304101124, 70.77739938441664, 5.2147377748042345, 76.37421832513064, 3.0049092427361757, 80.15705351345241, 0.0, 79.82587372139096, 0.0, 62.97495707683265], "area": 53.029368843067694, "bbox": [0.0, 62.97495707683265, 5.2147377748042345, 17.18209643661976], "iscrowd": 0}, {"id": 86, "image_id": 80, "category_id": 1, "segmentation": [67.90255079069175, 3.276597580872476, 28.28363055177033, 6.307983748614788, 27.799714812077582, 0.0, 71.94514406612143, 0.0, 67.90255079069175, 3.276597580872476], "area": 198.01462359240813, "bbox": [27.799714812077582, 0.0, 44.14542925404385, 6.307983748614788], "iscrowd": 0}, {"id": 87, "image_id": 1, "category_id": 1, "segmentation": [1.6062362873926759, 0.0, 1.2190176327712834, 1.9332001134753227, 0.0, 5.272445622831583, 0.0, 0.0, 1.6062362873926759, 0.0], "area": 4.766190177557586, "bbox": [0.0, 0.0, 1.6062362873926759, 5.272445622831583], "iscrowd": 0}, {"id": 88, "image_id": 64, "category_id": 1, "segmentation": [34.10728730587289, 0.0, 35.96344020427205, 5.102927703410387, 33.44890235946514, 4.764764592982829, 27.133644559653476, 3.8756691990420222, 25.402613184880465, 1.4097766196355224, 23.36717693414539, 3.967581197619438, 23.90543053066358, 8.526797778904438, 25.483251636382192, 13.08284202683717, 21.787694106809795, 16.08068347070366, 18.922195547260344, 18.858505848795176, 19.23201573966071, 14.056635465472937, 16.606901067774743, 9.94786886498332, 16.061761836288497, 4.3456138940528035, 14.30041885888204, 0.6374908359721303, 14.11581418896094, 0.0, 34.10728730587289, 0.0], "area": 163.2888762358447, "bbox": [14.11581418896094, 0.0, 21.847626015311107, 18.858505848795176], "iscrowd": 0}, {"id": 89, "image_id": 44, "category_id": 1, "segmentation": [27.799714812077582, 90.0, 27.03104694467038, 79.98018866404891, 40.409975677961484, 78.9433259088546, 40.000614465912804, 73.58191307913512, 44.70692550111562, 73.20067395456135, 44.32369863730855, 68.14936562720686, 55.53500795410946, 67.29858210776001, 55.99718666495755, 73.30238648783416, 74.21284357784316, 71.90326868090779, 75.38489323854446, 87.21201885771006, 71.94514406612143, 90.0, 27.799714812077582, 90.0], "area": 785.4688032755058, "bbox": [27.03104694467038, 67.29858210776001, 48.353846293874085, 22.701417892239988], "iscrowd": 0}], "categories": [{"id": 1, "name": "other"}], "images": [{"id": 1, "file_name": "sample_geotiff_733736_3725049.tif", "width": 90, "height": 90}, {"id": 2, "file_name": "sample_geotiff_733601_3725094.tif", "width": 90, "height": 90}, {"id": 3, "file_name": "sample_geotiff_733961_3725004.tif", "width": 90, "height": 90}, {"id": 4, "file_name": "sample_geotiff_733691_3725004.tif", "width": 90, "height": 90}, {"id": 5, "file_name": "sample_geotiff_733916_3724869.tif", "width": 90, "height": 90}, {"id": 6, "file_name": "sample_geotiff_734006_3724824.tif", "width": 90, "height": 90}, {"id": 7, "file_name": "sample_geotiff_733736_3724779.tif", "width": 90, "height": 90}, {"id": 8, "file_name": "sample_geotiff_733646_3724824.tif", "width": 90, "height": 90}, {"id": 9, "file_name": "sample_geotiff_733691_3724734.tif", "width": 90, "height": 90}, {"id": 10, "file_name": "sample_geotiff_733961_3724734.tif", "width": 90, "height": 90}, {"id": 11, "file_name": "sample_geotiff_733601_3724824.tif", "width": 90, "height": 90}, {"id": 12, "file_name": "sample_geotiff_733826_3724869.tif", "width": 90, "height": 90}, {"id": 13, "file_name": "sample_geotiff_733691_3724914.tif", "width": 90, "height": 90}, {"id": 14, "file_name": "sample_geotiff_733961_3724914.tif", "width": 90, "height": 90}, {"id": 15, "file_name": "sample_geotiff_733781_3724869.tif", "width": 90, "height": 90}, {"id": 16, "file_name": "sample_geotiff_733871_3724869.tif", "width": 90, "height": 90}, {"id": 17, "file_name": "sample_geotiff_733736_3724959.tif", "width": 90, "height": 90}, {"id": 18, "file_name": "sample_geotiff_734006_3725094.tif", "width": 90, "height": 90}, {"id": 19, "file_name": "sample_geotiff_733736_3725139.tif", "width": 90, "height": 90}, {"id": 20, "file_name": "sample_geotiff_733646_3725094.tif", "width": 90, "height": 90}, {"id": 21, "file_name": "sample_geotiff_733601_3724869.tif", "width": 90, "height": 90}, {"id": 22, "file_name": "sample_geotiff_733826_3724824.tif", "width": 90, "height": 90}, {"id": 23, "file_name": "sample_geotiff_733916_3725094.tif", "width": 90, "height": 90}, {"id": 24, "file_name": "sample_geotiff_733691_3724959.tif", "width": 90, "height": 90}, {"id": 25, "file_name": "sample_geotiff_733961_3724959.tif", "width": 90, "height": 90}, {"id": 26, "file_name": "sample_geotiff_733781_3724824.tif", "width": 90, "height": 90}, {"id": 27, "file_name": "sample_geotiff_733871_3724824.tif", "width": 90, "height": 90}, {"id": 28, "file_name": "sample_geotiff_733736_3724914.tif", "width": 90, "height": 90}, {"id": 29, "file_name": "sample_geotiff_733691_3725139.tif", "width": 90, "height": 90}, {"id": 30, "file_name": "sample_geotiff_733961_3725139.tif", "width": 90, "height": 90}, {"id": 31, "file_name": "sample_geotiff_733736_3725004.tif", "width": 90, "height": 90}, {"id": 32, "file_name": "sample_geotiff_733826_3725094.tif", "width": 90, "height": 90}, {"id": 33, "file_name": "sample_geotiff_733961_3725049.tif", "width": 90, "height": 90}, {"id": 34, "file_name": "sample_geotiff_733916_3724824.tif", "width": 90, "height": 90}, {"id": 35, "file_name": "sample_geotiff_733691_3725049.tif", "width": 90, "height": 90}, {"id": 36, "file_name": "sample_geotiff_733871_3725094.tif", "width": 90, "height": 90}, {"id": 37, "file_name": "sample_geotiff_733781_3725094.tif", "width": 90, "height": 90}, {"id": 38, "file_name": "sample_geotiff_734006_3724869.tif", "width": 90, "height": 90}, {"id": 39, "file_name": "sample_geotiff_733736_3724734.tif", "width": 90, "height": 90}, {"id": 40, "file_name": "sample_geotiff_733646_3724869.tif", "width": 90, "height": 90}, {"id": 41, "file_name": "sample_geotiff_733691_3724779.tif", "width": 90, "height": 90}, {"id": 42, "file_name": "sample_geotiff_733961_3724779.tif", "width": 90, "height": 90}, {"id": 43, "file_name": "sample_geotiff_733826_3724959.tif", "width": 90, "height": 90}, {"id": 44, "file_name": "sample_geotiff_734006_3725004.tif", "width": 90, "height": 90}, {"id": 45, "file_name": "sample_geotiff_733601_3724914.tif", "width": 90, "height": 90}, {"id": 46, "file_name": "sample_geotiff_733826_3725139.tif", "width": 90, "height": 90}, {"id": 47, "file_name": "sample_geotiff_733646_3725004.tif", "width": 90, "height": 90}, {"id": 48, "file_name": "sample_geotiff_733916_3724779.tif", "width": 90, "height": 90}, {"id": 49, "file_name": "sample_geotiff_733736_3724869.tif", "width": 90, "height": 90}, {"id": 50, "file_name": "sample_geotiff_733871_3724959.tif", "width": 90, "height": 90}, {"id": 51, "file_name": "sample_geotiff_733781_3724959.tif", "width": 90, "height": 90}, {"id": 52, "file_name": "sample_geotiff_733646_3724734.tif", "width": 90, "height": 90}, {"id": 53, "file_name": "sample_geotiff_733961_3724824.tif", "width": 90, "height": 90}, {"id": 54, "file_name": "sample_geotiff_733916_3725049.tif", "width": 90, "height": 90}, {"id": 55, "file_name": "sample_geotiff_733691_3724824.tif", "width": 90, "height": 90}, {"id": 56, "file_name": "sample_geotiff_733871_3725139.tif", "width": 90, "height": 90}, {"id": 57, "file_name": "sample_geotiff_733781_3725139.tif", "width": 90, "height": 90}, {"id": 58, "file_name": "sample_geotiff_734006_3724734.tif", "width": 90, "height": 90}, {"id": 59, "file_name": "sample_geotiff_733781_3725049.tif", "width": 90, "height": 90}, {"id": 60, "file_name": "sample_geotiff_733871_3725049.tif", "width": 90, "height": 90}, {"id": 61, "file_name": "sample_geotiff_733916_3724959.tif", "width": 90, "height": 90}, {"id": 62, "file_name": "sample_geotiff_733601_3724734.tif", "width": 90, "height": 90}, {"id": 63, "file_name": "sample_geotiff_733826_3724779.tif", "width": 90, "height": 90}, {"id": 64, "file_name": "sample_geotiff_733916_3725139.tif", "width": 90, "height": 90}, {"id": 65, "file_name": "sample_geotiff_733601_3725004.tif", "width": 90, "height": 90}, {"id": 66, "file_name": "sample_geotiff_734006_3724914.tif", "width": 90, "height": 90}, {"id": 67, "file_name": "sample_geotiff_733691_3725094.tif", "width": 90, "height": 90}, {"id": 68, "file_name": "sample_geotiff_733961_3725094.tif", "width": 90, "height": 90}, {"id": 69, "file_name": "sample_geotiff_733826_3725049.tif", "width": 90, "height": 90}, {"id": 70, "file_name": "sample_geotiff_733646_3724914.tif", "width": 90, "height": 90}, {"id": 71, "file_name": "sample_geotiff_733871_3724779.tif", "width": 90, "height": 90}, {"id": 72, "file_name": "sample_geotiff_733781_3724779.tif", "width": 90, "height": 90}, {"id": 73, "file_name": "sample_geotiff_733781_3725004.tif", "width": 90, "height": 90}, {"id": 74, "file_name": "sample_geotiff_733871_3725004.tif", "width": 90, "height": 90}, {"id": 75, "file_name": "sample_geotiff_733916_3724914.tif", "width": 90, "height": 90}, {"id": 76, "file_name": "sample_geotiff_733601_3724779.tif", "width": 90, "height": 90}, {"id": 77, "file_name": "sample_geotiff_733826_3724734.tif", "width": 90, "height": 90}, {"id": 78, "file_name": "sample_geotiff_733736_3725094.tif", "width": 90, "height": 90}, {"id": 79, "file_name": "sample_geotiff_733601_3725049.tif", "width": 90, "height": 90}, {"id": 80, "file_name": "sample_geotiff_734006_3724959.tif", "width": 90, "height": 90}, {"id": 81, "file_name": "sample_geotiff_733646_3725139.tif", "width": 90, "height": 90}, {"id": 82, "file_name": "sample_geotiff_733826_3725004.tif", "width": 90, "height": 90}, {"id": 83, "file_name": "sample_geotiff_733646_3724959.tif", "width": 90, "height": 90}, {"id": 84, "file_name": "sample_geotiff_733871_3724734.tif", "width": 90, "height": 90}, {"id": 85, "file_name": "sample_geotiff_733781_3724734.tif", "width": 90, "height": 90}, {"id": 86, "file_name": "sample_geotiff_734006_3725139.tif", "width": 90, "height": 90}, {"id": 87, "file_name": "sample_geotiff_733826_3724914.tif", "width": 90, "height": 90}, {"id": 88, "file_name": "sample_geotiff_734006_3725049.tif", "width": 90, "height": 90}, {"id": 89, "file_name": "sample_geotiff_733601_3724959.tif", "width": 90, "height": 90}, {"id": 90, "file_name": "sample_geotiff_733601_3725139.tif", "width": 90, "height": 90}, {"id": 91, "file_name": "sample_geotiff_733646_3725049.tif", "width": 90, "height": 90}, {"id": 92, "file_name": "sample_geotiff_733916_3724734.tif", "width": 90, "height": 90}, {"id": 93, "file_name": "sample_geotiff_733736_3724824.tif", "width": 90, "height": 90}, {"id": 94, "file_name": "sample_geotiff_733871_3724914.tif", "width": 90, "height": 90}, {"id": 95, "file_name": "sample_geotiff_733781_3724914.tif", "width": 90, "height": 90}, {"id": 96, "file_name": "sample_geotiff_733646_3724779.tif", "width": 90, "height": 90}, {"id": 97, "file_name": "sample_geotiff_733961_3724869.tif", "width": 90, "height": 90}, {"id": 98, "file_name": "sample_geotiff_733691_3724869.tif", "width": 90, "height": 90}, {"id": 99, "file_name": "sample_geotiff_733916_3725004.tif", "width": 90, "height": 90}, {"id": 100, "file_name": "sample_geotiff_734006_3724779.tif", "width": 90, "height": 90}]} \ No newline at end of file diff --git a/docker/solaris/solaris/data/competition_test_results.csv b/docker/solaris/solaris/data/competition_test_results.csv new file mode 100644 index 00000000..45ea2fe8 --- /dev/null +++ b/docker/solaris/solaris/data/competition_test_results.csv @@ -0,0 +1,2 @@ +F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos +1.0,0,0,1.0,1.0,2319 diff --git a/docker/solaris/solaris/data/competition_test_results_full.csv b/docker/solaris/solaris/data/competition_test_results_full.csv new file mode 100644 index 00000000..1849ef2c --- /dev/null +++ b/docker/solaris/solaris/data/competition_test_results_full.csv @@ -0,0 +1,34 @@ +F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos,imageID,iou_field,nadir-category +1.0,0,0,1.0,1.0,80,Atlanta_nadir8_catid_10300100023BC100_743501_3735489,iou_score,Nadir +1.0,0,0,1.0,1.0,112,Atlanta_nadir8_catid_10300100023BC100_743501_3739989,iou_score,Nadir +1.0,0,0,1.0,1.0,72,Atlanta_nadir8_catid_10300100023BC100_743501_3724689,iou_score,Nadir +1.0,0,0,1.0,1.0,1,Atlanta_nadir8_catid_10300100023BC100_743501_3732339,iou_score,Nadir +1.0,0,0,1.0,1.0,52,Atlanta_nadir8_catid_10300100023BC100_743501_3734589,iou_score,Nadir +1.0,0,0,1.0,1.0,67,Atlanta_nadir8_catid_10300100023BC100_743501_3733239,iou_score,Nadir +1.0,0,0,1.0,1.0,91,Atlanta_nadir8_catid_10300100023BC100_743501_3722439,iou_score,Nadir +1.0,0,0,1.0,1.0,121,Atlanta_nadir8_catid_10300100023BC100_743501_3723789,iou_score,Nadir +1.0,0,0,1.0,1.0,134,Atlanta_nadir8_catid_10300100023BC100_743501_3742689,iou_score,Nadir +1.0,0,0,1.0,1.0,154,Atlanta_nadir8_catid_10300100023BC100_743501_3738639,iou_score,Nadir +1.0,0,0,1.0,1.0,21,Atlanta_nadir8_catid_10300100023BC100_743501_3729189,iou_score,Nadir +1.0,0,0,1.0,1.0,101,Atlanta_nadir8_catid_10300100023BC100_743501_3740439,iou_score,Nadir +1.0,0,0,1.0,1.0,2,Atlanta_nadir8_catid_10300100023BC100_743501_3726489,iou_score,Nadir +1.0,0,0,1.0,1.0,28,Atlanta_nadir8_catid_10300100023BC100_743501_3725589,iou_score,Nadir +1.0,0,0,1.0,1.0,55,Atlanta_nadir8_catid_10300100023BC100_743501_3725139,iou_score,Nadir +1.0,0,0,1.0,1.0,139,Atlanta_nadir8_catid_10300100023BC100_743501_3737289,iou_score,Nadir +1.0,0,0,1.0,1.0,18,Atlanta_nadir8_catid_10300100023BC100_743501_3729639,iou_score,Nadir +1.0,0,0,1.0,1.0,122,Atlanta_nadir8_catid_10300100023BC100_743501_3739539,iou_score,Nadir +1.0,0,0,1.0,1.0,59,Atlanta_nadir8_catid_10300100023BC100_743501_3730539,iou_score,Nadir +1.0,0,0,1.0,1.0,45,Atlanta_nadir8_catid_10300100023BC100_743501_3734139,iou_score,Nadir +1.0,0,0,1.0,1.0,96,Atlanta_nadir8_catid_10300100023BC100_743501_3743139,iou_score,Nadir +1.0,0,0,1.0,1.0,3,Atlanta_nadir8_catid_10300100023BC100_743501_3727839,iou_score,Nadir +1.0,0,0,1.0,1.0,71,Atlanta_nadir8_catid_10300100023BC100_743501_3722889,iou_score,Nadir +1.0,0,0,1.0,1.0,117,Atlanta_nadir8_catid_10300100023BC100_743501_3736839,iou_score,Nadir +1.0,0,0,1.0,1.0,3,Atlanta_nadir8_catid_10300100023BC100_743501_3726039,iou_score,Nadir +1.0,0,0,1.0,1.0,96,Atlanta_nadir8_catid_10300100023BC100_743501_3721089,iou_score,Nadir +1.0,0,0,1.0,1.0,60,Atlanta_nadir8_catid_10300100023BC100_743501_3724239,iou_score,Nadir +1.0,0,0,1.0,1.0,59,Atlanta_nadir8_catid_10300100023BC100_743501_3721539,iou_score,Nadir +1.0,0,0,1.0,1.0,43,Atlanta_nadir8_catid_10300100023BC100_743501_3731439,iou_score,Nadir +1.0,0,0,1.0,1.0,144,Atlanta_nadir8_catid_10300100023BC100_743501_3739089,iou_score,Nadir +1.0,0,0,1.0,1.0,67,Atlanta_nadir8_catid_10300100023BC100_743501_3728739,iou_score,Nadir +1.0,0,0,1.0,1.0,78,Atlanta_nadir8_catid_10300100023BC100_743501_3741789,iou_score,Nadir +1.0,0,0,1.0,1.0,8,Atlanta_nadir8_catid_10300100023BC100_743501_3730089,iou_score,Nadir diff --git a/docker/solaris/solaris/data/datagen_sample/expected_im.tif b/docker/solaris/solaris/data/datagen_sample/expected_im.tif new file mode 100644 index 00000000..c0e6ef84 Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/expected_im.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_1.tif b/docker/solaris/solaris/data/datagen_sample/sample_1.tif new file mode 100644 index 00000000..e38e5ae9 Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_1.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_2.tif b/docker/solaris/solaris/data/datagen_sample/sample_2.tif new file mode 100644 index 00000000..e38e5ae9 Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_2.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_3.tif b/docker/solaris/solaris/data/datagen_sample/sample_3.tif new file mode 100644 index 00000000..e38e5ae9 Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_3.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_df.csv b/docker/solaris/solaris/data/datagen_sample/sample_df.csv new file mode 100644 index 00000000..55fcb64b --- /dev/null +++ b/docker/solaris/solaris/data/datagen_sample/sample_df.csv @@ -0,0 +1,4 @@ +image,label +sample_1.tif,sample_mask_1.tif +sample_2.tif,sample_mask_2.tif +sample_3.tif,sample_mask_3.tif diff --git a/docker/solaris/solaris/data/datagen_sample/sample_mask_1.tif b/docker/solaris/solaris/data/datagen_sample/sample_mask_1.tif new file mode 100644 index 00000000..89d3fa5e Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_mask_1.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_mask_2.tif b/docker/solaris/solaris/data/datagen_sample/sample_mask_2.tif new file mode 100644 index 00000000..89d3fa5e Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_mask_2.tif differ diff --git a/docker/solaris/solaris/data/datagen_sample/sample_mask_3.tif b/docker/solaris/solaris/data/datagen_sample/sample_mask_3.tif new file mode 100644 index 00000000..89d3fa5e Binary files /dev/null and b/docker/solaris/solaris/data/datagen_sample/sample_mask_3.tif differ diff --git a/docker/solaris/solaris/data/empty.geojson b/docker/solaris/solaris/data/empty.geojson new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/data/eval_vector/gt/108_1040010044D30600.geojson b/docker/solaris/solaris/data/eval_vector/gt/108_1040010044D30600.geojson new file mode 100755 index 00000000..29c5e07a --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/gt/108_1040010044D30600.geojson @@ -0,0 +1,138 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.864350443293258, "Wingspan": 15.498727599323365, "Area": 84.173995826752531, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088877689028763, 26.589432232595573 ], [ -80.088877689028763, 26.589533482415973 ], [ -80.088987993711143, 26.589533482415973 ], [ -80.088987993711143, 26.589432232595573 ], [ -80.088877689028763, 26.589432232595573 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 11.918589228862162, "Wingspan": 15.927227731544489, "Area": 94.798943646408517, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086455101862541, 26.589033818668202 ], [ -80.086455101862541, 26.589156882474299 ], [ -80.086560467529296, 26.589156882474299 ], [ -80.086560467529296, 26.589033818668202 ], [ -80.086455101862541, 26.589033818668202 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 11.126891332023144, "Wingspan": 15.492607943111297, "Area": 86.085820096113281, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.089019479935786, 26.588695496097575 ], [ -80.089019479935786, 26.588792424279301 ], [ -80.089138633687838, 26.588792424279301 ], [ -80.089138633687838, 26.588695496097575 ], [ -80.089019479935786, 26.588695496097575 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8841495524136542, "Wingspan": 11.075306315113178, "Area": 43.649045612517355, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086872860267903, 26.589684739769659 ], [ -80.086872860267903, 26.58976911461999 ], [ -80.086955177195065, 26.58976911461999 ], [ -80.086955177195065, 26.589684739769659 ], [ -80.086872860267903, 26.589684739769659 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.0703728464611668, "Wingspan": 9.2091507507870336, "Area": 32.553260992068516, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082769773033803, 26.589690501954585 ], [ -80.082769773033803, 26.589773642051011 ], [ -80.082840771383474, 26.589773642051011 ], [ -80.082840771383474, 26.589690501954585 ], [ -80.082769773033803, 26.589690501954585 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.1935371405699851, "Wingspan": 11.258320628919034, "Area": 46.097764390560947, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087004155766721, 26.58969132512383 ], [ -80.087004155766721, 26.589778992651247 ], [ -80.087084826355323, 26.589778992651247 ], [ -80.087084826355323, 26.58969132512383 ], [ -80.087004155766721, 26.58969132512383 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.3207923272017066, "Wingspan": 11.353070450350891, "Area": 47.221676359823007, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087310786320387, 26.58969502938556 ], [ -80.087310786320387, 26.589778169481985 ], [ -80.087397219093901, 26.589778169481985 ], [ -80.087397219093901, 26.58969502938556 ], [ -80.087310786320387, 26.58969502938556 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.3644156420226743, "Wingspan": 10.394335223437542, "Area": 43.459458701991849, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083171891222932, 26.589693794631668 ], [ -80.083171891222932, 26.589787635928623 ], [ -80.083255854488627, 26.589787635928623 ], [ -80.083255854488627, 26.589693794631668 ], [ -80.083171891222932, 26.589693794631668 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.6824571897800675, "Wingspan": 8.6351928127855011, "Area": 28.837828363798867, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087757767234777, 26.589723017140777 ], [ -80.087757767234777, 26.589788459097864 ], [ -80.0878248555304, 26.589788459097864 ], [ -80.0878248555304, 26.589723017140777 ], [ -80.087757767234777, 26.589723017140777 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 4.8605249282323468, "Wingspan": 7.4475294680774207, "Area": 18.06361210707492, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08789091486446, 26.589730219871907 ], [ -80.08789091486446, 26.589785989590052 ], [ -80.087943597697844, 26.589785989590052 ], [ -80.087943597697844, 26.589730219871907 ], [ -80.08789091486446, 26.589730219871907 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.467831031857485, "Wingspan": 11.117469783617505, "Area": 41.484998141006358, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088433383414426, 26.589746683257307 ], [ -80.088433383414426, 26.58983208706923 ], [ -80.088504999141037, 26.58983208706923 ], [ -80.088504999141037, 26.589746683257307 ], [ -80.088433383414426, 26.589746683257307 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4472373927363176, "Wingspan": 12.076774724521718, "Area": 44.933685283469913, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084330090387979, 26.589763352435114 ], [ -80.084330090387979, 26.589830440730744 ], [ -80.08445068468626, 26.589830440730744 ], [ -80.08445068468626, 26.589763352435114 ], [ -80.084330090387979, 26.589763352435114 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6021035830606376, "Wingspan": 11.301141103394597, "Area": 42.940968488077445, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084234191167852, 26.58955056317842 ], [ -80.084234191167852, 26.589651401414184 ], [ -80.084309922740829, 26.589651401414184 ], [ -80.084309922740829, 26.58955056317842 ], [ -80.084234191167852, 26.58955056317842 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8161041789053991, "Wingspan": 11.328675616156927, "Area": 44.24142473326976, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086513958465474, 26.589562293340521 ], [ -80.086513958465474, 26.589649137698668 ], [ -80.086597098561896, 26.589649137698668 ], [ -80.086597098561896, 26.589562293340521 ], [ -80.086513958465474, 26.589562293340521 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 5.9139759386628885, "Wingspan": 8.0109657298241643, "Area": 23.662250819622507, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086963408887755, 26.589591310057337 ], [ -80.086963408887755, 26.589648108737073 ], [ -80.087020619152128, 26.589648108737073 ], [ -80.087020619152128, 26.589591310057337 ], [ -80.086963408887755, 26.589591310057337 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9660148363786014, "Wingspan": 11.186613298867988, "Area": 44.503927331705228, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087387752647302, 26.589590486888074 ], [ -80.087387752647302, 26.589674861738406 ], [ -80.087468423235919, 26.589674861738406 ], [ -80.087468423235919, 26.589590486888074 ], [ -80.087387752647302, 26.589590486888074 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3851712111785979, "Wingspan": 10.905949789754816, "Area": 40.246460039419354, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087514520715118, 26.589595837488336 ], [ -80.087514520715118, 26.589685151354299 ], [ -80.087588605949549, 26.589685151354299 ], [ -80.087588605949549, 26.589595837488336 ], [ -80.087514520715118, 26.589595837488336 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.6628308706980999, "Wingspan": 10.510798568112037, "Area": 45.521567843788247, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087825061322732, 26.589602217050182 ], [ -80.087825061322732, 26.589684122392701 ], [ -80.087904702949743, 26.589684122392701 ], [ -80.087904702949743, 26.589602217050182 ], [ -80.087825061322732, 26.589602217050182 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8462733404229574, "Wingspan": 11.325116109586602, "Area": 44.418893751166699, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087950594636624, 26.58961230087376 ], [ -80.087950594636624, 26.589701820532039 ], [ -80.088026120417297, 26.589701820532039 ], [ -80.088026120417297, 26.58961230087376 ], [ -80.087950594636624, 26.58961230087376 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3537612028514108, "Wingspan": 11.01744573482795, "Area": 40.509047312124814, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083707774418684, 26.589622590489682 ], [ -80.083707774418684, 26.589721782386903 ], [ -80.083781448068478, 26.589721782386903 ], [ -80.083781448068478, 26.589622590489682 ], [ -80.083707774418684, 26.589622590489682 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.7321105180904155, "Wingspan": 9.2589801233925488, "Area": 40.423154123746492, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082862791161489, 26.589498703514305 ], [ -80.082862791161489, 26.589582255195367 ], [ -80.082950458688913, 26.589582255195367 ], [ -80.082950458688913, 26.589498703514305 ], [ -80.082862791161489, 26.589498703514305 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.7967912775375598, "Wingspan": 10.802672761438798, "Area": 36.678973549733712, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088037438994803, 26.589503436737598 ], [ -80.088037438994803, 26.58959172164197 ], [ -80.088101234613347, 26.58959172164197 ], [ -80.088101234613347, 26.589503436737598 ], [ -80.088037438994803, 26.589503436737598 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.839170052472161, "Wingspan": 9.1622458762994441, "Area": 40.480134350469633, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083265732519877, 26.589510639468749 ], [ -80.083265732519877, 26.589592956395901 ], [ -80.083353811631937, 26.589592956395901 ], [ -80.083353811631937, 26.589510639468749 ], [ -80.083265732519877, 26.589510639468749 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2300104923462927, "Wingspan": 9.6761454177857189, "Area": 34.97816766595146, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085172398345151, 26.589532041869809 ], [ -80.085172398345151, 26.589597278034578 ], [ -80.085269532319188, 26.589597278034578 ], [ -80.085269532319188, 26.589532041869809 ], [ -80.085172398345151, 26.589532041869809 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3872070368141332, "Wingspan": 11.229375386285167, "Area": 41.448922578145392, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085298960620662, 26.589534099792989 ], [ -80.085298960620662, 26.589600776503982 ], [ -80.085411529018543, 26.589600776503982 ], [ -80.085411529018543, 26.589534099792989 ], [ -80.085298960620662, 26.589534099792989 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3674114196983655, "Wingspan": 10.828233268962499, "Area": 39.819318448043852, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085438076227547, 26.589538833016306 ], [ -80.085438076227547, 26.589605303934984 ], [ -80.085546322986758, 26.589605303934984 ], [ -80.085546322986758, 26.589538833016306 ], [ -80.085438076227547, 26.589538833016306 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.0237351031471231, "Wingspan": 11.482108324164459, "Area": 46.033379416357135, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085572664403443, 26.589542537278028 ], [ -80.085572664403443, 26.589614976173927 ], [ -80.085687908101463, 26.589614976173927 ], [ -80.085687908101463, 26.589542537278028 ], [ -80.085572664403443, 26.589542537278028 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3890909792755739, "Wingspan": 9.6438778416288962, "Area": 35.553160097668908, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088580936506375, 26.589540479354792 ], [ -80.088580936506375, 26.589617445681682 ], [ -80.088650082725181, 26.589617445681682 ], [ -80.088650082725181, 26.589540479354792 ], [ -80.088580936506375, 26.589540479354792 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6606737684960198, "Wingspan": 9.5962266231102546, "Area": 36.754196494894828, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085999889255362, 26.589549122632196 ], [ -80.085999889255362, 26.589618268851005 ], [ -80.086096200060126, 26.589618268851005 ], [ -80.086096200060126, 26.589549122632196 ], [ -80.085999889255362, 26.589549122632196 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9233497089454383, "Wingspan": 11.391958219864421, "Area": 45.106566972125975, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088539160665846, 26.589436348441946 ], [ -80.088539160665846, 26.589522575423139 ], [ -80.088621889177645, 26.589522575423139 ], [ -80.088621889177645, 26.589436348441946 ], [ -80.088539160665846, 26.589436348441946 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.1185921406790449, "Wingspan": 10.578153566542168, "Area": 48.225240435672767, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08492935761771, 26.589453429204372 ], [ -80.08492935761771, 26.589535746131524 ], [ -80.085035546453739, 26.589535746131524 ], [ -80.085035546453739, 26.589453429204372 ], [ -80.08492935761771, 26.589453429204372 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8566859188618947, "Wingspan": 10.521224954069698, "Area": 41.328440405537862, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085238251886878, 26.589463307235636 ], [ -80.085238251886878, 26.589534099792989 ], [ -80.085343617553633, 26.589534099792989 ], [ -80.085343617553633, 26.589463307235636 ], [ -80.085238251886878, 26.589463307235636 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7946135611262601, "Wingspan": 10.94423114556156, "Area": 42.646663964979645, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086590101623088, 26.589458162427675 ], [ -80.086590101623088, 26.589540890939464 ], [ -80.086672418550251, 26.589540890939464 ], [ -80.086672418550251, 26.589458162427675 ], [ -80.086590101623088, 26.589458162427675 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3945937557149399, "Wingspan": 11.000459419411015, "Area": 40.671639625449728, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.0855125730466, 26.589472979474582 ], [ -80.0855125730466, 26.589539656185575 ], [ -80.085622877728994, 26.589539656185575 ], [ -80.085622877728994, 26.589472979474582 ], [ -80.0855125730466, 26.589472979474582 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2040810893674987, "Wingspan": 9.4680780269524956, "Area": 34.101840404467922, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085661566684763, 26.589474214228492 ], [ -80.085661566684763, 26.589539244600942 ], [ -80.085756642735632, 26.589539244600942 ], [ -80.085756642735632, 26.589474214228492 ], [ -80.085661566684763, 26.589474214228492 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9991664703200938, "Wingspan": 10.060655659244253, "Area": 39.896157520633281, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087034201445135, 26.589470715759063 ], [ -80.087034201445135, 26.589550563178403 ], [ -80.087104582417851, 26.589550563178403 ], [ -80.087104582417851, 26.589470715759063 ], [ -80.087034201445135, 26.589470715759063 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.9759337768983691, "Wingspan": 10.677070376116136, "Area": 37.233520506631308, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085375309570608, 26.589481622751929 ], [ -80.085375309570608, 26.589544389408886 ], [ -80.08548232157591, 26.589544389408886 ], [ -80.08548232157591, 26.589481622751929 ], [ -80.085375309570608, 26.589481622751929 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7528061872183969, "Wingspan": 11.150758478825743, "Area": 43.177344873148343, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085781337813771, 26.589485327013655 ], [ -80.085781337813771, 26.589555296401738 ], [ -80.085893288834711, 26.589555296401738 ], [ -80.085893288834711, 26.589485327013655 ], [ -80.085781337813771, 26.589485327013655 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7115823026591936, "Wingspan": 9.4287652533952127, "Area": 36.353393011252813, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087467600066631, 26.589488002313765 ], [ -80.087467600066631, 26.589560852794296 ], [ -80.08753880420862, 26.589560852794296 ], [ -80.08753880420862, 26.589488002313765 ], [ -80.087467600066631, 26.589488002313765 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7279016809168288, "Wingspan": 10.888543278593454, "Area": 42.071888979170062, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088047934403008, 26.58928365054209 ], [ -80.088047934403008, 26.589366379053878 ], [ -80.088126958653078, 26.589366379053878 ], [ -80.088126958653078, 26.58928365054209 ], [ -80.088047934403008, 26.58928365054209 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9618419971016623, "Wingspan": 10.700271182250109, "Area": 42.553715623451716, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088728695390614, 26.58932522059029 ], [ -80.088728695390614, 26.589408566479033 ], [ -80.088803809586636, 26.589408566479033 ], [ -80.088803809586636, 26.58932522059029 ], [ -80.088728695390614, 26.58932522059029 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4415171468672083, "Wingspan": 11.048192560246383, "Area": 41.098583112049603, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088620448631417, 26.589331600152139 ], [ -80.088620448631417, 26.589417827133332 ], [ -80.088694945450484, 26.589417827133332 ], [ -80.088694945450484, 26.589331600152139 ], [ -80.088620448631417, 26.589331600152139 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.706100723115437, "Wingspan": 10.762521704020191, "Area": 41.467611291604086, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088986347372597, 26.589342918729606 ], [ -80.088986347372597, 26.589424412487489 ], [ -80.089061667360937, 26.589424412487489 ], [ -80.089061667360937, 26.589342918729606 ], [ -80.088986347372597, 26.589342918729606 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 5.2214856387445483, "Wingspan": 7.2496673274491581, "Area": 18.891003459182031, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083159543683877, 26.589370289107947 ], [ -80.083159543683877, 26.589435731065038 ], [ -80.083211814932611, 26.589435731065038 ], [ -80.083211814932611, 26.589370289107947 ], [ -80.083159543683877, 26.589370289107947 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7666311025506829, "Wingspan": 10.928633421119038, "Area": 42.42152813109054, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087097997063665, 26.589365350092304 ], [ -80.087097997063665, 26.58944766701946 ], [ -80.087180313990814, 26.58944766701946 ], [ -80.087180313990814, 26.589365350092304 ], [ -80.087097997063665, 26.589365350092304 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6538680498639602, "Wingspan": 9.2373966535571466, "Area": 35.315458158599185, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087240405347629, 26.589383048231642 ], [ -80.087240405347629, 26.589452194450452 ], [ -80.087305024135446, 26.589452194450452 ], [ -80.087305024135446, 26.589383048231642 ], [ -80.087240405347629, 26.589383048231642 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5380831266902799, "Wingspan": 9.3043859041784938, "Area": 34.963228948948, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087543743224231, 26.589381401893103 ], [ -80.087543743224231, 26.589455487127541 ], [ -80.087611654689141, 26.589455487127541 ], [ -80.087611654689141, 26.589381401893103 ], [ -80.087543743224231, 26.589381401893103 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8589481233734277, "Wingspan": 9.6024219056711146, "Area": 37.651126556597447, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087673392384502, 26.589388398831911 ], [ -80.087673392384502, 26.589463718820255 ], [ -80.087741715434035, 26.589463718820255 ], [ -80.087741715434035, 26.589388398831911 ], [ -80.087673392384502, 26.589388398831911 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9399543128305776, "Wingspan": 9.2285196742907605, "Area": 36.632527250278045, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08797837659958, 26.589395807355341 ], [ -80.08797837659958, 26.589466188328057 ], [ -80.088055342926467, 26.589466188328057 ], [ -80.088055342926467, 26.589395807355341 ], [ -80.08797837659958, 26.589395807355341 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8314683459375152, "Wingspan": 11.354906504937466, "Area": 44.4623107149867, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085955232322362, 26.589154824551123 ], [ -80.085955232322362, 26.589225411316157 ], [ -80.086069035474154, 26.589225411316157 ], [ -80.086069035474154, 26.589154824551123 ], [ -80.085955232322362, 26.589154824551123 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.98753655702426, "Wingspan": 10.957186008015444, "Area": 43.7604313823837, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086098052190962, 26.589163467828481 ], [ -80.086098052190962, 26.589235495139739 ], [ -80.086207945288706, 26.589235495139739 ], [ -80.086207945288706, 26.589163467828481 ], [ -80.086098052190962, 26.589163467828481 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.0415668814952106, "Wingspan": 9.6165684424378508, "Area": 33.822553209709987, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087388575816533, 26.589164085205407 ], [ -80.087388575816533, 26.589236112516666 ], [ -80.087459368373885, 26.589236112516666 ], [ -80.087459368373885, 26.589164085205407 ], [ -80.087388575816533, 26.589164085205407 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7895072190573318, "Wingspan": 11.178474754383123, "Area": 43.535944958854692, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087677096646203, 26.589162027282235 ], [ -80.087677096646203, 26.589244550001705 ], [ -80.087758590404079, 26.589244550001705 ], [ -80.087758590404079, 26.589162027282235 ], [ -80.087677096646203, 26.589162027282235 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3886546435203408, "Wingspan": 10.235578382136993, "Area": 37.778477383349724, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088125312314546, 26.589177050121428 ], [ -80.088125312314546, 26.589253193279045 ], [ -80.088195899079579, 26.589253193279045 ], [ -80.088195899079579, 26.589177050121428 ], [ -80.088125312314546, 26.589177050121428 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.8147323987299986, "Wingspan": 8.1791725841095513, "Area": 27.845057181170993, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088260723659715, 26.589190632414404 ], [ -80.088260723659715, 26.589255868579176 ], [ -80.088322461355077, 26.589255868579176 ], [ -80.088322461355077, 26.589190632414404 ], [ -80.088260723659715, 26.589190632414404 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.734799089641756, "Wingspan": 11.224658843188262, "Area": 43.410240339380799, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088684244249976, 26.589232408254919 ], [ -80.088684244249976, 26.589310815128034 ], [ -80.088767795931034, 26.589310815128034 ], [ -80.088767795931034, 26.589232408254919 ], [ -80.088684244249976, 26.589232408254919 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 5.5933274882167314, "Wingspan": 8.6551762831493217, "Area": 24.205109043544553, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.089059197853118, 26.589247842678745 ], [ -80.089059197853118, 26.589309991958746 ], [ -80.089125874564118, 26.589309991958746 ], [ -80.089125874564118, 26.589247842678745 ], [ -80.089059197853118, 26.589247842678745 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2431453501131191, "Wingspan": 10.514723148468699, "Area": 38.079079777551229, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086737860507327, 26.589248254263431 ], [ -80.086737860507327, 26.589328513267407 ], [ -80.086814415249592, 26.589328513267407 ], [ -80.086814415249592, 26.589248254263431 ], [ -80.086737860507327, 26.589248254263431 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.9740810364361714, "Wingspan": 10.242093758572368, "Area": 35.714238149378744, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084570455815296, 26.589242492078551 ], [ -80.084570455815296, 26.589334892829282 ], [ -80.084640425203361, 26.589334892829282 ], [ -80.084640425203361, 26.589242492078551 ], [ -80.084570455815296, 26.589242492078551 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6064734579662217, "Wingspan": 8.4473923316235027, "Area": 32.104398689857135, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087179490821555, 26.58926307131031 ], [ -80.087179490821555, 26.589324809005674 ], [ -80.087249460209634, 26.589324809005674 ], [ -80.087249460209634, 26.58926307131031 ], [ -80.087179490821555, 26.58926307131031 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4843151739496312, "Wingspan": 9.3064289113028131, "Area": 34.823120359404903, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087621944305027, 26.589271508795349 ], [ -80.087621944305027, 26.589346005614424 ], [ -80.087691090523847, 26.589346005614424 ], [ -80.087691090523847, 26.589271508795349 ], [ -80.087621944305027, 26.589271508795349 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.4640634706847671, "Wingspan": 11.18272396782136, "Area": 47.303123787549829, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087730602648875, 26.589275624641708 ], [ -80.087730602648875, 26.589362057415219 ], [ -80.087813742745297, 26.589362057415219 ], [ -80.087813742745297, 26.589275624641708 ], [ -80.087730602648875, 26.589275624641708 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3004453919049332, "Wingspan": 9.1068540419980408, "Area": 33.213438867095142, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087328484459704, 26.589044931453358 ], [ -80.087328484459704, 26.589111608164352 ], [ -80.087396807509251, 26.589111608164352 ], [ -80.087396807509251, 26.589044931453358 ], [ -80.087328484459704, 26.589044931453358 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4305240011946676, "Wingspan": 10.926648688059194, "Area": 40.547474012405026, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087760442534943, 26.589056455823158 ], [ -80.087760442534943, 26.589142682804347 ], [ -80.087839672577331, 26.589142682804347 ], [ -80.087839672577331, 26.589056455823158 ], [ -80.087760442534943, 26.589056455823158 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8485158411213396, "Wingspan": 10.60352129716148, "Area": 41.595483816476914, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088200426510568, 26.589069420739172 ], [ -80.088200426510568, 26.589151531874005 ], [ -80.088280685514547, 26.589151531874005 ], [ -80.088280685514547, 26.589069420739172 ], [ -80.088200426510568, 26.589069420739172 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5833513093488181, "Wingspan": 8.6601339850651318, "Area": 32.830342439260576, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088754625222691, 26.589133010565391 ], [ -80.088754625222691, 26.589192690337576 ], [ -80.088823565649193, 26.589192690337576 ], [ -80.088823565649193, 26.589133010565391 ], [ -80.088754625222691, 26.589133010565391 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.175988838817366, "Wingspan": 9.9579303359707438, "Area": 45.597178590459471, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088864312528116, 26.589130746849889 ], [ -80.088864312528116, 26.589205243668964 ], [ -80.088951568470904, 26.589205243668964 ], [ -80.088951568470904, 26.589130746849889 ], [ -80.088864312528116, 26.589130746849889 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7946290368130207, "Wingspan": 11.25557416030448, "Area": 43.817423727625851, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086814415249592, 26.589128483134424 ], [ -80.086814415249592, 26.589216973831114 ], [ -80.086884796222307, 26.589216973831114 ], [ -80.086884796222307, 26.589128483134424 ], [ -80.086814415249592, 26.589128483134424 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.9420294522899288, "Wingspan": 10.148086313919633, "Area": 35.209925247804712, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.089135958387672, 26.589134245319276 ], [ -80.089135958387672, 26.589212446400072 ], [ -80.089210043622117, 26.589212446400072 ], [ -80.089210043622117, 26.589134245319276 ], [ -80.089135958387672, 26.589134245319276 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8451297416621681, "Wingspan": 10.925410922680708, "Area": 42.831773838154106, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085816939884765, 26.589150297120135 ], [ -80.085816939884765, 26.589221089677487 ], [ -80.085926421397872, 26.589221089677487 ], [ -80.085926421397872, 26.589150297120135 ], [ -80.085816939884765, 26.589150297120135 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7880853973829751, "Wingspan": 10.033636790604655, "Area": 39.065836399866676, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086929658947611, 26.588744474669248 ], [ -80.086929658947611, 26.588835023289118 ], [ -80.087007860028407, 26.588835023289118 ], [ -80.087007860028407, 26.588744474669248 ], [ -80.086929658947611, 26.588744474669248 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.6865443303232537, "Wingspan": 9.9502668815383721, "Area": 33.135002118053364, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088981614149276, 26.588804154441416 ], [ -80.088981614149276, 26.588879062845123 ], [ -80.089050142991127, 26.588879062845123 ], [ -80.089050142991127, 26.588804154441416 ], [ -80.088981614149276, 26.588804154441416 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4305305174877256, "Wingspan": 9.3897913891204432, "Area": 34.883951527121944, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08740380444803, 26.588935038355611 ], [ -80.08740380444803, 26.589007477251506 ], [ -80.087474185420746, 26.589007477251506 ], [ -80.087474185420746, 26.588935038355611 ], [ -80.08740380444803, 26.588935038355611 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.8090905072642869, "Wingspan": 12.199649545699659, "Area": 53.673133554130729, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087952446767545, 26.588946974310044 ], [ -80.087952446767545, 26.589041227191636 ], [ -80.088042995387411, 26.589041227191636 ], [ -80.088042995387411, 26.588946974310044 ], [ -80.087952446767545, 26.588946974310044 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.958657206081404, "Wingspan": 9.9844398595837163, "Area": 34.702447988490896, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088272865406452, 26.588959939226058 ], [ -80.088272865406452, 26.589036288175993 ], [ -80.088340571079044, 26.589036288175993 ], [ -80.088340571079044, 26.588959939226058 ], [ -80.088272865406452, 26.588959939226058 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.7946649841697191, "Wingspan": 10.075789293403513, "Area": 34.203076039545614, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088403131943679, 26.588973933103677 ], [ -80.088403131943679, 26.589050076261294 ], [ -80.088471249200893, 26.589050076261294 ], [ -80.088471249200893, 26.588973933103677 ], [ -80.088403131943679, 26.588973933103677 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9961897261030348, "Wingspan": 10.082185006297852, "Area": 40.163016448882928, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088839411657631, 26.589013856813331 ], [ -80.088839411657631, 26.589092469478764 ], [ -80.08891493743829, 26.589092469478764 ], [ -80.08891493743829, 26.589013856813331 ], [ -80.088839411657631, 26.589013856813331 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7131392626216861, "Wingspan": 11.574369259603639, "Area": 44.58213380476807, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088942513608885, 26.589008094628433 ], [ -80.088942513608885, 26.589099054832939 ], [ -80.089015569881738, 26.589099054832939 ], [ -80.089015569881738, 26.589008094628433 ], [ -80.088942513608885, 26.589008094628433 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4543295862996422, "Wingspan": 10.540531671467717, "Area": 39.285469443954632, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088201043887494, 26.588459658101268 ], [ -80.088201043887494, 26.588536418635837 ], [ -80.088273894368029, 26.588536418635837 ], [ -80.088273894368029, 26.588459658101268 ], [ -80.088201043887494, 26.588459658101268 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.3692144298797073, "Wingspan": 7.7939959507684025, "Area": 24.76538055902218, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08810185199026, 26.588472623017299 ], [ -80.08810185199026, 26.588531891204848 ], [ -80.088162560724044, 26.588531891204848 ], [ -80.088162560724044, 26.588472623017299 ], [ -80.08810185199026, 26.588472623017299 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.058780285566236, "Wingspan": 10.876951968357133, "Area": 43.826030996408733, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088819861387407, 26.588493819626052 ], [ -80.088819861387407, 26.588579840814926 ], [ -80.088903413068465, 26.588579840814926 ], [ -80.088903413068465, 26.588493819626052 ], [ -80.088819861387407, 26.588493819626052 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.1453772200792036, "Wingspan": 9.8991188312072698, "Area": 35.267585364979162, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088437293468459, 26.588501845526441 ], [ -80.088437293468459, 26.588577165514788 ], [ -80.088506851271902, 26.588577165514788 ], [ -80.088506851271902, 26.588501845526441 ], [ -80.088437293468459, 26.588501845526441 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5308661875985541, "Wingspan": 11.225281472215356, "Area": 42.238701221951985, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088544717058397, 26.588498141264719 ], [ -80.088544717058397, 26.58858169294578 ], [ -80.088617155954296, 26.58858169294578 ], [ -80.088617155954296, 26.588498141264719 ], [ -80.088544717058397, 26.588498141264719 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.0995050662042498, "Wingspan": 10.487420628588334, "Area": 42.470850102714195, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08801274391665, 26.588567081691206 ], [ -80.08801274391665, 26.588651662333863 ], [ -80.088092797128311, 26.588651662333863 ], [ -80.088092797128311, 26.588567081691206 ], [ -80.08801274391665, 26.588567081691206 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2191721586233415, "Wingspan": 9.3769944799384621, "Area": 33.845113124786259, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088748657245418, 26.588606593816252 ], [ -80.088748657245418, 26.588678209542877 ], [ -80.088818626633497, 26.588678209542877 ], [ -80.088818626633497, 26.588606593816252 ], [ -80.088748657245418, 26.588606593816252 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.2440553310411353, "Wingspan": 11.852423284740187, "Area": 48.854747929521025, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088467750731482, 26.588597127369621 ], [ -80.088467750731482, 26.58868849915876 ], [ -80.088540601212017, 26.58868849915876 ], [ -80.088540601212017, 26.588597127369621 ], [ -80.088467750731482, 26.588597127369621 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3886772579288627, "Wingspan": 8.974596868415075, "Area": 33.137869092595011, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088367324080352, 26.588617089224453 ], [ -80.088367324080352, 26.588683354350813 ], [ -80.088437293468431, 26.588683354350813 ], [ -80.088437293468431, 26.588617089224453 ], [ -80.088367324080352, 26.588617089224453 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.4237129868409752, "Wingspan": 11.686914742865682, "Area": 43.374751889464846, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086246016867563, 26.58868520648171 ], [ -80.086246016867563, 26.588790572148465 ], [ -80.08632051368663, 26.588790572148465 ], [ -80.08632051368663, 26.58868520648171 ], [ -80.086246016867563, 26.58868520648171 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.0163280496838221, "Wingspan": 10.300193921070653, "Area": 36.134628770745337, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088619419669783, 26.588392569805645 ], [ -80.088619419669783, 26.588474475148164 ], [ -80.088688977473225, 26.588474475148164 ], [ -80.088688977473225, 26.588392569805645 ], [ -80.088619419669783, 26.588392569805645 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2305637527681599, "Wingspan": 11.29852254469761, "Area": 40.651161571674493, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088507880233479, 26.58839401035187 ], [ -80.088507880233479, 26.588480648917699 ], [ -80.088580730714014, 26.588480648917699 ], [ -80.088580730714014, 26.58839401035187 ], [ -80.088507880233479, 26.58839401035187 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5231590051869013, "Wingspan": 10.964165101052117, "Area": 41.240471825892918, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085414204318596, 26.589835791331012 ], [ -80.085414204318596, 26.589903702795915 ], [ -80.085524303208658, 26.589903702795915 ], [ -80.085524303208658, 26.589835791331012 ], [ -80.085414204318596, 26.589835791331012 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9155968825494014, "Wingspan": 11.927002450364357, "Area": 47.200866278502922, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085547557740611, 26.589839084008098 ], [ -80.085547557740611, 26.589910082357768 ], [ -80.085666711492664, 26.589910082357768 ], [ -80.085666711492664, 26.589839084008098 ], [ -80.085547557740611, 26.589839084008098 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.7710548204033927, "Wingspan": 9.0799631814687025, "Area": 30.740458939446274, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085703548317568, 26.589846904116175 ], [ -80.085703548317568, 26.589908024434589 ], [ -80.085794714314389, 26.589908024434589 ], [ -80.085794714314389, 26.589846904116175 ], [ -80.085703548317568, 26.589846904116175 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3180368577236301, "Wingspan": 10.94431070920057, "Area": 40.028246444854432, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085830316385383, 26.589853283678028 ], [ -80.085830316385383, 26.58991934301207 ], [ -80.085940209483141, 26.58991934301207 ], [ -80.085940209483141, 26.589853283678028 ], [ -80.085830316385383, 26.589853283678028 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.1792449877329645, "Wingspan": 10.982336029894125, "Area": 44.913062202699976, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085968403030691, 26.589852666301081 ], [ -80.085968403030691, 26.589925928366245 ], [ -80.086077884543798, 26.589925928366245 ], [ -80.086077884543798, 26.589852666301081 ], [ -80.085968403030691, 26.589852666301081 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.498351786591126, "Wingspan": 11.10641009702368, "Area": 41.637839014419619, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.090476622257668, 26.589911035865448 ], [ -80.090476622257668, 26.590010973557131 ], [ -80.090551726947183, 26.590010973557131 ], [ -80.090551726947183, 26.589911035865448 ], [ -80.090476622257668, 26.589911035865448 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5893344864768979, "Wingspan": 9.2147535851771867, "Area": 34.949236541886137, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081471840884944, 26.589638230705841 ], [ -80.081471840884944, 26.589714785448095 ], [ -80.081540987103764, 26.589714785448095 ], [ -80.081540987103764, 26.589638230705841 ], [ -80.081471840884944, 26.589638230705841 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.6089538105233636, "Wingspan": 11.043461709143218, "Area": 36.490790519943239, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.080860637700823, 26.589640288629028 ], [ -80.080860637700823, 26.589739892110885 ], [ -80.080926902827187, 26.589739892110885 ], [ -80.080926902827187, 26.589640288629028 ], [ -80.080860637700823, 26.589640288629028 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2551967043646641, "Wingspan": 11.281984165040106, "Area": 40.884658779188833, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08120348770241, 26.589651401414191 ], [ -80.08120348770241, 26.589753062819227 ], [ -80.081275926598309, 26.589753062819227 ], [ -80.081275926598309, 26.589651401414191 ], [ -80.08120348770241, 26.589651401414191 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.9549556050245629, "Wingspan": 11.119244522073149, "Area": 38.662015120805854, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081683395387742, 26.589673626984517 ], [ -80.081683395387742, 26.589762529265844 ], [ -80.081751306852638, 26.589762529265844 ], [ -80.081751306852638, 26.589673626984517 ], [ -80.081683395387742, 26.589673626984517 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.8803849631940004, "Wingspan": 10.661449868530404, "Area": 42.006697622999184, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081153685961482, 26.589714373863465 ], [ -80.081153685961482, 26.589781462159095 ], [ -80.081254935781885, 26.589781462159095 ], [ -80.081254935781885, 26.589714373863465 ], [ -80.081153685961482, 26.589714373863465 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.6029006498985101, "Wingspan": 10.187030284202642, "Area": 43.806102193483241, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081468959792502, 26.589717254955907 ], [ -80.081468959792502, 26.589795867621341 ], [ -80.081546749288663, 26.589795867621341 ], [ -80.081546749288663, 26.589717254955907 ], [ -80.081468959792502, 26.589717254955907 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.8184087767815367, "Wingspan": 9.6735231588350299, "Area": 42.572697073603329, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081976032063722, 26.590146126146383 ], [ -80.081976032063722, 26.590233382089167 ], [ -80.082064111175782, 26.590233382089167 ], [ -80.082064111175782, 26.590146126146383 ], [ -80.081976032063722, 26.590146126146383 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.313645887107759, "Wingspan": 11.099311654658282, "Area": 40.574849216925045, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084932032917791, 26.589771789920146 ], [ -80.084932032917791, 26.58983764346187 ], [ -80.085043366561777, 26.58983764346187 ], [ -80.085043366561777, 26.589771789920146 ], [ -80.084932032917791, 26.589771789920146 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.3411007418727179, "Wingspan": 11.282511709950931, "Area": 41.385318737612735, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085210058339243, 26.5897804331975 ], [ -80.085210058339243, 26.589846492531539 ], [ -80.085323244114079, 26.589846492531539 ], [ -80.085323244114079, 26.5897804331975 ], [ -80.085210058339243, 26.5897804331975 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.1464118180575813, "Wingspan": 9.3871970949280037, "Area": 33.540482724299025, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085774752459557, 26.589797513959883 ], [ -80.085774752459557, 26.589861926955383 ], [ -80.085868799548834, 26.589861926955383 ], [ -80.085868799548834, 26.589797513959883 ], [ -80.085774752459557, 26.589797513959883 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.332002684330873, "Wingspan": 10.181585056125494, "Area": 37.260480747282351, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086807829895463, 26.589795867621316 ], [ -80.086807829895463, 26.589875303456019 ], [ -80.08688232671453, 26.589875303456019 ], [ -80.08688232671453, 26.589795867621316 ], [ -80.086807829895463, 26.589795867621316 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2189910014067964, "Wingspan": 10.907242740887824, "Area": 39.361670407725697, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085910369597045, 26.589811919422139 ], [ -80.085910369597045, 26.589875303456047 ], [ -80.086017381602346, 26.589875303456047 ], [ -80.086017381602346, 26.589811919422139 ], [ -80.085910369597045, 26.589811919422139 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7145238195414239, "Wingspan": 11.23546642559395, "Area": 43.152910226783888, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086936244301825, 26.589799983467675 ], [ -80.086936244301825, 26.589888474164365 ], [ -80.087009506366996, 26.589888474164365 ], [ -80.087009506366996, 26.589799983467675 ], [ -80.086936244301825, 26.589799983467675 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.841709566936534, "Wingspan": 11.108881144460513, "Area": 43.515392810385435, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087236701085956, 26.589804510898674 ], [ -80.087236701085956, 26.589889708918278 ], [ -80.087319018013105, 26.589889708918278 ], [ -80.087319018013105, 26.589804510898674 ], [ -80.087236701085956, 26.589804510898674 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.7282824549782898, "Wingspan": 10.248307914877444, "Area": 39.600838881321152, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085142352666651, 26.589826736469025 ], [ -80.085142352666651, 26.589896500064786 ], [ -80.085245248825601, 26.589896500064786 ], [ -80.085245248825601, 26.589826736469025 ], [ -80.085142352666651, 26.589826736469025 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9565413623305776, "Wingspan": 11.23094474535208, "Area": 44.678350169154982, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085276940842562, 26.589827971222931 ], [ -80.085276940842562, 26.589899792741875 ], [ -80.085389715032761, 26.589899792741875 ], [ -80.085389715032761, 26.589827971222931 ], [ -80.085276940842562, 26.589827971222931 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 1.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9008638263871243, "Wingspan": 10.871696086753303, "Area": 42.889418901055315, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087685328338893, 26.589822003245686 ], [ -80.087685328338893, 26.58991131711165 ], [ -80.087764764173599, 26.58991131711165 ], [ -80.087764764173599, 26.589822003245686 ], [ -80.087685328338893, 26.589822003245686 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.2154038852672979, "Wingspan": 11.330480619866067, "Area": 46.540580939920709, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088767384346369, 26.589655723052797 ], [ -80.088767384346369, 26.589740921072401 ], [ -80.088847231765712, 26.589740921072401 ], [ -80.088847231765712, 26.589655723052797 ], [ -80.088767384346369, 26.589655723052797 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.56553088583555, "Wingspan": 13.448728137351456, "Area": 71.045713651788674, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082302830264538, 26.589640700213657 ], [ -80.082302830264538, 26.589761706096571 ], [ -80.08240860751593, 26.589761706096571 ], [ -80.08240860751593, 26.589640700213657 ], [ -80.082302830264538, 26.589640700213657 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.0874044189679388, "Wingspan": 11.769054933719794, "Area": 53.419686210795945, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.0845325900288, 26.589678566000146 ], [ -80.0845325900288, 26.589784754836174 ], [ -80.084623550233317, 26.589784754836174 ], [ -80.084623550233317, 26.589678566000146 ], [ -80.0845325900288, 26.589678566000146 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 12.626898010436994, "Wingspan": 14.324805874370933, "Area": 90.428010773427459, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083590061212846, 26.589688444031403 ], [ -80.083590061212846, 26.589817681607034 ], [ -80.083716829280661, 26.589817681607034 ], [ -80.083716829280661, 26.589688444031403 ], [ -80.083590061212846, 26.589688444031403 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.533605186335404, "Wingspan": 11.990981759480951, "Area": 63.148230695700235, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084154755333145, 26.589713550694185 ], [ -80.084154755333145, 26.589821797453393 ], [ -80.084260532584537, 26.589821797453393 ], [ -80.084260532584537, 26.589713550694185 ], [ -80.084154755333145, 26.589713550694185 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.0746003056306108, "Wingspan": 11.914558538121241, "Area": 54.057883225461048, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083170656469022, 26.589558383286498 ], [ -80.083170656469022, 26.589665806876432 ], [ -80.083261616673525, 26.589665806876432 ], [ -80.083261616673525, 26.589558383286498 ], [ -80.083170656469022, 26.589558383286498 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.381056177700259, "Wingspan": 11.285054351243158, "Area": 58.574908639236767, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088494503732832, 26.589638848082735 ], [ -80.088494503732832, 26.589734129925915 ], [ -80.088591843499202, 26.589734129925915 ], [ -80.088591843499202, 26.589638848082735 ], [ -80.088494503732832, 26.589638848082735 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.628601667867105, "Wingspan": 14.881072260439037, "Area": 78.958035239043937, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083248445965182, 26.589621767320406 ], [ -80.083248445965182, 26.58975182806531 ], [ -80.083350107370208, 26.58975182806531 ], [ -80.083350107370208, 26.589621767320406 ], [ -80.083248445965182, 26.589621767320406 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.7290377084240838, "Wingspan": 11.628112277170855, "Area": 50.746246115615591, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088457049530987, 26.589550768970678 ], [ -80.088457049530987, 26.589622590489622 ], [ -80.088543482304502, 26.589622590489622 ], [ -80.088543482304502, 26.589550768970678 ], [ -80.088457049530987, 26.589550768970678 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9885621040113, "Wingspan": 11.698669901706733, "Area": 46.727528047939799, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086124599399994, 26.589552826893918 ], [ -80.086124599399994, 26.589624854205177 ], [ -80.086241901021197, 26.589624854205177 ], [ -80.086241901021197, 26.589552826893918 ], [ -80.086124599399994, 26.589552826893918 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.269268117980511, "Wingspan": 13.300263653346716, "Area": 68.219119985298136, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085086377156287, 26.589447049642523 ], [ -80.085086377156287, 26.589539656185572 ], [ -80.085219936370592, 26.589539656185572 ], [ -80.085219936370592, 26.589447049642523 ], [ -80.085086377156287, 26.589447049642523 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.1236262239070474, "Wingspan": 13.65328830993076, "Area": 55.453006434596624, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082994086660293, 26.589017972659732 ], [ -80.082994086660293, 26.589141036465829 ], [ -80.083075580418168, 26.589141036465829 ], [ -80.083075580418168, 26.589017972659732 ], [ -80.082994086660293, 26.589017972659732 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.730116105068454, "Wingspan": 14.688997231837904, "Area": 78.716477980930705, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082376503914332, 26.589045960414971 ], [ -80.082376503914332, 26.589176021159872 ], [ -80.082481046411829, 26.589176021159872 ], [ -80.082481046411829, 26.589045960414971 ], [ -80.082376503914332, 26.589045960414971 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.08221972094562, "Wingspan": 11.262918892195732, "Area": 56.703963394021194, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086395422090348, 26.58892886458608 ], [ -80.086395422090348, 26.589014062605685 ], [ -80.086481854863862, 26.589014062605685 ], [ -80.086481854863862, 26.58892886458608 ], [ -80.086395422090348, 26.58892886458608 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.166047322993581, "Wingspan": 12.697451310143169, "Area": 64.506203170282504, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082371153314071, 26.588934420978678 ], [ -80.082371153314071, 26.589036493968347 ], [ -80.082460467180042, 26.589036493968347 ], [ -80.082460467180042, 26.588934420978678 ], [ -80.082371153314071, 26.588934420978678 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 12.649640952109253, "Wingspan": 14.576206391983959, "Area": 92.191832538137604, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082236565138189, 26.58865989402662 ], [ -80.082236565138189, 26.588773902970729 ], [ -80.082382677683896, 26.588773902970729 ], [ -80.082382677683896, 26.58865989402662 ], [ -80.082236565138189, 26.58865989402662 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.967213157010667, "Wingspan": 11.882550761507554, "Area": 65.145435702421736, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088775410246782, 26.58870208145175 ], [ -80.088775410246782, 26.588793659033207 ], [ -80.08886822258215, 26.588793659033207 ], [ -80.08886822258215, 26.58870208145175 ], [ -80.088775410246782, 26.58870208145175 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.495400647841095, "Wingspan": 14.133624033814472, "Area": 74.165272894920889, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081913882783738, 26.58961888622796 ], [ -80.081913882783738, 26.589746065880412 ], [ -80.082018836865856, 26.589746065880412 ], [ -80.082018836865856, 26.58961888622796 ], [ -80.081913882783738, 26.58961888622796 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "1040010044D30600", "engines": 2.0, "loc_id": 108.0, "location": "Palm Beach County Park Airport, Lantana Airport, Palm Springs, Hypoluxo Village, Palm Beach County, Florida, 33462, USA", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 10.046720254335131, "Wingspan": 13.195291321276974, "Area": 66.242373422276387, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088681363157491, 26.589758619211736 ], [ -80.088681363157491, 26.589854518431871 ], [ -80.088775616039072, 26.589854518431871 ], [ -80.088775616039072, 26.589758619211736 ], [ -80.088681363157491, 26.589758619211736 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/eval_vector/gt/10_104005000FDC8300.geojson b/docker/solaris/solaris/data/eval_vector/gt/10_104005000FDC8300.geojson new file mode 100755 index 00000000..4a6dcaee --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/gt/10_104005000FDC8300.geojson @@ -0,0 +1,43 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 18.226419030477409, "Wingspan": 21.681116902165893, "Area": 197.21182432696207, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949714931613321, 43.578035656065914 ], [ 3.949714931613321, 43.578171536836344 ], [ 3.949506424913869, 43.578171536836344 ], [ 3.949506424913869, 43.578035656065914 ], [ 3.949714931613321, 43.578035656065914 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 13.636261797994667, "Wingspan": 16.815355759741706, "Area": 114.50396756215193, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963006727814833, 43.577991256513563 ], [ 3.963006727814833, 43.578120226939618 ], [ 3.962851226329701, 43.578120226939618 ], [ 3.962851226329701, 43.577991256513563 ], [ 3.963006727814833, 43.577991256513563 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 20.54486018644614, "Wingspan": 21.541649097583811, "Area": 221.27719740457212, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954143511649242, 43.577278971274858 ], [ 3.954143511649242, 43.577459529871334 ], [ 3.953882622901674, 43.577459529871334 ], [ 3.953882622901674, 43.577278971274858 ], [ 3.954143511649242, 43.577278971274858 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 13.845584472303255, "Wingspan": 17.268384151245943, "Area": 119.04680144632198, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.962621290484388, 43.577641930902416 ], [ 3.962621290484388, 43.577771638302337 ], [ 3.962462841103803, 43.577771638302337 ], [ 3.962462841103803, 43.577641930902416 ], [ 3.962621290484388, 43.577641930902416 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 35.886473744766278, "Wingspan": 30.024253218393003, "Area": 538.35398274617683, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953715698321675, 43.582576339403339 ], [ 3.953715698321675, 43.582799642483884 ], [ 3.953367109684389, 43.582799642483884 ], [ 3.953367109684389, 43.582576339403339 ], [ 3.953715698321675, 43.582576339403339 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 35.764427531440482, "Wingspan": 30.826408210332598, "Area": 551.24240700574342, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949860588043344, 43.578178447874883 ], [ 3.949860588043344, 43.578453339125851 ], [ 3.949535214082748, 43.578453339125851 ], [ 3.949535214082748, 43.578178447874883 ], [ 3.949860588043344, 43.578178447874883 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 36.369364346700927, "Wingspan": 29.446016451213033, "Area": 535.38984294101806, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954528580492767, 43.581782618552701 ], [ 3.954528580492767, 43.58200444768552 ], [ 3.9541475650055, 43.58200444768552 ], [ 3.9541475650055, 43.581782618552701 ], [ 3.954528580492767, 43.581782618552701 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 39.563126752864868, "Wingspan": 33.689257905575161, "Area": 666.12623856861455, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955229442636655, 43.581834575210031 ], [ 3.955229442636655, 43.582188322664365 ], [ 3.954813789377818, 43.582188322664365 ], [ 3.954813789377818, 43.581834575210031 ], [ 3.955229442636655, 43.581834575210031 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 46.281884875188126, "Wingspan": 34.224542160254046, "Area": 791.90757051862113, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954930968222066, 43.582041296378662 ], [ 3.954930968222066, 43.58237809343413 ], [ 3.954572798924558, 43.58237809343413 ], [ 3.954572798924558, 43.582041296378662 ], [ 3.954930968222066, 43.582041296378662 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 36.64505808945362, "Wingspan": 30.045611930362142, "Area": 549.08547822679827, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953997222337411, 43.58209583244453 ], [ 3.953997222337411, 43.582356721192099 ], [ 3.953688430288737, 43.582356721192099 ], [ 3.953688430288737, 43.58209583244453 ], [ 3.953997222337411, 43.58209583244453 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 48.592491293981517, "Wingspan": 33.644954889384614, "Area": 817.41471898063435, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954575009846147, 43.582227750766059 ], [ 3.954575009846147, 43.582586657037432 ], [ 3.954226421208861, 43.582586657037432 ], [ 3.954226421208861, 43.582227750766059 ], [ 3.954575009846147, 43.582227750766059 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 44.76328517201344, "Wingspan": 34.059356340768062, "Area": 762.06477159879159, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953589675791071, 43.582313976708058 ], [ 3.953589675791071, 43.582574865455626 ], [ 3.953137910812938, 43.582574865455626 ], [ 3.953137910812938, 43.582313976708058 ], [ 3.953589675791071, 43.582313976708058 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 48.235283942181539, "Wingspan": 34.203644143286411, "Area": 824.58646707211346, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954200627123651, 43.582406098440941 ], [ 3.954200627123651, 43.58277458537254 ], [ 3.953847616643186, 43.58277458537254 ], [ 3.953847616643186, 43.582406098440941 ], [ 3.954200627123651, 43.582406098440941 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.3444888677459996, "Wingspan": 11.980061828508633, "Area": 49.975934234810076, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949534845595817, 43.577817699168854 ], [ 3.949534845595817, 43.57792382340515 ], [ 3.949433511689629, 43.57792382340515 ], [ 3.949433511689629, 43.577817699168854 ], [ 3.949534845595817, 43.577817699168854 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9480613310678496, "Wingspan": 10.233304347414771, "Area": 40.663784555045787, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954313015637774, 43.578087431602739 ], [ 3.954313015637774, 43.578179553335637 ], [ 3.954214629627039, 43.578179553335637 ], [ 3.954214629627039, 43.578087431602739 ], [ 3.954313015637774, 43.578087431602739 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.2500127876809568, "Wingspan": 10.033467774858575, "Area": 41.386416627819735, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954721667644912, 43.578231878479926 ], [ 3.954721667644912, 43.578322157778167 ], [ 3.95461959676486, 43.578322157778167 ], [ 3.95461959676486, 43.578231878479926 ], [ 3.954721667644912, 43.578231878479926 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.2023130930421608, "Wingspan": 10.152684070741531, "Area": 41.592806644830972, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954338809722986, 43.578247354931051 ], [ 3.954338809722986, 43.578338739690089 ], [ 3.954237475816798, 43.578338739690089 ], [ 3.954237475816798, 43.578247354931051 ], [ 3.954338809722986, 43.578247354931051 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9382444434136845, "Wingspan": 9.8279289967367927, "Area": 38.970318994547121, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.95435576012184, 43.578413542537199 ], [ 3.95435576012184, 43.578501979400784 ], [ 3.954257742598036, 43.578501979400784 ], [ 3.954257742598036, 43.578413542537199 ], [ 3.95435576012184, 43.578413542537199 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.5406834111778185, "Wingspan": 10.425785182811948, "Area": 39.279649139528921, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956971464605425, 43.575593327806459 ], [ 3.956971464605425, 43.575668867627428 ], [ 3.956881922281053, 43.575668867627428 ], [ 3.956881922281053, 43.575593327806459 ], [ 3.956971464605425, 43.575593327806459 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.8331401851299205, "Wingspan": 9.7641019384928729, "Area": 43.055327768421044, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955867477758424, 43.575662234862634 ], [ 3.955867477758424, 43.575735563762017 ], [ 3.955777198460188, 43.575735563762017 ], [ 3.955777198460188, 43.575662234862634 ], [ 3.955867477758424, 43.575662234862634 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.09459835562091, "Wingspan": 11.809065185160016, "Area": 47.790001693561706, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.957153865636556, 43.57576541120352 ], [ 3.957153865636556, 43.575854953527895 ], [ 3.957050320808783, 43.575854953527895 ], [ 3.957050320808783, 43.57576541120352 ], [ 3.957153865636556, 43.57576541120352 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.4247457043905669, "Wingspan": 11.527610096965196, "Area": 48.490763721506752, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.957241197039339, 43.575857164449481 ], [ 3.957241197039339, 43.575945232826129 ], [ 3.957141337080882, 43.575945232826129 ], [ 3.957141337080882, 43.575857164449481 ], [ 3.957241197039339, 43.575857164449481 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.0084472559136879, "Wingspan": 9.3104204291275803, "Area": 31.930138960114959, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955484251349586, 43.575913174463039 ], [ 3.955484251349586, 43.575980976058446 ], [ 3.955401710276913, 43.575980976058446 ], [ 3.955401710276913, 43.575913174463039 ], [ 3.955484251349586, 43.575913174463039 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6905553055333709, "Wingspan": 10.949900231234274, "Area": 42.104609177917339, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.95740517372389, 43.576031090281177 ], [ 3.95740517372389, 43.576115842275442 ], [ 3.957312315017133, 43.576115842275442 ], [ 3.957312315017133, 43.576031090281177 ], [ 3.95740517372389, 43.576031090281177 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.0628825947815415, "Wingspan": 11.5285382779802, "Area": 46.471723507017458, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.951151766251642, 43.577291868317459 ], [ 3.951151766251642, 43.577363723269123 ], [ 3.951035324381259, 43.577363723269123 ], [ 3.951035324381259, 43.577291868317459 ], [ 3.951151766251642, 43.577291868317459 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.2191791713638036, "Wingspan": 13.33929900546994, "Area": 61.360063242375752, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963040260125609, 43.577363354782136 ], [ 3.963040260125609, 43.577463583227527 ], [ 3.962938557732489, 43.577463583227527 ], [ 3.962938557732489, 43.577363354782136 ], [ 3.963040260125609, 43.577363354782136 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.9416789998615096, "Wingspan": 11.492953180184687, "Area": 45.616155269544691, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.950887008391249, 43.577420654500116 ], [ 3.950887008391249, 43.577489930043257 ], [ 3.95076798711235, 43.577489930043257 ], [ 3.95076798711235, 43.577420654500116 ], [ 3.950887008391249, 43.577420654500116 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.7660701891983965, "Wingspan": 11.945783918339236, "Area": 52.345946405372572, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.964062258629735, 43.574941105937576 ], [ 3.964062258629735, 43.575019225167075 ], [ 3.963916337804831, 43.575019225167075 ], [ 3.963916337804831, 43.574941105937576 ], [ 3.964062258629735, 43.574941105937576 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 1.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.2123752682937834, "Wingspan": 9.326169404131285, "Area": 33.543280666487448, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956283131017241, 43.575570850103617 ], [ 3.956283131017241, 43.575639020185953 ], [ 3.956204274813884, 43.575639020185953 ], [ 3.956204274813884, 43.575570850103617 ], [ 3.956283131017241, 43.575570850103617 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 0.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "unpowered", "tail_fins": 1.0, "type_id": 134.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.835037976410903, "Wingspan": 13.252941204445785, "Area": 45.258465490866655, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955023458441884, 43.576228415032872 ], [ 3.955023458441884, 43.576327169530536 ], [ 3.954928388813533, 43.576327169530536 ], [ 3.954928388813533, 43.576228415032872 ], [ 3.955023458441884, 43.576228415032872 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.4326880468305809, "Wingspan": 13.668415591472886, "Area": 64.421334797130982, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954310804716185, 43.577909452414779 ], [ 3.954310804716185, 43.578032158562998 ], [ 3.954194731332734, 43.578032158562998 ], [ 3.954194731332734, 43.577909452414779 ], [ 3.954310804716185, 43.577909452414779 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.6212314027655008, "Wingspan": 11.938838770759922, "Area": 57.415484466829554, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963865302365444, 43.576400129942932 ], [ 3.963865302365444, 43.576485250424128 ], [ 3.963719381540534, 43.576485250424128 ], [ 3.963719381540534, 43.576400129942932 ], [ 3.963865302365444, 43.576400129942932 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.2478309767034474, "Wingspan": 9.7575988085091101, "Area": 40.239286159975208, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963887043094407, 43.576624906971198 ], [ 3.963887043094407, 43.576698972844447 ], [ 3.963766547867777, 43.576698972844447 ], [ 3.963766547867777, 43.576624906971198 ], [ 3.963887043094407, 43.576624906971198 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.633680190340419, "Wingspan": 13.407679449210098, "Area": 57.850028292147925, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.950467301776185, 43.577635482381226 ], [ 3.950467301776185, 43.577714338584578 ], [ 3.95033501496775, 43.577714338584578 ], [ 3.95033501496775, 43.577635482381226 ], [ 3.950467301776185, 43.577635482381226 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.3645746284947116, "Wingspan": 13.771730985528114, "Area": 57.597094856110203, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.981473254696825, 43.572068908873092 ], [ 3.981473254696825, 43.572146220345921 ], [ 3.981335031154491, 43.572146220345921 ], [ 3.981335031154491, 43.572068908873092 ], [ 3.981473254696825, 43.572068908873092 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.92538504514145, "Wingspan": 11.131916636106856, "Area": 44.110694880950341, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.9562779722002, 43.575446670007672 ], [ 3.9562779722002, 43.57553068502807 ], [ 3.956183271058785, 43.57553068502807 ], [ 3.956183271058785, 43.575446670007672 ], [ 3.9562779722002, 43.575446670007672 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "104005000FDC8300", "engines": 2.0, "loc_id": 10.0, "location": "Aéroport de Montpellier - Méditerranée, D 66, Le Boulidou, Pérols, Montpellier, Hérault, Occitania, Metropolitan France, 34470, France", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.5316237252159777, "Wingspan": 11.568461977514602, "Area": 49.267958226914153, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956132419862227, 43.575528474106484 ], [ 3.956132419862227, 43.575607330309843 ], [ 3.95603255990377, 43.575607330309843 ], [ 3.95603255990377, 43.575528474106484 ], [ 3.956132419862227, 43.575528474106484 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/eval_vector/gt/11_10400100213E1200.geojson b/docker/solaris/solaris/data/eval_vector/gt/11_10400100213E1200.geojson new file mode 100755 index 00000000..df526b89 --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/gt/11_10400100213E1200.geojson @@ -0,0 +1,24 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 16.732728784724575, "Wingspan": 17.067661563203607, "Area": 142.46347966169071, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.52504320418609, 47.674204764505923 ], [ 9.52504320418609, 47.67433693780076 ], [ 9.524851459828795, 47.67433693780076 ], [ 9.524851459828795, 47.674204764505923 ], [ 9.52504320418609, 47.674204764505923 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "swept", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 19.025753106346233, "Wingspan": 17.273577193781605, "Area": 164.23223510705151, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513384650837049, 47.668690407771038 ], [ 9.513384650837049, 47.668812652555481 ], [ 9.513200042596612, 47.668812652555481 ], [ 9.513200042596612, 47.668690407771038 ], [ 9.513384650837049, 47.668690407771038 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "jet", "tail_fins": 1.0, "type_id": 131.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 19.838556779415615, "Wingspan": 20.226423612865993, "Area": 198.8891210745584, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518115586047465, 47.670820073253942 ], [ 9.518115586047465, 47.670998786441324 ], [ 9.51786489115961, 47.670998786441324 ], [ 9.51786489115961, 47.670820073253942 ], [ 9.518115586047465, 47.670820073253942 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 9.9881405704745969, "Wingspan": 13.034734749378577, "Area": 64.970561884572589, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.517644602334881, 47.670879023784529 ], [ 9.517644602334881, 47.670996304313746 ], [ 9.517511808508148, 47.670996304313746 ], [ 9.517511808508148, 47.670879023784529 ], [ 9.517644602334881, 47.670879023784529 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 8.3433736373900125, "Wingspan": 11.780685737347524, "Area": 49.064118427246967, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513143263927702, 47.668642006282816 ], [ 9.513143263927702, 47.668719883036346 ], [ 9.513024432068732, 47.668719883036346 ], [ 9.513024432068732, 47.668642006282816 ], [ 9.513143263927702, 47.668642006282816 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 11.32900818435391, "Wingspan": 12.799280618005577, "Area": 72.467151916328987, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.512779321967983, 47.669011843295593 ], [ 9.512779321967983, 47.669099028027631 ], [ 9.512636289364888, 47.669099028027631 ], [ 9.512636289364888, 47.669011843295593 ], [ 9.512779321967983, 47.669011843295593 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 7.6225491099034857, "Wingspan": 10.153927053079649, "Area": 38.698200678713171, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513286606796749, 47.669306285682431 ], [ 9.513286606796749, 47.669373303127699 ], [ 9.513179565043892, 47.669373303127699 ], [ 9.513179565043892, 47.669306285682431 ], [ 9.513286606796749, 47.669306285682431 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Small Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 133.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Small Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 6.5277986264988233, "Wingspan": 8.6086496924725715, "Area": 28.003359970934987, "FAA_WingspanClass": 1, "subclass_id": 9, "make_subclass_id": 3.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513564915354189, 47.669805503596464 ], [ 9.513564915354189, 47.669865348531125 ], [ 9.513482905152385, 47.669865348531125 ], [ 9.513482905152385, 47.669805503596464 ], [ 9.513564915354189, 47.669805503596464 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 12.843307119499556, "Wingspan": 15.633820165024691, "Area": 100.39488451433645, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513008608505269, 47.668635800963784 ], [ 9.513008608505269, 47.668741911918787 ], [ 9.512851613934409, 47.668741911918787 ], [ 9.512851613934409, 47.668635800963784 ], [ 9.513008608505269, 47.668635800963784 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 12.932112875951084, "Wingspan": 16.311632938390964, "Area": 104.3288235076464, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513493864451558, 47.668810251302894 ], [ 9.513493864451558, 47.668956926222371 ], [ 9.513324459242687, 47.668956926222371 ], [ 9.513324459242687, 47.668810251302894 ], [ 9.513493864451558, 47.668810251302894 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 12.854174408082651, "Wingspan": 15.72691575688677, "Area": 100.56782856891012, "FAA_WingspanClass": 2, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513389304826303, 47.668899216755619 ], [ 9.513389304826303, 47.669040698028965 ], [ 9.513219279085531, 47.669040698028965 ], [ 9.513219279085531, 47.668899216755619 ], [ 9.513389304826303, 47.668899216755619 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Medium Civil Transport\/Utility", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 132.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Medium Civil Transport\/Utility", "Length_Truth": null, "Wingspan_Truth": null, "Length": 26.656200733725967, "Wingspan": 26.975849148136078, "Area": 359.09939347657297, "FAA_WingspanClass": 3, "subclass_id": 8, "make_subclass_id": 2.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.509195439975905, 47.667584309658167 ], [ 9.509195439975905, 47.667796531568186 ], [ 9.508893861472199, 47.667796531568186 ], [ 9.508893861472199, 47.667584309658167 ], [ 9.509195439975905, 47.667584309658167 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 1.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Fiat G.91", "propulsion": "jet", "tail_fins": 1.0, "type_id": 110.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Military Fighter\/Interceptor\/Attack", "Length_Truth": 10.3, "Wingspan_Truth": 8.56, "Length": 10.364532819414592, "Wingspan": 7.8368533802076756, "Area": 39.660926616388366, "FAA_WingspanClass": 1, "subclass_id": 5, "make_subclass_id": 60.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.517973173976269, 47.670827829902699 ], [ 9.517973173976269, 47.670896708943665 ], [ 9.517847826532343, 47.670896708943665 ], [ 9.517847826532343, 47.670827829902699 ], [ 9.517973173976269, 47.670827829902699 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Dornier Do 31", "propulsion": "jet", "tail_fins": 1.0, "type_id": 129.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Military Transport\/Utility\/AWAC", "Length_Truth": 20.88, "Wingspan_Truth": 18.06, "Length": 19.025894266935374, "Wingspan": 15.659909129582042, "Area": 147.66093065521918, "FAA_WingspanClass": 2, "subclass_id": 6, "make_subclass_id": 118.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518311053596159, 47.67024856337347 ], [ 9.518311053596159, 47.670389424114916 ], [ 9.518059117644505, 47.670389424114916 ], [ 9.518059117644505, 47.67024856337347 ], [ 9.518311053596159, 47.67024856337347 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Dornier Do 28", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 129.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Military Transport\/Utility\/AWAC", "Length_Truth": 9.0, "Wingspan_Truth": 13.8, "Length": 10.492160984236834, "Wingspan": 14.133923421580803, "Area": 73.779767919083753, "FAA_WingspanClass": 1, "subclass_id": 6, "make_subclass_id": 162.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518227902321476, 47.670901362932923 ], [ 9.518227902321476, 47.671023917983298 ], [ 9.518095108494743, 47.671023917983298 ], [ 9.518095108494743, 47.670901362932923 ], [ 9.518227902321476, 47.670901362932923 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Dornier Do 228", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 129.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Military Transport\/Utility\/AWAC", "Length_Truth": 16.56, "Wingspan_Truth": 16.97, "Length": 16.341176943638274, "Wingspan": 14.627937885214893, "Area": 117.95380540322427, "FAA_WingspanClass": 2, "subclass_id": 6, "make_subclass_id": 173.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518254274927255, 47.671014299738836 ], [ 9.518254274927255, 47.671143990906067 ], [ 9.518048568602197, 47.671143990906067 ], [ 9.518048568602197, 47.671014299738836 ], [ 9.518254274927255, 47.671014299738836 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Dornier Do 228", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 129.0, "wing_position": "high mounted", "wing_type": "straight", "subclass": "Military Transport\/Utility\/AWAC", "Length_Truth": 16.56, "Wingspan_Truth": 16.97, "Length": 12.359074775663972, "Wingspan": 15.257121800305864, "Area": 94.260160943086092, "FAA_WingspanClass": 2, "subclass_id": 6, "make_subclass_id": 173.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518622560610275, 47.671237380957109 ], [ 9.518622560610275, 47.671367692656247 ], [ 9.518467427635118, 47.671367692656247 ], [ 9.518467427635118, 47.671237380957109 ], [ 9.518622560610275, 47.671237380957109 ] ] ] } }, +{ "type": "Feature", "properties": { "canards": "no", "cat_id": "10400100213E1200", "engines": 2.0, "loc_id": 11.0, "location": "Flughafen Friedrichshafen, Flughafen, St. Georgen, Friedrichshafen, Verwaltungsgemeinschaft Friedrichshafen, Bodenseekreis, Regierungsbezirk Tübingen, Baden-Württemberg, 88046, Germany", "make": "Breguet Br. 1150 Atlantic", "propulsion": "propeller", "tail_fins": 1.0, "type_id": 129.0, "wing_position": "mid\/low mounted", "wing_type": "straight", "subclass": "Military Transport\/Utility\/AWAC", "Length_Truth": 31.62, "Wingspan_Truth": 37.42, "Length": 30.985218323316648, "Wingspan": 35.93718187171163, "Area": 555.69631037169427, "FAA_WingspanClass": 4, "subclass_id": 6, "make_subclass_id": 197.0, "pnp": "plane", "pnp_id": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518539409335594, 47.670994752983965 ], [ 9.518539409335594, 47.671311844785187 ], [ 9.518140407323486, 47.671311844785187 ], [ 9.518140407323486, 47.670994752983965 ], [ 9.518539409335594, 47.670994752983965 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/eval_vector/preds/108_1040010044D30600.geojson b/docker/solaris/solaris/data/eval_vector/preds/108_1040010044D30600.geojson new file mode 100755 index 00000000..c6098354 --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/preds/108_1040010044D30600.geojson @@ -0,0 +1,141 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.69326114654541016, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082235084585903, 26.588769862691187 ], [ -80.082235084585903, 26.588657756793651 ], [ -80.082395668709395, 26.588657756793651 ], [ -80.082395668709395, 26.588769862691187 ], [ -80.082235084585903, 26.588769862691187 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.73871439695358276, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083589445023691, 26.589818204327603 ], [ -80.083589445023691, 26.589690948984455 ], [ -80.08371973025595, 26.589690948984455 ], [ -80.08371973025595, 26.589818204327603 ], [ -80.083589445023691, 26.589818204327603 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98723280429840088, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082280532922738, 26.589757606545152 ], [ -80.082280532922738, 26.589642470758491 ], [ -80.082410818155012, 26.589642470758491 ], [ -80.082410818155012, 26.589757606545152 ], [ -80.082280532922738, 26.589757606545152 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.88810712099075317, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086455720133657, 26.589154658609758 ], [ -80.086455720133657, 26.589036492933975 ], [ -80.086564796142071, 26.589036492933975 ], [ -80.086564796142071, 26.589154658609758 ], [ -80.086455720133657, 26.589154658609758 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.87979769706726074, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08191694622802, 26.589742457099536 ], [ -80.08191694622802, 26.589621261534635 ], [ -80.082022992347319, 26.589621261534635 ], [ -80.082022992347319, 26.589742457099536 ], [ -80.08191694622802, 26.589742457099536 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.94132328033447266, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082383549152894, 26.589175867833614 ], [ -80.082383549152894, 26.589048612490465 ], [ -80.082477475715692, 26.589048612490465 ], [ -80.082477475715692, 26.589175867833614 ], [ -80.082383549152894, 26.589175867833614 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.65540307760238647, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08901900633137, 26.588794101804169 ], [ -80.08901900633137, 26.588697145352246 ], [ -80.089140201896271, 26.588697145352246 ], [ -80.089140201896271, 26.588794101804169 ], [ -80.08901900633137, 26.588794101804169 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.92210602760314941, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083256157220205, 26.589745486988662 ], [ -80.083256157220205, 26.589627321312879 ], [ -80.083353113672118, 26.589627321312879 ], [ -80.083353113672118, 26.589745486988662 ], [ -80.083256157220205, 26.589745486988662 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9961363673210144, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08415603428962, 26.589818204327603 ], [ -80.08415603428962, 26.589712158208311 ], [ -80.084262080408905, 26.589712158208311 ], [ -80.084262080408905, 26.589818204327603 ], [ -80.08415603428962, 26.589818204327603 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.8290095329284668, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088882661320852, 26.589533394750081 ], [ -80.088882661320852, 26.589433408409036 ], [ -80.088994767218381, 26.589433408409036 ], [ -80.088994767218381, 26.589533394750081 ], [ -80.088882661320852, 26.589533394750081 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9656640887260437, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085092270028483, 26.589536424639203 ], [ -80.085092270028483, 26.589448557854649 ], [ -80.085216495482513, 26.589448557854649 ], [ -80.085216495482513, 26.589536424639203 ], [ -80.085092270028483, 26.589536424639203 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.63846606016159058, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082992556866543, 26.589133449385898 ], [ -80.082992556866543, 26.589021343488362 ], [ -80.083074363872839, 26.589021343488362 ], [ -80.083074363872839, 26.589133449385898 ], [ -80.082992556866543, 26.589133449385898 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99601078033447266, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088497865402275, 26.589730337543049 ], [ -80.088497865402275, 26.589639440869369 ], [ -80.088594821854201, 26.589639440869369 ], [ -80.088594821854201, 26.589730337543049 ], [ -80.088497865402275, 26.589730337543049 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99906307458877563, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08453477042994, 26.589781845658131 ], [ -80.08453477042994, 26.589681859317086 ], [ -80.084622637214494, 26.589681859317086 ], [ -80.084622637214494, 26.589781845658131 ], [ -80.08453477042994, 26.589781845658131 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.55838525295257568, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087419224874637, 26.588915297369073 ], [ -80.087419224874637, 26.588830460473641 ], [ -80.087522241104807, 26.588830460473641 ], [ -80.087522241104807, 26.588915297369073 ], [ -80.087419224874637, 26.588915297369073 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.93419104814529419, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088682688638755, 26.58985153310795 ], [ -80.088682688638755, 26.589757606545152 ], [ -80.088773585312438, 26.589757606545152 ], [ -80.088773585312438, 26.58985153310795 ], [ -80.088682688638755, 26.58985153310795 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.95208966732025146, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082368399707278, 26.5890395228231 ], [ -80.082368399707278, 26.588942566371177 ], [ -80.082456266491846, 26.588942566371177 ], [ -80.082456266491846, 26.5890395228231 ], [ -80.082368399707278, 26.5890395228231 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99940609931945801, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085819443417904, 26.589221316170455 ], [ -80.085819443417904, 26.589145568942389 ], [ -80.085925489537203, 26.589145568942389 ], [ -80.085925489537203, 26.589221316170455 ], [ -80.085819443417904, 26.589221316170455 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99853777885437012, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088779645090682, 26.588794101804169 ], [ -80.088779645090682, 26.588703205130493 ], [ -80.088867511875236, 26.588703205130493 ], [ -80.088867511875236, 26.588794101804169 ], [ -80.088779645090682, 26.588794101804169 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99853813648223877, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084228751628558, 26.589645500647617 ], [ -80.084228751628558, 26.589548544195694 ], [ -80.084310558634868, 26.589548544195694 ], [ -80.084310558634868, 26.589645500647617 ], [ -80.084228751628558, 26.589645500647617 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99904602766036987, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08317738010301, 26.589660650093229 ], [ -80.08317738010301, 26.589560663752184 ], [ -80.083256157220205, 26.589560663752184 ], [ -80.083256157220205, 26.589660650093229 ], [ -80.08317738010301, 26.589660650093229 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99837565422058105, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085552813175113, 26.589909101001279 ], [ -80.085552813175113, 26.589839413551459 ], [ -80.085664919072656, 26.589839413551459 ], [ -80.085664919072656, 26.589909101001279 ], [ -80.085552813175113, 26.589909101001279 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9995417594909668, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086128492108415, 26.589621261534635 ], [ -80.086128492108415, 26.589551574084815 ], [ -80.086240598005958, 26.589551574084815 ], [ -80.086240598005958, 26.589621261534635 ], [ -80.086128492108415, 26.589621261534635 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9987911581993103, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084931685904991, 26.589527334971834 ], [ -80.084931685904991, 26.58945158774377 ], [ -80.085034702135161, 26.58945158774377 ], [ -80.085034702135161, 26.589527334971834 ], [ -80.084931685904991, 26.589527334971834 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99892187118530273, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085961848206665, 26.589224346059577 ], [ -80.085961848206665, 26.589151628720636 ], [ -80.086067894325964, 26.589151628720636 ], [ -80.086067894325964, 26.589224346059577 ], [ -80.085961848206665, 26.589224346059577 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99910283088684082, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081974514121356, 26.590227239359152 ], [ -80.081974514121356, 26.590142402463719 ], [ -80.082065410795039, 26.590142402463719 ], [ -80.082065410795039, 26.590227239359152 ], [ -80.081974514121356, 26.590227239359152 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99954944849014282, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086098193217197, 26.589236465616064 ], [ -80.086098193217197, 26.589163748277123 ], [ -80.086204239336482, 26.589163748277123 ], [ -80.086204239336482, 26.589236465616064 ], [ -80.086098193217197, 26.589236465616064 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99848651885986328, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08541646816461, 26.589906071112157 ], [ -80.08541646816461, 26.589833353773216 ], [ -80.085522514283895, 26.589833353773216 ], [ -80.085522514283895, 26.589906071112157 ], [ -80.08541646816461, 26.589906071112157 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99685400724411011, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084334797747843, 26.589830323884094 ], [ -80.084334797747843, 26.589763666323396 ], [ -80.084449933534501, 26.589763666323396 ], [ -80.084449933534501, 26.589830323884094 ], [ -80.084334797747843, 26.589830323884094 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99969398975372314, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085967907984909, 26.589924250446892 ], [ -80.085967907984909, 26.589854562997072 ], [ -80.086076983993323, 26.589854562997072 ], [ -80.086076983993323, 26.589924250446892 ], [ -80.085967907984909, 26.589924250446892 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99900835752487183, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086395122351206, 26.589012253820997 ], [ -80.086395122351206, 26.588930446814686 ], [ -80.086486019024889, 26.588930446814686 ], [ -80.086486019024889, 26.589012253820997 ], [ -80.086395122351206, 26.589012253820997 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99962913990020752, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085580082177216, 26.589612171867266 ], [ -80.085580082177216, 26.589542484417446 ], [ -80.085686128296516, 26.589542484417446 ], [ -80.085686128296516, 26.589612171867266 ], [ -80.085580082177216, 26.589612171867266 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9984859824180603, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085792174415801, 26.589554603973937 ], [ -80.085792174415801, 26.589484916524118 ], [ -80.0858982205351, 26.589484916524118 ], [ -80.0858982205351, 26.589554603973937 ], [ -80.085792174415801, 26.589554603973937 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9994465708732605, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085304362267067, 26.589603082199901 ], [ -80.085304362267067, 26.589533394750081 ], [ -80.085410408386352, 26.589533394750081 ], [ -80.085410408386352, 26.589603082199901 ], [ -80.085304362267067, 26.589603082199901 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99758720397949219, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.090476382999313, 26.590006057453202 ], [ -80.090476382999313, 26.589909101001279 ], [ -80.09055213022738, 26.589909101001279 ], [ -80.09055213022738, 26.590006057453202 ], [ -80.090476382999313, 26.590006057453202 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98386508226394653, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086249687673316, 26.588788042025925 ], [ -80.086249687673316, 26.58868805568488 ], [ -80.086322405012268, 26.58868805568488 ], [ -80.086322405012268, 26.588788042025925 ], [ -80.086249687673316, 26.588788042025925 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99964213371276855, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083174350213895, 26.589781845658131 ], [ -80.083174350213895, 26.589697008762698 ], [ -80.08325918710932, 26.589697008762698 ], [ -80.08325918710932, 26.589781845658131 ], [ -80.083174350213895, 26.589781845658131 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99873369932174683, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084937745683249, 26.589839413551459 ], [ -80.084937745683249, 26.58976972610164 ], [ -80.085040761913405, 26.58976972610164 ], [ -80.085040761913405, 26.589839413551459 ], [ -80.084937745683249, 26.589839413551459 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98621189594268799, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081201892395086, 26.589742457099536 ], [ -80.081201892395086, 26.58965156042586 ], [ -80.081280669512282, 26.58965156042586 ], [ -80.081280669512282, 26.589742457099536 ], [ -80.081201892395086, 26.589742457099536 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99977165460586548, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085280123154092, 26.589896981444788 ], [ -80.085280123154092, 26.589830323884094 ], [ -80.085386169273377, 26.589830323884094 ], [ -80.085386169273377, 26.589896981444788 ], [ -80.085280123154092, 26.589896981444788 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99982482194900513, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085831562974391, 26.589918190668648 ], [ -80.085831562974391, 26.589854562997072 ], [ -80.085940638982805, 26.589854562997072 ], [ -80.085940638982805, 26.589918190668648 ], [ -80.085831562974391, 26.589918190668648 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99767988920211792, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087961575027578, 26.589036492933975 ], [ -80.087961575027578, 26.588951656038542 ], [ -80.088043382033888, 26.588951656038542 ], [ -80.088043382033888, 26.589036492933975 ], [ -80.087961575027578, 26.589036492933975 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98339426517486572, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088767525534195, 26.589736397321293 ], [ -80.088767525534195, 26.589654590314982 ], [ -80.08885236242962, 26.589654590314982 ], [ -80.08885236242962, 26.589736397321293 ], [ -80.088767525534195, 26.589736397321293 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99903982877731323, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083704580810348, 26.589715188097436 ], [ -80.083704580810348, 26.589624291423757 ], [ -80.083780328038401, 26.589624291423757 ], [ -80.083780328038401, 26.589715188097436 ], [ -80.083704580810348, 26.589715188097436 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99966895580291748, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085216495482513, 26.589845473329706 ], [ -80.085216495482513, 26.589778815769009 ], [ -80.085319511712683, 26.589778815769009 ], [ -80.085319511712683, 26.589845473329706 ], [ -80.085216495482513, 26.589845473329706 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99984145164489746, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08514680803269, 26.589896981444788 ], [ -80.08514680803269, 26.589824264105847 ], [ -80.085240734595487, 26.589824264105847 ], [ -80.085240734595487, 26.589896981444788 ], [ -80.08514680803269, 26.589896981444788 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99736636877059937, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08854028384998, 26.589518245304468 ], [ -80.08854028384998, 26.589436438298158 ], [ -80.08862209085629, 26.589436438298158 ], [ -80.08862209085629, 26.589518245304468 ], [ -80.08854028384998, 26.589518245304468 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99946492910385132, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086934442615018, 26.588830460473641 ], [ -80.086934442615018, 26.588742593689084 ], [ -80.087010189843085, 26.588742593689084 ], [ -80.087010189843085, 26.588830460473641 ], [ -80.086934442615018, 26.588830460473641 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99960607290267944, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08115947394738, 26.589781845658131 ], [ -80.08115947394738, 26.589709128319189 ], [ -80.081250370621063, 26.589709128319189 ], [ -80.081250370621063, 26.589781845658131 ], [ -80.08115947394738, 26.589781845658131 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99741059541702271, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.080865574702486, 26.589736397321293 ], [ -80.080865574702486, 26.589645500647617 ], [ -80.080938292041424, 26.589645500647617 ], [ -80.080938292041424, 26.589736397321293 ], [ -80.080865574702486, 26.589736397321293 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99962735176086426, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085243764484616, 26.589530364860956 ], [ -80.085243764484616, 26.589463707300261 ], [ -80.085340720936543, 26.589463707300261 ], [ -80.085340720936543, 26.589530364860956 ], [ -80.085243764484616, 26.589530364860956 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99777078628540039, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087313178755338, 26.589775785879887 ], [ -80.087313178755338, 26.589693978873576 ], [ -80.087391955872533, 26.589693978873576 ], [ -80.087391955872533, 26.589775785879887 ], [ -80.087313178755338, 26.589775785879887 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99758791923522949, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088013083142656, 26.588645637237164 ], [ -80.088013083142656, 26.588563830230854 ], [ -80.088091860259851, 26.588563830230854 ], [ -80.088091860259851, 26.588645637237164 ], [ -80.088013083142656, 26.588645637237164 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99809235334396362, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086592065144174, 26.589536424639203 ], [ -80.086592065144174, 26.589457647522014 ], [ -80.086673872150484, 26.589457647522014 ], [ -80.086673872150484, 26.589536424639203 ], [ -80.086592065144174, 26.589536424639203 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99838912487030029, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087676765450055, 26.589242525394312 ], [ -80.087676765450055, 26.589163748277123 ], [ -80.087758572456366, 26.589163748277123 ], [ -80.087758572456366, 26.589242525394312 ], [ -80.087676765450055, 26.589242525394312 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99986362457275391, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085516454505651, 26.589533394750081 ], [ -80.085516454505651, 26.589472796967627 ], [ -80.085622500624936, 26.589472796967627 ], [ -80.085622500624936, 26.589533394750081 ], [ -80.085516454505651, 26.589533394750081 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99799025058746338, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.0872404614164, 26.589884861888301 ], [ -80.0872404614164, 26.589806084771112 ], [ -80.087319238533595, 26.589806084771112 ], [ -80.087319238533595, 26.589884861888301 ], [ -80.0872404614164, 26.589884861888301 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99974638223648071, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082868331412513, 26.589578843086919 ], [ -80.082868331412513, 26.58950006596973 ], [ -80.082947108529694, 26.58950006596973 ], [ -80.082947108529694, 26.589578843086919 ], [ -80.082868331412513, 26.589578843086919 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99933159351348877, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08782523001706, 26.589681859317086 ], [ -80.08782523001706, 26.589603082199901 ], [ -80.087904007134242, 26.589603082199901 ], [ -80.087904007134242, 26.589681859317086 ], [ -80.08782523001706, 26.589681859317086 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9969712495803833, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088049441812132, 26.589363720959213 ], [ -80.088049441812132, 26.589284943842028 ], [ -80.088128218929313, 26.589284943842028 ], [ -80.088128218929313, 26.589363720959213 ], [ -80.088049441812132, 26.589363720959213 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99893766641616821, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088822063538402, 26.588572919898219 ], [ -80.088822063538402, 26.588494142781034 ], [ -80.088900840655583, 26.588494142781034 ], [ -80.088900840655583, 26.588572919898219 ], [ -80.088822063538402, 26.588572919898219 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99880385398864746, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087388925983419, 26.589669739760595 ], [ -80.087388925983419, 26.58959096264341 ], [ -80.0874677031006, 26.58959096264341 ], [ -80.0874677031006, 26.589669739760595 ], [ -80.087388925983419, 26.589669739760595 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99834752082824707, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086516317916107, 26.589642470758491 ], [ -80.086516317916107, 26.589563693641306 ], [ -80.086595095033289, 26.589563693641306 ], [ -80.086595095033289, 26.589642470758491 ], [ -80.086516317916107, 26.589642470758491 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99839240312576294, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087761602345481, 26.589139509164145 ], [ -80.087761602345481, 26.589057702157834 ], [ -80.087837349573547, 26.589057702157834 ], [ -80.087837349573547, 26.589139509164145 ], [ -80.087761602345481, 26.589139509164145 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99893277883529663, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08868571852787, 26.589309182955009 ], [ -80.08868571852787, 26.589233435726943 ], [ -80.08876752553418, 26.589233435726943 ], [ -80.08876752553418, 26.589309182955009 ], [ -80.08868571852787, 26.589309182955009 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99702990055084229, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086813247050117, 26.589212226503086 ], [ -80.086813247050117, 26.589130419496776 ], [ -80.086888994278183, 26.589130419496776 ], [ -80.086888994278183, 26.589212226503086 ], [ -80.086813247050117, 26.589212226503086 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99842476844787598, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086873844832567, 26.589760636434274 ], [ -80.086873844832567, 26.589684889206207 ], [ -80.086955651838878, 26.589684889206207 ], [ -80.086955651838878, 26.589760636434274 ], [ -80.086873844832567, 26.589760636434274 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99987101554870605, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085913369980702, 26.58987274233181 ], [ -80.085913369980702, 26.589809114660234 ], [ -80.086010326432628, 26.589809114660234 ], [ -80.086010326432628, 26.58987274233181 ], [ -80.085913369980702, 26.58987274233181 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99928253889083862, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088470596400171, 26.588685025795755 ], [ -80.088470596400171, 26.588600188900323 ], [ -80.088543313739109, 26.588600188900323 ], [ -80.088543313739109, 26.588685025795755 ], [ -80.088470596400171, 26.588685025795755 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99979561567306519, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085443737166713, 26.589600052310775 ], [ -80.085443737166713, 26.589539454528325 ], [ -80.085543723507755, 26.589539454528325 ], [ -80.085543723507755, 26.589600052310775 ], [ -80.085443737166713, 26.589600052310775 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98732590675354004, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08168364476559, 26.589754576656027 ], [ -80.08168364476559, 26.589663679982351 ], [ -80.081750302326284, 26.589663679982351 ], [ -80.081750302326284, 26.589754576656027 ], [ -80.08168364476559, 26.589754576656027 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99965882301330566, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083271306665807, 26.58959096264341 ], [ -80.083271306665807, 26.589515215415343 ], [ -80.083350083783003, 26.589515215415343 ], [ -80.083350083783003, 26.58959096264341 ], [ -80.083271306665807, 26.58959096264341 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99703872203826904, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086807187271873, 26.589875772220932 ], [ -80.086807187271873, 26.589800024992869 ], [ -80.086885964389069, 26.589800024992869 ], [ -80.086885964389069, 26.589875772220932 ], [ -80.086807187271873, 26.589875772220932 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99816113710403442, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088200936268251, 26.589145568942389 ], [ -80.088200936268251, 26.589069821714325 ], [ -80.088279713385447, 26.589069821714325 ], [ -80.088279713385447, 26.589145568942389 ], [ -80.088200936268251, 26.589145568942389 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99900466203689575, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087685855117414, 26.589903041223035 ], [ -80.087685855117414, 26.589824264105847 ], [ -80.087761602345481, 26.589824264105847 ], [ -80.087761602345481, 26.589903041223035 ], [ -80.087685855117414, 26.589903041223035 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99877172708511353, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087740393121621, 26.589357661180969 ], [ -80.087740393121621, 26.589278884063781 ], [ -80.087816140349688, 26.589278884063781 ], [ -80.087816140349688, 26.589357661180969 ], [ -80.087740393121621, 26.589357661180969 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99937742948532104, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086937472504147, 26.589881831999175 ], [ -80.086937472504147, 26.58980305488199 ], [ -80.087013219732214, 26.58980305488199 ], [ -80.087013219732214, 26.589881831999175 ], [ -80.086937472504147, 26.589881831999175 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99900442361831665, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081471552527006, 26.589793965214621 ], [ -80.081471552527006, 26.589718217986558 ], [ -80.081550329644188, 26.589718217986558 ], [ -80.081550329644188, 26.589793965214621 ], [ -80.081471552527006, 26.589793965214621 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99929416179656982, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087952485360205, 26.589697008762698 ], [ -80.087952485360205, 26.589615201756388 ], [ -80.088025202699143, 26.589615201756388 ], [ -80.088025202699143, 26.589697008762698 ], [ -80.087952485360205, 26.589697008762698 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99985945224761963, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.084568099210287, 26.589327362289744 ], [ -80.084568099210287, 26.589245555283433 ], [ -80.084640816549225, 26.589245555283433 ], [ -80.084640816549225, 26.589327362289744 ], [ -80.084568099210287, 26.589327362289744 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99893981218338013, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088943259103303, 26.589094060827303 ], [ -80.088943259103303, 26.589012253820997 ], [ -80.089015976442241, 26.589012253820997 ], [ -80.089015976442241, 26.589094060827303 ], [ -80.088943259103303, 26.589094060827303 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99986159801483154, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085664919072656, 26.589536424639203 ], [ -80.085664919072656, 26.589472796967627 ], [ -80.085755815746339, 26.589472796967627 ], [ -80.085755815746339, 26.589536424639203 ], [ -80.085664919072656, 26.589536424639203 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9998629093170166, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085380109495134, 26.589539454528325 ], [ -80.085380109495134, 26.589481886634996 ], [ -80.085480095836175, 26.589481886634996 ], [ -80.085480095836175, 26.589539454528325 ], [ -80.085380109495134, 26.589539454528325 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99873834848403931, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087104116405882, 26.589442498076401 ], [ -80.087104116405882, 26.589366750848338 ], [ -80.087179863633949, 26.589366750848338 ], [ -80.087179863633949, 26.589442498076401 ], [ -80.087104116405882, 26.589442498076401 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99942868947982788, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088870541764351, 26.589200106946596 ], [ -80.088870541764351, 26.589127389607654 ], [ -80.088949318881546, 26.589127389607654 ], [ -80.088949318881546, 26.589200106946596 ], [ -80.088870541764351, 26.589200106946596 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99946147203445435, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088543313739109, 26.588578979676466 ], [ -80.088543313739109, 26.588500202559278 ], [ -80.088616031078047, 26.588500202559278 ], [ -80.088616031078047, 26.588578979676466 ], [ -80.088543313739109, 26.588578979676466 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99942421913146973, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088461506732799, 26.589621261534635 ], [ -80.088461506732799, 26.589548544195694 ], [ -80.08854028384998, 26.589548544195694 ], [ -80.08854028384998, 26.589621261534635 ], [ -80.088461506732799, 26.589621261534635 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99911969900131226, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.082768345071472, 26.58976972610164 ], [ -80.082768345071472, 26.589690948984455 ], [ -80.082841062410409, 26.589690948984455 ], [ -80.082841062410409, 26.58976972610164 ], [ -80.082768345071472, 26.58976972610164 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99982577562332153, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085174077034793, 26.589593992532532 ], [ -80.085174077034793, 26.589533394750081 ], [ -80.085268003597591, 26.589533394750081 ], [ -80.085268003597591, 26.589593992532532 ], [ -80.085174077034793, 26.589593992532532 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99937832355499268, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088731166864719, 26.589406139406933 ], [ -80.088731166864719, 26.589330392178866 ], [ -80.088803884203656, 26.589330392178866 ], [ -80.088803884203656, 26.589406139406933 ], [ -80.088731166864719, 26.589406139406933 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99894231557846069, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087013219732214, 26.589772755990765 ], [ -80.087013219732214, 26.589697008762698 ], [ -80.087085937071151, 26.589697008762698 ], [ -80.087085937071151, 26.589772755990765 ], [ -80.087013219732214, 26.589772755990765 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9993329644203186, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08797672447318, 26.589463707300261 ], [ -80.08797672447318, 26.589394019850442 ], [ -80.088055501590375, 26.589394019850442 ], [ -80.088055501590375, 26.589463707300261 ], [ -80.08797672447318, 26.589463707300261 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99933785200119019, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088434237730695, 26.589830323884094 ], [ -80.088434237730695, 26.589751546766905 ], [ -80.088503925180518, 26.589751546766905 ], [ -80.088503925180518, 26.589830323884094 ], [ -80.088434237730695, 26.589830323884094 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99931168556213379, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087519211215678, 26.589678829427964 ], [ -80.087519211215678, 26.589600052310775 ], [ -80.087588898665501, 26.589600052310775 ], [ -80.087588898665501, 26.589678829427964 ], [ -80.087519211215678, 26.589678829427964 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99962091445922852, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086004266654385, 26.589615201756388 ], [ -80.086004266654385, 26.589554603973937 ], [ -80.086092133438939, 26.589554603973937 ], [ -80.086092133438939, 26.589615201756388 ], [ -80.086004266654385, 26.589615201756388 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99977082014083862, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085707337520375, 26.589909101001279 ], [ -80.085707337520375, 26.589848503218828 ], [ -80.085795204304929, 26.589848503218828 ], [ -80.085795204304929, 26.589909101001279 ], [ -80.085707337520375, 26.589909101001279 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99909043312072754, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.089137172007142, 26.589209196613965 ], [ -80.089137172007142, 26.58913647927502 ], [ -80.08920988934608, 26.58913647927502 ], [ -80.08920988934608, 26.589209196613965 ], [ -80.089137172007142, 26.589209196613965 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99909079074859619, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087034428956073, 26.589545514306572 ], [ -80.087034428956073, 26.589472796967627 ], [ -80.087107146295011, 26.589472796967627 ], [ -80.087107146295011, 26.589545514306572 ], [ -80.087034428956073, 26.589545514306572 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99522709846496582, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.086740529711179, 26.589324332400622 ], [ -80.086740529711179, 26.589248585172555 ], [ -80.086810217161002, 26.589248585172555 ], [ -80.086810217161002, 26.589324332400622 ], [ -80.086740529711179, 26.589324332400622 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99889522790908813, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088988707440137, 26.58941825896342 ], [ -80.088988707440137, 26.589342511735357 ], [ -80.08905839488996, 26.589342511735357 ], [ -80.08905839488996, 26.58941825896342 ], [ -80.088988707440137, 26.58941825896342 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99860930442810059, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088840242873133, 26.589091030938182 ], [ -80.088840242873133, 26.589015283710118 ], [ -80.088909930322956, 26.589015283710118 ], [ -80.088909930322956, 26.589091030938182 ], [ -80.088840242873133, 26.589091030938182 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99737119674682617, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088513014847891, 26.588472933557174 ], [ -80.088513014847891, 26.588397186329111 ], [ -80.088582702297714, 26.588397186329111 ], [ -80.088582702297714, 26.588472933557174 ], [ -80.088513014847891, 26.588472933557174 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99860483407974243, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087401045539906, 26.589006194042749 ], [ -80.087401045539906, 26.58893650659293 ], [ -80.087476792767973, 26.58893650659293 ], [ -80.087476792767973, 26.589006194042749 ], [ -80.087401045539906, 26.589006194042749 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99933630228042603, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088625120745434, 26.589412199185176 ], [ -80.088625120745434, 26.589336451957113 ], [ -80.088694808195243, 26.589336451957113 ], [ -80.088694808195243, 26.589412199185176 ], [ -80.088625120745434, 26.589412199185176 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99645304679870605, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08820396615738, 26.588530501450503 ], [ -80.08820396615738, 26.588457784111561 ], [ -80.088273653607203, 26.588457784111561 ], [ -80.088273653607203, 26.588530501450503 ], [ -80.08820396615738, 26.588530501450503 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99778950214385986, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088437267619824, 26.588572919898219 ], [ -80.088437267619824, 26.588500202559278 ], [ -80.088506955069633, 26.588500202559278 ], [ -80.088506955069633, 26.588572919898219 ], [ -80.088437267619824, 26.588572919898219 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99856036901473999, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.081474582416135, 26.589706098430067 ], [ -80.081474582416135, 26.589633381091126 ], [ -80.081544269865944, 26.589633381091126 ], [ -80.081544269865944, 26.589706098430067 ], [ -80.081474582416135, 26.589706098430067 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99453496932983398, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088125189040198, 26.589248585172555 ], [ -80.088125189040198, 26.589175867833614 ], [ -80.088194876490007, 26.589175867833614 ], [ -80.088194876490007, 26.589248585172555 ], [ -80.088125189040198, 26.589248585172555 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99812811613082886, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085780054859313, 26.589854562997072 ], [ -80.085780054859313, 26.589796995103743 ], [ -80.085867921643867, 26.589796995103743 ], [ -80.085867921643867, 26.589854562997072 ], [ -80.085780054859313, 26.589854562997072 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99983394145965576, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08862209085629, 26.588469903668052 ], [ -80.08862209085629, 26.588394156439989 ], [ -80.088688748416999, 26.588394156439989 ], [ -80.088688748416999, 26.588469903668052 ], [ -80.08862209085629, 26.588469903668052 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99906069040298462, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088037322255644, 26.589584902865163 ], [ -80.088037322255644, 26.589506125747977 ], [ -80.08810094992721, 26.589506125747977 ], [ -80.08810094992721, 26.589584902865163 ], [ -80.088037322255644, 26.589584902865163 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99925822019577026, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08761919755672, 26.589342511735357 ], [ -80.08761919755672, 26.589272824285537 ], [ -80.087688885006543, 26.589272824285537 ], [ -80.087688885006543, 26.589342511735357 ], [ -80.08761919755672, 26.589342511735357 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99811917543411255, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08874934619945, 26.588675936128389 ], [ -80.08874934619945, 26.58860624867857 ], [ -80.088819033649273, 26.58860624867857 ], [ -80.088819033649273, 26.588675936128389 ], [ -80.08874934619945, 26.588675936128389 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99936312437057495, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.0874677031006, 26.589557633863059 ], [ -80.0874677031006, 26.589487946413239 ], [ -80.087537390550423, 26.589487946413239 ], [ -80.087537390550423, 26.589557633863059 ], [ -80.0874677031006, 26.589557633863059 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.74751776456832886, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.080368672886379, 26.590584766275615 ], [ -80.080368672886379, 26.590518108714917 ], [ -80.080441390225317, 26.590518108714917 ], [ -80.080441390225317, 26.590584766275615 ], [ -80.080368672886379, 26.590584766275615 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99930477142333984, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088403938839477, 26.589045582601344 ], [ -80.088403938839477, 26.588972865262402 ], [ -80.088470596400171, 26.588972865262402 ], [ -80.088470596400171, 26.589045582601344 ], [ -80.088403938839477, 26.589045582601344 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99938285350799561, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087388925983419, 26.589233435726943 ], [ -80.087388925983419, 26.589166778166248 ], [ -80.087461643322357, 26.589166778166248 ], [ -80.087461643322357, 26.589233435726943 ], [ -80.087388925983419, 26.589233435726943 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99816471338272095, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087673735560927, 26.589457647522014 ], [ -80.087673735560927, 26.58939098996132 ], [ -80.08774342301075, 26.58939098996132 ], [ -80.08774342301075, 26.589457647522014 ], [ -80.087673735560927, 26.589457647522014 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99961698055267334, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088585732186829, 26.589612171867266 ], [ -80.088585732186829, 26.589542484417446 ], [ -80.088652389747523, 26.589542484417446 ], [ -80.088652389747523, 26.589612171867266 ], [ -80.088585732186829, 26.589612171867266 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99932754039764404, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087540420439538, 26.58945158774377 ], [ -80.087540420439538, 26.589384930183073 ], [ -80.087610107889347, 26.589384930183073 ], [ -80.087610107889347, 26.58945158774377 ], [ -80.087540420439538, 26.58945158774377 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99560296535491943, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088985677551008, 26.588878938699601 ], [ -80.088985677551008, 26.58880622136066 ], [ -80.089049305222588, 26.58880622136066 ], [ -80.089049305222588, 26.588878938699601 ], [ -80.088985677551008, 26.588878938699601 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99918514490127563, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087243491305529, 26.589448557854649 ], [ -80.087243491305529, 26.589381900293951 ], [ -80.087310148866223, 26.589381900293951 ], [ -80.087310148866223, 26.589448557854649 ], [ -80.087243491305529, 26.589448557854649 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99847286939620972, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088367580170001, 26.588681995906633 ], [ -80.088367580170001, 26.588615338345935 ], [ -80.088434237730695, 26.588615338345935 ], [ -80.088434237730695, 26.588681995906633 ], [ -80.088367580170001, 26.588681995906633 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99894291162490845, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087325298311839, 26.589112240162041 ], [ -80.087325298311839, 26.589048612490465 ], [ -80.087394985761662, 26.589048612490465 ], [ -80.087394985761662, 26.589112240162041 ], [ -80.087325298311839, 26.589112240162041 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99861788749694824, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087179863633949, 26.589327362289744 ], [ -80.087179863633949, 26.589263734618168 ], [ -80.087249551083772, 26.589263734618168 ], [ -80.087249551083772, 26.589327362289744 ], [ -80.087179863633949, 26.589327362289744 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99747604131698608, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.089055365000831, 26.589312212844131 ], [ -80.089055365000831, 26.589248585172555 ], [ -80.089125052450655, 26.589248585172555 ], [ -80.089125052450655, 26.589312212844131 ], [ -80.089055365000831, 26.589312212844131 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 120, "class": "Cessna A-37", "confidence": 0.60905814170837402, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.085025612467803, 26.588288110320697 ], [ -80.085025612467803, 26.588203273425265 ], [ -80.085077120582881, 26.588203273425265 ], [ -80.085077120582881, 26.588288110320697 ], [ -80.085025612467803, 26.588288110320697 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9984889030456543, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088755405977693, 26.589197077057474 ], [ -80.088755405977693, 26.589133449385898 ], [ -80.088822063538402, 26.589133449385898 ], [ -80.088822063538402, 26.589197077057474 ], [ -80.088755405977693, 26.589197077057474 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99863690137863159, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088273653607203, 26.589030433155731 ], [ -80.088273653607203, 26.588963775595033 ], [ -80.088337281278768, 26.588963775595033 ], [ -80.088337281278768, 26.589030433155731 ], [ -80.088273653607203, 26.589030433155731 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99916791915893555, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.088261534050716, 26.589251615061681 ], [ -80.088261534050716, 26.589191017279227 ], [ -80.08832819161141, 26.589191017279227 ], [ -80.08832819161141, 26.589251615061681 ], [ -80.088261534050716, 26.589251615061681 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99748909473419189, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087758572456366, 26.589784875547252 ], [ -80.087758572456366, 26.589724277764802 ], [ -80.087822200127931, 26.589724277764802 ], [ -80.087822200127931, 26.589784875547252 ], [ -80.087758572456366, 26.589784875547252 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99490523338317871, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08810094992721, 26.588530501450503 ], [ -80.08810094992721, 26.588472933557174 ], [ -80.088164577598789, 26.588472933557174 ], [ -80.088164577598789, 26.588530501450503 ], [ -80.08810094992721, 26.588530501450503 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99025905132293701, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.08696474150625, 26.589645500647617 ], [ -80.08696474150625, 26.589593992532532 ], [ -80.087022309399586, 26.589593992532532 ], [ -80.087022309399586, 26.589645500647617 ], [ -80.08696474150625, 26.589645500647617 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9820590615272522, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.087891887577754, 26.589784875547252 ], [ -80.087891887577754, 26.589730337543049 ], [ -80.087943395692832, 26.589730337543049 ], [ -80.087943395692832, 26.589784875547252 ], [ -80.087891887577754, 26.589784875547252 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.98488706350326538, "image": "108_1040010044D30600" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -80.083162230657408, 26.589427348630789 ], [ -80.083162230657408, 26.589372810626582 ], [ -80.083210708883357, 26.589372810626582 ], [ -80.083210708883357, 26.589427348630789 ], [ -80.083162230657408, 26.589427348630789 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/eval_vector/preds/10_104005000FDC8300.geojson b/docker/solaris/solaris/data/eval_vector/preds/10_104005000FDC8300.geojson new file mode 100755 index 00000000..c81268f3 --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/preds/10_104005000FDC8300.geojson @@ -0,0 +1,44 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99935334920883179, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955229549072067, 43.582191816331502 ], [ 3.955229549072067, 43.581837502817137 ], [ 3.954817779312133, 43.581837502817137 ], [ 3.954817779312133, 43.582191816331502 ], [ 3.955229549072067, 43.582191816331502 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.97816532850265503, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.95419852866541, 43.582763186773583 ], [ 3.95419852866541, 43.582402489231939 ], [ 3.953847407164691, 43.582402489231939 ], [ 3.953847407164691, 43.582763186773583 ], [ 3.95419852866541, 43.582763186773583 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.98227846622467041, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954587954329844, 43.582574857968652 ], [ 3.954587954329844, 43.582230120495218 ], [ 3.954227256788196, 43.582230120495218 ], [ 3.954227256788196, 43.582574857968652 ], [ 3.954587954329844, 43.582574857968652 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99517917633056641, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.95493588381692, 43.582373761109153 ], [ 3.95493588381692, 43.582032215649363 ], [ 3.954578378288915, 43.582032215649363 ], [ 3.954578378288915, 43.582373761109153 ], [ 3.95493588381692, 43.582373761109153 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.98999959230422974, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.95358247003233, 43.582571665955015 ], [ 3.95358247003233, 43.582309920836295 ], [ 3.953145164163253, 43.582309920836295 ], [ 3.953145164163253, 43.582571665955015 ], [ 3.95358247003233, 43.582571665955015 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.9994317889213562, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954536882111558, 43.582009871553858 ], [ 3.954536882111558, 43.581764086503355 ], [ 3.954160224501695, 43.581764086503355 ], [ 3.954160224501695, 43.582009871553858 ], [ 3.954536882111558, 43.582009871553858 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99595069885253906, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949851006083778, 43.578457160369311 ], [ 3.949851006083778, 43.578173071155092 ], [ 3.949534996733131, 43.578173071155092 ], [ 3.949534996733131, 43.578457160369311 ], [ 3.949851006083778, 43.578457160369311 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99928802251815796, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953987855764979, 43.582360993054579 ], [ 3.953987855764979, 43.582099247935865 ], [ 3.95368142245526, 43.582099247935865 ], [ 3.95368142245526, 43.582360993054579 ], [ 3.953987855764979, 43.582360993054579 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99815768003463745, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953703766550761, 43.5828014909373 ], [ 3.953703766550761, 43.582574857968652 ], [ 3.953371797131899, 43.582574857968652 ], [ 3.953371797131899, 43.5828014909373 ], [ 3.953703766550761, 43.5828014909373 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99932575225830078, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954137880406195, 43.577451676071796 ], [ 3.954137880406195, 43.577279307335083 ], [ 3.95387932730112, 43.577279307335083 ], [ 3.95387932730112, 43.577451676071796 ], [ 3.954137880406195, 43.577451676071796 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99278998374938965, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949723325538062, 43.578169879141448 ], [ 3.949723325538062, 43.578042198595732 ], [ 3.94949030854213, 43.578042198595732 ], [ 3.94949030854213, 43.578169879141448 ], [ 3.949723325538062, 43.578169879141448 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99437570571899414, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.962625444682671, 43.577767685422444 ], [ 3.962625444682671, 43.577646388904014 ], [ 3.962462651986883, 43.577646388904014 ], [ 3.962462651986883, 43.577767685422444 ], [ 3.962625444682671, 43.577767685422444 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99708205461502075, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963005294306176, 43.578118806923165 ], [ 3.963005294306176, 43.577994318391092 ], [ 3.962848885637674, 43.577994318391092 ], [ 3.962848885637674, 43.578118806923165 ], [ 3.963005294306176, 43.578118806923165 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99262803792953491, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954307057129269, 43.578026238527521 ], [ 3.954307057129269, 43.577917710063659 ], [ 3.954192144638125, 43.577917710063659 ], [ 3.954192144638125, 43.578026238527521 ], [ 3.954307057129269, 43.578026238527521 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99946826696395874, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963860753962474, 43.576481303924353 ], [ 3.963860753962474, 43.576401503583277 ], [ 3.963723497375829, 43.576401503583277 ], [ 3.963723497375829, 43.576481303924353 ], [ 3.963860753962474, 43.576481303924353 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.83336251974105835, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.964061850821976, 43.575019361675906 ], [ 3.964061850821976, 43.574942753348473 ], [ 3.963921402221689, 43.574942753348473 ], [ 3.963921402221689, 43.575019361675906 ], [ 3.964061850821976, 43.575019361675906 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99247002601623535, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.98146790121672, 43.572146549397296 ], [ 3.98146790121672, 43.572063557042583 ], [ 3.981340220671004, 43.572063557042583 ], [ 3.981340220671004, 43.572146549397296 ], [ 3.98146790121672, 43.572146549397296 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99450868368148804, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.950463872703216, 43.577713421190516 ], [ 3.950463872703216, 43.57763362084944 ], [ 3.950336192157499, 43.57763362084944 ], [ 3.950336192157499, 43.577713421190516 ], [ 3.950463872703216, 43.577713421190516 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.96630311012268066, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.949534996733131, 43.577920902077302 ], [ 3.949534996733131, 43.577818757640735 ], [ 3.949439236323844, 43.577818757640735 ], [ 3.949439236323844, 43.577920902077302 ], [ 3.949534996733131, 43.577920902077302 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.89782452583312988, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955025260198922, 43.576331279283131 ], [ 3.955025260198922, 43.576232326860207 ], [ 3.954929499789635, 43.576232326860207 ], [ 3.954929499789635, 43.576331279283131 ], [ 3.955025260198922, 43.576331279283131 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99706995487213135, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963043598469891, 43.577461252112727 ], [ 3.963043598469891, 43.577371875730726 ], [ 3.962944646046961, 43.577371875730726 ], [ 3.962944646046961, 43.577461252112727 ], [ 3.963043598469891, 43.577461252112727 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99918097257614136, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956282913574225, 43.575536467886053 ], [ 3.956282913574225, 43.575450283517696 ], [ 3.956183961151295, 43.575450283517696 ], [ 3.956183961151295, 43.575536467886053 ], [ 3.956282913574225, 43.575536467886053 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99974101781845093, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954722018902846, 43.578319903782671 ], [ 3.954722018902846, 43.57823691142795 ], [ 3.954619874466273, 43.57823691142795 ], [ 3.954619874466273, 43.578319903782671 ], [ 3.954722018902846, 43.578319903782671 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.9997982382774353, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954335785252055, 43.578335863850882 ], [ 3.954335785252055, 43.578249679482525 ], [ 3.954240024842768, 43.578249679482525 ], [ 3.954240024842768, 43.578335863850882 ], [ 3.954335785252055, 43.578335863850882 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99986851215362549, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954313441156555, 43.578173071155092 ], [ 3.954313441156555, 43.578090078800379 ], [ 3.954217680747268, 43.578090078800379 ], [ 3.954217680747268, 43.578173071155092 ], [ 3.954313441156555, 43.578173071155092 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99930834770202637, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.963886290071617, 43.576695168838427 ], [ 3.963886290071617, 43.576628136551925 ], [ 3.963768185566829, 43.576628136551925 ], [ 3.963768185566829, 43.576695168838427 ], [ 3.963886290071617, 43.576695168838427 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99972110986709595, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.950888410517721, 43.577486788221869 ], [ 3.950888410517721, 43.577419755935367 ], [ 3.950773498026577, 43.577419755935367 ], [ 3.950773498026577, 43.577486788221869 ], [ 3.950888410517721, 43.577486788221869 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99965620040893555, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.951153347650082, 43.577359107676152 ], [ 3.951153347650082, 43.57729207538965 ], [ 3.951038435158938, 43.57729207538965 ], [ 3.951038435158938, 43.577359107676152 ], [ 3.951153347650082, 43.577359107676152 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99933016300201416, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.957406502376526, 43.576114222355415 ], [ 3.957406502376526, 43.576031230000702 ], [ 3.957313933980882, 43.576031230000702 ], [ 3.957313933980882, 43.576114222355415 ], [ 3.957406502376526, 43.576114222355415 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99836689233779907, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.957151141285094, 43.575849285223057 ], [ 3.957151141285094, 43.575769484881981 ], [ 3.957055380875807, 43.575769484881981 ], [ 3.957055380875807, 43.575849285223057 ], [ 3.957151141285094, 43.575849285223057 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99982553720474243, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.954354937333913, 43.578498656546671 ], [ 3.954354937333913, 43.578418856205595 ], [ 3.954259176924626, 43.578418856205595 ], [ 3.954259176924626, 43.578498656546671 ], [ 3.954354937333913, 43.578498656546671 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99935632944107056, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956139272960295, 43.575603500172555 ], [ 3.956139272960295, 43.575526891845122 ], [ 3.956040320537364, 43.575526891845122 ], [ 3.956040320537364, 43.575603500172555 ], [ 3.956139272960295, 43.575603500172555 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99895143508911133, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.957237325653453, 43.575941853618701 ], [ 3.957237325653453, 43.575865245291268 ], [ 3.957144757257808, 43.575865245291268 ], [ 3.957144757257808, 43.575941853618701 ], [ 3.957237325653453, 43.575941853618701 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99725323915481567, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956975580534735, 43.575667340445413 ], [ 3.956975580534735, 43.575593924131624 ], [ 3.956883012139091, 43.575593924131624 ], [ 3.956883012139091, 43.575667340445413 ], [ 3.956975580534735, 43.575667340445413 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99779319763183594, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.956286105587868, 43.575638612322628 ], [ 3.956286105587868, 43.575568388022482 ], [ 3.95619992121951, 43.575568388022482 ], [ 3.95619992121951, 43.575638612322628 ], [ 3.956286105587868, 43.575638612322628 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99938762187957764, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.955867951800648, 43.575731180718272 ], [ 3.955867951800648, 43.57566414843177 ], [ 3.955778575418647, 43.57566414843177 ], [ 3.955778575418647, 43.575731180718272 ], [ 3.955867951800648, 43.575731180718272 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99925810098648071, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.9554849101635, 43.575983349796061 ], [ 3.9554849101635, 43.575916317509559 ], [ 3.955405109822427, 43.575916317509559 ], [ 3.955405109822427, 43.575983349796061 ], [ 3.9554849101635, 43.575983349796061 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.52568131685256958, "image": "10_104005000FDC8300" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.953690998496189, 43.582357801040935 ], [ 3.953690998496189, 43.582175856263291 ], [ 3.953662270373403, 43.582175856263291 ], [ 3.953662270373403, 43.582357801040935 ], [ 3.953690998496189, 43.582357801040935 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/eval_vector/preds/11_10400100213E1200.geojson b/docker/solaris/solaris/data/eval_vector/preds/11_10400100213E1200.geojson new file mode 100755 index 00000000..a46f63f6 --- /dev/null +++ b/docker/solaris/solaris/data/eval_vector/preds/11_10400100213E1200.geojson @@ -0,0 +1,27 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "class_id": 197, "class": "Breguet Br. 1150 Atlantic", "confidence": 0.70753920078277588, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518536702936554, 47.671280335626633 ], [ 9.518536702936554, 47.670988397858999 ], [ 9.518198179354936, 47.670988397858999 ], [ 9.518198179354936, 47.671280335626633 ], [ 9.518536702936554, 47.671280335626633 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99987852573394775, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.509191588651337, 47.667798822461549 ], [ 9.509191588651337, 47.667584527717224 ], [ 9.508893439441838, 47.667584527717224 ], [ 9.508893439441838, 47.667798822461549 ], [ 9.509191588651337, 47.667798822461549 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.9996330738067627, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518114324889765, 47.670997715021798 ], [ 9.518114324889765, 47.670814477486793 ], [ 9.517865867215184, 47.670814477486793 ], [ 9.517865867215184, 47.670997715021798 ], [ 9.518114324889765, 47.670997715021798 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 118, "class": "Dornier Do 31", "confidence": 0.56089103221893311, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518319302471296, 47.670385887998137 ], [ 9.518319302471296, 47.670249236277115 ], [ 9.518101902006036, 47.670249236277115 ], [ 9.518101902006036, 47.670385887998137 ], [ 9.518319302471296, 47.670385887998137 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 173, "class": "Dornier Do 228", "confidence": 0.92769265174865723, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518244765168921, 47.67114678962654 ], [ 9.518244765168921, 47.67100703218459 ], [ 9.518042893308323, 47.67100703218459 ], [ 9.518042893308323, 47.67114678962654 ], [ 9.518244765168921, 47.67114678962654 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99868589639663696, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.525043188289674, 47.674330153582126 ], [ 9.525043188289674, 47.67420592474484 ], [ 9.524847527870939, 47.67420592474484 ], [ 9.524847527870939, 47.674330153582126 ], [ 9.525043188289674, 47.674330153582126 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.98512911796569824, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513381206188976, 47.669034899392599 ], [ 9.513381206188976, 47.668895141950649 ], [ 9.513213497258635, 47.668895141950649 ], [ 9.513213497258635, 47.669034899392599 ], [ 9.513381206188976, 47.669034899392599 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99051278829574585, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513486800700674, 47.668954150648361 ], [ 9.513486800700674, 47.668814393206411 ], [ 9.513322197491263, 47.668814393206411 ], [ 9.513322197491263, 47.668954150648361 ], [ 9.513486800700674, 47.668954150648361 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.79077893495559692, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513365677584316, 47.668826816090139 ], [ 9.513365677584316, 47.668699481531917 ], [ 9.513197968653973, 47.668699481531917 ], [ 9.513197968653973, 47.668826816090139 ], [ 9.513365677584316, 47.668826816090139 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 173, "class": "Dornier Do 228", "confidence": 0.90649533271789551, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518642297448253, 47.671364190091801 ], [ 9.518642297448253, 47.671243066975443 ], [ 9.518468377076045, 47.671243066975443 ], [ 9.518468377076045, 47.671364190091801 ], [ 9.518642297448253, 47.671364190091801 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 162, "class": "Dornier Do 28", "confidence": 0.85791546106338501, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.51822923656426, 47.671041195114846 ], [ 9.51822923656426, 47.670892120510096 ], [ 9.518098796285104, 47.670892120510096 ], [ 9.518098796285104, 47.671041195114846 ], [ 9.51822923656426, 47.671041195114846 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.99960094690322876, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513011625398036, 47.668736750183101 ], [ 9.513011625398036, 47.66862804995047 ], [ 9.512850127909559, 47.66862804995047 ], [ 9.512850127909559, 47.668736750183101 ], [ 9.513011625398036, 47.668736750183101 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.74271434545516968, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.51764225530806, 47.670997715021798 ], [ 9.51764225530806, 47.670882803347304 ], [ 9.517508709307972, 47.670882803347304 ], [ 9.517508709307972, 47.670997715021798 ], [ 9.51764225530806, 47.670997715021798 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.50610750913619995, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.51278490776998, 47.669093908090311 ], [ 9.51278490776998, 47.669006947904208 ], [ 9.512635833165231, 47.669006947904208 ], [ 9.512635833165231, 47.669093908090311 ], [ 9.51278490776998, 47.669093908090311 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.76217836141586304, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513117219909734, 47.668715010136573 ], [ 9.513117219909734, 47.66863736711327 ], [ 9.513024048281766, 47.66863736711327 ], [ 9.513024048281766, 47.668715010136573 ], [ 9.513117219909734, 47.668715010136573 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99836450815200806, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513284928840076, 47.669370317253282 ], [ 9.513284928840076, 47.669305097113707 ], [ 9.513182440049311, 47.669305097113707 ], [ 9.513182440049311, 47.669370317253282 ], [ 9.513284928840076, 47.669370317253282 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 2, "class": "Medium Civil Transport\/Utility", "confidence": 0.87490624189376831, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.518207496517734, 47.671227538370779 ], [ 9.518207496517734, 47.671134366742812 ], [ 9.518139170657225, 47.671134366742812 ], [ 9.518139170657225, 47.671227538370779 ], [ 9.518207496517734, 47.671227538370779 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.73400759696960449, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.517350317540426, 47.670873486184504 ], [ 9.517350317540426, 47.67079894888213 ], [ 9.517275780238052, 47.67079894888213 ], [ 9.517275780238052, 47.670873486184504 ], [ 9.517350317540426, 47.670873486184504 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.99908244609832764, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.513567549444913, 47.669861021160585 ], [ 9.513567549444913, 47.669805118183803 ], [ 9.513486800700674, 47.669805118183803 ], [ 9.513486800700674, 47.669861021160585 ], [ 9.513567549444913, 47.669861021160585 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.94752192497253418, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.514260125212811, 47.669755426648884 ], [ 9.514260125212811, 47.669696417951172 ], [ 9.514188693631368, 47.669696417951172 ], [ 9.514188693631368, 47.669755426648884 ], [ 9.514260125212811, 47.669755426648884 ] ] ] } }, +{ "type": "Feature", "properties": { "class_id": 3, "class": "Small Civil Transport\/Utility", "confidence": 0.83418089151382446, "image": "11_10400100213E1200" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 9.514232173724421, 47.669851703997786 ], [ 9.514232173724421, 47.669802012462874 ], [ 9.514166953584843, 47.669802012462874 ], [ 9.514166953584843, 47.669851703997786 ], [ 9.514232173724421, 47.669851703997786 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/gdf_from_mask_1.geojson b/docker/solaris/solaris/data/gdf_from_mask_1.geojson new file mode 100644 index 00000000..139cd822 --- /dev/null +++ b/docker/solaris/solaris/data/gdf_from_mask_1.geojson @@ -0,0 +1,48 @@ +{ +"type": "FeatureCollection", +"features": [ +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 644.0, 0.0 ], [ 644.0, 1.0 ], [ 645.0, 1.0 ], [ 645.0, 3.0 ], [ 646.0, 3.0 ], [ 646.0, 9.0 ], [ 647.0, 9.0 ], [ 647.0, 11.0 ], [ 648.0, 11.0 ], [ 648.0, 13.0 ], [ 649.0, 13.0 ], [ 649.0, 18.0 ], [ 650.0, 18.0 ], [ 650.0, 17.0 ], [ 651.0, 17.0 ], [ 651.0, 16.0 ], [ 652.0, 16.0 ], [ 653.0, 16.0 ], [ 653.0, 15.0 ], [ 654.0, 15.0 ], [ 654.0, 14.0 ], [ 655.0, 14.0 ], [ 655.0, 10.0 ], [ 654.0, 10.0 ], [ 654.0, 5.0 ], [ 653.0, 5.0 ], [ 653.0, 4.0 ], [ 654.0, 4.0 ], [ 654.0, 3.0 ], [ 655.0, 3.0 ], [ 655.0, 2.0 ], [ 656.0, 2.0 ], [ 656.0, 3.0 ], [ 657.0, 3.0 ], [ 657.0, 4.0 ], [ 662.0, 4.0 ], [ 662.0, 5.0 ], [ 665.0, 5.0 ], [ 666.0, 5.0 ], [ 666.0, 4.0 ], [ 665.0, 4.0 ], [ 665.0, 1.0 ], [ 664.0, 1.0 ], [ 664.0, 0.0 ], [ 644.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 459.0, 0.0 ], [ 459.0, 2.0 ], [ 458.0, 2.0 ], [ 458.0, 4.0 ], [ 457.0, 4.0 ], [ 457.0, 6.0 ], [ 456.0, 6.0 ], [ 456.0, 8.0 ], [ 455.0, 8.0 ], [ 455.0, 10.0 ], [ 454.0, 10.0 ], [ 454.0, 12.0 ], [ 453.0, 12.0 ], [ 453.0, 15.0 ], [ 452.0, 15.0 ], [ 452.0, 17.0 ], [ 451.0, 17.0 ], [ 451.0, 19.0 ], [ 450.0, 19.0 ], [ 450.0, 21.0 ], [ 449.0, 21.0 ], [ 449.0, 23.0 ], [ 448.0, 23.0 ], [ 448.0, 25.0 ], [ 447.0, 25.0 ], [ 447.0, 28.0 ], [ 448.0, 28.0 ], [ 448.0, 29.0 ], [ 451.0, 29.0 ], [ 451.0, 30.0 ], [ 453.0, 30.0 ], [ 453.0, 31.0 ], [ 455.0, 31.0 ], [ 455.0, 32.0 ], [ 457.0, 32.0 ], [ 457.0, 33.0 ], [ 459.0, 33.0 ], [ 459.0, 34.0 ], [ 461.0, 34.0 ], [ 461.0, 35.0 ], [ 464.0, 35.0 ], [ 464.0, 36.0 ], [ 465.0, 36.0 ], [ 465.0, 34.0 ], [ 466.0, 34.0 ], [ 466.0, 32.0 ], [ 467.0, 32.0 ], [ 467.0, 30.0 ], [ 468.0, 30.0 ], [ 468.0, 28.0 ], [ 469.0, 28.0 ], [ 469.0, 26.0 ], [ 470.0, 26.0 ], [ 470.0, 24.0 ], [ 471.0, 24.0 ], [ 471.0, 21.0 ], [ 472.0, 21.0 ], [ 472.0, 19.0 ], [ 473.0, 19.0 ], [ 473.0, 17.0 ], [ 474.0, 17.0 ], [ 474.0, 15.0 ], [ 475.0, 15.0 ], [ 475.0, 13.0 ], [ 476.0, 13.0 ], [ 476.0, 11.0 ], [ 477.0, 11.0 ], [ 477.0, 9.0 ], [ 479.0, 9.0 ], [ 480.0, 9.0 ], [ 480.0, 8.0 ], [ 481.0, 8.0 ], [ 481.0, 6.0 ], [ 482.0, 6.0 ], [ 482.0, 4.0 ], [ 483.0, 4.0 ], [ 483.0, 2.0 ], [ 484.0, 2.0 ], [ 484.0, 0.0 ], [ 459.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 816.0, 1.0 ], [ 816.0, 2.0 ], [ 815.0, 2.0 ], [ 795.0, 2.0 ], [ 795.0, 30.0 ], [ 796.0, 30.0 ], [ 796.0, 38.0 ], [ 797.0, 38.0 ], [ 797.0, 39.0 ], [ 798.0, 39.0 ], [ 798.0, 40.0 ], [ 799.0, 40.0 ], [ 799.0, 42.0 ], [ 800.0, 42.0 ], [ 800.0, 43.0 ], [ 801.0, 43.0 ], [ 801.0, 44.0 ], [ 802.0, 44.0 ], [ 802.0, 46.0 ], [ 803.0, 46.0 ], [ 803.0, 47.0 ], [ 804.0, 47.0 ], [ 804.0, 48.0 ], [ 817.0, 48.0 ], [ 818.0, 48.0 ], [ 818.0, 30.0 ], [ 817.0, 30.0 ], [ 817.0, 1.0 ], [ 816.0, 1.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 0.0, 3.0 ], [ 0.0, 54.0 ], [ 2.0, 54.0 ], [ 2.0, 55.0 ], [ 7.0, 55.0 ], [ 7.0, 56.0 ], [ 12.0, 56.0 ], [ 12.0, 57.0 ], [ 17.0, 57.0 ], [ 17.0, 58.0 ], [ 18.0, 58.0 ], [ 18.0, 52.0 ], [ 19.0, 52.0 ], [ 19.0, 37.0 ], [ 17.0, 37.0 ], [ 17.0, 36.0 ], [ 16.0, 36.0 ], [ 16.0, 35.0 ], [ 14.0, 35.0 ], [ 14.0, 34.0 ], [ 12.0, 34.0 ], [ 12.0, 33.0 ], [ 11.0, 33.0 ], [ 11.0, 32.0 ], [ 9.0, 32.0 ], [ 9.0, 31.0 ], [ 8.0, 31.0 ], [ 8.0, 30.0 ], [ 6.0, 30.0 ], [ 6.0, 20.0 ], [ 7.0, 20.0 ], [ 7.0, 10.0 ], [ 8.0, 10.0 ], [ 8.0, 8.0 ], [ 7.0, 8.0 ], [ 7.0, 7.0 ], [ 6.0, 7.0 ], [ 6.0, 6.0 ], [ 4.0, 6.0 ], [ 4.0, 5.0 ], [ 3.0, 5.0 ], [ 3.0, 4.0 ], [ 1.0, 4.0 ], [ 1.0, 3.0 ], [ 0.0, 3.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 59.0, 67.0 ], [ 59.0, 69.0 ], [ 58.0, 69.0 ], [ 58.0, 71.0 ], [ 57.0, 71.0 ], [ 57.0, 73.0 ], [ 56.0, 73.0 ], [ 56.0, 75.0 ], [ 55.0, 75.0 ], [ 55.0, 77.0 ], [ 54.0, 77.0 ], [ 54.0, 79.0 ], [ 55.0, 79.0 ], [ 55.0, 80.0 ], [ 57.0, 80.0 ], [ 57.0, 81.0 ], [ 58.0, 81.0 ], [ 58.0, 82.0 ], [ 60.0, 82.0 ], [ 60.0, 83.0 ], [ 62.0, 83.0 ], [ 62.0, 84.0 ], [ 64.0, 84.0 ], [ 64.0, 85.0 ], [ 66.0, 85.0 ], [ 66.0, 86.0 ], [ 68.0, 86.0 ], [ 68.0, 87.0 ], [ 70.0, 87.0 ], [ 70.0, 88.0 ], [ 72.0, 88.0 ], [ 72.0, 89.0 ], [ 74.0, 89.0 ], [ 74.0, 90.0 ], [ 76.0, 90.0 ], [ 76.0, 91.0 ], [ 77.0, 91.0 ], [ 77.0, 92.0 ], [ 79.0, 92.0 ], [ 79.0, 93.0 ], [ 81.0, 93.0 ], [ 81.0, 94.0 ], [ 83.0, 94.0 ], [ 83.0, 95.0 ], [ 85.0, 95.0 ], [ 85.0, 96.0 ], [ 87.0, 96.0 ], [ 87.0, 97.0 ], [ 89.0, 97.0 ], [ 89.0, 98.0 ], [ 91.0, 98.0 ], [ 91.0, 99.0 ], [ 95.0, 99.0 ], [ 95.0, 100.0 ], [ 98.0, 100.0 ], [ 98.0, 99.0 ], [ 99.0, 99.0 ], [ 99.0, 97.0 ], [ 100.0, 97.0 ], [ 100.0, 95.0 ], [ 101.0, 95.0 ], [ 101.0, 94.0 ], [ 102.0, 94.0 ], [ 102.0, 92.0 ], [ 103.0, 92.0 ], [ 103.0, 90.0 ], [ 104.0, 90.0 ], [ 104.0, 88.0 ], [ 105.0, 88.0 ], [ 105.0, 87.0 ], [ 106.0, 87.0 ], [ 106.0, 85.0 ], [ 107.0, 85.0 ], [ 107.0, 84.0 ], [ 106.0, 84.0 ], [ 106.0, 83.0 ], [ 104.0, 83.0 ], [ 104.0, 82.0 ], [ 102.0, 82.0 ], [ 102.0, 81.0 ], [ 100.0, 81.0 ], [ 100.0, 80.0 ], [ 99.0, 80.0 ], [ 99.0, 79.0 ], [ 97.0, 79.0 ], [ 97.0, 78.0 ], [ 95.0, 78.0 ], [ 95.0, 77.0 ], [ 94.0, 77.0 ], [ 94.0, 76.0 ], [ 92.0, 76.0 ], [ 92.0, 75.0 ], [ 90.0, 75.0 ], [ 90.0, 74.0 ], [ 88.0, 74.0 ], [ 88.0, 73.0 ], [ 86.0, 73.0 ], [ 86.0, 74.0 ], [ 85.0, 74.0 ], [ 85.0, 75.0 ], [ 84.0, 75.0 ], [ 83.0, 75.0 ], [ 83.0, 76.0 ], [ 82.0, 76.0 ], [ 81.0, 76.0 ], [ 81.0, 77.0 ], [ 80.0, 77.0 ], [ 80.0, 78.0 ], [ 79.0, 78.0 ], [ 79.0, 77.0 ], [ 77.0, 77.0 ], [ 77.0, 76.0 ], [ 76.0, 76.0 ], [ 76.0, 75.0 ], [ 74.0, 75.0 ], [ 74.0, 74.0 ], [ 72.0, 74.0 ], [ 72.0, 73.0 ], [ 70.0, 73.0 ], [ 70.0, 72.0 ], [ 68.0, 72.0 ], [ 68.0, 71.0 ], [ 66.0, 71.0 ], [ 66.0, 70.0 ], [ 64.0, 70.0 ], [ 64.0, 69.0 ], [ 62.0, 69.0 ], [ 62.0, 68.0 ], [ 60.0, 68.0 ], [ 60.0, 67.0 ], [ 59.0, 67.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 434.0, 44.0 ], [ 434.0, 48.0 ], [ 433.0, 48.0 ], [ 433.0, 55.0 ], [ 432.0, 55.0 ], [ 432.0, 61.0 ], [ 431.0, 61.0 ], [ 431.0, 67.0 ], [ 430.0, 67.0 ], [ 430.0, 74.0 ], [ 429.0, 74.0 ], [ 429.0, 80.0 ], [ 428.0, 80.0 ], [ 428.0, 87.0 ], [ 427.0, 87.0 ], [ 427.0, 94.0 ], [ 433.0, 94.0 ], [ 433.0, 95.0 ], [ 440.0, 95.0 ], [ 440.0, 96.0 ], [ 446.0, 96.0 ], [ 446.0, 97.0 ], [ 447.0, 97.0 ], [ 447.0, 91.0 ], [ 448.0, 91.0 ], [ 448.0, 85.0 ], [ 449.0, 85.0 ], [ 449.0, 78.0 ], [ 450.0, 78.0 ], [ 450.0, 72.0 ], [ 451.0, 72.0 ], [ 451.0, 69.0 ], [ 452.0, 69.0 ], [ 452.0, 70.0 ], [ 455.0, 70.0 ], [ 456.0, 70.0 ], [ 456.0, 65.0 ], [ 457.0, 65.0 ], [ 457.0, 58.0 ], [ 458.0, 58.0 ], [ 458.0, 52.0 ], [ 459.0, 52.0 ], [ 459.0, 48.0 ], [ 458.0, 48.0 ], [ 458.0, 47.0 ], [ 452.0, 47.0 ], [ 452.0, 46.0 ], [ 446.0, 46.0 ], [ 446.0, 45.0 ], [ 439.0, 45.0 ], [ 439.0, 44.0 ], [ 434.0, 44.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 813.0, 58.0 ], [ 813.0, 59.0 ], [ 812.0, 59.0 ], [ 806.0, 59.0 ], [ 806.0, 60.0 ], [ 805.0, 60.0 ], [ 805.0, 61.0 ], [ 804.0, 61.0 ], [ 804.0, 62.0 ], [ 803.0, 62.0 ], [ 795.0, 62.0 ], [ 795.0, 65.0 ], [ 796.0, 65.0 ], [ 796.0, 106.0 ], [ 806.0, 106.0 ], [ 806.0, 105.0 ], [ 816.0, 105.0 ], [ 817.0, 105.0 ], [ 817.0, 60.0 ], [ 816.0, 60.0 ], [ 816.0, 58.0 ], [ 813.0, 58.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 571.0, 78.0 ], [ 571.0, 79.0 ], [ 570.0, 79.0 ], [ 568.0, 79.0 ], [ 568.0, 80.0 ], [ 567.0, 80.0 ], [ 567.0, 81.0 ], [ 566.0, 81.0 ], [ 566.0, 82.0 ], [ 565.0, 82.0 ], [ 565.0, 83.0 ], [ 564.0, 83.0 ], [ 564.0, 84.0 ], [ 563.0, 84.0 ], [ 563.0, 85.0 ], [ 562.0, 85.0 ], [ 562.0, 86.0 ], [ 561.0, 86.0 ], [ 561.0, 88.0 ], [ 560.0, 88.0 ], [ 560.0, 89.0 ], [ 559.0, 89.0 ], [ 559.0, 90.0 ], [ 558.0, 90.0 ], [ 558.0, 91.0 ], [ 557.0, 91.0 ], [ 557.0, 92.0 ], [ 556.0, 92.0 ], [ 556.0, 93.0 ], [ 555.0, 93.0 ], [ 555.0, 94.0 ], [ 554.0, 94.0 ], [ 554.0, 96.0 ], [ 553.0, 96.0 ], [ 553.0, 97.0 ], [ 552.0, 97.0 ], [ 552.0, 98.0 ], [ 551.0, 98.0 ], [ 551.0, 99.0 ], [ 550.0, 99.0 ], [ 550.0, 100.0 ], [ 549.0, 100.0 ], [ 549.0, 101.0 ], [ 548.0, 101.0 ], [ 548.0, 105.0 ], [ 547.0, 105.0 ], [ 547.0, 111.0 ], [ 546.0, 111.0 ], [ 546.0, 118.0 ], [ 547.0, 118.0 ], [ 547.0, 121.0 ], [ 548.0, 121.0 ], [ 548.0, 124.0 ], [ 549.0, 124.0 ], [ 549.0, 126.0 ], [ 550.0, 126.0 ], [ 550.0, 127.0 ], [ 551.0, 127.0 ], [ 551.0, 128.0 ], [ 552.0, 128.0 ], [ 552.0, 129.0 ], [ 553.0, 129.0 ], [ 553.0, 130.0 ], [ 554.0, 130.0 ], [ 554.0, 129.0 ], [ 555.0, 129.0 ], [ 555.0, 127.0 ], [ 556.0, 127.0 ], [ 556.0, 126.0 ], [ 557.0, 126.0 ], [ 557.0, 125.0 ], [ 558.0, 125.0 ], [ 558.0, 124.0 ], [ 559.0, 124.0 ], [ 559.0, 123.0 ], [ 560.0, 123.0 ], [ 560.0, 122.0 ], [ 561.0, 122.0 ], [ 561.0, 121.0 ], [ 562.0, 121.0 ], [ 562.0, 120.0 ], [ 563.0, 120.0 ], [ 563.0, 118.0 ], [ 564.0, 118.0 ], [ 564.0, 117.0 ], [ 565.0, 117.0 ], [ 565.0, 116.0 ], [ 566.0, 116.0 ], [ 566.0, 115.0 ], [ 567.0, 115.0 ], [ 567.0, 114.0 ], [ 568.0, 114.0 ], [ 568.0, 113.0 ], [ 569.0, 113.0 ], [ 569.0, 112.0 ], [ 570.0, 112.0 ], [ 570.0, 111.0 ], [ 571.0, 111.0 ], [ 571.0, 110.0 ], [ 572.0, 110.0 ], [ 572.0, 108.0 ], [ 573.0, 108.0 ], [ 573.0, 107.0 ], [ 574.0, 107.0 ], [ 574.0, 106.0 ], [ 575.0, 106.0 ], [ 575.0, 105.0 ], [ 576.0, 105.0 ], [ 576.0, 104.0 ], [ 577.0, 104.0 ], [ 577.0, 103.0 ], [ 578.0, 103.0 ], [ 578.0, 102.0 ], [ 579.0, 102.0 ], [ 579.0, 101.0 ], [ 580.0, 101.0 ], [ 580.0, 99.0 ], [ 581.0, 99.0 ], [ 581.0, 98.0 ], [ 582.0, 98.0 ], [ 582.0, 97.0 ], [ 583.0, 97.0 ], [ 583.0, 96.0 ], [ 584.0, 96.0 ], [ 584.0, 95.0 ], [ 585.0, 95.0 ], [ 585.0, 94.0 ], [ 586.0, 94.0 ], [ 586.0, 93.0 ], [ 587.0, 93.0 ], [ 587.0, 92.0 ], [ 588.0, 92.0 ], [ 588.0, 91.0 ], [ 586.0, 91.0 ], [ 586.0, 90.0 ], [ 582.0, 90.0 ], [ 582.0, 89.0 ], [ 578.0, 89.0 ], [ 578.0, 88.0 ], [ 576.0, 88.0 ], [ 576.0, 86.0 ], [ 575.0, 86.0 ], [ 575.0, 85.0 ], [ 574.0, 85.0 ], [ 574.0, 78.0 ], [ 571.0, 78.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 228.0, 118.0 ], [ 228.0, 119.0 ], [ 227.0, 119.0 ], [ 227.0, 124.0 ], [ 226.0, 124.0 ], [ 226.0, 126.0 ], [ 228.0, 126.0 ], [ 228.0, 127.0 ], [ 233.0, 127.0 ], [ 233.0, 128.0 ], [ 234.0, 128.0 ], [ 235.0, 128.0 ], [ 235.0, 123.0 ], [ 236.0, 123.0 ], [ 236.0, 119.0 ], [ 231.0, 119.0 ], [ 231.0, 118.0 ], [ 228.0, 118.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 133.0, 98.0 ], [ 133.0, 100.0 ], [ 132.0, 100.0 ], [ 132.0, 102.0 ], [ 131.0, 102.0 ], [ 131.0, 104.0 ], [ 130.0, 104.0 ], [ 130.0, 105.0 ], [ 129.0, 105.0 ], [ 129.0, 107.0 ], [ 128.0, 107.0 ], [ 128.0, 109.0 ], [ 127.0, 109.0 ], [ 127.0, 110.0 ], [ 126.0, 110.0 ], [ 126.0, 112.0 ], [ 125.0, 112.0 ], [ 125.0, 114.0 ], [ 126.0, 114.0 ], [ 126.0, 115.0 ], [ 127.0, 115.0 ], [ 127.0, 116.0 ], [ 128.0, 116.0 ], [ 128.0, 117.0 ], [ 129.0, 117.0 ], [ 129.0, 118.0 ], [ 131.0, 118.0 ], [ 131.0, 119.0 ], [ 132.0, 119.0 ], [ 132.0, 120.0 ], [ 133.0, 120.0 ], [ 133.0, 121.0 ], [ 134.0, 121.0 ], [ 134.0, 122.0 ], [ 135.0, 122.0 ], [ 135.0, 123.0 ], [ 136.0, 123.0 ], [ 136.0, 124.0 ], [ 137.0, 124.0 ], [ 137.0, 126.0 ], [ 138.0, 126.0 ], [ 138.0, 127.0 ], [ 139.0, 127.0 ], [ 139.0, 128.0 ], [ 140.0, 128.0 ], [ 140.0, 129.0 ], [ 142.0, 129.0 ], [ 142.0, 130.0 ], [ 143.0, 130.0 ], [ 143.0, 131.0 ], [ 145.0, 131.0 ], [ 145.0, 132.0 ], [ 147.0, 132.0 ], [ 147.0, 133.0 ], [ 149.0, 133.0 ], [ 149.0, 134.0 ], [ 151.0, 134.0 ], [ 151.0, 135.0 ], [ 152.0, 135.0 ], [ 152.0, 136.0 ], [ 153.0, 136.0 ], [ 153.0, 137.0 ], [ 154.0, 137.0 ], [ 154.0, 139.0 ], [ 155.0, 139.0 ], [ 155.0, 140.0 ], [ 156.0, 140.0 ], [ 156.0, 141.0 ], [ 159.0, 141.0 ], [ 159.0, 142.0 ], [ 161.0, 142.0 ], [ 161.0, 143.0 ], [ 164.0, 143.0 ], [ 164.0, 144.0 ], [ 167.0, 144.0 ], [ 167.0, 142.0 ], [ 168.0, 142.0 ], [ 168.0, 139.0 ], [ 169.0, 139.0 ], [ 169.0, 137.0 ], [ 170.0, 137.0 ], [ 170.0, 134.0 ], [ 171.0, 134.0 ], [ 171.0, 133.0 ], [ 170.0, 133.0 ], [ 170.0, 132.0 ], [ 169.0, 132.0 ], [ 169.0, 131.0 ], [ 167.0, 131.0 ], [ 167.0, 130.0 ], [ 165.0, 130.0 ], [ 165.0, 129.0 ], [ 163.0, 129.0 ], [ 163.0, 128.0 ], [ 162.0, 128.0 ], [ 162.0, 126.0 ], [ 161.0, 126.0 ], [ 161.0, 123.0 ], [ 160.0, 123.0 ], [ 160.0, 120.0 ], [ 159.0, 120.0 ], [ 159.0, 117.0 ], [ 158.0, 117.0 ], [ 158.0, 116.0 ], [ 156.0, 116.0 ], [ 156.0, 115.0 ], [ 155.0, 115.0 ], [ 155.0, 114.0 ], [ 153.0, 114.0 ], [ 153.0, 112.0 ], [ 152.0, 112.0 ], [ 152.0, 110.0 ], [ 151.0, 110.0 ], [ 151.0, 108.0 ], [ 149.0, 108.0 ], [ 149.0, 107.0 ], [ 148.0, 107.0 ], [ 148.0, 106.0 ], [ 146.0, 106.0 ], [ 146.0, 105.0 ], [ 145.0, 105.0 ], [ 145.0, 104.0 ], [ 143.0, 104.0 ], [ 143.0, 103.0 ], [ 142.0, 103.0 ], [ 142.0, 102.0 ], [ 140.0, 102.0 ], [ 140.0, 101.0 ], [ 138.0, 101.0 ], [ 138.0, 100.0 ], [ 137.0, 100.0 ], [ 137.0, 99.0 ], [ 135.0, 99.0 ], [ 135.0, 98.0 ], [ 133.0, 98.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 417.0, 115.0 ], [ 417.0, 124.0 ], [ 416.0, 124.0 ], [ 416.0, 131.0 ], [ 417.0, 131.0 ], [ 417.0, 133.0 ], [ 418.0, 133.0 ], [ 418.0, 134.0 ], [ 419.0, 134.0 ], [ 419.0, 136.0 ], [ 420.0, 136.0 ], [ 420.0, 141.0 ], [ 421.0, 141.0 ], [ 421.0, 145.0 ], [ 420.0, 145.0 ], [ 420.0, 148.0 ], [ 419.0, 148.0 ], [ 419.0, 154.0 ], [ 420.0, 154.0 ], [ 420.0, 155.0 ], [ 423.0, 155.0 ], [ 423.0, 156.0 ], [ 428.0, 156.0 ], [ 428.0, 155.0 ], [ 429.0, 155.0 ], [ 429.0, 154.0 ], [ 430.0, 154.0 ], [ 430.0, 153.0 ], [ 431.0, 153.0 ], [ 431.0, 151.0 ], [ 432.0, 151.0 ], [ 432.0, 150.0 ], [ 433.0, 150.0 ], [ 433.0, 148.0 ], [ 434.0, 148.0 ], [ 434.0, 147.0 ], [ 435.0, 147.0 ], [ 435.0, 139.0 ], [ 436.0, 139.0 ], [ 436.0, 118.0 ], [ 437.0, 118.0 ], [ 437.0, 115.0 ], [ 417.0, 115.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 803.0, 127.0 ], [ 803.0, 128.0 ], [ 802.0, 128.0 ], [ 777.0, 128.0 ], [ 777.0, 153.0 ], [ 778.0, 153.0 ], [ 778.0, 164.0 ], [ 787.0, 164.0 ], [ 787.0, 163.0 ], [ 802.0, 163.0 ], [ 803.0, 163.0 ], [ 803.0, 152.0 ], [ 802.0, 152.0 ], [ 802.0, 147.0 ], [ 803.0, 147.0 ], [ 803.0, 146.0 ], [ 818.0, 146.0 ], [ 819.0, 146.0 ], [ 819.0, 142.0 ], [ 818.0, 142.0 ], [ 818.0, 127.0 ], [ 803.0, 127.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 525.0, 150.0 ], [ 525.0, 166.0 ], [ 526.0, 166.0 ], [ 526.0, 167.0 ], [ 528.0, 167.0 ], [ 528.0, 168.0 ], [ 529.0, 168.0 ], [ 529.0, 170.0 ], [ 530.0, 170.0 ], [ 530.0, 172.0 ], [ 529.0, 172.0 ], [ 529.0, 174.0 ], [ 528.0, 174.0 ], [ 528.0, 176.0 ], [ 527.0, 176.0 ], [ 527.0, 178.0 ], [ 526.0, 178.0 ], [ 526.0, 179.0 ], [ 525.0, 179.0 ], [ 525.0, 180.0 ], [ 524.0, 180.0 ], [ 524.0, 181.0 ], [ 523.0, 181.0 ], [ 523.0, 182.0 ], [ 522.0, 182.0 ], [ 522.0, 192.0 ], [ 524.0, 192.0 ], [ 524.0, 189.0 ], [ 525.0, 189.0 ], [ 525.0, 188.0 ], [ 526.0, 188.0 ], [ 526.0, 187.0 ], [ 527.0, 187.0 ], [ 528.0, 187.0 ], [ 528.0, 186.0 ], [ 529.0, 186.0 ], [ 529.0, 185.0 ], [ 530.0, 185.0 ], [ 531.0, 185.0 ], [ 531.0, 184.0 ], [ 532.0, 184.0 ], [ 532.0, 183.0 ], [ 533.0, 183.0 ], [ 534.0, 183.0 ], [ 534.0, 182.0 ], [ 535.0, 182.0 ], [ 535.0, 181.0 ], [ 536.0, 181.0 ], [ 537.0, 181.0 ], [ 537.0, 180.0 ], [ 538.0, 180.0 ], [ 538.0, 179.0 ], [ 539.0, 179.0 ], [ 539.0, 175.0 ], [ 538.0, 175.0 ], [ 538.0, 171.0 ], [ 537.0, 171.0 ], [ 537.0, 167.0 ], [ 536.0, 167.0 ], [ 536.0, 165.0 ], [ 537.0, 165.0 ], [ 537.0, 159.0 ], [ 536.0, 159.0 ], [ 536.0, 150.0 ], [ 525.0, 150.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 237.0, 154.0 ], [ 237.0, 156.0 ], [ 236.0, 156.0 ], [ 236.0, 158.0 ], [ 235.0, 158.0 ], [ 235.0, 160.0 ], [ 234.0, 160.0 ], [ 234.0, 161.0 ], [ 233.0, 161.0 ], [ 233.0, 163.0 ], [ 232.0, 163.0 ], [ 232.0, 165.0 ], [ 231.0, 165.0 ], [ 231.0, 167.0 ], [ 230.0, 167.0 ], [ 230.0, 169.0 ], [ 229.0, 169.0 ], [ 229.0, 171.0 ], [ 228.0, 171.0 ], [ 228.0, 173.0 ], [ 227.0, 173.0 ], [ 227.0, 175.0 ], [ 226.0, 175.0 ], [ 226.0, 177.0 ], [ 225.0, 177.0 ], [ 225.0, 179.0 ], [ 224.0, 179.0 ], [ 224.0, 181.0 ], [ 223.0, 181.0 ], [ 223.0, 182.0 ], [ 222.0, 182.0 ], [ 222.0, 184.0 ], [ 224.0, 184.0 ], [ 224.0, 185.0 ], [ 226.0, 185.0 ], [ 226.0, 186.0 ], [ 227.0, 186.0 ], [ 227.0, 187.0 ], [ 229.0, 187.0 ], [ 229.0, 188.0 ], [ 231.0, 188.0 ], [ 231.0, 189.0 ], [ 233.0, 189.0 ], [ 233.0, 190.0 ], [ 235.0, 190.0 ], [ 235.0, 191.0 ], [ 237.0, 191.0 ], [ 237.0, 192.0 ], [ 239.0, 192.0 ], [ 239.0, 193.0 ], [ 241.0, 193.0 ], [ 241.0, 194.0 ], [ 243.0, 194.0 ], [ 243.0, 195.0 ], [ 245.0, 195.0 ], [ 245.0, 196.0 ], [ 247.0, 196.0 ], [ 247.0, 197.0 ], [ 249.0, 197.0 ], [ 249.0, 198.0 ], [ 251.0, 198.0 ], [ 251.0, 199.0 ], [ 253.0, 199.0 ], [ 253.0, 200.0 ], [ 257.0, 200.0 ], [ 257.0, 201.0 ], [ 261.0, 201.0 ], [ 261.0, 200.0 ], [ 262.0, 200.0 ], [ 262.0, 199.0 ], [ 263.0, 199.0 ], [ 263.0, 198.0 ], [ 264.0, 198.0 ], [ 264.0, 196.0 ], [ 265.0, 196.0 ], [ 265.0, 195.0 ], [ 266.0, 195.0 ], [ 266.0, 193.0 ], [ 267.0, 193.0 ], [ 267.0, 191.0 ], [ 268.0, 191.0 ], [ 268.0, 189.0 ], [ 269.0, 189.0 ], [ 269.0, 187.0 ], [ 270.0, 187.0 ], [ 270.0, 184.0 ], [ 271.0, 184.0 ], [ 271.0, 181.0 ], [ 272.0, 181.0 ], [ 272.0, 176.0 ], [ 273.0, 176.0 ], [ 273.0, 171.0 ], [ 272.0, 171.0 ], [ 272.0, 170.0 ], [ 270.0, 170.0 ], [ 270.0, 169.0 ], [ 268.0, 169.0 ], [ 268.0, 168.0 ], [ 266.0, 168.0 ], [ 266.0, 167.0 ], [ 265.0, 167.0 ], [ 265.0, 166.0 ], [ 263.0, 166.0 ], [ 263.0, 165.0 ], [ 261.0, 165.0 ], [ 261.0, 164.0 ], [ 259.0, 164.0 ], [ 259.0, 163.0 ], [ 257.0, 163.0 ], [ 257.0, 162.0 ], [ 255.0, 162.0 ], [ 255.0, 161.0 ], [ 253.0, 161.0 ], [ 253.0, 155.0 ], [ 252.0, 155.0 ], [ 252.0, 154.0 ], [ 245.0, 154.0 ], [ 245.0, 155.0 ], [ 244.0, 155.0 ], [ 244.0, 157.0 ], [ 243.0, 157.0 ], [ 243.0, 156.0 ], [ 241.0, 156.0 ], [ 241.0, 155.0 ], [ 240.0, 155.0 ], [ 240.0, 154.0 ], [ 237.0, 154.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 412.0, 165.0 ], [ 412.0, 180.0 ], [ 411.0, 180.0 ], [ 411.0, 208.0 ], [ 410.0, 208.0 ], [ 410.0, 216.0 ], [ 425.0, 216.0 ], [ 425.0, 217.0 ], [ 430.0, 217.0 ], [ 431.0, 217.0 ], [ 431.0, 194.0 ], [ 432.0, 194.0 ], [ 432.0, 167.0 ], [ 433.0, 167.0 ], [ 433.0, 166.0 ], [ 415.0, 166.0 ], [ 415.0, 165.0 ], [ 412.0, 165.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 778.0, 197.0 ], [ 778.0, 198.0 ], [ 777.0, 198.0 ], [ 776.0, 198.0 ], [ 776.0, 199.0 ], [ 775.0, 199.0 ], [ 774.0, 199.0 ], [ 774.0, 200.0 ], [ 773.0, 200.0 ], [ 772.0, 200.0 ], [ 772.0, 201.0 ], [ 771.0, 201.0 ], [ 771.0, 202.0 ], [ 770.0, 202.0 ], [ 769.0, 202.0 ], [ 769.0, 203.0 ], [ 768.0, 203.0 ], [ 767.0, 203.0 ], [ 767.0, 219.0 ], [ 766.0, 219.0 ], [ 766.0, 220.0 ], [ 792.0, 220.0 ], [ 793.0, 220.0 ], [ 793.0, 207.0 ], [ 780.0, 207.0 ], [ 780.0, 197.0 ], [ 778.0, 197.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 523.0, 198.0 ], [ 523.0, 214.0 ], [ 524.0, 214.0 ], [ 524.0, 215.0 ], [ 525.0, 215.0 ], [ 525.0, 216.0 ], [ 526.0, 216.0 ], [ 526.0, 218.0 ], [ 527.0, 218.0 ], [ 527.0, 219.0 ], [ 528.0, 219.0 ], [ 528.0, 221.0 ], [ 529.0, 221.0 ], [ 529.0, 222.0 ], [ 528.0, 222.0 ], [ 528.0, 226.0 ], [ 527.0, 226.0 ], [ 527.0, 229.0 ], [ 526.0, 229.0 ], [ 526.0, 230.0 ], [ 525.0, 230.0 ], [ 524.0, 230.0 ], [ 524.0, 231.0 ], [ 523.0, 231.0 ], [ 522.0, 231.0 ], [ 522.0, 232.0 ], [ 521.0, 232.0 ], [ 521.0, 233.0 ], [ 520.0, 233.0 ], [ 519.0, 233.0 ], [ 519.0, 236.0 ], [ 520.0, 236.0 ], [ 520.0, 241.0 ], [ 521.0, 241.0 ], [ 521.0, 245.0 ], [ 522.0, 245.0 ], [ 522.0, 248.0 ], [ 524.0, 248.0 ], [ 524.0, 249.0 ], [ 537.0, 249.0 ], [ 537.0, 250.0 ], [ 542.0, 250.0 ], [ 542.0, 248.0 ], [ 543.0, 248.0 ], [ 543.0, 228.0 ], [ 544.0, 228.0 ], [ 544.0, 208.0 ], [ 545.0, 208.0 ], [ 545.0, 199.0 ], [ 531.0, 199.0 ], [ 531.0, 198.0 ], [ 523.0, 198.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 35.0, 221.0 ], [ 35.0, 226.0 ], [ 34.0, 226.0 ], [ 27.0, 226.0 ], [ 27.0, 245.0 ], [ 26.0, 245.0 ], [ 26.0, 266.0 ], [ 29.0, 266.0 ], [ 29.0, 267.0 ], [ 48.0, 267.0 ], [ 48.0, 266.0 ], [ 49.0, 266.0 ], [ 49.0, 265.0 ], [ 50.0, 265.0 ], [ 51.0, 265.0 ], [ 51.0, 264.0 ], [ 52.0, 264.0 ], [ 52.0, 263.0 ], [ 53.0, 263.0 ], [ 54.0, 263.0 ], [ 54.0, 262.0 ], [ 55.0, 262.0 ], [ 55.0, 251.0 ], [ 56.0, 251.0 ], [ 56.0, 230.0 ], [ 44.0, 230.0 ], [ 44.0, 228.0 ], [ 45.0, 228.0 ], [ 45.0, 221.0 ], [ 35.0, 221.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 407.0, 225.0 ], [ 407.0, 232.0 ], [ 410.0, 232.0 ], [ 410.0, 240.0 ], [ 409.0, 240.0 ], [ 409.0, 241.0 ], [ 408.0, 241.0 ], [ 408.0, 242.0 ], [ 407.0, 242.0 ], [ 407.0, 245.0 ], [ 406.0, 245.0 ], [ 406.0, 249.0 ], [ 407.0, 249.0 ], [ 407.0, 251.0 ], [ 408.0, 251.0 ], [ 408.0, 252.0 ], [ 409.0, 252.0 ], [ 409.0, 253.0 ], [ 410.0, 253.0 ], [ 410.0, 255.0 ], [ 411.0, 255.0 ], [ 411.0, 256.0 ], [ 412.0, 256.0 ], [ 412.0, 259.0 ], [ 413.0, 259.0 ], [ 413.0, 261.0 ], [ 414.0, 261.0 ], [ 414.0, 275.0 ], [ 427.0, 275.0 ], [ 427.0, 276.0 ], [ 428.0, 276.0 ], [ 428.0, 271.0 ], [ 429.0, 271.0 ], [ 429.0, 259.0 ], [ 425.0, 259.0 ], [ 425.0, 256.0 ], [ 426.0, 256.0 ], [ 426.0, 246.0 ], [ 427.0, 246.0 ], [ 427.0, 245.0 ], [ 430.0, 245.0 ], [ 431.0, 245.0 ], [ 431.0, 234.0 ], [ 432.0, 234.0 ], [ 432.0, 226.0 ], [ 423.0, 226.0 ], [ 423.0, 225.0 ], [ 407.0, 225.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 129.0, 257.0 ], [ 129.0, 259.0 ], [ 128.0, 259.0 ], [ 128.0, 261.0 ], [ 127.0, 261.0 ], [ 127.0, 263.0 ], [ 126.0, 263.0 ], [ 126.0, 265.0 ], [ 125.0, 265.0 ], [ 125.0, 267.0 ], [ 124.0, 267.0 ], [ 124.0, 272.0 ], [ 125.0, 272.0 ], [ 125.0, 274.0 ], [ 127.0, 274.0 ], [ 127.0, 275.0 ], [ 129.0, 275.0 ], [ 129.0, 276.0 ], [ 131.0, 276.0 ], [ 131.0, 277.0 ], [ 133.0, 277.0 ], [ 133.0, 278.0 ], [ 134.0, 278.0 ], [ 134.0, 279.0 ], [ 136.0, 279.0 ], [ 136.0, 280.0 ], [ 138.0, 280.0 ], [ 138.0, 281.0 ], [ 140.0, 281.0 ], [ 140.0, 282.0 ], [ 142.0, 282.0 ], [ 142.0, 283.0 ], [ 144.0, 283.0 ], [ 144.0, 284.0 ], [ 146.0, 284.0 ], [ 146.0, 285.0 ], [ 148.0, 285.0 ], [ 148.0, 286.0 ], [ 150.0, 286.0 ], [ 150.0, 287.0 ], [ 152.0, 287.0 ], [ 152.0, 288.0 ], [ 154.0, 288.0 ], [ 154.0, 289.0 ], [ 155.0, 289.0 ], [ 155.0, 287.0 ], [ 156.0, 287.0 ], [ 156.0, 285.0 ], [ 157.0, 285.0 ], [ 157.0, 283.0 ], [ 158.0, 283.0 ], [ 158.0, 281.0 ], [ 159.0, 281.0 ], [ 159.0, 280.0 ], [ 160.0, 280.0 ], [ 160.0, 278.0 ], [ 161.0, 278.0 ], [ 161.0, 276.0 ], [ 162.0, 276.0 ], [ 162.0, 274.0 ], [ 161.0, 274.0 ], [ 161.0, 273.0 ], [ 159.0, 273.0 ], [ 159.0, 272.0 ], [ 157.0, 272.0 ], [ 157.0, 271.0 ], [ 155.0, 271.0 ], [ 155.0, 270.0 ], [ 153.0, 270.0 ], [ 153.0, 269.0 ], [ 151.0, 269.0 ], [ 151.0, 268.0 ], [ 150.0, 268.0 ], [ 150.0, 267.0 ], [ 148.0, 267.0 ], [ 148.0, 266.0 ], [ 146.0, 266.0 ], [ 146.0, 265.0 ], [ 144.0, 265.0 ], [ 144.0, 264.0 ], [ 142.0, 264.0 ], [ 142.0, 263.0 ], [ 140.0, 263.0 ], [ 140.0, 262.0 ], [ 138.0, 262.0 ], [ 138.0, 261.0 ], [ 136.0, 261.0 ], [ 136.0, 260.0 ], [ 134.0, 260.0 ], [ 134.0, 259.0 ], [ 132.0, 259.0 ], [ 132.0, 258.0 ], [ 130.0, 258.0 ], [ 130.0, 257.0 ], [ 129.0, 257.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 527.0, 262.0 ], [ 527.0, 267.0 ], [ 526.0, 267.0 ], [ 526.0, 301.0 ], [ 525.0, 301.0 ], [ 518.0, 301.0 ], [ 518.0, 314.0 ], [ 543.0, 314.0 ], [ 543.0, 315.0 ], [ 551.0, 315.0 ], [ 552.0, 315.0 ], [ 552.0, 293.0 ], [ 545.0, 293.0 ], [ 545.0, 262.0 ], [ 527.0, 262.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 407.0, 293.0 ], [ 407.0, 304.0 ], [ 406.0, 304.0 ], [ 404.0, 304.0 ], [ 404.0, 314.0 ], [ 403.0, 314.0 ], [ 403.0, 332.0 ], [ 402.0, 332.0 ], [ 402.0, 344.0 ], [ 408.0, 344.0 ], [ 408.0, 345.0 ], [ 425.0, 345.0 ], [ 425.0, 336.0 ], [ 426.0, 336.0 ], [ 426.0, 318.0 ], [ 427.0, 318.0 ], [ 427.0, 300.0 ], [ 428.0, 300.0 ], [ 428.0, 294.0 ], [ 410.0, 294.0 ], [ 410.0, 293.0 ], [ 407.0, 293.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 589.0, 341.0 ], [ 589.0, 356.0 ], [ 591.0, 356.0 ], [ 591.0, 362.0 ], [ 596.0, 362.0 ], [ 596.0, 360.0 ], [ 595.0, 360.0 ], [ 595.0, 352.0 ], [ 596.0, 352.0 ], [ 596.0, 351.0 ], [ 595.0, 351.0 ], [ 595.0, 350.0 ], [ 594.0, 350.0 ], [ 594.0, 348.0 ], [ 593.0, 348.0 ], [ 593.0, 344.0 ], [ 594.0, 344.0 ], [ 594.0, 342.0 ], [ 595.0, 342.0 ], [ 595.0, 341.0 ], [ 589.0, 341.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 863.0, 337.0 ], [ 863.0, 338.0 ], [ 862.0, 338.0 ], [ 854.0, 338.0 ], [ 854.0, 340.0 ], [ 855.0, 340.0 ], [ 855.0, 343.0 ], [ 854.0, 343.0 ], [ 851.0, 343.0 ], [ 851.0, 344.0 ], [ 850.0, 344.0 ], [ 850.0, 349.0 ], [ 849.0, 349.0 ], [ 843.0, 349.0 ], [ 843.0, 350.0 ], [ 842.0, 350.0 ], [ 837.0, 350.0 ], [ 837.0, 356.0 ], [ 838.0, 356.0 ], [ 838.0, 366.0 ], [ 849.0, 366.0 ], [ 849.0, 365.0 ], [ 861.0, 365.0 ], [ 862.0, 365.0 ], [ 862.0, 364.0 ], [ 874.0, 364.0 ], [ 875.0, 364.0 ], [ 875.0, 363.0 ], [ 878.0, 363.0 ], [ 879.0, 363.0 ], [ 879.0, 362.0 ], [ 880.0, 362.0 ], [ 880.0, 361.0 ], [ 881.0, 361.0 ], [ 881.0, 360.0 ], [ 882.0, 360.0 ], [ 883.0, 360.0 ], [ 883.0, 359.0 ], [ 884.0, 359.0 ], [ 884.0, 358.0 ], [ 885.0, 358.0 ], [ 885.0, 346.0 ], [ 884.0, 346.0 ], [ 884.0, 342.0 ], [ 876.0, 342.0 ], [ 876.0, 343.0 ], [ 866.0, 343.0 ], [ 866.0, 337.0 ], [ 863.0, 337.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 108.0, 315.0 ], [ 108.0, 316.0 ], [ 110.0, 316.0 ], [ 110.0, 317.0 ], [ 111.0, 317.0 ], [ 111.0, 318.0 ], [ 112.0, 318.0 ], [ 112.0, 319.0 ], [ 114.0, 319.0 ], [ 114.0, 320.0 ], [ 113.0, 320.0 ], [ 113.0, 327.0 ], [ 112.0, 327.0 ], [ 112.0, 328.0 ], [ 111.0, 328.0 ], [ 111.0, 330.0 ], [ 110.0, 330.0 ], [ 110.0, 332.0 ], [ 109.0, 332.0 ], [ 109.0, 334.0 ], [ 108.0, 334.0 ], [ 108.0, 335.0 ], [ 107.0, 335.0 ], [ 107.0, 337.0 ], [ 106.0, 337.0 ], [ 105.0, 337.0 ], [ 105.0, 338.0 ], [ 104.0, 338.0 ], [ 104.0, 339.0 ], [ 103.0, 339.0 ], [ 103.0, 340.0 ], [ 102.0, 340.0 ], [ 101.0, 340.0 ], [ 101.0, 341.0 ], [ 100.0, 341.0 ], [ 100.0, 342.0 ], [ 99.0, 342.0 ], [ 99.0, 343.0 ], [ 98.0, 343.0 ], [ 97.0, 343.0 ], [ 97.0, 344.0 ], [ 96.0, 344.0 ], [ 96.0, 346.0 ], [ 95.0, 346.0 ], [ 95.0, 350.0 ], [ 94.0, 350.0 ], [ 94.0, 353.0 ], [ 93.0, 353.0 ], [ 93.0, 356.0 ], [ 92.0, 356.0 ], [ 92.0, 359.0 ], [ 91.0, 359.0 ], [ 91.0, 362.0 ], [ 90.0, 362.0 ], [ 90.0, 364.0 ], [ 93.0, 364.0 ], [ 93.0, 365.0 ], [ 96.0, 365.0 ], [ 96.0, 366.0 ], [ 99.0, 366.0 ], [ 99.0, 367.0 ], [ 103.0, 367.0 ], [ 103.0, 368.0 ], [ 106.0, 368.0 ], [ 106.0, 369.0 ], [ 109.0, 369.0 ], [ 109.0, 370.0 ], [ 111.0, 370.0 ], [ 111.0, 369.0 ], [ 112.0, 369.0 ], [ 112.0, 366.0 ], [ 113.0, 366.0 ], [ 113.0, 363.0 ], [ 114.0, 363.0 ], [ 114.0, 360.0 ], [ 115.0, 360.0 ], [ 115.0, 357.0 ], [ 116.0, 357.0 ], [ 116.0, 354.0 ], [ 117.0, 354.0 ], [ 117.0, 351.0 ], [ 118.0, 351.0 ], [ 118.0, 348.0 ], [ 119.0, 348.0 ], [ 119.0, 345.0 ], [ 120.0, 345.0 ], [ 120.0, 342.0 ], [ 121.0, 342.0 ], [ 121.0, 339.0 ], [ 122.0, 339.0 ], [ 122.0, 336.0 ], [ 123.0, 336.0 ], [ 123.0, 333.0 ], [ 124.0, 333.0 ], [ 124.0, 330.0 ], [ 125.0, 330.0 ], [ 125.0, 327.0 ], [ 126.0, 327.0 ], [ 126.0, 324.0 ], [ 127.0, 324.0 ], [ 127.0, 321.0 ], [ 126.0, 321.0 ], [ 126.0, 320.0 ], [ 123.0, 320.0 ], [ 123.0, 319.0 ], [ 120.0, 319.0 ], [ 120.0, 318.0 ], [ 117.0, 318.0 ], [ 117.0, 317.0 ], [ 114.0, 317.0 ], [ 114.0, 316.0 ], [ 111.0, 316.0 ], [ 111.0, 315.0 ], [ 108.0, 315.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 230.0, 331.0 ], [ 230.0, 332.0 ], [ 229.0, 332.0 ], [ 228.0, 332.0 ], [ 228.0, 333.0 ], [ 227.0, 333.0 ], [ 226.0, 333.0 ], [ 226.0, 334.0 ], [ 225.0, 334.0 ], [ 224.0, 334.0 ], [ 224.0, 335.0 ], [ 223.0, 335.0 ], [ 223.0, 336.0 ], [ 222.0, 336.0 ], [ 221.0, 336.0 ], [ 221.0, 337.0 ], [ 220.0, 337.0 ], [ 219.0, 337.0 ], [ 219.0, 338.0 ], [ 218.0, 338.0 ], [ 218.0, 339.0 ], [ 217.0, 339.0 ], [ 216.0, 339.0 ], [ 216.0, 340.0 ], [ 215.0, 340.0 ], [ 214.0, 340.0 ], [ 214.0, 341.0 ], [ 213.0, 341.0 ], [ 213.0, 342.0 ], [ 212.0, 342.0 ], [ 211.0, 342.0 ], [ 211.0, 343.0 ], [ 210.0, 343.0 ], [ 209.0, 343.0 ], [ 209.0, 344.0 ], [ 208.0, 344.0 ], [ 208.0, 345.0 ], [ 207.0, 345.0 ], [ 206.0, 345.0 ], [ 206.0, 346.0 ], [ 205.0, 346.0 ], [ 204.0, 346.0 ], [ 204.0, 349.0 ], [ 205.0, 349.0 ], [ 205.0, 350.0 ], [ 206.0, 350.0 ], [ 206.0, 352.0 ], [ 207.0, 352.0 ], [ 207.0, 353.0 ], [ 208.0, 353.0 ], [ 208.0, 355.0 ], [ 209.0, 355.0 ], [ 209.0, 356.0 ], [ 210.0, 356.0 ], [ 210.0, 358.0 ], [ 211.0, 358.0 ], [ 211.0, 359.0 ], [ 213.0, 359.0 ], [ 213.0, 358.0 ], [ 214.0, 358.0 ], [ 215.0, 358.0 ], [ 215.0, 357.0 ], [ 216.0, 357.0 ], [ 217.0, 357.0 ], [ 217.0, 356.0 ], [ 218.0, 356.0 ], [ 219.0, 356.0 ], [ 219.0, 355.0 ], [ 220.0, 355.0 ], [ 221.0, 355.0 ], [ 221.0, 354.0 ], [ 223.0, 354.0 ], [ 223.0, 356.0 ], [ 224.0, 356.0 ], [ 224.0, 357.0 ], [ 225.0, 357.0 ], [ 225.0, 359.0 ], [ 226.0, 359.0 ], [ 226.0, 360.0 ], [ 225.0, 360.0 ], [ 224.0, 360.0 ], [ 224.0, 361.0 ], [ 223.0, 361.0 ], [ 222.0, 361.0 ], [ 222.0, 362.0 ], [ 221.0, 362.0 ], [ 221.0, 365.0 ], [ 222.0, 365.0 ], [ 222.0, 367.0 ], [ 223.0, 367.0 ], [ 223.0, 369.0 ], [ 224.0, 369.0 ], [ 224.0, 371.0 ], [ 225.0, 371.0 ], [ 225.0, 373.0 ], [ 226.0, 373.0 ], [ 226.0, 372.0 ], [ 227.0, 372.0 ], [ 228.0, 372.0 ], [ 228.0, 371.0 ], [ 229.0, 371.0 ], [ 230.0, 371.0 ], [ 230.0, 370.0 ], [ 231.0, 370.0 ], [ 232.0, 370.0 ], [ 232.0, 369.0 ], [ 233.0, 369.0 ], [ 234.0, 369.0 ], [ 234.0, 368.0 ], [ 235.0, 368.0 ], [ 236.0, 368.0 ], [ 236.0, 367.0 ], [ 237.0, 367.0 ], [ 238.0, 367.0 ], [ 238.0, 366.0 ], [ 239.0, 366.0 ], [ 240.0, 366.0 ], [ 240.0, 365.0 ], [ 241.0, 365.0 ], [ 241.0, 364.0 ], [ 242.0, 364.0 ], [ 242.0, 363.0 ], [ 243.0, 363.0 ], [ 243.0, 362.0 ], [ 244.0, 362.0 ], [ 244.0, 361.0 ], [ 245.0, 361.0 ], [ 245.0, 360.0 ], [ 246.0, 360.0 ], [ 246.0, 359.0 ], [ 247.0, 359.0 ], [ 247.0, 358.0 ], [ 248.0, 358.0 ], [ 248.0, 356.0 ], [ 247.0, 356.0 ], [ 247.0, 354.0 ], [ 246.0, 354.0 ], [ 246.0, 353.0 ], [ 245.0, 353.0 ], [ 245.0, 351.0 ], [ 244.0, 351.0 ], [ 244.0, 349.0 ], [ 243.0, 349.0 ], [ 243.0, 348.0 ], [ 242.0, 348.0 ], [ 242.0, 346.0 ], [ 241.0, 346.0 ], [ 241.0, 345.0 ], [ 240.0, 345.0 ], [ 240.0, 343.0 ], [ 239.0, 343.0 ], [ 239.0, 342.0 ], [ 238.0, 342.0 ], [ 238.0, 340.0 ], [ 237.0, 340.0 ], [ 237.0, 339.0 ], [ 236.0, 339.0 ], [ 236.0, 337.0 ], [ 235.0, 337.0 ], [ 235.0, 336.0 ], [ 234.0, 336.0 ], [ 234.0, 334.0 ], [ 233.0, 334.0 ], [ 233.0, 332.0 ], [ 232.0, 332.0 ], [ 232.0, 331.0 ], [ 230.0, 331.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 482.0, 358.0 ], [ 482.0, 363.0 ], [ 481.0, 363.0 ], [ 481.0, 369.0 ], [ 480.0, 369.0 ], [ 480.0, 375.0 ], [ 479.0, 375.0 ], [ 479.0, 382.0 ], [ 478.0, 382.0 ], [ 478.0, 388.0 ], [ 477.0, 388.0 ], [ 477.0, 394.0 ], [ 482.0, 394.0 ], [ 482.0, 395.0 ], [ 488.0, 395.0 ], [ 488.0, 396.0 ], [ 492.0, 396.0 ], [ 492.0, 392.0 ], [ 493.0, 392.0 ], [ 493.0, 393.0 ], [ 499.0, 393.0 ], [ 499.0, 394.0 ], [ 506.0, 394.0 ], [ 506.0, 395.0 ], [ 512.0, 395.0 ], [ 512.0, 396.0 ], [ 519.0, 396.0 ], [ 519.0, 397.0 ], [ 525.0, 397.0 ], [ 525.0, 393.0 ], [ 526.0, 393.0 ], [ 526.0, 386.0 ], [ 527.0, 386.0 ], [ 527.0, 380.0 ], [ 528.0, 380.0 ], [ 528.0, 377.0 ], [ 522.0, 377.0 ], [ 522.0, 376.0 ], [ 516.0, 376.0 ], [ 516.0, 375.0 ], [ 510.0, 375.0 ], [ 510.0, 374.0 ], [ 503.0, 374.0 ], [ 503.0, 373.0 ], [ 497.0, 373.0 ], [ 497.0, 372.0 ], [ 494.0, 372.0 ], [ 494.0, 369.0 ], [ 495.0, 369.0 ], [ 495.0, 362.0 ], [ 496.0, 362.0 ], [ 496.0, 360.0 ], [ 492.0, 360.0 ], [ 492.0, 359.0 ], [ 486.0, 359.0 ], [ 486.0, 358.0 ], [ 482.0, 358.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 562.0, 379.0 ], [ 562.0, 382.0 ], [ 561.0, 382.0 ], [ 561.0, 384.0 ], [ 560.0, 384.0 ], [ 546.0, 384.0 ], [ 546.0, 386.0 ], [ 545.0, 386.0 ], [ 545.0, 404.0 ], [ 559.0, 404.0 ], [ 559.0, 405.0 ], [ 593.0, 405.0 ], [ 594.0, 405.0 ], [ 594.0, 385.0 ], [ 574.0, 385.0 ], [ 574.0, 384.0 ], [ 573.0, 384.0 ], [ 573.0, 379.0 ], [ 562.0, 379.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 84.0, 379.0 ], [ 84.0, 381.0 ], [ 83.0, 381.0 ], [ 83.0, 384.0 ], [ 82.0, 384.0 ], [ 82.0, 386.0 ], [ 81.0, 386.0 ], [ 81.0, 393.0 ], [ 82.0, 393.0 ], [ 82.0, 397.0 ], [ 83.0, 397.0 ], [ 83.0, 401.0 ], [ 84.0, 401.0 ], [ 84.0, 409.0 ], [ 83.0, 409.0 ], [ 83.0, 416.0 ], [ 82.0, 416.0 ], [ 75.0, 416.0 ], [ 75.0, 415.0 ], [ 74.0, 415.0 ], [ 74.0, 418.0 ], [ 73.0, 418.0 ], [ 73.0, 421.0 ], [ 72.0, 421.0 ], [ 72.0, 425.0 ], [ 77.0, 425.0 ], [ 77.0, 426.0 ], [ 81.0, 426.0 ], [ 81.0, 427.0 ], [ 84.0, 427.0 ], [ 84.0, 428.0 ], [ 86.0, 428.0 ], [ 86.0, 429.0 ], [ 89.0, 429.0 ], [ 89.0, 430.0 ], [ 91.0, 430.0 ], [ 91.0, 431.0 ], [ 93.0, 431.0 ], [ 93.0, 432.0 ], [ 95.0, 432.0 ], [ 95.0, 433.0 ], [ 96.0, 433.0 ], [ 96.0, 428.0 ], [ 97.0, 428.0 ], [ 97.0, 422.0 ], [ 98.0, 422.0 ], [ 98.0, 415.0 ], [ 99.0, 415.0 ], [ 99.0, 409.0 ], [ 100.0, 409.0 ], [ 100.0, 403.0 ], [ 101.0, 403.0 ], [ 101.0, 397.0 ], [ 102.0, 397.0 ], [ 102.0, 390.0 ], [ 103.0, 390.0 ], [ 103.0, 384.0 ], [ 104.0, 384.0 ], [ 104.0, 379.0 ], [ 84.0, 379.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 66.0, 443.0 ], [ 66.0, 452.0 ], [ 65.0, 452.0 ], [ 65.0, 478.0 ], [ 64.0, 478.0 ], [ 64.0, 493.0 ], [ 79.0, 493.0 ], [ 79.0, 494.0 ], [ 84.0, 494.0 ], [ 84.0, 484.0 ], [ 85.0, 484.0 ], [ 85.0, 458.0 ], [ 86.0, 458.0 ], [ 86.0, 444.0 ], [ 70.0, 444.0 ], [ 70.0, 443.0 ], [ 66.0, 443.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 70.0, 502.0 ], [ 70.0, 535.0 ], [ 71.0, 535.0 ], [ 71.0, 553.0 ], [ 77.0, 553.0 ], [ 77.0, 552.0 ], [ 85.0, 552.0 ], [ 86.0, 552.0 ], [ 86.0, 551.0 ], [ 92.0, 551.0 ], [ 93.0, 551.0 ], [ 93.0, 539.0 ], [ 92.0, 539.0 ], [ 92.0, 523.0 ], [ 91.0, 523.0 ], [ 91.0, 511.0 ], [ 90.0, 511.0 ], [ 90.0, 508.0 ], [ 89.0, 508.0 ], [ 89.0, 506.0 ], [ 88.0, 506.0 ], [ 88.0, 503.0 ], [ 83.0, 503.0 ], [ 83.0, 502.0 ], [ 70.0, 502.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 72.0, 562.0 ], [ 72.0, 574.0 ], [ 73.0, 574.0 ], [ 73.0, 594.0 ], [ 72.0, 594.0 ], [ 72.0, 608.0 ], [ 73.0, 608.0 ], [ 73.0, 611.0 ], [ 86.0, 611.0 ], [ 86.0, 610.0 ], [ 87.0, 610.0 ], [ 88.0, 610.0 ], [ 88.0, 609.0 ], [ 89.0, 609.0 ], [ 90.0, 609.0 ], [ 90.0, 608.0 ], [ 91.0, 608.0 ], [ 92.0, 608.0 ], [ 92.0, 607.0 ], [ 93.0, 607.0 ], [ 93.0, 606.0 ], [ 94.0, 606.0 ], [ 94.0, 596.0 ], [ 95.0, 596.0 ], [ 95.0, 591.0 ], [ 94.0, 591.0 ], [ 94.0, 587.0 ], [ 93.0, 587.0 ], [ 93.0, 583.0 ], [ 92.0, 583.0 ], [ 92.0, 579.0 ], [ 91.0, 579.0 ], [ 91.0, 572.0 ], [ 90.0, 572.0 ], [ 90.0, 565.0 ], [ 89.0, 565.0 ], [ 89.0, 562.0 ], [ 72.0, 562.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 691.0, 599.0 ], [ 691.0, 620.0 ], [ 698.0, 620.0 ], [ 698.0, 619.0 ], [ 699.0, 619.0 ], [ 699.0, 618.0 ], [ 700.0, 618.0 ], [ 700.0, 617.0 ], [ 701.0, 617.0 ], [ 701.0, 616.0 ], [ 702.0, 616.0 ], [ 703.0, 616.0 ], [ 703.0, 615.0 ], [ 707.0, 615.0 ], [ 707.0, 616.0 ], [ 714.0, 616.0 ], [ 714.0, 617.0 ], [ 715.0, 617.0 ], [ 715.0, 618.0 ], [ 717.0, 618.0 ], [ 717.0, 619.0 ], [ 718.0, 619.0 ], [ 718.0, 620.0 ], [ 723.0, 620.0 ], [ 723.0, 619.0 ], [ 724.0, 619.0 ], [ 724.0, 618.0 ], [ 725.0, 618.0 ], [ 725.0, 611.0 ], [ 724.0, 611.0 ], [ 724.0, 610.0 ], [ 723.0, 610.0 ], [ 723.0, 609.0 ], [ 722.0, 609.0 ], [ 722.0, 608.0 ], [ 721.0, 608.0 ], [ 721.0, 607.0 ], [ 720.0, 607.0 ], [ 720.0, 599.0 ], [ 691.0, 599.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 638.0, 606.0 ], [ 638.0, 607.0 ], [ 637.0, 607.0 ], [ 634.0, 607.0 ], [ 634.0, 608.0 ], [ 633.0, 608.0 ], [ 629.0, 608.0 ], [ 629.0, 609.0 ], [ 628.0, 609.0 ], [ 625.0, 609.0 ], [ 625.0, 610.0 ], [ 624.0, 610.0 ], [ 620.0, 610.0 ], [ 620.0, 611.0 ], [ 619.0, 611.0 ], [ 616.0, 611.0 ], [ 616.0, 612.0 ], [ 615.0, 612.0 ], [ 611.0, 612.0 ], [ 611.0, 613.0 ], [ 612.0, 613.0 ], [ 612.0, 618.0 ], [ 613.0, 618.0 ], [ 613.0, 622.0 ], [ 614.0, 622.0 ], [ 614.0, 624.0 ], [ 617.0, 624.0 ], [ 617.0, 623.0 ], [ 620.0, 623.0 ], [ 621.0, 623.0 ], [ 621.0, 622.0 ], [ 626.0, 622.0 ], [ 626.0, 624.0 ], [ 627.0, 624.0 ], [ 627.0, 629.0 ], [ 628.0, 629.0 ], [ 628.0, 633.0 ], [ 629.0, 633.0 ], [ 629.0, 638.0 ], [ 630.0, 638.0 ], [ 630.0, 642.0 ], [ 631.0, 642.0 ], [ 631.0, 643.0 ], [ 636.0, 643.0 ], [ 636.0, 642.0 ], [ 639.0, 642.0 ], [ 640.0, 642.0 ], [ 640.0, 641.0 ], [ 644.0, 641.0 ], [ 645.0, 641.0 ], [ 645.0, 640.0 ], [ 648.0, 640.0 ], [ 649.0, 640.0 ], [ 649.0, 639.0 ], [ 650.0, 639.0 ], [ 650.0, 636.0 ], [ 649.0, 636.0 ], [ 649.0, 632.0 ], [ 648.0, 632.0 ], [ 648.0, 627.0 ], [ 647.0, 627.0 ], [ 647.0, 623.0 ], [ 646.0, 623.0 ], [ 646.0, 619.0 ], [ 645.0, 619.0 ], [ 645.0, 614.0 ], [ 644.0, 614.0 ], [ 644.0, 610.0 ], [ 643.0, 610.0 ], [ 643.0, 606.0 ], [ 638.0, 606.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 74.0, 635.0 ], [ 74.0, 651.0 ], [ 73.0, 651.0 ], [ 73.0, 653.0 ], [ 77.0, 653.0 ], [ 77.0, 652.0 ], [ 78.0, 652.0 ], [ 78.0, 648.0 ], [ 79.0, 648.0 ], [ 79.0, 645.0 ], [ 81.0, 645.0 ], [ 82.0, 645.0 ], [ 82.0, 644.0 ], [ 84.0, 644.0 ], [ 85.0, 644.0 ], [ 85.0, 643.0 ], [ 93.0, 643.0 ], [ 94.0, 643.0 ], [ 94.0, 642.0 ], [ 95.0, 642.0 ], [ 95.0, 639.0 ], [ 96.0, 639.0 ], [ 96.0, 636.0 ], [ 94.0, 636.0 ], [ 94.0, 635.0 ], [ 74.0, 635.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 392.0, 671.0 ], [ 392.0, 673.0 ], [ 393.0, 673.0 ], [ 393.0, 685.0 ], [ 415.0, 685.0 ], [ 415.0, 684.0 ], [ 418.0, 684.0 ], [ 419.0, 684.0 ], [ 419.0, 683.0 ], [ 418.0, 683.0 ], [ 418.0, 673.0 ], [ 417.0, 673.0 ], [ 417.0, 671.0 ], [ 392.0, 671.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 835.0, 798.0 ], [ 835.0, 799.0 ], [ 834.0, 799.0 ], [ 826.0, 799.0 ], [ 826.0, 800.0 ], [ 825.0, 800.0 ], [ 821.0, 800.0 ], [ 821.0, 810.0 ], [ 822.0, 810.0 ], [ 822.0, 813.0 ], [ 831.0, 813.0 ], [ 831.0, 815.0 ], [ 832.0, 815.0 ], [ 832.0, 816.0 ], [ 833.0, 816.0 ], [ 833.0, 817.0 ], [ 834.0, 817.0 ], [ 834.0, 818.0 ], [ 835.0, 818.0 ], [ 835.0, 819.0 ], [ 837.0, 819.0 ], [ 837.0, 818.0 ], [ 842.0, 818.0 ], [ 843.0, 818.0 ], [ 843.0, 817.0 ], [ 847.0, 817.0 ], [ 848.0, 817.0 ], [ 848.0, 816.0 ], [ 852.0, 816.0 ], [ 853.0, 816.0 ], [ 853.0, 815.0 ], [ 857.0, 815.0 ], [ 858.0, 815.0 ], [ 858.0, 814.0 ], [ 863.0, 814.0 ], [ 864.0, 814.0 ], [ 864.0, 812.0 ], [ 863.0, 812.0 ], [ 863.0, 811.0 ], [ 862.0, 811.0 ], [ 862.0, 809.0 ], [ 861.0, 809.0 ], [ 861.0, 808.0 ], [ 860.0, 808.0 ], [ 860.0, 807.0 ], [ 842.0, 807.0 ], [ 842.0, 806.0 ], [ 841.0, 806.0 ], [ 841.0, 803.0 ], [ 840.0, 803.0 ], [ 840.0, 800.0 ], [ 839.0, 800.0 ], [ 839.0, 798.0 ], [ 835.0, 798.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 886.0, 802.0 ], [ 886.0, 820.0 ], [ 888.0, 820.0 ], [ 888.0, 819.0 ], [ 889.0, 819.0 ], [ 890.0, 819.0 ], [ 890.0, 818.0 ], [ 891.0, 818.0 ], [ 892.0, 818.0 ], [ 892.0, 817.0 ], [ 897.0, 817.0 ], [ 897.0, 818.0 ], [ 899.0, 818.0 ], [ 899.0, 819.0 ], [ 900.0, 819.0 ], [ 900.0, 802.0 ], [ 886.0, 802.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 775.0, 796.0 ], [ 775.0, 797.0 ], [ 774.0, 797.0 ], [ 773.0, 797.0 ], [ 773.0, 798.0 ], [ 772.0, 798.0 ], [ 772.0, 799.0 ], [ 771.0, 799.0 ], [ 770.0, 799.0 ], [ 770.0, 800.0 ], [ 769.0, 800.0 ], [ 769.0, 801.0 ], [ 768.0, 801.0 ], [ 768.0, 802.0 ], [ 767.0, 802.0 ], [ 766.0, 802.0 ], [ 766.0, 803.0 ], [ 765.0, 803.0 ], [ 765.0, 804.0 ], [ 764.0, 804.0 ], [ 763.0, 804.0 ], [ 763.0, 805.0 ], [ 762.0, 805.0 ], [ 762.0, 806.0 ], [ 761.0, 806.0 ], [ 761.0, 808.0 ], [ 760.0, 808.0 ], [ 760.0, 819.0 ], [ 761.0, 819.0 ], [ 761.0, 820.0 ], [ 763.0, 820.0 ], [ 763.0, 821.0 ], [ 765.0, 821.0 ], [ 765.0, 822.0 ], [ 767.0, 822.0 ], [ 767.0, 823.0 ], [ 775.0, 823.0 ], [ 775.0, 822.0 ], [ 787.0, 822.0 ], [ 788.0, 822.0 ], [ 788.0, 821.0 ], [ 801.0, 821.0 ], [ 802.0, 821.0 ], [ 802.0, 820.0 ], [ 806.0, 820.0 ], [ 807.0, 820.0 ], [ 807.0, 819.0 ], [ 808.0, 819.0 ], [ 808.0, 813.0 ], [ 809.0, 813.0 ], [ 809.0, 812.0 ], [ 810.0, 812.0 ], [ 810.0, 811.0 ], [ 811.0, 811.0 ], [ 811.0, 808.0 ], [ 810.0, 808.0 ], [ 810.0, 805.0 ], [ 809.0, 805.0 ], [ 809.0, 801.0 ], [ 808.0, 801.0 ], [ 808.0, 800.0 ], [ 794.0, 800.0 ], [ 794.0, 799.0 ], [ 793.0, 799.0 ], [ 793.0, 798.0 ], [ 792.0, 798.0 ], [ 792.0, 797.0 ], [ 791.0, 797.0 ], [ 791.0, 796.0 ], [ 775.0, 796.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 478.0, 829.0 ], [ 478.0, 830.0 ], [ 477.0, 830.0 ], [ 476.0, 830.0 ], [ 476.0, 831.0 ], [ 475.0, 831.0 ], [ 473.0, 831.0 ], [ 473.0, 832.0 ], [ 472.0, 832.0 ], [ 471.0, 832.0 ], [ 471.0, 833.0 ], [ 470.0, 833.0 ], [ 468.0, 833.0 ], [ 468.0, 834.0 ], [ 467.0, 834.0 ], [ 466.0, 834.0 ], [ 466.0, 835.0 ], [ 465.0, 835.0 ], [ 463.0, 835.0 ], [ 463.0, 836.0 ], [ 462.0, 836.0 ], [ 461.0, 836.0 ], [ 461.0, 837.0 ], [ 460.0, 837.0 ], [ 458.0, 837.0 ], [ 458.0, 838.0 ], [ 457.0, 838.0 ], [ 456.0, 838.0 ], [ 456.0, 839.0 ], [ 455.0, 839.0 ], [ 453.0, 839.0 ], [ 453.0, 840.0 ], [ 452.0, 840.0 ], [ 451.0, 840.0 ], [ 451.0, 841.0 ], [ 450.0, 841.0 ], [ 448.0, 841.0 ], [ 448.0, 842.0 ], [ 447.0, 842.0 ], [ 446.0, 842.0 ], [ 446.0, 843.0 ], [ 447.0, 843.0 ], [ 447.0, 845.0 ], [ 448.0, 845.0 ], [ 448.0, 847.0 ], [ 449.0, 847.0 ], [ 449.0, 850.0 ], [ 450.0, 850.0 ], [ 450.0, 852.0 ], [ 451.0, 852.0 ], [ 451.0, 855.0 ], [ 452.0, 855.0 ], [ 452.0, 857.0 ], [ 453.0, 857.0 ], [ 453.0, 860.0 ], [ 454.0, 860.0 ], [ 454.0, 861.0 ], [ 455.0, 861.0 ], [ 455.0, 860.0 ], [ 456.0, 860.0 ], [ 457.0, 860.0 ], [ 457.0, 859.0 ], [ 459.0, 859.0 ], [ 460.0, 859.0 ], [ 460.0, 858.0 ], [ 461.0, 858.0 ], [ 462.0, 858.0 ], [ 462.0, 857.0 ], [ 464.0, 857.0 ], [ 465.0, 857.0 ], [ 465.0, 856.0 ], [ 466.0, 856.0 ], [ 467.0, 856.0 ], [ 467.0, 855.0 ], [ 469.0, 855.0 ], [ 470.0, 855.0 ], [ 470.0, 854.0 ], [ 472.0, 854.0 ], [ 473.0, 854.0 ], [ 473.0, 853.0 ], [ 474.0, 853.0 ], [ 475.0, 853.0 ], [ 475.0, 852.0 ], [ 477.0, 852.0 ], [ 478.0, 852.0 ], [ 478.0, 851.0 ], [ 479.0, 851.0 ], [ 480.0, 851.0 ], [ 480.0, 850.0 ], [ 482.0, 850.0 ], [ 483.0, 850.0 ], [ 483.0, 849.0 ], [ 484.0, 849.0 ], [ 485.0, 849.0 ], [ 485.0, 848.0 ], [ 487.0, 848.0 ], [ 488.0, 848.0 ], [ 488.0, 847.0 ], [ 489.0, 847.0 ], [ 489.0, 846.0 ], [ 488.0, 846.0 ], [ 488.0, 844.0 ], [ 487.0, 844.0 ], [ 487.0, 841.0 ], [ 486.0, 841.0 ], [ 486.0, 839.0 ], [ 485.0, 839.0 ], [ 485.0, 836.0 ], [ 484.0, 836.0 ], [ 484.0, 834.0 ], [ 483.0, 834.0 ], [ 483.0, 831.0 ], [ 482.0, 831.0 ], [ 482.0, 829.0 ], [ 478.0, 829.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 213.0, 866.0 ], [ 213.0, 867.0 ], [ 212.0, 867.0 ], [ 212.0, 868.0 ], [ 211.0, 868.0 ], [ 210.0, 868.0 ], [ 210.0, 869.0 ], [ 209.0, 869.0 ], [ 209.0, 870.0 ], [ 208.0, 870.0 ], [ 208.0, 871.0 ], [ 207.0, 871.0 ], [ 207.0, 872.0 ], [ 206.0, 872.0 ], [ 206.0, 873.0 ], [ 205.0, 873.0 ], [ 204.0, 873.0 ], [ 204.0, 875.0 ], [ 207.0, 875.0 ], [ 207.0, 876.0 ], [ 210.0, 876.0 ], [ 210.0, 877.0 ], [ 213.0, 877.0 ], [ 213.0, 880.0 ], [ 214.0, 880.0 ], [ 214.0, 886.0 ], [ 213.0, 886.0 ], [ 213.0, 887.0 ], [ 212.0, 887.0 ], [ 212.0, 888.0 ], [ 211.0, 888.0 ], [ 211.0, 891.0 ], [ 212.0, 891.0 ], [ 212.0, 893.0 ], [ 213.0, 893.0 ], [ 213.0, 894.0 ], [ 214.0, 894.0 ], [ 214.0, 896.0 ], [ 215.0, 896.0 ], [ 215.0, 898.0 ], [ 216.0, 898.0 ], [ 216.0, 899.0 ], [ 217.0, 899.0 ], [ 217.0, 898.0 ], [ 218.0, 898.0 ], [ 218.0, 897.0 ], [ 219.0, 897.0 ], [ 219.0, 896.0 ], [ 220.0, 896.0 ], [ 220.0, 895.0 ], [ 221.0, 895.0 ], [ 221.0, 894.0 ], [ 222.0, 894.0 ], [ 222.0, 892.0 ], [ 223.0, 892.0 ], [ 223.0, 891.0 ], [ 224.0, 891.0 ], [ 224.0, 890.0 ], [ 225.0, 890.0 ], [ 225.0, 889.0 ], [ 226.0, 889.0 ], [ 226.0, 888.0 ], [ 227.0, 888.0 ], [ 227.0, 887.0 ], [ 228.0, 887.0 ], [ 228.0, 886.0 ], [ 229.0, 886.0 ], [ 229.0, 885.0 ], [ 230.0, 885.0 ], [ 230.0, 883.0 ], [ 231.0, 883.0 ], [ 231.0, 881.0 ], [ 230.0, 881.0 ], [ 230.0, 880.0 ], [ 228.0, 880.0 ], [ 228.0, 879.0 ], [ 227.0, 879.0 ], [ 227.0, 878.0 ], [ 226.0, 878.0 ], [ 226.0, 877.0 ], [ 225.0, 877.0 ], [ 225.0, 876.0 ], [ 224.0, 876.0 ], [ 224.0, 875.0 ], [ 223.0, 875.0 ], [ 223.0, 874.0 ], [ 222.0, 874.0 ], [ 222.0, 873.0 ], [ 221.0, 873.0 ], [ 221.0, 872.0 ], [ 220.0, 872.0 ], [ 220.0, 871.0 ], [ 219.0, 871.0 ], [ 219.0, 870.0 ], [ 218.0, 870.0 ], [ 218.0, 869.0 ], [ 217.0, 869.0 ], [ 217.0, 868.0 ], [ 216.0, 868.0 ], [ 216.0, 867.0 ], [ 215.0, 867.0 ], [ 215.0, 866.0 ], [ 213.0, 866.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 59.0, 885.0 ], [ 59.0, 886.0 ], [ 58.0, 886.0 ], [ 58.0, 887.0 ], [ 57.0, 887.0 ], [ 57.0, 888.0 ], [ 56.0, 888.0 ], [ 56.0, 889.0 ], [ 55.0, 889.0 ], [ 55.0, 890.0 ], [ 54.0, 890.0 ], [ 54.0, 891.0 ], [ 53.0, 891.0 ], [ 53.0, 892.0 ], [ 52.0, 892.0 ], [ 52.0, 893.0 ], [ 51.0, 893.0 ], [ 51.0, 894.0 ], [ 50.0, 894.0 ], [ 49.0, 894.0 ], [ 49.0, 895.0 ], [ 48.0, 895.0 ], [ 48.0, 897.0 ], [ 49.0, 897.0 ], [ 49.0, 898.0 ], [ 50.0, 898.0 ], [ 50.0, 899.0 ], [ 51.0, 899.0 ], [ 51.0, 900.0 ], [ 72.0, 900.0 ], [ 73.0, 900.0 ], [ 73.0, 899.0 ], [ 72.0, 899.0 ], [ 72.0, 897.0 ], [ 71.0, 897.0 ], [ 71.0, 896.0 ], [ 70.0, 896.0 ], [ 70.0, 895.0 ], [ 69.0, 895.0 ], [ 69.0, 894.0 ], [ 68.0, 894.0 ], [ 68.0, 893.0 ], [ 67.0, 893.0 ], [ 67.0, 892.0 ], [ 66.0, 892.0 ], [ 66.0, 891.0 ], [ 65.0, 891.0 ], [ 65.0, 890.0 ], [ 64.0, 890.0 ], [ 64.0, 889.0 ], [ 63.0, 889.0 ], [ 63.0, 888.0 ], [ 62.0, 888.0 ], [ 62.0, 886.0 ], [ 61.0, 886.0 ], [ 61.0, 885.0 ], [ 59.0, 885.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 415.0, 871.0 ], [ 415.0, 872.0 ], [ 414.0, 872.0 ], [ 414.0, 873.0 ], [ 413.0, 873.0 ], [ 413.0, 874.0 ], [ 412.0, 874.0 ], [ 412.0, 875.0 ], [ 411.0, 875.0 ], [ 411.0, 876.0 ], [ 410.0, 876.0 ], [ 410.0, 877.0 ], [ 409.0, 877.0 ], [ 409.0, 878.0 ], [ 408.0, 878.0 ], [ 408.0, 879.0 ], [ 407.0, 879.0 ], [ 407.0, 880.0 ], [ 406.0, 880.0 ], [ 406.0, 881.0 ], [ 405.0, 881.0 ], [ 405.0, 882.0 ], [ 404.0, 882.0 ], [ 404.0, 883.0 ], [ 403.0, 883.0 ], [ 403.0, 884.0 ], [ 402.0, 884.0 ], [ 401.0, 884.0 ], [ 401.0, 885.0 ], [ 400.0, 885.0 ], [ 400.0, 886.0 ], [ 399.0, 886.0 ], [ 399.0, 887.0 ], [ 398.0, 887.0 ], [ 398.0, 888.0 ], [ 397.0, 888.0 ], [ 397.0, 889.0 ], [ 396.0, 889.0 ], [ 396.0, 890.0 ], [ 395.0, 890.0 ], [ 395.0, 891.0 ], [ 394.0, 891.0 ], [ 394.0, 892.0 ], [ 393.0, 892.0 ], [ 393.0, 893.0 ], [ 392.0, 893.0 ], [ 392.0, 894.0 ], [ 391.0, 894.0 ], [ 391.0, 895.0 ], [ 390.0, 895.0 ], [ 390.0, 896.0 ], [ 389.0, 896.0 ], [ 389.0, 897.0 ], [ 388.0, 897.0 ], [ 388.0, 898.0 ], [ 387.0, 898.0 ], [ 387.0, 899.0 ], [ 386.0, 899.0 ], [ 386.0, 900.0 ], [ 418.0, 900.0 ], [ 418.0, 899.0 ], [ 419.0, 899.0 ], [ 419.0, 898.0 ], [ 420.0, 898.0 ], [ 421.0, 898.0 ], [ 421.0, 897.0 ], [ 422.0, 897.0 ], [ 422.0, 896.0 ], [ 423.0, 896.0 ], [ 423.0, 895.0 ], [ 424.0, 895.0 ], [ 424.0, 894.0 ], [ 425.0, 894.0 ], [ 426.0, 894.0 ], [ 426.0, 893.0 ], [ 425.0, 893.0 ], [ 425.0, 886.0 ], [ 424.0, 886.0 ], [ 424.0, 880.0 ], [ 423.0, 880.0 ], [ 423.0, 878.0 ], [ 422.0, 878.0 ], [ 422.0, 877.0 ], [ 421.0, 877.0 ], [ 421.0, 876.0 ], [ 420.0, 876.0 ], [ 420.0, 875.0 ], [ 419.0, 875.0 ], [ 419.0, 874.0 ], [ 418.0, 874.0 ], [ 418.0, 873.0 ], [ 417.0, 873.0 ], [ 417.0, 872.0 ], [ 416.0, 872.0 ], [ 416.0, 871.0 ], [ 415.0, 871.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/gdf_from_mask_2.geojson b/docker/solaris/solaris/data/gdf_from_mask_2.geojson new file mode 100644 index 00000000..cde03cdd --- /dev/null +++ b/docker/solaris/solaris/data/gdf_from_mask_2.geojson @@ -0,0 +1,41 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733830.5, 3725139.0 ], [ 733843.0, 3725139.0 ], [ 733841.5, 3725135.0 ], [ 733839.5, 3725134.5 ], [ 733833.5, 3725121.0 ], [ 733824.5, 3725125.0 ], [ 733824.5, 3725126.5 ], [ 733827.5, 3725131.5 ], [ 733827.5, 3725133.0 ], [ 733830.5, 3725139.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734009.0, 3725138.5 ], [ 734010.0, 3725115.0 ], [ 734003.0, 3725115.0 ], [ 734002.0, 3725117.0 ], [ 734000.5, 3725118.0 ], [ 734000.5, 3725119.0 ], [ 733999.0, 3725120.0 ], [ 733998.5, 3725138.0 ], [ 734009.0, 3725138.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733601.0, 3725137.5 ], [ 733605.0, 3725135.0 ], [ 733604.0, 3725124.0 ], [ 733610.5, 3725120.5 ], [ 733610.0, 3725110.0 ], [ 733601.0, 3725112.0 ], [ 733601.0, 3725137.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733630.5, 3725105.5 ], [ 733639.0, 3725101.5 ], [ 733640.5, 3725100.0 ], [ 733645.0, 3725102.5 ], [ 733648.0, 3725101.0 ], [ 733648.5, 3725100.0 ], [ 733650.5, 3725099.5 ], [ 733651.0, 3725098.5 ], [ 733654.0, 3725097.5 ], [ 733654.5, 3725096.5 ], [ 733653.0, 3725095.0 ], [ 733650.0, 3725089.0 ], [ 733646.5, 3725089.5 ], [ 733628.5, 3725099.0 ], [ 733628.0, 3725100.5 ], [ 733630.5, 3725105.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733818.0, 3725117.0 ], [ 733830.5, 3725115.0 ], [ 733829.0, 3725104.0 ], [ 733826.5, 3725104.5 ], [ 733826.0, 3725103.0 ], [ 733824.5, 3725090.5 ], [ 733814.5, 3725092.0 ], [ 733818.0, 3725117.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734007.5, 3725110.0 ], [ 734009.0, 3725110.0 ], [ 734009.5, 3725109.0 ], [ 734009.5, 3725086.5 ], [ 733999.0, 3725086.0 ], [ 733998.5, 3725108.0 ], [ 734003.0, 3725108.0 ], [ 734004.0, 3725109.5 ], [ 734007.5, 3725110.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733886.5, 3725100.0 ], [ 733888.0, 3725100.0 ], [ 733888.0, 3725096.5 ], [ 733889.0, 3725096.0 ], [ 733889.0, 3725095.0 ], [ 733894.0, 3725094.0 ], [ 733895.0, 3725093.0 ], [ 733891.0, 3725089.5 ], [ 733891.0, 3725088.5 ], [ 733887.0, 3725085.0 ], [ 733887.0, 3725084.0 ], [ 733882.5, 3725080.0 ], [ 733882.5, 3725079.0 ], [ 733877.5, 3725074.0 ], [ 733875.5, 3725076.0 ], [ 733874.0, 3725080.0 ], [ 733875.0, 3725088.5 ], [ 733881.5, 3725095.0 ], [ 733881.5, 3725096.0 ], [ 733885.0, 3725099.5 ], [ 733886.5, 3725100.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733667.5, 3725090.0 ], [ 733669.5, 3725089.5 ], [ 733670.0, 3725088.5 ], [ 733672.0, 3725088.0 ], [ 733672.5, 3725087.0 ], [ 733676.5, 3725085.0 ], [ 733677.5, 3725082.0 ], [ 733680.5, 3725080.5 ], [ 733682.0, 3725075.0 ], [ 733686.5, 3725072.5 ], [ 733684.5, 3725067.0 ], [ 733679.0, 3725068.5 ], [ 733676.5, 3725072.0 ], [ 733671.0, 3725074.5 ], [ 733669.5, 3725076.0 ], [ 733669.5, 3725077.0 ], [ 733663.5, 3725082.0 ], [ 733667.5, 3725090.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733809.5, 3725081.5 ], [ 733819.5, 3725081.5 ], [ 733818.5, 3725065.5 ], [ 733815.0, 3725061.0 ], [ 733812.5, 3725061.0 ], [ 733810.5, 3725062.0 ], [ 733811.5, 3725068.5 ], [ 733811.0, 3725071.0 ], [ 733809.0, 3725073.5 ], [ 733809.5, 3725081.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734002.5, 3725075.5 ], [ 734010.0, 3725075.5 ], [ 734010.5, 3725066.0 ], [ 734002.0, 3725065.5 ], [ 734002.5, 3725057.5 ], [ 733990.0, 3725057.0 ], [ 733989.5, 3725075.0 ], [ 734002.5, 3725075.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733863.5, 3725064.0 ], [ 733869.0, 3725064.0 ], [ 733869.0, 3725055.5 ], [ 733870.5, 3725049.5 ], [ 733866.5, 3725046.5 ], [ 733865.5, 3725046.5 ], [ 733865.0, 3725045.5 ], [ 733864.0, 3725045.5 ], [ 733863.0, 3725044.5 ], [ 733863.0, 3725043.0 ], [ 733862.0, 3725043.0 ], [ 733862.0, 3725048.0 ], [ 733864.5, 3725050.0 ], [ 733866.0, 3725053.0 ], [ 733865.5, 3725055.0 ], [ 733863.5, 3725056.0 ], [ 733863.5, 3725064.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733719.5, 3725062.0 ], [ 733721.0, 3725062.0 ], [ 733722.5, 3725060.5 ], [ 733723.5, 3725062.0 ], [ 733727.0, 3725062.0 ], [ 733727.5, 3725058.5 ], [ 733737.5, 3725053.5 ], [ 733736.0, 3725045.5 ], [ 733731.5, 3725038.5 ], [ 733727.5, 3725039.0 ], [ 733712.0, 3725047.0 ], [ 733719.5, 3725062.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733807.0, 3725056.5 ], [ 733817.5, 3725056.0 ], [ 733816.5, 3725030.5 ], [ 733806.0, 3725031.0 ], [ 733807.0, 3725056.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733990.0, 3725040.5 ], [ 733991.0, 3725040.5 ], [ 733991.0, 3725035.5 ], [ 733997.5, 3725035.5 ], [ 733997.5, 3725029.0 ], [ 733984.0, 3725029.0 ], [ 733984.5, 3725037.5 ], [ 733986.5, 3725038.0 ], [ 733987.0, 3725039.0 ], [ 733990.0, 3725040.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733862.5, 3725040.0 ], [ 733873.5, 3725039.5 ], [ 733872.0, 3725014.0 ], [ 733862.0, 3725015.0 ], [ 733860.5, 3725022.5 ], [ 733864.5, 3725024.5 ], [ 733865.5, 3725028.0 ], [ 733864.0, 3725031.0 ], [ 733862.5, 3725032.0 ], [ 733862.5, 3725040.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733618.5, 3725028.5 ], [ 733623.5, 3725028.5 ], [ 733623.0, 3725024.0 ], [ 733629.0, 3725024.0 ], [ 733628.5, 3725008.0 ], [ 733625.5, 3725006.5 ], [ 733625.0, 3725005.5 ], [ 733614.0, 3725006.0 ], [ 733614.5, 3725026.0 ], [ 733618.5, 3725026.0 ], [ 733618.5, 3725028.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733804.5, 3725026.5 ], [ 733817.0, 3725026.0 ], [ 733816.5, 3725016.5 ], [ 733814.0, 3725016.0 ], [ 733813.5, 3725011.0 ], [ 733813.5, 3725009.5 ], [ 733815.5, 3725009.5 ], [ 733815.0, 3725001.0 ], [ 733808.0, 3725001.5 ], [ 733808.0, 3725008.5 ], [ 733807.0, 3725009.5 ], [ 733807.0, 3725011.0 ], [ 733804.0, 3725014.5 ], [ 733804.5, 3725018.0 ], [ 733806.0, 3725019.0 ], [ 733806.0, 3725023.0 ], [ 733804.5, 3725023.0 ], [ 733804.5, 3725026.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733665.5, 3725010.5 ], [ 733682.0, 3725002.0 ], [ 733678.5, 3724994.5 ], [ 733663.5, 3725002.0 ], [ 733663.0, 3725005.5 ], [ 733665.5, 3725010.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733864.5, 3725008.0 ], [ 733873.5, 3725008.0 ], [ 733873.5, 3724992.5 ], [ 733877.0, 3724992.5 ], [ 733877.0, 3724981.5 ], [ 733860.0, 3724982.0 ], [ 733860.0, 3724988.5 ], [ 733864.0, 3724988.5 ], [ 733864.5, 3725008.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733804.5, 3724992.5 ], [ 733815.0, 3724992.0 ], [ 733813.5, 3724966.5 ], [ 733802.0, 3724967.0 ], [ 733802.5, 3724982.0 ], [ 733803.0, 3724987.0 ], [ 733804.5, 3724987.0 ], [ 733804.5, 3724992.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734032.5, 3724970.5 ], [ 734034.0, 3724970.5 ], [ 734034.0, 3724967.5 ], [ 734043.0, 3724968.0 ], [ 734043.5, 3724960.0 ], [ 734041.5, 3724959.0 ], [ 734040.5, 3724957.5 ], [ 734038.5, 3724957.0 ], [ 734020.0, 3724956.0 ], [ 734019.5, 3724964.0 ], [ 734026.0, 3724964.5 ], [ 734026.0, 3724967.0 ], [ 734028.5, 3724967.5 ], [ 734028.0, 3724970.0 ], [ 734032.5, 3724970.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733655.0, 3724981.5 ], [ 733664.5, 3724978.5 ], [ 733656.5, 3724954.0 ], [ 733646.0, 3724957.0 ], [ 733649.0, 3724967.0 ], [ 733652.5, 3724969.0 ], [ 733653.5, 3724970.5 ], [ 733654.5, 3724970.5 ], [ 733656.5, 3724975.0 ], [ 733657.5, 3724975.5 ], [ 733658.0, 3724979.5 ], [ 733657.0, 3724979.5 ], [ 733655.0, 3724981.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733716.0, 3724973.5 ], [ 733717.5, 3724973.0 ], [ 733718.0, 3724971.0 ], [ 733723.0, 3724964.5 ], [ 733725.0, 3724960.0 ], [ 733721.0, 3724956.0 ], [ 733713.5, 3724952.5 ], [ 733711.5, 3724956.5 ], [ 733711.5, 3724958.0 ], [ 733714.0, 3724959.0 ], [ 733712.5, 3724962.0 ], [ 733706.5, 3724959.5 ], [ 733704.0, 3724964.0 ], [ 733703.0, 3724964.5 ], [ 733703.0, 3724966.0 ], [ 733705.0, 3724966.5 ], [ 733705.5, 3724967.5 ], [ 733707.5, 3724968.0 ], [ 733708.0, 3724969.0 ], [ 733710.0, 3724969.5 ], [ 733710.5, 3724970.5 ], [ 733712.5, 3724971.0 ], [ 733713.0, 3724972.0 ], [ 733716.0, 3724973.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733842.0, 3724960.0 ], [ 733849.0, 3724959.0 ], [ 733848.0, 3724953.0 ], [ 733865.0, 3724950.5 ], [ 733863.5, 3724940.5 ], [ 733850.5, 3724942.0 ], [ 733847.5, 3724943.0 ], [ 733847.0, 3724941.0 ], [ 733839.5, 3724942.0 ], [ 733842.0, 3724960.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733882.0, 3724949.5 ], [ 733887.5, 3724949.5 ], [ 733888.0, 3724946.5 ], [ 733898.0, 3724946.5 ], [ 733898.0, 3724936.5 ], [ 733873.5, 3724937.0 ], [ 733874.0, 3724947.0 ], [ 733881.5, 3724947.0 ], [ 733882.0, 3724949.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733643.0, 3724949.5 ], [ 733653.0, 3724949.5 ], [ 733649.0, 3724922.5 ], [ 733643.0, 3724925.5 ], [ 733637.0, 3724926.5 ], [ 733638.0, 3724931.5 ], [ 733642.5, 3724931.0 ], [ 733643.0, 3724938.5 ], [ 733641.5, 3724942.5 ], [ 733641.5, 3724946.0 ], [ 733642.5, 3724947.0 ], [ 733643.0, 3724949.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733634.0, 3724917.5 ], [ 733644.0, 3724917.0 ], [ 733643.0, 3724892.0 ], [ 733633.0, 3724892.5 ], [ 733634.0, 3724917.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733636.0, 3724888.0 ], [ 733645.0, 3724887.5 ], [ 733645.0, 3724886.0 ], [ 733646.5, 3724883.5 ], [ 733647.5, 3724863.5 ], [ 733636.5, 3724862.5 ], [ 733636.0, 3724888.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733637.0, 3724858.0 ], [ 733645.5, 3724858.0 ], [ 733646.0, 3724856.5 ], [ 733646.5, 3724849.5 ], [ 733648.5, 3724843.5 ], [ 733648.5, 3724841.0 ], [ 733648.0, 3724836.0 ], [ 733644.0, 3724833.5 ], [ 733637.5, 3724833.5 ], [ 733637.0, 3724835.0 ], [ 733637.0, 3724858.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733946.5, 3724839.5 ], [ 733961.0, 3724839.5 ], [ 733961.0, 3724835.5 ], [ 733963.5, 3724833.5 ], [ 733963.5, 3724830.0 ], [ 733962.5, 3724829.0 ], [ 733960.0, 3724829.0 ], [ 733958.0, 3724831.0 ], [ 733952.5, 3724831.5 ], [ 733950.0, 3724829.0 ], [ 733946.5, 3724829.0 ], [ 733946.5, 3724839.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733920.0, 3724836.0 ], [ 733922.5, 3724836.0 ], [ 733926.0, 3724819.5 ], [ 733919.0, 3724817.5 ], [ 733916.5, 3724817.5 ], [ 733914.0, 3724828.0 ], [ 733908.0, 3724827.0 ], [ 733906.5, 3724833.0 ], [ 733909.0, 3724833.0 ], [ 733920.0, 3724836.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734018.5, 3724740.0 ], [ 734020.5, 3724740.0 ], [ 734022.0, 3724735.5 ], [ 734031.0, 3724735.5 ], [ 734033.0, 3724732.0 ], [ 734030.0, 3724732.0 ], [ 734018.5, 3724729.5 ], [ 734016.5, 3724731.5 ], [ 734016.5, 3724732.5 ], [ 734012.0, 3724732.5 ], [ 734011.5, 3724739.0 ], [ 734018.5, 3724740.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733988.5, 3724741.0 ], [ 733996.5, 3724741.0 ], [ 733998.0, 3724739.0 ], [ 734005.0, 3724739.0 ], [ 734006.5, 3724735.0 ], [ 734006.5, 3724733.5 ], [ 734005.0, 3724732.5 ], [ 734005.0, 3724729.5 ], [ 734002.0, 3724728.5 ], [ 733995.0, 3724728.5 ], [ 733988.5, 3724727.5 ], [ 733984.5, 3724727.5 ], [ 733981.5, 3724729.0 ], [ 733981.5, 3724736.0 ], [ 733988.5, 3724741.0 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733840.0, 3724724.5 ], [ 733842.0, 3724724.5 ], [ 733845.5, 3724715.5 ], [ 733828.0, 3724708.5 ], [ 733824.0, 3724718.0 ], [ 733834.0, 3724721.5 ], [ 733835.0, 3724722.5 ], [ 733836.5, 3724722.5 ], [ 733837.5, 3724723.5 ], [ 733839.0, 3724723.5 ], [ 733840.0, 3724724.5 ] ] ] } }, +{ "type": "Feature", "properties": { "value": 255.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733808.5, 3724703.5 ], [ 733813.0, 3724699.0 ], [ 733814.0, 3724692.0 ], [ 733813.0, 3724692.0 ], [ 733810.0, 3724689.0 ], [ 733794.0, 3724689.0 ], [ 733808.5, 3724703.5 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/geotiff_custom_proj_labels.geojson b/docker/solaris/solaris/data/geotiff_custom_proj_labels.geojson new file mode 100644 index 00000000..c1d78e85 --- /dev/null +++ b/docker/solaris/solaris/data/geotiff_custom_proj_labels.geojson @@ -0,0 +1,24 @@ +{ +"type": "FeatureCollection", +"features": [ +{ "type": "Feature", "properties": { "id": 43127 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658589.384257903206162, 2119553.812619875650853 ], [ -658577.288778321584687, 2119629.622372117824852 ], [ -658553.285590448882431, 2119702.484052273444831 ], [ -658518.005186571157537, 2119770.483718048315495 ], [ -658472.374295899178833, 2119831.835142846684903 ], [ -658417.591543267364614, 2119884.92673698766157 ], [ -658355.095964830950834, 2119928.363881513476372 ], [ -658286.52920765907038, 2119961.005562471225858 ], [ -658213.692406120127998, 2119981.994343436323106 ], [ -658138.498867915594019, 2119990.778888852801174 ], [ -658062.923812753288075, 2119987.128446566406637 ], [ -657988.952484210836701, 2119971.138909094501287 ], [ -657918.527998040663078, 2119943.230294490233064 ], [ -657853.500297219958156, 2119904.13571294862777 ], [ -657795.577554859919474, 2119854.882108986377716 ], [ -657746.281301830429584, 2119796.763285214547068 ], [ -657706.906458006822504, 2119731.305916173849255 ], [ -657678.487317130551673, 2119660.22944527072832 ], [ -657661.770378812332638, 2119585.400917990598828 ], [ -657657.194741239887662, 2119508.785938089247793 ], [ -657664.880569423199631, 2119432.397034936584532 ], [ -657684.625941646983847, 2119358.240798329934478 ], [ -657715.912156658247113, 2119288.265169409103692 ], [ -657757.917361841071397, 2119224.308272304944694 ], [ -657809.538144117803313, 2119168.050130397547036 ], [ -657869.418516154866666, 2119120.968535693828017 ], [ -657935.985536298365332, 2119084.300230347085744 ], [ -658007.490626496262848, 2119059.008420037571341 ], [ -658082.055502896895632, 2119045.757472535129637 ], [ -658157.721512741176412, 2119044.895466048736125 ], [ -658232.501081775058992, 2119056.445045804604888 ], [ -658304.429921047529206, 2119080.102828956674784 ], [ -658371.61862201790791, 2119115.247373646125197 ], [ -658432.30228501255624, 2119160.955502684693784 ], [ -658484.886877641314641, 2119216.026553315576166 ], [ -658527.991105676628649, 2119279.013915992807597 ], [ -658560.482696639373899, 2119348.263033763039857 ], [ -658581.508143070968799, 2119421.954864094499499 ], [ -658589.384257903206162, 2119553.812619875650853 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43137 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662702.666498517501168, 2126587.452882915735245 ], [ -662695.501050548395142, 2126651.66860979096964 ], [ -662646.853386092116125, 2126770.599066926166415 ], [ -662558.725325931911357, 2126863.336458752863109 ], [ -662443.336543769808486, 2126917.021936025004834 ], [ -662316.686776358401403, 2126924.211544466204941 ], [ -662196.337272424250841, 2126883.908394473604858 ], [ -662098.97568173869513, 2126801.700882449746132 ], [ -662038.102083388133906, 2126688.987801236566156 ], [ -662022.157051441608928, 2126561.39779051579535 ], [ -662053.351335313753225, 2126436.622288749087602 ], [ -662127.359409167896956, 2126331.962468302343041 ], [ -662233.919334881473333, 2126261.930289819836617 ], [ -662358.255713250837289, 2126236.236304953694344 ], [ -662483.128394665312953, 2126258.443210243247449 ], [ -662591.222891321522184, 2126325.47184578794986 ], [ -662667.551083773723803, 2126428.028137710876763 ], [ -662690.278055311879143, 2126488.32739687897265 ], [ -662702.666498517501168, 2126587.452882915735245 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43140 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661153.83058622374665, 2127328.616901022382081 ], [ -661135.572882248554379, 2127396.691046867053956 ], [ -661105.812480075517669, 2127460.480009274091572 ], [ -661065.475744732189924, 2127517.998154572676867 ], [ -661015.818270102958195, 2127567.455048063304275 ], [ -660958.385796234360896, 2127607.311187216080725 ], [ -660894.966093846131116, 2127636.325923815835267 ], [ -660827.533313687890768, 2127653.596083151642233 ], [ -660758.186533141997643, 2127658.584078218322247 ], [ -660689.084413231350482, 2127651.13464369205758 ], [ -660622.378000342752784, 2127631.479668885003775 ], [ -660560.143764850799926, 2127600.23097911849618 ], [ -660504.318961397977546, 2127558.361290441360325 ], [ -660456.641323366085999, 2127507.173930419608951 ], [ -660418.594969000900164, 2127448.262267667800188 ], [ -660391.364203251083381, 2127383.460112993605435 ], [ -660375.796653394470923, 2127314.784636141732335 ], [ -660372.376885888981633, 2127244.373575075063854 ], [ -660381.211325464537367, 2127174.418692280072719 ], [ -660402.024945559096523, 2127107.097549493890256 ], [ -660434.169832728570327, 2127044.505724612157792 ], [ -660476.645357989589684, 2126988.591580656822771 ], [ -660528.129326881375164, 2126941.095617241691798 ], [ -660587.019138261326589, 2126903.496292402967811 ], [ -660651.481670495471917, 2126876.964001296553761 ], [ -660719.510342149878852, 2126862.32464409712702 ], [ -660788.987571105943061, 2126860.033917336724699 ], [ -660857.750688107567839, 2126870.163128829095513 ], [ -660923.659253327758051, 2126892.396977754775435 ], [ -660984.661680917371996, 2126926.043369080871344 ], [ -661038.859098049462773, 2126970.054956822656095 ], [ -661084.564451013226062, 2127023.061745565384626 ], [ -661120.355018777307123, 2127083.41373552242294 ], [ -661145.116699527367018, 2127149.232283691409975 ], [ -661158.078691621427424, 2127218.468582318630069 ], [ -661153.83058622374665, 2127328.616901022382081 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43414 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659420.346183134359308, 2129520.998052256181836 ], [ -659461.383203490171582, 2129399.606295715086162 ], [ -659547.086564990342595, 2129298.315927427727729 ], [ -659661.62028721626848, 2129234.975180191453546 ], [ -659798.579059214680456, 2129218.564515012316406 ], [ -659927.475170377991162, 2129254.094571889843792 ], [ -660027.724878244567662, 2129325.433824636507779 ], [ -660059.225607082247734, 2129372.70192670635879 ], [ -660101.998273776727729, 2129441.900743046309799 ], [ -660127.751540555735119, 2129559.633796967100352 ], [ -660110.56129486777354, 2129691.13266032282263 ], [ -660045.32403637690004, 2129811.623265770729631 ], [ -659942.061952754273079, 2129896.894823632668704 ], [ -659819.280962530174293, 2129938.417467396706343 ], [ -659683.268217500881292, 2129930.279335264116526 ], [ -659567.182027778704651, 2129876.788248814176768 ], [ -659473.929295641952194, 2129781.125478959176689 ], [ -659423.975672627915628, 2129662.491195299196988 ], [ -659420.346183134359308, 2129520.998052256181836 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43432 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661405.676939211669378, 2123288.86178601719439 ], [ -661403.782982936012559, 2123337.963227344676852 ], [ -661359.605677559156902, 2123462.322005654685199 ], [ -661267.62126125767827, 2123569.535078656394035 ], [ -661156.119410120882094, 2123632.996182947885245 ], [ -661006.832222920143977, 2123655.098585824482143 ], [ -660883.638621504884213, 2123629.000263110734522 ], [ -660780.015362312318757, 2123566.751423150766641 ], [ -660687.361338537652045, 2123455.738921721931547 ], [ -660643.935131138539873, 2123325.046108578331769 ], [ -660649.618192790658213, 2123177.741813613567501 ], [ -660699.846238650381565, 2123053.608332986477762 ], [ -660797.762410051655024, 2122949.689418350812048 ], [ -660915.195275436271913, 2122889.522383940406144 ], [ -661064.126183984219097, 2122876.626461438834667 ], [ -661181.150594006758183, 2122905.568316395394504 ], [ -661296.281015769927762, 2122983.611613548360765 ], [ -661373.810528091271408, 2123094.061052629258484 ], [ -661408.399509966839105, 2123218.278411462437361 ], [ -661405.676939211669378, 2123288.86178601719439 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43442 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662585.537716711056419, 2120972.474544008728117 ], [ -662672.193167518475093, 2121003.358062254730612 ], [ -662683.819169561844319, 2121016.083868674002588 ], [ -662730.204866083106026, 2121070.055986914318055 ], [ -662753.759822092601098, 2121166.205419245176017 ], [ -662722.394043221371248, 2121272.60513280890882 ], [ -662653.243543352698907, 2121337.645981988403946 ], [ -662552.476378657971509, 2121358.484082465525717 ], [ -662480.353142374427989, 2121343.507765955291688 ], [ -662406.980393495410681, 2121282.385037379804999 ], [ -662373.522658198489808, 2121207.38051318237558 ], [ -662377.310087871621363, 2121109.174894538708031 ], [ -662419.592080263071693, 2121033.914584087673575 ], [ -662494.318861099774949, 2120981.374514529015869 ], [ -662585.537716711056419, 2120972.474544008728117 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44985 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661693.049046312924474, 2125281.44501457316801 ], [ -661666.960651054629125, 2125279.469046130776405 ], [ -661641.099107037181966, 2125275.496953601017594 ], [ -661615.612548492848873, 2125269.55148894013837 ], [ -661590.646961719612591, 2125261.666707387659699 ], [ -661566.345348845352419, 2125251.887772443704307 ], [ -661542.846908688894473, 2125240.270697164814919 ], [ -661520.286239411914721, 2125226.882023290265352 ], [ -661498.792567527620122, 2125211.798440108075738 ], [ -661478.48900767264422, 2125195.106345217209309 ], [ -661459.491857402725145, 2125176.901349597610533 ], [ -661441.909931039670482, 2125157.287729956675321 ], [ -661425.843936376972124, 2125136.377831485122442 ], [ -661411.385897836997174, 2125114.291424304712564 ], [ -661398.618629367672838, 2125091.155017446260899 ], [ -661387.615260096616112, 2125067.101134225260466 ], [ -661378.438815474859439, 2125042.267553140874952 ], [ -661371.141856281319633, 2125016.796518718823791 ], [ -661365.766177598387003, 2124990.833926709834486 ], [ -661362.342569427099079, 2124964.528488450683653 ], [ -661360.89064036833588, 2124938.030879037920386 ], [ -661361.418705336167477, 2124911.492874273564667 ], [ -661363.923737964709289, 2124885.066481336485595 ], [ -661368.391387987649068, 2124858.903068054933101 ], [ -661374.796063468093053, 2124833.152495936490595 ], [ -661383.101077428204007, 2124807.962261753622442 ], [ -661393.25885801948607, 2124783.476652692537755 ], [ -661405.211221050121821, 2124759.835919931530952 ], [ -661418.889703286229633, 2124737.175475241150707 ], [ -661434.215954630053602, 2124715.625115408562124 ], [ -661451.102186925476417, 2124695.308278769254684 ], [ -661469.451676818658598, 2124676.341338146477938 ], [ -661489.159319791593589, 2124658.832934310659766 ], [ -661510.112232200684957, 2124642.883353668265045 ], [ -661532.190397863741964, 2124628.58395386300981 ], [ -661555.267355497344397, 2124616.016640475951135 ], [ -661579.210923062753864, 2124605.253397871740162 ], [ -661603.883954883087426, 2124596.355876897461712 ], [ -661629.145127173513174, 2124589.375041739083827 ], [ -661654.84974751342088, 2124584.350878013297915 ], [ -661680.850583590101451, 2124581.312163736671209 ], [ -661706.998706517973915, 2124580.276304449886084 ], [ -661733.14434383274056, 2124581.249233581591398 ], [ -661759.137737356009893, 2124584.225378401111811 ], [ -661784.830000945483334, 2124589.187691974919289 ], [ -661810.073973272694275, 2124596.107750783208758 ], [ -661834.725060706492513, 2124604.945917534641922 ], [ -661858.642065498512238, 2124615.65156820975244 ], [ -661881.68799451738596, 2124628.163381983991712 ], [ -661903.730843905941583, 2124642.409692525397986 ], [ -661924.644355155993253, 2124658.308898446150124 ], [ -661944.308738291496411, 2124675.769930709153414 ], [ -661962.611357989604585, 2124694.692774272989482 ], [ -661979.447378740878776, 2124714.969040958676487 ], [ -661994.720365330926143, 2124736.482590271625668 ], [ -662008.342835211078636, 2124759.110194658860564 ], [ -662020.236759598483332, 2124782.722245336975902 ], [ -662030.33401042688638, 2124807.183494668453932 ], [ -662038.576750600477681, 2124832.353830857668072 ], [ -662044.917765291407704, 2124858.089080506004393 ], [ -662049.320732422638685, 2124884.241834385786206 ], [ -662051.760430733207613, 2124910.662291845306754 ], [ -662052.222884287592024, 2124937.199118802323937 ], [ -662050.705442559788935, 2124963.700314601883292 ], [ -662047.216795651824214, 2124990.014082662761211 ], [ -662041.776924558798783, 2125015.989699948579073 ], [ -662034.416986754979007, 2125041.478380300104618 ], [ -662025.179137763916515, 2125066.33412669133395 ], [ -662014.11628973193001, 2125090.414567464962602 ], [ -662001.291808383306488, 2125113.581771849654615 ], [ -661986.779150097514503, 2125135.703039995860308 ], [ -661970.661441179923713, 2125156.651663101743907 ], [ -661953.031001744908281, 2125176.307649174239486 ], [ -661933.988816928351298, 2125194.55841034417972 ], [ -661913.643958465545438, 2125211.299407756421715 ], [ -661892.112959937076084, 2125226.434750369749963 ], [ -661869.519149279803969, 2125239.877744222991168 ], [ -661845.991942365537398, 2125251.551388973370194 ], [ -661821.666101702605374, 2125261.388819009531289 ], [ -661796.680964516592212, 2125269.333686395082623 ], [ -661771.179644609219395, 2125275.340483661741018 ], [ -661745.308212590985931, 2125279.374804461374879 ], [ -661719.214859174564481, 2125281.413540632463992 ], [ -661693.049046312924474, 2125281.44501457316801 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44986 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658389.357993305195123, 2126666.714983499608934 ], [ -658361.791610523476265, 2126664.686247928068042 ], [ -658334.442908015451394, 2126660.667545913718641 ], [ -658307.45125024439767, 2126654.679356 ], [ -658280.954182202811353, 2126646.752192817628384 ], [ -658255.086728487163782, 2126636.926451556384563 ], [ -658229.980705206049606, 2126625.252202128525823 ], [ -658205.764048243523575, 2126611.788934027310461 ], [ -658182.560161303495988, 2126596.605253192596138 ], [ -658160.487287048832513, 2126579.778532353229821 ], [ -658139.65790453995578, 2126561.394516810309142 ], [ -658120.178156056674197, 2126541.546887464355677 ], [ -658102.14730620617047, 2126520.336783428676426 ], [ -658085.657236095168628, 2126497.872286662925035 ], [ -658070.791975114378147, 2126474.267871207091957 ], [ -658057.627272758516483, 2126449.643819838762283 ], [ -658046.230212623020634, 2126424.125611137598753 ], [ -658036.658870588755235, 2126397.843280079308897 ], [ -658028.962018886813894, 2126370.930755404755473 ], [ -658023.178877599537373, 2126343.525177144445479 ], [ -658019.338914823485538, 2126315.766197797842324 ], [ -658017.461696540238336, 2126287.795270692091435 ], [ -658017.556786946952343, 2126259.754929140210152 ], [ -658019.623699746211059, 2126231.788060187362134 ], [ -658023.651900666416623, 2126204.037176440004259 ], [ -658029.620861177914776, 2126176.643689867574722 ], [ -658037.500163129996508, 2126149.747191241942346 ], [ -658047.249653797363862, 2126123.484738753642887 ], [ -658058.81965051824227, 2126097.990159647073597 ], [ -658072.151193896192126, 2126073.393368262331933 ], [ -658087.176348270149902, 2126049.819704017601907 ], [ -658103.818547927658074, 2126027.389292704872787 ], [ -658121.992987278616056, 2126006.216434383764863 ], [ -658141.607053027721122, 2125986.40902090864256 ], [ -658162.560796114616096, 2125968.067986173555255 ], [ -658184.747441042563878, 2125951.286791754886508 ], [ -658208.053929978981614, 2125936.150950652081519 ], [ -658232.361498868558556, 2125922.737591560930014 ], [ -658257.546282618888654, 2125911.115065815858543 ], [ -658283.479946272447705, 2125901.342599121388048 ], [ -658310.030338955344632, 2125893.469989716541022 ], [ -658337.062167264288291, 2125887.537354629021138 ], [ -658364.437684660428204, 2125883.574925265274942 ], [ -658392.017393372021616, 2125881.602893332950771 ], [ -658419.660755204269662, 2125881.631307951174676 ], [ -658447.226907656295225, 2125883.66002444922924 ], [ -658474.575381690519862, 2125887.678705106955022 ], [ -658501.566817496553995, 2125893.66687180660665 ], [ -658528.063674598117359, 2125901.594010395463556 ], [ -658553.930932691902854, 2125911.419726196676493 ], [ -658579.036779655958526, 2125923.093949794303626 ], [ -658603.253283190540969, 2125936.557192228734493 ], [ -658626.457042718422599, 2125951.740848090033978 ], [ -658648.529818170121871, 2125968.567545146681368 ], [ -658669.359132494777441, 2125986.951538590714335 ], [ -658688.838844802579843, 2126006.799147974699736 ], [ -658706.869691220344976, 2126028.009234580677003 ], [ -658723.359790714690462, 2126050.473716807551682 ], [ -658738.225113293272443, 2126074.078120913822204 ], [ -658751.389908211887814, 2126098.702164346817881 ], [ -658762.78708998428192, 2126124.220368698704988 ], [ -658772.358580259489827, 2126150.502699077595025 ], [ -658780.055603784625418, 2126177.415226754266769 ], [ -658785.838936982327141, 2126204.820811631157994 ], [ -658789.679107847972773, 2126232.579801086802036 ], [ -658791.556546163395979, 2126260.550741584505886 ], [ -658791.46168325364124, 2126288.591099532321095 ], [ -658789.39500078279525, 2126316.557987560052425 ], [ -658785.367028334876522, 2126344.308892672415823 ], [ -658779.398289790959097, 2126371.702402450144291 ], [ -658771.519198779133148, 2126398.598925666883588 ], [ -658761.769903731532395, 2126424.861403628252447 ], [ -658750.200083329807967, 2126450.356008558068424 ], [ -658736.868693378637545, 2126474.952825609128922 ], [ -658721.843666416825727, 2126498.526514826342463 ], [ -658705.201565562398173, 2126520.956949916202575 ], [ -658687.027194395894185, 2126542.129830337595195 ], [ -658667.413164822966792, 2126561.937263780273497 ], [ -658646.459425167995505, 2126580.278315940406173 ], [ -658624.272750857053325, 2126597.059524904936552 ], [ -658600.966200321912766, 2126612.195377354510128 ], [ -658576.658538872026838, 2126625.60874437680468 ], [ -658551.473633483168669, 2126637.231274466961622 ], [ -658525.539821589482017, 2126647.003741845488548 ], [ -658498.989257082226686, 2126654.876348257996142 ], [ -658471.957236864138395, 2126660.808976714499295 ], [ -658444.581511377356946, 2126664.771395970601588 ], [ -658417.001582632656209, 2126666.743414520751685 ], [ -658389.357993305195123, 2126666.714983499608934 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44987 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -657466.402500477503054, 2122615.744329270441085 ], [ -657504.459532589418814, 2122619.44776436407119 ], [ -657542.27652326412499, 2122625.113126998767257 ], [ -657579.75531230669003, 2122632.725711655337363 ], [ -657616.798617354244925, 2122642.26575839985162 ], [ -657653.310286374413408, 2122653.708504125010222 ], [ -657689.195547221810557, 2122667.024246860761195 ], [ -657724.361253622919321, 2122682.178422848694026 ], [ -657758.716126931016333, 2122699.131696274038404 ], [ -657792.170993045903742, 2122717.840061356313527 ], [ -657824.639013865729794, 2122738.254956576507539 ], [ -657856.035912677296437, 2122760.323390714358538 ], [ -657886.280192902311683, 2122783.988080429378897 ], [ -657915.293349626474082, 2122809.187598920427263 ], [ -657943.000073368195444, 2122835.856535359751433 ], [ -657969.328445552149788, 2122863.925664736423641 ], [ -657994.210125187295489, 2122893.32212746469304 ], [ -658017.580526254954748, 2122923.969618564937264 ], [ -658039.378985360846855, 2122955.788585698232055 ], [ -658059.548919199965894, 2122988.696435683406889 ], [ -658078.037971434532665, 2123022.607748864218593 ], [ -658094.79814860841725, 2123057.434500847477466 ], [ -658109.785944731906056, 2123093.08629096718505 ], [ -658122.962454223888926, 2123129.470576967578381 ], [ -658134.293472913559526, 2123166.492915213573724 ], [ -658143.749586842372082, 2123204.057205812074244 ], [ -658151.30624863167759, 2123242.065942096523941 ], [ -658156.943841221509501, 2123280.420463698450476 ], [ -658160.647728810086846, 2123319.021212670952082 ], [ -658162.408294869586825, 2123357.767991894390434 ], [ -658162.220967128174379, 2123396.560225179884583 ], [ -658160.086229468462989, 2123435.297218301799148 ], [ -658156.009620686760172, 2123473.878420419525355 ], [ -658150.001720148255117, 2123512.203685072716326 ], [ -658142.078120347578079, 2123550.173530114348978 ], [ -658132.259386461810209, 2123587.689395941328257 ], [ -658120.571002987446263, 2123624.653901375830173 ], [ -658107.043307624408044, 2123660.97109637549147 ], [ -658091.711412533768453, 2123696.546711169183254 ], [ -658074.615113232517615, 2123731.28840088378638 ], [ -658055.798785308026709, 2123765.105985307134688 ], [ -658035.311269244528376, 2123797.911682960577309 ], [ -658013.205743674538098, 2123829.620338936336339 ], [ -657989.539587346720509, 2123860.149645953439176 ], [ -657964.374230198329315, 2123889.42035801988095 ], [ -657937.774993914645165, 2123917.356496119871736 ], [ -657909.810922376811504, 2123943.885545442812145 ], [ -657880.554602447198704, 2123968.938643611036241 ], [ -657850.081975555396639, 2123992.450759433675557 ], [ -657818.47214057482779, 2124014.360861715860665 ], [ -657785.807148503372446, 2124034.612077671568841 ], [ -657752.171789475018159, 2124053.151840555015951 ], [ -657717.653372659697197, 2124069.932026114314795 ], [ -657682.341499619651586, 2124084.909077508375049 ], [ -657646.327831724542193, 2124098.044118352700025 ], [ -657609.705852197948843, 2124109.30305366916582 ], [ -657572.570623452309519, 2124118.656658348627388 ], [ -657535.018540311255492, 2124126.080653048120439 ], [ -657497.147079776856117, 2124131.555767158512026 ], [ -657459.054547981591895, 2124135.067788891494274 ], [ -657420.839824993163347, 2124136.607602118980139 ], [ -657382.602108124294318, 2124136.171210059896111 ], [ -657354.824107498629019, 2124134.61220397753641 ], [ -657466.402500477503054, 2122615.744329270441085 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44988 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658457.540065760724247, 2122953.522529276553541 ], [ -658473.490459658904001, 2122935.695226454176009 ], [ -658490.808612616616301, 2122919.233438171446323 ], [ -658509.380740088527091, 2122904.245322190225124 ], [ -658529.084818860283121, 2122890.82935388199985 ], [ -658549.79138875589706, 2122879.073679281398654 ], [ -658571.364403223269619, 2122869.05553590785712 ], [ -658593.662123170797713, 2122860.840745320543647 ], [ -658616.538048211135902, 2122854.483280651271343 ], [ -658639.841879179119132, 2122850.024911969900131 ], [ -658663.4205055993516, 2122847.494931864086539 ], [ -658687.119011628208682, 2122846.909962984733284 ], [ -658710.781693853903562, 2122848.27384881535545 ], [ -658734.253084263764322, 2122851.577628406696022 ], [ -658757.378971670870669, 2122856.799595279619098 ], [ -658780.007414877065457, 2122863.905440015718341 ], [ -658801.989740930031985, 2122872.84847567928955 ], [ -658823.181521901977248, 2122883.569944557268173 ], [ -658843.443523784750141, 2122895.999404221307486 ], [ -658862.642621265025809, 2122910.055190309882164 ], [ -658880.652672362164594, 2122925.644953129347414 ], [ -658897.355347199714743, 2122942.666264357510954 ], [ -658912.640905439970084, 2122961.007290055509657 ], [ -658926.40891730831936, 2122980.547525425907224 ], [ -658938.568923432962038, 2123001.158586544916034 ], [ -658949.041029194486327, 2123022.70505391061306 ], [ -658957.756429665139876, 2123045.045362114906311 ], [ -658964.657861690968275, 2123068.032730029895902 ], [ -658969.699980147765018, 2123091.516125136986375 ], [ -658972.849655898171477, 2123115.341255904175341 ], [ -658974.086193488794379, 2123139.351585451513529 ], [ -658973.401467161602341, 2123163.389360094908625 ], [ -658970.799974273191765, 2123187.296645811758935 ], [ -658966.298805792117491, 2123210.916365884244442 ], [ -658959.927534040762112, 2123234.093332968652248 ], [ -658951.728018435882404, 2123256.675268687307835 ], [ -658941.754130501183681, 2123278.513804163783789 ], [ -658930.071399945300072, 2123299.465454828925431 ], [ -658916.756584150716662, 2123319.392563161440194 ], [ -658901.897163884015754, 2123338.164203129708767 ], [ -658885.590768542140722, 2123355.657040425110608 ], [ -658867.944534721202217, 2123371.756142770405859 ], [ -658849.074402312631719, 2123386.355735100340098 ], [ -658829.104352750815451, 2123399.359894497785717 ], [ -658808.165594420861453, 2123410.683180436491966 ], [ -658786.39570057974197, 2123420.251196170691401 ], [ -658763.937705448712222, 2123428.001077524386346 ], [ -658740.939164424780756, 2123433.881905917543918 ], [ -658717.551184582174756, 2123437.855042927432805 ], [ -658693.927431826130487, 2123439.894384145271033 ], [ -658670.2231212410843, 2123439.986530691385269 ], [ -658646.473029853543267, 2123438.116326023824513 ], [ -658622.956551702925935, 2123434.290675894357264 ], [ -658599.82978837727569, 2123428.534974757581949 ], [ -658577.246254518977366, 2123420.887428554240614 ], [ -658555.355858770548366, 2123411.398801142349839 ], [ -658534.303908661357127, 2123400.132077276706696 ], [ -658514.230146035901271, 2123387.162044564262033 ], [ -658495.267819437780418, 2123372.574797005392611 ], [ -658477.542799591552466, 2123356.467163515742868 ], [ -658461.172743874136358, 2123338.946065162308514 ], [ -658446.266315299551934, 2123320.12780545046553 ], [ -658432.922461228445172, 2123300.137298292014748 ], [ -658421.229756557382643, 2123279.107238846831024 ], [ -658411.265815782360733, 2123257.177222690545022 ], [ -658403.096777815138921, 2123234.492819210048765 ], [ -658396.776866976753809, 2123211.2046053041704 ], [ -658392.348033092450351, 2123187.467165869195014 ], [ -658389.839673056616448, 2123163.438067727722228 ], [ -658389.268435738747939, 2123139.276813630014658 ], [ -658546.601961093838327, 2123136.563198985997587 ], [ -658457.540065760724247, 2122953.522529276553541 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 45023 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662180.079371348954737, 2129426.582739786244929 ], [ -662153.667974605807103, 2129424.594752465374768 ], [ -662127.481689385371283, 2129420.610031333751976 ], [ -662101.666961695766076, 2129414.650860731489956 ], [ -662076.368159586563706, 2129406.750566921196878 ], [ -662051.72676575742662, 2129396.953331731725484 ], [ -662027.88058628689032, 2129385.313945461530238 ], [ -662004.962979941163212, 2129371.897500451188534 ], [ -661983.102112341905013, 2129356.779027095064521 ], [ -661962.420239194761962, 2129340.043074189685285 ], [ -661943.033022560644895, 2129321.783236138522625 ], [ -661925.048884019372053, 2129302.101629505399615 ], [ -661908.56839831720572, 2129281.108321944717318 ], [ -661893.683730909717269, 2129258.920716643333435 ], [ -661880.478122539469041, 2129235.662895787972957 ], [ -661869.025423728860915, 2129211.464926595333964 ], [ -661859.389681786647998, 2129186.46213395986706 ], [ -661851.624782655620947, 2129160.794343632645905 ], [ -661845.774149572127499, 2129134.605100294575095 ], [ -661841.870500267134048, 2129108.04086477169767 ], [ -661839.935664013843052, 2129081.250194957014173 ], [ -661839.980459591839463, 2129054.382915051653981 ], [ -661842.004634817363694, 2129027.589277653954923 ], [ -661845.996867987909354, 2129001.019123523496091 ], [ -661851.934831241727807, 2128974.821043573319912 ], [ -661859.78531545388978, 2128949.141547931823879 ], [ -661869.504416000796482, 2128924.124246568419039 ], [ -661881.037778317695484, 2128899.909046212211251 ], [ -661894.320901909377426, 2128876.631367898546159 ], [ -661909.279501089360565, 2128854.421389662660658 ], [ -661925.82992043858394, 2128833.403318539727479 ], [ -661943.879602658911608, 2128813.694695955142379 ], [ -661963.327606209204532, 2128795.4057403691113 ], [ -661984.065169813809916, 2128778.638730920385569 ], [ -662005.976320713292807, 2128763.487435414455831 ], [ -662028.938523216173053, 2128750.036585953552276 ], [ -662052.823363971081562, 2128738.361405096016824 ], [ -662077.49727008480113, 2128728.527185152284801 ], [ -662102.822256104671396, 2128720.588923064991832 ], [ -662128.656695660669357, 2128714.591012839693576 ], [ -662154.856113473535515, 2128710.566997264511883 ], [ -662181.273993284790777, 2128708.539380367845297 ], [ -662207.762597204186022, 2128708.519501485396177 ], [ -662234.173791883862577, 2128710.507471913471818 ], [ -662260.359876902075484, 2128714.492174254730344 ], [ -662286.174410723964684, 2128720.451324599329382 ], [ -662311.473029629676603, 2128728.351597126107663 ], [ -662336.114255011081696, 2128738.148810496088117 ], [ -662359.960284553235397, 2128749.788174892310053 ], [ -662382.877762841060758, 2128763.204598455224186 ], [ -662404.738527109497227, 2128778.323051281739026 ], [ -662425.420323965139687, 2128795.058985020965338 ], [ -662444.807493047672324, 2128813.318805709481239 ], [ -662462.791613842360675, 2128833.000397164840251 ], [ -662479.272111999685876, 2128853.993692078627646 ], [ -662494.156821784097701, 2128876.181287525687367 ], [ -662507.362501504831016, 2128899.439101570751518 ], [ -662518.815299049252644, 2128923.637067125178874 ], [ -662528.451164900208823, 2128948.639859389513731 ], [ -662529.036220424692146, 2128950.573794241063297 ], [ -662504.430104913422838, 2129239.355072253849357 ], [ -662493.522054941044189, 2129258.470812628511339 ], [ -662478.563559090718627, 2129280.680811395402998 ], [ -662462.013216034742072, 2129301.698901677038521 ], [ -662443.963581364718266, 2129321.40754162427038 ], [ -662424.515595561126247, 2129339.696512387134135 ], [ -662403.778019501478411, 2129356.463534492067993 ], [ -662381.866826227051206, 2129371.61483984393999 ], [ -662358.904552371706814, 2129385.065696123987436 ], [ -662335.019612883334048, 2129396.740880620200187 ], [ -662310.345582859939896, 2129406.575100938789546 ], [ -662285.020450522075407, 2129414.51336013013497 ], [ -662259.185845508240163, 2129420.511264255270362 ], [ -662232.986246794578619, 2129424.535270656459033 ], [ -662206.568174679763615, 2129426.56287552928552 ], [ -662180.079371348954737, 2129426.582739786244929 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 49388 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659311.63530308578629, 2118898.249322732910514 ], [ -659324.960144641227089, 2118871.126656083390117 ], [ -659339.996072151698172, 2118844.937861145008355 ], [ -659356.679213025839999, 2118819.794185576494783 ], [ -659374.938697636476718, 2118795.802437491249293 ], [ -659394.696960386587307, 2118773.064531718846411 ], [ -659415.870069225900806, 2118751.677056812215596 ], [ -659438.368082174099982, 2118731.730864941142499 ], [ -659462.095429417560808, 2118713.310685710050166 ], [ -659486.95131926285103, 2118696.494766448158771 ], [ -659512.830166305880994, 2118681.354539667721838 ], [ -659539.622039922629483, 2118667.954319780226797 ], [ -659567.213131252327003, 2118656.351029764860868 ], [ -659582.062515574623831, 2118651.226497760042548 ], [ -659770.753831441979855, 2118636.181507399771363 ], [ -660013.512340991874225, 2118769.101699479855597 ], [ -660109.498503531562164, 2118897.351060654502362 ], [ -660131.677862829528749, 2119097.582143804524094 ], [ -659724.850629267864861, 2119090.861519446130842 ], [ -659855.950715026585385, 2119471.2081599999219 ], [ -659443.917992893373594, 2119455.862715763505548 ], [ -659435.41632107400801, 2119449.192769099958241 ], [ -659412.969644859666005, 2119429.060237135272473 ], [ -659391.874188715824857, 2119407.476520008873194 ], [ -659372.220500705880113, 2119384.534261402208358 ], [ -659354.092940157279372, 2119360.331936274189502 ], [ -659337.569315572269261, 2119334.973428127821535 ], [ -659322.720550647587515, 2119308.567583160474896 ], [ -659309.610379873192869, 2119281.227742935530841 ], [ -659298.295074963360094, 2119253.07125798612833 ], [ -659288.823203338659368, 2119224.218984119594097 ], [ -659281.235419691773131, 2119194.794763611163944 ], [ -659279.559241548296995, 2119185.966326669324189 ], [ -659305.303102059056982, 2118913.558468156959862 ], [ -659311.63530308578629, 2118898.249322732910514 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 49389 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -663283.073593405890279, 2119779.762437678407878 ], [ -663253.822182945325039, 2120444.366674417164177 ], [ -663252.602443580864929, 2120458.681742427870631 ], [ -663248.521530967671424, 2120458.6886699013412 ], [ -663222.924045348539948, 2120456.738284546416253 ], [ -663197.553661965415813, 2120452.805372496601194 ], [ -663172.559265924151987, 2120446.913013793993741 ], [ -663148.087535830563866, 2120439.09578731097281 ], [ -663124.282082987134345, 2120429.399567834567279 ], [ -663101.282608592882752, 2120417.881256833206862 ], [ -663079.224083889159374, 2120404.608448529615998 ], [ -663058.235958066419698, 2120389.659033250529319 ], [ -663038.44139857776463, 2120373.120740294456482 ], [ -663019.956568334018812, 2120355.090623137541115 ], [ -663002.889943994348869, 2120335.674489835742861 ], [ -662987.341679371893406, 2120314.986282147467136 ], [ -662973.403017695178278, 2120293.147406825330108 ], [ -662961.155756158172153, 2120270.286023183260113 ], [ -662950.671765910810791, 2120246.536290997639298 ], [ -662942.012570308870636, 2120222.037583190016448 ], [ -662935.228983889217488, 2120196.933667931240052 ], [ -662930.360814187210053, 2120171.371864984277636 ], [ -662927.436628174269572, 2120145.502181126270443 ], [ -662926.473584638326429, 2120119.476429886650294 ], [ -662927.477333525661379, 2120093.447340620681643 ], [ -662930.441982820746489, 2120067.567662256769836 ], [ -662935.350133164785802, 2120041.989266885910183 ], [ -662942.172979998518713, 2120016.862258536275476 ], [ -662950.870482631260529, 2119992.334092317149043 ], [ -662961.391599258990027, 2119968.548709051683545 ], [ -662973.674586524371989, 2119945.645690641365945 ], [ -662987.647361883427948, 2119923.759440896566957 ], [ -663003.227926639490761, 2119903.018396837171167 ], [ -663020.324847167124972, 2119883.544274964835495 ], [ -663038.837791492813267, 2119865.451356981415302 ], [ -663058.658118097577244, 2119848.845819177106023 ], [ -663079.669513472821563, 2119833.825109289959073 ], [ -663101.74867468723096, 2119820.477374695241451 ], [ -663124.766032977961004, 2119808.880945119075477 ], [ -663148.58651409659069, 2119799.103872939012945 ], [ -663173.070330958231352, 2119791.203533890191466 ], [ -663198.073803949402645, 2119785.22629027068615 ], [ -663223.450204065418802, 2119781.207218958530575 ], [ -663249.050613947096281, 2119779.169905507471412 ], [ -663274.724801751435734, 2119779.126305778045207 ], [ -663283.073593405890279, 2119779.762437678407878 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 49390 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -663346.62979113869369, 2119279.060939743649215 ], [ -663344.000321470550261, 2119308.702645138371736 ], [ -663339.404817673144862, 2119338.101558745838702 ], [ -663332.864246130455285, 2119367.123539878055453 ], [ -663324.40844818088226, 2119395.636167690623552 ], [ -663314.076003984780982, 2119423.509345383383334 ], [ -663301.914056529174559, 2119450.615893825422972 ], [ -663287.978096553124487, 2119476.832131807692349 ], [ -663272.331709379563108, 2119502.038440420757979 ], [ -663255.046284817159176, 2119526.119808808900416 ], [ -663236.200691437814385, 2119548.966359012294561 ], [ -663215.880916743539274, 2119570.473847224842757 ], [ -663194.179674824583344, 2119590.544139507692307 ], [ -663171.195983343175612, 2119609.085659546777606 ], [ -663147.034711733227596, 2119626.01380647206679 ], [ -663121.806102711590938, 2119641.251340882387012 ], [ -663095.625269248150289, 2119654.728737305384129 ], [ -663068.611669332254678, 2119666.384501378517598 ], [ -663040.888560887891799, 2119676.165450478903949 ], [ -663012.582439361605793, 2119684.026956347748637 ], [ -662983.82246052834671, 2119689.933148756623268 ], [ -662954.739851160906255, 2119693.857079146429896 ], [ -662925.467310235835612, 2119695.780843579676002 ], [ -662896.138403434073552, 2119695.695664482191205 ], [ -662866.8869536772836, 2119693.601930618751794 ], [ -662837.846430486883037, 2119689.509195384103805 ], [ -662809.149340966134332, 2119683.436133151873946 ], [ -662780.926625158521347, 2119675.410454124212265 ], [ -662753.307058575563133, 2119665.468777829781175 ], [ -662726.41666458058171, 2119653.656466093380004 ], [ -662700.378139353590086, 2119640.02741600619629 ], [ -662675.310292023699731, 2119624.64381402824074 ], [ -662651.327502547646873, 2119607.575852252542973 ], [ -662633.458797906874679, 2119592.932897321414202 ], [ -662649.665591727825813, 2119259.050650794059038 ], [ -662861.041964669013396, 2119240.197332561481744 ], [ -662936.911776579800062, 2119207.398018904961646 ], [ -663015.696438976679929, 2119099.011640863027424 ], [ -663165.474128923728131, 2118896.517805930227041 ], [ -663186.449792176950723, 2118912.547428961377591 ], [ -663208.601418016827665, 2118931.993573479354382 ], [ -663229.404818454175256, 2118952.915762934368104 ], [ -663248.765072659938596, 2118975.218534371349961 ], [ -663266.593844299437478, 2118998.800125535111874 ], [ -663282.80978459387552, 2119023.552939210087061 ], [ -663297.338903489755467, 2119049.364034105092287 ], [ -663310.114907275652513, 2119076.115640216972679 ], [ -663321.079501070897095, 2119103.685696195345372 ], [ -663330.18265483470168, 2119131.948406239971519 ], [ -663337.382831657887436, 2119160.774814092088491 ], [ -663342.647177310776897, 2119190.033391451463103 ], [ -663345.951670174137689, 2119219.590638105757535 ], [ -663347.28123087878339, 2119249.311691022012383 ], [ -663346.62979113869369, 2119279.060939743649215 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51975 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658921.496904977597296, 2124657.013779395725578 ], [ -658854.282888242392801, 2125590.437160382978618 ], [ -658825.412765480112284, 2125588.400236253626645 ], [ -658795.497885896358639, 2125584.280913686845452 ], [ -658765.917407608008943, 2125578.185100108850747 ], [ -658736.798000635113567, 2125570.138898885343224 ], [ -658708.264360537752509, 2125560.176765322685242 ], [ -658680.438674429082312, 2125548.341358996462077 ], [ -658653.440097719896585, 2125534.683361158240587 ], [ -658627.384243847103789, 2125519.261257669422776 ], [ -658602.382689178804867, 2125502.141088569536805 ], [ -658578.54249520751182, 2125483.396165246609598 ], [ -658555.965750074828975, 2125463.106756548397243 ], [ -658534.749131407472305, 2125441.359745018649846 ], [ -658514.983492311206646, 2125418.248254877515137 ], [ -658496.753472328535281, 2125393.871253212913871 ], [ -658480.1371349922847, 2125368.333126229699701 ], [ -658465.205633551348001, 2125341.743232206441462 ], [ -658452.022906283498742, 2125314.21543324412778 ], [ -658440.645402727532201, 2125285.86760764149949 ], [ -658431.121841961285099, 2125256.82114516897127 ], [ -658423.49300400132779, 2125227.200427231844515 ], [ -658417.791555201401934, 2125197.132294275332242 ], [ -658414.04190839570947, 2125166.745502574369311 ], [ -658412.260118386242539, 2125136.170172972604632 ], [ -658412.453813223401085, 2125105.537233613897115 ], [ -658414.62216157768853, 2125074.977859297767282 ], [ -658418.755876322626136, 2125044.622909801546484 ], [ -658424.837254349142313, 2125014.602369507309049 ], [ -658432.840252396184951, 2124985.0447907904163 ], [ -658442.730598602094688, 2124956.07674353942275 ], [ -658454.465939305373468, 2124927.822273160330951 ], [ -658467.996020425693132, 2124900.402369406074286 ], [ -658483.262902688235044, 2124873.934448328334838 ], [ -658500.201209762366489, 2124848.531849378719926 ], [ -658518.738408219069242, 2124824.30335018504411 ], [ -658538.795118156820536, 2124801.352700711693615 ], [ -658560.285453132237308, 2124779.778178946115077 ], [ -658583.117387934704311, 2124759.672170166857541 ], [ -658607.193152680760249, 2124741.120771209243685 ], [ -658632.40965145919472, 2124724.203421888872981 ], [ -658658.658903812174685, 2124708.99256477644667 ], [ -658685.828507106285542, 2124695.553335041273385 ], [ -658713.802117862040177, 2124683.943281442858279 ], [ -658742.459949916810729, 2124674.212120003532618 ], [ -658771.679287369130179, 2124666.401521049439907 ], [ -658801.335010028094985, 2124660.544930789619684 ], [ -658831.300129182054661, 2124656.66742812236771 ], [ -658861.446331353392452, 2124654.785617183428258 ], [ -658891.644527719821781, 2124654.907556267920882 ], [ -658921.496904977597296, 2124657.013779395725578 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51976 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -657935.574715439113788, 2125013.364574597682804 ], [ -657957.886944658122957, 2125028.368760372977704 ], [ -657979.055997524759732, 2125044.987679026089609 ], [ -657998.968928661313839, 2125063.132661627139896 ], [ -658017.519494415493682, 2125082.706896985881031 ], [ -658034.608719696989283, 2125103.605948258191347 ], [ -658050.145426055416465, 2125125.718310072552413 ], [ -658064.046718155965209, 2125148.926003519911319 ], [ -658076.238426065654494, 2125173.105205598287284 ], [ -658086.655500992550515, 2125198.126909851562232 ], [ -658095.242362370248884, 2125223.85761469649151 ], [ -658101.953194423113018, 2125250.160035675857216 ], [ -658106.752190634142607, 2125276.89383796742186 ], [ -658109.613744819769636, 2125303.916385096497834 ], [ -658110.522587778861634, 2125331.083500024396926 ], [ -658109.473868797649629, 2125358.250234310980886 ], [ -658106.473181562148966, 2125385.271641551051289 ], [ -658101.536534349550493, 2125412.003550697583705 ], [ -658094.690264659002423, 2125438.303335254080594 ], [ -658085.970898708677851, 2125464.030674356501549 ], [ -658075.424956610891968, 2125489.048301302827895 ], [ -658063.108704176149331, 2125513.222736048512161 ], [ -658049.087852749973536, 2125536.424997323192656 ], [ -658033.437208641669713, 2125558.531290855258703 ], [ -658016.240274019539356, 2125579.423669791314751 ], [ -657997.588801398989744, 2125598.990664108190686 ], [ -657977.582304133451544, 2125617.127875248901546 ], [ -657956.327525451546535, 2125633.738533235155046 ], [ -657933.9378689636942, 2125648.73401289479807 ], [ -657910.532793580321595, 2125662.034306736662984 ], [ -657886.237176139373332, 2125673.568451914936304 ], [ -657861.180645138258114, 2125683.274908720050007 ], [ -657835.49688907712698, 2125691.10188898909837 ], [ -657809.322943148436025, 2125697.007632420398295 ], [ -657802.526830313028768, 2125698.02047174051404 ], [ -657791.492645345628262, 2125658.056809708476067 ], [ -657739.362688409048133, 2125539.166614615358412 ], [ -657734.917427351349033, 2125321.482915705535561 ], [ -657697.537186143454164, 2124992.690990759991109 ], [ -657923.31402577960398, 2125006.371951649896801 ], [ -657935.574715439113788, 2125013.364574597682804 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51977 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659593.499317524489015, 2121101.25985866971314 ], [ -659621.161475712549873, 2121107.280303706414998 ], [ -659648.337560845422558, 2121115.244646563660353 ], [ -659674.895175419631414, 2121125.114085969515145 ], [ -659700.704934947192669, 2121136.840539240743965 ], [ -659725.641098271706142, 2121150.36687646061182 ], [ -659749.582180132973008, 2121165.627198915462941 ], [ -659772.411543014924973, 2121182.547160051763058 ], [ -659794.01796537428163, 2121201.044327731244266 ], [ -659814.296183471102268, 2121221.028585822787136 ], [ -659833.147404208779335, 2121242.402573185972869 ], [ -659850.479786423034966, 2121265.062158104963601 ], [ -659866.208888332010247, 2121288.896945472806692 ], [ -659880.258078924147412, 2121313.790814733598381 ], [ -659892.558911308413371, 2121339.622485547792166 ], [ -659903.051456188433804, 2121366.266108673531562 ], [ -659911.684593858197331, 2121393.591879102867097 ], [ -659918.416263263789006, 2121421.466668413951993 ], [ -659923.213666951516643, 2121449.75467342697084 ], [ -659926.053430877509527, 2121478.318077754229307 ], [ -659926.921718316501938, 2121507.017723280005157 ], [ -659925.814297307748348, 2121535.713788097724319 ], [ -659922.736561301746406, 2121564.266467738896608 ], [ -659917.703502923017368, 2121592.536656247451901 ], [ -659910.739640963030979, 2121620.386623923666775 ], [ -659901.878900956362486, 2121647.680688340216875 ], [ -659891.164449935546145, 2121674.285875346977264 ], [ -659878.648486159043387, 2121700.072566939517856 ], [ -659864.391984826768748, 2121724.915132720023394 ], [ -659848.464401056640781, 2121748.692541981115937 ], [ -659830.943331506918184, 2121771.288953385315835 ], [ -659811.914136369829066, 2121792.594279269687831 ], [ -659791.469523506239057, 2121812.504722049459815 ], [ -659769.709096802282147, 2121830.923279865179211 ], [ -659746.738870900124311, 2121847.760219188872725 ], [ -659722.670754716498777, 2121862.933512050658464 ], [ -659697.622006225050427, 2121876.369235567748547 ], [ -659671.714661172241904, 2121888.001932152081281 ], [ -659645.074938520323485, 2121897.774928473401815 ], [ -659629.673720736638643, 2121902.221729316748679 ], [ -659343.090929316589609, 2121876.186674251686782 ], [ -659342.073580209165812, 2121875.724451997317374 ], [ -659317.137249083491042, 2121862.198084950447083 ], [ -659293.196026191464625, 2121846.937733049038798 ], [ -659270.366551788640209, 2121830.017743398901075 ], [ -659248.760049596894532, 2121811.520548693370074 ], [ -659228.481784906936809, 2121791.536265584174544 ], [ -659209.630551721085794, 2121770.16225570673123 ], [ -659192.298191446112469, 2121747.502651233691722 ], [ -659176.569145439192653, 2121723.667847617994994 ], [ -659162.520043621538207, 2121698.77396573824808 ], [ -659150.219331156695262, 2121672.942286207340658 ], [ -659139.726935007725842, 2121646.298658392392099 ], [ -659131.09397198271472, 2121618.972887450363487 ], [ -659124.362499741604552, 2121591.098101755138487 ], [ -659123.915658929967321, 2121588.463178135920316 ], [ -659150.505961999180727, 2121341.036335645243526 ], [ -659151.616387362941168, 2121338.279078538063914 ], [ -659164.132531132898293, 2121312.492416725028306 ], [ -659178.389187256689183, 2121287.649880652315915 ], [ -659194.316897609969601, 2121263.872500442434102 ], [ -659211.838063069386408, 2121241.27611686848104 ], [ -659230.867321574594826, 2121219.970817078370601 ], [ -659251.311964030377567, 2121200.060398116707802 ], [ -659273.072385977604426, 2121181.641861391719431 ], [ -659296.042572864680551, 2121164.804940011817962 ], [ -659320.110616532503627, 2121149.631661616731435 ], [ -659337.60418646549806, 2121140.248377581126988 ], [ -659359.625597864855081, 2121160.16248986730352 ], [ -659406.979154465836473, 2121215.917041466571391 ], [ -659441.146196235786192, 2121162.595604790840298 ], [ -659436.128465855610557, 2121104.536155052483082 ], [ -659527.27129629557021, 2121126.892843056004494 ], [ -659566.8505983594805, 2121097.409812904894352 ], [ -659593.499317524489015, 2121101.25985866971314 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51978 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659497.381202135933563, 2121065.58143145730719 ], [ -659472.24446488043759, 2121063.652641850057989 ], [ -659447.335413984721527, 2121059.753171474672854 ], [ -659422.803856978425756, 2121053.90647235698998 ], [ -659398.797331044799648, 2121046.147707412019372 ], [ -659375.460215675993823, 2121036.523538936395198 ], [ -659352.93286432872992, 2121025.091847995296121 ], [ -659331.350760281318799, 2121011.921386326197535 ], [ -659310.843701803358272, 2120997.091362826060504 ], [ -659291.535021508578211, 2120980.690967177972198 ], [ -659273.540844593662769, 2120962.818833486642689 ], [ -659256.969390446203761, 2120943.582447006832808 ], [ -659241.920321780722588, 2120923.097497791517526 ], [ -659228.484145266585983, 2120901.487184851430357 ], [ -659216.741667206399143, 2120878.881475246977061 ], [ -659206.763507572119124, 2120855.416322454344481 ], [ -659198.609675305662677, 2120831.232848693616688 ], [ -659192.329207431874238, 2120806.476496257353574 ], [ -659187.959874180727638, 2120781.296152719762176 ], [ -659185.527951854164712, 2120755.843255601357669 ], [ -659185.04806483455468, 2120730.270881538745016 ], [ -659186.523097666911781, 2120704.732825704384595 ], [ -659189.944177747122012, 2120679.382676833774894 ], [ -659195.290728722582571, 2120654.372893572319299 ], [ -659202.530594277428463, 2120629.853887521661818 ], [ -659211.62023155996576, 2120605.973118708003312 ], [ -659222.504973092465661, 2120582.874208681751043 ], [ -659235.119355578557588, 2120560.696076818276197 ], [ -659249.387513631605543, 2120539.572104820981622 ], [ -659265.223636070732027, 2120519.62933454522863 ], [ -659282.532482007867657, 2120500.987703995313495 ], [ -659301.209953660843894, 2120483.75932594994083 ], [ -659321.143722414155491, 2120468.047813758719712 ], [ -659342.213904378353618, 2120453.947658159770072 ], [ -659364.293781381682493, 2120441.543659069109708 ], [ -659387.250563065055758, 2120430.910415486432612 ], [ -659410.946185475564562, 2120422.111876958049834 ], [ -659435.238141383626498, 2120415.200958903413266 ], [ -659459.980337314424105, 2120410.219224413391203 ], [ -659485.023972139810212, 2120407.196634254418314 ], [ -659510.21843195729889, 2120406.151366705074906 ], [ -659535.412195857381448, 2120407.089708221610636 ], [ -659560.453747158870101, 2120410.006015637889504 ], [ -659585.192484604660422, 2120414.882750074379146 ], [ -659609.479628061293624, 2120421.690582444425672 ], [ -659633.169113263254985, 2120430.38856980483979 ], [ -659656.118470219196752, 2120440.924401632044464 ], [ -659678.189680017065257, 2120453.234714389778674 ], [ -659699.250004857312888, 2120467.245472597889602 ], [ -659719.172786321258172, 2120482.872414099983871 ], [ -659737.838207094813697, 2120500.021556816995144 ], [ -659755.134011550107971, 2120518.589763960335404 ], [ -659770.956180849694647, 2120538.465364293195307 ], [ -659785.209558523609303, 2120559.528823764529079 ], [ -659797.808422759757377, 2120581.653464351315051 ], [ -659808.677001946023665, 2120604.706225960981101 ], [ -659817.749930387944914, 2120628.548466613516212 ], [ -659824.97264144080691, 2120653.036796306725591 ], [ -659830.301695706904866, 2120678.023939332924783 ], [ -659833.705042308545671, 2120703.359620058909059 ], [ -659835.162211681832559, 2120728.891466641798615 ], [ -659834.66443871811498, 2120754.465927465353161 ], [ -659832.214715508394875, 2120779.929194598458707 ], [ -659827.827773392898962, 2120805.128128825221211 ], [ -659821.529994401265867, 2120829.911180628929287 ], [ -659813.359252625377849, 2120854.129301641602069 ], [ -659803.364686473272741, 2120877.636841059662402 ], [ -659791.606403183541261, 2120900.292421570513397 ], [ -659778.155117357731797, 2120921.959789655636996 ], [ -659763.091725705424324, 2120942.508635034319013 ], [ -659746.506820542388596, 2120961.815374337136745 ], [ -659728.500144974444993, 2120979.763894383795559 ], [ -659709.179993038182147, 2120996.246250514406711 ], [ -659688.662558421376161, 2121011.163315750192851 ], [ -659667.071235650684685, 2121024.425377008039504 ], [ -659644.535877985181287, 2121035.952674577012658 ], [ -659621.192016443586908, 2121045.675881882663816 ], [ -659597.180044692126103, 2121053.536522374488413 ], [ -659572.644374677794985, 2121059.487321235239506 ], [ -659547.732568088686094, 2121063.492489673662931 ], [ -659522.594448870862834, 2121065.527940206229687 ], [ -659497.381202135933563, 2121065.58143145730719 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51979 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658946.965828784857877, 2120371.286821369081736 ], [ -658944.344651411520317, 2120400.81970789982006 ], [ -658939.764597370987758, 2120430.11056350544095 ], [ -658933.246562565560453, 2120459.025740545708686 ], [ -658924.820285517838784, 2120487.433305508457124 ], [ -658914.524211706244387, 2120515.203640935476869 ], [ -658902.405318174976856, 2120542.210036988835782 ], [ -658888.518899226561189, 2120568.329269421752542 ], [ -658872.928314155549742, 2120593.44216194562614 ], [ -658855.704698162153363, 2120617.434129951521754 ], [ -658836.926637807278894, 2120640.195703411474824 ], [ -658816.679812461021356, 2120661.623026246204972 ], [ -658795.056603368720971, 2120681.618330281693488 ], [ -658772.155672146822326, 2120700.090381413232535 ], [ -658748.08151061553508, 2120716.954895707312971 ], [ -658722.943964025354944, 2120732.134924151934683 ], [ -658696.857729850686155, 2120745.561203632503748 ], [ -658489.060621699900366, 2120761.666761284228414 ], [ -658475.741586894029751, 2120784.763515352737159 ], [ -658468.949286051560193, 2120784.276907002553344 ], [ -658440.014508809661493, 2120780.197267948184162 ], [ -658411.422001351136714, 2120774.144583037123084 ], [ -658383.302226528176107, 2120766.146469464991242 ], [ -658355.783490139059722, 2120756.239421003963798 ], [ -658328.991355475969613, 2120744.4686414510943 ], [ -658303.04807036567945, 2120730.887838416732848 ], [ -658278.072009374969639, 2120715.558978194836527 ], [ -658254.1771336508682, 2120698.552003097720444 ], [ -658231.472470933687873, 2120679.944512263871729 ], [ -658210.061618061736226, 2120659.821407643146813 ], [ -658190.042268284596503, 2120638.274506502784789 ], [ -658171.505765492212959, 2120615.402122605126351 ], [ -658154.536687429994345, 2120591.308617565780878 ], [ -658139.212459800532088, 2120566.103924655355513 ], [ -658125.603002985124476, 2120539.903047208674252 ], [ -658113.770413023768924, 2120512.82553390879184 ], [ -658103.768678305554204, 2120484.994933296460658 ], [ -658095.643433243385516, 2120456.538230045232922 ], [ -658089.431750078452751, 2120427.585265572648495 ], [ -658085.161969757988118, 2120398.268145555630326 ], [ -658082.853572644176893, 2120368.720637251622975 ], [ -658082.517089662607759, 2120339.077559052035213 ], [ -658084.154054292128421, 2120309.474165397230536 ], [ -658087.756995575735345, 2120280.04552966170013 ], [ -658093.309472275548615, 2120250.925927776377648 ], [ -658100.786147905746475, 2120222.248225611634552 ], [ -658110.152906366158277, 2120194.143272779881954 ], [ -658121.367007654625922, 2120166.739305472467095 ], [ -658134.377282890025526, 2120140.161361502483487 ], [ -658149.124367825686932, 2120114.530709736514837 ], [ -658158.593653818708844, 2120100.360524518415332 ], [ -658455.335083308862522, 2119913.349948056507856 ], [ -658459.05264056159649, 2119912.718425014521927 ], [ -658488.140612175338902, 2119909.805534073617309 ], [ -658517.351258776616305, 2119908.890994604676962 ], [ -658546.551300692837685, 2119909.978979546111077 ], [ -658575.60750667960383, 2119913.064524822868407 ], [ -658604.387301785987802, 2119918.13355194311589 ], [ -658632.759372225613333, 2119925.162932321429253 ], [ -658660.594264514511451, 2119934.120592666324228 ], [ -658687.764976084465161, 2119944.965661464259028 ], [ -658714.147534753428772, 2119957.648655370343477 ], [ -658739.621564340079203, 2119972.111705029383302 ], [ -658764.070833902922459, 2119988.28881906485185 ], [ -658787.383788040606305, 2120006.106185271404684 ], [ -658809.454055875889026, 2120025.482507315464318 ], [ -658830.180936396471225, 2120046.329375660978258 ], [ -658849.469857916235924, 2120068.551671048160642 ], [ -658867.232809569453821, 2120092.047998384106904 ], [ -658883.388742897077464, 2120116.711149514652789 ], [ -658897.863941647927277, 2120142.428592210635543 ], [ -658910.592358138761483, 2120169.082983812317252 ], [ -658921.515914622345008, 2120196.552706511225551 ], [ -658924.112807311699726, 2120204.61632668832317 ], [ -658931.591654603835195, 2120281.948986494913697 ], [ -658947.582965830923058, 2120343.160043694078922 ], [ -658946.965828784857877, 2120371.286821369081736 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/geotiff_labels.geojson b/docker/solaris/solaris/data/geotiff_labels.geojson new file mode 100755 index 00000000..8abbc8cc --- /dev/null +++ b/docker/solaris/solaris/data/geotiff_labels.geojson @@ -0,0 +1,49 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102932, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 250.90410248692208, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733633.917563493945636, 3724917.327059258706868 ], [ 733644.026576642645523, 3724916.940842032898217 ], [ 733643.061781426658854, 3724892.15789200225845 ], [ 733632.952742423396558, 3724892.544111663941294 ], [ 733633.917563493945636, 3724917.327059258706868 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102940, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 247.85698155347222, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733653.132692800136283, 3724949.324520394671708 ], [ 733648.855268120998517, 3724922.585283832624555 ], [ 733642.8083045554813, 3724925.412150491029024 ], [ 733636.785855265101418, 3724926.852379398420453 ], [ 733638.080213227891363, 3724931.256500165909529 ], [ 733642.490936725749634, 3724931.197525009978563 ], [ 733643.028865780681372, 3724937.691814653109759 ], [ 733641.684800133458339, 3724942.564334524329752 ], [ 733641.276853826944716, 3724944.829464028123766 ], [ 733643.267351940623485, 3724949.61678282963112 ], [ 733653.132692800136283, 3724949.324520394671708 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135943, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 293.80215091143663, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733614.692407695925795, 3725025.917704849969596 ], [ 733618.232930953730829, 3725025.759830723050982 ], [ 733618.361258932505734, 3725028.493043820839375 ], [ 733623.40737501042895, 3725028.260883178096265 ], [ 733623.226638415828347, 3725024.25013302732259 ], [ 733629.22064378275536, 3725023.974487836007029 ], [ 733628.499683096189983, 3725008.231178386602551 ], [ 733624.467038923874497, 3725005.347326850984246 ], [ 733613.770959213492461, 3725005.830230219755322 ], [ 733614.692407695925795, 3725025.917704849969596 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733644.636568528716452, 3725102.534991428721696 ], [ 733654.492900374345481, 3725096.893328425474465 ], [ 733649.850147562567145, 3725088.956138697918504 ], [ 733646.575522312079556, 3725089.630984119605273 ], [ 733627.684122170670889, 3725099.591503564734012 ], [ 733630.799799439031631, 3725105.438350759446621 ], [ 733640.795360824326053, 3725100.177396830171347 ], [ 733644.636568528716452, 3725102.534991428721696 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102923, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 371.9610521071786, "origlen": 0, "partialDec": 0.40890572019922689, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733601.0, 3725137.577448081690818 ], [ 733604.893619947950356, 3725135.093213116750121 ], [ 733604.174474695930257, 3725128.416942054405808 ], [ 733603.743797679431736, 3725124.377905528992414 ], [ 733610.68987981416285, 3725120.074717226438224 ], [ 733610.059207651182078, 3725110.148913879878819 ], [ 733601.0, 3725111.934446161147207 ], [ 733601.0, 3725137.577448081690818 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93019, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 223.11504089232326, "origlen": 0, "partialDec": 0.71792570273177214, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733808.825040770112537, 3724703.694553496781737 ], [ 733812.694460124941543, 3724699.57170610036701 ], [ 733813.810271165333688, 3724692.263191219884902 ], [ 733809.834030322846957, 3724689.0 ], [ 733793.844497522106394, 3724689.0 ], [ 733808.825040770112537, 3724703.694553496781737 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134694, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 98.143961268411516, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733707.923785710008815, 3724706.18411343311891 ], [ 733716.751506693311967, 3724697.820616477634758 ], [ 733709.214741862844676, 3724689.402245204430073 ], [ 733706.274902248056605, 3724694.235860752407461 ], [ 733708.154135095421225, 3724696.734307382255793 ], [ 733707.539887185208499, 3724700.22627462958917 ], [ 733702.747399608721025, 3724701.85182610200718 ], [ 733707.923785710008815, 3724706.18411343311891 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93018, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 189.51651421072748, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733824.194954353850335, 3724717.886300034821033 ], [ 733841.671712412848137, 3724724.860320621170104 ], [ 733845.427343191811815, 3724715.507587827276438 ], [ 733827.959576514316723, 3724708.544878867920488 ], [ 733824.194954353850335, 3724717.886300034821033 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92640, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 140.04971670918377, "origlen": 0, "partialDec": 0.38635210049961444, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734044.256298960419372, 3724728.538379620760679 ], [ 734044.149200432701036, 3724737.8691356764175 ], [ 734051.0, 3724737.92929164506495 ], [ 734051.0, 3724729.625226940959692 ], [ 734048.398788033635356, 3724730.725917739327997 ], [ 734045.49420094571542, 3724729.545217602048069 ], [ 734044.256298960419372, 3724728.538379620760679 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733998.172171951970086, 3724738.955371181480587 ], [ 734005.087085773120634, 3724738.990968229714781 ], [ 734006.539364616852254, 3724733.876970435027033 ], [ 734005.036672754795291, 3724732.308777935802937 ], [ 734004.700672700069845, 3724728.960097201168537 ], [ 733985.17671379854437, 3724727.518070545978844 ], [ 733980.793273733812384, 3724729.49750511161983 ], [ 733981.333166613709182, 3724735.903093201573938 ], [ 733988.48206047678832, 3724740.80528543330729 ], [ 733992.534389728447422, 3724740.981871583964676 ], [ 733996.584479778190143, 3724740.86985838599503 ], [ 733998.172171951970086, 3724738.955371181480587 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92641, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 127.89418152890968, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734011.306465386645868, 3724738.998495681677014 ], [ 734020.362710424815305, 3724739.918715209700167 ], [ 734021.868912066216581, 3724735.638380894903094 ], [ 734030.806304456898943, 3724735.723363324068487 ], [ 734033.127172784181312, 3724732.295262097381055 ], [ 734018.804850079119205, 3724729.581800156738609 ], [ 734016.222719809040427, 3724732.304357184097171 ], [ 734011.883401251398027, 3724732.475890559609979 ], [ 734011.306465386645868, 3724738.998495681677014 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93146, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 85.499318876340681, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733797.19361286086496, 3724803.425375143066049 ], [ 733809.65190442930907, 3724803.396256368607283 ], [ 733810.314092590706423, 3724796.798043267335743 ], [ 733797.529922557529062, 3724796.486277254763991 ], [ 733797.19361286086496, 3724803.425375143066049 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733922.277427969733253, 3724836.252271949779242 ], [ 733926.087521769106388, 3724819.343201762996614 ], [ 733916.41197619389277, 3724817.176084883511066 ], [ 733913.923271550564095, 3724828.246590849943459 ], [ 733907.995921526453458, 3724826.925592739600688 ], [ 733906.674282207386568, 3724832.775251807179302 ], [ 733922.277427969733253, 3724836.252271949779242 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85996, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 147.78075500471959, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733960.773279143380933, 3724839.677568545099348 ], [ 733961.184794776490889, 3724835.747843090444803 ], [ 733963.223460652050562, 3724833.61130030779168 ], [ 733963.607368887402117, 3724830.812890837434679 ], [ 733962.502454621368088, 3724828.921473243273795 ], [ 733960.194102098466828, 3724829.175883492454886 ], [ 733957.964056476601399, 3724830.786153580993414 ], [ 733952.526077670161612, 3724831.474698279984295 ], [ 733949.761529997806065, 3724829.121061434503645 ], [ 733946.739561799215153, 3724828.947437366005033 ], [ 733946.682754538487643, 3724839.644461845047772 ], [ 733960.773279143380933, 3724839.677568545099348 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86005, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.65732906731142, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733873.500091013731435, 3724936.93015677202493 ], [ 733873.77284804196097, 3724947.058124471455812 ], [ 733881.724138652090915, 3724946.841479253023863 ], [ 733881.803844684036449, 3724949.662298649549484 ], [ 733887.590841862838715, 3724949.503837334923446 ], [ 733887.518520563142374, 3724946.760883453302085 ], [ 733898.116790315601975, 3724946.486752765718848 ], [ 733897.845682443701662, 3724936.292234980501235 ], [ 733873.500091013731435, 3724936.93015677202493 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86006, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 300.47300947479948, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733842.117838621605188, 3724960.036273914854974 ], [ 733848.919941028230824, 3724958.981422422919422 ], [ 733847.980877452413552, 3724952.954536063130945 ], [ 733864.939495211467147, 3724950.327385626267642 ], [ 733863.365486120572314, 3724940.267557054758072 ], [ 733847.021852035541087, 3724942.798733331263065 ], [ 733846.740707991877571, 3724941.005108900833875 ], [ 733839.323592993430793, 3724942.155940910801291 ], [ 733842.117838621605188, 3724960.036273914854974 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134689, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 28.431725003743587, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733898.297767688753083, 3724968.365196610335261 ], [ 733897.317986879032105, 3724965.910844214726239 ], [ 733898.783516068593599, 3724963.294194012414664 ], [ 733898.249386645853519, 3724961.216946474276483 ], [ 733898.518854100489989, 3724959.303578331600875 ], [ 733899.437801164225675, 3724957.783386053517461 ], [ 733896.328320293803699, 3724957.77410851046443 ], [ 733896.316138221649453, 3724960.936722508631647 ], [ 733895.295106926350854, 3724960.934007437899709 ], [ 733895.271784850629047, 3724968.35795633494854 ], [ 733898.297767688753083, 3724968.365196610335261 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86004, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 245.87085671707746, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734039.951275395345874, 3724957.361701209563762 ], [ 734020.141815275885165, 3724955.846008125692606 ], [ 734019.51552347233519, 3724964.009905667975545 ], [ 734026.204987838980742, 3724964.528337045572698 ], [ 734026.000307232956402, 3724967.20904346043244 ], [ 734028.35346275055781, 3724967.399663022719324 ], [ 734028.161849318654276, 3724969.925317186396569 ], [ 734033.76750397705473, 3724970.350708946119994 ], [ 734033.998593332478777, 3724967.348806756082922 ], [ 734043.10642178892158, 3724968.048365659546107 ], [ 734043.692446619272232, 3724960.393990571144968 ], [ 734039.951275395345874, 3724957.361701209563762 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102925, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 235.62354171707437, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733716.606242132140324, 3724973.75564177101478 ], [ 733725.094902680953965, 3724960.633992579299957 ], [ 733721.837239123298787, 3724956.803474905434996 ], [ 733713.769853547797538, 3724952.64484176505357 ], [ 733711.143068718956783, 3724957.585964888799936 ], [ 733714.034097261959687, 3724959.32113200193271 ], [ 733712.084855253109708, 3724961.981503693852574 ], [ 733706.699992214213125, 3724959.353198084048927 ], [ 733702.652436110540293, 3724965.458262465894222 ], [ 733716.606242132140324, 3724973.75564177101478 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733653.939914708724245, 3724982.094132591970265 ], [ 733664.747478068107739, 3724978.562062832526863 ], [ 733656.60582423210144, 3724953.837236555293202 ], [ 733645.967119043460116, 3724957.295747282914817 ], [ 733649.078211254440248, 3724966.760403643827885 ], [ 733654.418122929870151, 3724970.852504897862673 ], [ 733657.441431801649742, 3724975.920252304058522 ], [ 733657.77753429254517, 3724979.268915843218565 ], [ 733653.939914708724245, 3724982.094132591970265 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86007, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 288.75968082249199, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733804.608246845542453, 3724992.315301816910505 ], [ 733814.888789982767776, 3724991.744788535870612 ], [ 733813.477104315534234, 3724966.273909805342555 ], [ 733801.90457292238716, 3724966.912798856850713 ], [ 733803.019650391303003, 3724987.038337231613696 ], [ 733804.311609080410562, 3724986.969964746385813 ], [ 733804.608246845542453, 3724992.315301816910505 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733864.315949483308941, 3725008.232279121875763 ], [ 733873.641557777300477, 3725007.993668059818447 ], [ 733873.247122836532071, 3724992.580114854499698 ], [ 733877.046077472623438, 3724992.48412902886048 ], [ 733876.762816092930734, 3724981.645636139903218 ], [ 733859.904205449391156, 3724982.077810946386307 ], [ 733860.067030058125965, 3724988.341016149614006 ], [ 733863.800775169045664, 3724988.254532156512141 ], [ 733864.315949483308941, 3725008.232279121875763 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102924, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.59457935772951, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733665.773527625598945, 3725010.34930036123842 ], [ 733682.16668538027443, 3725001.970434435643256 ], [ 733678.337399649550207, 3724994.552469162270427 ], [ 733663.362111695343629, 3725002.26673267967999 ], [ 733662.883814595406875, 3725004.752105239313096 ], [ 733665.773527625598945, 3725010.34930036123842 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86008, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 231.57048190271922, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733817.060308103798889, 3725026.023760433774441 ], [ 733816.30381879361812, 3725016.316818532999605 ], [ 733814.319134745746851, 3725016.623545987997204 ], [ 733813.591841582325287, 3725009.525329754687846 ], [ 733815.572198782116175, 3725009.396063346415758 ], [ 733815.140452889609151, 3725001.217458509840071 ], [ 733808.102239650906995, 3725001.678382671438158 ], [ 733808.237916652811691, 3725007.152969629038125 ], [ 733806.608725623344071, 3725011.152880643028766 ], [ 733803.99797402555123, 3725014.673837821930647 ], [ 733804.473099919268861, 3725017.648571964353323 ], [ 733806.2293102737749, 3725019.478170173708349 ], [ 733806.057821782305837, 3725022.703484487254173 ], [ 733804.33324929792434, 3725023.38278932031244 ], [ 733804.607062857598066, 3725026.61896403087303 ], [ 733817.060308103798889, 3725026.023760433774441 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86011, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.33402454110015, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733862.708252763142809, 3725039.888753946404904 ], [ 733873.467645566677675, 3725039.474261729512364 ], [ 733872.213034232496284, 3725014.040485232602805 ], [ 733862.252876899787225, 3725014.796318253036588 ], [ 733861.065796526381746, 3725019.317511857487261 ], [ 733860.456022532889619, 3725022.2435885919258 ], [ 733864.300192805239931, 3725024.47926747566089 ], [ 733865.408540649805218, 3725028.512659384403378 ], [ 733862.343076531891711, 3725032.788266675546765 ], [ 733862.708252763142809, 3725039.888753946404904 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86607, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 109.09963485201257, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733984.247434898978099, 3725029.170712447725236 ], [ 733984.469720786786638, 3725037.177755209617317 ], [ 733988.266932822414674, 3725039.434536231681705 ], [ 733990.833932525943965, 3725040.751259885728359 ], [ 733991.039521950762719, 3725035.751105143688619 ], [ 733997.483796045300551, 3725035.664246738888323 ], [ 733997.398187489830889, 3725028.903508787043393 ], [ 733984.247434898978099, 3725029.170712447725236 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86009, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 259.07434426271021, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733807.020737620303407, 3725056.298199352808297 ], [ 733817.2592352849897, 3725055.926431227475405 ], [ 733816.334671155898832, 3725030.65609390148893 ], [ 733806.096146776108071, 3725031.027864479925483 ], [ 733807.020737620303407, 3725056.298199352808297 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733719.886159604880959, 3725062.341508098412305 ], [ 733722.739838666282594, 3725060.657610790804029 ], [ 733723.763353180838749, 3725062.080902691464871 ], [ 733727.644603378139436, 3725061.653928931802511 ], [ 733727.184225898468867, 3725058.834930552169681 ], [ 733737.701844487339258, 3725053.486916756257415 ], [ 733736.609508816385642, 3725048.033399943262339 ], [ 733735.121561629115604, 3725043.957477799151093 ], [ 733732.919629639131017, 3725040.230379342567176 ], [ 733731.032719954149798, 3725038.43090241169557 ], [ 733727.129203135380521, 3725039.390032827854156 ], [ 733715.550556391477585, 3725045.233793378807604 ], [ 733711.973885777289979, 3725047.255200332496315 ], [ 733719.886159604880959, 3725062.341508098412305 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134680, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 101.16730006427393, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733869.037673469400033, 3725063.914816930424422 ], [ 733868.948807091452181, 3725060.328016446903348 ], [ 733869.699439586838707, 3725057.716118558309972 ], [ 733869.12773916113656, 3725056.037481418810785 ], [ 733870.693983497098088, 3725049.672183774411678 ], [ 733867.962342513957992, 3725047.874265642371029 ], [ 733866.364284909097478, 3725046.792074438650161 ], [ 733863.243467739550397, 3725044.585138092748821 ], [ 733863.183629220700823, 3725042.852400838397443 ], [ 733862.012820417294279, 3725042.901524438522756 ], [ 733862.195312751107849, 3725047.97773150773719 ], [ 733863.91026226838585, 3725049.595474375877529 ], [ 733866.030543527216651, 3725053.242929659318179 ], [ 733865.282733293948695, 3725054.978159906808287 ], [ 733863.307564386399463, 3725056.417098031844944 ], [ 733863.502039127284661, 3725064.046122215222567 ], [ 733869.037673469400033, 3725063.914816930424422 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86606, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 298.78711714810817, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734010.322496132925153, 3725066.053069988265634 ], [ 734002.152090000803582, 3725065.742649260908365 ], [ 734002.463489620480686, 3725057.548868575133383 ], [ 733989.952479137689807, 3725057.088115018326789 ], [ 733989.289290938875638, 3725074.750953383278102 ], [ 734009.97067632782273, 3725075.522128228098154 ], [ 734010.322496132925153, 3725066.053069988265634 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86013, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 168.5733291901596, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733819.335845608613454, 3725081.535611427389085 ], [ 733818.589080677134916, 3725066.102373758796602 ], [ 733815.155530380317941, 3725061.113343438133597 ], [ 733811.214473494095728, 3725061.327962883748114 ], [ 733810.384644210571423, 3725062.239949475973845 ], [ 733810.523116171592847, 3725064.174366268794984 ], [ 733811.440289434045553, 3725067.293057959526777 ], [ 733810.916349143604748, 3725070.887105803471059 ], [ 733809.174980869051069, 3725073.397157665342093 ], [ 733809.349399076891132, 3725081.469605537131429 ], [ 733819.335845608613454, 3725081.535611427389085 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117299, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 17.925629600560526, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733714.078736086841673, 3725075.905853402335197 ], [ 733714.823168934090063, 3725080.019134919159114 ], [ 733719.046768112573773, 3725079.256469215732068 ], [ 733718.293059892137535, 3725075.14296110207215 ], [ 733714.078736086841673, 3725075.905853402335197 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102920, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 226.41975901786444, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733667.977394764660858, 3725090.163731965236366 ], [ 733676.199185358826071, 3725084.937261826358736 ], [ 733677.677698997664265, 3725082.16552930790931 ], [ 733680.178836030769162, 3725080.473026779480278 ], [ 733682.073956778040156, 3725075.081243939697742 ], [ 733686.535091507947072, 3725072.570883402135223 ], [ 733684.217532261973247, 3725066.732374109327793 ], [ 733679.064045730396174, 3725068.704256920609623 ], [ 733676.880473150406033, 3725071.458802872337401 ], [ 733671.348778790328652, 3725074.475772328674793 ], [ 733667.915225020376965, 3725078.243055400904268 ], [ 733663.415438750991598, 3725082.339483730494976 ], [ 733667.977394764660858, 3725090.163731965236366 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86015, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 251.33309650398371, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733884.809803572017699, 3725099.258101164363325 ], [ 733887.820667246589437, 3725100.263786776456982 ], [ 733887.97904269839637, 3725096.81619278434664 ], [ 733889.399402834707871, 3725094.908708232920617 ], [ 733892.969486426794901, 3725093.91931293066591 ], [ 733895.139970269752666, 3725093.606038105674088 ], [ 733877.763453568681143, 3725074.182422619313002 ], [ 733875.624623022042215, 3725075.861519815400243 ], [ 733873.833603138453327, 3725081.167029351461679 ], [ 733874.96624890586827, 3725088.008793785702437 ], [ 733884.809803572017699, 3725099.258101164363325 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734009.23638294194825, 3725109.785761667881161 ], [ 734009.752270760363899, 3725086.37058871705085 ], [ 733999.194156195735559, 3725086.14615699602291 ], [ 733998.721341388998553, 3725107.797806410584599 ], [ 734002.757127131801099, 3725107.885221907868981 ], [ 734004.217231130925938, 3725109.674339018762112 ], [ 734009.23638294194825, 3725109.785761667881161 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733830.582235559704714, 3725115.192502366378903 ], [ 733828.823811884387396, 3725103.940700557082891 ], [ 733826.438338553067297, 3725104.315333605743945 ], [ 733824.310515563818626, 3725090.701761959586293 ], [ 733814.219372081570327, 3725092.264592101797462 ], [ 733818.105646131793037, 3725117.12995953951031 ], [ 733830.582235559704714, 3725115.192502366378903 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86604, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 243.05210583905563, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733998.347550055012107, 3725137.941802813205868 ], [ 734009.348738314933144, 3725138.265837647020817 ], [ 734010.00927426526323, 3725115.009565961547196 ], [ 734003.339624392450787, 3725114.824559542350471 ], [ 733998.844389547826722, 3725120.630035975016654 ], [ 733998.347550055012107, 3725137.941802813205868 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86014, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 269.2410676574849, "origlen": 0, "partialDec": 0.67827110406115843, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733843.10121820657514, 3725139.0 ], [ 733840.877073246985674, 3725134.199127878062427 ], [ 733839.731750019127503, 3725134.726086068432778 ], [ 733833.373904321575537, 3725120.998228457290679 ], [ 733824.230409230804071, 3725125.192175134085119 ], [ 733830.626115061459132, 3725139.0 ], [ 733843.10121820657514, 3725139.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134690, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 75.233357615263444, "origlen": 0, "partialDec": 0.5426079647770381, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733933.053643652936444, 3725139.0 ], [ 733933.981720102136023, 3725136.448536148294806 ], [ 733932.724451179732569, 3725136.617617703508586 ], [ 733929.566822279826738, 3725137.062165400478989 ], [ 733928.701306592440233, 3725138.295111690182239 ], [ 733927.683588467072695, 3725137.016209401190281 ], [ 733927.95271526533179, 3725134.736601110547781 ], [ 733928.741625818191096, 3725132.458578986581415 ], [ 733926.893847053404897, 3725130.959658264648169 ], [ 733925.461097773630172, 3725129.570747075602412 ], [ 733925.616007869830355, 3725131.971682267263532 ], [ 733924.303450533887371, 3725134.02606556750834 ], [ 733924.030880918144248, 3725136.827193052973598 ], [ 733923.15020942944102, 3725138.681254582013935 ], [ 733923.05790709448047, 3725139.0 ], [ 733933.053643652936444, 3725139.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134696, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 160.8812060779413, "origlen": 0, "partialDec": 0.33276744108985457, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733631.017092985799536, 3724696.563397473655641 ], [ 733637.916874720831402, 3724689.0 ], [ 733626.758141876780428, 3724689.0 ], [ 733624.904465531464666, 3724691.031963157467544 ], [ 733631.017092985799536, 3724696.563397473655641 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117300, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 56.69138110426168, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733637.712568847462535, 3724812.644189648330212 ], [ 733637.935811243369244, 3724818.720185527112335 ], [ 733637.983977672527544, 3724821.69559862697497 ], [ 733645.835462932707742, 3724821.387535042595118 ], [ 733648.828701670165174, 3724821.216328571550548 ], [ 733648.656602984643541, 3724817.993740823119879 ], [ 733646.185421441216022, 3724817.312033404130489 ], [ 733644.187826864537783, 3724817.38542799977586 ], [ 733642.700126820825972, 3724817.105018282774836 ], [ 733640.485648880014196, 3724816.174316430930048 ], [ 733639.697913700132631, 3724812.692572094965726 ], [ 733637.712568847462535, 3724812.644189648330212 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102938, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 241.49355060420999, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733645.532881900900975, 3724858.180826778523624 ], [ 733646.794668328016996, 3724849.066900496836752 ], [ 733648.42761501041241, 3724842.625518082175404 ], [ 733647.709180656238459, 3724835.538641034159809 ], [ 733643.618271068204194, 3724833.519004478584975 ], [ 733637.692060588742606, 3724833.674225233960897 ], [ 733636.89257507189177, 3724836.007498409133404 ], [ 733637.419333599274978, 3724844.865368209313601 ], [ 733637.096330924076028, 3724858.119497096166015 ], [ 733645.532881900900975, 3724858.180826778523624 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733644.666782932821661, 3724887.62466867826879 ], [ 733646.397882855031639, 3724883.249889441765845 ], [ 733647.617427871096879, 3724863.680702774319798 ], [ 733636.398522177478299, 3724862.375186257530004 ], [ 733636.446408499730751, 3724866.504775203764439 ], [ 733636.068220987217501, 3724875.928781074937433 ], [ 733635.770354353357106, 3724888.151417502202094 ], [ 733644.666782932821661, 3724887.62466867826879 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/gj_to_px_result.geojson b/docker/solaris/solaris/data/gj_to_px_result.geojson new file mode 100644 index 00000000..5867abc9 --- /dev/null +++ b/docker/solaris/solaris/data/gj_to_px_result.geojson @@ -0,0 +1,49 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102923, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 371.9610521071786, "origlen": 0, "partialDec": 0.40890572019922689, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 0.0, 3.0 ], [ 8.0, 8.0 ], [ 6.0, 21.0 ], [ 5.0, 29.0 ], [ 19.0, 38.0 ], [ 18.0, 58.0 ], [ 0.0, 54.0 ], [ 0.0, 3.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135943, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 293.80215091143663, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 27.0, 226.0 ], [ 34.0, 226.0 ], [ 35.0, 221.0 ], [ 45.0, 221.0 ], [ 44.0, 229.0 ], [ 56.0, 230.0 ], [ 55.0, 262.0 ], [ 47.0, 267.0 ], [ 26.0, 266.0 ], [ 27.0, 226.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134696, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 160.8812060779413, "origlen": 0, "partialDec": 0.33276744108985457, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 60.0, 885.0 ], [ 74.0, 900.0 ], [ 52.0, 900.0 ], [ 48.0, 896.0 ], [ 60.0, 885.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102932, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 250.90410248692208, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 66.0, 443.0 ], [ 86.0, 444.0 ], [ 84.0, 494.0 ], [ 64.0, 493.0 ], [ 66.0, 443.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 87.0, 73.0 ], [ 107.0, 84.0 ], [ 98.0, 100.0 ], [ 91.0, 99.0 ], [ 53.0, 79.0 ], [ 60.0, 67.0 ], [ 80.0, 78.0 ], [ 87.0, 73.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 87.0, 503.0 ], [ 91.0, 512.0 ], [ 93.0, 551.0 ], [ 71.0, 553.0 ], [ 71.0, 545.0 ], [ 70.0, 526.0 ], [ 70.0, 502.0 ], [ 87.0, 503.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102938, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 241.49355060420999, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 89.0, 562.0 ], [ 92.0, 580.0 ], [ 95.0, 593.0 ], [ 93.0, 607.0 ], [ 85.0, 611.0 ], [ 73.0, 611.0 ], [ 72.0, 606.0 ], [ 73.0, 588.0 ], [ 72.0, 562.0 ], [ 89.0, 562.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117300, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 56.69138110426168, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 73.0, 653.0 ], [ 74.0, 641.0 ], [ 74.0, 635.0 ], [ 90.0, 635.0 ], [ 96.0, 636.0 ], [ 95.0, 642.0 ], [ 90.0, 643.0 ], [ 86.0, 643.0 ], [ 83.0, 644.0 ], [ 79.0, 646.0 ], [ 77.0, 653.0 ], [ 73.0, 653.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102940, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 247.85698155347222, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 104.0, 379.0 ], [ 96.0, 433.0 ], [ 84.0, 427.0 ], [ 72.0, 424.0 ], [ 74.0, 415.0 ], [ 83.0, 416.0 ], [ 84.0, 403.0 ], [ 81.0, 393.0 ], [ 81.0, 388.0 ], [ 85.0, 379.0 ], [ 104.0, 379.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 106.0, 314.0 ], [ 127.0, 321.0 ], [ 111.0, 370.0 ], [ 90.0, 363.0 ], [ 96.0, 344.0 ], [ 107.0, 336.0 ], [ 113.0, 326.0 ], [ 114.0, 319.0 ], [ 106.0, 314.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102924, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.59457935772951, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 130.0, 257.0 ], [ 162.0, 274.0 ], [ 155.0, 289.0 ], [ 125.0, 273.0 ], [ 124.0, 268.0 ], [ 130.0, 257.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102920, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 226.41975901786444, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 134.0, 98.0 ], [ 150.0, 108.0 ], [ 153.0, 114.0 ], [ 158.0, 117.0 ], [ 162.0, 128.0 ], [ 171.0, 133.0 ], [ 166.0, 145.0 ], [ 156.0, 141.0 ], [ 152.0, 135.0 ], [ 141.0, 129.0 ], [ 134.0, 122.0 ], [ 125.0, 113.0 ], [ 134.0, 98.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134694, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 98.143961268411516, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 214.0, 866.0 ], [ 232.0, 882.0 ], [ 216.0, 899.0 ], [ 211.0, 890.0 ], [ 214.0, 885.0 ], [ 213.0, 878.0 ], [ 203.0, 874.0 ], [ 214.0, 866.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102925, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 235.62354171707437, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 231.0, 330.0 ], [ 248.0, 357.0 ], [ 242.0, 364.0 ], [ 226.0, 373.0 ], [ 220.0, 363.0 ], [ 226.0, 359.0 ], [ 222.0, 354.0 ], [ 211.0, 359.0 ], [ 203.0, 347.0 ], [ 231.0, 330.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117299, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 17.925629600560526, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 226.0, 126.0 ], [ 228.0, 118.0 ], [ 236.0, 119.0 ], [ 235.0, 128.0 ], [ 226.0, 126.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 238.0, 153.0 ], [ 243.0, 157.0 ], [ 246.0, 154.0 ], [ 253.0, 155.0 ], [ 252.0, 160.0 ], [ 273.0, 171.0 ], [ 271.0, 182.0 ], [ 268.0, 190.0 ], [ 264.0, 198.0 ], [ 260.0, 201.0 ], [ 252.0, 199.0 ], [ 229.0, 188.0 ], [ 222.0, 183.0 ], [ 238.0, 153.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93146, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 85.499318876340681, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 392.0, 671.0 ], [ 417.0, 671.0 ], [ 419.0, 684.0 ], [ 393.0, 685.0 ], [ 392.0, 671.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93019, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 223.11504089232326, "origlen": 0, "partialDec": 0.71792570273177214, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 416.0, 871.0 ], [ 423.0, 879.0 ], [ 426.0, 893.0 ], [ 418.0, 900.0 ], [ 386.0, 900.0 ], [ 416.0, 871.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86007, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 288.75968082249199, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 407.0, 293.0 ], [ 428.0, 295.0 ], [ 425.0, 345.0 ], [ 402.0, 344.0 ], [ 404.0, 304.0 ], [ 407.0, 304.0 ], [ 407.0, 293.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86008, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 231.57048190271922, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 432.0, 226.0 ], [ 431.0, 245.0 ], [ 427.0, 245.0 ], [ 425.0, 259.0 ], [ 429.0, 259.0 ], [ 428.0, 276.0 ], [ 414.0, 275.0 ], [ 414.0, 264.0 ], [ 411.0, 256.0 ], [ 406.0, 249.0 ], [ 407.0, 243.0 ], [ 410.0, 239.0 ], [ 410.0, 233.0 ], [ 407.0, 231.0 ], [ 407.0, 225.0 ], [ 432.0, 226.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86009, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 259.07434426271021, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 412.0, 165.0 ], [ 433.0, 166.0 ], [ 431.0, 217.0 ], [ 410.0, 216.0 ], [ 412.0, 165.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86013, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 168.5733291901596, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 437.0, 115.0 ], [ 435.0, 146.0 ], [ 428.0, 156.0 ], [ 420.0, 155.0 ], [ 419.0, 154.0 ], [ 419.0, 150.0 ], [ 421.0, 143.0 ], [ 420.0, 136.0 ], [ 416.0, 131.0 ], [ 417.0, 115.0 ], [ 437.0, 115.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 459.0, 48.0 ], [ 456.0, 70.0 ], [ 451.0, 69.0 ], [ 447.0, 97.0 ], [ 426.0, 93.0 ], [ 434.0, 44.0 ], [ 459.0, 48.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86014, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 269.2410676574849, "origlen": 0, "partialDec": 0.67827110406115843, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 484.0, 0.0 ], [ 480.0, 10.0 ], [ 477.0, 9.0 ], [ 465.0, 36.0 ], [ 446.0, 28.0 ], [ 459.0, 0.0 ], [ 484.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93018, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 189.51651421072748, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 446.0, 842.0 ], [ 481.0, 828.0 ], [ 489.0, 847.0 ], [ 454.0, 861.0 ], [ 446.0, 842.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86006, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 300.47300947479948, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 482.0, 358.0 ], [ 496.0, 360.0 ], [ 494.0, 372.0 ], [ 528.0, 377.0 ], [ 525.0, 397.0 ], [ 492.0, 392.0 ], [ 491.0, 396.0 ], [ 477.0, 394.0 ], [ 482.0, 358.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134680, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 101.16730006427393, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 536.0, 150.0 ], [ 536.0, 157.0 ], [ 537.0, 163.0 ], [ 536.0, 166.0 ], [ 539.0, 179.0 ], [ 534.0, 182.0 ], [ 531.0, 184.0 ], [ 524.0, 189.0 ], [ 524.0, 192.0 ], [ 522.0, 192.0 ], [ 522.0, 182.0 ], [ 526.0, 179.0 ], [ 530.0, 172.0 ], [ 529.0, 168.0 ], [ 525.0, 165.0 ], [ 525.0, 150.0 ], [ 536.0, 150.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86011, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.33402454110015, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 523.0, 198.0 ], [ 545.0, 199.0 ], [ 542.0, 250.0 ], [ 523.0, 248.0 ], [ 520.0, 239.0 ], [ 519.0, 234.0 ], [ 527.0, 229.0 ], [ 529.0, 221.0 ], [ 523.0, 212.0 ], [ 523.0, 198.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 527.0, 262.0 ], [ 545.0, 262.0 ], [ 544.0, 293.0 ], [ 552.0, 293.0 ], [ 552.0, 315.0 ], [ 518.0, 314.0 ], [ 518.0, 301.0 ], [ 526.0, 301.0 ], [ 527.0, 262.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86015, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 251.33309650398371, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 568.0, 79.0 ], [ 574.0, 77.0 ], [ 574.0, 84.0 ], [ 577.0, 88.0 ], [ 584.0, 90.0 ], [ 588.0, 91.0 ], [ 554.0, 130.0 ], [ 549.0, 126.0 ], [ 546.0, 116.0 ], [ 548.0, 102.0 ], [ 568.0, 79.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86005, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.65732906731142, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 545.0, 404.0 ], [ 546.0, 384.0 ], [ 561.0, 384.0 ], [ 562.0, 379.0 ], [ 573.0, 379.0 ], [ 573.0, 384.0 ], [ 594.0, 385.0 ], [ 594.0, 405.0 ], [ 545.0, 404.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134689, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 28.431725003743587, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 595.0, 341.0 ], [ 593.0, 346.0 ], [ 596.0, 351.0 ], [ 594.0, 356.0 ], [ 595.0, 359.0 ], [ 597.0, 362.0 ], [ 591.0, 362.0 ], [ 591.0, 356.0 ], [ 589.0, 356.0 ], [ 589.0, 341.0 ], [ 595.0, 341.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 643.0, 605.0 ], [ 650.0, 639.0 ], [ 631.0, 644.0 ], [ 626.0, 622.0 ], [ 614.0, 624.0 ], [ 611.0, 612.0 ], [ 643.0, 605.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134690, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 75.233357615263444, "origlen": 0, "partialDec": 0.5426079647770381, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 664.0, 0.0 ], [ 666.0, 5.0 ], [ 663.0, 5.0 ], [ 657.0, 4.0 ], [ 655.0, 1.0 ], [ 653.0, 4.0 ], [ 654.0, 9.0 ], [ 655.0, 13.0 ], [ 652.0, 16.0 ], [ 649.0, 19.0 ], [ 649.0, 14.0 ], [ 647.0, 10.0 ], [ 646.0, 4.0 ], [ 644.0, 1.0 ], [ 644.0, 0.0 ], [ 664.0, 0.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85996, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 147.78075500471959, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 720.0, 599.0 ], [ 720.0, 607.0 ], [ 724.0, 611.0 ], [ 725.0, 616.0 ], [ 723.0, 620.0 ], [ 718.0, 620.0 ], [ 714.0, 616.0 ], [ 703.0, 615.0 ], [ 698.0, 620.0 ], [ 691.0, 620.0 ], [ 691.0, 599.0 ], [ 720.0, 599.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86607, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 109.09963485201257, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 766.0, 220.0 ], [ 767.0, 204.0 ], [ 775.0, 199.0 ], [ 780.0, 196.0 ], [ 780.0, 206.0 ], [ 793.0, 207.0 ], [ 793.0, 220.0 ], [ 766.0, 220.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 794.0, 800.0 ], [ 808.0, 800.0 ], [ 811.0, 810.0 ], [ 808.0, 813.0 ], [ 807.0, 820.0 ], [ 768.0, 823.0 ], [ 760.0, 819.0 ], [ 761.0, 806.0 ], [ 775.0, 796.0 ], [ 783.0, 796.0 ], [ 791.0, 796.0 ], [ 794.0, 800.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86606, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 298.78711714810817, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 819.0, 146.0 ], [ 802.0, 147.0 ], [ 803.0, 163.0 ], [ 778.0, 164.0 ], [ 777.0, 128.0 ], [ 818.0, 127.0 ], [ 819.0, 146.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86604, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 243.05210583905563, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 795.0, 2.0 ], [ 817.0, 1.0 ], [ 818.0, 48.0 ], [ 805.0, 48.0 ], [ 796.0, 37.0 ], [ 795.0, 2.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 816.0, 58.0 ], [ 818.0, 105.0 ], [ 796.0, 106.0 ], [ 795.0, 62.0 ], [ 804.0, 62.0 ], [ 806.0, 59.0 ], [ 816.0, 58.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92641, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 127.89418152890968, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 821.0, 800.0 ], [ 839.0, 798.0 ], [ 842.0, 807.0 ], [ 860.0, 807.0 ], [ 864.0, 813.0 ], [ 836.0, 819.0 ], [ 830.0, 813.0 ], [ 822.0, 813.0 ], [ 821.0, 800.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86004, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 245.87085671707746, "origlen": 0, "partialDec": 1.0, "truncated": 0, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 878.0, 363.0 ], [ 838.0, 366.0 ], [ 837.0, 350.0 ], [ 850.0, 349.0 ], [ 850.0, 344.0 ], [ 855.0, 343.0 ], [ 854.0, 338.0 ], [ 866.0, 337.0 ], [ 866.0, 343.0 ], [ 884.0, 342.0 ], [ 885.0, 357.0 ], [ 878.0, 363.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92640, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 140.04971670918377, "origlen": 0, "partialDec": 0.38635210049961444, "truncated": 1, "image_fname": "sample_geotiff.tif" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 887.0, 821.0 ], [ 886.0, 802.0 ], [ 900.0, 802.0 ], [ 900.0, 819.0 ], [ 895.0, 817.0 ], [ 889.0, 819.0 ], [ 887.0, 821.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/gt.geojson b/docker/solaris/solaris/data/gt.geojson new file mode 100644 index 00000000..bec9a480 --- /dev/null +++ b/docker/solaris/solaris/data/gt.geojson @@ -0,0 +1,34 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112379, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 391.91383579611608, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736687.54563533468172, 3722455.067802789621055 ], [ 736686.930121065350249, 3722464.963263520039618 ], [ 736691.639786917716265, 3722470.905968100298196 ], [ 736705.544305954361334, 3722472.614050497766584 ], [ 736706.899210122646764, 3722462.858909503556788 ], [ 736704.866059878026135, 3722459.457111885305494 ], [ 736713.144347417633981, 3722452.103498172480613 ], [ 736710.031280528288335, 3722447.30998557060957 ], [ 736700.388616721378639, 3722454.263705271296203 ], [ 736698.457744072075002, 3722451.985345270019025 ], [ 736690.127276806393638, 3722451.291527833789587 ], [ 736689.410866743884981, 3722455.11381392320618 ], [ 736687.54563533468172, 3722455.067802789621055 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 386, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 2624.7975056988498, "origlen": 0, "partialDec": 0.19529179922156292, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736751.0, 3722460.573565732687712 ], [ 736738.113781600724906, 3722460.343964175321162 ], [ 736736.791128790122457, 3722498.144925817381591 ], [ 736751.0, 3722498.422295336145908 ], [ 736751.0, 3722460.573565732687712 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112358, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 58.470938413055272, "origlen": 0, "partialDec": 0.84938424165096427, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736397.409104686463252, 3722439.0 ], [ 736397.181703229551204, 3722444.158429131377488 ], [ 736406.647051504696719, 3722444.391635047737509 ], [ 736406.853222909849137, 3722440.923003434203565 ], [ 736406.524663215968758, 3722439.183601674623787 ], [ 736405.575140941189602, 3722439.0 ], [ 736397.409104686463252, 3722439.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112363, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 16.648158736312929, "origlen": 0, "partialDec": 0.98964372574588277, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736484.027086415444501, 3722439.672882508486509 ], [ 736483.826080948347226, 3722442.930777621921152 ], [ 736487.966752305626869, 3722443.332480486482382 ], [ 736488.02169913391117, 3722441.103111260570586 ], [ 736488.071998692466877, 3722439.062295656185597 ], [ 736487.32207741017919, 3722439.0 ], [ 736485.148234838736244, 3722439.0 ], [ 736484.027086415444501, 3722439.672882508486509 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94297, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 183.20412136950409, "origlen": 0, "partialDec": 0.93523287290728463, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736363.946201358223334, 3722456.346642858348787 ], [ 736364.408703304128721, 3722439.0 ], [ 736353.693128296523355, 3722439.0 ], [ 736353.446917289751582, 3722448.330427123233676 ], [ 736355.126278227078728, 3722448.382892823778093 ], [ 736354.92656793363858, 3722456.113359274342656 ], [ 736363.946201358223334, 3722456.346642858348787 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112359, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 176.34374484678875, "origlen": 0, "partialDec": 0.96463496551767225, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736417.351756769232452, 3722439.0 ], [ 736416.305264746421017, 3722456.393609915394336 ], [ 736424.937325929058716, 3722456.905952396802604 ], [ 736425.450442405068316, 3722448.517318034078926 ], [ 736427.230787994340062, 3722448.616677135229111 ], [ 736427.810252295457758, 3722439.0 ], [ 736417.351756769232452, 3722439.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112362, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 179.02465416204927, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736466.591233940678649, 3722439.209873514715582 ], [ 736466.404814333305694, 3722446.774200418964028 ], [ 736464.621447641286068, 3722447.174175225198269 ], [ 736464.298580141505226, 3722454.246821955777705 ], [ 736465.292277529719286, 3722455.74736173870042 ], [ 736465.248816089704633, 3722457.510892521124333 ], [ 736467.468574301688932, 3722457.865247134119272 ], [ 736469.230344797018915, 3722458.341492664534599 ], [ 736474.857422852423042, 3722458.335897943470627 ], [ 736476.035026877070777, 3722446.722986677661538 ], [ 736474.307710953289643, 3722444.471889103762805 ], [ 736473.353922696085647, 3722441.352005653548986 ], [ 736473.401212287833914, 3722439.433195420540869 ], [ 736466.591233940678649, 3722439.209873514715582 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112365, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 7.4751673398446687, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736496.77675381465815, 3722451.318312388379127 ], [ 736496.697474075248465, 3722454.534815385006368 ], [ 736498.387208432541229, 3722454.543169409967959 ], [ 736498.920558606390841, 3722453.624072678852826 ], [ 736499.660265666199848, 3722452.243940894491971 ], [ 736499.232060643378645, 3722451.53420439735055 ], [ 736496.77675381465815, 3722451.318312388379127 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94343, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.28750827276107, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736314.363320976961404, 3722461.961782470345497 ], [ 736314.847857188433409, 3722445.304353397805244 ], [ 736305.773356888443232, 3722445.0364711843431 ], [ 736305.288836469873786, 3722461.693899831734598 ], [ 736314.363320976961404, 3722461.961782470345497 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112364, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 21.963567885446508, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736490.577403310569935, 3722463.240276603028178 ], [ 736490.20043297635857, 3722471.377010900061578 ], [ 736491.70156253199093, 3722471.502794798463583 ], [ 736492.537633634987287, 3722469.603425742592663 ], [ 736493.288273783982731, 3722467.779637967702001 ], [ 736493.494933491572738, 3722466.552839444484562 ], [ 736493.712254900136031, 3722464.893476727884263 ], [ 736493.398444178747013, 3722463.309807919431478 ], [ 736490.577403310569935, 3722463.240276603028178 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112378, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 1978.5412509861878, "origlen": 0, "partialDec": 0.95008661457365517, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736641.135498640942387, 3722439.0 ], [ 736641.44608521531336, 3722442.166714551858604 ], [ 736638.775406126398593, 3722442.400498348288238 ], [ 736639.149403739487752, 3722450.955290583427995 ], [ 736642.116759263793938, 3722450.739922123495489 ], [ 736643.01822438032832, 3722456.3556182035245 ], [ 736640.333051251480356, 3722457.177245275583118 ], [ 736641.623067400534637, 3722464.733600644860417 ], [ 736644.009624472819269, 3722464.348534908611327 ], [ 736646.569863889133558, 3722486.663454041816294 ], [ 736649.23314510146156, 3722486.729140755254775 ], [ 736649.475658227340318, 3722488.943652479443699 ], [ 736650.936362006003037, 3722489.578979117330164 ], [ 736648.084327072487213, 3722497.166354874614626 ], [ 736668.787249097484164, 3722504.457959180697799 ], [ 736667.979486411670223, 3722507.090491399634629 ], [ 736675.625032334472053, 3722509.199059344828129 ], [ 736676.715247352141887, 3722507.161696563940495 ], [ 736682.577697755070403, 3722509.659114402253181 ], [ 736681.480094538535923, 3722511.995944298338145 ], [ 736688.823462490807287, 3722514.685271090827882 ], [ 736690.665108178276569, 3722511.92287040874362 ], [ 736697.266098092193715, 3722514.593890508636832 ], [ 736701.087026222492568, 3722503.490119367837906 ], [ 736703.869775006431155, 3722504.735172503627837 ], [ 736705.133919100742787, 3722501.669975460972637 ], [ 736702.644563336041756, 3722500.576435842551291 ], [ 736703.158942219335586, 3722497.792392146773636 ], [ 736688.912387389224023, 3722493.012784410268068 ], [ 736689.866466320352629, 3722490.09531101025641 ], [ 736682.822196552762762, 3722487.701914799399674 ], [ 736682.007041912060231, 3722490.633913781028241 ], [ 736664.981266265152954, 3722484.465108423028141 ], [ 736661.109181975829415, 3722455.48113471781835 ], [ 736663.935447486350313, 3722454.96264321077615 ], [ 736662.632336766226217, 3722447.561334693804383 ], [ 736659.668535659788176, 3722447.632508754264563 ], [ 736658.620385286398232, 3722439.0 ], [ 736641.135498640942387, 3722439.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94296, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 126.17363166362102, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736369.075709116645157, 3722476.915734668727964 ], [ 736363.244285021908581, 3722464.109130891505629 ], [ 736355.075292770634405, 3722467.792239647358656 ], [ 736360.906451368588023, 3722480.609929433558136 ], [ 736369.075709116645157, 3722476.915734668727964 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112360, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 180.75176621340333, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736416.143644711235538, 3722466.344641777221113 ], [ 736407.65411069674883, 3722481.905895890202373 ], [ 736415.118411558913067, 3722486.473567737732083 ], [ 736419.349931529257447, 3722479.042207289487123 ], [ 736421.091488246805966, 3722479.961870929691941 ], [ 736425.890768180717714, 3722470.968569246120751 ], [ 736416.143644711235538, 3722466.344641777221113 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112361, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 177.29104209284444, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736466.394711766741239, 3722469.036790397018194 ], [ 736473.645322980242781, 3722487.172250971198082 ], [ 736482.084543753881007, 3722483.828844431322068 ], [ 736474.833944755955599, 3722465.693376823328435 ], [ 736466.394711766741239, 3722469.036790397018194 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94295, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 159.47798255248392, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736315.809539304929785, 3722489.953544122632593 ], [ 736315.495809184387326, 3722470.646201250143349 ], [ 736307.237940698862076, 3722470.775764404330403 ], [ 736307.55168734502513, 3722490.08310608798638 ], [ 736315.809539304929785, 3722489.953544122632593 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112373, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 494.45818962400131, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736372.680536549771205, 3722519.044206998310983 ], [ 736372.643417015555315, 3722528.842930405866355 ], [ 736423.104116770089604, 3722529.042965376283973 ], [ 736423.141287761623971, 3722519.244239298626781 ], [ 736372.680536549771205, 3722519.044206998310983 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112376, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.97436340611333, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736538.675348197342828, 3722532.179978611413389 ], [ 736538.52198518125806, 3722543.673863848205656 ], [ 736560.799196747131646, 3722543.978929038625211 ], [ 736560.952312994049862, 3722532.496134063694626 ], [ 736538.675348197342828, 3722532.179978611413389 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94241, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 529.29603533983379, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736327.616773990448564, 3722564.745991075411439 ], [ 736328.134527753572911, 3722518.091175336390734 ], [ 736316.790937224403024, 3722517.967153953388333 ], [ 736316.273238607216626, 3722564.621967230923474 ], [ 736327.616773990448564, 3722564.745991075411439 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112371, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 502.08685271218383, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736443.92724241502583, 3722519.401302315294743 ], [ 736443.363607329665683, 3722566.387970147188753 ], [ 736454.047750120749697, 3722566.518090064637363 ], [ 736454.611437526298687, 3722519.531419942621142 ], [ 736443.92724241502583, 3722519.401302315294743 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94240, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 514.86470663648481, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736365.526830352959223, 3722574.613818577956408 ], [ 736366.278800731059164, 3722523.359040684532374 ], [ 736356.235733798705041, 3722523.211519918870181 ], [ 736355.483817089698277, 3722574.466295608319342 ], [ 736365.526830352959223, 3722574.613818577956408 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112375, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 694.47253909490576, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736468.50609065790195, 3722542.358654133975506 ], [ 736468.760668412433006, 3722558.401721917558461 ], [ 736512.037742168758996, 3722557.7149120522663 ], [ 736511.783236389281228, 3722541.671839113812894 ], [ 736468.50609065790195, 3722542.358654133975506 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112372, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 360.54645618916322, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736407.684152557281777, 3722533.446308027952909 ], [ 736407.460298574413173, 3722569.287724438589066 ], [ 736417.514786850544624, 3722569.34679905558005 ], [ 736417.747958019608632, 3722533.505609442014247 ], [ 736407.684152557281777, 3722533.446308027952909 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112377, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 289.94182610795599, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736530.744800130603835, 3722561.97162597393617 ], [ 736532.217653167201206, 3722577.556419963482767 ], [ 736532.467297722003423, 3722579.482551428955048 ], [ 736536.001178764970973, 3722580.013596748933196 ], [ 736538.468926818808541, 3722581.983313422650099 ], [ 736539.174929537111893, 3722583.47677054349333 ], [ 736547.342147199902683, 3722582.501720760483295 ], [ 736546.468247159384191, 3722576.143140569794923 ], [ 736547.159897308796644, 3722575.205752962268889 ], [ 736546.611806952860206, 3722570.320159194990993 ], [ 736544.826288024778478, 3722570.431513085495681 ], [ 736543.813346480717883, 3722560.673476526513696 ], [ 736530.744800130603835, 3722561.97162597393617 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94239, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 430.56656045400211, "origlen": 0, "partialDec": 0.61880994996342276, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736325.45189977355767, 3722587.421605918556452 ], [ 736325.681690517580137, 3722576.584408206865191 ], [ 736301.0, 3722576.059337061829865 ], [ 736301.0, 3722586.908257981296629 ], [ 736325.45189977355767, 3722587.421605918556452 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112374, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 489.52426447714623, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736406.218373467680067, 3722589.167154558934271 ], [ 736407.878821145510301, 3722599.407241638284177 ], [ 736454.470669962931424, 3722591.932123596314341 ], [ 736452.819548748200759, 3722581.692254566121846 ], [ 736406.218373467680067, 3722589.167154558934271 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94237, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 525.7717667099605, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736399.809332579723559, 3722600.939721549861133 ], [ 736398.351412292104214, 3722590.016543004196137 ], [ 736351.075145132141188, 3722596.254277244210243 ], [ 736352.532843629131094, 3722607.188537154812366 ], [ 736399.809332579723559, 3722600.939721549861133 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94238, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 498.71535465444168, "origlen": 0, "partialDec": 0.8949086745538668, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736301.0, 3722616.962805293034762 ], [ 736343.597317913314328, 3722610.708488081116229 ], [ 736342.053352136164904, 3722600.260422327090055 ], [ 736301.0, 3722606.296359290368855 ], [ 736301.0, 3722616.962805293034762 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94236, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 152.8742559217601, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736351.470539042027667, 3722758.485215371940285 ], [ 736351.878608449478634, 3722747.197382184211165 ], [ 736338.352984992321581, 3722746.708811732474715 ], [ 736337.944931550649926, 3722757.996644604951143 ], [ 736351.470539042027667, 3722758.485215371940285 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/gt_epsg4326.json b/docker/solaris/solaris/data/gt_epsg4326.json new file mode 100644 index 00000000..550d2c7d --- /dev/null +++ b/docker/solaris/solaris/data/gt_epsg4326.json @@ -0,0 +1,34 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112379, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 391.91383579611608, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4487639, 33.6156071 ], [ -84.4487679, 33.6156964 ], [ -84.4487156, 33.6157489 ], [ -84.4485654, 33.6157612 ], [ -84.4485534, 33.615673 ], [ -84.4485762, 33.6156428 ], [ -84.448489, 33.6155747 ], [ -84.4485238, 33.6155322 ], [ -84.4486258, 33.615597 ], [ -84.4486472, 33.6155769 ], [ -84.4487371, 33.6155725 ], [ -84.4487438, 33.6156071 ], [ -84.4487639, 33.6156071 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 386, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 2624.7975056988498, "origlen": 0, "partialDec": 0.19529179922156292, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.448079059714743, 33.615642603932443 ], [ -84.4482179, 33.6156434 ], [ -84.4482221, 33.6159843 ], [ -84.448069001991314, 33.615983640258932 ], [ -84.448079059714743, 33.615642603932443 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112358, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 58.470938413055272, "origlen": 0, "partialDec": 0.84938424165096427, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.451892819685412, 33.615526769304253 ], [ -84.4518939, 33.6155733 ], [ -84.4517919, 33.6155733 ], [ -84.4517906, 33.615542 ], [ -84.4517946, 33.6155264 ], [ -84.451804874697416, 33.615524956448077 ], [ -84.451892819685412, 33.615526769304253 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112363, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 16.648158736312929, "origlen": 0, "partialDec": 0.98964372574588277, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4509598, 33.6155136 ], [ -84.4509611, 33.615543 ], [ -84.4509164, 33.6155457 ], [ -84.4509164, 33.6155256 ], [ -84.4509164, 33.6155072 ], [ -84.450924492884155, 33.615506805225834 ], [ -84.450947904289066, 33.615507287990347 ], [ -84.4509598, 33.6155136 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94297, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 183.20412136950409, "origlen": 0, "partialDec": 0.93523287290728463, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4522486, 33.6156905 ], [ -84.452248221065886, 33.615534094741641 ], [ -84.452363623633815, 33.615536473168348 ], [ -84.4523638, 33.6156206 ], [ -84.4523457, 33.6156207 ], [ -84.4523458, 33.6156904 ], [ -84.4522486, 33.6156905 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112359, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 176.34374484678875, "origlen": 0, "partialDec": 0.96463496551767225, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.451678045206066, 33.615522341935012 ], [ -84.4516847, 33.6156793 ], [ -84.4515916, 33.615682 ], [ -84.4515883, 33.6156063 ], [ -84.4515691, 33.6156068 ], [ -84.451565411362395, 33.615520019947454 ], [ -84.451678045206066, 33.615522341935012 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112362, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 179.02465416204927, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4511477, 33.6155133 ], [ -84.4511477, 33.6155815 ], [ -84.4511668, 33.6155855 ], [ -84.4511684, 33.6156493 ], [ -84.4511573, 33.6156626 ], [ -84.4511573, 33.6156785 ], [ -84.4511333, 33.6156812 ], [ -84.4511142, 33.6156851 ], [ -84.4510536, 33.6156838 ], [ -84.451044, 33.61557890000001 ], [ -84.4510632, 33.615559 ], [ -84.4510743, 33.615531099999984 ], [ -84.4510743, 33.615513799999988 ], [ -84.4511477, 33.6155133 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112365, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 7.4751673398446687, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4508194, 33.6156157 ], [ -84.4508194, 33.6156447 ], [ -84.4508012, 33.6156444 ], [ -84.4507957, 33.615636 ], [ -84.4507881, 33.6156234 ], [ -84.4507929, 33.6156171 ], [ -84.4508194, 33.6156157 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94343, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.28750827276107, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.45278110000001, 33.6157521 ], [ -84.452780300000015, 33.6156019 ], [ -84.4528781, 33.6156015 ], [ -84.4528789, 33.6157517 ], [ -84.45278110000001, 33.6157521 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112364, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 21.963567885446508, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.450883, 33.6157245 ], [ -84.4508849, 33.6157979 ], [ -84.4508687, 33.6157987 ], [ -84.4508602, 33.6157814 ], [ -84.4508526, 33.6157648 ], [ -84.4508507, 33.6157537 ], [ -84.4508488, 33.6157387 ], [ -84.4508526, 33.6157245 ], [ -84.450883, 33.6157245 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112378, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 1978.5412509861878, "origlen": 0, "partialDec": 0.95008661457365517, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.449267985989621, 33.615472635281179 ], [ -84.4492638, 33.61550110000001 ], [ -84.4492925, 33.6155038 ], [ -84.449286200000017, 33.6155808 ], [ -84.4492543, 33.6155782 ], [ -84.4492431, 33.6156286 ], [ -84.4492718, 33.6156366 ], [ -84.4492559, 33.6157044 ], [ -84.449230300000011, 33.6157004 ], [ -84.4491968, 33.6159009 ], [ -84.4491681, 33.6159009 ], [ -84.4491649, 33.6159208 ], [ -84.449149, 33.6159262 ], [ -84.449177700000021, 33.6159952 ], [ -84.4489528, 33.6160563 ], [ -84.4489608, 33.6160802 ], [ -84.448877900000014, 33.6160975 ], [ -84.4488667, 33.6160789 ], [ -84.4488029, 33.6161001 ], [ -84.4488141, 33.6161214 ], [ -84.4487343, 33.616144 ], [ -84.4487152, 33.6161187 ], [ -84.4486434, 33.6161413 ], [ -84.4486052, 33.6160404 ], [ -84.4485749, 33.616051 ], [ -84.4485621, 33.616023099999985 ], [ -84.4485892, 33.6160138 ], [ -84.4485844, 33.615988599999987 ], [ -84.4487391, 33.6159487 ], [ -84.4487296, 33.6159222 ], [ -84.4488061, 33.6159022 ], [ -84.4488141, 33.6159288 ], [ -84.4489991, 33.615877 ], [ -84.4490485, 33.6156167 ], [ -84.4490182, 33.6156114 ], [ -84.4490342, 33.615545 ], [ -84.4490661, 33.6155463 ], [ -84.449079681173004, 33.615468749578028 ], [ -84.449267985989621, 33.615472635281179 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94296, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 126.17363166362102, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4521879, 33.6158747 ], [ -84.4522541, 33.6157606 ], [ -84.4523411, 33.6157956 ], [ -84.4522749, 33.6159098 ], [ -84.4521879, 33.6158747 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112360, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 180.75176621340333, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4516838, 33.615769 ], [ -84.4517711, 33.6159111 ], [ -84.4516895, 33.6159506 ], [ -84.4516459, 33.615882699999986 ], [ -84.4516269, 33.6158906 ], [ -84.4515776, 33.6158085 ], [ -84.4516838, 33.615769 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112361, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 177.29104209284444, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4511419, 33.6157821 ], [ -84.451059, 33.6159439 ], [ -84.450969, 33.6159119 ], [ -84.4510519, 33.6157501 ], [ -84.4511419, 33.6157821 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94295, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 159.47798255248392, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4527581, 33.616004 ], [ -84.4527666, 33.6158301 ], [ -84.4528555, 33.6158331 ], [ -84.452847, 33.616007 ], [ -84.4527581, 33.616004 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112373, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 494.45818962400131, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4521379, 33.6162535 ], [ -84.4521357, 33.6163418 ], [ -84.4515922, 33.6163324 ], [ -84.4515944, 33.61624410000001 ], [ -84.4521379, 33.6162535 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112376, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.97436340611333, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4503467, 33.616335 ], [ -84.4503453, 33.616438600000016 ], [ -84.4501053, 33.6164364 ], [ -84.4501067, 33.61633290000001 ], [ -84.4503467, 33.616335 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94241, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 529.29603533983379, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4526111, 33.6166753 ], [ -84.4526179, 33.6162548 ], [ -84.4527401, 33.6162562 ], [ -84.45273330000002, 33.6166767 ], [ -84.4526111, 33.6166753 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112371, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 502.08685271218383, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4513705, 33.6162409 ], [ -84.4513641, 33.6166644 ], [ -84.451249, 33.6166632 ], [ -84.4512554, 33.6162397 ], [ -84.4513705, 33.6162409 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94240, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 514.86470663648481, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4522002, 33.6167558 ], [ -84.4522057, 33.6162938 ], [ -84.4523139, 33.6162947 ], [ -84.4523084, 33.6167567 ], [ -84.4522002, 33.6167558 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112375, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 694.47253909490576, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4510997, 33.61644230000001 ], [ -84.4510927, 33.6165868 ], [ -84.4506268, 33.616571 ], [ -84.4506338, 33.6164265 ], [ -84.4510997, 33.61644230000001 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112372, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 360.54645618916322, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4517571, 33.6163755 ], [ -84.45175, 33.6166985 ], [ -84.4516417, 33.6166968 ], [ -84.451648700000021, 33.6163738 ], [ -84.4517571, 33.6163755 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112377, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 289.94182610795599, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4504242, 33.6166052 ], [ -84.4504042, 33.6167453 ], [ -84.450401, 33.6167626 ], [ -84.4503628, 33.616766599999984 ], [ -84.4503357, 33.6167838 ], [ -84.4503277, 33.6167971 ], [ -84.45024, 33.6167865 ], [ -84.4502511, 33.6167294 ], [ -84.4502439, 33.616720799999989 ], [ -84.4502511, 33.6166769 ], [ -84.4502703, 33.616678299999982 ], [ -84.4502838, 33.6165906 ], [ -84.4504242, 33.6166052 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94239, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 430.56656045400211, "origlen": 0, "partialDec": 0.61880994996342276, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4526284, 33.6168801 ], [ -84.452628800000014, 33.6167824 ], [ -84.452894755393373, 33.616783146366444 ], [ -84.452891877787877, 33.616880901019456 ], [ -84.4526284, 33.6168801 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 112374, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 489.52426447714623, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4517581, 33.6168779 ], [ -84.4517375, 33.6169698 ], [ -84.4512377, 33.6168921 ], [ -84.4512582, 33.6168002 ], [ -84.4517581, 33.6168779 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94237, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 525.7717667099605, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.451824, 33.6169854 ], [ -84.4518426, 33.6168873 ], [ -84.4523501, 33.616954 ], [ -84.4523315, 33.61705220000001 ], [ -84.451824, 33.6169854 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94238, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 498.71535465444168, "origlen": 0, "partialDec": 0.8949086745538668, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.45288390594277, 33.617151708758335 ], [ -84.4524268, 33.6170859 ], [ -84.4524462, 33.6169921 ], [ -84.452886735185913, 33.617055598307978 ], [ -84.45288390594277, 33.617151708758335 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94236, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 152.8742559217601, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -84.4523028, 33.6184157 ], [ -84.4523014, 33.6183139 ], [ -84.4524472, 33.618312499999981 ], [ -84.4524486, 33.6184143 ], [ -84.4523028, 33.6184157 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/inference_tiler_test_output.npy b/docker/solaris/solaris/data/inference_tiler_test_output.npy new file mode 100644 index 00000000..197d41f4 Binary files /dev/null and b/docker/solaris/solaris/data/inference_tiler_test_output.npy differ diff --git a/docker/solaris/solaris/data/nebraska_landsat5_with_nodata_wgs84.tif b/docker/solaris/solaris/data/nebraska_landsat5_with_nodata_wgs84.tif new file mode 100644 index 00000000..ee1b33ef Binary files /dev/null and b/docker/solaris/solaris/data/nebraska_landsat5_with_nodata_wgs84.tif differ diff --git a/docker/solaris/solaris/data/nebraska_wgs84_with_nodata_labels.geojson b/docker/solaris/solaris/data/nebraska_wgs84_with_nodata_labels.geojson new file mode 100644 index 00000000..b99c411a --- /dev/null +++ b/docker/solaris/solaris/data/nebraska_wgs84_with_nodata_labels.geojson @@ -0,0 +1,16 @@ +{ +"type": "FeatureCollection", +"name": "nebraska_wgs84_with_nodata_labels", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 724, "AREA": 6975423.905, "PERIMETER": 9362.892, "ACRES": 160.134, "HECTARES": 64.804 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.105563544642948, 41.728275581313468 ], [ -101.105382960788944, 41.728275074220257 ], [ -101.105202498070824, 41.728270094395427 ], [ -101.105022353859226, 41.728260647285339 ], [ -101.104842725176226, 41.728246743222186 ], [ -101.104663808479785, 41.728228397412693 ], [ -101.104485799448696, 41.728205629921433 ], [ -101.104308892768501, 41.728178465648881 ], [ -101.104133281918436, 41.72814693430422 ], [ -101.103959158959753, 41.728111070372762 ], [ -101.103786714325466, 41.72807091307822 ], [ -101.103616136612061, 41.72802650633983 ], [ -101.103447612373103, 41.727977898724284 ], [ -101.103281325915148, 41.727925143392511 ], [ -101.103117459095969, 41.727868298041606 ], [ -101.102956191125756, 41.727807424841664 ], [ -101.102797698370921, 41.727742590367683 ], [ -101.102642154161146, 41.727673865526825 ], [ -101.102489728599892, 41.727601325480784 ], [ -101.102340588378112, 41.727525049563468 ], [ -101.102194896592096, 41.727445121194364 ], [ -101.102052812564949, 41.727361627787076 ], [ -101.101914491672375, 41.72727466065384 ], [ -101.10178008517272, 41.727184314905514 ], [ -101.101649740041566, 41.727090689347555 ], [ -101.101523598810971, 41.726993886371901 ], [ -101.101401799413608, 41.72689401184499 ], [ -101.101284475031946, 41.72679117499186 ], [ -101.10117175395267, 41.726685488276757 ], [ -101.101063759426353, 41.726577067279969 ], [ -101.100960609532791, 41.726466030571515 ], [ -101.100862417051857, 41.726352499581296 ], [ -101.100769289340334, 41.726236598466365 ], [ -101.100681328214478, 41.726118453975133 ], [ -101.100598629838856, 41.725998195308549 ], [ -101.100521284621195, 41.725875953978949 ], [ -101.100449377113691, 41.72575186366609 ], [ -101.100382985920604, 41.725626060071001 ], [ -101.100322183612462, 41.725498680767473 ], [ -101.100267036646741, 41.725369865051682 ], [ -101.100217605295441, 41.725239753789722 ], [ -101.100173943579236, 41.725108489263597 ], [ -101.100136099208513, 41.724976215015616 ], [ -101.100104113531444, 41.724843075691396 ], [ -101.100078021488841, 41.724709216881621 ], [ -101.100057851576153, 41.724574784962911 ], [ -101.100043625812404, 41.724439926937606 ], [ -101.10003535971633, 41.724304790273187 ], [ -101.10003306228954, 41.724169522740858 ], [ -101.100036736006871, 41.724034272254002 ], [ -101.100046376813779, 41.723899186706561 ], [ -101.100061974131037, 41.723764413811033 ], [ -101.100083510866341, 41.723630100937285 ], [ -101.100110963433266, 41.723496394951219 ], [ -101.100144301777163, 41.723363442054179 ], [ -101.100183489408209, 41.72323138762323 ], [ -101.100228483441441, 41.723100376052102 ], [ -101.100279234643779, 41.722970550593374 ], [ -101.100335687488055, 41.722842053201845 ], [ -101.100397780213811, 41.722715024379333 ], [ -101.100465444895022, 41.722589603021106 ], [ -101.100538607514494, 41.722465926263936 ], [ -101.100617188044822, 41.722344129336413 ], [ -101.100701100536128, 41.722224345410815 ], [ -101.100790253210121, 41.722106705457811 ], [ -101.100884548560472, 41.721991338103066 ], [ -101.10098388345962, 41.721878369486845 ], [ -101.101088149271547, 41.721767923126016 ], [ -101.101197231970673, 41.721660119779024 ], [ -101.101311012266549, 41.72155507731398 ], [ -101.101429365734404, 41.721452910579792 ], [ -101.101552162951222, 41.721353731280622 ], [ -101.101679269637202, 41.721257647853804 ], [ -101.101810546802739, 41.721164765351439 ], [ -101.101945850900336, 41.721075185325354 ], [ -101.10208503398151, 41.720989005716312 ], [ -101.102227943858679, 41.720906320746941 ], [ -101.102374424271432, 41.720827220818684 ], [ -101.102524315057408, 41.720751792413054 ], [ -101.102677452327384, 41.720680117997162 ], [ -101.102833668644394, 41.72061227593354 ], [ -101.102992793206781, 41.720548340394501 ], [ -101.10315465203486, 41.720488381281157 ], [ -101.103319068161056, 41.720432464146946 ], [ -101.103485861823401, 41.720380650126074 ], [ -101.103654850661854, 41.720332995866649 ], [ -101.10382584991774, 41.720289553468788 ], [ -101.103998672635541, 41.720250370427756 ], [ -101.104173129867277, 41.720215489581967 ], [ -101.104349030878936, 41.720184949066265 ], [ -101.104526183358942, 41.720158782270182 ], [ -101.104704393628225, 41.72013701780147 ], [ -101.104883466851959, 41.720119679454939 ], [ -101.105063207252329, 41.720106786186236 ], [ -101.105243418322559, 41.720098352091412 ], [ -101.105423903041611, 41.720094386391253 ], [ -101.105604464089396, 41.720094893421411 ], [ -101.10578490406246, 41.72009987262755 ], [ -101.105965025689613, 41.720109318566031 ], [ -101.106144632047545, 41.720123220909734 ], [ -101.106323526775924, 41.720141564459503 ], [ -101.10650151429202, 41.720164329160603 ], [ -101.106678400004341, 41.720191490124776 ], [ -101.106853990525366, 41.720223017657318 ], [ -101.107028093882747, 41.720258877289588 ], [ -101.107200519729147, 41.720299029816644 ], [ -101.107371079550248, 41.720343431340126 ], [ -101.107539586870715, 41.720392033316124 ], [ -101.107705857458015, 41.720444782608389 ], [ -101.107869709523712, 41.720501621546227 ], [ -101.108030963922175, 41.7205624879876 ], [ -101.108189444346337, 41.72062731538702 ], [ -101.108344977520389, 41.720696032868304 ], [ -101.108497393389186, 41.720768565301952 ], [ -101.10864652530411, 41.720844833387268 ], [ -101.108792210205223, 41.720924753739091 ], [ -101.108934288799503, 41.721008238978818 ], [ -101.109072605735037, 41.72109519782996 ], [ -101.109207009770742, 41.72118553521787 ], [ -101.109337353941839, 41.721279152373668 ], [ -101.109463495720476, 41.721375946942096 ], [ -101.109585297171549, 41.721475813093548 ], [ -101.109702625103623, 41.721578641639539 ], [ -101.109815351214493, 41.721684320152235 ], [ -101.109923352231576, 41.721792733087149 ], [ -101.110026510046723, 41.721903761909573 ], [ -101.110124711845373, 41.722017285223998 ], [ -101.110217850230043, 41.722133178906986 ], [ -101.110305823337768, 41.722251316242641 ], [ -101.110388534951625, 41.722371568061291 ], [ -101.11046589460598, 41.72249380288055 ], [ -101.110537817685483, 41.72261788704909 ], [ -101.110604225517832, 41.722743684892812 ], [ -101.11066504545974, 41.722871058863028 ], [ -101.110720210976567, 41.722999869686916 ], [ -101.110769661715238, 41.723129976519743 ], [ -101.1108133435703, 41.723261237098882 ], [ -101.110851208743284, 41.723393507899267 ], [ -101.11088321579507, 41.723526644290395 ], [ -101.110909329691353, 41.723660500694386 ], [ -101.110929521841186, 41.723794930745164 ], [ -101.110943770128245, 41.72392978744849 ], [ -101.110952058935325, 41.724064923342745 ], [ -101.110954379161527, 41.724200190660035 ], [ -101.110950728232297, 41.724335441487924 ], [ -101.110941110102516, 41.724470527931039 ], [ -101.110925535252321, 41.72460530227287 ], [ -101.11090402067569, 41.724739617137288 ], [ -101.110876589862187, 41.724873325649696 ], [ -101.110843272771334, 41.725006281597665 ], [ -101.110804105800042, 41.725138339590806 ], [ -101.11075913174291, 41.725269355219801 ], [ -101.11070839974569, 41.725399185214336 ], [ -101.110651965251535, 41.725527687599801 ], [ -101.110589889940684, 41.7256547218525 ], [ -101.110522241663006, 41.725780149053463 ], [ -101.110449094364, 41.725903832040295 ], [ -101.110370528004012, 41.726025635557271 ], [ -101.110286628471016, 41.726145426403207 ], [ -101.110197487486602, 41.72626307357725 ], [ -101.110103202506011, 41.726378448422075 ], [ -101.110003876611444, 41.726491424764689 ], [ -101.109899618399552, 41.726601879054513 ], [ -101.109790541862679, 41.72670969049836 ], [ -101.109676766264329, 41.726814741192712 ], [ -101.109558416008724, 41.726916916252705 ], [ -101.10943562050484, 41.727016103937778 ], [ -101.109308514025003, 41.727112195773849 ], [ -101.109177235557866, 41.727205086672164 ], [ -101.10904192865668, 41.727294675044106 ], [ -101.108902741282151, 41.727380862912426 ], [ -101.108759825640647, 41.7274635560184 ], [ -101.108613338017776, 41.727542663924993 ], [ -101.108463438607444, 41.727618100115791 ], [ -101.108310291336565, 41.727689782089676 ], [ -101.108154063685845, 41.727757631451041 ], [ -101.107994926506507, 41.727821573995669 ], [ -101.107833053833403, 41.727881539791802 ], [ -101.107668622694646, 41.727937463256779 ], [ -101.10750181291786, 41.727989283228759 ], [ -101.107332806933528, 41.728036943033572 ], [ -101.107161789575315, 41.728080390546836 ], [ -101.10698894787788, 41.72811957825094 ], [ -101.106814470872237, 41.728154463287069 ], [ -101.106638549378815, 41.728185007502084 ], [ -101.106461375798773, 41.728211177490259 ], [ -101.106283143903468, 41.728232944629845 ], [ -101.106104048622271, 41.728250285114449 ], [ -101.105924285829431, 41.728263179978946 ], [ -101.105744052129666, 41.728271615120434 ], [ -101.105563544642948, 41.728275581313468 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 725, "AREA": 5476760.938, "PERIMETER": 8296.389, "ACRES": 125.729, "HECTARES": 50.881 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.111815161434677, 41.715330608692398 ], [ -101.111645348776378, 41.715330011671831 ], [ -101.111475668706532, 41.715324949556383 ], [ -101.111306330305197, 41.715315428583601 ], [ -101.111137542231177, 41.715301460485222 ], [ -101.110969512464891, 41.715283062472679 ], [ -101.110802448051842, 41.715260257215945 ], [ -101.110636554847488, 41.715233072815579 ], [ -101.110472037263392, 41.715201542768014 ], [ -101.110309098015222, 41.715165705924363 ], [ -101.110147937872952, 41.715125606442449 ], [ -101.109988755413227, 41.715081293732354 ], [ -101.109831746774702, 41.715032822395649 ], [ -101.109677105416225, 41.714980252157872 ], [ -101.109525021878326, 41.714923647795082 ], [ -101.10937568354845, 41.714863079053906 ], [ -101.109229274429907, 41.714798620565595 ], [ -101.10908597491516, 41.714730351754014 ], [ -101.108945961563435, 41.714658356737743 ], [ -101.10880940688314, 41.714582724226396 ], [ -101.108676479119325, 41.714503547411276 ], [ -101.108547342046307, 41.71442092385044 ], [ -101.108422154765819, 41.714334955348527 ], [ -101.108301071511121, 41.714245747831271 ], [ -101.108184241456769, 41.714153411214831 ], [ -101.108071808535001, 41.714058059270407 ], [ -101.107963911258338, 41.713959809484038 ], [ -101.107860682548946, 41.713858782911707 ], [ -101.107762249574989, 41.71375510403017 ], [ -101.107668733593897, 41.713648900583593 ], [ -101.107580249803121, 41.713540303425994 ], [ -101.107496907198254, 41.713429446360102 ], [ -101.107418808438794, 41.713316465972412 ], [ -101.107346049721826, 41.713201501464795 ], [ -101.10727872066353, 41.713084694483008 ], [ -101.107216904188917, 41.712966188942154 ], [ -101.107160676429814, 41.712846130849293 ], [ -101.107110106631154, 41.712724668123528 ], [ -101.107065257065827, 41.712601950413763 ], [ -101.107026182958094, 41.712478128914221 ], [ -101.106992932415665, 41.712353356178205 ], [ -101.106965546370688, 41.712227785930153 ], [ -101.106944058529336, 41.712101572876158 ], [ -101.106928495330578, 41.711974872513345 ], [ -101.106918875913692, 41.711847840938361 ], [ -101.106915212094862, 41.711720634654966 ], [ -101.106917508352851, 41.711593410381283 ], [ -101.106925761823604, 41.711466324856708 ], [ -101.10693996230394, 41.711339534648729 ], [ -101.10696009226433, 41.711213195960148 ], [ -101.106986126870638, 41.711087464436595 ], [ -101.107018034014899, 41.710962494974829 ], [ -101.107055774355047, 41.710838441531855 ], [ -101.107099301363547, 41.710715456935318 ], [ -101.107148561384818, 41.710593692695284 ], [ -101.107203493701547, 41.710473298817526 ], [ -101.107264030609656, 41.710354423618845 ], [ -101.107330097501773, 41.710237213544289 ], [ -101.107401612959293, 41.710121812986877 ], [ -101.107478488852891, 41.71000836410964 ], [ -101.107560630451104, 41.709897006670623 ], [ -101.107647936537191, 41.709787877850744 ], [ -101.107740299533901, 41.709681112084745 ], [ -101.107837605636163, 41.709576840895771 ], [ -101.107939734951231, 41.709475192733279 ], [ -101.108046561646574, 41.709376292814902 ], [ -101.108157954104868, 41.709280262972193 ], [ -101.108273775086246, 41.709187221500699 ], [ -101.108393881897371, 41.709097283014117 ], [ -101.108518126567191, 41.709010558303333 ], [ -101.108646356029368, 41.708927154199849 ], [ -101.108778412310727, 41.708847173444362 ], [ -101.108914132725872, 41.708770714560117 ], [ -101.109053350077659, 41.708697871731651 ], [ -101.109195892863013, 41.708628734688858 ], [ -101.109341585484259, 41.708563388596453 ], [ -101.109490248465363, 41.708501913949078 ], [ -101.109641698672917, 41.708444386472266 ], [ -101.109795749541689, 41.708390877029188 ], [ -101.109952211304403, 41.70834145153335 ], [ -101.110110891225432, 41.708296170867435 ], [ -101.110271593837993, 41.708255090808414 ], [ -101.110434121185023, 41.708218261958834 ], [ -101.110598273062763, 41.708185729684502 ], [ -101.110763847267393, 41.708157534058593 ], [ -101.110930639843858, 41.708133709812429 ], [ -101.111098445337163, 41.708114286292584 ], [ -101.111267057045112, 41.708099287424837 ], [ -101.111436267273021, 41.708088731684711 ], [ -101.111605867589262, 41.708082632074685 ], [ -101.111775649081949, 41.708080996108187 ], [ -101.111945402616143, 41.708083825800429 ], [ -101.112114919091354, 41.708091117665759 ], [ -101.112283989698909, 41.708102862722193 ], [ -101.112452406179102, 41.708119046502198 ], [ -101.112619961077556, 41.708139649070709 ], [ -101.112786448000676, 41.708164645049592 ], [ -101.112951661869701, 41.70819400364892 ], [ -101.113115399173267, 41.708227688704831 ], [ -101.113277458217965, 41.708265658724109 ], [ -101.113437639376613, 41.708307866935243 ], [ -101.113595745334152, 41.708354261346031 ], [ -101.113751581330533, 41.70840478480752 ], [ -101.113904955400542, 41.708459375084509 ], [ -101.1140556786102, 41.708517964932064 ], [ -101.114203565289429, 41.708580482178313 ], [ -101.114348433260673, 41.708646849813434 ], [ -101.11449010406325, 41.708716986084276 ], [ -101.114628403173171, 41.70879080459521 ], [ -101.114763160218004, 41.708868214414437 ], [ -101.114894209186829, 41.708949120185849 ], [ -101.115021388634631, 41.709033422246669 ], [ -101.115144541881222, 41.709121016750004 ], [ -101.115263517204156, 41.709211795792719 ], [ -101.115378168025785, 41.709305647548412 ], [ -101.115488353093724, 41.70940245640503 ], [ -101.115593936654932, 41.70950210310734 ], [ -101.115694788623003, 41.709604464903656 ], [ -101.115790784738422, 41.709709415697063 ], [ -101.115881806721802, 41.709816826200765 ], [ -101.115967742419514, 41.709926564097266 ], [ -101.116048485942073, 41.7100384942012 ], [ -101.116123937794612, 41.710152478626107 ], [ -101.116194004999556, 41.710268376953955 ], [ -101.116258601211285, 41.71038604640831 ], [ -101.116317646822651, 41.710505342030032 ], [ -101.116371069063106, 41.710626116855934 ], [ -101.11641880208856, 41.710748222099681 ], [ -101.116460787062579, 41.710871507335192 ], [ -101.116496972229058, 41.710995820681809 ], [ -101.116527312976174, 41.711121008991476 ], [ -101.11655177189138, 41.71124691803729 ], [ -101.11657031880776, 41.711373392703607 ], [ -101.116582930841361, 41.711500277177016 ], [ -101.116589592419501, 41.711627415138381 ], [ -101.116590295300142, 41.711754649955296 ], [ -101.116585038582258, 41.711881824875135 ], [ -101.116573828707047, 41.712008783218288 ], [ -101.116556679450269, 41.71213536857082 ], [ -101.116533611905325, 41.712261424977655 ], [ -101.116504654457515, 41.712386797134364 ], [ -101.116469842749254, 41.712511330578664 ], [ -101.116429219636217, 41.712634871880752 ], [ -101.116382835134829, 41.712757268832299 ], [ -101.116330746360745, 41.712878370634002 ], [ -101.116273017458553, 41.712998028081429 ], [ -101.116209719523042, 41.713116093748908 ], [ -101.116140930511605, 41.713232422171096 ], [ -101.116066735148436, 41.713346870022356 ], [ -101.115987224820145, 41.713459296293259 ], [ -101.115902497463424, 41.713569562464443 ], [ -101.115812657444323, 41.713677532677295 ], [ -101.115717815429917, 41.713783073901403 ], [ -101.115618088251892, 41.713886056098438 ], [ -101.115513598762789, 41.713986352382555 ], [ -101.11540447568467, 41.714083839176588 ], [ -101.115290853450546, 41.714178396364495 ], [ -101.115172872038812, 41.714269907439416 ], [ -101.115050676800806, 41.71435825964717 ], [ -101.11492441828176, 41.7144433441253 ], [ -101.114794252035281, 41.714525056037239 ], [ -101.114660338431733, 41.714603294701583 ], [ -101.114522842460616, 41.714677963716056 ], [ -101.11438193352727, 41.71474897107651 ], [ -101.114237785244043, 41.714816229290165 ], [ -101.114090575216508, 41.714879655483628 ], [ -101.113940484824326, 41.714939171504938 ], [ -101.113787698997953, 41.714994704019894 ], [ -101.113632405990529, 41.715046184602535 ], [ -101.113474797145955, 41.715093549819443 ], [ -101.113315066662963, 41.715136741307923 ], [ -101.113153411355782, 41.71517570584804 ], [ -101.112990030411495, 41.715210395428066 ], [ -101.11282512514461, 41.715240767303833 ], [ -101.112658898748705, 41.715266784051316 ], [ -101.112491556046137, 41.715288413612804 ], [ -101.112323303235414, 41.715305629336449 ], [ -101.112154347637031, 41.715318410009111 ], [ -101.111984897437893, 41.715326739882457 ], [ -101.111815161434677, 41.715330608692398 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 726, "AREA": 5621916.146, "PERIMETER": 8405.609, "ACRES": 129.061, "HECTARES": 52.23 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.092358560362669, 41.712236645375988 ], [ -101.092187476272855, 41.712236027505718 ], [ -101.092016525201814, 41.712230935878544 ], [ -101.091845915462471, 41.71222137669892 ], [ -101.091675854951504, 41.712207361615128 ], [ -101.091506550896057, 41.712188907705233 ], [ -101.091338209600977, 41.712166037456207 ], [ -101.091171036197437, 41.712138778736531 ], [ -101.09100523439264, 41.712107164762187 ], [ -101.090841006221723, 41.712071234056168 ], [ -101.090678551801261, 41.712031030401548 ], [ -101.090518069085391, 41.711986602788066 ], [ -101.090359753624497, 41.711938005352415 ], [ -101.090203798326854, 41.711885297312257 ], [ -101.090050393223422, 41.71182854289404 ], [ -101.089899725236208, 41.711767811254639 ], [ -101.089751977950485, 41.711703176397116 ], [ -101.089607331390965, 41.711634717080472 ], [ -101.089465961802446, 41.711562516723617 ], [ -101.089328041434939, 41.711486663303752 ], [ -101.089193738333805, 41.711407249249085 ], [ -101.089063216134861, 41.711324371326086 ], [ -101.088936633865188, 41.711238130521629 ], [ -101.088814145749097, 41.711148631919848 ], [ -101.088695901020387, 41.711055984574088 ], [ -101.088582043740487, 41.710960301373937 ], [ -101.088472712622874, 41.710861698907621 ], [ -101.088368040864225, 41.710760297319908 ], [ -101.088268155981993, 41.710656220165646 ], [ -101.088173179659279, 41.710549594259213 ], [ -101.088083227596513, 41.710440549519916 ], [ -101.087998409370556, 41.710329218813698 ], [ -101.087918828301383, 41.710215737791096 ], [ -101.087844581326138, 41.710100244722028 ], [ -101.087775758881222, 41.709982880327225 ], [ -101.08771244479226, 41.709863787606778 ], [ -101.087654716171983, 41.709743111665787 ], [ -101.087602643326434, 41.709620999537606 ], [ -101.087556289669521, 41.709497600004646 ], [ -101.087515711645779, 41.709373063417026 ], [ -101.087480958661871, 41.709247541509406 ], [ -101.087452073026412, 41.709121187216155 ], [ -101.08742908989872, 41.708994154484792 ], [ -101.087412037246025, 41.708866598088676 ], [ -101.087400935809669, 41.708738673438191 ], [ -101.087395799079857, 41.708610536391518 ], [ -101.087396633279582, 41.708482343064702 ], [ -101.087403437357068, 41.70835424964141 ], [ -101.087416202987271, 41.708226412182761 ], [ -101.087434914582204, 41.708098986437001 ], [ -101.087459549310097, 41.70797212764996 ], [ -101.087490077123377, 41.707845990375809 ], [ -101.087526460795445, 41.707720728288791 ], [ -101.087568655966152, 41.70759649399605 ], [ -101.087616611196069, 41.707473438851657 ], [ -101.087670268029314, 41.707351712772336 ], [ -101.087729561064805, 41.707231464054779 ], [ -101.087794418036225, 41.707112839195005 ], [ -101.087864759900086, 41.706995982709998 ], [ -101.087940500932234, 41.706881036961569 ], [ -101.088021548832344, 41.706768141983048 ], [ -101.088107804836511, 41.706657435308621 ], [ -101.088199163837672, 41.706549051805972 ], [ -101.088295514513732, 41.706443123511917 ], [ -101.088396739463306, 41.70633977947157 ], [ -101.088502715348753, 41.706239145581264 ], [ -101.088613313046537, 41.706141344435196 ], [ -101.088728397804601, 41.706046495176075 ], [ -101.088847829406532, 41.705954713350145 ], [ -101.088971462342442, 41.705866110766308 ], [ -101.089099145986211, 41.705780795360148 ], [ -101.089230724779028, 41.705698871062339 ], [ -101.089366038418902, 41.705620437672188 ], [ -101.089504922055909, 41.705545590736023 ], [ -101.089647206492998, 41.705474421430935 ], [ -101.089792718392104, 41.705407016453698 ], [ -101.089941280485206, 41.70534345791517 ], [ -101.090092711790305, 41.705283823240414 ], [ -101.090246827831777, 41.705228185074311 ], [ -101.090403440865018, 41.705176611193181 ], [ -101.090562360105139, 41.705129164422253 ], [ -101.090723391959258, 41.705085902559084 ], [ -101.090886340262216, 41.705046878303285 ], [ -101.091051006515443, 41.70501213919237 ], [ -101.091217190128731, 41.704981727543725 ], [ -101.091384688664334, 41.704955680403259 ], [ -101.09155329808361, 41.704934029500201 ], [ -101.091722812995371, 41.704916801208455 ], [ -101.091893026905936, 41.704904016514604 ], [ -101.092063732470578, 41.704895690992224 ], [ -101.092234721745982, 41.704891834782991 ], [ -101.09240578644345, 41.704892452584275 ], [ -101.0925767181824, 41.704897543643568 ], [ -101.092747308744137, 41.704907101759176 ], [ -101.092917350325365, 41.704921115288009 ], [ -101.093086635791209, 41.704939567159542 ], [ -101.093254958927346, 41.704962434896757 ], [ -101.093422114691123, 41.704989690643394 ], [ -101.093587899461212, 41.705021301197988 ], [ -101.093752111285539, 41.705057228054173 ], [ -101.093914550127124, 41.705097427447669 ], [ -101.094075018107745, 41.705141850409454 ], [ -101.094233319748781, 41.705190442825504 ], [ -101.094389262209319, 41.705243145502642 ], [ -101.094542655520925, 41.705299894240532 ], [ -101.094693312819047, 41.705360619909953 ], [ -101.094841050570466, 41.705425248536955 ], [ -101.094985688796925, 41.705493701392903 ], [ -101.095127051294256, 41.705565895090309 ], [ -101.09526496584698, 41.70564174168446 ], [ -101.095399264438072, 41.705721148780405 ], [ -101.095529783453628, 41.705804019645576 ], [ -101.095656363882171, 41.705890253327446 ], [ -101.0957788515083, 41.70597974477662 ], [ -101.095897097100604, 41.706072384974561 ], [ -101.09601095659346, 41.706168061066549 ], [ -101.096120291262594, 41.706266656498968 ], [ -101.096224967894088, 41.706368051161334 ], [ -101.096324858946659, 41.706472121532485 ], [ -101.096419842707192, 41.706578740831169 ], [ -101.096509803439034, 41.706687779170217 ], [ -101.096594631523033, 41.706799103714886 ], [ -101.096674223591279, 41.706912578844658 ], [ -101.09674848265297, 41.707028066318294 ], [ -101.0968173182128, 41.70714542544237 ], [ -101.096880646381322, 41.707264513242443 ], [ -101.096938389977169, 41.707385184637403 ], [ -101.096990478621336, 41.707507292616043 ], [ -101.097036848822981, 41.707630688416138 ], [ -101.097077444056907, 41.707755221705803 ], [ -101.097112214832663, 41.707880740766385 ], [ -101.097141118754919, 41.708007092677434 ], [ -101.097164120575258, 41.708134123503022 ], [ -101.097181192235411, 41.70826167847909 ], [ -101.097192312901441, 41.708389602202139 ], [ -101.097197468989407, 41.708517738818522 ], [ -101.097196654182056, 41.708645932214303 ], [ -101.097189869436662, 41.708774026205475 ], [ -101.097177122984078, 41.708901864728148 ], [ -101.097158430318871, 41.709029292028895 ], [ -101.09713381418058, 41.709156152854284 ], [ -101.097103304526229, 41.709282292640246 ], [ -101.097066938493967, 41.709407557700274 ], [ -101.097024760357939, 41.709531795412786 ], [ -101.096976821474598, 41.709654854407006 ], [ -101.096923180220244, 41.70977658474748 ], [ -101.096863901920017, 41.70989683811672 ], [ -101.09679905876844, 41.710015467996087 ], [ -101.096728729741685, 41.710132329844114 ], [ -101.096653000501362, 41.710247281272878 ], [ -101.096571963290359, 41.710360182221379 ], [ -101.096485716820496, 41.710470895126313 ], [ -101.096394366152367, 41.710579285089665 ], [ -101.096298022567481, 41.710685220043167 ], [ -101.096196803432704, 41.710788570909223 ], [ -101.096090832057328, 41.710889211758257 ], [ -101.095980237542918, 41.710987019962168 ], [ -101.095865154625997, 41.711081876343876 ], [ -101.095745723514014, 41.711173665322434 ], [ -101.09562208971451, 41.711262275054047 ], [ -101.095494403857785, 41.71134759756837 ], [ -101.095362821513433, 41.711429528900069 ], [ -101.095227503000743, 41.711507969215589 ], [ -101.095088613193383, 41.711582822934844 ], [ -101.09494632131846, 41.711653998847709 ], [ -101.094800800750292, 41.711721410225188 ], [ -101.09465222879912, 41.711784974925266 ], [ -101.094500786494976, 41.711844615492851 ], [ -101.094346658367073, 41.711900259254385 ], [ -101.09419003221889, 41.711951838406229 ], [ -101.094031098899208, 41.711999290097545 ], [ -101.093870052069533, 41.712042556506759 ], [ -101.093707087967942, 41.71208158491207 ], [ -101.093542405169998, 41.712116327755865 ], [ -101.093376204346526, 41.71214674270248 ], [ -101.093208688019075, 41.712172792689977 ], [ -101.093040060312987, 41.712194445975236 ], [ -101.09287052670858, 41.712211676172728 ], [ -101.092700293790571, 41.712224462286642 ], [ -101.09252956899627, 41.712232788736458 ], [ -101.092358560362669, 41.712236645375988 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 727, "AREA": 5539022.672, "PERIMETER": 8343.414, "ACRES": 127.158, "HECTARES": 51.459 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.07329900677108, 41.692684692434021 ], [ -101.073128291648388, 41.692684035022793 ], [ -101.072957712488247, 41.692678887227785 ], [ -101.072787479478762, 41.692669255392126 ], [ -101.072617802381302, 41.692655151384095 ], [ -101.072448890271886, 41.692636592582694 ], [ -101.072280951283432, 41.69261360185601 ], [ -101.072114192349176, 41.692586207533154 ], [ -101.071948818947575, 41.692554443369303 ], [ -101.071785034849043, 41.692518348504017 ], [ -101.071623041864626, 41.692477967413105 ], [ -101.071463039597361, 41.692433349853751 ], [ -101.071305225196141, 41.692384550803112 ], [ -101.071149793112752, 41.69233163039064 ], [ -101.070996934862123, 41.692274653823908 ], [ -101.070846838786338, 41.69221369130824 ], [ -101.070699689822433, 41.692148817960096 ], [ -101.070555669274512, 41.692080113714638 ], [ -101.07041495459022, 41.692007663227024 ], [ -101.070277719142126, 41.691931555768157 ], [ -101.07014413201405, 41.691851885114559 ], [ -101.070014357792715, 41.69176874943291 ], [ -101.069888556364845, 41.691682251158944 ], [ -101.069766882720302, 41.691592496871138 ], [ -101.069649486761008, 41.691499597159456 ], [ -101.069536513116319, 41.691403666489002 ], [ -101.069428100964842, 41.691304823058928 ], [ -101.069324383862991, 41.691203188656701 ], [ -101.069225489580447, 41.69109888850813 ], [ -101.069131539942887, 41.690992051122848 ], [ -101.069042650681794, 41.690882808136045 ], [ -101.068958931292116, 41.690771294146259 ], [ -101.068880484897363, 41.690657646549361 ], [ -101.068807408122595, 41.690542005369387 ], [ -101.068739790975613, 41.69042451308588 ], [ -101.06867771673609, 41.690305314458278 ], [ -101.068621261853082, 41.69018455634766 ], [ -101.068570495851063, 41.690062387535598 ], [ -101.068525481244308, 41.689938958540971 ], [ -101.068486273460039, 41.689814421434356 ], [ -101.068452920770355, 41.689688929650806 ], [ -101.068425464232845, 41.689562637800577 ], [ -101.068403937640213, 41.689435701478864 ], [ -101.068388367478761, 41.689308277073842 ], [ -101.068378772895969, 41.689180521574237 ], [ -101.068375165677082, 41.689052592375667 ], [ -101.068377550230736, 41.688924647086871 ], [ -101.068385923583733, 41.688796843335489 ], [ -101.068400275384803, 41.688669338573895 ], [ -101.068420587917643, 41.688542289885135 ], [ -101.068446836122845, 41.688415853789529 ], [ -101.068478987628936, 41.688290186051809 ], [ -101.068517002792476, 41.68816544148919 ], [ -101.068560834746989, 41.688041773780682 ], [ -101.068610429460904, 41.687919335277876 ], [ -101.068665725804323, 41.687798276817155 ], [ -101.068726655624332, 41.68767874753393 ], [ -101.068793143829282, 41.687560894679017 ], [ -101.068865108481276, 41.687444863437193 ], [ -101.068942460897361, 41.687330796748384 ], [ -101.069025105758797, 41.687218835131674 ], [ -101.069112941228639, 41.687109116512147 ], [ -101.069205859077357, 41.687001776051098 ], [ -101.069303744816096, 41.68689694597947 ], [ -101.069406477837944, 41.686794755435187 ], [ -101.069513931566533, 41.68669533030392 ], [ -101.069625973611991, 41.686598793064128 ], [ -101.069742465934127, 41.686505262636317 ], [ -101.069863265012515, 41.686414854236453 ], [ -101.069988222023298, 41.686327679234218 ], [ -101.070117183022575, 41.686243845015703 ], [ -101.070249989136002, 41.686163454851297 ], [ -101.070386476754507, 41.686086607768587 ], [ -101.070526477735882, 41.68601339843017 ], [ -101.070669819611808, 41.68594391701739 ], [ -101.070816325800294, 41.685878249119028 ], [ -101.070965815823186, 41.685816475626126 ], [ -101.071118105528413, 41.685758672632261 ], [ -101.071273007316734, 41.685704911339911 ], [ -101.07143033037282, 41.685655257972755 ], [ -101.071589880900163, 41.685609773694118 ], [ -101.071751462359757, 41.685568514531738 ], [ -101.071914875712082, 41.68553153130874 ], [ -101.072079919662201, 41.685498869581011 ], [ -101.072246390907537, 41.68547056958117 ], [ -101.072414084388313, 41.685446666169092 ], [ -101.072582793539894, 41.685427188788786 ], [ -101.07275231054723, 41.685412161432431 ], [ -101.072922426600655, 41.685401602610568 ], [ -101.073092932152903, 41.685395525329469 ], [ -101.07326361717729, 41.685393937075077 ], [ -101.073434271426052, 41.685396839803779 ], [ -101.0736046846894, 41.685404229940048 ], [ -101.073774647054222, 41.685416098380735 ], [ -101.073943949162484, 41.685432430506424 ], [ -101.07411238246911, 41.685453206199327 ], [ -101.074279739498635, 41.685478399868067 ], [ -101.074445814100685, 41.685507980479251 ], [ -101.074610401703779, 41.685541911595543 ], [ -101.074773299567269, 41.685580151420709 ], [ -101.074934307030816, 41.685622652850896 ], [ -101.075093225761634, 41.68566936353276 ], [ -101.075249859998593, 41.685720225927831 ], [ -101.075404016793314, 41.685775177383483 ], [ -101.075555506247682, 41.685834150209857 ], [ -101.075704141747821, 41.685897071763492 ], [ -101.075849740193775, 41.685963864536483 ], [ -101.075992122225074, 41.68603444625213 ], [ -101.076131112441601, 41.686108729966108 ], [ -101.076266539619624, 41.686186624173644 ], [ -101.076398236922657, 41.686268032922051 ], [ -101.076526042106977, 41.686352855928973 ], [ -101.076649797721473, 41.686440988705897 ], [ -101.07676935130165, 41.686532322686702 ], [ -101.076884555557356, 41.686626745361444 ], [ -101.076995268554313, 41.686724140414839 ], [ -101.07710135388902, 41.686824387869578 ], [ -101.077202680856743, 41.686927364234052 ], [ -101.077299124612637, 41.687032942654291 ], [ -101.077390566325661, 41.687140993070422 ], [ -101.077476893324885, 41.687251382376694 ], [ -101.07755799923855, 41.687363974585374 ], [ -101.077633784125069, 41.687478630994356 ], [ -101.077704154596361, 41.687595210357891 ], [ -101.077769023932859, 41.687713569060556 ], [ -101.077828312190675, 41.687833561294198 ], [ -101.07788194630001, 41.687955039237458 ], [ -101.077929860155422, 41.6880778532379 ], [ -101.077971994697378, 41.688201851996276 ], [ -101.078008297985164, 41.688326882752968 ], [ -101.078038725261052, 41.688452791476102 ], [ -101.078063239005559, 41.688579423051294 ], [ -101.078081808983839, 41.688706621472754 ], [ -101.078094412283178, 41.688834230035432 ], [ -101.078101033341227, 41.688962091528126 ], [ -101.078101663965555, 41.689090048427047 ], [ -101.078096303343742, 41.689217943090007 ], [ -101.078084958044656, 41.689345617950543 ], [ -101.078067642010467, 41.689472915712081 ], [ -101.078044376539765, 41.689599679541693 ], [ -101.078015190261283, 41.68972575326341 ], [ -101.077980119099024, 41.689850981550599 ], [ -101.077939206228024, 41.689975210117275 ], [ -101.077892502021314, 41.690098285908356 ], [ -101.077840063988134, 41.690220057288123 ], [ -101.07778195670312, 41.690340374227162 ], [ -101.077718251726893, 41.690459088487152 ], [ -101.077649027518106, 41.690576053803596 ], [ -101.077574369336887, 41.690691126066127 ], [ -101.077494369139828, 41.690804163495827 ], [ -101.077409125466929, 41.690915026820363 ], [ -101.07731874332022, 41.691023579445243 ], [ -101.0772233340345, 41.691129687622386 ], [ -101.077123015140231, 41.691233220614876 ], [ -101.077017910218885, 41.691334050858067 ], [ -101.076908148750633, 41.691432054116888 ], [ -101.076793865954969, 41.691527109638855 ], [ -101.076675202624088, 41.691619100302972 ], [ -101.076552304949459, 41.691707912764109 ], [ -101.07642532434177, 41.691793437592594 ], [ -101.076294417244284, 41.691875569409227 ], [ -101.076159744940227, 41.691954207015115 ], [ -101.076021473353904, 41.692029253516381 ], [ -101.075879772846349, 41.692100616443625 ], [ -101.075734818005358, 41.692168207865947 ], [ -101.075586787430282, 41.692231944499234 ], [ -101.07543586351197, 41.692291747808994 ], [ -101.075282232207996, 41.69234754410698 ], [ -101.075126082813398, 41.692399264642113 ], [ -101.074967607727388, 41.692446845685254 ], [ -101.074807002216204, 41.69249022860776 ], [ -101.074644464172437, 41.692529359953703 ], [ -101.074480193871011, 41.692564191505788 ], [ -101.074314393722418, 41.692594680344918 ], [ -101.074147268023125, 41.692620788902836 ], [ -101.073979022703753, 41.69264248500874 ], [ -101.073809865075177, 41.69265974192875 ], [ -101.073640003573033, 41.692672538398888 ], [ -101.073469647500644, 41.692680858651386 ], [ -101.07329900677108, 41.692684692434021 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 728, "AREA": 5163530.754, "PERIMETER": 8055.665, "ACRES": 118.538, "HECTARES": 47.971 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.09117611353696, 41.693027572351383 ], [ -101.091008475708321, 41.693026915187247 ], [ -101.09084097600909, 41.693021773500362 ], [ -101.090673827926977, 41.693012153844087 ], [ -101.090507244501296, 41.69299806847917 ], [ -101.090341438051354, 41.692979535358141 ], [ -101.090176619905648, 41.692956578102397 ], [ -101.09001300013243, 41.692929225972108 ], [ -101.089850787271828, 41.692897513828903 ], [ -101.089690188069966, 41.692861482091345 ], [ -101.08953140721529, 41.692821176683502 ], [ -101.089374647077605, 41.692776648976348 ], [ -101.089220107450032, 41.692727955722169 ], [ -101.089067985294292, 41.692675158982375 ], [ -101.08891847448956, 41.692618326048212 ], [ -101.088771765585278, 41.692557529354964 ], [ -101.088628045558252, 41.692492846389754 ], [ -101.088487497574263, 41.692424359592536 ], [ -101.08835030075457, 41.692352156251133 ], [ -101.088216629947567, 41.692276328389845 ], [ -101.088086655505933, 41.692196972652141 ], [ -101.087960543069443, 41.69211419017752 ], [ -101.087838453353925, 41.692028086472455 ], [ -101.087720541946325, 41.691938771275879 ], [ -101.08760695910658, 41.69184635841934 ], [ -101.087497849575939, 41.691750965681869 ], [ -101.087393352392681, 41.69165271463973 ], [ -101.087293600714915, 41.691551730511556 ], [ -101.087198721650893, 41.691448141998599 ], [ -101.087108836097144, 41.691342081120787 ], [ -101.08702405858439, 41.691233683048196 ], [ -101.086944497131768, 41.691123085929064 ], [ -101.086870253109183, 41.691010430713369 ], [ -101.086801421108277, 41.690895860973342 ], [ -101.086738088821946, 41.690779522720376 ], [ -101.086680336932773, 41.690661564219006 ], [ -101.086628239010238, 41.690542135797756 ], [ -101.086581861417201, 41.690421389657686 ], [ -101.086541263225456, 41.690299479678309 ], [ -101.086506496140515, 41.690176561221499 ], [ -101.08647760443597, 41.6900527909334 ], [ -101.086454624897215, 41.68992832654493 ], [ -101.086437586774707, 41.689803326670564 ], [ -101.086426511746893, 41.689677950606338 ], [ -101.086421413892751, 41.689552358126782 ], [ -101.08642229967397, 41.689426709281257 ], [ -101.086429167927008, 41.68930116419012 ], [ -101.086442009864612, 41.689175882840487 ], [ -101.086460809087313, 41.689051024882573 ], [ -101.086485541604446, 41.688926749426038 ], [ -101.086516175864872, 41.688803214837449 ], [ -101.08655267279741, 41.688680578538282 ], [ -101.086594985860742, 41.688558996804524 ], [ -101.086643061102933, 41.688438624567418 ], [ -101.086696837230306, 41.688319615216081 ], [ -101.086756245685677, 41.688202120402188 ], [ -101.086821210735934, 41.688086289846488 ], [ -101.086891649568599, 41.687972271148418 ], [ -101.086967472397546, 41.687860209597709 ], [ -101.087048582577495, 41.687750247989499 ], [ -101.087134876727319, 41.687642526442353 ], [ -101.08722624486181, 41.687537182219735 ], [ -101.087322570532024, 41.687434349555197 ], [ -101.08742373097364, 41.687334159481303 ], [ -101.087529597263526, 41.687236739662815 ], [ -101.087640034484096, 41.687142214233944 ], [ -101.087754901895238, 41.687050703640352 ], [ -101.08787405311368, 41.686962324485577 ], [ -101.087997336299622, 41.686877189382663 ], [ -101.088124594350134, 41.686795406810617 ], [ -101.088255665099396, 41.686717080976273 ], [ -101.088390381525329, 41.686642311681524 ], [ -101.088528571962371, 41.68657119419624 ], [ -101.088670060320226, 41.686503819136888 ], [ -101.088814666308181, 41.686440272351213 ], [ -101.088962205664757, 41.686380634808714 ], [ -101.089112490392495, 41.68632498249773 ], [ -101.089265328997385, 41.68627338632848 ], [ -101.089420526732781, 41.686225912042893 ], [ -101.089577885847547, 41.686182620130744 ], [ -101.089737205837793, 41.686143565752715 ], [ -101.089898283702368, 41.686108798670148 ], [ -101.090060914201374, 41.686078363181572 ], [ -101.09022489011754, 41.686052298066386 ], [ -101.090390002520166, 41.686030636535399 ], [ -101.090556041031192, 41.686013406188628 ], [ -101.090722794093111, 41.686000628980047 ], [ -101.090890049238467, 41.685992321189666 ], [ -101.091057593360361, 41.685988493402803 ], [ -101.091225212983872, 41.685989150496631 ], [ -101.09139269453793, 41.685994291633918 ], [ -101.091559824627296, 41.686003910264098 ], [ -101.091726390304359, 41.686017994131639 ], [ -101.091892179340249, 41.686036525291676 ], [ -101.092056980495215, 41.686059480132762 ], [ -101.092220583787565, 41.686086829407053 ], [ -101.092382780761156, 41.686118538267472 ], [ -101.092543364750838, 41.686154566312183 ], [ -101.092702131145657, 41.686194867635948 ], [ -101.092858877649533, 41.686239390888581 ], [ -101.093013404538766, 41.686288079340507 ], [ -101.093165514916606, 41.686340870954801 ], [ -101.093315014963849, 41.686397698466344 ], [ -101.093461714185892, 41.686458489467398 ], [ -101.093605425655255, 41.686523166499974 ], [ -101.093745966249756, 41.686591647154238 ], [ -101.093883156885838, 41.686663844173751 ], [ -101.094016822746568, 41.686739665566407 ], [ -101.094146793504507, 41.686819014721635 ], [ -101.094272903538624, 41.686901790533575 ], [ -101.094394992145354, 41.686987887529661 ], [ -101.09451290374335, 41.68707719600512 ], [ -101.094626488071796, 41.687169602162712 ], [ -101.094735600381782, 41.687264988257496 ], [ -101.094840101620903, 41.687363232747067 ], [ -101.09493985861036, 41.687464210446208 ], [ -101.095034744214843, 41.687567792686401 ], [ -101.095124637504512, 41.687673847479701 ], [ -101.095209423909182, 41.68778223968701 ], [ -101.095288995364456, 41.687892831190005 ], [ -101.095363250449395, 41.688005481067307 ], [ -101.095432094516056, 41.688120045773935 ], [ -101.095495439810009, 41.688236379324053 ], [ -101.095553205582419, 41.688354333477179 ], [ -101.095605318193037, 41.688473757926893 ], [ -101.095651711204155, 41.688594500492407 ], [ -101.095692325465436, 41.688716407312405 ], [ -101.095727109189468, 41.68883932304113 ], [ -101.095756018017852, 41.688963091046304 ], [ -101.095779015077966, 41.68908755360868 ], [ -101.095796071030037, 41.689212552123067 ], [ -101.095807164104784, 41.689337927300379 ], [ -101.09581228013127, 41.689463519370577 ], [ -101.095811412555179, 41.689589168286389 ], [ -101.095804562447327, 41.689714713927145 ], [ -101.095791738502456, 41.689839996302872 ], [ -101.095772957028359, 41.68996485575817 ], [ -101.095748241925278, 41.690089133175704 ], [ -101.095717624655606, 41.690212670178958 ], [ -101.095681144203937, 41.69033530933411 ], [ -101.095638847027615, 41.690456894350646 ], [ -101.09559078699759, 41.690577270280606 ], [ -101.095537025330088, 41.690696283716008 ], [ -101.095477630508526, 41.690813782984449 ], [ -101.09541267819661, 41.690929618342359 ], [ -101.095342251141858, 41.691043642165944 ], [ -101.0952664390704, 41.691155709139295 ], [ -101.095185338572676, 41.69126567643967 ], [ -101.09509905298053, 41.691373403919485 ], [ -101.095007692235498, 41.691478754285022 ], [ -101.094911372748911, 41.691581593271465 ], [ -101.094810217253567, 41.69168178981402 ], [ -101.094704354647391, 41.691779216214968 ], [ -101.094593919829194, 41.691873748306499 ], [ -101.094479053526854, 41.69196526560912 ], [ -101.094359902117972, 41.692053651484976 ], [ -101.094236617443343, 41.692138793286837 ], [ -101.094109356613487, 41.692220582501562 ], [ -101.093978281808361, 41.692298914888482 ], [ -101.093843560070752, 41.692373690612264 ], [ -101.09370536309325, 41.692444814370319 ], [ -101.093563866999474, 41.692512195514162 ], [ -101.093419252119617, 41.692575748165147 ], [ -101.093271702760376, 41.692635391323755 ], [ -101.093121406970269, 41.692691048973138 ], [ -101.092968556299638, 41.692742650175802 ], [ -101.092813345556607, 41.692790129164223 ], [ -101.092655972558603, 41.692833425424638 ], [ -101.092496637880288, 41.692872483774181 ], [ -101.092335544597617, 41.692907254431297 ], [ -101.092172898029077, 41.692937693079259 ], [ -101.092008905473833, 41.692963760922552 ], [ -101.091843775947368, 41.692985424736435 ], [ -101.091677719914998, 41.693002656909293 ], [ -101.091510949023501, 41.693015435477861 ], [ -101.091343675831197, 41.69302374415517 ], [ -101.09117611353696, 41.693027572351383 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 729, "AREA": 5250033.07, "PERIMETER": 8122.856, "ACRES": 120.524, "HECTARES": 48.775 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.093373895150492, 41.666682720015125 ], [ -101.093205882274361, 41.666682077016581 ], [ -101.093038005700407, 41.666676962986571 ], [ -101.092870476986292, 41.666667384369802 ], [ -101.09270350725113, 41.666653353237152 ], [ -101.092537306909236, 41.666634887270611 ], [ -101.092372085404875, 41.666612009740838 ], [ -101.092208050948187, 41.666584749477927 ], [ -101.092045410252695, 41.666553140834992 ], [ -101.091884368274648, 41.666517223644874 ], [ -101.09172512795466, 41.666477043169976 ], [ -101.091567889961865, 41.666432650045088 ], [ -101.091412852440897, 41.666384100213648 ], [ -101.091260210762144, 41.666331454857136 ], [ -101.091110157275438, 41.666274780318041 ], [ -101.090962881067554, 41.666214148016103 ], [ -101.090818567723915, 41.666149634358312 ], [ -101.090677399094616, 41.666081320642647 ], [ -101.090539553065241, 41.666009292955522 ], [ -101.090405203332665, 41.665933642063194 ], [ -101.090274519186067, 41.665854463297528 ], [ -101.090147665293642, 41.665771856435626 ], [ -101.090024801495105, 41.665685925574117 ], [ -101.08990608260018, 41.665596778998001 ], [ -101.089791658193633, 41.665504529044028 ], [ -101.089681672446716, 41.665409291959136 ], [ -101.08957626393557, 41.665311187753971 ], [ -101.0894755654666, 41.665210340051516 ], [ -101.089379703909245, 41.665106875931379 ], [ -101.089288800036115, 41.665000925769426 ], [ -101.089202968370941, 41.664892623073726 ], [ -101.089122317044229, 41.664782104315954 ], [ -101.089046947657266, 41.664669508759673 ], [ -101.088976955154038, 41.664554978284578 ], [ -101.088912427701842, 41.664438657207839 ], [ -101.088853446580217, 41.664320692102102 ], [ -101.088800086078663, 41.664201231610839 ], [ -101.088752413403242, 41.664080426260995 ], [ -101.088710488591957, 41.663958428273261 ], [ -101.088674364439299, 41.663835391370256 ], [ -101.088644086429852, 41.663711470582832 ], [ -101.088619692681149, 41.66358682205464 ], [ -101.088601213895842, 41.663461602845494 ], [ -101.08858867332313, 41.663335970733328 ], [ -101.088582086729673, 41.663210084015397 ], [ -101.088581462379864, 41.663084101308947 ], [ -101.088586801025656, 41.662958181351193 ], [ -101.088598095905709, 41.662832482799381 ], [ -101.088615332754159, 41.662707164030877 ], [ -101.088638489818706, 41.662582382943675 ], [ -101.088667537888213, 41.662458296757336 ], [ -101.088702440329698, 41.662335061815057 ], [ -101.088743153134644, 41.662212833386548 ], [ -101.088789624974595, 41.662091765472589 ], [ -101.088841797266014, 41.661972010610896 ], [ -101.088899604244205, 41.66185371968394 ], [ -101.088962973046364, 41.661737041729033 ], [ -101.089031823803481, 41.661622123750398 ], [ -101.089106069741121, 41.661509110534105 ], [ -101.089185617288848, 41.661398144465714 ], [ -101.089270366198321, 41.661289365350754 ], [ -101.089360209669522, 41.661182910238814 ], [ -101.089455034485582, 41.661078913250833 ], [ -101.089554721155466, 41.660977505410145 ], [ -101.089659144064541, 41.66087881447752 ], [ -101.089768171632926, 41.660782964790158 ], [ -101.089881666481347, 41.660690077105194 ], [ -101.089999485604238, 41.66060026844751 ], [ -101.090121480549925, 41.660513651962411 ], [ -101.090247497607749, 41.660430336773118 ], [ -101.090377378001619, 41.660350427843298 ], [ -101.090510958090164, 41.660274025844963 ], [ -101.090648069572794, 41.660201227031557 ], [ -101.090788539701748, 41.660132123116888 ], [ -101.090932191499718, 41.660066801159495 ], [ -101.0910788439827, 41.660005343453122 ], [ -101.09122831238804, 41.659947827422997 ], [ -101.09138040840709, 41.659894325528406 ], [ -101.091534940422378, 41.659844905171404 ], [ -101.091691713748943, 41.659799628611971 ], [ -101.091850530879469, 41.659758552889542 ], [ -101.092011191733121, 41.65972172975129 ], [ -101.092173493907367, 41.659689205586844 ], [ -101.092337232933005, 41.659661021370006 ], [ -101.092502202531477, 41.659637212607024 ], [ -101.092668194874804, 41.659617809291987 ], [ -101.092835000847117, 41.659602835868974 ], [ -101.093002410308102, 41.659592311201358 ], [ -101.093170212357492, 41.65958624854801 ], [ -101.093338195600737, 41.659584655546553 ], [ -101.093506148415102, 41.659587534203858 ], [ -101.093673859216196, 41.659594880893401 ], [ -101.093841116724363, 41.659606686359922 ], [ -101.09400771023067, 41.659622935731043 ], [ -101.094173429862323, 41.659643608535958 ], [ -101.094338066846859, 41.659668678731286 ], [ -101.094501413775092, 41.659698114733786 ], [ -101.094663264862248, 41.65973187946021 ], [ -101.094823416207049, 41.659769930373876 ], [ -101.094981666048611, 41.659812219538381 ], [ -101.095137815020379, 41.65985869367784 ], [ -101.095291666401266, 41.659909294244066 ], [ -101.095443026363398, 41.65996395749022 ], [ -101.095591704216176, 41.660022614551096 ], [ -101.095737512646437, 41.660085191529888 ], [ -101.095880267954414, 41.660151609591196 ], [ -101.096019790285055, 41.660221785060365 ], [ -101.096155903854637, 41.660295629528768 ], [ -101.096288437172063, 41.660373049965187 ], [ -101.096417223255017, 41.660453948832995 ], [ -101.096542099840192, 41.660538224212928 ], [ -101.096662909587849, 41.660625769931535 ], [ -101.096779500279879, 41.660716475694777 ], [ -101.096891725011773, 41.660810227226982 ], [ -101.096999442377594, 41.660906906414802 ], [ -101.097102516648164, 41.661006391455913 ], [ -101.097200817942237, 41.661108557012447 ], [ -101.097294222390062, 41.661213274368883 ], [ -101.097382612289579, 41.661320411594104 ], [ -101.097465876254731, 41.661429833707587 ], [ -101.097543909355991, 41.661541402849501 ], [ -101.097616613252555, 41.661654978454202 ], [ -101.097683896316411, 41.661770417427455 ], [ -101.09774567374788, 41.661887574326528 ], [ -101.097801867682563, 41.662006301543535 ], [ -101.097852407289636, 41.662126449491289 ], [ -101.097897228861143, 41.662247866791738 ], [ -101.097936275892522, 41.662370400466777 ], [ -101.097969499153834, 41.662493896130776 ], [ -101.097996856752047, 41.662618198185264 ], [ -101.098018314183903, 41.662743150014833 ], [ -101.0980338443796, 41.662868594184495 ], [ -101.098043427737124, 41.662994372638025 ], [ -101.098047052146953, 41.663120326897143 ], [ -101.098044713007667, 41.663246298261043 ], [ -101.098036413231824, 41.6633721280066 ], [ -101.098022163242518, 41.663497657588117 ], [ -101.098001980960348, 41.663622728837197 ], [ -101.097975891781076, 41.663747184162119 ], [ -101.097943928543785, 41.663870866746244 ], [ -101.097906131489623, 41.663993620745806 ], [ -101.097862548211367, 41.664115291486212 ], [ -101.097813233593456, 41.66423572565688 ], [ -101.097758249743151, 41.66435477150462 ], [ -101.097697665912349, 41.664472279024722 ], [ -101.097631558410413, 41.664588100150098 ], [ -101.097560010508246, 41.66470208893778 ], [ -101.097483112333478, 41.664814101753024 ], [ -101.097400960756929, 41.664923997450209 ], [ -101.097313659270768, 41.665031637550683 ], [ -101.097221317858114, 41.665136886417464 ], [ -101.097124052854639, 41.665239611426102 ], [ -101.09702198680192, 41.665339683131826 ], [ -101.096915248293243, 41.665436975432755 ], [ -101.096803971811482, 41.665531365728803 ], [ -101.096688297559808, 41.665622735076298 ], [ -101.096568371285002, 41.66571096833782 ], [ -101.096444344093754, 41.665795954327329 ], [ -101.096316372262436, 41.665877585950454 ], [ -101.096184617040009, 41.665955760339386 ], [ -101.096049244444913, 41.666030378982519 ], [ -101.095910425055834, 41.666101347848794 ], [ -101.095768333796698, 41.666168577506077 ], [ -101.095623149716275, 41.666231983233956 ], [ -101.09547505576235, 41.666291485130643 ], [ -101.095324238551328, 41.666347008213542 ], [ -101.095170888132841, 41.666398482513863 ], [ -101.095015197750271, 41.666445843164873 ], [ -101.094857363597058, 41.666489030483589 ], [ -101.094697584569502, 41.666527990046141 ], [ -101.094536062015905, 41.6665626727562 ], [ -101.094372999482772, 41.666593034907073 ], [ -101.094208602458238, 41.666619038236703 ], [ -101.094043078112932, 41.666640649975939 ], [ -101.093876635038811, 41.66665784288984 ], [ -101.093709482986185, 41.666670595312013 ], [ -101.093541832599229, 41.666678891171912 ], [ -101.093373895150492, 41.666682720015125 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 730, "AREA": 5555421.276, "PERIMETER": 8355.751, "ACRES": 127.535, "HECTARES": 51.612 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.053446719493977, 41.713052995259162 ], [ -101.053276648430568, 41.713052323701795 ], [ -101.053106712269866, 41.713047204970984 ], [ -101.052937118087783, 41.713037645304148 ], [ -101.05276807254333, 41.713023656350167 ], [ -101.052599781626611, 41.713005255155295 ], [ -101.052432450407778, 41.7129824641422 ], [ -101.052266282786888, 41.712955311082794 ], [ -101.052101481245444, 41.712923829064273 ], [ -101.051938246599462, 41.712888056448811 ], [ -101.051776777754739, 41.71284803682677 ], [ -101.051617271464266, 41.7128038189636 ], [ -101.051459922088483, 41.71275545674041 ], [ -101.051304921358295, 41.712703009088123 ], [ -101.051152458141374, 41.712646539915866 ], [ -101.051002718211905, 41.71258611803286 ], [ -101.05085588402423, 41.712521817064648 ], [ -101.05071213449034, 41.712453715363317 ], [ -101.050571644761916, 41.712381895911967 ], [ -101.050434586016848, 41.712306446223572 ], [ -101.05030112525057, 41.71222745823426 ], [ -101.050171425072548, 41.712145028191294 ], [ -101.050045643508199, 41.712059256535738 ], [ -101.049923933806255, 41.711970247780016 ], [ -101.049806444252113, 41.711878110380482 ], [ -101.049693317987092, 41.711782956605269 ], [ -101.049584692834102, 41.711684902397408 ], [ -101.049480701129696, 41.711584067233588 ], [ -101.049381469562931, 41.711480573978378 ], [ -101.049287119020988, 41.711374548734625 ], [ -101.049197764442027, 41.711266120689679 ], [ -101.049113514675142, 41.711155421957983 ], [ -101.049034472347898, 41.711042587419954 ], [ -101.048960733741282, 41.710927754557744 ], [ -101.04889238867257, 41.710811063287636 ], [ -101.048829520386022, 41.710692655789472 ], [ -101.048772205451499, 41.710572676333378 ], [ -101.04872051367137, 41.710451271104063 ], [ -101.048674507995571, 41.710328588022584 ], [ -101.04863424444504, 41.710204776566073 ], [ -101.048599772043616, 41.710079987585701 ], [ -101.048571132758468, 41.709954373122727 ], [ -101.048548361449164, 41.709828086223347 ], [ -101.048531485825265, 41.709701280752057 ], [ -101.048520526412773, 41.709574111204368 ], [ -101.048515496529376, 41.709446732518487 ], [ -101.048516402268206, 41.709319299886445 ], [ -101.048523242490802, 41.709191968565143 ], [ -101.048536008828478, 41.70906489368717 ], [ -101.048554685692821, 41.708938230071709 ], [ -101.048579250294793, 41.708812132036087 ], [ -101.04860967267264, 41.708686753207651 ], [ -101.048645915728642, 41.708562246336605 ], [ -101.048687935274359, 41.708438763110017 ], [ -101.048735680084718, 41.708316453967022 ], [ -101.048789091960558, 41.70819546791553 ], [ -101.048848105799621, 41.708075952350647 ], [ -101.048912649676069, 41.707958052875306 ], [ -101.048982644928188, 41.70784191312277 ], [ -101.049058006254427, 41.707727674581783 ], [ -101.049138641817351, 41.707615476424124 ], [ -101.049224453355663, 41.70750545533518 ], [ -101.049315336304076, 41.707397745347443 ], [ -101.049411179920696, 41.707292477677299 ], [ -101.049511867422069, 41.707189780565137 ], [ -101.049617276125517, 41.707089779119272 ], [ -101.049727277598663, 41.706992595163449 ], [ -101.049841737815967, 41.706898347088668 ], [ -101.049960517321978, 41.70680714970878 ], [ -101.050083471401322, 41.706719114120872 ], [ -101.050210450254994, 41.706634347569825 ], [ -101.050341299182833, 41.706552953317797 ], [ -101.050475858772032, 41.706475030518469 ], [ -101.050613965091301, 41.70640067409623 ], [ -101.050755449890502, 41.706329974630734 ], [ -101.05090014080568, 41.706263018246439 ], [ -101.051047861568918, 41.706199886507825 ], [ -101.051198432223032, 41.706140656320088 ], [ -101.051351669340789, 41.706085399835487 ], [ -101.051507386248161, 41.706034184365429 ], [ -101.051665393251795, 41.705987072298548 ], [ -101.051825497869956, 41.705944121024764 ], [ -101.051987505066862, 41.705905382865438 ], [ -101.052151217490234, 41.705870905009519 ], [ -101.052316435711575, 41.705840729456284 ], [ -101.052482958469028, 41.705814892963978 ], [ -101.052650582912378, 41.705793427005354 ], [ -101.052819104850073, 41.705776357729007 ], [ -101.052988318997862, 41.705763705927843 ], [ -101.053158019228661, 41.705755487013555 ], [ -101.05332799882359, 41.705751710997959 ], [ -101.053498050723675, 41.70575238248081 ], [ -101.053667967781834, 41.705757500644125 ], [ -101.053837543015206, 41.705767059253262 ], [ -101.054006569857108, 41.705781046664441 ], [ -101.054174842408528, 41.705799445838977 ], [ -101.054342155688801, 41.705822234364014 ], [ -101.054508305885278, 41.705849384479784 ], [ -101.054673090601341, 41.705880863113414 ], [ -101.054836309102924, 41.705916631919237 ], [ -101.054997762562934, 41.705956647325436 ], [ -101.055157254303268, 41.706000860587096 ], [ -101.055314590034428, 41.706049217845575 ], [ -101.055469578091945, 41.706101660194058 ], [ -101.055622029669905, 41.706158123749354 ], [ -101.055771759050813, 41.706218539729512 ], [ -101.055918583831755, 41.706282834537802 ], [ -101.056062325146527, 41.706350929852121 ], [ -101.056202807883508, 41.706422742720513 ], [ -101.056339860898859, 41.706498185662056 ], [ -101.056473317225098, 41.706577166773492 ], [ -101.056603014274359, 41.706659589841074 ], [ -101.056728794036474, 41.706745354457709 ], [ -101.056850503271463, 41.706834356145293 ], [ -101.056967993696233, 41.706926486481876 ], [ -101.057081122165229, 41.707021633233772 ], [ -101.057189750844799, 41.707119680492113 ], [ -101.057293747381209, 41.707220508814132 ], [ -101.057392985061853, 41.707323995368526 ], [ -101.057487342969765, 41.707430014085141 ], [ -101.057576706130902, 41.707538435808402 ], [ -101.05766096565435, 41.707649128454705 ], [ -101.057740018865033, 41.707761957173219 ], [ -101.057813769428932, 41.707876784510169 ], [ -101.057882127470492, 41.707993470576206 ], [ -101.057945009682285, 41.708111873216879 ], [ -101.058002339426579, 41.708231848185605 ], [ -101.058054046828872, 41.708353249319664 ], [ -101.058100068863126, 41.708475928717903 ], [ -101.058140349428669, 41.708599736921094 ], [ -101.058174839418811, 41.708724523093949 ], [ -101.058203496780649, 41.708850135208905 ], [ -101.058226286566594, 41.708976420231238 ], [ -101.058243180977044, 41.709103224305501 ], [ -101.058254159394451, 41.709230392943027 ], [ -101.0582592084086, 41.709357771210072 ], [ -101.058258321833065, 41.709485203916635 ], [ -101.058251500713041, 41.709612535805419 ], [ -101.058238753324062, 41.709739611741128 ], [ -101.058220095162284, 41.709866276899369 ], [ -101.05819554892561, 41.709992376955341 ], [ -101.05816514448631, 41.710117758271828 ], [ -101.058128918854734, 41.710242268086525 ], [ -101.0580869161344, 41.710365754697982 ], [ -101.05803918746841, 41.710488067650608 ], [ -101.057985790977298, 41.710609057917949 ], [ -101.057926791688374, 41.710728578084343 ], [ -101.057862261456563, 41.710846482524403 ], [ -101.057792278877116, 41.710962627580699 ], [ -101.057716929189823, 41.711076871738712 ], [ -101.05763630417546, 41.711189075799204 ], [ -101.057550502043881, 41.71129910304807 ], [ -101.057459627314586, 41.711406819422812 ], [ -101.057363790689408, 41.711512093675921 ], [ -101.057263108917752, 41.711614797534935 ], [ -101.057157704654358, 41.711714805858676 ], [ -101.057047706309973, 41.711811996789862 ], [ -101.056933247894918, 41.711906251903571 ], [ -101.056814468855848, 41.71199745635159 ], [ -101.056691513905861, 41.712085499002448 ], [ -101.056564532848213, 41.712170272576834 ], [ -101.056433680393795, 41.712251673778347 ], [ -101.056299115972593, 41.712329603419505 ], [ -101.056161003539458, 41.712403966542553 ], [ -101.056019511374288, 41.712474672535194 ], [ -101.055874811876933, 41.71254163524118 ], [ -101.055727081357119, 41.712604773065188 ], [ -101.055576499819566, 41.712664009072419 ], [ -101.055423250744553, 41.712719271082278 ], [ -101.055267520864305, 41.712770491756402 ], [ -101.05510949993544, 41.712817608680808 ], [ -101.054949380507495, 41.712860564441932 ], [ -101.05478735768844, 41.712899306696528 ], [ -101.054623628906626, 41.712933788235695 ], [ -101.054458393670245, 41.712963967042256 ], [ -101.054291853324088, 41.712989806342044 ], [ -101.054124210804034, 41.713011274648693 ], [ -101.053955670389698, 41.71302834580213 ], [ -101.053786437455372, 41.713040999000299 ], [ -101.053616718219644, 41.713049218824736 ], [ -101.053446719493977, 41.713052995259162 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 759, "AREA": 5628503.107, "PERIMETER": 8410.962, "ACRES": 129.213, "HECTARES": 52.291 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.02986150942003, 41.753320763387563 ], [ -101.029618736775561, 41.753318431460016 ], [ -101.029376415257985, 41.753307111572269 ], [ -101.029135137968922, 41.753286831430636 ], [ -101.0288954954529, 41.753257640672238 ], [ -101.028658074251169, 41.753219610743585 ], [ -101.028423455465443, 41.753172834725504 ], [ -101.02819221333489, 41.753117427105281 ], [ -101.027964913830047, 41.753053523496206 ], [ -101.027742113266996, 41.752981280305683 ], [ -101.027524356945193, 41.752900874352044 ], [ -101.027312177812405, 41.752812502431638 ], [ -101.027106095159809, 41.752716380837029 ], [ -101.026906613350775, 41.752612744827267 ], [ -101.026714220586101, 41.752501848051921 ], [ -101.026529387708976, 41.752383961929972 ], [ -101.026352567052498, 41.75225937498525 ], [ -101.02618419133259, 41.752128392140079 ], [ -101.026024672589017, 41.751991333968618 ], [ -101.025874401177035, 41.75184853591206 ], [ -101.025733744812371, 41.751700347457387 ], [ -101.025603047671453, 41.751547131281775 ], [ -101.025482629549629, 41.751389262364597 ], [ -101.025372785078858, 41.751227127069669 ], [ -101.025273783007265, 41.75106112219926 ], [ -101.025185865542056, 41.750891654022801 ], [ -101.025109247757442, 41.750719137282502 ], [ -101.025044117069058, 41.750543994178003 ], [ -101.024990632776081, 41.750366653333082 ], [ -101.024948925672291, 41.75018754874651 ], [ -101.024919097726837, 41.750007118729819 ], [ -101.024901221835705, 41.749825804834586 ], [ -101.024895341644239, 41.749644050771778 ], [ -101.024901471441297, 41.749462301325806 ], [ -101.024919596125301, 41.749281001266191 ], [ -101.024949671242197, 41.749100594258984 ], [ -101.024991623095175, 41.748921521781199 ], [ -101.025045348926, 41.748744222040585 ], [ -101.025110717167394, 41.748569128903192 ], [ -101.025187567765926, 41.74839667083198 ], [ -101.025275712574498, 41.748227269838416 ], [ -101.025374935813559, 41.74806134044988 ], [ -101.025484994599992, 41.747899288695649 ], [ -101.025605619541921, 41.747741511113439 ], [ -101.025736515398776, 41.747588393779317 ], [ -101.025877361804163, 41.747440311363249 ], [ -101.026027814050309, 41.747297626212536 ], [ -101.026187503931908, 41.747160687465453 ], [ -101.026356040647471, 41.747029830197135 ], [ -101.026533011755774, 41.746905374600097 ], [ -101.026717984185197, 41.746787625200838 ], [ -101.0269105052935, 41.746676870115124 ], [ -101.027110103975289, 41.746573380343264 ], [ -101.027316291814742, 41.746477409107207 ], [ -101.027528564280402, 41.746389191231316 ], [ -101.027746401959419, 41.746308942567843 ], [ -101.027969271828184, 41.746236859469292 ], [ -101.028196628556032, 41.746173118307951 ], [ -101.028427915839188, 41.746117875044682 ], [ -101.028662567761288, 41.746071264847444 ], [ -101.028900010177509, 41.746033401760762 ], [ -101.029139662118766, 41.746004378426797 ], [ -101.029380937212494, 41.745984265858766 ], [ -101.029623245116696, 41.745973113267269 ], [ -101.029865992963565, 41.745970947940087 ], [ -101.03010858680949, 41.745977775175191 ], [ -101.030350433087435, 41.745993578267985 ], [ -101.030590940058559, 41.746018318552146 ], [ -101.030829519259285, 41.746051935494044 ], [ -101.031065586940471, 41.746094346840955 ], [ -101.031298565494993, 41.746145448822062 ], [ -101.031527884870385, 41.746205116402379 ], [ -101.03175298396296, 41.746273203588366 ], [ -101.031973311990214, 41.746349543785186 ], [ -101.032188329837993, 41.746433950203922 ], [ -101.032397511379074, 41.746526216318536 ], [ -101.032600344760155, 41.746626116370997 ], [ -101.032796333654019, 41.746733405923251 ], [ -101.032984998473651, 41.746847822455287 ], [ -101.033165877545628, 41.746969086007084 ], [ -101.03333852823971, 41.747096899863443 ], [ -101.033502528051883, 41.747230951279619 ], [ -101.033657475638279, 41.74737091224646 ], [ -101.033802991797515, 41.7475164402926 ], [ -101.033938720398694, 41.747667179322242 ], [ -101.034064329253326, 41.74782276048623 ], [ -101.034179510928539, 41.747982803084348 ], [ -101.034283983499918, 41.748146915496669 ], [ -101.034377491241955, 41.748314696141506 ], [ -101.034459805254457, 41.748485734457994 ], [ -101.03453072402344, 41.748659611910433 ], [ -101.03459007391497, 41.748835903012377 ], [ -101.034637709600943, 41.749014176367631 ], [ -101.034673514415573, 41.749193995725712 ], [ -101.034697400641832, 41.749374921049352 ], [ -101.034709309727035, 41.749556509591351 ], [ -101.034709212427046, 41.749738316977663 ], [ -101.034697108878916, 41.749919898295182 ], [ -101.034673028601532, 41.750100809180225 ], [ -101.034637030424278, 41.750280606906259 ], [ -101.034589202344094, 41.750458851467179 ], [ -101.034529661311083, 41.750635106654258 ], [ -101.034458552943249, 41.750808941123822 ], [ -101.034376051170938, 41.750979929452981 ], [ -101.034282357812089, 41.751147653180794 ], [ -101.034177702079077, 41.751311701832734 ], [ -101.034062340018508, 41.751471673925217 ], [ -101.033936553885269, 41.751627177948656 ], [ -101.033800651452282, 41.75177783332564 ], [ -101.03365496525781, 41.751923271342662 ], [ -101.033499851792129, 41.752063136052797 ], [ -101.033335690625293, 41.752197085147046 ], [ -101.033162883478568, 41.752324790792429 ], [ -101.032981853241267, 41.752445940434605 ], [ -101.032793042936092, 41.752560237563124 ], [ -101.032596914634624, 41.752667402437325 ], [ -101.032393948326543, 41.752767172771364 ], [ -101.032184640744617, 41.752859304376344 ], [ -101.031969504148734, 41.75294357175828 ], [ -101.031749065071807, 41.753019768670157 ], [ -101.031523863030799, 41.753087708617038 ], [ -101.031294449205674, 41.753147225312667 ], [ -101.031061385089899, 41.753198173086709 ], [ -101.03082524111565, 41.75324042724138 ], [ -101.030586595256949, 41.753273884356865 ], [ -101.0303460316144, 41.753298462544471 ], [ -101.030104138984981, 41.753314101647341 ], [ -101.02986150942003, 41.753320763387563 ] ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 760, "AREA": 5453408.859, "PERIMETER": 8279.116, "ACRES": 125.193, "HECTARES": 50.664 }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.016621281545525, 41.751606983733346 ], [ -101.016380425397784, 41.751604607113471 ], [ -101.016140026919231, 41.751593242642009 ], [ -101.015900683877803, 41.751572918577466 ], [ -101.015662991415752, 41.75154368545693 ], [ -101.015427540569007, 41.751505615970402 ], [ -101.015194916796801, 41.751458804779809 ], [ -101.014965698525245, 41.751403368283711 ], [ -101.01474045570842, 41.751339444327542 ], [ -101.014519748410535, 41.75126719186084 ], [ -101.014304125412821, 41.751186790541702 ], [ -101.014094122848434, 41.751098440290036 ], [ -101.013890262868998, 41.751002360789911 ], [ -101.0136930523459, 41.750898790943502 ], [ -101.013502981609747, 41.750787988276457 ], [ -101.013320523230945, 41.750670228297523 ], [ -101.013146130844518, 41.750545803813012 ], [ -101.012980238022308, 41.750415024198745 ], [ -101.012823257194853, 41.750278214630342 ], [ -101.012675578626101, 41.750135715274467 ], [ -101.012537569443381, 41.749987880442745 ], [ -101.012409572724806, 41.749835077710564 ], [ -101.012291906646752, 41.749677687002823 ], [ -101.012184863693221, 41.749516099649121 ], [ -101.012088709929188, 41.749350717410493 ], [ -101.012003684339746, 41.74918195148031 ], [ -101.011929998236624, 41.749010221461646 ], [ -101.011867834733607, 41.748835954323887 ], [ -101.011817348292027, 41.748659583341031 ], [ -101.011778664337683, 41.748481547014286 ], [ -101.011751878949894, 41.748302287981687 ], [ -101.011737058623567, 41.74812225191765 ], [ -101.011734240104872, 41.747941886424719 ], [ -101.011743430300854, 41.747761639920789 ], [ -101.011764606263284, 41.747581960524229 ], [ -101.011797715246658, 41.7474032949399 ], [ -101.011842674840366, 41.747226087348501 ], [ -101.011899373174515, 41.747050778302516 ], [ -101.011967669198938, 41.746877803630937 ], [ -101.012047393034862, 41.746707593355985 ], [ -101.012138346397975, 41.746540570624127 ], [ -101.012240303092213, 41.746377150654375 ], [ -101.012353009572848, 41.746217739706168 ], [ -101.012476185577484, 41.746062734069717 ], [ -101.012609524823347, 41.745912519080996 ], [ -101.012752695769251, 41.745767468164075 ], [ -101.012905342440291, 41.745627941903059 ], [ -101.013067085313054, 41.745494287146016 ], [ -101.013237522259445, 41.745366836142971 ], [ -101.013416229546536, 41.74524590572026 ], [ -101.01360276288996, 41.745131796493204 ], [ -101.013796658558448, 41.745024792119175 ], [ -101.01399743452653, 41.744925158592601 ], [ -101.014204591672595, 41.744833143584096 ], [ -101.014417615019383, 41.744748975825068 ], [ -101.014635975013903, 41.744672864539375 ], [ -101.014859128843355, 41.744604998923343 ], [ -101.015086521784028, 41.744545547675898 ], [ -101.015317588579691, 41.744494658579171 ], [ -101.015551754846186, 41.744452458131498 ], [ -101.015788438498546, 41.744419051233052 ], [ -101.016027051197284, 41.744394520925191 ], [ -101.016266999810185, 41.744378928184155 ], [ -101.016507687885905, 41.744372311769617 ], [ -101.016748517135966, 41.744374688128303 ], [ -101.016988888921134, 41.744386051353217 ], [ -101.017228205738675, 41.744406373198188 ], [ -101.017465872707007, 41.744435603148197 ], [ -101.01770129904358, 41.744473668544806 ], [ -101.017933899532736, 41.744520474766688 ], [ -101.018163095979759, 41.744575905464657 ], [ -101.018388318647467, 41.744639822850928 ], [ -101.018609007671927, 41.74471206804126 ], [ -101.018824614453507, 41.74479246144984 ], [ -101.019034603020188, 41.744880803235475 ], [ -101.019238451359499, 41.744976873798095 ], [ -101.0194356527157, 41.745080434324422 ], [ -101.019625716849319, 41.74519122738144 ], [ -101.01980817125559, 41.745308977555943 ], [ -101.019982562338896, 41.74543339213897 ], [ -101.020148456540397, 41.745564161853139 ], [ -101.020305441415843, 41.745700961621274 ], [ -101.020453126661053, 41.74584345137405 ], [ -101.020591145082349, 41.745991276895332 ], [ -101.020719153509802, 41.746144070702229 ], [ -101.020836833650634, 41.746301452958541 ], [ -101.020943892881121, 41.746463032418667 ], [ -101.021040064974514, 41.746628407400024 ], [ -101.021125110763535, 41.746797166781406 ], [ -101.021198818735726, 41.746968891024792 ], [ -101.021261005559936, 41.747143153218161 ], [ -101.021311516543079, 41.747319520136685 ], [ -101.021350226015429, 41.747497553319583 ], [ -101.021377037644072, 41.747676810159994 ], [ -101.021391884673307, 41.747856845005337 ], [ -101.021394730091544, 41.748037210265188 ], [ -101.021385566724462, 41.748217457523957 ], [ -101.021364417253608, 41.74839713865574 ], [ -101.021331334161204, 41.748575806938483 ], [ -101.021286399600555, 41.748753018164599 ], [ -101.021229725192782, 41.74892833174556 ], [ -101.021161451750217, 41.749101311807237 ], [ -101.021081748927216, 41.749271528273901 ], [ -101.020990814799291, 41.749438557937509 ], [ -101.020888875371313, 41.749601985510182 ], [ -101.020776184016384, 41.74976140465688 ], [ -101.020653020846609, 41.749916419005928 ], [ -101.020519692017217, 41.75006664313478 ], [ -101.020376528965841, 41.750211703528542 ], [ -101.020223887588926, 41.75035123950888 ], [ -101.02006214735728, 41.750484904131163 ], [ -101.019891710372747, 41.750612365047324 ], [ -101.01971300036864, 41.750733305332439 ], [ -101.019526461656312, 41.750847424273239 ], [ -101.019332558020395, 41.750954438115841 ], [ -101.019131771565554, 41.751054080771638 ], [ -101.018924601517639, 41.751146104479297 ], [ -101.018711562982091, 41.75123028042097 ], [ -101.018493185662876, 41.751306399291529 ], [ -101.018270012544974, 41.751374271819181 ], [ -101.018042598543815, 41.751433729236368 ], [ -101.017811509124883, 41.751484623699596 ], [ -101.017577318897239, 41.751526828657212 ], [ -101.017340610183936, 41.751560239164242 ], [ -101.017101971573553, 41.751584772143467 ], [ -101.016861996455845, 41.751600366592022 ], [ -101.016621281545525, 41.751606983733346 ] ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/overlap_test.geojson b/docker/solaris/solaris/data/overlap_test.geojson new file mode 100644 index 00000000..56138466 --- /dev/null +++ b/docker/solaris/solaris/data/overlap_test.geojson @@ -0,0 +1,6 @@ +{ +"type": "FeatureCollection", +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 94236, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 152.8742559217601, "origlen": 0, "partialDec": 1.0, "truncated": 0, "iou_score": 0.073499798744833519 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736351.470539042027667, 3722758.485215371940285 ], [ 736351.878608449478634, 3722747.197382184211165 ], [ 736338.352984992321581, 3722746.708811732474715 ], [ 736337.944931550649926, 3722757.996644604951143 ], [ 736351.470539042027667, 3722758.485215371940285 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/pred.geojson b/docker/solaris/solaris/data/pred.geojson new file mode 100644 index 00000000..3f6a980c --- /dev/null +++ b/docker/solaris/solaris/data/pred.geojson @@ -0,0 +1,34 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736348.0, 3722762.5 ], [ 736353.0, 3722762.0 ], [ 736354.0, 3722759.0 ], [ 736352.0, 3722755.5 ], [ 736348.5, 3722755.5 ], [ 736346.0, 3722757.5 ], [ 736348.0, 3722762.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736301.0, 3722760.5 ], [ 736310.5, 3722760.0 ], [ 736314.0, 3722758.0 ], [ 736315.0, 3722752.0 ], [ 736310.5, 3722746.5 ], [ 736308.0, 3722746.0 ], [ 736306.0, 3722750.0 ], [ 736301.0, 3722752.0 ], [ 736301.0, 3722760.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736306.5, 3722614.0 ], [ 736334.5, 3722611.0 ], [ 736339.5, 3722610.0 ], [ 736339.5, 3722608.5 ], [ 736335.0, 3722603.0 ], [ 736316.0, 3722603.5 ], [ 736304.0, 3722607.5 ], [ 736306.5, 3722614.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736359.5, 3722611.5 ], [ 736379.5, 3722608.5 ], [ 736398.0, 3722609.0 ], [ 736399.5, 3722600.0 ], [ 736396.5, 3722596.5 ], [ 736380.5, 3722600.5 ], [ 736373.5, 3722598.5 ], [ 736365.0, 3722600.5 ], [ 736364.0, 3722598.5 ], [ 736360.0, 3722600.0 ], [ 736356.0, 3722605.0 ], [ 736356.0, 3722609.5 ], [ 736359.5, 3722611.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736412.5, 3722602.0 ], [ 736429.5, 3722598.0 ], [ 736439.0, 3722601.5 ], [ 736443.5, 3722600.0 ], [ 736454.0, 3722600.0 ], [ 736455.0, 3722594.0 ], [ 736453.5, 3722591.5 ], [ 736444.0, 3722589.5 ], [ 736440.5, 3722587.0 ], [ 736432.5, 3722589.5 ], [ 736427.5, 3722587.5 ], [ 736419.5, 3722587.5 ], [ 736416.0, 3722589.0 ], [ 736410.0, 3722596.0 ], [ 736409.5, 3722600.0 ], [ 736412.5, 3722602.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736311.5, 3722591.0 ], [ 736323.5, 3722589.0 ], [ 736325.5, 3722582.0 ], [ 736324.5, 3722579.5 ], [ 736320.5, 3722577.5 ], [ 736302.5, 3722579.0 ], [ 736301.0, 3722580.5 ], [ 736301.0, 3722588.5 ], [ 736311.5, 3722591.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736544.0, 3722576.0 ], [ 736547.5, 3722576.0 ], [ 736551.5, 3722573.0 ], [ 736552.0, 3722564.0 ], [ 736550.0, 3722561.0 ], [ 736543.5, 3722559.0 ], [ 736538.5, 3722561.0 ], [ 736537.5, 3722570.5 ], [ 736540.0, 3722575.0 ], [ 736544.0, 3722576.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736508.0, 3722564.5 ], [ 736513.5, 3722562.0 ], [ 736515.0, 3722557.0 ], [ 736510.0, 3722539.0 ], [ 736495.0, 3722539.0 ], [ 736491.0, 3722537.0 ], [ 736485.5, 3722537.0 ], [ 736478.5, 3722540.0 ], [ 736472.5, 3722549.0 ], [ 736471.0, 3722556.5 ], [ 736473.0, 3722559.0 ], [ 736497.5, 3722559.0 ], [ 736505.0, 3722561.5 ], [ 736508.0, 3722564.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736552.0, 3722551.0 ], [ 736557.0, 3722550.0 ], [ 736559.0, 3722548.0 ], [ 736560.0, 3722543.0 ], [ 736554.5, 3722539.5 ], [ 736547.5, 3722538.5 ], [ 736542.5, 3722535.0 ], [ 736536.0, 3722533.0 ], [ 736534.0, 3722537.0 ], [ 736535.0, 3722543.0 ], [ 736538.0, 3722548.0 ], [ 736552.0, 3722551.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736361.0, 3722579.0 ], [ 736365.0, 3722579.0 ], [ 736364.0, 3722522.0 ], [ 736360.0, 3722525.0 ], [ 736357.5, 3722544.0 ], [ 736358.0, 3722565.5 ], [ 736359.5, 3722569.0 ], [ 736358.0, 3722575.5 ], [ 736361.0, 3722579.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736319.0, 3722572.0 ], [ 736326.0, 3722570.0 ], [ 736328.0, 3722566.0 ], [ 736330.5, 3722565.0 ], [ 736329.5, 3722547.5 ], [ 736331.0, 3722528.5 ], [ 736330.0, 3722524.5 ], [ 736326.5, 3722520.5 ], [ 736320.0, 3722523.0 ], [ 736318.0, 3722527.0 ], [ 736317.5, 3722570.5 ], [ 736319.0, 3722572.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736414.0, 3722573.0 ], [ 736417.5, 3722572.5 ], [ 736420.0, 3722568.0 ], [ 736421.0, 3722556.0 ], [ 736418.5, 3722538.0 ], [ 736424.0, 3722532.5 ], [ 736424.0, 3722527.0 ], [ 736422.5, 3722525.5 ], [ 736412.0, 3722524.0 ], [ 736410.5, 3722521.5 ], [ 736407.0, 3722520.5 ], [ 736383.5, 3722521.0 ], [ 736376.5, 3722528.5 ], [ 736378.0, 3722532.5 ], [ 736402.0, 3722532.0 ], [ 736410.0, 3722539.0 ], [ 736411.0, 3722544.0 ], [ 736408.5, 3722553.5 ], [ 736409.0, 3722569.0 ], [ 736414.0, 3722573.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736451.0, 3722575.0 ], [ 736455.0, 3722574.0 ], [ 736457.0, 3722569.0 ], [ 736455.5, 3722555.5 ], [ 736457.0, 3722531.5 ], [ 736454.5, 3722525.0 ], [ 736454.5, 3722516.5 ], [ 736449.0, 3722518.0 ], [ 736449.0, 3722524.0 ], [ 736446.0, 3722525.5 ], [ 736443.5, 3722547.0 ], [ 736445.0, 3722564.5 ], [ 736443.0, 3722569.0 ], [ 736446.0, 3722574.0 ], [ 736451.0, 3722575.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736733.5, 3722519.5 ], [ 736735.5, 3722519.0 ], [ 736738.0, 3722512.5 ], [ 736738.0, 3722510.0 ], [ 736736.0, 3722508.0 ], [ 736732.0, 3722510.0 ], [ 736730.5, 3722514.5 ], [ 736732.0, 3722519.0 ], [ 736733.5, 3722519.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736448.0, 3722488.5 ], [ 736450.5, 3722488.0 ], [ 736450.5, 3722484.5 ], [ 736448.0, 3722485.5 ], [ 736448.0, 3722488.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736412.0, 3722492.0 ], [ 736418.0, 3722492.0 ], [ 736422.0, 3722487.5 ], [ 736423.5, 3722481.5 ], [ 736422.0, 3722478.5 ], [ 736415.0, 3722478.0 ], [ 736408.0, 3722484.5 ], [ 736408.0, 3722490.0 ], [ 736412.0, 3722492.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736477.0, 3722491.5 ], [ 736483.0, 3722490.0 ], [ 736482.5, 3722482.5 ], [ 736476.5, 3722474.0 ], [ 736473.0, 3722474.0 ], [ 736469.5, 3722477.5 ], [ 736469.0, 3722482.5 ], [ 736470.0, 3722486.0 ], [ 736477.0, 3722491.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736498.0, 3722478.0 ], [ 736500.0, 3722477.5 ], [ 736500.5, 3722473.5 ], [ 736496.0, 3722470.5 ], [ 736494.0, 3722474.5 ], [ 736498.0, 3722478.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736744.0, 3722505.0 ], [ 736750.0, 3722505.0 ], [ 736751.0, 3722503.5 ], [ 736751.0, 3722460.0 ], [ 736749.5, 3722459.5 ], [ 736746.0, 3722460.5 ], [ 736741.5, 3722466.0 ], [ 736739.5, 3722492.0 ], [ 736745.0, 3722497.0 ], [ 736745.5, 3722500.5 ], [ 736742.5, 3722502.0 ], [ 736744.0, 3722505.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736700.0, 3722476.5 ], [ 736706.0, 3722476.5 ], [ 736708.0, 3722474.0 ], [ 736708.0, 3722465.0 ], [ 736705.5, 3722461.5 ], [ 736696.0, 3722463.0 ], [ 736692.0, 3722468.5 ], [ 736693.5, 3722474.0 ], [ 736700.0, 3722476.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736417.5, 3722456.5 ], [ 736422.0, 3722456.5 ], [ 736422.5, 3722454.5 ], [ 736418.0, 3722453.5 ], [ 736417.5, 3722456.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736395.5, 3722456.0 ], [ 736401.5, 3722456.0 ], [ 736404.0, 3722452.5 ], [ 736406.5, 3722453.0 ], [ 736406.5, 3722449.0 ], [ 736404.0, 3722449.0 ], [ 736402.0, 3722446.5 ], [ 736396.0, 3722447.0 ], [ 736394.0, 3722448.5 ], [ 736395.5, 3722456.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736311.5, 3722494.5 ], [ 736314.5, 3722494.5 ], [ 736318.0, 3722491.5 ], [ 736319.0, 3722484.0 ], [ 736318.0, 3722479.0 ], [ 736316.0, 3722478.5 ], [ 736316.0, 3722475.5 ], [ 736319.0, 3722472.5 ], [ 736316.0, 3722465.0 ], [ 736316.0, 3722462.0 ], [ 736318.5, 3722459.0 ], [ 736318.5, 3722454.5 ], [ 736315.5, 3722452.5 ], [ 736316.0, 3722446.0 ], [ 736314.5, 3722444.5 ], [ 736310.0, 3722445.0 ], [ 736308.5, 3722454.0 ], [ 736308.0, 3722470.5 ], [ 736309.5, 3722480.0 ], [ 736307.5, 3722492.0 ], [ 736311.5, 3722494.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736364.0, 3722484.5 ], [ 736365.0, 3722445.5 ], [ 736363.0, 3722439.0 ], [ 736356.0, 3722439.0 ], [ 736353.0, 3722454.5 ], [ 736357.5, 3722463.0 ], [ 736362.5, 3722467.5 ], [ 736357.5, 3722473.0 ], [ 736356.5, 3722478.0 ], [ 736364.0, 3722484.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736494.5, 3722447.5 ], [ 736501.0, 3722447.0 ], [ 736501.5, 3722444.0 ], [ 736498.5, 3722442.0 ], [ 736495.0, 3722442.0 ], [ 736495.0, 3722439.0 ], [ 736490.5, 3722439.0 ], [ 736492.0, 3722446.0 ], [ 736494.5, 3722447.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736399.0, 3722441.5 ], [ 736403.0, 3722439.0 ], [ 736397.0, 3722439.0 ], [ 736399.0, 3722441.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736472.0, 3722466.5 ], [ 736476.0, 3722465.5 ], [ 736477.5, 3722458.0 ], [ 736477.0, 3722443.5 ], [ 736475.5, 3722439.0 ], [ 736469.5, 3722439.0 ], [ 736467.0, 3722456.0 ], [ 736468.5, 3722463.5 ], [ 736472.0, 3722466.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736660.0, 3722528.5 ], [ 736672.5, 3722528.5 ], [ 736679.5, 3722525.5 ], [ 736688.0, 3722527.5 ], [ 736698.5, 3722524.5 ], [ 736704.0, 3722515.5 ], [ 736705.0, 3722503.5 ], [ 736702.0, 3722501.5 ], [ 736702.0, 3722498.0 ], [ 736704.0, 3722496.0 ], [ 736701.5, 3722491.5 ], [ 736694.0, 3722489.0 ], [ 736684.0, 3722482.0 ], [ 736674.0, 3722482.5 ], [ 736668.0, 3722479.0 ], [ 736666.0, 3722465.5 ], [ 736664.5, 3722463.5 ], [ 736665.5, 3722457.0 ], [ 736664.0, 3722452.5 ], [ 736664.0, 3722440.0 ], [ 736650.5, 3722439.0 ], [ 736649.0, 3722445.5 ], [ 736644.0, 3722452.0 ], [ 736642.5, 3722471.5 ], [ 736650.0, 3722501.0 ], [ 736656.0, 3722515.5 ], [ 736660.0, 3722519.5 ], [ 736657.5, 3722528.0 ], [ 736660.0, 3722528.5 ] ], [ [ 736700.0, 3722500.0 ], [ 736698.0, 3722503.0 ], [ 736695.0, 3722502.5 ], [ 736697.0, 3722499.0 ], [ 736700.0, 3722500.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/preproc_tutorial/example1flowchart.png b/docker/solaris/solaris/data/preproc_tutorial/example1flowchart.png new file mode 100644 index 00000000..758d4cf6 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/example1flowchart.png differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/example2flowchart.png b/docker/solaris/solaris/data/preproc_tutorial/example2flowchart.png new file mode 100644 index 00000000..a5d15a39 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/example2flowchart.png differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/example3flowchart.png b/docker/solaris/solaris/data/preproc_tutorial/example3flowchart.png new file mode 100644 index 00000000..d9a8591e Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/example3flowchart.png differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/ms1.tif b/docker/solaris/solaris/data/preproc_tutorial/ms1.tif new file mode 100644 index 00000000..7b91b99f Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/ms1.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/ms2.tif b/docker/solaris/solaris/data/preproc_tutorial/ms2.tif new file mode 100644 index 00000000..97083f97 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/ms2.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/ms3.tif b/docker/solaris/solaris/data/preproc_tutorial/ms3.tif new file mode 100644 index 00000000..e935efca Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/ms3.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/pan1.tif b/docker/solaris/solaris/data/preproc_tutorial/pan1.tif new file mode 100644 index 00000000..264b4079 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/pan1.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/pan2.tif b/docker/solaris/solaris/data/preproc_tutorial/pan2.tif new file mode 100644 index 00000000..68bea199 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/pan2.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/pan3.tif b/docker/solaris/solaris/data/preproc_tutorial/pan3.tif new file mode 100644 index 00000000..8a27f853 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/pan3.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/rgb_unmasked.tif b/docker/solaris/solaris/data/preproc_tutorial/rgb_unmasked.tif new file mode 100644 index 00000000..a9b6a20e Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/rgb_unmasked.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/sar_hh.tif b/docker/solaris/solaris/data/preproc_tutorial/sar_hh.tif new file mode 100644 index 00000000..0c37ea9e Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/sar_hh.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/sar_hv.tif b/docker/solaris/solaris/data/preproc_tutorial/sar_hv.tif new file mode 100644 index 00000000..5baf347a Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/sar_hv.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/sar_masked.tif b/docker/solaris/solaris/data/preproc_tutorial/sar_masked.tif new file mode 100644 index 00000000..956664a0 Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/sar_masked.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/sar_vh.tif b/docker/solaris/solaris/data/preproc_tutorial/sar_vh.tif new file mode 100644 index 00000000..b62d46cb Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/sar_vh.tif differ diff --git a/docker/solaris/solaris/data/preproc_tutorial/sar_vv.tif b/docker/solaris/solaris/data/preproc_tutorial/sar_vv.tif new file mode 100644 index 00000000..01ad2a0c Binary files /dev/null and b/docker/solaris/solaris/data/preproc_tutorial/sar_vv.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2118645.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2118645.tif new file mode 100644 index 00000000..c17877f6 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2118645.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2122485.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2122485.tif new file mode 100644 index 00000000..5ee97bba Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2122485.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2126325.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2126325.tif new file mode 100644 index 00000000..58d2c047 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2126325.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2130165.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2130165.tif new file mode 100644 index 00000000..90d64c58 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-661905_2130165.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2118645.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2118645.tif new file mode 100644 index 00000000..454a74ac Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2118645.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2122485.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2122485.tif new file mode 100644 index 00000000..d1c077b9 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2122485.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2126325.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2126325.tif new file mode 100644 index 00000000..75a296d1 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2126325.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2130165.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2130165.tif new file mode 100644 index 00000000..b8eea1dd Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-665745_2130165.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2118645.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2118645.tif new file mode 100644 index 00000000..0057a74c Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2118645.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2122485.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2122485.tif new file mode 100644 index 00000000..baa78255 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2122485.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2126325.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2126325.tif new file mode 100644 index 00000000..07a1c1d6 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2126325.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2130165.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2130165.tif new file mode 100644 index 00000000..df2e4221 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-669585_2130165.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2118645.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2118645.tif new file mode 100644 index 00000000..592e1655 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2118645.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2122485.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2122485.tif new file mode 100644 index 00000000..bd8f1cb8 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2122485.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2126325.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2126325.tif new file mode 100644 index 00000000..eb55a230 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2126325.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2130165.tif b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2130165.tif new file mode 100644 index 00000000..ebdee159 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_custom_proj_expected/sample_geotiff_custom_proj_-673425_2130165.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724734.tif new file mode 100644 index 00000000..e1421d9d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724779.tif new file mode 100644 index 00000000..818f36de Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724824.tif new file mode 100644 index 00000000..a25a4888 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724869.tif new file mode 100644 index 00000000..fcc54f37 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724914.tif new file mode 100644 index 00000000..4b144681 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724959.tif new file mode 100644 index 00000000..98a0f461 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725004.tif new file mode 100644 index 00000000..3d786d83 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725049.tif new file mode 100644 index 00000000..c769585e Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725094.tif new file mode 100644 index 00000000..ad0df649 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725139.tif new file mode 100644 index 00000000..25565fc3 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733601_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724734.tif new file mode 100644 index 00000000..1c750ff2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724779.tif new file mode 100644 index 00000000..20b601fb Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724824.tif new file mode 100644 index 00000000..1c116bee Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724869.tif new file mode 100644 index 00000000..288bb202 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724914.tif new file mode 100644 index 00000000..e1e97883 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724959.tif new file mode 100644 index 00000000..3b905f50 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725004.tif new file mode 100644 index 00000000..59412865 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725049.tif new file mode 100644 index 00000000..5b17f44e Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725094.tif new file mode 100644 index 00000000..55c8b78b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725139.tif new file mode 100644 index 00000000..dfe05db2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733646_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724734.tif new file mode 100644 index 00000000..7504c895 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724779.tif new file mode 100644 index 00000000..842c72d7 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724824.tif new file mode 100644 index 00000000..0ad72d67 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724869.tif new file mode 100644 index 00000000..451dce71 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724914.tif new file mode 100644 index 00000000..f0f7553b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724959.tif new file mode 100644 index 00000000..aa3cc74b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725004.tif new file mode 100644 index 00000000..3cf1e826 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725049.tif new file mode 100644 index 00000000..55182ccb Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725094.tif new file mode 100644 index 00000000..9fac53c6 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725139.tif new file mode 100644 index 00000000..6cb139fa Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733691_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724734.tif new file mode 100644 index 00000000..024cbed7 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724779.tif new file mode 100644 index 00000000..5d4c4c00 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724824.tif new file mode 100644 index 00000000..6f68f8d3 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724869.tif new file mode 100644 index 00000000..2c0a475d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724914.tif new file mode 100644 index 00000000..d4e71b4d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724959.tif new file mode 100644 index 00000000..a21cee96 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725004.tif new file mode 100644 index 00000000..1bfadb0f Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725049.tif new file mode 100644 index 00000000..c4676f78 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725094.tif new file mode 100644 index 00000000..423d80e9 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725139.tif new file mode 100644 index 00000000..f0ca7f5a Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733736_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724734.tif new file mode 100644 index 00000000..ffec0496 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724779.tif new file mode 100644 index 00000000..2b4464c5 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724824.tif new file mode 100644 index 00000000..ed7f06f1 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724869.tif new file mode 100644 index 00000000..fe2e4935 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724914.tif new file mode 100644 index 00000000..94a17f11 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724959.tif new file mode 100644 index 00000000..89068782 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725004.tif new file mode 100644 index 00000000..6056a49d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725049.tif new file mode 100644 index 00000000..dfd4b09d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725094.tif new file mode 100644 index 00000000..1c54d990 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725139.tif new file mode 100644 index 00000000..da01b6df Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733781_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724734.tif new file mode 100644 index 00000000..c0ddada2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724779.tif new file mode 100644 index 00000000..7a0870d6 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724824.tif new file mode 100644 index 00000000..ab5e175c Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724869.tif new file mode 100644 index 00000000..18109898 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724914.tif new file mode 100644 index 00000000..0c7fdb05 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724959.tif new file mode 100644 index 00000000..7db46392 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725004.tif new file mode 100644 index 00000000..9d082c19 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725049.tif new file mode 100644 index 00000000..a59583ca Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725094.tif new file mode 100644 index 00000000..1610be5c Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725139.tif new file mode 100644 index 00000000..f50651bb Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733826_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724734.tif new file mode 100644 index 00000000..8c58c0e2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724779.tif new file mode 100644 index 00000000..cc03cd90 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724824.tif new file mode 100644 index 00000000..fa748243 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724869.tif new file mode 100644 index 00000000..2f701da4 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724914.tif new file mode 100644 index 00000000..930c7343 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724959.tif new file mode 100644 index 00000000..9a79ecb1 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725004.tif new file mode 100644 index 00000000..501ed72c Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725049.tif new file mode 100644 index 00000000..df007605 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725094.tif new file mode 100644 index 00000000..2f654052 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725139.tif new file mode 100644 index 00000000..2f5186ce Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733871_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724734.tif new file mode 100644 index 00000000..76e0420b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724779.tif new file mode 100644 index 00000000..3c0cafde Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724824.tif new file mode 100644 index 00000000..5ae4321d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724869.tif new file mode 100644 index 00000000..2e089c95 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724914.tif new file mode 100644 index 00000000..40528897 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724959.tif new file mode 100644 index 00000000..57891ceb Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725004.tif new file mode 100644 index 00000000..6f64b0a3 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725049.tif new file mode 100644 index 00000000..e7d2287d Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725094.tif new file mode 100644 index 00000000..d91a95a9 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725139.tif new file mode 100644 index 00000000..a6ca086b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733916_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724734.tif new file mode 100644 index 00000000..c1fabe07 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724779.tif new file mode 100644 index 00000000..2399e421 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724824.tif new file mode 100644 index 00000000..46ed483a Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724869.tif new file mode 100644 index 00000000..12c001c4 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724914.tif new file mode 100644 index 00000000..b34a3620 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724959.tif new file mode 100644 index 00000000..b39b9cc4 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725004.tif new file mode 100644 index 00000000..589b8e6b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725049.tif new file mode 100644 index 00000000..fa11a8a3 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725094.tif new file mode 100644 index 00000000..92d17596 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725139.tif new file mode 100644 index 00000000..fec575ad Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_733961_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724734.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724734.tif new file mode 100644 index 00000000..317a2006 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724734.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724779.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724779.tif new file mode 100644 index 00000000..5c7443b2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724779.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724824.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724824.tif new file mode 100644 index 00000000..44be8bd5 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724824.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724869.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724869.tif new file mode 100644 index 00000000..60883342 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724869.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724914.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724914.tif new file mode 100644 index 00000000..ab2a9ca9 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724914.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724959.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724959.tif new file mode 100644 index 00000000..09fc3149 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3724959.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725004.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725004.tif new file mode 100644 index 00000000..ece13bd1 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725004.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725049.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725049.tif new file mode 100644 index 00000000..be81dcad Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725049.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725094.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725094.tif new file mode 100644 index 00000000..99796408 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725094.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725139.tif b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725139.tif new file mode 100644 index 00000000..e19d36e6 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_expected/sample_geotiff_734006_3725139.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.003_41.774.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.003_41.774.tif new file mode 100644 index 00000000..fa3742d9 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.003_41.774.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.691.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.691.tif new file mode 100644 index 00000000..75a7fbd4 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.691.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.732.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.732.tif new file mode 100644 index 00000000..7a8b0f12 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.732.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.774.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.774.tif new file mode 100644 index 00000000..a6d3bc9e Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.044_41.774.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.691.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.691.tif new file mode 100644 index 00000000..ce461694 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.691.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.732.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.732.tif new file mode 100644 index 00000000..9b8c2fe1 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.732.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.774.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.774.tif new file mode 100644 index 00000000..41e84e29 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.085_41.774.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.691.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.691.tif new file mode 100644 index 00000000..f9bcefb2 Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.691.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.732.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.732.tif new file mode 100644 index 00000000..8def1a5c Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.732.tif differ diff --git a/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.774.tif b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.774.tif new file mode 100644 index 00000000..033d472b Binary files /dev/null and b/docker/solaris/solaris/data/rastertile_test_fill_nodata_expected/aoi_restricted_nebraska_landsat5_with_nodata_wgs84_-101.127_41.774.tif differ diff --git a/docker/solaris/solaris/data/restrict_aoi_test.geojson b/docker/solaris/solaris/data/restrict_aoi_test.geojson new file mode 100644 index 00000000..a8adb683 --- /dev/null +++ b/docker/solaris/solaris/data/restrict_aoi_test.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"name": "restrict_aoi_test", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": null }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -101.122733978747405, 41.737200029527671 ], [ -101.064449359873706, 41.767397042817478 ], [ -101.002339286354697, 41.751349690809022 ], [ -101.047567608320904, 41.677423309487807 ], [ -101.096527512847558, 41.649695858211281 ], [ -101.125247881319964, 41.674698280229421 ], [ -101.126670019207381, 41.707062031182794 ], [ -101.122733978747405, 41.737200029527671 ] ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/road_mask_input.tif b/docker/solaris/solaris/data/road_mask_input.tif new file mode 100755 index 00000000..73827d99 Binary files /dev/null and b/docker/solaris/solaris/data/road_mask_input.tif differ diff --git a/docker/solaris/solaris/data/sample.csv b/docker/solaris/solaris/data/sample.csv new file mode 100644 index 00000000..bb8b31c3 --- /dev/null +++ b/docker/solaris/solaris/data/sample.csv @@ -0,0 +1,152 @@ +ImageId,BuildingId,PolygonWKT_Pix,PolygonWKT_Geo +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,0,"POLYGON ((131.60 895.50, 164.33 889.06, 165.81 900.00, 133.85 900.00, 131.60 895.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,1,"POLYGON ((403.49 891.48, 417.58 891.12, 432.38 890.75, 436.71 900.00, 402.68 900.00, 403.49 891.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,2,"POLYGON ((199.79 878.96, 208.24 894.84, 198.46 900.00, 174.73 900.00, 171.49 893.90, 199.79 878.96))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,3,"POLYGON ((379.23 875.22, 389.31 875.34, 389.14 887.13, 397.24 887.24, 399.23 891.25, 399.45 900.00, 371.36 900.00, 371.37 899.28, 370.67 896.50, 371.16 890.87, 369.87 884.02, 372.02 881.48, 373.27 882.07, 379.92 877.69, 379.23 875.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,4,"POLYGON ((553.60 882.37, 558.59 884.82, 564.66 883.27, 569.06 880.12, 571.29 884.72, 575.30 885.80, 576.78 898.15, 560.59 898.56, 554.41 895.45, 553.60 882.37))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,5,"POLYGON ((522.61 878.25, 521.63 874.14, 534.96 874.98, 534.47 879.12, 548.38 878.77, 550.65 898.16, 519.85 897.76, 513.58 895.54, 514.88 888.45, 518.31 883.95, 522.61 878.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,6,"POLYGON ((811.87 196.52, 697.91 200.08, 693.30 0.00, 809.85 0.00, 811.87 196.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,7,"POLYGON ((566.25 392.67, 585.96 391.24, 591.79 417.73, 577.05 419.99, 572.01 415.68, 566.25 392.67))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,8,"POLYGON ((551.82 362.93, 583.89 361.24, 586.09 383.87, 555.80 386.12, 551.82 362.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,9,"POLYGON ((65.35 385.12, 64.22 357.89, 72.19 356.80, 73.43 358.83, 77.57 358.88, 77.71 370.22, 78.08 384.93, 65.35 385.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,10,"POLYGON ((345.42 360.87, 363.92 360.56, 363.85 363.96, 358.33 367.91, 360.05 371.42, 360.80 377.73, 346.73 377.64, 345.42 360.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,11,"POLYGON ((0.00 359.94, 11.09 359.58, 11.64 377.77, 0.00 378.13, 0.00 359.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,12,"POLYGON ((56.13 359.45, 55.94 362.09, 55.71 367.74, 56.69 372.31, 57.50 375.42, 56.45 378.00, 49.21 378.20, 48.66 359.79, 56.13 359.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,13,"POLYGON ((128.52 352.36, 140.80 351.99, 143.09 384.14, 123.67 383.90, 123.90 376.85, 122.67 369.80, 126.23 361.79, 128.86 357.77, 128.52 352.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,14,"POLYGON ((171.38 354.77, 173.12 372.37, 153.61 374.26, 151.86 356.33, 160.24 355.52, 159.94 352.24, 169.70 351.28, 170.05 354.94, 171.38 354.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,15,"POLYGON ((299.07 353.93, 299.40 367.04, 289.33 366.72, 289.12 370.10, 281.13 369.72, 281.16 353.96, 299.07 353.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,16,"POLYGON ((28.24 351.54, 45.19 351.13, 45.71 372.49, 35.30 372.76, 35.08 363.95, 22.80 364.24, 22.63 356.45, 28.36 356.30, 28.24 351.54))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,17,"POLYGON ((489.45 352.83, 490.20 368.75, 469.76 367.68, 470.65 353.81, 489.45 352.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,18,"POLYGON ((459.13 352.30, 459.48 364.79, 439.92 366.28, 440.97 353.45, 459.13 352.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,19,"POLYGON ((428.51 351.28, 428.59 366.01, 405.21 366.45, 404.24 351.29, 428.51 351.28))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,20,"POLYGON ((332.70 348.14, 333.37 361.39, 331.96 361.45, 332.22 366.80, 308.50 367.97, 307.58 349.37, 332.70 348.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,21,"POLYGON ((573.39 332.05, 573.62 336.90, 569.23 337.19, 572.96 356.36, 555.98 357.97, 550.95 334.08, 573.39 332.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,22,"POLYGON ((553.64 306.17, 567.30 301.57, 568.67 326.70, 552.85 326.97, 553.64 306.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,23,"POLYGON ((554.00 273.16, 569.99 272.75, 572.30 294.65, 569.09 296.06, 567.96 298.44, 557.01 298.43, 554.00 273.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,24,"POLYGON ((316.50 278.05, 337.20 278.15, 337.10 286.27, 331.46 286.19, 331.37 292.92, 320.86 292.81, 320.91 287.59, 310.93 287.49, 310.99 282.58, 316.50 278.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,25,"POLYGON ((392.39 270.59, 393.80 282.56, 391.99 289.29, 371.54 287.96, 370.85 270.37, 392.39 270.59))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,26,"POLYGON ((64.82 272.66, 83.14 272.06, 83.00 267.83, 89.88 267.61, 90.01 271.96, 92.60 271.87, 93.21 290.90, 65.41 291.78, 64.82 272.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,27,"POLYGON ((403.18 267.87, 421.39 267.50, 421.54 274.87, 426.03 274.78, 426.37 291.37, 410.44 291.71, 410.32 286.23, 400.53 286.45, 400.38 279.24, 403.42 279.19, 403.18 267.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,28,"POLYGON ((160.71 274.46, 161.27 263.62, 186.85 265.06, 186.44 274.04, 186.13 294.67, 162.88 293.59, 160.71 274.46))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,29,"POLYGON ((433.54 269.42, 441.62 268.97, 441.90 274.91, 453.46 274.87, 454.52 286.60, 447.91 286.28, 448.22 288.49, 444.87 288.71, 444.41 286.13, 434.11 286.87, 433.54 269.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,30,"POLYGON ((467.59 271.34, 490.45 269.58, 491.69 285.58, 465.26 287.62, 464.32 275.61, 467.90 275.35, 467.59 271.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,31,"POLYGON ((307.24 284.83, 284.59 285.15, 281.36 270.36, 287.39 272.45, 289.15 278.36, 292.14 278.28, 294.84 277.46, 298.58 277.12, 302.25 279.76, 306.37 280.39, 307.24 284.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,32,"POLYGON ((209.74 266.13, 210.98 280.08, 204.23 282.47, 204.08 288.51, 192.99 288.63, 190.80 266.45, 209.74 266.13))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,33,"POLYGON ((244.25 266.61, 245.97 284.70, 223.83 284.66, 223.19 267.36, 244.25 266.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,34,"POLYGON ((260.47 263.52, 260.90 268.66, 272.71 268.23, 273.87 274.52, 279.48 274.76, 279.66 281.93, 276.78 281.75, 277.01 285.72, 252.77 286.33, 252.95 263.91, 260.47 263.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,35,"POLYGON ((133.56 253.33, 138.86 253.68, 138.08 265.40, 148.79 266.08, 149.61 270.64, 158.21 271.20, 156.99 289.83, 131.27 288.17, 133.56 253.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,36,"POLYGON ((118.73 251.79, 119.38 259.19, 116.49 259.26, 119.35 278.48, 117.41 278.08, 115.10 280.65, 116.20 283.26, 119.07 284.97, 119.50 287.31, 101.18 286.51, 99.89 251.42, 118.73 251.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,37,"POLYGON ((552.85 240.11, 570.58 239.46, 571.35 260.47, 576.59 260.27, 576.79 266.01, 553.82 266.86, 552.85 240.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,38,"POLYGON ((197.58 222.39, 198.08 234.19, 188.49 234.61, 187.99 222.79, 197.58 222.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,39,"POLYGON ((547.98 207.58, 562.52 206.31, 564.23 225.68, 569.31 225.25, 570.04 233.60, 562.30 234.26, 562.04 231.31, 550.17 232.37, 547.98 207.58))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,40,"POLYGON ((425.46 194.57, 426.17 208.76, 413.55 209.39, 412.85 195.18, 425.46 194.57))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,41,"POLYGON ((547.58 178.36, 564.48 175.65, 568.47 201.58, 550.11 203.71, 547.58 178.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,42,"POLYGON ((308.55 148.90, 329.14 147.65, 329.96 184.50, 320.73 183.80, 321.10 193.89, 310.20 193.77, 309.00 182.85, 308.55 148.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,43,"POLYGON ((11.39 153.16, 12.45 156.11, 11.07 158.85, 12.22 161.80, 14.28 163.36, 14.09 166.79, 15.62 168.72, 15.59 171.79, 2.18 172.17, 0.00 169.19, 0.00 153.39, 11.39 153.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,44,"POLYGON ((217.75 149.19, 241.15 148.58, 242.11 170.22, 217.33 171.66, 217.75 149.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,45,"POLYGON ((462.20 152.34, 485.74 151.66, 486.22 162.70, 475.44 162.88, 474.94 168.20, 466.89 168.34, 465.66 159.93, 461.43 159.39, 462.20 152.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,46,"POLYGON ((561.51 147.15, 562.29 155.08, 570.25 154.30, 571.44 166.30, 563.26 167.11, 563.61 170.63, 547.72 172.18, 545.41 148.73, 561.51 147.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,47,"POLYGON ((339.04 147.16, 361.97 146.74, 362.78 161.43, 345.02 161.15, 344.57 167.06, 338.50 167.06, 339.04 147.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,48,"POLYGON ((81.26 141.17, 85.54 146.90, 86.56 171.60, 69.54 172.30, 69.03 160.15, 63.26 160.38, 62.83 149.76, 70.95 149.42, 70.62 141.57, 81.26 141.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,49,"POLYGON ((246.59 147.89, 266.25 147.15, 266.20 165.24, 246.51 164.49, 246.59 147.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,50,"POLYGON ((125.71 148.62, 139.71 147.85, 141.12 161.77, 126.09 163.62, 125.71 148.62))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,51,"POLYGON ((120.19 145.83, 120.25 164.58, 94.51 164.61, 94.14 149.84, 103.54 149.60, 103.46 146.25, 120.19 145.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,52,"POLYGON ((186.04 162.88, 185.22 146.43, 207.58 145.47, 209.52 155.61, 205.53 155.51, 206.37 163.84, 186.04 162.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,53,"POLYGON ((369.93 145.43, 392.12 145.73, 392.71 155.20, 388.66 154.94, 389.03 163.17, 370.74 163.45, 369.93 145.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,54,"POLYGON ((172.29 146.94, 180.11 150.07, 179.57 161.74, 161.09 159.92, 161.52 151.78, 161.19 146.60, 172.29 146.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,55,"POLYGON ((429.24 144.22, 431.28 147.86, 435.61 152.48, 442.54 155.10, 451.04 149.98, 455.62 150.22, 455.90 161.42, 430.96 163.27, 429.24 144.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,56,"POLYGON ((302.24 145.15, 302.63 155.38, 297.73 155.57, 298.00 162.38, 286.48 162.82, 286.37 159.87, 279.57 160.13, 279.05 146.36, 280.41 146.30, 280.35 144.62, 283.70 144.49, 283.76 146.09, 285.47 146.02, 285.39 143.80, 289.43 143.63, 289.51 145.96, 290.53 145.94, 290.44 143.72, 294.55 143.57, 294.62 145.46, 302.24 145.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,57,"POLYGON ((422.24 145.60, 422.90 162.21, 400.22 163.09, 399.59 146.70, 405.86 146.45, 405.74 143.48, 411.21 143.26, 411.32 146.03, 422.24 145.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,58,"POLYGON ((543.94 117.65, 561.21 116.04, 562.14 129.29, 568.08 130.16, 568.98 142.21, 545.61 142.80, 543.94 117.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,59,"POLYGON ((545.25 87.27, 561.29 86.58, 562.74 111.87, 547.11 113.75, 545.25 87.27))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,60,"POLYGON ((40.58 84.52, 40.02 68.65, 42.08 67.31, 43.98 65.97, 45.05 63.70, 44.50 60.99, 46.56 59.49, 50.26 59.06, 53.80 58.98, 54.51 61.22, 55.05 63.30, 56.97 63.25, 58.62 64.32, 59.18 67.52, 60.24 71.33, 59.41 76.66, 60.43 79.03, 60.82 81.28, 60.41 84.03, 56.08 85.40, 55.66 87.83, 57.52 90.98, 54.64 92.03, 52.05 91.76, 50.56 90.20, 47.15 89.49, 45.82 87.43, 45.11 85.21, 42.35 84.48, 40.58 84.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,61,"POLYGON ((590.16 66.25, 591.66 78.89, 573.05 80.38, 571.85 68.05, 590.16 66.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,62,"POLYGON ((221.11 63.87, 241.02 63.77, 242.32 80.58, 237.09 80.09, 236.66 82.70, 229.42 82.15, 229.13 80.67, 221.54 80.73, 221.11 63.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,63,"POLYGON ((468.76 50.18, 468.91 59.81, 478.03 59.44, 480.55 69.35, 487.41 73.44, 487.72 94.92, 476.35 95.09, 476.11 78.91, 460.62 79.15, 460.19 50.30, 468.76 50.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,64,"POLYGON ((280.22 63.02, 289.05 62.56, 289.00 71.24, 298.48 71.73, 298.69 74.70, 301.49 77.85, 306.48 78.48, 320.72 75.50, 323.09 70.25, 334.07 67.13, 335.42 80.72, 290.39 81.35, 285.17 81.72, 281.17 81.34, 279.86 78.77, 280.22 63.02))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,65,"POLYGON ((369.16 62.87, 384.11 63.36, 383.91 67.21, 374.47 68.33, 374.19 74.95, 378.50 75.60, 381.43 79.94, 370.18 80.07, 369.16 62.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,66,"POLYGON ((63.69 57.84, 109.17 55.36, 110.50 79.61, 85.46 80.98, 76.43 87.09, 67.54 86.33, 64.93 80.85, 63.69 57.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,67,"POLYGON ((248.41 59.87, 272.27 59.14, 273.52 79.17, 265.94 79.50, 266.79 83.18, 248.48 82.65, 248.10 67.81, 248.41 59.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,68,"POLYGON ((401.12 60.85, 403.38 67.45, 419.33 68.93, 423.77 79.66, 401.60 79.99, 401.12 60.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,69,"POLYGON ((157.03 66.35, 168.69 65.05, 169.25 57.85, 182.48 59.00, 183.25 79.29, 157.16 81.70, 157.03 66.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,70,"POLYGON ((542.10 55.86, 559.86 54.72, 560.34 81.32, 544.67 81.21, 542.10 55.86))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,71,"POLYGON ((143.56 51.79, 143.09 61.86, 150.47 62.20, 149.83 75.98, 153.68 76.15, 153.48 80.04, 147.98 79.78, 147.75 84.65, 129.34 84.67, 128.19 80.32, 114.79 78.75, 114.74 62.13, 128.65 61.78, 128.57 51.08, 143.56 51.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,72,"POLYGON ((338.57 53.88, 350.29 53.80, 350.70 61.69, 364.30 61.77, 365.53 77.17, 339.80 77.82, 338.57 53.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,73,"POLYGON ((187.58 53.66, 214.10 51.52, 214.71 58.59, 205.91 58.41, 206.16 68.62, 211.82 68.47, 212.68 78.44, 187.97 77.40, 187.58 53.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,74,"POLYGON ((560.92 0.00, 562.35 15.92, 552.54 17.57, 548.84 17.31, 547.52 13.83, 548.87 11.87, 549.00 9.78, 548.59 7.68, 547.32 5.60, 546.37 3.36, 544.55 1.12, 542.25 0.67, 541.82 0.00, 560.92 0.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,75,"POLYGON ((831.82 858.92, 845.09 860.56, 846.92 862.09, 849.18 865.96, 847.49 893.13, 842.90 891.29, 839.36 891.38, 837.06 894.57, 829.51 893.38, 831.82 858.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,76,"POLYGON ((870.38 813.51, 878.27 813.11, 879.05 828.43, 877.20 833.20, 876.99 840.38, 881.15 840.45, 880.91 853.04, 857.66 852.61, 858.04 833.49, 860.39 832.63, 864.93 832.51, 867.28 831.28, 871.09 826.28, 871.45 817.01, 870.38 813.51))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,77,"POLYGON ((783.37 762.98, 793.82 762.30, 793.27 773.56, 789.42 770.33, 784.00 770.89, 783.37 762.98))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,78,"POLYGON ((787.56 702.08, 789.04 740.93, 770.36 740.91, 770.26 706.71, 775.48 706.58, 775.87 702.37, 787.56 702.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,79,"POLYGON ((840.64 705.89, 846.53 727.29, 799.67 726.88, 799.87 705.54, 840.64 705.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,80,"POLYGON ((803.37 617.39, 824.09 618.43, 828.00 623.94, 829.21 634.52, 825.78 635.23, 825.89 639.60, 828.15 654.84, 817.47 653.86, 814.20 636.17, 813.65 626.81, 802.97 626.46, 803.37 617.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,81,"POLYGON ((764.00 614.12, 766.58 616.57, 773.33 618.48, 791.42 618.36, 791.50 633.85, 790.72 635.96, 790.84 640.95, 785.26 643.18, 776.65 642.77, 774.63 636.79, 774.63 625.24, 764.00 625.25, 764.00 614.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,82,"POLYGON ((845.17 621.34, 862.13 621.84, 864.24 618.35, 868.01 618.56, 867.85 624.83, 862.88 626.51, 856.52 635.41, 850.18 635.22, 845.43 631.32, 845.17 621.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,83,"POLYGON ((724.57 610.94, 732.04 616.39, 736.02 616.92, 750.80 614.97, 751.63 621.32, 750.13 629.59, 725.77 625.50, 724.57 610.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,84,"POLYGON ((696.37 223.25, 811.13 223.17, 812.93 462.08, 697.54 464.99, 696.37 223.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,85,"POLYGON ((578.97 852.43, 595.04 850.47, 604.87 853.18, 607.15 896.74, 604.50 900.00, 585.78 900.00, 585.79 882.83, 582.46 880.27, 578.97 852.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,86,"POLYGON ((34.56 842.14, 37.53 847.23, 39.21 847.41, 39.01 849.13, 45.39 858.78, 42.53 860.67, 43.28 864.65, 31.03 871.44, 28.52 866.93, 26.64 868.00, 23.16 861.82, 17.46 852.04, 34.56 842.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,87,"POLYGON ((64.98 830.41, 72.22 842.72, 71.54 845.31, 78.52 857.19, 65.44 864.78, 50.28 838.94, 64.98 830.41))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,88,"POLYGON ((81.91 841.50, 85.59 838.83, 90.02 837.08, 92.16 837.96, 94.26 837.22, 99.55 832.87, 107.39 845.54, 89.36 857.21, 81.91 841.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,89,"POLYGON ((674.38 824.77, 687.43 828.39, 692.79 823.82, 694.04 834.18, 702.54 835.96, 702.72 842.88, 695.26 843.07, 695.90 848.51, 676.52 849.98, 674.38 824.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,90,"POLYGON ((105.68 826.36, 124.73 815.61, 137.24 837.16, 118.29 848.07, 105.68 826.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,91,"POLYGON ((572.69 812.84, 598.35 811.83, 597.80 817.80, 601.55 826.83, 600.77 838.06, 589.87 838.33, 588.23 829.25, 570.01 832.53, 570.95 827.93, 574.77 825.39, 576.35 819.05, 572.69 812.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,92,"POLYGON ((129.82 812.93, 146.13 801.68, 159.62 820.61, 160.41 823.85, 150.23 830.19, 145.81 831.70, 140.17 831.62, 132.18 822.46, 129.82 812.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,93,"POLYGON ((369.11 796.89, 402.18 794.64, 403.41 812.44, 399.04 815.19, 395.51 816.47, 386.33 816.71, 378.37 817.06, 370.52 817.59, 369.11 796.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,94,"POLYGON ((404.94 796.08, 428.32 793.61, 431.77 797.72, 435.57 799.17, 436.68 813.82, 417.12 815.31, 413.75 813.17, 410.24 814.59, 407.19 817.31, 404.94 796.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,95,"POLYGON ((343.18 791.91, 361.32 789.81, 364.49 816.99, 349.09 818.82, 346.06 815.65, 343.18 791.91))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,96,"POLYGON ((451.55 796.80, 466.37 791.25, 476.06 794.11, 477.69 811.74, 475.34 812.98, 453.10 814.87, 451.55 796.80))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,97,"POLYGON ((508.61 792.01, 517.46 790.45, 520.74 787.04, 531.96 783.63, 540.16 785.73, 541.73 789.84, 540.84 795.92, 542.21 800.26, 540.86 804.67, 533.81 807.98, 528.32 806.03, 511.60 808.98, 508.61 792.01))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,98,"POLYGON ((180.17 784.69, 186.11 777.99, 190.90 778.85, 190.23 782.55, 194.53 785.73, 202.50 784.82, 205.72 788.82, 196.44 796.20, 196.75 798.77, 190.88 802.29, 180.17 784.69))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,99,"POLYGON ((571.49 765.21, 589.72 762.29, 594.26 774.80, 599.05 783.43, 597.23 795.39, 594.55 800.37, 584.00 800.63, 580.85 787.39, 579.88 777.25, 572.40 773.24, 571.49 765.21))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,100,"POLYGON ((230.09 774.87, 213.94 785.68, 208.06 769.72, 214.47 767.09, 218.41 765.50, 224.82 763.10, 230.41 767.42, 231.77 772.34, 230.09 774.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,101,"POLYGON ((25.50 789.42, 16.05 785.53, 5.84 771.76, 16.29 764.11, 35.34 757.79, 42.33 778.41, 25.50 789.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,102,"POLYGON ((677.36 756.48, 701.26 755.46, 702.26 779.16, 682.22 780.02, 681.61 766.96, 674.50 764.41, 677.36 756.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,103,"POLYGON ((68.10 743.25, 78.72 761.25, 69.51 766.63, 63.71 775.14, 59.18 772.08, 52.87 764.87, 51.35 756.23, 57.29 749.56, 68.10 743.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,104,"POLYGON ((259.45 744.40, 263.46 746.04, 266.41 744.23, 270.59 752.05, 264.68 754.42, 256.34 759.33, 250.65 751.06, 259.45 744.40))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,105,"POLYGON ((283.78 740.00, 277.65 731.61, 274.03 728.46, 265.07 725.75, 266.13 720.71, 275.15 714.29, 280.63 720.05, 289.36 726.32, 289.24 733.38, 283.78 740.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,106,"POLYGON ((577.72 713.44, 592.39 713.07, 593.91 733.35, 584.95 733.57, 585.07 728.86, 584.72 724.41, 581.92 722.26, 578.55 716.88, 577.72 713.44))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,107,"POLYGON ((688.05 694.48, 704.66 693.62, 705.21 715.67, 704.28 740.64, 686.56 740.65, 688.05 694.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,108,"POLYGON ((181.89 703.43, 174.73 700.37, 165.90 702.94, 157.78 709.05, 155.25 714.71, 155.74 722.35, 144.19 710.26, 174.82 692.42, 181.89 703.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,109,"POLYGON ((499.76 695.52, 533.07 695.12, 535.39 703.25, 532.18 706.38, 527.03 706.51, 524.97 708.89, 518.41 709.99, 513.33 713.15, 508.44 714.92, 499.99 715.02, 499.76 695.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,110,"POLYGON ((441.60 673.47, 452.48 683.25, 443.30 693.12, 448.58 698.14, 421.80 723.29, 406.27 710.23, 441.60 673.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,111,"POLYGON ((282.48 683.07, 290.08 676.84, 297.78 683.31, 309.14 694.28, 314.68 689.54, 321.28 694.39, 306.45 712.26, 302.40 709.03, 303.56 705.05, 303.43 699.84, 302.70 695.90, 297.32 689.98, 294.94 687.13, 282.48 683.07))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,112,"POLYGON ((3.82 660.85, 11.40 662.99, 19.24 659.59, 27.06 677.55, 10.46 695.53, 4.55 690.26, 0.00 681.32, 0.00 672.95, 2.25 671.81, 0.00 667.38, 0.00 662.78, 3.82 660.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,113,"POLYGON ((317.67 660.30, 324.60 665.03, 329.57 666.33, 338.27 657.32, 345.25 664.04, 342.42 673.24, 334.95 683.95, 328.60 683.89, 317.42 678.31, 314.21 672.55, 314.80 667.85, 314.20 662.25, 317.67 660.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,114,"POLYGON ((36.83 653.31, 49.24 647.87, 54.93 660.85, 60.97 664.32, 62.72 669.55, 65.36 677.06, 58.28 679.52, 41.62 685.78, 37.98 676.19, 41.64 674.81, 39.89 670.17, 35.97 660.55, 36.83 653.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,115,"POLYGON ((185.25 641.90, 202.29 653.21, 182.27 683.11, 165.21 671.79, 185.25 641.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,116,"POLYGON ((216.87 639.66, 221.64 643.09, 215.52 651.53, 210.76 648.12, 216.87 639.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,117,"POLYGON ((562.44 626.39, 577.37 623.75, 583.69 659.42, 577.40 665.10, 569.80 662.63, 562.44 626.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,118,"POLYGON ((108.09 632.92, 114.18 639.54, 118.35 640.03, 120.79 643.50, 115.57 647.76, 111.23 651.40, 107.57 647.67, 109.25 643.50, 104.32 635.66, 108.09 632.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,119,"POLYGON ((363.25 636.33, 358.08 642.63, 359.88 647.78, 362.83 650.15, 354.99 659.85, 343.85 650.92, 347.30 646.63, 346.69 639.17, 340.19 630.37, 346.52 622.73, 363.25 636.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,120,"POLYGON ((475.42 623.68, 485.45 614.21, 489.37 613.14, 493.92 614.00, 498.77 617.63, 499.93 632.74, 498.66 637.10, 486.73 648.28, 472.70 633.56, 479.10 627.51, 475.42 623.68))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,121,"POLYGON ((216.60 627.26, 207.89 638.93, 191.67 626.62, 195.37 623.62, 189.60 618.55, 197.97 610.42, 202.80 610.72, 211.07 615.52, 214.10 619.60, 210.87 623.85, 216.60 627.26))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,122,"POLYGON ((689.46 593.45, 710.47 591.43, 713.64 624.29, 702.96 625.32, 700.33 619.92, 692.08 620.73, 689.46 593.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,123,"POLYGON ((72.98 602.93, 86.65 593.75, 90.03 598.08, 90.70 601.30, 91.98 605.69, 92.69 610.40, 85.97 613.81, 83.10 617.12, 72.98 602.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,124,"POLYGON ((382.15 588.60, 397.32 599.45, 383.62 618.44, 378.78 614.99, 372.35 610.20, 378.06 602.58, 375.58 597.71, 382.15 588.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,125,"POLYGON ((553.65 582.11, 581.72 580.89, 580.65 587.18, 579.94 592.03, 579.08 599.16, 578.18 607.90, 580.18 617.13, 563.52 616.00, 559.80 591.39, 553.99 589.98, 553.65 582.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,126,"POLYGON ((11.31 572.00, 24.63 590.34, 5.98 603.19, 0.82 597.66, 0.00 596.29, 0.00 588.57, 7.24 583.60, 7.47 575.78, 11.31 572.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,127,"POLYGON ((227.26 577.47, 239.09 565.85, 242.44 565.59, 244.25 563.12, 253.69 560.67, 262.78 566.19, 264.71 568.36, 251.95 587.06, 247.03 585.78, 244.68 594.86, 230.38 582.59, 227.26 577.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,128,"POLYGON ((332.28 572.45, 343.10 571.00, 344.31 580.01, 333.48 581.08, 332.28 572.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,129,"POLYGON ((61.62 559.22, 76.74 581.32, 57.89 594.12, 61.62 588.07, 59.42 577.25, 55.26 572.81, 50.67 572.57, 48.01 571.77, 44.75 566.79, 36.64 572.03, 27.80 558.51, 42.58 548.97, 47.48 556.47, 53.14 552.79, 55.71 556.72, 58.70 554.78, 61.62 559.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,130,"POLYGON ((402.32 553.20, 417.37 554.57, 417.60 563.62, 416.96 569.92, 396.17 568.29, 396.27 556.30, 399.80 555.61, 402.32 553.20))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,131,"POLYGON ((680.79 544.19, 700.09 542.50, 702.67 571.87, 683.37 573.56, 680.79 544.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,132,"POLYGON ((369.03 541.65, 385.86 542.78, 384.25 565.24, 368.40 563.89, 360.09 563.30, 360.51 548.15, 368.99 548.13, 369.03 541.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,133,"POLYGON ((281.86 541.93, 306.16 541.90, 308.56 542.88, 308.93 557.57, 306.88 559.46, 281.89 559.49, 281.86 541.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,134,"POLYGON ((323.56 540.42, 347.68 541.19, 348.09 557.69, 323.17 556.37, 323.56 540.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,135,"POLYGON ((121.51 554.60, 117.97 538.93, 130.25 536.18, 131.10 539.96, 137.01 538.63, 140.01 551.85, 136.64 554.05, 133.76 554.89, 129.35 557.89, 126.21 558.50, 121.10 554.68, 121.51 554.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,136,"POLYGON ((681.04 497.35, 700.18 496.78, 697.49 501.04, 697.90 504.18, 700.11 507.99, 701.63 512.14, 703.14 516.30, 701.88 522.30, 700.21 525.85, 681.56 525.21, 681.04 497.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,137,"POLYGON ((138.05 464.18, 152.73 462.79, 155.07 487.17, 140.39 488.58, 138.05 464.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,138,"POLYGON ((241.88 463.78, 259.13 463.04, 259.61 473.24, 263.98 475.88, 261.61 479.76, 256.35 480.16, 247.61 478.80, 242.66 481.54, 241.88 463.78))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,139,"POLYGON ((114.39 458.49, 130.98 457.63, 132.21 481.44, 128.10 481.65, 128.28 485.38, 115.81 486.00, 114.39 458.49))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,140,"POLYGON ((334.76 461.45, 352.98 459.32, 354.97 482.23, 343.47 484.00, 341.24 479.33, 336.69 480.09, 334.76 461.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,141,"POLYGON ((71.72 454.50, 79.53 455.33, 79.04 460.05, 83.50 460.51, 83.13 464.05, 91.15 464.89, 94.12 467.70, 94.49 473.66, 88.93 474.00, 89.68 486.35, 78.73 487.02, 78.36 480.95, 71.37 475.35, 68.02 467.56, 68.10 460.48, 71.22 459.09, 71.72 454.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,142,"POLYGON ((199.00 461.31, 201.01 464.24, 201.75 475.94, 183.59 477.08, 182.94 466.98, 191.27 462.22, 199.00 461.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,143,"POLYGON ((435.58 457.18, 435.97 480.92, 422.82 481.23, 421.48 456.85, 425.45 460.25, 431.76 459.03, 435.58 457.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,144,"POLYGON ((392.17 449.08, 411.47 450.48, 409.48 477.08, 403.51 480.12, 389.95 479.06, 392.17 449.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,145,"POLYGON ((209.20 446.25, 223.55 444.76, 220.50 449.03, 222.59 458.96, 227.96 463.02, 229.37 470.91, 222.18 472.18, 223.33 478.70, 213.81 480.36, 211.76 468.72, 210.94 462.79, 209.20 446.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,146,"POLYGON ((300.29 442.36, 309.71 441.79, 308.75 446.03, 310.18 450.46, 316.86 453.17, 322.91 453.02, 323.04 458.26, 321.15 466.72, 321.93 479.88, 302.56 481.01, 300.29 442.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,147,"POLYGON ((487.12 444.61, 505.68 436.77, 516.18 461.43, 514.73 462.05, 517.84 469.36, 501.44 476.28, 494.16 459.12, 489.11 461.25, 485.19 452.00, 489.50 450.18, 487.12 444.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,148,"POLYGON ((23.88 446.03, 26.10 456.18, 24.04 460.59, 26.42 464.57, 22.88 466.70, 6.48 465.80, 2.89 461.54, 3.51 445.21, 23.88 446.03))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,149,"POLYGON ((379.75 420.36, 400.11 420.23, 400.21 436.07, 379.85 436.23, 379.75 420.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,150,"POLYGON ((222.41 405.96, 242.39 406.57, 242.01 418.72, 222.05 418.14, 222.41 405.96))",1 \ No newline at end of file diff --git a/docker/solaris/solaris/data/sample.geojson b/docker/solaris/solaris/data/sample.geojson new file mode 100644 index 00000000..3f6a980c --- /dev/null +++ b/docker/solaris/solaris/data/sample.geojson @@ -0,0 +1,34 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736348.0, 3722762.5 ], [ 736353.0, 3722762.0 ], [ 736354.0, 3722759.0 ], [ 736352.0, 3722755.5 ], [ 736348.5, 3722755.5 ], [ 736346.0, 3722757.5 ], [ 736348.0, 3722762.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736301.0, 3722760.5 ], [ 736310.5, 3722760.0 ], [ 736314.0, 3722758.0 ], [ 736315.0, 3722752.0 ], [ 736310.5, 3722746.5 ], [ 736308.0, 3722746.0 ], [ 736306.0, 3722750.0 ], [ 736301.0, 3722752.0 ], [ 736301.0, 3722760.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736306.5, 3722614.0 ], [ 736334.5, 3722611.0 ], [ 736339.5, 3722610.0 ], [ 736339.5, 3722608.5 ], [ 736335.0, 3722603.0 ], [ 736316.0, 3722603.5 ], [ 736304.0, 3722607.5 ], [ 736306.5, 3722614.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736359.5, 3722611.5 ], [ 736379.5, 3722608.5 ], [ 736398.0, 3722609.0 ], [ 736399.5, 3722600.0 ], [ 736396.5, 3722596.5 ], [ 736380.5, 3722600.5 ], [ 736373.5, 3722598.5 ], [ 736365.0, 3722600.5 ], [ 736364.0, 3722598.5 ], [ 736360.0, 3722600.0 ], [ 736356.0, 3722605.0 ], [ 736356.0, 3722609.5 ], [ 736359.5, 3722611.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736412.5, 3722602.0 ], [ 736429.5, 3722598.0 ], [ 736439.0, 3722601.5 ], [ 736443.5, 3722600.0 ], [ 736454.0, 3722600.0 ], [ 736455.0, 3722594.0 ], [ 736453.5, 3722591.5 ], [ 736444.0, 3722589.5 ], [ 736440.5, 3722587.0 ], [ 736432.5, 3722589.5 ], [ 736427.5, 3722587.5 ], [ 736419.5, 3722587.5 ], [ 736416.0, 3722589.0 ], [ 736410.0, 3722596.0 ], [ 736409.5, 3722600.0 ], [ 736412.5, 3722602.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736311.5, 3722591.0 ], [ 736323.5, 3722589.0 ], [ 736325.5, 3722582.0 ], [ 736324.5, 3722579.5 ], [ 736320.5, 3722577.5 ], [ 736302.5, 3722579.0 ], [ 736301.0, 3722580.5 ], [ 736301.0, 3722588.5 ], [ 736311.5, 3722591.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736544.0, 3722576.0 ], [ 736547.5, 3722576.0 ], [ 736551.5, 3722573.0 ], [ 736552.0, 3722564.0 ], [ 736550.0, 3722561.0 ], [ 736543.5, 3722559.0 ], [ 736538.5, 3722561.0 ], [ 736537.5, 3722570.5 ], [ 736540.0, 3722575.0 ], [ 736544.0, 3722576.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736508.0, 3722564.5 ], [ 736513.5, 3722562.0 ], [ 736515.0, 3722557.0 ], [ 736510.0, 3722539.0 ], [ 736495.0, 3722539.0 ], [ 736491.0, 3722537.0 ], [ 736485.5, 3722537.0 ], [ 736478.5, 3722540.0 ], [ 736472.5, 3722549.0 ], [ 736471.0, 3722556.5 ], [ 736473.0, 3722559.0 ], [ 736497.5, 3722559.0 ], [ 736505.0, 3722561.5 ], [ 736508.0, 3722564.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736552.0, 3722551.0 ], [ 736557.0, 3722550.0 ], [ 736559.0, 3722548.0 ], [ 736560.0, 3722543.0 ], [ 736554.5, 3722539.5 ], [ 736547.5, 3722538.5 ], [ 736542.5, 3722535.0 ], [ 736536.0, 3722533.0 ], [ 736534.0, 3722537.0 ], [ 736535.0, 3722543.0 ], [ 736538.0, 3722548.0 ], [ 736552.0, 3722551.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736361.0, 3722579.0 ], [ 736365.0, 3722579.0 ], [ 736364.0, 3722522.0 ], [ 736360.0, 3722525.0 ], [ 736357.5, 3722544.0 ], [ 736358.0, 3722565.5 ], [ 736359.5, 3722569.0 ], [ 736358.0, 3722575.5 ], [ 736361.0, 3722579.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736319.0, 3722572.0 ], [ 736326.0, 3722570.0 ], [ 736328.0, 3722566.0 ], [ 736330.5, 3722565.0 ], [ 736329.5, 3722547.5 ], [ 736331.0, 3722528.5 ], [ 736330.0, 3722524.5 ], [ 736326.5, 3722520.5 ], [ 736320.0, 3722523.0 ], [ 736318.0, 3722527.0 ], [ 736317.5, 3722570.5 ], [ 736319.0, 3722572.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736414.0, 3722573.0 ], [ 736417.5, 3722572.5 ], [ 736420.0, 3722568.0 ], [ 736421.0, 3722556.0 ], [ 736418.5, 3722538.0 ], [ 736424.0, 3722532.5 ], [ 736424.0, 3722527.0 ], [ 736422.5, 3722525.5 ], [ 736412.0, 3722524.0 ], [ 736410.5, 3722521.5 ], [ 736407.0, 3722520.5 ], [ 736383.5, 3722521.0 ], [ 736376.5, 3722528.5 ], [ 736378.0, 3722532.5 ], [ 736402.0, 3722532.0 ], [ 736410.0, 3722539.0 ], [ 736411.0, 3722544.0 ], [ 736408.5, 3722553.5 ], [ 736409.0, 3722569.0 ], [ 736414.0, 3722573.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736451.0, 3722575.0 ], [ 736455.0, 3722574.0 ], [ 736457.0, 3722569.0 ], [ 736455.5, 3722555.5 ], [ 736457.0, 3722531.5 ], [ 736454.5, 3722525.0 ], [ 736454.5, 3722516.5 ], [ 736449.0, 3722518.0 ], [ 736449.0, 3722524.0 ], [ 736446.0, 3722525.5 ], [ 736443.5, 3722547.0 ], [ 736445.0, 3722564.5 ], [ 736443.0, 3722569.0 ], [ 736446.0, 3722574.0 ], [ 736451.0, 3722575.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736733.5, 3722519.5 ], [ 736735.5, 3722519.0 ], [ 736738.0, 3722512.5 ], [ 736738.0, 3722510.0 ], [ 736736.0, 3722508.0 ], [ 736732.0, 3722510.0 ], [ 736730.5, 3722514.5 ], [ 736732.0, 3722519.0 ], [ 736733.5, 3722519.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736448.0, 3722488.5 ], [ 736450.5, 3722488.0 ], [ 736450.5, 3722484.5 ], [ 736448.0, 3722485.5 ], [ 736448.0, 3722488.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736412.0, 3722492.0 ], [ 736418.0, 3722492.0 ], [ 736422.0, 3722487.5 ], [ 736423.5, 3722481.5 ], [ 736422.0, 3722478.5 ], [ 736415.0, 3722478.0 ], [ 736408.0, 3722484.5 ], [ 736408.0, 3722490.0 ], [ 736412.0, 3722492.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736477.0, 3722491.5 ], [ 736483.0, 3722490.0 ], [ 736482.5, 3722482.5 ], [ 736476.5, 3722474.0 ], [ 736473.0, 3722474.0 ], [ 736469.5, 3722477.5 ], [ 736469.0, 3722482.5 ], [ 736470.0, 3722486.0 ], [ 736477.0, 3722491.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736498.0, 3722478.0 ], [ 736500.0, 3722477.5 ], [ 736500.5, 3722473.5 ], [ 736496.0, 3722470.5 ], [ 736494.0, 3722474.5 ], [ 736498.0, 3722478.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736744.0, 3722505.0 ], [ 736750.0, 3722505.0 ], [ 736751.0, 3722503.5 ], [ 736751.0, 3722460.0 ], [ 736749.5, 3722459.5 ], [ 736746.0, 3722460.5 ], [ 736741.5, 3722466.0 ], [ 736739.5, 3722492.0 ], [ 736745.0, 3722497.0 ], [ 736745.5, 3722500.5 ], [ 736742.5, 3722502.0 ], [ 736744.0, 3722505.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736700.0, 3722476.5 ], [ 736706.0, 3722476.5 ], [ 736708.0, 3722474.0 ], [ 736708.0, 3722465.0 ], [ 736705.5, 3722461.5 ], [ 736696.0, 3722463.0 ], [ 736692.0, 3722468.5 ], [ 736693.5, 3722474.0 ], [ 736700.0, 3722476.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736417.5, 3722456.5 ], [ 736422.0, 3722456.5 ], [ 736422.5, 3722454.5 ], [ 736418.0, 3722453.5 ], [ 736417.5, 3722456.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736395.5, 3722456.0 ], [ 736401.5, 3722456.0 ], [ 736404.0, 3722452.5 ], [ 736406.5, 3722453.0 ], [ 736406.5, 3722449.0 ], [ 736404.0, 3722449.0 ], [ 736402.0, 3722446.5 ], [ 736396.0, 3722447.0 ], [ 736394.0, 3722448.5 ], [ 736395.5, 3722456.0 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736311.5, 3722494.5 ], [ 736314.5, 3722494.5 ], [ 736318.0, 3722491.5 ], [ 736319.0, 3722484.0 ], [ 736318.0, 3722479.0 ], [ 736316.0, 3722478.5 ], [ 736316.0, 3722475.5 ], [ 736319.0, 3722472.5 ], [ 736316.0, 3722465.0 ], [ 736316.0, 3722462.0 ], [ 736318.5, 3722459.0 ], [ 736318.5, 3722454.5 ], [ 736315.5, 3722452.5 ], [ 736316.0, 3722446.0 ], [ 736314.5, 3722444.5 ], [ 736310.0, 3722445.0 ], [ 736308.5, 3722454.0 ], [ 736308.0, 3722470.5 ], [ 736309.5, 3722480.0 ], [ 736307.5, 3722492.0 ], [ 736311.5, 3722494.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736364.0, 3722484.5 ], [ 736365.0, 3722445.5 ], [ 736363.0, 3722439.0 ], [ 736356.0, 3722439.0 ], [ 736353.0, 3722454.5 ], [ 736357.5, 3722463.0 ], [ 736362.5, 3722467.5 ], [ 736357.5, 3722473.0 ], [ 736356.5, 3722478.0 ], [ 736364.0, 3722484.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736494.5, 3722447.5 ], [ 736501.0, 3722447.0 ], [ 736501.5, 3722444.0 ], [ 736498.5, 3722442.0 ], [ 736495.0, 3722442.0 ], [ 736495.0, 3722439.0 ], [ 736490.5, 3722439.0 ], [ 736492.0, 3722446.0 ], [ 736494.5, 3722447.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736399.0, 3722441.5 ], [ 736403.0, 3722439.0 ], [ 736397.0, 3722439.0 ], [ 736399.0, 3722441.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736472.0, 3722466.5 ], [ 736476.0, 3722465.5 ], [ 736477.5, 3722458.0 ], [ 736477.0, 3722443.5 ], [ 736475.5, 3722439.0 ], [ 736469.5, 3722439.0 ], [ 736467.0, 3722456.0 ], [ 736468.5, 3722463.5 ], [ 736472.0, 3722466.5 ] ] ] } }, +{ "type": "Feature", "properties": { "rasterVal": 1.0, "conf": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 736660.0, 3722528.5 ], [ 736672.5, 3722528.5 ], [ 736679.5, 3722525.5 ], [ 736688.0, 3722527.5 ], [ 736698.5, 3722524.5 ], [ 736704.0, 3722515.5 ], [ 736705.0, 3722503.5 ], [ 736702.0, 3722501.5 ], [ 736702.0, 3722498.0 ], [ 736704.0, 3722496.0 ], [ 736701.5, 3722491.5 ], [ 736694.0, 3722489.0 ], [ 736684.0, 3722482.0 ], [ 736674.0, 3722482.5 ], [ 736668.0, 3722479.0 ], [ 736666.0, 3722465.5 ], [ 736664.5, 3722463.5 ], [ 736665.5, 3722457.0 ], [ 736664.0, 3722452.5 ], [ 736664.0, 3722440.0 ], [ 736650.5, 3722439.0 ], [ 736649.0, 3722445.5 ], [ 736644.0, 3722452.0 ], [ 736642.5, 3722471.5 ], [ 736650.0, 3722501.0 ], [ 736656.0, 3722515.5 ], [ 736660.0, 3722519.5 ], [ 736657.5, 3722528.0 ], [ 736660.0, 3722528.5 ] ], [ [ 736700.0, 3722500.0 ], [ 736698.0, 3722503.0 ], [ 736695.0, 3722502.5 ], [ 736697.0, 3722499.0 ], [ 736700.0, 3722500.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/sample_b_from_df2px.tif b/docker/solaris/solaris/data/sample_b_from_df2px.tif new file mode 100644 index 00000000..1d7d7a9c Binary files /dev/null and b/docker/solaris/solaris/data/sample_b_from_df2px.tif differ diff --git a/docker/solaris/solaris/data/sample_b_mask_inner.tif b/docker/solaris/solaris/data/sample_b_mask_inner.tif new file mode 100644 index 00000000..92add2f6 Binary files /dev/null and b/docker/solaris/solaris/data/sample_b_mask_inner.tif differ diff --git a/docker/solaris/solaris/data/sample_b_mask_outer.tif b/docker/solaris/solaris/data/sample_b_mask_outer.tif new file mode 100644 index 00000000..1b560fa9 Binary files /dev/null and b/docker/solaris/solaris/data/sample_b_mask_outer.tif differ diff --git a/docker/solaris/solaris/data/sample_b_mask_outer_10.tif b/docker/solaris/solaris/data/sample_b_mask_outer_10.tif new file mode 100644 index 00000000..036131e6 Binary files /dev/null and b/docker/solaris/solaris/data/sample_b_mask_outer_10.tif differ diff --git a/docker/solaris/solaris/data/sample_c_from_df2px.tif b/docker/solaris/solaris/data/sample_c_from_df2px.tif new file mode 100644 index 00000000..6940a554 Binary files /dev/null and b/docker/solaris/solaris/data/sample_c_from_df2px.tif differ diff --git a/docker/solaris/solaris/data/sample_c_mask.tif b/docker/solaris/solaris/data/sample_c_mask.tif new file mode 100644 index 00000000..3d5072c4 Binary files /dev/null and b/docker/solaris/solaris/data/sample_c_mask.tif differ diff --git a/docker/solaris/solaris/data/sample_fbc_from_df2px.tif b/docker/solaris/solaris/data/sample_fbc_from_df2px.tif new file mode 100644 index 00000000..3fc29c25 Binary files /dev/null and b/docker/solaris/solaris/data/sample_fbc_from_df2px.tif differ diff --git a/docker/solaris/solaris/data/sample_fp_from_df2px.tif b/docker/solaris/solaris/data/sample_fp_from_df2px.tif new file mode 100644 index 00000000..f00c0a32 Binary files /dev/null and b/docker/solaris/solaris/data/sample_fp_from_df2px.tif differ diff --git a/docker/solaris/solaris/data/sample_fp_mask.tif b/docker/solaris/solaris/data/sample_fp_mask.tif new file mode 100644 index 00000000..8fe56578 Binary files /dev/null and b/docker/solaris/solaris/data/sample_fp_mask.tif differ diff --git a/docker/solaris/solaris/data/sample_fp_mask_from_geojson.tif b/docker/solaris/solaris/data/sample_fp_mask_from_geojson.tif new file mode 100644 index 00000000..cf9ad5c6 Binary files /dev/null and b/docker/solaris/solaris/data/sample_fp_mask_from_geojson.tif differ diff --git a/docker/solaris/solaris/data/sample_geotiff.tif b/docker/solaris/solaris/data/sample_geotiff.tif new file mode 100644 index 00000000..ca68a7cf Binary files /dev/null and b/docker/solaris/solaris/data/sample_geotiff.tif differ diff --git a/docker/solaris/solaris/data/sample_geotiff.tif.txt b/docker/solaris/solaris/data/sample_geotiff.tif.txt new file mode 100644 index 00000000..f21bb906 --- /dev/null +++ b/docker/solaris/solaris/data/sample_geotiff.tif.txt @@ -0,0 +1,43 @@ +0 0.010766533126847612 0.033637375598255956 0.021533066253695225 0.06095229822624889 +0 0.04554622555138647 0.2712884770313071 0.034332632376139774 0.05143492659967806 +0 0.06757926694699563 0.9915962250292715 0.028916464865259412 0.01680754994145698 +0 0.08331035451782454 0.5205722763767052 0.024608520487219922 0.05593148279210759 +0 0.08908558060557374 0.09289501171403874 0.059575062674832424 0.036627137914506926 +0 0.0904308691382822 0.5860815513780755 0.02632683003942172 0.05728051387704909 +0 0.09257798898033798 0.651444631936546 0.02563342208560142 0.05480404955541922 +0 0.09393474501958635 0.7151780130274387 0.02470251738378364 0.02011424217476613 +0 0.09768727562804189 0.4508865925974937 0.03632630563341081 0.0600699977711257 +0 0.12079399679063095 0.3800762565030406 0.041734131165883606 0.06279310230372681 +0 0.15894499997297923 0.3034424783072124 0.042850823966372346 0.03510406933103998 +0 0.1643894780654874 0.13455988213937314 0.051377006126567724 0.052069684124241275 +0 0.24166545144670332 0.9804596015138345 0.03112023796575765 0.037293040508197414 +0 0.2508303764349936 0.3906661294043685 0.049872147934252604 0.04691288890213602 +0 0.25680611577712825 0.1364865599764097 0.011040071168293556 0.01083594181574881 +0 0.27519525584958804 0.19691954387765792 0.057173241577887286 0.05313467930381497 +0 0.45056411716841266 0.7534314973357444 0.029156621621869917 0.015420217529560128 +0 0.45072752076382233 0.9836727183369092 0.04436838587383843 0.032654563326181635 +0 0.46088151433906105 0.35490087597527437 0.0288538156897347 0.057869760025706554 +0 0.4656203134770557 0.2779595305103188 0.029027409062772576 0.05644779004673991 +0 0.46817264673455306 0.2122730074952253 0.024806863353070287 0.05698245655848748 +0 0.47390091830851616 0.15039005014941925 0.022579699421249745 0.045382817753901086 +0 0.4920017862680834 0.07796475388989266 0.036361918840298636 0.058729327955386705 +0 0.517035141597088 0.020001968380788136 0.041935131057269044 0.04000393676157627 +0 0.5195803306062913 0.9384386672343438 0.047183086306581065 0.03625653722944359 +0 0.5580700980054422 0.41966241003376326 0.0569242271511919 0.043930481911326445 +0 0.5896742265715471 0.19011275216264442 0.01929147351067513 0.047097158615166945 +0 0.5910262978884081 0.24896751202332476 0.028914717852862346 0.05744059714178244 +0 0.5943892032466829 0.32013564970913444 0.03809304894051618 0.05908142884882788 +0 0.6299706371202288 0.11505976733803334 0.047347482513998534 0.057958587015875515 +0 0.6329076459214815 0.4356060737443881 0.05470377622637898 0.029711252597885 +0 0.6585662066831719 0.3909563276435559 0.00925781403021473 0.02353575133304629 +0 0.7008464488627699 0.6939684924074552 0.04314053235937738 0.04239152681392928 +0 0.7278218079962405 0.010476947693775097 0.02427514001701234 0.020953895387550193 +0 0.7869890260287664 0.6771121757906965 0.03761025410869883 0.023902434004056786 +0 0.8663680343825318 0.23149470147469806 0.02941413588071656 0.02632833577485548 +0 0.8725918203896273 0.8994445087445072 0.05721353529564415 0.02991955786219074 +0 0.8862353189686676 0.16154417417063896 0.0467404559867767 0.040964473799491925 +0 0.8959520270225282 0.027677336456254125 0.02591494268944694 0.052091729121489655 +0 0.8960817912770694 0.09118675704010659 0.02451317638081188 0.05253245482635167 +0 0.9360373757453635 0.898332760703957 0.0484904608834121 0.022970922339914573 +0 0.9568977445462304 0.3908925365868749 0.053726495882082316 0.03223266848983864 +0 0.9923880004807789 0.9017025874824159 0.015223999038442142 0.020868693387342825 diff --git a/docker/solaris/solaris/data/sample_geotiff_custom_proj.tif b/docker/solaris/solaris/data/sample_geotiff_custom_proj.tif new file mode 100644 index 00000000..9bc42a49 Binary files /dev/null and b/docker/solaris/solaris/data/sample_geotiff_custom_proj.tif differ diff --git a/docker/solaris/solaris/data/sample_graph.pkl b/docker/solaris/solaris/data/sample_graph.pkl new file mode 100644 index 00000000..7d597534 Binary files /dev/null and b/docker/solaris/solaris/data/sample_graph.pkl differ diff --git a/docker/solaris/solaris/data/sample_inst_mask.tif b/docker/solaris/solaris/data/sample_inst_mask.tif new file mode 100644 index 00000000..cf461249 Binary files /dev/null and b/docker/solaris/solaris/data/sample_inst_mask.tif differ diff --git a/docker/solaris/solaris/data/sample_preds.csv b/docker/solaris/solaris/data/sample_preds.csv new file mode 100644 index 00000000..4b7ec028 --- /dev/null +++ b/docker/solaris/solaris/data/sample_preds.csv @@ -0,0 +1,152 @@ +ImageId,BuildingId,PolygonWKT_Pix,Confidence +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,0,"POLYGON ((131.60 895.50, 164.33 889.06, 165.81 900.00, 133.85 900.00, 131.60 895.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,1,"POLYGON ((403.49 891.48, 417.58 891.12, 432.38 890.75, 436.71 900.00, 402.68 900.00, 403.49 891.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,2,"POLYGON ((199.79 878.96, 208.24 894.84, 198.46 900.00, 174.73 900.00, 171.49 893.90, 199.79 878.96))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,3,"POLYGON ((379.23 875.22, 389.31 875.34, 389.14 887.13, 397.24 887.24, 399.23 891.25, 399.45 900.00, 371.36 900.00, 371.37 899.28, 370.67 896.50, 371.16 890.87, 369.87 884.02, 372.02 881.48, 373.27 882.07, 379.92 877.69, 379.23 875.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,4,"POLYGON ((553.60 882.37, 558.59 884.82, 564.66 883.27, 569.06 880.12, 571.29 884.72, 575.30 885.80, 576.78 898.15, 560.59 898.56, 554.41 895.45, 553.60 882.37))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,5,"POLYGON ((522.61 878.25, 521.63 874.14, 534.96 874.98, 534.47 879.12, 548.38 878.77, 550.65 898.16, 519.85 897.76, 513.58 895.54, 514.88 888.45, 518.31 883.95, 522.61 878.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,6,"POLYGON ((811.87 196.52, 697.91 200.08, 693.30 0.00, 809.85 0.00, 811.87 196.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,7,"POLYGON ((566.25 392.67, 585.96 391.24, 591.79 417.73, 577.05 419.99, 572.01 415.68, 566.25 392.67))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,8,"POLYGON ((551.82 362.93, 583.89 361.24, 586.09 383.87, 555.80 386.12, 551.82 362.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,9,"POLYGON ((65.35 385.12, 64.22 357.89, 72.19 356.80, 73.43 358.83, 77.57 358.88, 77.71 370.22, 78.08 384.93, 65.35 385.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,10,"POLYGON ((345.42 360.87, 363.92 360.56, 363.85 363.96, 358.33 367.91, 360.05 371.42, 360.80 377.73, 346.73 377.64, 345.42 360.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,11,"POLYGON ((0.00 359.94, 11.09 359.58, 11.64 377.77, 0.00 378.13, 0.00 359.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,12,"POLYGON ((56.13 359.45, 55.94 362.09, 55.71 367.74, 56.69 372.31, 57.50 375.42, 56.45 378.00, 49.21 378.20, 48.66 359.79, 56.13 359.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,13,"POLYGON ((128.52 352.36, 140.80 351.99, 143.09 384.14, 123.67 383.90, 123.90 376.85, 122.67 369.80, 126.23 361.79, 128.86 357.77, 128.52 352.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,14,"POLYGON ((171.38 354.77, 173.12 372.37, 153.61 374.26, 151.86 356.33, 160.24 355.52, 159.94 352.24, 169.70 351.28, 170.05 354.94, 171.38 354.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,15,"POLYGON ((299.07 353.93, 299.40 367.04, 289.33 366.72, 289.12 370.10, 281.13 369.72, 281.16 353.96, 299.07 353.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,16,"POLYGON ((28.24 351.54, 45.19 351.13, 45.71 372.49, 35.30 372.76, 35.08 363.95, 22.80 364.24, 22.63 356.45, 28.36 356.30, 28.24 351.54))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,17,"POLYGON ((489.45 352.83, 490.20 368.75, 469.76 367.68, 470.65 353.81, 489.45 352.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,18,"POLYGON ((459.13 352.30, 459.48 364.79, 439.92 366.28, 440.97 353.45, 459.13 352.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,19,"POLYGON ((428.51 351.28, 428.59 366.01, 405.21 366.45, 404.24 351.29, 428.51 351.28))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,20,"POLYGON ((332.70 348.14, 333.37 361.39, 331.96 361.45, 332.22 366.80, 308.50 367.97, 307.58 349.37, 332.70 348.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,21,"POLYGON ((573.39 332.05, 573.62 336.90, 569.23 337.19, 572.96 356.36, 555.98 357.97, 550.95 334.08, 573.39 332.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,22,"POLYGON ((553.64 306.17, 567.30 301.57, 568.67 326.70, 552.85 326.97, 553.64 306.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,23,"POLYGON ((554.00 273.16, 569.99 272.75, 572.30 294.65, 569.09 296.06, 567.96 298.44, 557.01 298.43, 554.00 273.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,24,"POLYGON ((316.50 278.05, 337.20 278.15, 337.10 286.27, 331.46 286.19, 331.37 292.92, 320.86 292.81, 320.91 287.59, 310.93 287.49, 310.99 282.58, 316.50 278.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,25,"POLYGON ((392.39 270.59, 393.80 282.56, 391.99 289.29, 371.54 287.96, 370.85 270.37, 392.39 270.59))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,26,"POLYGON ((64.82 272.66, 83.14 272.06, 83.00 267.83, 89.88 267.61, 90.01 271.96, 92.60 271.87, 93.21 290.90, 65.41 291.78, 64.82 272.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,27,"POLYGON ((403.18 267.87, 421.39 267.50, 421.54 274.87, 426.03 274.78, 426.37 291.37, 410.44 291.71, 410.32 286.23, 400.53 286.45, 400.38 279.24, 403.42 279.19, 403.18 267.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,28,"POLYGON ((160.71 274.46, 161.27 263.62, 186.85 265.06, 186.44 274.04, 186.13 294.67, 162.88 293.59, 160.71 274.46))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,29,"POLYGON ((433.54 269.42, 441.62 268.97, 441.90 274.91, 453.46 274.87, 454.52 286.60, 447.91 286.28, 448.22 288.49, 444.87 288.71, 444.41 286.13, 434.11 286.87, 433.54 269.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,30,"POLYGON ((467.59 271.34, 490.45 269.58, 491.69 285.58, 465.26 287.62, 464.32 275.61, 467.90 275.35, 467.59 271.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,31,"POLYGON ((307.24 284.83, 284.59 285.15, 281.36 270.36, 287.39 272.45, 289.15 278.36, 292.14 278.28, 294.84 277.46, 298.58 277.12, 302.25 279.76, 306.37 280.39, 307.24 284.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,32,"POLYGON ((209.74 266.13, 210.98 280.08, 204.23 282.47, 204.08 288.51, 192.99 288.63, 190.80 266.45, 209.74 266.13))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,33,"POLYGON ((244.25 266.61, 245.97 284.70, 223.83 284.66, 223.19 267.36, 244.25 266.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,34,"POLYGON ((260.47 263.52, 260.90 268.66, 272.71 268.23, 273.87 274.52, 279.48 274.76, 279.66 281.93, 276.78 281.75, 277.01 285.72, 252.77 286.33, 252.95 263.91, 260.47 263.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,35,"POLYGON ((133.56 253.33, 138.86 253.68, 138.08 265.40, 148.79 266.08, 149.61 270.64, 158.21 271.20, 156.99 289.83, 131.27 288.17, 133.56 253.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,36,"POLYGON ((118.73 251.79, 119.38 259.19, 116.49 259.26, 119.35 278.48, 117.41 278.08, 115.10 280.65, 116.20 283.26, 119.07 284.97, 119.50 287.31, 101.18 286.51, 99.89 251.42, 118.73 251.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,37,"POLYGON ((552.85 240.11, 570.58 239.46, 571.35 260.47, 576.59 260.27, 576.79 266.01, 553.82 266.86, 552.85 240.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,38,"POLYGON ((197.58 222.39, 198.08 234.19, 188.49 234.61, 187.99 222.79, 197.58 222.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,39,"POLYGON ((547.98 207.58, 562.52 206.31, 564.23 225.68, 569.31 225.25, 570.04 233.60, 562.30 234.26, 562.04 231.31, 550.17 232.37, 547.98 207.58))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,40,"POLYGON ((425.46 194.57, 426.17 208.76, 413.55 209.39, 412.85 195.18, 425.46 194.57))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,41,"POLYGON ((547.58 178.36, 564.48 175.65, 568.47 201.58, 550.11 203.71, 547.58 178.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,42,"POLYGON ((308.55 148.90, 329.14 147.65, 329.96 184.50, 320.73 183.80, 321.10 193.89, 310.20 193.77, 309.00 182.85, 308.55 148.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,43,"POLYGON ((11.39 153.16, 12.45 156.11, 11.07 158.85, 12.22 161.80, 14.28 163.36, 14.09 166.79, 15.62 168.72, 15.59 171.79, 2.18 172.17, 0.00 169.19, 0.00 153.39, 11.39 153.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,44,"POLYGON ((217.75 149.19, 241.15 148.58, 242.11 170.22, 217.33 171.66, 217.75 149.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,45,"POLYGON ((462.20 152.34, 485.74 151.66, 486.22 162.70, 475.44 162.88, 474.94 168.20, 466.89 168.34, 465.66 159.93, 461.43 159.39, 462.20 152.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,46,"POLYGON ((561.51 147.15, 562.29 155.08, 570.25 154.30, 571.44 166.30, 563.26 167.11, 563.61 170.63, 547.72 172.18, 545.41 148.73, 561.51 147.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,47,"POLYGON ((339.04 147.16, 361.97 146.74, 362.78 161.43, 345.02 161.15, 344.57 167.06, 338.50 167.06, 339.04 147.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,48,"POLYGON ((81.26 141.17, 85.54 146.90, 86.56 171.60, 69.54 172.30, 69.03 160.15, 63.26 160.38, 62.83 149.76, 70.95 149.42, 70.62 141.57, 81.26 141.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,49,"POLYGON ((246.59 147.89, 266.25 147.15, 266.20 165.24, 246.51 164.49, 246.59 147.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,50,"POLYGON ((125.71 148.62, 139.71 147.85, 141.12 161.77, 126.09 163.62, 125.71 148.62))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,51,"POLYGON ((120.19 145.83, 120.25 164.58, 94.51 164.61, 94.14 149.84, 103.54 149.60, 103.46 146.25, 120.19 145.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,52,"POLYGON ((186.04 162.88, 185.22 146.43, 207.58 145.47, 209.52 155.61, 205.53 155.51, 206.37 163.84, 186.04 162.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,53,"POLYGON ((369.93 145.43, 392.12 145.73, 392.71 155.20, 388.66 154.94, 389.03 163.17, 370.74 163.45, 369.93 145.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,54,"POLYGON ((172.29 146.94, 180.11 150.07, 179.57 161.74, 161.09 159.92, 161.52 151.78, 161.19 146.60, 172.29 146.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,55,"POLYGON ((429.24 144.22, 431.28 147.86, 435.61 152.48, 442.54 155.10, 451.04 149.98, 455.62 150.22, 455.90 161.42, 430.96 163.27, 429.24 144.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,56,"POLYGON ((302.24 145.15, 302.63 155.38, 297.73 155.57, 298.00 162.38, 286.48 162.82, 286.37 159.87, 279.57 160.13, 279.05 146.36, 280.41 146.30, 280.35 144.62, 283.70 144.49, 283.76 146.09, 285.47 146.02, 285.39 143.80, 289.43 143.63, 289.51 145.96, 290.53 145.94, 290.44 143.72, 294.55 143.57, 294.62 145.46, 302.24 145.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,57,"POLYGON ((422.24 145.60, 422.90 162.21, 400.22 163.09, 399.59 146.70, 405.86 146.45, 405.74 143.48, 411.21 143.26, 411.32 146.03, 422.24 145.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,58,"POLYGON ((543.94 117.65, 561.21 116.04, 562.14 129.29, 568.08 130.16, 568.98 142.21, 545.61 142.80, 543.94 117.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,59,"POLYGON ((545.25 87.27, 561.29 86.58, 562.74 111.87, 547.11 113.75, 545.25 87.27))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,60,"POLYGON ((40.58 84.52, 40.02 68.65, 42.08 67.31, 43.98 65.97, 45.05 63.70, 44.50 60.99, 46.56 59.49, 50.26 59.06, 53.80 58.98, 54.51 61.22, 55.05 63.30, 56.97 63.25, 58.62 64.32, 59.18 67.52, 60.24 71.33, 59.41 76.66, 60.43 79.03, 60.82 81.28, 60.41 84.03, 56.08 85.40, 55.66 87.83, 57.52 90.98, 54.64 92.03, 52.05 91.76, 50.56 90.20, 47.15 89.49, 45.82 87.43, 45.11 85.21, 42.35 84.48, 40.58 84.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,61,"POLYGON ((590.16 66.25, 591.66 78.89, 573.05 80.38, 571.85 68.05, 590.16 66.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,62,"POLYGON ((221.11 63.87, 241.02 63.77, 242.32 80.58, 237.09 80.09, 236.66 82.70, 229.42 82.15, 229.13 80.67, 221.54 80.73, 221.11 63.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,63,"POLYGON ((468.76 50.18, 468.91 59.81, 478.03 59.44, 480.55 69.35, 487.41 73.44, 487.72 94.92, 476.35 95.09, 476.11 78.91, 460.62 79.15, 460.19 50.30, 468.76 50.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,64,"POLYGON ((280.22 63.02, 289.05 62.56, 289.00 71.24, 298.48 71.73, 298.69 74.70, 301.49 77.85, 306.48 78.48, 320.72 75.50, 323.09 70.25, 334.07 67.13, 335.42 80.72, 290.39 81.35, 285.17 81.72, 281.17 81.34, 279.86 78.77, 280.22 63.02))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,65,"POLYGON ((369.16 62.87, 384.11 63.36, 383.91 67.21, 374.47 68.33, 374.19 74.95, 378.50 75.60, 381.43 79.94, 370.18 80.07, 369.16 62.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,66,"POLYGON ((63.69 57.84, 109.17 55.36, 110.50 79.61, 85.46 80.98, 76.43 87.09, 67.54 86.33, 64.93 80.85, 63.69 57.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,67,"POLYGON ((248.41 59.87, 272.27 59.14, 273.52 79.17, 265.94 79.50, 266.79 83.18, 248.48 82.65, 248.10 67.81, 248.41 59.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,68,"POLYGON ((401.12 60.85, 403.38 67.45, 419.33 68.93, 423.77 79.66, 401.60 79.99, 401.12 60.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,69,"POLYGON ((157.03 66.35, 168.69 65.05, 169.25 57.85, 182.48 59.00, 183.25 79.29, 157.16 81.70, 157.03 66.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,70,"POLYGON ((542.10 55.86, 559.86 54.72, 560.34 81.32, 544.67 81.21, 542.10 55.86))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,71,"POLYGON ((143.56 51.79, 143.09 61.86, 150.47 62.20, 149.83 75.98, 153.68 76.15, 153.48 80.04, 147.98 79.78, 147.75 84.65, 129.34 84.67, 128.19 80.32, 114.79 78.75, 114.74 62.13, 128.65 61.78, 128.57 51.08, 143.56 51.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,72,"POLYGON ((338.57 53.88, 350.29 53.80, 350.70 61.69, 364.30 61.77, 365.53 77.17, 339.80 77.82, 338.57 53.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,73,"POLYGON ((187.58 53.66, 214.10 51.52, 214.71 58.59, 205.91 58.41, 206.16 68.62, 211.82 68.47, 212.68 78.44, 187.97 77.40, 187.58 53.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,74,"POLYGON ((560.92 0.00, 562.35 15.92, 552.54 17.57, 548.84 17.31, 547.52 13.83, 548.87 11.87, 549.00 9.78, 548.59 7.68, 547.32 5.60, 546.37 3.36, 544.55 1.12, 542.25 0.67, 541.82 0.00, 560.92 0.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,75,"POLYGON ((831.82 858.92, 845.09 860.56, 846.92 862.09, 849.18 865.96, 847.49 893.13, 842.90 891.29, 839.36 891.38, 837.06 894.57, 829.51 893.38, 831.82 858.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,76,"POLYGON ((870.38 813.51, 878.27 813.11, 879.05 828.43, 877.20 833.20, 876.99 840.38, 881.15 840.45, 880.91 853.04, 857.66 852.61, 858.04 833.49, 860.39 832.63, 864.93 832.51, 867.28 831.28, 871.09 826.28, 871.45 817.01, 870.38 813.51))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,77,"POLYGON ((783.37 762.98, 793.82 762.30, 793.27 773.56, 789.42 770.33, 784.00 770.89, 783.37 762.98))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,78,"POLYGON ((787.56 702.08, 789.04 740.93, 770.36 740.91, 770.26 706.71, 775.48 706.58, 775.87 702.37, 787.56 702.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,79,"POLYGON ((840.64 705.89, 846.53 727.29, 799.67 726.88, 799.87 705.54, 840.64 705.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,80,"POLYGON ((803.37 617.39, 824.09 618.43, 828.00 623.94, 829.21 634.52, 825.78 635.23, 825.89 639.60, 828.15 654.84, 817.47 653.86, 814.20 636.17, 813.65 626.81, 802.97 626.46, 803.37 617.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,81,"POLYGON ((764.00 614.12, 766.58 616.57, 773.33 618.48, 791.42 618.36, 791.50 633.85, 790.72 635.96, 790.84 640.95, 785.26 643.18, 776.65 642.77, 774.63 636.79, 774.63 625.24, 764.00 625.25, 764.00 614.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,82,"POLYGON ((845.17 621.34, 862.13 621.84, 864.24 618.35, 868.01 618.56, 867.85 624.83, 862.88 626.51, 856.52 635.41, 850.18 635.22, 845.43 631.32, 845.17 621.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,83,"POLYGON ((724.57 610.94, 732.04 616.39, 736.02 616.92, 750.80 614.97, 751.63 621.32, 750.13 629.59, 725.77 625.50, 724.57 610.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,84,"POLYGON ((696.37 223.25, 811.13 223.17, 812.93 462.08, 697.54 464.99, 696.37 223.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,85,"POLYGON ((578.97 852.43, 595.04 850.47, 604.87 853.18, 607.15 896.74, 604.50 900.00, 585.78 900.00, 585.79 882.83, 582.46 880.27, 578.97 852.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,86,"POLYGON ((34.56 842.14, 37.53 847.23, 39.21 847.41, 39.01 849.13, 45.39 858.78, 42.53 860.67, 43.28 864.65, 31.03 871.44, 28.52 866.93, 26.64 868.00, 23.16 861.82, 17.46 852.04, 34.56 842.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,87,"POLYGON ((64.98 830.41, 72.22 842.72, 71.54 845.31, 78.52 857.19, 65.44 864.78, 50.28 838.94, 64.98 830.41))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,88,"POLYGON ((81.91 841.50, 85.59 838.83, 90.02 837.08, 92.16 837.96, 94.26 837.22, 99.55 832.87, 107.39 845.54, 89.36 857.21, 81.91 841.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,89,"POLYGON ((674.38 824.77, 687.43 828.39, 692.79 823.82, 694.04 834.18, 702.54 835.96, 702.72 842.88, 695.26 843.07, 695.90 848.51, 676.52 849.98, 674.38 824.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,90,"POLYGON ((105.68 826.36, 124.73 815.61, 137.24 837.16, 118.29 848.07, 105.68 826.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,91,"POLYGON ((572.69 812.84, 598.35 811.83, 597.80 817.80, 601.55 826.83, 600.77 838.06, 589.87 838.33, 588.23 829.25, 570.01 832.53, 570.95 827.93, 574.77 825.39, 576.35 819.05, 572.69 812.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,92,"POLYGON ((129.82 812.93, 146.13 801.68, 159.62 820.61, 160.41 823.85, 150.23 830.19, 145.81 831.70, 140.17 831.62, 132.18 822.46, 129.82 812.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,93,"POLYGON ((369.11 796.89, 402.18 794.64, 403.41 812.44, 399.04 815.19, 395.51 816.47, 386.33 816.71, 378.37 817.06, 370.52 817.59, 369.11 796.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,94,"POLYGON ((404.94 796.08, 428.32 793.61, 431.77 797.72, 435.57 799.17, 436.68 813.82, 417.12 815.31, 413.75 813.17, 410.24 814.59, 407.19 817.31, 404.94 796.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,95,"POLYGON ((343.18 791.91, 361.32 789.81, 364.49 816.99, 349.09 818.82, 346.06 815.65, 343.18 791.91))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,96,"POLYGON ((451.55 796.80, 466.37 791.25, 476.06 794.11, 477.69 811.74, 475.34 812.98, 453.10 814.87, 451.55 796.80))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,97,"POLYGON ((508.61 792.01, 517.46 790.45, 520.74 787.04, 531.96 783.63, 540.16 785.73, 541.73 789.84, 540.84 795.92, 542.21 800.26, 540.86 804.67, 533.81 807.98, 528.32 806.03, 511.60 808.98, 508.61 792.01))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,98,"POLYGON ((180.17 784.69, 186.11 777.99, 190.90 778.85, 190.23 782.55, 194.53 785.73, 202.50 784.82, 205.72 788.82, 196.44 796.20, 196.75 798.77, 190.88 802.29, 180.17 784.69))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,99,"POLYGON ((571.49 765.21, 589.72 762.29, 594.26 774.80, 599.05 783.43, 597.23 795.39, 594.55 800.37, 584.00 800.63, 580.85 787.39, 579.88 777.25, 572.40 773.24, 571.49 765.21))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,100,"POLYGON ((230.09 774.87, 213.94 785.68, 208.06 769.72, 214.47 767.09, 218.41 765.50, 224.82 763.10, 230.41 767.42, 231.77 772.34, 230.09 774.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,101,"POLYGON ((25.50 789.42, 16.05 785.53, 5.84 771.76, 16.29 764.11, 35.34 757.79, 42.33 778.41, 25.50 789.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,102,"POLYGON ((677.36 756.48, 701.26 755.46, 702.26 779.16, 682.22 780.02, 681.61 766.96, 674.50 764.41, 677.36 756.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,103,"POLYGON ((68.10 743.25, 78.72 761.25, 69.51 766.63, 63.71 775.14, 59.18 772.08, 52.87 764.87, 51.35 756.23, 57.29 749.56, 68.10 743.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,104,"POLYGON ((259.45 744.40, 263.46 746.04, 266.41 744.23, 270.59 752.05, 264.68 754.42, 256.34 759.33, 250.65 751.06, 259.45 744.40))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,105,"POLYGON ((283.78 740.00, 277.65 731.61, 274.03 728.46, 265.07 725.75, 266.13 720.71, 275.15 714.29, 280.63 720.05, 289.36 726.32, 289.24 733.38, 283.78 740.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,106,"POLYGON ((577.72 713.44, 592.39 713.07, 593.91 733.35, 584.95 733.57, 585.07 728.86, 584.72 724.41, 581.92 722.26, 578.55 716.88, 577.72 713.44))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,107,"POLYGON ((688.05 694.48, 704.66 693.62, 705.21 715.67, 704.28 740.64, 686.56 740.65, 688.05 694.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,108,"POLYGON ((181.89 703.43, 174.73 700.37, 165.90 702.94, 157.78 709.05, 155.25 714.71, 155.74 722.35, 144.19 710.26, 174.82 692.42, 181.89 703.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,109,"POLYGON ((499.76 695.52, 533.07 695.12, 535.39 703.25, 532.18 706.38, 527.03 706.51, 524.97 708.89, 518.41 709.99, 513.33 713.15, 508.44 714.92, 499.99 715.02, 499.76 695.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,110,"POLYGON ((441.60 673.47, 452.48 683.25, 443.30 693.12, 448.58 698.14, 421.80 723.29, 406.27 710.23, 441.60 673.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,111,"POLYGON ((282.48 683.07, 290.08 676.84, 297.78 683.31, 309.14 694.28, 314.68 689.54, 321.28 694.39, 306.45 712.26, 302.40 709.03, 303.56 705.05, 303.43 699.84, 302.70 695.90, 297.32 689.98, 294.94 687.13, 282.48 683.07))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,112,"POLYGON ((3.82 660.85, 11.40 662.99, 19.24 659.59, 27.06 677.55, 10.46 695.53, 4.55 690.26, 0.00 681.32, 0.00 672.95, 2.25 671.81, 0.00 667.38, 0.00 662.78, 3.82 660.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,113,"POLYGON ((317.67 660.30, 324.60 665.03, 329.57 666.33, 338.27 657.32, 345.25 664.04, 342.42 673.24, 334.95 683.95, 328.60 683.89, 317.42 678.31, 314.21 672.55, 314.80 667.85, 314.20 662.25, 317.67 660.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,114,"POLYGON ((36.83 653.31, 49.24 647.87, 54.93 660.85, 60.97 664.32, 62.72 669.55, 65.36 677.06, 58.28 679.52, 41.62 685.78, 37.98 676.19, 41.64 674.81, 39.89 670.17, 35.97 660.55, 36.83 653.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,115,"POLYGON ((185.25 641.90, 202.29 653.21, 182.27 683.11, 165.21 671.79, 185.25 641.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,116,"POLYGON ((216.87 639.66, 221.64 643.09, 215.52 651.53, 210.76 648.12, 216.87 639.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,117,"POLYGON ((562.44 626.39, 577.37 623.75, 583.69 659.42, 577.40 665.10, 569.80 662.63, 562.44 626.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,118,"POLYGON ((108.09 632.92, 114.18 639.54, 118.35 640.03, 120.79 643.50, 115.57 647.76, 111.23 651.40, 107.57 647.67, 109.25 643.50, 104.32 635.66, 108.09 632.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,119,"POLYGON ((363.25 636.33, 358.08 642.63, 359.88 647.78, 362.83 650.15, 354.99 659.85, 343.85 650.92, 347.30 646.63, 346.69 639.17, 340.19 630.37, 346.52 622.73, 363.25 636.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,120,"POLYGON ((475.42 623.68, 485.45 614.21, 489.37 613.14, 493.92 614.00, 498.77 617.63, 499.93 632.74, 498.66 637.10, 486.73 648.28, 472.70 633.56, 479.10 627.51, 475.42 623.68))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,121,"POLYGON ((216.60 627.26, 207.89 638.93, 191.67 626.62, 195.37 623.62, 189.60 618.55, 197.97 610.42, 202.80 610.72, 211.07 615.52, 214.10 619.60, 210.87 623.85, 216.60 627.26))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,122,"POLYGON ((689.46 593.45, 710.47 591.43, 713.64 624.29, 702.96 625.32, 700.33 619.92, 692.08 620.73, 689.46 593.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,123,"POLYGON ((72.98 602.93, 86.65 593.75, 90.03 598.08, 90.70 601.30, 91.98 605.69, 92.69 610.40, 85.97 613.81, 83.10 617.12, 72.98 602.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,124,"POLYGON ((382.15 588.60, 397.32 599.45, 383.62 618.44, 378.78 614.99, 372.35 610.20, 378.06 602.58, 375.58 597.71, 382.15 588.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,125,"POLYGON ((553.65 582.11, 581.72 580.89, 580.65 587.18, 579.94 592.03, 579.08 599.16, 578.18 607.90, 580.18 617.13, 563.52 616.00, 559.80 591.39, 553.99 589.98, 553.65 582.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,126,"POLYGON ((11.31 572.00, 24.63 590.34, 5.98 603.19, 0.82 597.66, 0.00 596.29, 0.00 588.57, 7.24 583.60, 7.47 575.78, 11.31 572.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,127,"POLYGON ((227.26 577.47, 239.09 565.85, 242.44 565.59, 244.25 563.12, 253.69 560.67, 262.78 566.19, 264.71 568.36, 251.95 587.06, 247.03 585.78, 244.68 594.86, 230.38 582.59, 227.26 577.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,128,"POLYGON ((332.28 572.45, 343.10 571.00, 344.31 580.01, 333.48 581.08, 332.28 572.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,129,"POLYGON ((61.62 559.22, 76.74 581.32, 57.89 594.12, 61.62 588.07, 59.42 577.25, 55.26 572.81, 50.67 572.57, 48.01 571.77, 44.75 566.79, 36.64 572.03, 27.80 558.51, 42.58 548.97, 47.48 556.47, 53.14 552.79, 55.71 556.72, 58.70 554.78, 61.62 559.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,130,"POLYGON ((402.32 553.20, 417.37 554.57, 417.60 563.62, 416.96 569.92, 396.17 568.29, 396.27 556.30, 399.80 555.61, 402.32 553.20))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,131,"POLYGON ((680.79 544.19, 700.09 542.50, 702.67 571.87, 683.37 573.56, 680.79 544.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,132,"POLYGON ((369.03 541.65, 385.86 542.78, 384.25 565.24, 368.40 563.89, 360.09 563.30, 360.51 548.15, 368.99 548.13, 369.03 541.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,133,"POLYGON ((281.86 541.93, 306.16 541.90, 308.56 542.88, 308.93 557.57, 306.88 559.46, 281.89 559.49, 281.86 541.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,134,"POLYGON ((323.56 540.42, 347.68 541.19, 348.09 557.69, 323.17 556.37, 323.56 540.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,135,"POLYGON ((121.51 554.60, 117.97 538.93, 130.25 536.18, 131.10 539.96, 137.01 538.63, 140.01 551.85, 136.64 554.05, 133.76 554.89, 129.35 557.89, 126.21 558.50, 121.10 554.68, 121.51 554.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,136,"POLYGON ((681.04 497.35, 700.18 496.78, 697.49 501.04, 697.90 504.18, 700.11 507.99, 701.63 512.14, 703.14 516.30, 701.88 522.30, 700.21 525.85, 681.56 525.21, 681.04 497.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,137,"POLYGON ((138.05 464.18, 152.73 462.79, 155.07 487.17, 140.39 488.58, 138.05 464.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,138,"POLYGON ((241.88 463.78, 259.13 463.04, 259.61 473.24, 263.98 475.88, 261.61 479.76, 256.35 480.16, 247.61 478.80, 242.66 481.54, 241.88 463.78))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,139,"POLYGON ((114.39 458.49, 130.98 457.63, 132.21 481.44, 128.10 481.65, 128.28 485.38, 115.81 486.00, 114.39 458.49))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,140,"POLYGON ((334.76 461.45, 352.98 459.32, 354.97 482.23, 343.47 484.00, 341.24 479.33, 336.69 480.09, 334.76 461.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,141,"POLYGON ((71.72 454.50, 79.53 455.33, 79.04 460.05, 83.50 460.51, 83.13 464.05, 91.15 464.89, 94.12 467.70, 94.49 473.66, 88.93 474.00, 89.68 486.35, 78.73 487.02, 78.36 480.95, 71.37 475.35, 68.02 467.56, 68.10 460.48, 71.22 459.09, 71.72 454.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,142,"POLYGON ((199.00 461.31, 201.01 464.24, 201.75 475.94, 183.59 477.08, 182.94 466.98, 191.27 462.22, 199.00 461.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,143,"POLYGON ((435.58 457.18, 435.97 480.92, 422.82 481.23, 421.48 456.85, 425.45 460.25, 431.76 459.03, 435.58 457.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,144,"POLYGON ((392.17 449.08, 411.47 450.48, 409.48 477.08, 403.51 480.12, 389.95 479.06, 392.17 449.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,145,"POLYGON ((209.20 446.25, 223.55 444.76, 220.50 449.03, 222.59 458.96, 227.96 463.02, 229.37 470.91, 222.18 472.18, 223.33 478.70, 213.81 480.36, 211.76 468.72, 210.94 462.79, 209.20 446.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,146,"POLYGON ((300.29 442.36, 309.71 441.79, 308.75 446.03, 310.18 450.46, 316.86 453.17, 322.91 453.02, 323.04 458.26, 321.15 466.72, 321.93 479.88, 302.56 481.01, 300.29 442.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,147,"POLYGON ((487.12 444.61, 505.68 436.77, 516.18 461.43, 514.73 462.05, 517.84 469.36, 501.44 476.28, 494.16 459.12, 489.11 461.25, 485.19 452.00, 489.50 450.18, 487.12 444.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,148,"POLYGON ((23.88 446.03, 26.10 456.18, 24.04 460.59, 26.42 464.57, 22.88 466.70, 6.48 465.80, 2.89 461.54, 3.51 445.21, 23.88 446.03))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,149,"POLYGON ((379.75 420.36, 400.11 420.23, 400.21 436.07, 379.85 436.23, 379.75 420.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,150,"POLYGON ((222.41 405.96, 242.39 406.57, 242.01 418.72, 222.05 418.14, 222.41 405.96))",1 diff --git a/docker/solaris/solaris/data/sample_preds_competition.csv b/docker/solaris/solaris/data/sample_preds_competition.csv new file mode 100644 index 00000000..715e029c --- /dev/null +++ b/docker/solaris/solaris/data/sample_preds_competition.csv @@ -0,0 +1,2320 @@ +ImageId,BuildingId,PolygonWKT_Pix,Confidence +Atlanta_nadir8_catid_10300100023BC100_743501_3726489,0,"POLYGON ((0.00 712.83, 158.37 710.28, 160.59 699.81, 197.58 699.22, 197.93 721.99, 188.25 722.15, 191.07 900.00, 0.00 900.00, 0.00 712.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726489,1,"POLYGON ((665.82 0.00, 676.56 1.50, 591.36 603.57, 368.07 572.29, 366.50 583.45, 338.74 579.56, 342.41 553.70, 351.93 555.03, 430.49 0.00, 665.82 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,0,"POLYGON ((182.62 324.15, 194.25 323.52, 197.97 335.12, 201.28 335.30, 205.87 332.59, 204.91 324.51, 203.32 319.07, 214.47 318.87, 215.14 354.57, 186.94 355.25, 183.99 349.35, 182.62 324.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,1,"POLYGON ((92.99 96.94, 117.20 99.64, 114.72 121.68, 110.90 121.26, 109.86 130.50, 92.34 128.55, 93.47 118.35, 89.25 117.88, 89.87 112.41, 93.74 112.84, 94.46 106.56, 91.94 106.27, 92.99 96.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,2,"POLYGON ((0.82 29.96, 3.48 40.71, 2.80 51.00, 0.00 51.51, 0.00 30.16, 0.82 29.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,0,"POLYGON ((476.88 884.61, 485.59 877.64, 490.50 883.69, 501.08 875.21, 510.57 886.91, 494.26 900.00, 489.33 900.00, 476.88 884.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,1,"POLYGON ((459.45 858.97, 467.41 853.09, 463.37 847.69, 473.92 839.87, 491.62 863.55, 473.11 877.25, 459.45 858.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,2,"POLYGON ((407.34 754.17, 434.90 780.55, 420.27 795.69, 414.77 790.43, 405.88 792.34, 390.17 783.38, 400.26 772.75, 394.62 767.35, 407.34 754.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,3,"POLYGON ((311.00 760.22, 318.38 746.78, 341.02 759.10, 333.64 772.54, 311.00 760.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,4,"POLYGON ((490.49 742.67, 509.81 731.14, 534.12 771.57, 514.79 783.07, 490.49 742.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,5,"POLYGON ((319.28 723.07, 339.97 698.22, 354.29 708.97, 358.58 713.28, 356.47 722.39, 351.91 738.05, 350.69 740.92, 338.71 730.61, 333.62 734.63, 327.30 731.95, 319.28 723.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,6,"POLYGON ((466.49 709.69, 484.26 696.45, 502.59 720.85, 484.79 734.06, 466.49 709.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,7,"POLYGON ((433.84 673.34, 443.90 663.96, 448.70 666.97, 452.01 665.31, 471.95 686.44, 458.26 699.25, 433.84 673.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,8,"POLYGON ((459.24 649.03, 467.38 641.90, 472.84 645.11, 481.72 649.79, 470.13 661.94, 459.24 649.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,9,"POLYGON ((403.55 643.50, 416.98 630.51, 440.36 654.44, 426.94 667.43, 403.55 643.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,10,"POLYGON ((369.12 614.88, 382.54 600.91, 406.33 623.54, 392.92 637.51, 369.12 614.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,11,"POLYGON ((24.86 585.97, 45.12 583.63, 47.43 603.64, 40.92 602.18, 32.55 604.35, 26.62 601.26, 24.86 585.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,12,"POLYGON ((192.24 526.97, 217.80 533.82, 210.59 560.40, 213.69 599.32, 198.64 600.50, 195.50 561.05, 183.84 557.93, 192.24 526.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,13,"POLYGON ((340.54 582.71, 357.91 563.11, 351.06 557.09, 379.47 525.07, 375.45 521.53, 389.78 505.40, 420.55 532.46, 360.43 600.20, 340.54 582.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,14,"POLYGON ((328.04 500.00, 342.08 505.49, 319.73 562.15, 305.69 556.67, 328.04 500.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,15,"POLYGON ((204.86 506.54, 209.61 490.74, 204.85 489.33, 208.87 476.00, 214.61 477.72, 221.47 454.99, 238.66 460.13, 223.02 511.96, 204.86 506.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,16,"POLYGON ((327.22 455.36, 340.03 425.56, 411.42 455.95, 398.61 485.73, 327.22 455.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,17,"POLYGON ((239.02 426.00, 250.66 401.97, 267.34 409.98, 255.70 434.01, 239.02 426.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,18,"POLYGON ((396.36 315.67, 447.39 329.15, 438.70 361.67, 387.69 348.21, 396.36 315.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,19,"POLYGON ((531.30 281.18, 539.07 274.27, 545.03 280.94, 556.14 271.09, 558.49 273.69, 568.18 265.10, 562.05 258.24, 569.70 251.45, 593.53 278.06, 557.28 310.19, 531.30 281.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,20,"POLYGON ((441.50 261.02, 461.97 220.32, 488.44 233.50, 467.96 274.20, 441.50 261.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,21,"POLYGON ((260.80 197.32, 272.78 166.62, 284.29 171.08, 274.76 195.50, 295.23 203.41, 292.77 209.67, 260.80 197.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,22,"POLYGON ((487.08 158.93, 510.80 136.48, 557.55 185.39, 533.83 207.86, 487.08 158.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,23,"POLYGON ((272.75 138.17, 278.76 125.18, 283.79 124.26, 287.47 117.19, 297.89 122.57, 297.41 126.55, 298.72 130.69, 306.06 133.94, 311.49 132.81, 313.73 136.10, 310.84 141.35, 306.98 142.33, 304.42 145.35, 291.10 138.90, 293.15 134.69, 290.62 130.23, 286.29 128.39, 280.17 130.23, 278.79 136.57, 279.99 141.49, 272.75 138.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,24,"POLYGON ((593.72 470.20, 637.72 519.60, 615.80 538.96, 579.92 498.68, 573.87 504.00, 565.75 494.89, 593.72 470.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,25,"POLYGON ((715.23 492.01, 770.00 451.95, 813.62 414.14, 834.44 431.66, 832.45 433.49, 797.60 470.69, 798.95 474.27, 788.92 482.56, 783.77 481.08, 740.14 519.81, 715.23 492.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,26,"POLYGON ((648.04 452.41, 666.87 435.15, 701.60 472.65, 670.22 470.74, 668.52 463.59, 660.83 457.79, 648.04 452.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,27,"POLYGON ((571.40 394.03, 584.01 397.04, 589.66 443.18, 586.08 457.96, 559.90 451.70, 567.15 421.71, 564.82 421.17, 571.40 394.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,28,"POLYGON ((868.09 363.96, 900.00 364.38, 900.00 372.62, 896.98 372.31, 895.34 402.47, 900.00 402.79, 900.00 453.54, 898.32 453.05, 894.79 453.19, 890.15 453.26, 888.79 455.69, 871.48 453.29, 872.10 442.40, 872.41 416.88, 865.19 415.96, 868.09 363.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,29,"POLYGON ((650.23 407.96, 697.21 368.76, 715.95 391.01, 668.97 430.21, 650.23 407.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,30,"POLYGON ((780.04 377.48, 809.37 342.00, 830.13 356.36, 804.14 385.75, 803.64 390.16, 799.40 394.28, 780.04 377.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,31,"POLYGON ((580.95 330.28, 612.45 336.14, 608.87 355.21, 604.68 358.40, 599.10 365.11, 589.85 363.22, 585.90 366.98, 582.09 387.23, 573.74 385.69, 577.87 363.70, 574.77 363.11, 580.95 330.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,32,"POLYGON ((645.57 304.24, 667.36 285.46, 718.66 344.44, 696.84 363.22, 645.57 304.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,33,"POLYGON ((747.60 290.07, 764.14 269.32, 799.62 297.36, 803.16 294.87, 829.13 315.39, 809.97 339.38, 747.60 290.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,34,"POLYGON ((611.15 259.41, 630.34 244.20, 626.79 239.76, 664.56 209.83, 682.91 232.76, 647.39 260.91, 649.98 264.15, 628.53 281.14, 611.15 259.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,35,"POLYGON ((540.66 204.71, 562.84 184.30, 598.62 222.86, 570.20 248.98, 564.03 242.34, 570.27 236.61, 540.66 204.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,36,"POLYGON ((607.46 49.04, 605.71 66.27, 609.43 68.88, 606.51 81.57, 601.80 81.80, 583.10 74.97, 584.61 67.85, 581.43 67.00, 584.30 60.05, 584.44 57.44, 587.39 58.10, 589.65 44.28, 607.46 49.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,37,"POLYGON ((840.99 891.66, 851.72 890.34, 852.28 887.84, 868.66 885.82, 870.42 900.00, 842.01 900.00, 840.99 891.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,38,"POLYGON ((879.53 888.01, 888.10 887.01, 886.65 874.60, 895.41 873.58, 898.51 900.00, 880.94 900.00, 879.53 888.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,39,"POLYGON ((733.84 869.12, 758.63 871.91, 756.90 887.16, 752.42 886.65, 751.80 892.06, 731.49 889.76, 733.84 869.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,40,"POLYGON ((745.92 841.53, 761.61 842.51, 761.28 847.74, 763.89 847.91, 763.41 855.52, 766.54 856.53, 766.50 862.52, 752.38 862.46, 753.99 858.84, 751.86 854.41, 748.72 853.09, 745.19 852.87, 745.92 841.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,41,"POLYGON ((828.19 815.66, 849.43 813.04, 849.69 815.03, 857.88 814.02, 859.77 829.24, 830.34 832.88, 828.19 815.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,42,"POLYGON ((867.27 816.07, 875.11 815.76, 875.01 813.05, 884.67 812.67, 884.88 818.17, 892.71 817.86, 892.97 824.47, 886.06 829.26, 867.81 829.97, 867.27 816.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,43,"POLYGON ((743.14 805.76, 761.53 807.09, 761.15 812.44, 766.24 812.80, 765.13 828.13, 752.67 827.24, 747.98 823.21, 742.07 820.46, 743.14 805.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,44,"POLYGON ((868.97 790.41, 869.09 796.38, 859.87 796.54, 859.76 790.60, 868.97 790.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,45,"POLYGON ((743.49 774.40, 761.34 774.93, 761.21 779.35, 765.56 779.48, 765.14 793.85, 742.95 793.22, 743.49 774.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,46,"POLYGON ((827.26 772.58, 847.97 770.37, 850.11 790.24, 834.14 791.96, 833.90 789.70, 830.49 790.08, 829.59 781.76, 820.63 782.72, 820.12 777.87, 827.75 777.05, 827.26 772.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,47,"POLYGON ((889.28 755.33, 900.00 754.63, 900.00 772.50, 890.45 773.12, 889.28 755.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,48,"POLYGON ((741.53 741.71, 758.98 742.00, 758.90 747.44, 762.29 747.49, 762.16 755.50, 765.62 755.55, 765.48 763.21, 741.15 762.81, 741.53 741.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,49,"POLYGON ((814.79 742.71, 844.28 739.69, 846.15 757.78, 825.49 759.91, 824.33 748.68, 815.50 749.59, 814.79 742.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,50,"POLYGON ((889.59 723.16, 900.00 722.92, 900.00 741.95, 890.02 742.17, 889.59 723.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,51,"POLYGON ((750.02 710.31, 762.44 710.26, 762.46 714.67, 766.62 714.66, 766.67 728.26, 750.89 728.33, 750.87 721.78, 747.11 721.81, 747.07 715.40, 750.06 715.39, 750.02 710.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,52,"POLYGON ((814.43 708.18, 843.81 706.85, 844.66 725.54, 822.90 726.52, 822.35 714.55, 814.75 714.90, 814.43 708.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,53,"POLYGON ((678.59 673.30, 703.05 674.61, 702.27 689.06, 705.43 689.22, 702.23 748.06, 678.76 746.80, 680.29 718.25, 676.15 718.03, 678.59 673.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,54,"POLYGON ((891.86 694.17, 900.00 693.82, 900.00 713.55, 892.69 713.86, 891.86 694.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,55,"POLYGON ((813.51 682.21, 842.81 680.71, 843.76 698.88, 820.96 700.04, 820.40 689.05, 813.88 689.39, 813.51 682.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,56,"POLYGON ((744.51 673.80, 759.45 674.33, 759.32 677.59, 764.41 677.78, 763.84 693.99, 747.13 693.42, 747.41 685.58, 744.10 685.46, 744.51 673.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,57,"POLYGON ((754.68 635.89, 767.70 639.29, 764.22 657.67, 755.36 654.37, 755.83 648.12, 755.27 642.11, 754.68 635.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,58,"POLYGON ((828.95 633.53, 843.10 620.43, 855.57 633.77, 842.07 646.27, 840.62 644.74, 836.70 648.36, 827.43 638.43, 830.71 635.40, 828.95 633.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,59,"POLYGON ((877.72 617.11, 882.84 617.75, 883.30 614.12, 900.00 616.29, 900.00 632.62, 883.18 630.44, 883.34 629.28, 876.24 628.38, 877.72 617.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,60,"POLYGON ((675.10 589.46, 701.66 590.83, 698.35 653.51, 671.80 652.12, 675.10 589.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,61,"POLYGON ((761.75 598.77, 776.83 606.96, 774.74 610.79, 778.27 612.72, 774.27 620.03, 777.28 621.66, 775.29 625.29, 767.37 620.98, 766.41 622.74, 753.59 615.79, 756.20 613.06, 758.55 612.84, 760.24 615.17, 762.12 613.06, 764.87 605.47, 763.59 601.68, 761.75 598.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,62,"POLYGON ((772.12 587.37, 786.25 574.00, 798.38 586.70, 796.59 588.39, 799.60 591.53, 787.24 603.21, 772.12 587.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,63,"POLYGON ((796.09 565.29, 811.54 554.58, 821.69 569.06, 820.21 570.09, 823.27 574.45, 809.30 584.16, 796.09 565.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,64,"POLYGON ((824.91 549.84, 841.47 542.20, 848.80 557.95, 847.06 558.78, 849.02 562.97, 834.18 569.80, 824.91 549.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,65,"POLYGON ((860.89 547.41, 885.04 546.24, 885.54 556.20, 881.00 556.42, 881.27 562.12, 861.66 563.09, 860.89 547.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,66,"POLYGON ((615.15 543.37, 644.55 518.87, 679.90 560.90, 652.75 583.53, 647.20 576.92, 652.55 572.48, 625.23 540.01, 617.64 546.33, 615.15 543.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,0,"POLYGON ((394.89 770.59, 402.04 768.06, 406.44 769.57, 410.42 769.95, 410.60 771.81, 409.63 773.08, 405.41 773.68, 405.36 781.11, 410.55 784.44, 410.10 786.56, 408.51 787.47, 406.93 789.00, 402.95 789.34, 399.70 802.81, 409.22 805.79, 409.38 807.14, 407.90 807.31, 407.33 809.79, 383.80 804.81, 384.87 797.86, 394.20 797.74, 395.06 786.95, 387.92 785.75, 390.12 778.64, 402.45 779.19, 403.06 773.85, 395.08 773.18, 394.89 770.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,1,"POLYGON ((340.68 613.32, 342.65 616.20, 342.43 619.25, 342.24 622.21, 341.98 628.85, 343.31 632.04, 345.35 634.23, 351.40 637.29, 355.68 636.83, 356.57 644.84, 353.71 649.86, 338.26 648.77, 333.39 643.43, 336.19 616.37, 340.68 613.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,2,"POLYGON ((199.73 437.91, 225.61 438.29, 225.94 457.55, 223.64 457.61, 223.77 462.69, 201.03 461.34, 199.73 437.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,3,"POLYGON ((11.94 347.48, 11.74 339.54, 13.72 339.49, 13.20 332.06, 19.97 330.40, 20.18 338.66, 30.60 337.08, 32.84 346.13, 31.76 349.62, 31.33 352.45, 32.64 364.64, 29.00 365.07, 26.85 365.28, 24.97 363.69, 23.21 359.58, 21.61 355.49, 21.69 352.03, 17.31 349.98, 15.77 347.87, 11.94 347.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,4,"POLYGON ((188.19 347.68, 188.99 338.20, 209.04 337.69, 211.33 344.47, 211.45 349.02, 205.12 349.00, 204.67 345.15, 203.73 343.27, 202.29 341.55, 200.01 342.47, 197.91 342.17, 196.72 344.13, 195.54 345.92, 194.17 347.70, 192.64 349.50, 190.35 350.09, 188.05 349.44, 188.19 347.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,5,"POLYGON ((555.34 211.96, 565.52 175.87, 571.74 173.85, 574.25 173.56, 577.20 174.11, 579.32 175.52, 580.24 178.21, 580.51 180.71, 580.78 183.41, 580.85 186.31, 580.93 189.22, 580.79 191.73, 580.22 194.26, 579.86 196.75, 578.68 199.49, 577.68 201.82, 575.65 203.54, 575.29 206.04, 572.59 207.15, 570.10 207.83, 566.76 207.92, 564.71 209.63, 565.81 212.11, 564.39 214.02, 555.34 211.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,6,"POLYGON ((465.25 194.94, 464.57 188.17, 501.48 185.61, 502.77 187.55, 502.60 190.90, 491.52 195.40, 483.15 193.89, 474.72 195.21, 467.75 195.63, 465.25 194.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,7,"POLYGON ((642.02 892.27, 651.42 870.23, 658.94 872.39, 661.88 865.37, 672.70 870.67, 664.35 889.35, 657.20 887.18, 656.15 889.56, 654.48 892.44, 649.59 891.32, 647.09 896.23, 642.02 892.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,8,"POLYGON ((860.84 719.75, 874.25 718.72, 875.19 730.99, 861.80 732.02, 860.84 719.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,9,"POLYGON ((705.56 692.49, 721.67 696.92, 721.22 698.80, 726.42 700.42, 725.82 704.58, 720.08 702.98, 713.95 722.31, 707.09 718.40, 704.92 713.77, 703.20 711.24, 702.45 704.54, 700.00 702.98, 700.40 700.50, 702.59 701.02, 705.56 692.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,10,"POLYGON ((786.25 613.52, 782.83 636.52, 752.44 632.05, 756.13 607.23, 776.14 610.16, 775.87 611.99, 786.25 613.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,11,"POLYGON ((698.79 609.62, 722.04 611.49, 721.22 625.08, 718.16 625.35, 716.72 623.33, 715.41 622.27, 712.67 623.12, 711.00 623.85, 709.22 623.52, 708.48 621.85, 706.50 621.61, 704.06 622.47, 702.23 620.54, 703.57 619.33, 701.35 617.33, 699.02 614.92, 698.12 614.66, 698.79 609.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,12,"POLYGON ((709.43 585.13, 685.48 577.24, 687.31 576.39, 688.44 572.70, 686.61 569.78, 688.71 565.86, 692.61 556.22, 700.67 558.54, 705.37 559.46, 707.34 557.37, 710.87 558.55, 711.34 560.84, 716.71 563.48, 709.43 585.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,13,"POLYGON ((754.86 555.85, 753.37 562.92, 750.69 562.37, 746.12 559.60, 747.25 554.27, 754.86 555.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,14,"POLYGON ((769.76 211.11, 795.89 219.26, 790.23 237.21, 787.96 236.51, 784.44 236.60, 782.79 240.84, 784.14 245.00, 781.56 247.17, 780.47 250.58, 759.41 244.01, 769.76 211.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,15,"POLYGON ((819.20 146.10, 823.60 139.71, 833.97 146.77, 829.56 153.16, 819.20 146.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,16,"POLYGON ((888.79 140.84, 883.20 148.02, 876.44 142.78, 882.01 135.62, 888.79 140.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,17,"POLYGON ((864.16 130.59, 856.42 141.47, 849.68 128.92, 849.40 121.09, 848.78 112.58, 845.72 102.36, 859.81 112.30, 852.59 122.45, 864.16 130.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,18,"POLYGON ((702.60 88.14, 699.99 95.04, 701.83 97.22, 700.62 102.09, 696.58 100.32, 692.97 110.67, 685.16 108.67, 685.19 105.48, 678.90 102.53, 676.47 107.23, 673.00 105.45, 675.78 101.85, 673.42 100.36, 675.93 94.77, 679.11 93.27, 682.45 85.46, 693.78 89.43, 695.17 86.40, 702.60 88.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,19,"POLYGON ((890.40 80.22, 877.84 95.13, 861.76 81.71, 865.38 77.42, 862.71 75.18, 864.96 72.48, 867.15 75.58, 871.44 78.44, 875.30 77.83, 877.54 76.20, 880.80 72.08, 890.40 80.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,20,"POLYGON ((710.48 44.79, 713.47 40.63, 717.15 41.64, 719.18 43.26, 719.47 46.00, 719.40 47.53, 719.70 50.52, 722.80 54.31, 719.96 64.97, 703.94 58.21, 710.48 44.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,0,"POLYGON ((604.65 313.72, 620.87 319.81, 620.72 322.03, 681.40 345.97, 682.11 344.06, 691.84 347.72, 689.91 349.75, 696.82 352.35, 687.66 376.55, 597.35 341.16, 604.65 313.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,1,"POLYGON ((697.92 233.12, 724.60 242.29, 701.17 309.73, 674.49 300.55, 697.92 233.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,2,"POLYGON ((592.94 222.12, 596.18 231.96, 597.73 292.85, 597.06 295.84, 574.21 297.42, 570.92 295.75, 568.98 222.68, 592.94 222.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,3,"POLYGON ((715.14 155.83, 739.51 157.47, 735.89 210.13, 709.20 208.32, 712.60 158.87, 715.14 155.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,4,"POLYGON ((567.21 164.97, 663.90 165.12, 663.96 188.74, 662.23 191.54, 657.16 192.84, 647.46 192.18, 645.01 190.62, 637.11 189.34, 636.15 188.12, 633.54 188.54, 631.35 192.88, 625.90 191.66, 625.23 190.02, 624.57 189.10, 621.76 189.37, 620.01 190.46, 619.88 192.68, 617.05 192.51, 614.99 192.34, 615.25 188.87, 603.52 187.86, 600.02 185.98, 596.70 187.72, 589.73 187.01, 566.22 192.32, 566.70 185.05, 567.02 178.63, 567.17 173.45, 567.21 164.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,5,"POLYGON ((707.78 53.68, 734.23 53.19, 735.88 139.56, 709.41 140.06, 707.78 53.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,6,"POLYGON ((650.16 43.14, 652.47 137.65, 646.29 141.13, 635.60 140.36, 632.63 138.99, 625.57 139.26, 621.97 44.02, 650.16 43.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,7,"POLYGON ((812.43 46.49, 812.09 41.83, 819.98 41.23, 822.57 44.61, 875.55 43.88, 878.61 40.98, 886.69 40.55, 888.28 70.24, 813.93 74.24, 812.43 46.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,0,"POLYGON ((730.28 414.30, 729.56 434.03, 696.66 432.85, 697.38 413.12, 730.28 414.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,1,"POLYGON ((692.70 417.03, 692.24 432.69, 667.32 432.00, 667.77 416.20, 656.00 415.86, 656.07 405.67, 670.69 406.07, 670.30 416.40, 692.70 417.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,2,"POLYGON ((773.80 407.37, 803.77 407.34, 803.78 415.95, 799.46 415.95, 799.45 419.35, 773.81 419.38, 773.80 407.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,3,"POLYGON ((625.16 188.72, 624.81 194.43, 627.79 203.81, 631.59 215.90, 633.19 219.83, 632.02 222.61, 620.03 222.65, 614.28 222.56, 613.21 209.66, 612.64 197.49, 611.91 188.81, 625.16 188.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,4,"POLYGON ((625.01 153.65, 626.68 160.06, 625.12 167.56, 625.12 177.53, 625.81 184.97, 611.07 184.83, 610.29 154.02, 625.01 153.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,5,"POLYGON ((617.90 119.51, 631.15 120.17, 632.44 131.33, 630.02 134.87, 627.14 138.92, 625.24 142.94, 624.18 150.18, 616.95 151.37, 611.48 152.26, 609.42 150.07, 608.49 142.37, 609.32 135.64, 610.17 120.46, 617.90 119.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,6,"POLYGON ((614.33 77.58, 625.82 77.78, 623.93 81.31, 622.84 88.31, 623.60 98.23, 616.16 100.66, 609.63 99.32, 609.40 79.93, 614.33 77.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,7,"POLYGON ((610.54 36.63, 626.01 36.24, 626.74 68.45, 609.84 68.77, 609.39 40.90, 610.54 36.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,8,"POLYGON ((626.13 0.00, 627.38 23.82, 631.79 23.59, 632.00 27.47, 623.14 27.94, 623.39 32.44, 609.12 33.18, 607.38 0.00, 626.13 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,9,"POLYGON ((532.50 547.35, 535.57 584.63, 518.81 585.99, 516.33 555.87, 524.29 555.22, 523.70 548.07, 532.50 547.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,10,"POLYGON ((429.64 542.36, 440.18 541.49, 441.66 553.00, 443.98 556.49, 444.76 563.86, 440.77 564.41, 435.26 564.68, 431.37 563.45, 431.64 556.65, 430.80 552.83, 429.64 542.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,11,"POLYGON ((27.89 557.23, 27.45 548.07, 34.33 544.14, 38.35 539.54, 60.35 538.49, 61.18 555.65, 27.89 557.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,12,"POLYGON ((135.78 551.46, 114.32 552.61, 112.66 521.77, 134.12 520.63, 135.78 551.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,13,"POLYGON ((359.92 526.78, 362.00 526.72, 362.95 523.30, 369.18 523.14, 369.53 525.36, 383.94 524.83, 384.77 534.14, 383.83 537.84, 381.47 538.50, 365.72 538.59, 360.09 539.05, 359.92 526.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,14,"POLYGON ((275.20 519.77, 277.21 540.40, 268.91 540.93, 267.87 535.18, 256.15 535.48, 254.96 523.39, 275.20 519.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,15,"POLYGON ((69.34 525.58, 77.38 525.27, 77.02 516.29, 82.29 516.06, 82.59 523.63, 88.58 523.39, 88.76 527.71, 95.59 527.45, 96.13 540.75, 70.01 541.84, 69.34 525.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,16,"POLYGON ((0.00 515.48, 4.52 515.45, 4.55 521.00, 15.83 520.93, 15.92 534.65, 2.51 534.74, 2.53 537.67, 0.00 537.69, 0.00 515.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,17,"POLYGON ((319.33 513.91, 317.66 529.92, 302.41 531.64, 299.97 529.92, 296.00 531.36, 293.17 530.98, 291.11 518.72, 319.33 513.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,18,"POLYGON ((315.60 417.78, 315.77 424.48, 310.90 431.31, 317.05 433.44, 321.38 433.44, 321.42 441.45, 314.10 441.46, 299.90 436.81, 297.31 433.94, 293.29 432.80, 290.65 428.47, 288.07 419.39, 293.11 417.95, 293.10 413.85, 299.52 413.82, 304.05 418.10, 315.60 417.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,19,"POLYGON ((563.50 415.13, 562.19 422.62, 559.59 428.66, 553.42 431.81, 543.65 430.55, 537.76 425.49, 539.11 419.24, 543.75 414.90, 563.50 415.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,20,"POLYGON ((272.99 411.14, 275.87 416.93, 276.75 426.94, 267.57 429.26, 261.89 429.20, 255.00 431.05, 250.60 431.16, 249.65 427.43, 245.46 427.54, 241.53 421.78, 245.41 417.48, 258.42 416.75, 266.85 417.78, 272.99 411.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,21,"POLYGON ((433.18 390.64, 434.30 399.62, 433.94 402.89, 431.86 402.94, 431.65 417.73, 422.33 419.15, 415.24 415.49, 415.68 391.81, 433.18 390.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,22,"POLYGON ((382.45 391.99, 383.25 404.65, 387.76 404.38, 388.57 417.34, 366.23 418.76, 364.31 388.51, 376.57 387.73, 376.87 392.36, 382.45 391.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,23,"POLYGON ((466.13 388.86, 468.17 386.53, 472.39 386.82, 474.19 392.01, 472.00 396.24, 470.63 400.05, 472.78 402.08, 474.30 404.35, 474.01 409.57, 473.45 411.67, 467.95 410.77, 465.39 409.17, 466.13 388.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,24,"POLYGON ((386.42 376.82, 386.29 372.94, 392.61 372.73, 392.75 376.90, 402.30 376.55, 402.78 390.37, 382.48 391.08, 381.99 376.98, 386.42 376.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,25,"POLYGON ((45.19 372.37, 45.92 410.55, 42.20 410.62, 42.27 414.26, 21.92 414.65, 21.87 410.92, 19.32 410.96, 18.20 346.42, 43.28 345.98, 43.32 348.51, 48.63 348.42, 48.57 345.40, 106.87 344.52, 107.30 373.10, 50.60 373.94, 50.11 372.26, 45.19 372.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,26,"POLYGON ((303.95 323.40, 305.67 332.35, 275.40 331.66, 275.50 327.28, 277.31 324.93, 282.57 325.41, 286.92 322.79, 303.95 323.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,27,"POLYGON ((521.46 314.31, 540.69 313.96, 538.75 321.35, 542.28 322.37, 542.61 325.72, 538.83 329.05, 535.40 337.22, 523.51 335.66, 522.15 326.99, 521.46 314.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,28,"POLYGON ((390.10 314.95, 392.72 318.86, 393.92 325.09, 392.77 329.51, 391.64 333.93, 385.99 334.92, 381.34 334.00, 379.12 329.04, 376.31 326.58, 373.59 326.65, 371.66 333.20, 365.74 332.09, 365.61 326.65, 366.94 321.60, 381.90 315.16, 390.10 314.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,29,"POLYGON ((394.21 303.13, 407.21 301.95, 409.26 308.18, 404.92 311.22, 393.39 312.14, 392.46 308.61, 394.21 303.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,30,"POLYGON ((510.35 276.81, 535.84 275.52, 536.11 281.86, 539.15 281.74, 539.64 293.12, 540.90 293.06, 541.12 297.81, 537.39 297.97, 537.67 304.60, 521.71 305.29, 521.62 303.12, 519.62 303.22, 519.45 299.38, 520.89 299.32, 520.75 295.84, 510.32 296.66, 510.35 276.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,31,"POLYGON ((295.20 260.05, 296.94 262.12, 296.77 272.35, 290.38 277.11, 292.78 297.14, 288.80 297.44, 285.81 295.01, 283.83 291.51, 277.33 292.09, 278.02 278.49, 276.99 270.97, 274.64 269.79, 274.53 265.40, 277.90 265.11, 276.56 261.79, 295.20 260.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,32,"POLYGON ((402.04 251.23, 402.96 267.47, 379.98 268.75, 379.84 266.20, 372.26 266.61, 371.85 259.30, 379.89 258.85, 379.55 252.49, 402.04 251.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,33,"POLYGON ((536.20 243.24, 537.60 256.74, 539.14 261.63, 534.94 262.96, 519.44 264.75, 519.79 257.71, 516.93 243.02, 523.81 242.13, 529.84 243.40, 536.20 243.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,34,"POLYGON ((535.34 226.32, 516.78 227.37, 515.92 211.92, 521.98 211.59, 525.06 207.98, 524.75 203.24, 533.89 202.65, 535.34 226.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,35,"POLYGON ((268.89 182.39, 285.10 184.26, 285.63 179.67, 291.57 180.34, 289.48 198.24, 267.34 195.70, 268.89 182.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,36,"POLYGON ((517.14 159.97, 530.84 158.89, 531.25 164.58, 534.76 165.49, 537.09 168.92, 536.01 175.40, 534.90 180.67, 536.03 185.61, 525.67 186.66, 524.64 181.42, 523.55 166.13, 517.14 159.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,37,"POLYGON ((516.25 118.53, 529.51 118.81, 533.88 123.80, 533.77 134.86, 536.73 133.05, 537.16 140.26, 534.72 142.05, 534.20 146.30, 533.12 147.95, 526.87 147.98, 525.20 146.64, 522.74 142.36, 519.50 143.33, 508.57 135.39, 507.39 126.41, 514.18 125.55, 516.25 118.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,38,"POLYGON ((297.03 73.39, 297.43 83.43, 268.49 84.33, 265.77 83.20, 264.53 75.26, 270.46 74.22, 297.03 73.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,39,"POLYGON ((503.67 53.18, 508.74 56.05, 508.84 60.02, 510.35 70.68, 494.37 70.58, 492.68 53.47, 503.67 53.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,40,"POLYGON ((413.66 40.09, 414.06 49.98, 401.56 49.42, 400.15 46.50, 398.91 38.85, 413.66 40.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,41,"POLYGON ((246.25 38.61, 256.36 38.73, 256.30 45.03, 246.17 44.92, 246.25 38.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,42,"POLYGON ((120.80 27.20, 123.62 39.05, 122.31 44.94, 113.08 46.22, 110.89 42.52, 107.49 40.92, 106.12 36.58, 104.17 34.52, 106.42 31.76, 108.32 23.96, 110.80 22.23, 114.85 24.64, 120.80 27.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,43,"POLYGON ((516.55 19.78, 544.41 20.67, 544.06 30.27, 546.04 39.67, 538.50 47.33, 532.66 48.07, 531.01 35.00, 515.47 36.95, 514.26 27.30, 516.55 19.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,44,"POLYGON ((164.57 21.31, 175.70 21.03, 175.80 24.29, 179.72 26.56, 183.44 26.91, 184.75 37.51, 183.93 40.35, 179.09 42.69, 152.50 42.88, 152.15 24.60, 164.66 24.28, 164.57 21.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,45,"POLYGON ((488.34 20.01, 503.54 18.87, 505.96 25.78, 506.10 30.99, 506.25 37.20, 504.18 43.74, 498.70 44.61, 496.99 36.46, 487.97 35.69, 488.34 20.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,46,"POLYGON ((442.60 22.15, 468.48 22.80, 472.04 29.28, 472.69 39.46, 459.29 40.31, 456.58 37.38, 442.37 37.28, 442.45 28.02, 442.60 22.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,47,"POLYGON ((213.84 22.10, 222.82 21.96, 222.79 19.32, 232.58 19.20, 232.63 22.80, 239.00 22.12, 238.33 27.22, 236.01 31.34, 233.96 34.15, 232.79 38.46, 213.09 37.10, 211.57 28.46, 212.58 25.88, 213.84 22.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,48,"POLYGON ((276.38 20.39, 284.66 19.01, 295.95 18.72, 301.38 21.82, 302.40 27.12, 299.82 30.74, 293.35 32.97, 287.43 34.32, 280.86 33.00, 276.29 28.39, 276.38 20.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,49,"POLYGON ((378.36 1.41, 387.76 2.42, 389.37 10.28, 387.33 20.37, 384.57 30.11, 374.00 31.07, 371.07 26.75, 372.57 23.03, 376.93 20.99, 378.14 13.03, 376.07 8.18, 375.01 1.68, 378.36 1.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,50,"POLYGON ((804.92 820.15, 878.90 817.71, 882.04 830.10, 882.37 843.21, 805.75 845.35, 804.92 820.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,51,"POLYGON ((809.26 761.06, 874.68 757.75, 874.47 754.05, 897.80 752.74, 899.74 787.21, 811.00 792.16, 809.26 761.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,52,"POLYGON ((797.29 624.76, 824.75 623.21, 825.10 629.60, 823.02 629.72, 825.30 671.93, 826.80 671.84, 827.29 681.29, 825.60 681.37, 826.22 694.08, 828.37 693.98, 828.76 701.87, 826.22 701.98, 826.66 711.47, 829.05 711.36, 829.41 718.77, 802.65 720.21, 797.29 624.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,53,"POLYGON ((900.00 701.98, 894.53 702.13, 878.43 703.05, 878.24 695.81, 880.65 695.69, 880.09 688.93, 878.33 688.98, 877.74 679.76, 879.15 679.72, 877.94 655.53, 876.66 655.57, 876.30 645.76, 877.69 645.73, 877.29 634.53, 875.42 634.69, 875.09 625.93, 876.36 625.65, 875.78 616.56, 873.31 616.74, 873.16 610.88, 876.09 610.81, 875.77 606.97, 900.00 606.11, 900.00 701.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,54,"POLYGON ((640.34 562.10, 651.00 561.25, 652.15 575.74, 641.50 576.56, 640.34 562.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,55,"POLYGON ((720.27 527.54, 720.94 600.51, 692.71 600.85, 692.60 593.64, 694.51 593.61, 694.38 585.87, 692.84 585.89, 692.74 578.28, 694.08 578.24, 693.87 563.64, 692.00 563.67, 691.89 555.10, 693.14 555.09, 693.01 542.93, 690.60 542.95, 690.52 533.89, 693.89 533.85, 693.83 527.79, 720.27 527.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,56,"POLYGON ((558.71 549.26, 565.63 549.15, 565.61 547.40, 575.10 547.24, 575.14 549.62, 616.54 548.90, 616.51 546.88, 626.01 546.70, 626.05 548.97, 632.70 548.84, 633.14 574.00, 559.15 575.29, 558.71 549.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,57,"POLYGON ((814.63 512.51, 814.93 517.78, 817.25 517.65, 817.80 527.47, 815.04 527.63, 815.60 537.45, 817.38 537.36, 818.09 549.75, 816.03 549.87, 817.22 571.20, 819.61 571.07, 820.19 581.33, 818.43 581.42, 818.87 589.00, 821.24 588.88, 821.64 595.81, 794.19 597.36, 789.52 513.90, 814.63 512.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,58,"POLYGON ((895.66 514.28, 900.00 576.14, 900.00 587.41, 875.02 589.04, 874.68 582.21, 871.05 582.50, 870.75 573.92, 873.18 573.63, 872.13 551.28, 870.35 551.48, 870.14 543.43, 871.83 543.26, 871.38 531.59, 868.73 531.53, 868.10 523.62, 870.19 523.41, 870.00 515.78, 895.66 514.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,0,"POLYGON ((275.97 836.50, 277.61 868.87, 261.10 869.71, 259.46 837.35, 275.97 836.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,1,"POLYGON ((366.05 814.01, 367.56 814.73, 366.41 818.46, 366.39 827.39, 367.07 835.05, 372.33 836.14, 372.96 841.09, 354.82 842.04, 353.86 814.56, 366.05 814.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,2,"POLYGON ((144.24 815.17, 160.41 813.83, 162.44 838.15, 146.29 839.52, 144.24 815.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,3,"POLYGON ((258.61 803.21, 276.17 802.18, 277.37 822.53, 259.79 823.55, 258.61 803.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,4,"POLYGON ((58.66 784.46, 59.46 807.54, 44.18 808.07, 43.68 793.52, 35.91 793.78, 35.79 790.63, 46.32 790.25, 46.15 784.89, 58.66 784.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,5,"POLYGON ((354.43 777.99, 371.34 777.36, 372.24 801.11, 355.33 801.76, 354.43 777.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,6,"POLYGON ((142.23 775.34, 146.50 777.67, 147.60 779.39, 152.25 782.07, 157.52 781.94, 159.74 800.11, 143.94 801.22, 142.23 775.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,7,"POLYGON ((260.30 767.69, 276.69 766.70, 278.48 796.00, 262.08 796.99, 260.30 767.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,8,"POLYGON ((41.16 774.67, 40.95 764.93, 33.03 765.09, 32.78 753.02, 46.78 752.75, 46.69 748.38, 56.07 748.18, 56.38 763.27, 59.61 763.21, 59.83 774.30, 41.16 774.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,9,"POLYGON ((354.95 748.30, 372.49 747.70, 373.34 772.20, 355.80 772.80, 354.95 748.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,10,"POLYGON ((144.58 743.04, 160.75 742.99, 163.16 768.14, 159.99 768.22, 155.38 766.59, 150.41 765.32, 147.24 765.40, 144.44 765.47, 144.58 743.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,11,"POLYGON ((252.16 758.37, 251.94 746.26, 277.53 745.79, 277.75 757.90, 252.16 758.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,12,"POLYGON ((16.56 745.15, 17.28 757.03, 5.42 757.73, 4.71 745.85, 16.56 745.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,13,"POLYGON ((212.34 727.25, 229.23 727.35, 229.12 743.40, 212.23 743.30, 212.34 727.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,14,"POLYGON ((354.51 709.95, 371.68 709.42, 372.45 733.67, 355.28 734.22, 354.51 709.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,15,"POLYGON ((40.17 710.87, 56.13 710.34, 56.88 732.71, 40.92 733.23, 40.17 710.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,16,"POLYGON ((364.81 671.93, 373.74 671.59, 374.85 699.82, 359.60 700.41, 359.50 697.99, 362.97 696.51, 363.21 692.30, 365.22 688.03, 364.34 681.40, 362.59 678.22, 366.95 675.83, 364.81 671.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,17,"POLYGON ((141.68 668.73, 146.80 668.51, 146.97 672.19, 157.40 671.75, 158.58 698.51, 142.05 688.37, 141.68 668.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,18,"POLYGON ((40.32 660.08, 58.21 659.36, 59.49 690.78, 41.60 691.50, 40.32 660.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,19,"POLYGON ((346.03 637.51, 372.27 636.49, 372.55 643.79, 377.13 643.60, 377.45 651.83, 370.56 652.09, 370.92 661.10, 358.61 661.59, 358.14 649.66, 346.51 650.11, 346.03 637.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,20,"POLYGON ((141.13 631.65, 148.10 631.37, 149.58 634.84, 162.11 634.34, 162.44 642.21, 157.90 642.39, 158.59 659.42, 142.30 660.08, 141.13 631.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,21,"POLYGON ((248.50 628.52, 261.99 627.98, 271.04 637.89, 268.35 643.22, 249.13 644.00, 248.50 628.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,22,"POLYGON ((43.90 567.63, 47.69 580.78, 29.29 586.04, 25.50 572.89, 43.90 567.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,23,"POLYGON ((271.12 531.65, 288.25 531.12, 289.58 574.87, 272.45 575.39, 271.12 531.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,24,"POLYGON ((0.00 523.36, 9.93 522.96, 11.13 552.85, 0.00 553.30, 0.00 523.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,25,"POLYGON ((459.66 553.84, 458.67 534.09, 428.37 535.28, 427.06 522.68, 471.39 520.49, 473.02 553.19, 459.66 553.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,26,"POLYGON ((30.79 550.33, 29.75 531.23, 34.75 530.97, 34.44 525.25, 71.27 523.26, 72.63 548.07, 30.79 550.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,27,"POLYGON ((250.47 511.29, 258.07 511.03, 258.24 515.66, 262.39 515.53, 263.62 550.42, 245.71 551.05, 244.51 517.26, 250.67 517.05, 250.47 511.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,28,"POLYGON ((157.82 512.00, 167.00 511.68, 167.22 517.54, 170.82 517.40, 171.41 533.92, 176.58 533.72, 176.77 539.18, 171.80 539.35, 172.07 546.74, 155.88 547.33, 154.79 516.90, 158.00 516.79, 157.82 512.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,29,"POLYGON ((180.17 512.48, 188.37 512.11, 188.61 517.21, 191.26 517.08, 191.02 512.05, 203.31 511.49, 203.52 516.21, 205.80 516.11, 206.09 522.30, 217.61 521.76, 217.87 527.57, 208.14 528.02, 208.61 538.48, 205.07 538.64, 205.39 545.82, 191.75 546.46, 191.44 539.43, 189.64 539.50, 188.97 524.86, 180.76 525.23, 180.17 512.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,30,"POLYGON ((122.62 516.36, 138.12 515.66, 139.12 537.41, 128.44 537.90, 128.20 532.69, 123.38 532.93, 122.62 516.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,31,"POLYGON ((407.25 442.23, 418.91 441.73, 419.69 459.70, 408.34 460.16, 413.23 456.95, 416.58 452.29, 414.61 447.64, 407.25 442.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,32,"POLYGON ((390.45 363.26, 404.78 350.95, 435.78 388.45, 422.74 399.68, 422.03 395.28, 421.30 389.86, 418.61 388.45, 417.60 383.61, 414.45 381.49, 411.81 376.99, 406.42 374.33, 404.24 376.45, 403.89 379.99, 390.45 363.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,33,"POLYGON ((30.83 145.38, 36.18 151.94, 68.03 126.23, 90.44 153.74, 98.51 147.23, 76.49 120.20, 85.65 112.79, 103.48 134.67, 122.08 119.63, 139.87 141.49, 121.75 156.14, 193.22 243.82, 240.80 205.40, 248.44 214.77, 143.62 299.47, 138.23 292.88, 121.07 306.77, 100.18 281.15, 0.00 362.14, 0.00 302.42, 1.97 300.83, 0.00 298.42, 0.00 281.36, 3.93 286.11, 2.02 287.69, 7.96 294.86, 83.53 232.85, 79.09 227.50, 61.88 241.63, 48.55 225.55, 0.00 265.38, 0.00 207.27, 20.55 190.90, 8.47 175.89, 28.08 160.25, 22.64 153.49, 0.00 171.52, 0.00 114.17, 16.54 100.81, 44.11 134.65, 30.83 145.38), (97.39 164.39, 121.39 192.39, 88.13 218.84, 95.18 227.11, 134.58 193.86, 143.19 203.96, 145.75 201.79, 106.86 156.14, 97.39 164.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,34,"POLYGON ((836.69 529.29, 838.00 580.73, 770.08 581.07, 769.63 577.24, 756.36 587.81, 818.03 667.06, 784.95 690.55, 669.89 543.35, 702.32 517.64, 729.63 554.08, 755.51 533.45, 770.62 533.06, 769.12 529.26, 836.69 529.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,35,"POLYGON ((900.00 376.06, 758.85 379.04, 760.48 394.40, 734.12 394.65, 733.71 378.44, 683.52 379.30, 684.76 427.99, 643.33 427.80, 643.43 399.05, 611.85 424.43, 615.30 428.09, 600.53 439.73, 572.95 408.77, 583.21 401.01, 423.11 194.32, 454.29 169.77, 567.20 314.36, 900.00 309.11, 900.00 376.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,36,"POLYGON ((860.32 204.24, 900.00 203.55, 900.00 243.90, 861.34 244.57, 860.32 204.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,37,"POLYGON ((36.54 895.81, 52.78 896.48, 57.28 900.00, 36.36 900.00, 36.54 895.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,38,"POLYGON ((356.58 896.52, 374.32 895.33, 374.63 900.00, 356.81 900.00, 356.58 896.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,39,"POLYGON ((143.94 887.86, 149.22 887.63, 149.33 890.41, 158.19 890.05, 158.60 900.00, 144.44 900.00, 143.94 887.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,40,"POLYGON ((276.04 878.68, 281.29 887.38, 278.85 896.56, 259.35 897.35, 258.89 879.12, 276.04 878.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,41,"POLYGON ((356.23 851.40, 372.12 849.48, 374.87 872.28, 359.00 874.19, 356.23 851.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,42,"POLYGON ((148.74 849.16, 160.42 854.61, 151.30 867.80, 144.91 861.04, 144.71 853.39, 148.74 849.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3732339,0,"POLYGON ((74.14 523.87, 104.12 520.89, 104.91 528.64, 98.51 529.29, 99.52 539.41, 75.93 541.76, 74.14 523.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,0,"POLYGON ((622.25 117.12, 653.93 116.04, 654.63 136.76, 622.95 137.81, 622.25 117.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,1,"POLYGON ((639.82 85.57, 664.37 86.25, 663.90 103.18, 639.34 102.50, 639.82 85.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,2,"POLYGON ((875.90 99.55, 875.66 90.14, 880.44 92.26, 884.89 91.64, 888.11 90.58, 888.53 87.35, 887.96 84.63, 893.45 85.22, 894.31 99.32, 875.90 99.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,3,"POLYGON ((858.48 99.75, 842.32 99.67, 841.95 95.46, 845.19 94.89, 848.82 91.07, 849.43 86.10, 849.09 82.16, 845.02 78.80, 840.75 77.42, 840.64 72.96, 858.08 74.01, 856.53 90.87, 858.36 94.80, 858.48 99.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,4,"POLYGON ((778.91 0.00, 780.70 109.41, 730.82 110.22, 729.01 0.00, 778.91 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,5,"POLYGON ((619.37 6.98, 619.01 0.00, 654.25 0.00, 654.41 3.20, 659.12 2.97, 659.67 13.65, 655.57 13.87, 655.74 17.39, 627.80 18.81, 623.63 6.78, 619.37 6.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,6,"POLYGON ((572.26 0.56, 569.96 0.64, 569.93 0.00, 591.62 0.00, 591.96 10.14, 584.75 10.39, 585.05 19.33, 572.91 19.75, 572.26 0.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,7,"POLYGON ((615.73 0.00, 616.13 5.62, 608.90 6.11, 608.47 0.00, 615.73 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,8,"POLYGON ((238.07 751.25, 263.44 761.82, 268.64 749.47, 311.29 767.26, 273.46 857.19, 205.43 828.84, 238.07 751.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,9,"POLYGON ((534.27 771.29, 533.42 769.01, 533.37 766.57, 534.17 764.24, 535.55 762.47, 537.43 761.22, 539.60 760.61, 541.85 760.69, 543.97 761.48, 545.73 762.88, 546.96 764.78, 547.54 766.94, 547.41 769.18, 546.59 771.27, 545.01 773.13, 542.87 774.36, 540.45 774.78, 538.02 774.37, 535.88 773.16, 534.27 771.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,10,"POLYGON ((295.94 696.84, 300.62 698.67, 302.38 694.25, 358.96 716.56, 349.32 740.78, 291.26 717.91, 293.97 711.11, 290.76 709.84, 295.94 696.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,11,"POLYGON ((366.83 698.71, 365.98 696.43, 365.93 693.96, 366.73 691.66, 368.11 689.89, 369.99 688.62, 372.16 688.01, 374.41 688.11, 376.53 688.90, 378.29 690.30, 379.52 692.17, 380.10 694.36, 379.97 696.58, 379.15 698.69, 377.57 700.55, 375.43 701.76, 373.01 702.20, 370.58 701.77, 368.44 700.56, 366.83 698.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,12,"POLYGON ((119.25 572.86, 97.19 595.90, 52.30 553.32, 70.72 534.11, 68.86 532.34, 79.93 520.76, 81.70 522.42, 90.72 513.00, 105.32 526.86, 88.87 544.04, 119.25 572.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,13,"POLYGON ((28.64 487.13, 36.35 486.97, 36.49 493.21, 28.78 493.38, 28.64 487.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,14,"POLYGON ((244.34 478.90, 269.44 478.11, 270.08 498.27, 253.21 498.81, 253.03 493.00, 244.80 493.25, 244.34 478.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,15,"POLYGON ((294.01 472.55, 306.75 471.85, 307.20 479.96, 294.46 480.69, 294.01 472.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,16,"POLYGON ((79.60 459.83, 83.95 482.68, 74.94 484.37, 70.59 461.53, 79.60 459.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,17,"POLYGON ((250.86 405.86, 267.16 405.55, 267.20 407.60, 298.90 407.01, 298.83 403.57, 317.40 403.23, 317.80 424.91, 308.64 425.07, 308.96 442.58, 284.83 443.02, 284.92 447.06, 262.89 447.48, 262.46 425.67, 251.26 425.89, 250.86 405.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,18,"POLYGON ((226.74 258.32, 226.35 281.64, 197.77 281.17, 198.17 257.85, 226.74 258.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,19,"POLYGON ((230.07 244.99, 255.13 244.77, 255.39 279.19, 244.65 279.29, 244.71 285.99, 232.80 286.09, 232.75 278.26, 230.32 278.28, 230.07 244.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,20,"POLYGON ((132.95 232.50, 89.89 278.68, 65.99 256.60, 77.20 244.60, 70.36 238.29, 84.77 222.85, 91.80 229.35, 109.26 210.62, 132.95 232.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,21,"POLYGON ((474.91 210.31, 487.32 209.55, 497.08 209.88, 501.89 247.76, 493.91 247.52, 495.18 262.65, 479.34 262.47, 478.66 247.75, 471.56 246.91, 474.91 210.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,22,"POLYGON ((202.49 208.31, 261.82 208.86, 261.81 231.86, 226.00 231.55, 225.96 234.35, 202.21 234.09, 202.49 208.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,23,"POLYGON ((148.18 217.77, 147.41 192.13, 161.91 191.72, 162.67 217.34, 148.18 217.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,24,"POLYGON ((56.60 158.88, 56.73 183.81, 30.74 183.96, 30.72 178.08, 25.23 178.11, 25.12 159.06, 56.60 158.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,25,"POLYGON ((79.26 149.34, 92.92 149.43, 92.87 155.72, 79.24 155.64, 79.26 149.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,26,"POLYGON ((398.13 139.19, 398.51 154.81, 394.32 154.89, 394.44 159.69, 385.53 159.89, 385.61 163.22, 374.84 163.47, 374.76 160.50, 366.49 160.71, 366.47 159.09, 361.00 159.23, 360.81 151.11, 366.36 150.97, 366.08 139.96, 398.13 139.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,27,"POLYGON ((117.36 125.64, 118.02 151.68, 114.98 151.76, 115.17 159.23, 111.20 159.33, 111.29 162.88, 100.93 163.15, 99.98 126.08, 117.36 125.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,28,"POLYGON ((449.09 118.14, 472.92 117.91, 473.02 127.45, 476.95 127.41, 477.03 134.14, 457.80 134.34, 457.84 138.91, 449.31 139.00, 449.09 118.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,29,"POLYGON ((203.59 117.47, 204.00 133.33, 195.56 133.54, 195.16 117.68, 203.59 117.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,30,"POLYGON ((79.05 114.67, 92.76 114.96, 92.60 123.05, 78.88 122.78, 79.05 114.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,31,"POLYGON ((499.12 112.20, 499.24 119.74, 486.57 119.93, 486.45 112.39, 499.12 112.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,32,"POLYGON ((211.95 109.80, 218.31 109.61, 218.57 118.44, 212.19 118.62, 211.95 109.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,33,"POLYGON ((362.64 97.39, 376.62 97.19, 376.66 100.45, 387.01 100.32, 387.05 103.27, 398.01 103.12, 398.21 118.46, 386.16 118.63, 386.25 125.11, 363.04 125.44, 362.64 97.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,34,"POLYGON ((74.56 93.50, 74.51 107.04, 68.35 107.02, 68.32 118.21, 40.57 118.11, 40.59 111.72, 36.16 111.72, 36.19 97.89, 42.07 97.90, 42.09 93.39, 74.56 93.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,35,"POLYGON ((476.28 83.10, 476.61 99.68, 470.16 99.82, 470.23 103.08, 442.75 103.62, 442.40 85.50, 448.59 85.38, 448.56 83.65, 476.28 83.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,36,"POLYGON ((139.11 78.76, 158.37 78.38, 158.86 103.18, 150.25 103.33, 150.20 100.67, 142.19 100.83, 142.09 95.37, 139.44 95.42, 139.11 78.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,37,"POLYGON ((295.25 64.39, 314.49 64.21, 314.56 72.20, 317.04 72.18, 317.38 107.13, 295.66 107.35, 295.25 64.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,38,"POLYGON ((213.57 86.78, 215.29 78.52, 214.07 72.16, 209.53 68.13, 216.67 67.86, 216.80 71.40, 221.82 71.23, 222.76 96.58, 217.96 96.77, 218.07 99.52, 213.90 99.67, 213.99 102.44, 207.69 102.67, 207.44 95.95, 203.29 96.10, 208.93 93.20, 213.57 86.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,39,"POLYGON ((281.58 67.33, 281.12 94.87, 254.91 94.43, 255.16 79.30, 260.20 79.40, 260.42 66.70, 261.81 66.73, 261.87 63.33, 278.22 63.60, 278.15 67.29, 281.58 67.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,40,"POLYGON ((249.25 63.74, 249.38 91.26, 226.86 91.37, 226.75 65.89, 238.49 65.83, 238.47 63.79, 249.25 63.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,41,"POLYGON ((520.21 63.49, 521.28 89.26, 508.25 89.79, 508.06 85.16, 502.94 85.35, 502.25 68.45, 505.01 68.34, 504.83 64.13, 520.21 63.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,42,"POLYGON ((366.94 58.99, 389.00 58.18, 389.34 67.23, 396.62 66.97, 397.08 79.06, 385.61 79.49, 385.74 82.99, 374.65 83.38, 374.48 78.66, 367.67 78.90, 366.94 58.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,43,"POLYGON ((336.48 59.72, 342.32 59.44, 343.24 78.17, 337.43 78.47, 336.48 59.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,44,"POLYGON ((479.74 55.16, 480.21 74.72, 448.75 75.46, 448.28 55.89, 479.74 55.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,45,"POLYGON ((20.82 6.12, 40.87 5.36, 41.26 7.00, 45.61 7.35, 46.16 28.96, 39.33 28.07, 39.36 29.35, 32.81 29.99, 31.79 26.86, 28.66 24.25, 26.18 23.14, 22.10 24.42, 22.60 16.46, 21.64 10.87, 20.82 6.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,46,"POLYGON ((487.51 15.42, 469.48 14.82, 469.95 0.00, 492.47 0.00, 492.38 7.86, 487.65 7.83, 487.51 15.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,47,"POLYGON ((541.47 10.41, 541.44 17.89, 529.18 17.84, 529.24 0.00, 548.63 0.00, 548.60 10.45, 541.47 10.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,48,"POLYGON ((517.83 12.92, 498.75 14.16, 499.07 0.00, 518.64 0.00, 517.83 12.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,49,"POLYGON ((403.07 12.58, 398.11 11.91, 398.07 10.33, 395.45 10.84, 393.18 11.17, 389.92 11.34, 386.12 10.73, 382.96 10.47, 382.23 6.72, 382.71 4.27, 383.96 1.77, 384.17 0.00, 404.78 0.00, 404.41 3.62, 402.50 4.27, 403.07 12.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,50,"POLYGON ((466.05 0.00, 466.21 6.75, 459.72 6.90, 459.85 12.51, 439.06 13.00, 438.76 0.00, 466.05 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,51,"POLYGON ((248.37 0.00, 248.34 2.01, 248.44 5.76, 246.36 7.14, 242.26 7.25, 238.86 8.44, 231.89 8.62, 230.21 8.33, 229.81 5.48, 231.34 4.33, 233.77 4.27, 234.49 1.94, 233.75 0.00, 248.37 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,52,"POLYGON ((258.11 6.55, 257.71 0.00, 276.00 0.00, 274.38 1.52, 275.11 3.26, 270.34 2.60, 268.08 2.66, 265.41 2.73, 262.79 3.97, 260.18 2.95, 258.11 6.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,53,"POLYGON ((21.58 2.75, 21.35 0.00, 39.54 0.00, 41.24 2.36, 21.58 2.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,54,"POLYGON ((402.96 766.12, 414.53 739.34, 435.70 748.24, 432.07 756.78, 568.02 814.34, 571.26 806.82, 592.62 815.96, 580.66 843.72, 569.09 838.78, 560.84 857.88, 548.53 852.62, 546.29 857.83, 595.92 879.14, 586.87 900.00, 549.39 900.00, 549.44 899.88, 493.83 875.88, 490.92 882.55, 437.04 859.26, 439.73 853.15, 385.49 829.61, 382.23 837.08, 348.47 822.40, 368.17 777.53, 416.69 798.62, 417.70 796.31, 406.13 791.28, 414.90 771.30, 402.96 766.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,55,"POLYGON ((605.99 653.39, 625.46 661.86, 627.95 656.21, 655.06 667.99, 652.33 674.25, 658.16 676.79, 653.90 686.49, 678.06 696.99, 675.19 703.55, 699.05 713.93, 664.19 793.35, 609.64 769.64, 606.99 775.68, 561.09 755.72, 605.99 653.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,56,"POLYGON ((659.22 537.96, 674.17 544.46, 671.03 551.64, 789.23 602.81, 804.97 566.83, 825.90 575.86, 828.11 570.79, 844.62 577.89, 843.08 581.44, 863.28 590.05, 850.21 620.39, 844.83 618.09, 841.11 626.71, 900.00 651.72, 900.00 693.78, 816.07 657.20, 802.87 687.22, 883.74 722.49, 880.57 729.67, 874.51 727.01, 866.38 745.33, 874.06 748.69, 870.04 757.73, 784.26 719.99, 774.44 742.08, 751.38 731.19, 761.05 708.91, 680.99 673.74, 684.15 666.65, 687.79 668.26, 695.35 651.22, 690.08 648.92, 694.96 637.89, 769.53 670.64, 780.55 645.79, 654.21 590.36, 650.93 597.77, 635.81 591.16, 659.22 537.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,57,"POLYGON ((887.84 311.12, 900.00 310.93, 900.00 347.15, 888.41 347.33, 887.84 311.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,58,"POLYGON ((820.39 281.30, 840.07 281.84, 839.04 323.73, 833.41 323.88, 828.69 319.40, 824.88 317.63, 827.25 312.16, 829.86 307.72, 830.26 299.58, 825.36 295.76, 822.81 293.93, 819.88 294.01, 820.39 281.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,59,"POLYGON ((748.29 299.61, 747.48 260.74, 767.93 260.35, 768.77 299.15, 748.29 299.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,60,"POLYGON ((650.24 217.25, 664.56 216.59, 667.63 284.53, 653.33 285.18, 650.24 217.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,61,"POLYGON ((615.65 221.28, 620.38 221.07, 620.52 224.24, 620.67 226.61, 622.76 226.47, 624.20 225.39, 626.69 228.66, 629.16 228.51, 631.65 226.51, 632.39 224.32, 632.27 221.59, 637.29 221.35, 639.38 266.98, 617.81 267.98, 615.65 221.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,62,"POLYGON ((635.59 191.74, 650.89 191.70, 650.91 199.96, 640.92 199.99, 640.45 198.61, 638.53 197.28, 635.61 198.60, 635.59 191.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,63,"POLYGON ((873.71 175.25, 881.03 175.07, 881.05 167.78, 897.60 168.61, 897.16 191.92, 890.05 192.53, 885.86 191.81, 883.13 191.88, 878.27 189.50, 876.30 186.64, 873.61 187.33, 873.71 175.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,64,"POLYGON ((710.91 152.21, 749.29 151.21, 749.94 176.83, 772.39 176.26, 773.18 207.05, 712.35 208.60, 710.91 152.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,65,"POLYGON ((842.72 176.20, 856.27 175.81, 855.98 165.18, 863.74 164.96, 864.43 189.63, 843.11 190.22, 842.72 176.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,66,"POLYGON ((629.43 140.95, 654.71 140.20, 655.26 158.89, 651.52 159.01, 651.72 165.49, 630.18 166.13, 629.43 140.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,0,"POLYGON ((570.90 367.76, 578.81 402.30, 560.04 406.57, 553.23 376.84, 559.83 375.34, 558.73 370.51, 570.90 367.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,1,"POLYGON ((504.83 148.20, 519.08 147.68, 520.36 183.27, 547.06 182.30, 546.83 175.96, 554.46 175.68, 554.73 182.53, 573.11 181.82, 572.52 166.78, 581.70 166.41, 582.36 183.16, 612.59 181.96, 613.70 209.50, 583.33 210.75, 583.71 219.90, 586.90 219.78, 587.81 241.13, 547.37 242.85, 548.18 262.32, 523.84 270.87, 524.77 281.10, 454.09 309.01, 451.81 243.02, 506.70 241.14, 506.19 226.30, 499.41 226.54, 498.96 213.81, 507.17 213.51, 504.83 148.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,2,"POLYGON ((514.43 18.27, 515.20 28.91, 515.28 56.23, 510.16 56.25, 511.29 72.80, 501.39 73.08, 496.39 71.72, 494.59 69.52, 491.95 63.66, 493.20 18.66, 514.43 18.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,3,"POLYGON ((214.49 26.21, 217.24 23.65, 226.66 23.70, 226.65 28.19, 233.96 28.22, 235.34 32.87, 235.28 43.15, 228.28 50.83, 214.71 50.95, 214.49 26.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,4,"POLYGON ((245.24 26.38, 265.54 25.22, 270.61 27.60, 265.33 43.05, 245.99 44.61, 245.24 26.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,5,"POLYGON ((404.37 28.67, 426.48 27.35, 428.71 36.95, 405.36 38.30, 400.64 38.18, 399.15 28.56, 404.37 28.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,6,"POLYGON ((203.29 25.16, 203.50 37.72, 196.41 42.59, 186.37 42.53, 184.04 36.66, 183.88 26.03, 188.05 25.97, 187.97 21.54, 197.56 21.38, 197.62 25.26, 203.29 25.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,7,"POLYGON ((277.28 25.87, 277.33 22.68, 300.60 23.08, 302.54 29.58, 297.96 30.96, 298.02 38.95, 277.38 39.08, 277.28 25.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,8,"POLYGON ((338.91 25.01, 362.97 24.20, 362.28 36.00, 339.61 37.18, 338.91 25.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,9,"POLYGON ((478.56 12.08, 478.90 17.82, 482.43 17.62, 484.07 46.88, 471.38 47.58, 465.41 37.59, 464.02 12.90, 478.56 12.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,10,"POLYGON ((313.43 19.45, 320.99 19.08, 321.19 23.20, 330.19 22.77, 330.55 30.13, 323.84 37.78, 307.52 38.55, 306.84 24.12, 313.64 23.79, 313.43 19.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,11,"POLYGON ((430.87 21.18, 433.26 19.79, 443.26 20.42, 445.72 21.91, 451.26 21.57, 452.03 34.42, 431.74 35.63, 430.87 21.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,12,"POLYGON ((711.39 667.36, 713.79 772.29, 690.15 772.83, 687.77 667.89, 711.39 667.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,13,"POLYGON ((761.92 666.78, 763.70 765.60, 740.71 766.03, 738.91 667.21, 761.92 666.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,14,"POLYGON ((819.30 702.01, 835.16 702.11, 838.34 699.37, 844.55 699.21, 844.74 706.57, 832.98 709.51, 831.61 713.97, 819.23 713.91, 819.30 702.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,15,"POLYGON ((780.67 690.52, 786.25 690.62, 787.81 689.78, 788.77 688.20, 789.29 686.01, 791.39 682.63, 806.55 681.24, 807.30 679.67, 809.85 679.20, 813.62 680.09, 813.85 681.06, 817.81 681.35, 824.47 679.41, 830.08 679.51, 829.88 691.44, 826.95 691.38, 822.64 692.64, 818.59 696.85, 818.24 698.44, 818.66 699.60, 818.47 707.29, 813.00 709.58, 809.22 708.10, 806.46 708.26, 806.07 708.31, 803.50 708.65, 802.13 709.66, 792.53 712.66, 791.25 708.56, 790.02 706.84, 785.44 705.38, 783.92 707.57, 780.32 707.51, 780.67 690.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,16,"POLYGON ((619.73 655.49, 620.57 680.26, 606.00 680.74, 605.15 655.99, 619.73 655.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,17,"POLYGON ((727.84 624.49, 728.58 647.01, 684.85 648.43, 684.10 625.92, 727.84 624.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,18,"POLYGON ((829.05 630.37, 834.57 639.15, 821.42 642.04, 819.32 632.50, 829.05 630.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,19,"POLYGON ((727.06 569.91, 727.64 594.62, 694.25 595.41, 684.32 592.15, 683.81 570.94, 727.06 569.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,20,"POLYGON ((848.72 568.35, 849.17 583.86, 830.73 584.37, 830.47 575.10, 842.09 574.78, 841.92 568.55, 848.72 568.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,21,"POLYGON ((897.33 548.02, 900.00 547.99, 900.00 579.24, 897.74 579.27, 897.33 548.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,22,"POLYGON ((852.66 528.63, 852.80 536.35, 849.62 538.43, 849.95 556.25, 831.98 556.59, 831.68 540.93, 812.97 541.27, 812.76 529.38, 852.66 528.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,23,"POLYGON ((623.64 511.92, 625.76 538.37, 612.27 539.45, 611.78 533.33, 590.34 535.04, 589.04 518.75, 595.70 518.21, 595.37 514.17, 623.64 511.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,24,"POLYGON ((709.22 461.01, 711.38 549.17, 687.97 549.73, 685.81 461.59, 709.22 461.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,25,"POLYGON ((855.58 499.21, 856.05 509.01, 812.41 511.14, 811.91 501.32, 855.58 499.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,26,"POLYGON ((757.51 453.23, 760.83 479.65, 752.91 476.46, 749.80 478.98, 746.08 480.89, 746.58 488.70, 748.65 494.97, 752.64 517.53, 760.02 529.13, 759.85 545.03, 737.52 545.75, 735.04 454.38, 757.51 453.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,27,"POLYGON ((854.29 461.62, 854.67 481.65, 830.95 482.08, 830.71 468.74, 822.83 468.88, 822.71 462.20, 854.29 461.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,28,"POLYGON ((834.00 421.78, 834.65 432.06, 816.35 433.22, 811.05 430.42, 799.74 431.13, 799.29 423.95, 834.00 421.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,29,"POLYGON ((706.23 373.74, 707.32 454.09, 686.57 454.38, 685.48 374.03, 706.23 373.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,30,"POLYGON ((757.75 444.44, 736.71 444.93, 734.63 372.84, 758.53 371.87, 759.65 378.75, 764.93 378.55, 766.55 416.55, 760.64 419.85, 757.75 444.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,31,"POLYGON ((808.20 371.27, 823.40 370.90, 823.72 384.04, 852.75 383.34, 852.81 385.83, 858.25 385.69, 858.45 394.45, 853.22 394.56, 853.35 399.55, 830.33 400.12, 830.48 406.77, 809.07 407.30, 808.20 371.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,32,"POLYGON ((629.75 335.65, 631.29 355.23, 592.61 355.86, 592.83 350.60, 629.75 335.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,33,"POLYGON ((879.39 200.48, 878.83 320.79, 812.53 320.48, 812.43 342.88, 798.45 342.82, 798.59 312.82, 769.22 312.69, 769.09 342.28, 699.86 341.98, 700.18 273.00, 810.76 227.43, 811.15 313.10, 817.72 313.09, 817.31 225.68, 879.39 200.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,34,"POLYGON ((692.18 176.85, 708.00 176.09, 711.88 177.03, 712.70 195.57, 693.01 195.74, 692.18 176.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,35,"POLYGON ((706.46 73.88, 706.98 84.66, 710.07 84.49, 710.47 92.71, 705.64 97.06, 692.30 99.02, 685.87 94.74, 685.27 85.83, 696.07 85.11, 695.37 74.52, 700.72 74.16, 706.46 73.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,36,"POLYGON ((753.52 49.79, 753.43 64.49, 737.72 64.43, 737.79 49.71, 753.52 49.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,37,"POLYGON ((717.82 45.16, 718.14 53.98, 722.18 53.84, 722.52 63.42, 686.14 64.72, 685.49 46.31, 717.82 45.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,38,"POLYGON ((622.69 37.71, 622.90 53.46, 610.87 50.13, 601.65 52.69, 595.10 56.48, 592.56 61.52, 585.01 61.62, 584.69 38.23, 622.69 37.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,39,"POLYGON ((706.66 17.56, 706.46 35.79, 689.59 35.62, 689.73 25.01, 697.55 18.97, 699.54 14.23, 706.65 14.21, 706.66 17.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,40,"POLYGON ((603.38 5.10, 615.57 14.69, 615.80 27.47, 575.50 28.21, 575.30 16.92, 586.82 16.71, 586.62 5.42, 603.38 5.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,41,"POLYGON ((824.72 0.00, 825.44 30.64, 806.90 31.10, 806.17 0.00, 824.72 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,42,"POLYGON ((846.22 0.00, 846.05 21.15, 842.74 22.34, 842.69 29.09, 827.96 28.98, 828.18 0.00, 846.22 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,43,"POLYGON ((802.91 0.00, 803.89 21.40, 802.43 29.50, 785.33 30.29, 783.93 0.00, 802.91 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,44,"POLYGON ((900.00 20.69, 897.21 20.78, 897.37 26.34, 881.80 26.79, 881.05 0.00, 900.00 0.00, 900.00 20.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,45,"POLYGON ((766.86 790.65, 767.94 869.20, 746.10 869.49, 743.63 866.03, 742.63 790.98, 766.86 790.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,46,"POLYGON ((713.71 775.60, 717.07 871.83, 698.54 872.48, 694.08 871.42, 690.76 776.41, 713.71 775.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,47,"POLYGON ((850.63 784.00, 850.89 795.92, 854.69 795.84, 854.90 804.61, 821.51 805.35, 821.06 784.65, 850.63 784.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,48,"POLYGON ((583.73 764.33, 585.46 796.83, 559.18 798.23, 557.46 765.71, 583.73 764.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,49,"POLYGON ((739.19 769.07, 740.13 785.38, 733.35 785.78, 732.40 769.46, 739.19 769.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,50,"POLYGON ((852.95 746.61, 853.10 766.16, 844.70 766.22, 844.74 772.03, 818.37 772.24, 818.20 751.25, 827.83 751.18, 827.79 746.78, 852.95 746.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,51,"POLYGON ((575.21 734.29, 613.08 733.31, 613.10 748.05, 588.28 748.10, 580.43 741.82, 575.21 734.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,0,"POLYGON ((725.02 692.67, 725.14 697.39, 731.48 697.23, 732.07 719.75, 725.88 719.93, 725.95 722.52, 695.74 723.38, 695.88 728.09, 682.75 728.49, 682.46 718.44, 691.34 718.17, 695.57 710.22, 695.29 698.44, 713.54 698.00, 713.43 692.96, 725.02 692.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,1,"POLYGON ((820.90 671.55, 835.12 671.23, 835.18 673.52, 843.21 673.33, 843.42 682.30, 849.81 682.16, 849.91 686.88, 844.61 687.00, 844.70 691.19, 850.43 691.06, 851.05 717.58, 822.00 718.27, 820.90 671.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,2,"POLYGON ((774.38 670.36, 783.67 670.24, 783.77 678.74, 787.46 678.69, 787.81 706.25, 784.34 706.29, 784.47 717.21, 773.76 717.35, 773.64 706.88, 767.32 706.95, 766.96 679.90, 774.49 679.80, 774.38 670.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,3,"POLYGON ((795.69 661.83, 807.23 661.93, 807.14 670.70, 814.74 670.78, 814.63 681.59, 817.82 681.62, 817.62 701.36, 815.23 701.33, 815.08 717.25, 800.11 717.10, 800.27 701.54, 795.28 701.48, 795.69 661.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,4,"POLYGON ((899.75 671.00, 900.00 671.75, 900.00 702.25, 885.42 703.31, 883.90 682.91, 885.23 681.90, 884.74 674.92, 896.27 674.09, 896.07 671.25, 899.75 671.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,5,"POLYGON ((795.93 645.75, 806.44 646.15, 806.07 655.88, 805.31 658.50, 795.47 658.13, 795.93 645.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,6,"POLYGON ((724.58 641.51, 724.62 646.64, 719.97 646.67, 720.05 655.88, 703.64 656.03, 703.70 661.00, 684.76 661.18, 684.63 646.64, 692.14 646.56, 692.12 643.50, 698.52 643.44, 698.55 647.59, 704.82 647.54, 704.75 639.24, 710.93 639.20, 710.95 641.64, 724.58 641.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,7,"POLYGON ((834.80 638.30, 848.86 637.63, 849.32 647.56, 835.26 648.21, 834.80 638.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,8,"POLYGON ((743.31 623.74, 746.72 623.72, 746.69 620.33, 751.05 620.28, 751.08 623.90, 759.28 623.82, 759.36 632.72, 743.39 632.86, 743.31 623.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,9,"POLYGON ((900.00 632.38, 897.25 632.42, 886.06 632.35, 886.15 615.46, 891.84 615.49, 891.87 609.98, 900.00 610.03, 900.00 632.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,10,"POLYGON ((683.48 614.41, 713.00 614.82, 714.53 608.76, 740.04 609.24, 739.87 618.28, 725.62 618.02, 725.38 630.73, 709.37 630.42, 707.85 632.53, 698.79 632.16, 698.25 630.69, 683.37 630.60, 683.48 614.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,11,"POLYGON ((823.74 618.49, 823.92 630.78, 806.81 631.04, 806.43 605.26, 825.81 604.96, 825.89 610.33, 822.40 610.40, 822.47 615.22, 827.07 615.14, 827.13 618.43, 823.74 618.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,12,"POLYGON ((900.00 604.73, 899.73 604.74, 899.55 598.38, 892.10 598.57, 891.91 592.13, 886.48 592.29, 886.19 581.62, 900.00 581.25, 900.00 604.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,13,"POLYGON ((694.18 589.77, 698.19 589.87, 698.51 575.52, 724.95 576.08, 726.76 578.50, 727.54 591.71, 725.99 592.75, 726.05 601.81, 690.38 602.05, 690.37 600.30, 684.21 600.34, 684.17 589.82, 694.18 589.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,14,"POLYGON ((826.90 575.21, 826.65 597.51, 806.60 597.22, 806.87 578.88, 809.13 578.91, 809.23 573.00, 820.82 573.17, 821.32 575.20, 826.90 575.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,15,"POLYGON ((900.00 572.78, 891.07 572.80, 886.80 571.29, 886.77 554.02, 900.00 553.99, 900.00 572.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,16,"POLYGON ((724.19 560.85, 719.64 565.59, 726.07 565.47, 726.17 570.79, 715.55 571.02, 712.50 568.50, 700.70 569.16, 697.41 571.66, 692.32 572.03, 686.13 567.11, 686.07 564.18, 684.03 564.21, 683.78 550.08, 723.97 549.34, 724.19 560.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,17,"POLYGON ((806.18 548.79, 826.08 548.69, 828.61 550.66, 828.82 557.30, 823.98 557.44, 824.23 566.42, 813.78 566.71, 807.01 566.78, 806.93 556.50, 803.93 556.53, 803.89 552.09, 806.21 552.08, 806.18 548.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,18,"POLYGON ((757.19 522.45, 769.57 522.25, 769.80 536.12, 757.41 536.32, 757.19 522.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,19,"POLYGON ((900.00 541.30, 886.04 541.38, 886.00 532.75, 893.31 532.72, 888.72 526.80, 884.63 526.88, 884.41 516.10, 891.38 515.94, 900.00 523.28, 900.00 541.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,20,"POLYGON ((708.24 511.94, 740.30 511.21, 740.53 520.61, 708.46 521.32, 708.24 511.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,21,"POLYGON ((886.59 496.40, 886.57 493.11, 900.00 492.97, 900.00 514.37, 884.63 514.09, 884.92 497.13, 886.58 497.15, 886.59 496.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,22,"POLYGON ((888.79 464.09, 888.71 461.07, 900.00 460.75, 900.00 483.23, 893.29 483.42, 893.17 478.71, 885.18 478.94, 884.79 464.19, 888.79 464.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,23,"POLYGON ((795.15 473.67, 804.80 465.74, 804.59 455.47, 816.12 455.22, 816.36 465.91, 822.16 465.79, 822.49 481.07, 818.05 481.19, 818.16 486.56, 785.39 487.26, 785.12 473.88, 795.15 473.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,24,"POLYGON ((692.97 454.59, 706.09 454.90, 705.44 483.57, 693.50 483.30, 693.60 478.37, 686.90 478.23, 687.13 468.30, 692.64 468.43, 692.97 454.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,25,"POLYGON ((889.22 440.17, 889.23 432.04, 900.00 432.06, 900.00 454.28, 882.35 454.24, 882.38 440.16, 889.22 440.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,26,"POLYGON ((692.69 426.72, 703.10 426.14, 703.29 429.89, 709.62 429.55, 710.07 437.88, 708.09 442.19, 709.28 443.72, 709.51 444.96, 709.03 446.19, 708.38 447.74, 707.85 448.86, 707.30 449.98, 706.63 451.24, 705.87 452.60, 705.14 453.81, 687.78 453.86, 690.41 449.93, 690.78 448.56, 690.74 447.12, 690.30 445.78, 687.37 445.85, 685.22 443.71, 683.90 441.35, 683.87 439.99, 684.57 438.62, 686.21 436.65, 688.60 433.66, 687.56 432.82, 687.41 431.27, 688.33 430.42, 692.19 429.59, 692.79 428.64, 692.69 426.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,27,"POLYGON ((715.40 401.70, 715.54 405.87, 711.30 411.75, 712.59 413.64, 714.71 414.46, 716.16 415.99, 716.05 418.44, 716.63 420.71, 722.44 420.92, 718.50 424.86, 683.33 426.16, 682.47 402.89, 715.40 401.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,28,"POLYGON ((680.50 369.11, 701.76 368.75, 712.53 377.22, 717.99 377.61, 719.89 376.34, 722.18 375.75, 724.13 376.57, 725.93 378.27, 726.34 380.37, 726.67 393.33, 711.87 392.64, 708.42 395.53, 706.33 395.76, 703.86 395.64, 702.06 394.11, 701.60 389.93, 699.27 388.06, 696.10 388.14, 694.56 389.58, 693.19 391.19, 691.46 392.47, 689.00 392.54, 685.83 391.91, 683.86 390.91, 681.90 389.90, 680.28 388.21, 680.94 386.26, 680.50 369.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,29,"POLYGON ((808.35 347.73, 824.41 346.37, 826.05 385.69, 823.26 387.00, 821.69 391.52, 824.91 398.41, 826.46 401.81, 820.70 401.34, 816.95 398.19, 815.74 396.14, 815.88 392.72, 815.15 388.98, 814.78 386.79, 812.61 383.74, 814.83 380.56, 810.72 375.04, 809.61 372.48, 808.35 347.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,30,"POLYGON ((872.15 370.83, 869.11 370.40, 865.99 367.06, 867.24 363.68, 867.14 362.13, 878.20 361.45, 877.88 356.19, 886.95 355.65, 887.27 361.04, 894.79 360.60, 896.48 388.04, 891.72 388.31, 891.94 391.84, 883.55 392.36, 883.33 388.86, 876.66 389.28, 874.74 386.77, 872.15 370.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,31,"POLYGON ((780.21 368.68, 779.64 347.89, 783.33 347.80, 783.13 340.72, 801.32 340.23, 801.50 346.84, 805.04 346.73, 805.83 376.10, 786.78 376.61, 786.67 372.35, 785.59 370.89, 782.57 368.62, 780.21 368.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,32,"POLYGON ((717.84 337.35, 719.15 339.93, 719.36 344.83, 719.75 349.73, 719.45 351.85, 714.11 356.18, 713.70 354.08, 709.28 353.15, 704.72 353.80, 703.71 355.73, 704.19 360.63, 706.41 364.77, 704.00 367.11, 698.33 367.28, 698.14 360.40, 682.53 360.85, 681.90 338.38, 717.84 337.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,33,"POLYGON ((717.08 258.94, 717.98 260.27, 717.99 270.06, 716.50 270.59, 715.03 270.76, 713.68 271.79, 713.22 273.27, 710.54 275.20, 709.95 276.70, 709.99 278.43, 709.04 279.57, 707.55 280.23, 706.77 283.60, 705.68 284.74, 704.20 285.51, 702.71 285.19, 701.21 284.47, 699.70 284.27, 698.48 285.30, 694.80 287.24, 686.09 287.35, 681.49 282.14, 681.76 273.21, 690.45 267.92, 699.78 267.93, 702.34 270.22, 712.48 258.95, 717.08 258.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,34,"POLYGON ((734.97 258.77, 751.38 258.69, 751.44 265.88, 747.49 268.04, 747.52 271.37, 735.05 271.45, 734.97 258.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,35,"POLYGON ((851.13 272.06, 843.33 272.12, 843.09 279.34, 824.40 279.55, 824.15 275.65, 821.10 276.02, 823.01 263.74, 826.86 261.42, 828.87 258.04, 827.18 240.99, 832.67 237.65, 839.91 237.18, 840.14 229.27, 850.17 228.46, 851.57 261.35, 851.13 272.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,36,"POLYGON ((887.28 238.61, 894.14 239.15, 893.04 229.03, 900.00 228.75, 900.00 277.60, 888.40 276.81, 888.67 271.10, 891.98 269.35, 892.29 265.46, 892.04 261.00, 888.37 259.30, 888.20 257.90, 887.54 254.72, 887.06 251.96, 887.89 245.70, 887.28 238.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,37,"POLYGON ((4.85 877.72, 11.18 884.44, 5.02 890.22, 7.57 892.91, 13.85 896.96, 11.87 900.00, 6.64 900.00, 6.91 899.58, 2.41 894.83, 0.00 897.09, 0.00 882.27, 4.85 877.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,38,"POLYGON ((82.01 891.94, 79.02 900.00, 72.95 900.00, 76.66 889.99, 82.01 891.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,39,"POLYGON ((142.02 892.30, 147.05 892.17, 152.22 886.44, 165.35 890.81, 164.96 898.77, 164.54 900.00, 146.94 900.00, 142.02 892.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,40,"POLYGON ((378.39 779.58, 412.74 786.99, 410.48 797.30, 415.66 803.36, 414.06 809.88, 403.35 818.68, 385.40 883.78, 392.80 885.79, 389.96 896.05, 353.00 885.92, 349.36 882.77, 352.68 867.63, 353.41 864.80, 360.35 866.57, 375.72 807.24, 365.35 804.58, 370.25 785.67, 373.69 780.88, 378.39 779.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,41,"POLYGON ((98.62 833.40, 92.16 833.81, 92.57 798.22, 87.12 798.16, 87.43 793.78, 107.94 794.30, 110.71 820.86, 104.76 825.39, 98.62 833.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,42,"POLYGON ((447.81 728.71, 443.27 788.58, 426.73 787.34, 428.85 759.30, 425.21 759.01, 426.01 748.34, 428.69 748.53, 430.30 727.38, 447.81 728.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,43,"POLYGON ((97.04 729.04, 97.93 742.12, 69.12 744.07, 68.21 731.00, 97.04 729.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,44,"POLYGON ((391.53 692.17, 397.70 690.52, 397.61 691.90, 412.81 687.92, 415.47 692.75, 418.24 693.11, 423.66 717.12, 412.60 718.42, 411.33 709.09, 404.62 708.86, 402.93 699.73, 394.41 701.62, 391.53 692.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,45,"POLYGON ((178.11 667.35, 178.44 673.83, 168.09 674.42, 160.88 681.27, 161.87 699.44, 169.36 703.80, 175.66 709.44, 173.56 716.66, 164.87 720.57, 160.74 717.17, 161.28 710.67, 161.38 707.87, 154.97 704.70, 152.35 698.82, 153.33 681.81, 159.69 669.58, 168.08 667.61, 178.11 667.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,46,"POLYGON ((64.37 677.69, 80.59 676.70, 81.14 685.79, 84.91 686.11, 84.75 689.45, 81.06 692.47, 67.28 693.02, 67.78 687.79, 65.17 684.53, 64.37 677.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,47,"POLYGON ((133.51 607.25, 133.77 625.46, 134.10 635.96, 104.04 636.86, 104.50 651.98, 82.09 652.66, 81.93 647.21, 66.57 647.67, 66.13 633.47, 68.59 624.48, 110.51 623.84, 110.26 607.59, 133.51 607.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,48,"POLYGON ((226.92 595.99, 231.84 603.36, 231.31 605.73, 226.23 609.41, 221.99 611.43, 219.73 610.02, 217.09 605.36, 216.13 602.01, 226.92 595.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,49,"POLYGON ((348.55 593.10, 354.04 587.97, 356.47 592.19, 372.06 583.31, 384.41 604.84, 377.11 608.98, 372.57 611.40, 375.90 617.58, 372.35 619.47, 364.16 604.21, 356.64 608.19, 348.55 593.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,50,"POLYGON ((79.25 565.43, 92.12 574.69, 86.87 581.93, 74.00 572.69, 79.25 565.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,51,"POLYGON ((438.33 553.57, 474.43 580.10, 470.36 585.56, 465.98 582.34, 457.93 593.18, 426.23 569.88, 438.33 553.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,52,"POLYGON ((422.70 526.86, 438.07 544.87, 422.31 558.10, 407.09 539.96, 422.70 526.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,53,"POLYGON ((347.45 463.23, 368.96 462.53, 369.94 492.91, 373.26 494.89, 373.36 498.86, 371.94 501.54, 372.50 505.94, 379.55 509.74, 392.91 509.31, 394.43 556.40, 383.22 556.77, 383.01 550.23, 343.75 551.49, 341.58 484.82, 348.14 484.61, 347.45 463.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,54,"POLYGON ((41.64 507.26, 64.17 494.07, 68.97 502.70, 46.08 515.53, 41.64 507.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,55,"POLYGON ((150.80 457.41, 162.10 456.92, 162.57 468.21, 157.93 474.52, 153.07 470.98, 152.94 468.01, 151.25 468.08, 150.80 457.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,56,"POLYGON ((893.36 889.37, 900.00 889.10, 900.00 900.00, 893.81 900.00, 893.36 889.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,57,"POLYGON ((839.25 883.74, 839.42 890.26, 841.12 895.21, 844.14 896.56, 844.23 900.00, 816.56 900.00, 816.35 883.15, 823.30 883.08, 823.27 880.26, 838.15 880.06, 839.25 883.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,58,"POLYGON ((717.46 886.50, 732.04 886.26, 731.92 879.47, 739.04 879.35, 738.98 875.47, 751.95 875.25, 752.37 900.00, 717.70 900.00, 717.46 886.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,59,"POLYGON ((794.64 814.58, 800.71 814.62, 804.92 817.65, 819.82 817.78, 819.73 830.30, 794.52 830.12, 794.64 814.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,60,"POLYGON ((719.87 799.68, 719.91 801.90, 724.24 801.84, 724.64 823.74, 734.78 823.54, 734.90 830.71, 695.90 831.44, 695.59 814.33, 702.73 814.19, 702.82 819.14, 704.51 819.12, 704.16 799.97, 719.87 799.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,61,"POLYGON ((845.57 775.19, 845.04 791.83, 849.09 791.97, 848.84 799.70, 843.98 799.56, 843.75 806.66, 825.04 806.08, 826.10 773.24, 841.63 773.71, 842.95 775.10, 845.57 775.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,62,"POLYGON ((900.00 802.47, 888.35 802.75, 888.17 795.52, 884.62 790.77, 884.34 785.23, 882.56 782.99, 882.39 777.15, 900.00 776.65, 900.00 802.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,63,"POLYGON ((82.45 444.02, 101.45 443.62, 103.48 447.14, 103.49 453.11, 98.91 453.12, 98.92 456.54, 88.29 456.56, 88.29 454.32, 82.69 454.33, 82.45 444.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,64,"POLYGON ((129.17 444.31, 142.32 444.07, 142.51 455.40, 129.39 455.65, 129.17 444.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,65,"POLYGON ((97.36 402.04, 97.74 411.15, 102.30 410.95, 103.21 432.17, 80.87 433.13, 79.58 402.80, 97.36 402.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,66,"POLYGON ((138.38 393.15, 152.00 392.63, 153.34 424.94, 151.87 426.09, 114.22 427.58, 113.51 409.97, 123.93 409.57, 123.78 405.80, 138.86 405.22, 138.38 393.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,67,"POLYGON ((0.00 394.33, 2.16 393.94, 4.81 391.48, 6.21 392.68, 8.04 395.24, 13.06 395.22, 16.17 394.41, 18.95 392.87, 19.64 410.97, 16.71 411.04, 13.06 415.62, 10.66 419.74, 0.00 419.65, 0.00 394.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,68,"POLYGON ((142.52 289.18, 153.71 293.98, 165.28 312.42, 164.63 335.73, 149.10 354.50, 129.33 358.33, 129.24 362.00, 130.21 364.95, 126.31 370.67, 118.99 366.66, 124.25 358.82, 106.29 351.57, 67.05 350.64, 66.81 347.85, 45.66 346.11, 41.83 354.44, 32.15 354.51, 33.91 340.81, 30.72 340.18, 27.86 338.50, 26.06 336.11, 25.78 332.61, 27.27 329.24, 31.63 326.86, 34.37 317.34, 34.45 306.48, 33.62 301.60, 28.82 299.63, 26.94 295.11, 28.08 291.59, 31.02 289.05, 33.98 288.29, 42.01 285.98, 42.43 282.30, 47.52 281.82, 47.58 283.90, 71.41 286.80, 110.16 289.32, 122.04 286.22, 127.47 285.37, 127.70 280.46, 131.40 274.06, 136.84 280.23, 136.82 286.71, 142.52 289.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,69,"POLYGON ((267.71 3.43, 280.69 5.59, 285.69 16.09, 286.24 18.17, 288.60 17.66, 291.16 31.29, 286.46 32.59, 282.55 35.62, 275.55 39.64, 265.23 40.92, 260.12 37.52, 255.75 28.35, 252.56 19.47, 256.72 8.75, 267.71 3.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,70,"POLYGON ((320.69 0.00, 319.42 5.15, 316.98 8.17, 313.49 10.48, 307.43 10.48, 303.32 5.56, 301.59 1.79, 302.02 0.00, 320.69 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,71,"POLYGON ((854.83 225.15, 875.85 223.08, 877.97 267.75, 880.36 268.40, 880.80 274.63, 878.30 274.69, 878.48 276.64, 854.61 276.83, 856.15 260.81, 854.83 225.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,72,"POLYGON ((684.73 247.23, 684.76 235.35, 689.31 235.37, 689.32 232.28, 701.99 232.29, 701.98 243.48, 705.45 243.48, 705.44 246.79, 708.50 246.77, 708.49 253.14, 688.96 253.11, 688.98 247.23, 684.73 247.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,73,"POLYGON ((709.79 207.25, 710.52 227.94, 683.88 229.04, 679.17 224.90, 679.10 217.82, 688.10 214.37, 690.26 212.23, 689.82 207.67, 709.79 207.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,74,"POLYGON ((679.75 177.94, 710.42 179.13, 711.85 197.94, 699.84 198.87, 679.75 198.23, 679.75 177.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,75,"POLYGON ((684.88 122.87, 718.83 120.38, 721.74 124.88, 720.77 126.81, 719.33 128.60, 718.79 130.39, 718.85 132.74, 718.59 134.53, 718.34 136.29, 717.80 138.08, 717.10 139.85, 715.80 141.37, 714.67 142.71, 713.68 144.80, 712.84 146.59, 711.70 148.38, 689.97 148.64, 689.17 146.31, 678.96 146.13, 679.01 124.77, 684.88 122.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,76,"POLYGON ((677.67 95.39, 716.70 94.64, 716.87 106.27, 721.86 106.14, 723.00 107.11, 724.50 108.07, 725.66 109.02, 726.80 110.10, 727.10 112.33, 727.14 114.06, 727.08 116.04, 687.56 117.43, 679.51 118.12, 677.88 113.35, 677.67 95.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,77,"POLYGON ((679.61 64.15, 706.84 63.57, 715.98 71.51, 718.25 72.20, 717.61 76.41, 717.02 78.05, 718.77 78.12, 718.31 79.62, 717.23 81.02, 716.26 82.40, 715.32 83.91, 715.23 85.40, 715.51 87.12, 715.44 88.72, 686.14 91.09, 683.58 88.18, 679.32 86.94, 679.61 64.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,78,"POLYGON ((692.42 32.04, 692.45 33.93, 693.13 36.24, 694.88 36.82, 696.15 37.65, 698.40 37.86, 701.04 39.01, 701.56 40.49, 703.63 43.16, 705.12 43.50, 706.48 42.85, 707.56 41.58, 711.80 36.89, 711.76 40.25, 711.77 41.71, 711.85 43.31, 711.98 44.79, 712.13 46.28, 712.38 48.13, 712.63 49.62, 713.79 55.89, 713.10 58.64, 692.98 59.77, 689.42 56.78, 685.56 56.74, 683.05 50.61, 682.80 32.17, 692.42 32.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,79,"POLYGON ((791.99 8.39, 809.03 8.46, 809.39 27.26, 806.67 27.57, 804.82 28.50, 802.88 30.40, 803.20 33.50, 794.76 34.20, 792.75 28.19, 791.99 8.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,0,"POLYGON ((69.96 571.69, 67.43 574.86, 67.58 577.14, 71.54 580.68, 75.36 582.67, 79.96 582.77, 81.73 582.31, 81.74 587.19, 77.73 585.43, 66.13 590.10, 58.60 597.99, 45.69 598.59, 45.61 597.08, 39.85 597.34, 38.69 572.84, 41.54 572.72, 49.70 576.98, 56.73 577.95, 60.33 575.88, 63.07 572.06, 69.96 571.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,1,"POLYGON ((267.33 570.25, 268.06 585.12, 265.01 589.91, 265.28 595.32, 253.46 595.91, 253.62 599.12, 241.43 599.72, 241.18 594.40, 230.35 594.92, 230.20 592.02, 219.57 592.53, 218.60 572.62, 267.33 570.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,2,"POLYGON ((389.47 552.37, 390.10 568.53, 350.75 570.09, 350.12 553.93, 389.47 552.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,3,"POLYGON ((258.14 529.26, 258.67 540.50, 262.56 540.31, 262.93 548.34, 258.21 548.55, 259.06 566.86, 224.13 568.49, 223.55 556.09, 239.85 555.34, 238.91 535.25, 251.92 534.66, 251.68 529.58, 258.14 529.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,4,"POLYGON ((209.53 529.77, 211.02 556.79, 205.74 559.96, 208.22 564.27, 196.19 564.96, 197.52 559.00, 197.02 554.17, 190.15 538.39, 194.44 530.60, 209.53 529.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,5,"POLYGON ((118.23 525.17, 129.96 524.72, 130.62 542.10, 125.79 542.29, 126.27 554.76, 103.75 555.62, 103.20 541.54, 101.29 541.63, 101.06 535.44, 104.67 535.31, 104.25 524.66, 112.57 524.34, 112.72 528.04, 118.32 527.81, 118.23 525.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,6,"POLYGON ((95.95 526.78, 99.07 527.32, 100.88 544.10, 97.31 546.17, 94.91 549.58, 97.63 553.60, 75.88 554.51, 74.65 525.59, 83.96 525.20, 84.07 527.75, 88.51 527.55, 88.39 525.07, 93.37 524.85, 95.95 526.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,7,"POLYGON ((53.76 525.37, 55.56 527.68, 59.43 528.20, 62.35 525.66, 65.47 525.56, 65.75 534.43, 61.41 534.57, 61.88 549.49, 41.02 550.16, 40.55 535.41, 37.16 535.52, 36.84 525.91, 53.76 525.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,8,"POLYGON ((402.28 545.29, 401.80 529.39, 419.69 528.87, 420.15 544.75, 402.28 545.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,9,"POLYGON ((383.91 520.03, 384.61 544.52, 352.46 545.45, 351.76 520.97, 383.91 520.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,10,"POLYGON ((430.06 513.35, 454.08 512.78, 454.96 550.67, 445.97 550.88, 445.74 540.54, 430.69 540.90, 430.06 513.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,11,"POLYGON ((583.55 434.00, 584.33 474.87, 523.24 476.03, 522.76 450.80, 541.06 450.47, 540.75 434.83, 583.55 434.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,12,"POLYGON ((422.39 427.02, 422.39 465.35, 408.68 465.35, 408.70 458.69, 406.79 452.30, 397.39 445.13, 397.39 427.03, 422.39 427.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,13,"POLYGON ((428.33 425.71, 436.00 425.63, 435.98 423.39, 454.19 423.17, 454.60 459.60, 441.23 459.75, 441.28 465.45, 428.79 465.59, 428.33 425.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,14,"POLYGON ((359.39 460.30, 335.19 460.74, 334.67 431.70, 337.14 430.46, 338.71 428.20, 358.80 427.84, 359.39 460.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,15,"POLYGON ((517.96 463.29, 506.66 463.45, 506.60 458.48, 495.31 458.61, 494.95 429.94, 501.20 429.85, 501.13 424.83, 517.48 424.63, 517.96 463.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,16,"POLYGON ((387.82 428.34, 390.60 428.36, 390.40 452.99, 382.57 448.72, 375.64 450.39, 372.28 454.42, 367.12 457.29, 366.57 444.91, 376.79 446.63, 385.90 442.44, 387.75 437.20, 387.82 428.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,17,"POLYGON ((487.28 419.97, 487.74 451.68, 471.70 451.91, 471.61 445.16, 464.15 445.26, 463.95 430.17, 470.07 430.09, 470.04 427.62, 477.44 427.50, 477.33 420.11, 487.28 419.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,18,"POLYGON ((287.60 389.39, 289.64 465.06, 282.43 465.24, 282.56 469.72, 272.65 469.98, 272.50 464.12, 265.11 464.31, 264.05 425.69, 243.77 426.25, 242.79 389.89, 264.03 389.31, 263.98 387.69, 275.99 387.36, 276.05 389.71, 287.60 389.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,19,"POLYGON ((570.32 368.43, 571.15 384.02, 543.68 385.47, 542.86 369.89, 570.32 368.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,20,"POLYGON ((247.37 352.79, 281.44 352.39, 281.75 377.55, 249.14 377.94, 249.00 365.80, 247.52 365.82, 247.37 352.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,21,"POLYGON ((113.61 252.61, 114.37 312.03, 147.66 311.61, 147.22 275.92, 177.40 275.53, 181.01 286.27, 187.13 296.50, 195.22 301.71, 219.47 301.52, 219.99 343.83, 212.78 343.93, 213.20 377.17, 138.96 378.11, 140.08 466.37, 41.33 467.62, 40.46 398.07, 7.46 398.49, 5.63 253.98, 113.61 252.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,22,"POLYGON ((252.40 326.20, 259.13 325.90, 261.41 329.53, 266.94 332.87, 271.23 332.07, 276.79 325.71, 282.06 325.67, 282.29 346.97, 277.30 347.01, 277.31 348.25, 246.52 348.57, 246.46 343.47, 248.11 342.45, 249.08 339.36, 248.73 331.91, 252.65 331.72, 252.40 326.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,23,"POLYGON ((551.71 316.30, 552.55 347.73, 462.61 350.12, 462.01 327.25, 475.58 326.88, 475.47 322.75, 504.68 321.98, 504.56 317.55, 551.71 316.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,24,"POLYGON ((457.93 256.01, 459.02 298.02, 450.53 298.24, 451.99 353.72, 341.74 356.60, 341.41 344.31, 343.71 344.25, 342.43 294.76, 344.35 294.71, 343.70 269.82, 342.29 269.86, 341.95 256.55, 391.39 255.24, 391.46 257.75, 457.93 256.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,25,"POLYGON ((273.13 265.90, 266.73 269.90, 267.06 276.55, 260.99 286.34, 261.11 288.93, 229.83 290.37, 228.76 267.45, 273.13 265.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,26,"POLYGON ((559.77 257.42, 560.17 275.82, 527.88 276.51, 527.49 258.12, 559.77 257.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,27,"POLYGON ((826.43 26.30, 825.78 43.79, 816.52 43.71, 811.60 47.10, 800.62 45.98, 800.21 24.33, 818.05 23.72, 821.27 26.90, 826.43 26.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,28,"POLYGON ((870.23 20.79, 878.34 18.51, 879.41 11.41, 889.61 9.68, 891.14 12.35, 895.32 11.82, 897.33 17.19, 900.00 16.50, 900.00 33.00, 870.58 34.52, 870.23 20.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,29,"POLYGON ((740.79 14.61, 744.55 14.50, 744.46 11.66, 755.80 11.28, 755.86 12.81, 759.43 12.70, 760.10 32.43, 747.54 32.87, 747.42 29.50, 741.30 29.70, 740.79 14.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,30,"POLYGON ((674.78 10.64, 699.45 9.37, 700.52 30.07, 675.85 31.32, 674.78 10.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,31,"POLYGON ((798.03 5.16, 823.34 4.18, 823.65 16.43, 815.52 15.21, 798.64 14.96, 798.03 5.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,32,"POLYGON ((900.00 7.44, 897.28 7.44, 897.27 0.00, 900.00 0.00, 900.00 7.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,33,"POLYGON ((894.54 0.00, 894.66 7.31, 872.22 7.73, 872.08 0.00, 894.54 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,34,"POLYGON ((761.58 0.00, 761.82 7.40, 739.06 7.98, 738.91 0.00, 761.58 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,35,"POLYGON ((700.90 0.00, 701.05 3.75, 674.19 4.79, 674.00 0.00, 700.90 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,36,"POLYGON ((828.10 466.32, 807.23 466.71, 806.80 433.87, 817.64 433.73, 817.68 436.57, 822.02 436.50, 822.28 454.74, 827.82 454.67, 828.10 466.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,37,"POLYGON ((779.20 462.86, 759.34 462.94, 759.21 433.00, 779.09 432.92, 779.20 462.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,38,"POLYGON ((734.44 431.66, 755.63 431.54, 755.79 462.44, 748.89 462.48, 748.88 456.77, 734.56 456.85, 734.44 431.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,39,"POLYGON ((880.98 424.83, 896.20 424.60, 896.30 440.20, 900.00 440.19, 900.00 458.45, 881.55 458.67, 880.98 424.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,40,"POLYGON ((686.64 407.31, 698.13 407.01, 698.55 423.45, 686.84 423.75, 686.64 407.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,41,"POLYGON ((850.52 407.63, 850.85 418.43, 820.09 419.35, 819.97 415.40, 816.19 415.52, 815.99 408.13, 823.79 407.89, 823.71 405.60, 834.44 405.29, 836.90 408.02, 850.52 407.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,42,"POLYGON ((879.11 392.87, 897.06 392.88, 897.05 395.41, 900.00 395.40, 900.00 408.34, 898.05 408.34, 898.03 416.29, 888.24 416.30, 885.42 409.71, 882.74 407.87, 882.99 403.82, 885.06 402.02, 883.90 398.21, 879.11 392.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,43,"POLYGON ((752.57 374.10, 753.52 403.22, 751.29 403.28, 751.51 409.78, 734.86 410.32, 734.31 393.26, 736.51 393.18, 735.97 376.12, 740.78 375.96, 740.75 374.47, 752.57 374.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,44,"POLYGON ((774.72 373.89, 775.32 405.86, 766.48 406.02, 766.37 401.16, 761.57 401.24, 761.06 374.15, 774.72 373.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,45,"POLYGON ((802.95 373.13, 803.82 402.34, 781.98 402.99, 781.12 373.77, 802.95 373.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,46,"POLYGON ((726.34 373.64, 727.30 399.76, 713.94 400.26, 713.00 374.11, 726.34 373.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,47,"POLYGON ((806.34 370.80, 817.88 370.48, 818.22 382.35, 823.11 382.20, 823.38 391.81, 819.84 391.90, 820.12 401.63, 807.23 402.01, 806.34 370.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,48,"POLYGON ((851.90 376.58, 852.35 394.44, 828.21 395.06, 827.75 377.20, 851.90 376.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,49,"POLYGON ((708.56 395.56, 699.77 395.85, 699.68 393.06, 697.01 390.64, 693.06 390.01, 688.91 390.82, 685.09 391.70, 684.86 379.12, 685.73 379.09, 690.82 379.21, 695.77 377.84, 697.25 376.23, 698.55 374.93, 701.16 374.84, 705.50 374.62, 707.89 375.42, 708.56 395.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,50,"POLYGON ((875.50 373.40, 900.00 373.32, 900.00 390.68, 875.55 390.78, 875.50 373.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,51,"POLYGON ((850.94 314.52, 851.81 348.41, 840.73 348.70, 840.58 342.86, 824.26 343.08, 824.18 315.20, 850.94 314.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,52,"POLYGON ((787.67 344.79, 766.87 345.32, 766.08 314.22, 786.87 313.69, 787.67 344.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,53,"POLYGON ((700.03 309.54, 700.07 339.26, 691.08 339.29, 691.08 344.35, 673.89 344.39, 673.85 315.87, 684.12 315.87, 684.12 309.57, 700.03 309.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,54,"POLYGON ((802.70 300.97, 804.29 346.50, 792.80 346.79, 791.64 301.45, 802.70 300.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,55,"POLYGON ((736.60 301.37, 757.18 301.22, 757.48 346.30, 736.88 346.42, 736.60 301.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,56,"POLYGON ((886.78 301.19, 900.00 301.04, 900.00 327.14, 898.40 327.16, 898.46 330.77, 887.21 330.91, 887.09 321.10, 884.75 321.11, 884.69 315.92, 886.95 315.91, 886.78 301.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,57,"POLYGON ((647.66 268.03, 666.57 267.70, 666.65 271.70, 674.01 271.55, 674.29 287.64, 668.40 287.75, 668.50 293.94, 636.55 294.49, 636.38 284.37, 647.93 284.19, 647.66 268.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,58,"POLYGON ((780.12 239.57, 800.51 239.42, 800.72 271.34, 780.32 271.48, 780.12 239.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,59,"POLYGON ((732.35 235.17, 744.40 234.95, 745.08 272.56, 733.01 272.78, 732.35 235.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,60,"POLYGON ((758.12 233.49, 770.94 233.58, 770.70 262.29, 757.91 262.20, 758.12 233.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,61,"POLYGON ((815.46 182.86, 814.86 160.87, 834.41 160.33, 834.81 175.04, 829.64 175.19, 829.84 182.47, 815.46 182.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,62,"POLYGON ((739.78 157.86, 761.20 157.56, 761.58 184.01, 748.09 184.20, 748.02 179.16, 740.10 179.28, 739.78 157.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,63,"POLYGON ((764.73 150.46, 780.52 150.21, 780.64 156.93, 786.02 156.84, 786.45 183.80, 772.73 184.01, 772.64 179.20, 765.17 179.30, 764.73 150.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,64,"POLYGON ((810.81 182.80, 790.58 182.91, 790.43 150.91, 804.74 150.85, 804.78 159.02, 810.68 159.00, 810.81 182.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,65,"POLYGON ((869.29 184.26, 868.88 165.29, 864.99 165.36, 864.44 140.76, 900.00 139.97, 900.00 187.98, 899.69 187.99, 899.75 191.22, 892.88 191.38, 892.85 189.60, 876.58 189.97, 876.64 193.19, 866.86 193.42, 866.66 184.32, 869.29 184.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,66,"POLYGON ((653.73 136.80, 692.01 136.06, 692.37 154.52, 654.07 155.26, 653.73 136.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,67,"POLYGON ((774.00 130.82, 774.12 148.40, 741.91 148.62, 741.80 131.04, 774.00 130.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,68,"POLYGON ((802.05 123.06, 826.74 122.66, 826.99 136.90, 822.97 136.98, 823.11 145.63, 802.43 145.98, 802.05 123.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,69,"POLYGON ((773.74 109.09, 774.06 126.64, 741.90 127.22, 741.59 109.69, 773.74 109.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,70,"POLYGON ((812.84 102.88, 812.73 99.17, 820.68 98.90, 820.77 101.67, 826.25 101.49, 826.85 119.88, 802.62 120.67, 802.04 103.24, 812.84 102.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,71,"POLYGON ((773.26 87.99, 773.74 105.41, 741.37 106.28, 740.91 88.86, 773.26 87.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,72,"POLYGON ((803.35 79.90, 827.62 78.66, 830.28 91.38, 827.39 94.58, 804.20 94.86, 803.35 79.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,73,"POLYGON ((879.97 63.56, 887.78 62.16, 888.78 66.71, 900.00 66.97, 900.00 83.61, 897.19 83.56, 880.19 84.00, 880.74 82.23, 880.34 78.11, 878.65 75.93, 876.45 75.99, 873.77 75.64, 873.04 70.19, 879.96 69.13, 879.97 63.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,74,"POLYGON ((743.00 66.39, 744.42 63.98, 774.12 62.80, 774.84 80.69, 743.62 81.94, 743.00 66.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,75,"POLYGON ((674.08 61.58, 701.01 60.89, 702.04 83.02, 680.74 83.25, 680.51 74.36, 674.40 74.05, 674.08 61.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,76,"POLYGON ((799.19 51.95, 809.87 52.45, 809.92 48.70, 820.56 47.65, 821.56 50.60, 827.35 49.98, 827.54 57.46, 825.52 64.22, 823.33 64.89, 822.70 70.53, 802.82 71.19, 802.48 58.10, 799.04 58.50, 799.19 51.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,77,"POLYGON ((741.45 38.84, 745.23 38.76, 745.20 36.97, 762.96 36.62, 763.28 56.51, 760.14 59.09, 748.43 59.53, 741.77 55.81, 741.45 38.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,78,"POLYGON ((877.02 36.62, 900.00 36.14, 900.00 53.95, 899.59 54.09, 896.76 56.14, 896.76 59.02, 878.81 58.95, 877.37 56.25, 873.59 54.00, 872.68 52.53, 872.44 39.64, 877.09 39.55, 877.02 36.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,79,"POLYGON ((679.83 35.40, 699.73 35.51, 700.63 58.42, 678.21 57.59, 677.58 39.36, 680.10 39.45, 679.83 35.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,80,"POLYGON ((891.69 813.31, 891.51 804.82, 894.36 804.77, 897.15 803.65, 900.00 803.29, 900.00 813.13, 891.69 813.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,81,"POLYGON ((879.55 705.47, 882.66 705.37, 882.15 691.07, 894.99 690.61, 895.12 694.44, 896.79 697.89, 899.82 699.54, 900.00 704.69, 900.00 730.07, 880.46 730.76, 879.55 705.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,82,"POLYGON ((885.09 655.85, 895.75 655.05, 896.00 658.53, 896.13 659.65, 896.58 662.80, 897.01 665.38, 900.00 667.55, 900.00 674.34, 889.60 675.12, 888.81 664.44, 885.76 664.67, 885.09 655.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,83,"POLYGON ((657.85 530.22, 689.16 529.78, 689.23 534.84, 704.95 534.61, 704.90 529.86, 779.00 528.88, 778.93 524.93, 791.10 524.89, 790.87 513.86, 797.02 513.70, 797.21 524.75, 806.93 524.57, 807.23 548.20, 852.02 547.64, 852.55 587.80, 842.98 587.91, 844.04 669.44, 792.56 670.11, 792.19 641.71, 778.44 641.91, 778.52 648.21, 731.20 648.86, 730.68 611.14, 709.67 611.43, 709.54 601.96, 728.19 601.70, 727.98 586.12, 746.17 585.88, 745.98 572.97, 705.33 573.54, 705.43 581.11, 688.69 581.36, 688.59 573.66, 658.21 574.08, 657.83 547.85, 648.06 548.01, 647.85 533.23, 657.88 533.08, 657.85 530.22), (795.52 553.70, 781.96 553.96, 782.29 571.20, 758.26 571.65, 758.58 587.69, 762.96 587.63, 763.00 589.20, 790.06 588.69, 790.09 590.64, 796.24 590.53, 795.52 553.70), (809.51 602.42, 809.74 617.87, 824.34 617.63, 824.10 602.20, 809.51 602.42), (768.59 594.63, 768.81 609.34, 777.60 609.21, 778.00 635.19, 791.22 634.99, 790.59 594.27, 768.59 594.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,84,"POLYGON ((900.00 588.81, 882.98 589.47, 882.35 572.55, 900.00 571.88, 900.00 588.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,85,"POLYGON ((885.60 547.49, 885.09 530.32, 880.64 530.46, 880.40 522.43, 882.88 522.34, 882.53 510.56, 886.73 510.43, 886.82 513.32, 898.78 512.97, 899.81 547.06, 885.60 547.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,86,"POLYGON ((682.35 469.39, 659.11 469.92, 658.56 446.21, 664.02 446.09, 671.95 445.71, 678.33 447.12, 681.83 447.05, 682.35 469.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,87,"POLYGON ((853.92 466.08, 830.41 465.99, 830.44 451.34, 835.26 449.86, 839.73 444.80, 843.94 443.82, 853.99 443.88, 853.92 466.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,88,"POLYGON ((636.87 469.20, 636.21 439.56, 645.47 439.35, 645.78 453.57, 655.23 453.37, 655.57 468.79, 636.87 469.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,89,"POLYGON ((619.00 433.67, 634.06 433.29, 635.11 474.20, 620.05 474.87, 619.00 433.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,90,"POLYGON ((805.04 466.13, 782.53 466.12, 782.55 440.91, 805.05 440.91, 805.04 466.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,91,"POLYGON ((709.19 439.34, 729.23 438.90, 729.84 466.36, 709.80 466.81, 709.19 439.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,92,"POLYGON ((266.68 829.87, 265.03 833.42, 265.29 837.25, 269.80 841.69, 276.95 842.17, 276.51 848.71, 277.70 854.63, 283.38 856.93, 246.30 858.23, 249.35 853.24, 247.46 848.56, 244.93 845.30, 241.37 843.81, 240.75 831.11, 266.68 829.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,93,"POLYGON ((43.65 822.02, 47.04 821.95, 54.12 817.40, 57.01 812.75, 57.91 807.49, 61.93 807.04, 62.19 809.32, 67.56 812.66, 67.06 828.08, 61.02 832.45, 62.40 824.00, 58.54 824.64, 57.27 830.44, 53.84 833.86, 50.86 840.75, 47.04 842.95, 44.00 840.75, 43.65 822.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,94,"POLYGON ((71.94 806.27, 84.52 805.95, 85.69 808.83, 87.86 811.68, 86.39 819.42, 87.76 824.49, 91.92 827.60, 91.39 836.14, 79.18 836.25, 76.27 832.00, 70.38 835.97, 68.53 812.28, 70.15 810.38, 71.94 806.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,95,"POLYGON ((192.19 805.33, 191.22 815.17, 186.81 821.05, 179.25 828.44, 173.06 834.01, 172.16 805.69, 192.19 805.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,96,"POLYGON ((220.76 801.47, 228.31 807.23, 244.86 808.38, 248.65 804.96, 250.46 800.36, 255.58 800.92, 252.16 805.22, 251.39 809.26, 255.15 811.78, 259.37 811.32, 264.37 807.53, 266.73 804.32, 270.10 805.45, 273.20 809.24, 273.87 821.47, 270.29 821.68, 270.61 827.42, 237.48 828.55, 237.51 826.20, 233.36 826.44, 232.68 814.18, 221.51 814.80, 220.76 801.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,97,"POLYGON ((350.74 800.58, 382.46 799.55, 383.10 819.57, 351.38 820.61, 350.74 800.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,98,"POLYGON ((260.33 731.45, 260.47 735.44, 264.77 736.78, 266.73 737.35, 268.00 737.69, 269.04 740.46, 269.15 743.50, 273.51 743.35, 273.72 749.69, 271.37 749.77, 271.62 756.85, 243.18 757.86, 242.56 740.69, 248.82 740.47, 248.51 731.89, 260.33 731.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,99,"POLYGON ((358.17 729.09, 401.00 727.42, 401.62 743.14, 409.16 742.84, 409.67 755.56, 380.40 756.73, 380.33 754.54, 356.26 755.48, 355.64 739.61, 358.58 739.49, 358.17 729.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,100,"POLYGON ((129.39 732.82, 133.99 727.84, 146.87 723.25, 147.51 719.26, 146.85 716.48, 153.53 716.79, 150.03 730.82, 151.17 734.61, 157.58 736.51, 157.34 755.61, 130.14 755.88, 130.74 740.15, 129.07 740.08, 129.39 732.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,101,"POLYGON ((109.94 718.59, 111.89 725.16, 119.74 731.89, 124.06 731.78, 124.60 752.27, 102.31 752.89, 101.38 718.83, 109.94 718.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,102,"POLYGON ((90.34 721.27, 91.24 742.96, 87.50 740.79, 83.71 737.20, 79.24 734.16, 75.91 735.29, 76.10 728.83, 81.72 728.15, 85.18 725.62, 90.34 721.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,103,"POLYGON ((426.10 734.50, 411.87 734.93, 411.56 724.35, 425.80 723.92, 426.10 734.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,104,"POLYGON ((262.06 700.17, 262.29 706.32, 273.49 705.88, 274.11 721.95, 263.05 722.39, 263.22 726.94, 238.14 727.91, 237.94 723.16, 243.09 721.55, 246.09 718.23, 245.50 715.71, 241.03 712.85, 237.55 712.99, 237.23 704.54, 250.14 704.03, 250.01 700.64, 262.06 700.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,105,"POLYGON ((385.22 699.78, 386.01 725.11, 376.04 725.41, 376.11 727.52, 362.26 727.96, 361.95 718.22, 359.84 718.28, 363.59 714.16, 368.97 703.88, 368.80 698.78, 375.39 698.57, 375.45 700.08, 385.22 699.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,106,"POLYGON ((548.54 635.15, 549.04 654.98, 502.99 656.15, 503.94 693.20, 532.98 692.46, 532.58 676.89, 559.19 676.21, 560.47 726.19, 532.61 726.90, 532.22 711.33, 505.64 712.00, 506.92 761.96, 479.06 762.67, 476.32 655.30, 482.79 655.14, 482.32 636.84, 548.54 635.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,107,"POLYGON ((258.26 669.81, 255.21 673.82, 254.73 679.96, 258.44 683.42, 264.94 676.57, 267.37 675.18, 269.30 675.15, 269.54 686.60, 274.67 686.49, 274.86 695.25, 242.44 695.92, 242.35 691.53, 250.63 691.36, 247.12 684.71, 237.37 684.71, 237.36 675.59, 241.62 675.57, 244.85 675.53, 248.39 675.42, 248.95 673.47, 247.89 669.81, 258.26 669.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,108,"POLYGON ((371.25 669.93, 387.06 669.39, 387.10 671.30, 395.02 671.03, 395.68 691.35, 355.42 692.69, 355.07 682.64, 360.83 680.74, 368.93 674.05, 371.25 669.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,109,"POLYGON ((261.73 638.67, 261.82 642.11, 260.63 644.23, 261.93 646.37, 264.04 647.16, 272.96 647.00, 273.36 668.47, 254.62 668.82, 254.56 665.74, 250.81 665.81, 246.54 669.98, 244.45 670.03, 244.41 668.37, 237.55 668.52, 237.46 664.95, 232.77 665.07, 232.20 642.00, 238.11 641.85, 238.19 644.78, 252.66 644.41, 252.54 638.91, 261.73 638.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,110,"POLYGON ((352.77 639.12, 387.15 638.38, 387.39 649.25, 392.60 649.14, 392.86 661.52, 382.63 661.73, 383.02 666.16, 376.49 666.55, 373.21 666.55, 356.72 666.41, 353.31 663.84, 352.77 639.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,111,"POLYGON ((66.29 637.29, 66.29 640.86, 67.28 644.81, 74.61 650.88, 77.87 651.38, 77.49 653.83, 71.25 658.69, 71.26 662.78, 45.32 662.84, 45.28 642.15, 51.01 642.14, 51.01 644.40, 56.13 644.38, 56.11 637.30, 66.29 637.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,112,"POLYGON ((117.51 656.22, 121.48 653.15, 124.76 650.96, 129.73 645.92, 129.48 642.62, 134.40 642.83, 138.26 642.22, 140.99 639.51, 144.14 638.56, 145.21 645.71, 140.09 652.32, 135.07 655.42, 117.51 656.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,113,"POLYGON ((41.33 617.57, 47.85 619.38, 51.30 618.80, 53.25 617.26, 50.56 613.85, 48.17 612.31, 47.72 606.35, 55.32 606.29, 55.90 608.76, 57.10 611.84, 63.82 616.62, 87.67 615.87, 88.00 628.85, 73.83 628.99, 71.79 627.56, 70.00 625.00, 64.68 626.25, 62.37 628.15, 62.33 626.02, 52.08 626.15, 52.20 635.09, 41.56 635.23, 41.33 617.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,114,"POLYGON ((351.33 627.64, 351.22 618.14, 354.49 615.57, 356.51 612.59, 356.42 609.04, 376.49 608.55, 382.75 616.09, 382.60 619.00, 375.64 624.80, 374.67 627.33, 351.33 627.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,115,"POLYGON ((265.70 601.65, 266.39 628.74, 254.38 629.05, 254.42 630.53, 243.11 630.82, 243.06 628.51, 231.16 628.80, 230.91 618.92, 216.60 619.29, 216.39 610.53, 213.93 610.59, 213.70 601.65, 215.79 601.60, 215.74 599.58, 230.31 599.21, 230.35 600.98, 252.61 600.41, 252.65 601.99, 265.70 601.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,116,"POLYGON ((529.56 578.38, 553.20 577.98, 553.57 600.66, 529.94 601.06, 529.56 578.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,0,"POLYGON ((784.89 334.23, 824.94 333.80, 825.07 345.56, 823.20 350.14, 793.60 350.70, 791.91 346.21, 784.24 347.01, 781.64 345.50, 781.77 335.28, 784.89 334.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,1,"POLYGON ((731.22 326.61, 731.40 322.89, 747.28 323.69, 744.93 329.63, 744.88 333.61, 748.52 343.21, 733.28 342.74, 730.28 341.19, 731.22 326.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,2,"POLYGON ((790.30 323.21, 792.38 324.02, 796.09 323.79, 799.76 323.26, 804.02 322.11, 806.51 320.86, 806.70 316.75, 808.28 315.38, 816.70 315.01, 820.86 315.79, 821.69 319.17, 821.68 324.61, 823.22 332.80, 809.47 333.02, 808.93 329.50, 790.77 329.97, 790.30 323.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,3,"POLYGON ((857.63 320.16, 872.93 319.55, 875.78 307.57, 890.75 307.30, 891.10 325.19, 883.45 329.33, 858.04 330.36, 857.63 320.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,4,"POLYGON ((667.62 309.36, 698.80 308.32, 699.16 318.55, 667.98 319.61, 667.62 309.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,5,"POLYGON ((823.22 297.77, 824.17 311.89, 808.78 311.68, 806.61 307.63, 803.43 304.76, 800.73 303.21, 795.11 303.06, 789.35 297.46, 823.22 297.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,6,"POLYGON ((732.29 291.40, 764.37 291.69, 764.27 303.41, 760.43 303.37, 758.35 297.54, 754.33 296.18, 746.35 296.96, 741.11 300.05, 732.27 300.21, 732.23 297.32, 732.29 291.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,7,"POLYGON ((702.62 289.38, 701.43 300.91, 693.98 301.83, 674.11 303.29, 673.84 299.50, 667.14 299.99, 666.42 289.88, 677.90 289.08, 677.22 279.48, 693.29 278.36, 696.54 288.78, 702.62 289.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,8,"POLYGON ((864.94 276.84, 875.80 276.43, 876.52 296.03, 882.58 295.81, 882.77 300.96, 856.20 301.95, 858.07 293.64, 865.57 293.38, 864.94 276.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,9,"POLYGON ((822.97 270.48, 823.91 288.57, 810.43 289.29, 809.45 285.92, 797.93 286.37, 792.19 275.90, 792.02 272.02, 822.97 270.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,10,"POLYGON ((671.13 264.39, 698.66 264.11, 698.74 271.90, 669.84 272.21, 669.79 267.04, 671.13 264.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,11,"POLYGON ((730.34 262.45, 763.89 262.06, 764.02 268.32, 764.20 272.64, 760.52 272.83, 733.49 273.76, 730.48 273.79, 730.34 262.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,12,"POLYGON ((604.88 255.78, 643.76 255.01, 644.01 272.70, 643.49 277.06, 600.01 277.97, 600.08 256.12, 604.88 255.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,13,"POLYGON ((699.50 248.46, 699.69 256.78, 677.73 257.27, 677.56 248.95, 699.50 248.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,14,"POLYGON ((776.51 246.14, 793.83 247.02, 794.02 248.64, 795.38 250.23, 797.32 250.91, 799.22 249.68, 802.99 247.08, 817.62 247.15, 819.28 254.03, 819.26 258.87, 795.29 258.46, 793.92 256.59, 790.47 255.06, 788.13 255.69, 786.10 257.52, 776.49 257.04, 776.63 250.99, 776.51 246.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,15,"POLYGON ((759.32 240.36, 763.79 240.38, 764.66 248.35, 761.77 249.22, 755.71 254.61, 732.39 253.90, 728.40 252.29, 727.57 241.17, 759.32 240.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,16,"POLYGON ((794.94 222.93, 814.87 222.27, 816.47 226.65, 816.76 235.76, 820.80 235.64, 821.03 242.49, 780.51 243.84, 780.27 236.80, 795.39 236.31, 794.94 222.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,17,"POLYGON ((623.50 215.35, 634.15 214.23, 635.11 243.78, 630.76 245.55, 625.09 245.08, 623.50 215.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,18,"POLYGON ((733.14 215.99, 756.86 215.38, 757.05 222.72, 757.02 227.32, 738.33 227.51, 736.48 226.03, 735.61 224.80, 733.36 224.60, 733.02 221.96, 733.14 215.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,19,"POLYGON ((678.45 217.81, 678.54 214.03, 699.71 214.49, 699.60 219.00, 699.36 225.71, 678.19 224.94, 678.45 217.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,20,"POLYGON ((820.61 198.61, 821.41 201.72, 821.27 208.48, 802.65 208.22, 794.60 208.42, 795.00 199.38, 820.61 198.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,21,"POLYGON ((857.63 186.75, 871.49 181.18, 880.42 203.21, 866.56 208.76, 857.63 186.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,22,"POLYGON ((728.72 176.52, 732.08 179.50, 734.21 181.44, 741.26 188.19, 743.99 194.98, 757.11 194.75, 757.34 207.11, 729.27 207.61, 728.72 176.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,23,"POLYGON ((822.69 181.16, 823.00 193.51, 795.81 193.92, 792.94 185.45, 796.09 181.55, 806.72 180.97, 808.31 185.34, 814.29 187.85, 822.69 181.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,24,"POLYGON ((756.41 168.04, 774.21 165.64, 777.45 189.44, 759.65 191.85, 756.41 168.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,25,"POLYGON ((779.50 158.73, 780.13 154.30, 802.45 144.63, 811.14 164.54, 809.67 164.58, 807.92 165.51, 805.17 168.53, 804.53 171.48, 803.10 173.58, 784.93 180.41, 781.77 172.06, 783.77 169.81, 779.50 158.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,26,"POLYGON ((873.17 146.77, 881.92 143.84, 891.13 171.24, 881.39 174.49, 876.79 160.82, 869.11 163.39, 866.62 155.99, 875.30 153.11, 873.17 146.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,27,"POLYGON ((570.97 110.80, 609.05 95.43, 624.49 132.79, 585.89 148.20, 570.97 110.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,28,"POLYGON ((655.90 0.00, 656.20 59.06, 607.06 59.00, 604.65 0.00, 655.90 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,29,"POLYGON ((654.08 650.54, 663.20 650.13, 674.00 649.68, 672.72 679.56, 663.78 680.35, 655.22 680.57, 654.08 650.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,30,"POLYGON ((715.89 646.34, 715.59 683.82, 697.79 683.23, 699.15 646.35, 715.89 646.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,31,"POLYGON ((744.41 649.76, 744.32 678.91, 739.95 679.87, 733.82 677.52, 733.62 660.87, 724.19 660.27, 723.93 650.09, 744.41 649.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,32,"POLYGON ((797.14 661.27, 826.99 659.66, 827.52 663.62, 830.44 663.13, 830.77 668.11, 797.34 669.17, 797.14 661.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,33,"POLYGON ((676.13 645.36, 689.53 645.19, 691.37 659.07, 693.83 660.11, 693.85 668.26, 690.89 669.27, 690.65 681.50, 684.13 682.05, 675.92 681.53, 676.13 645.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,34,"POLYGON ((873.39 641.52, 900.00 641.35, 900.00 667.98, 878.17 668.10, 874.57 666.64, 873.51 663.71, 873.39 641.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,35,"POLYGON ((603.17 643.58, 624.16 643.49, 624.24 663.22, 603.25 663.29, 603.17 643.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,36,"POLYGON ((897.30 619.46, 888.39 620.47, 886.59 623.18, 887.84 629.38, 886.81 631.90, 883.28 634.65, 862.29 635.34, 859.08 632.45, 858.38 610.80, 861.45 608.06, 877.22 606.41, 888.02 605.83, 897.91 606.82, 898.13 609.01, 897.30 619.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,37,"POLYGON ((600.45 583.34, 640.39 582.10, 640.80 595.07, 620.22 595.71, 621.17 626.74, 601.81 627.35, 600.45 583.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,38,"POLYGON ((862.02 589.04, 862.98 583.07, 900.00 582.85, 900.00 605.90, 863.20 606.44, 862.02 589.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,39,"POLYGON ((668.27 585.11, 701.63 585.04, 703.02 593.26, 701.15 597.62, 669.18 597.66, 667.84 591.61, 668.27 585.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,40,"POLYGON ((825.41 565.72, 828.03 568.02, 828.70 589.81, 826.00 592.43, 820.43 590.79, 818.47 588.20, 803.78 590.04, 801.99 575.86, 803.90 573.64, 809.94 570.55, 810.86 568.35, 814.37 566.31, 825.41 565.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,41,"POLYGON ((673.99 569.63, 701.99 568.91, 703.38 576.74, 700.73 581.13, 674.89 581.59, 673.99 569.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,42,"POLYGON ((862.02 567.53, 864.79 566.35, 898.29 565.30, 898.77 576.78, 862.84 577.90, 862.02 567.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,43,"POLYGON ((748.77 541.01, 764.88 540.11, 768.92 541.57, 770.99 544.82, 771.62 552.28, 777.31 566.48, 776.91 586.42, 760.31 586.09, 756.65 581.79, 753.37 566.65, 750.82 561.87, 739.42 562.50, 738.47 545.70, 749.00 545.09, 748.77 541.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,44,"POLYGON ((668.10 551.02, 702.95 549.78, 703.41 564.70, 668.53 565.51, 668.10 551.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,45,"POLYGON ((794.28 547.93, 796.69 546.12, 821.64 545.12, 823.46 547.54, 824.63 551.88, 824.41 557.13, 824.55 562.56, 822.49 564.70, 794.34 564.02, 794.67 556.49, 794.28 547.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,46,"POLYGON ((601.33 542.25, 632.82 543.18, 632.11 567.01, 600.62 566.09, 601.33 542.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,47,"POLYGON ((860.47 557.72, 860.38 528.53, 898.85 528.21, 900.00 531.92, 900.00 539.98, 898.31 539.99, 898.34 557.66, 860.47 557.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,48,"POLYGON ((797.20 525.77, 803.11 525.44, 815.41 524.97, 823.86 524.82, 824.08 536.36, 824.09 543.77, 797.81 543.82, 797.77 536.92, 797.69 534.59, 797.20 525.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,49,"POLYGON ((666.63 526.82, 703.48 526.99, 705.03 533.34, 705.21 540.28, 666.97 540.15, 666.63 526.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,50,"POLYGON ((732.57 510.68, 758.06 509.83, 760.44 529.13, 733.67 529.17, 732.57 510.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,51,"POLYGON ((859.19 507.34, 893.30 507.95, 894.39 514.23, 900.00 516.40, 900.00 527.03, 859.49 526.24, 859.19 507.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,52,"POLYGON ((668.14 509.64, 701.79 507.85, 703.40 522.31, 668.50 523.66, 668.14 509.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,53,"POLYGON ((599.78 505.49, 629.86 504.47, 629.44 526.26, 600.01 527.01, 599.78 505.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,54,"POLYGON ((824.93 497.69, 826.91 513.47, 825.26 517.88, 825.03 523.90, 793.30 522.65, 793.47 518.87, 801.15 516.92, 805.81 512.92, 803.62 510.38, 802.31 507.10, 807.42 500.85, 809.40 499.46, 824.93 497.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,55,"POLYGON ((673.96 494.44, 702.85 492.84, 703.43 503.39, 674.55 505.00, 673.96 494.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,56,"POLYGON ((599.68 477.74, 642.93 477.88, 648.01 492.42, 642.29 502.22, 599.41 503.63, 599.68 477.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,57,"POLYGON ((788.76 481.17, 795.57 480.04, 803.71 479.96, 817.75 480.09, 819.95 482.68, 820.27 486.62, 819.94 490.16, 820.68 494.51, 821.61 498.44, 814.55 499.89, 809.40 499.46, 806.58 499.25, 802.56 497.49, 797.54 497.61, 795.30 499.96, 791.91 497.96, 789.59 496.97, 788.21 492.44, 787.52 489.95, 788.76 481.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,58,"POLYGON ((749.18 474.28, 753.44 475.57, 751.97 479.20, 753.66 484.13, 754.39 488.02, 758.97 489.61, 762.20 493.12, 762.75 496.84, 737.05 496.10, 734.98 494.75, 732.86 491.52, 733.10 476.24, 736.04 474.61, 749.18 474.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,59,"POLYGON ((674.55 476.01, 702.40 474.61, 702.97 485.82, 675.13 487.24, 674.55 476.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,60,"POLYGON ((853.40 472.55, 897.20 471.56, 898.91 478.62, 898.52 485.00, 880.26 485.76, 854.17 486.42, 853.40 472.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,61,"POLYGON ((598.74 454.92, 629.26 453.39, 636.89 453.39, 639.37 462.79, 640.44 475.54, 599.27 475.66, 599.33 470.67, 598.74 454.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,62,"POLYGON ((802.98 453.52, 828.49 452.56, 829.70 456.26, 829.56 463.14, 825.77 468.06, 825.57 472.44, 801.91 472.73, 801.93 467.42, 802.98 453.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,63,"POLYGON ((857.52 456.55, 888.02 456.50, 889.04 460.18, 888.37 462.80, 888.45 467.39, 884.96 467.43, 857.52 467.47, 857.52 456.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,64,"POLYGON ((732.69 450.12, 767.57 447.86, 767.93 469.27, 733.20 470.16, 732.35 467.43, 728.99 467.11, 729.02 453.17, 732.74 452.28, 732.69 450.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,65,"POLYGON ((673.36 446.93, 676.24 444.06, 699.35 445.83, 702.30 451.03, 702.26 470.46, 678.24 470.36, 673.56 466.35, 673.36 446.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,66,"POLYGON ((857.40 445.96, 857.48 441.14, 882.52 441.52, 882.49 443.86, 886.78 451.54, 886.64 455.43, 857.09 454.36, 857.40 445.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,67,"POLYGON ((734.28 429.77, 764.08 429.59, 764.14 439.91, 734.34 440.07, 734.28 429.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,68,"POLYGON ((858.07 428.72, 860.27 427.73, 882.45 427.72, 885.18 432.29, 885.13 436.24, 858.00 436.00, 858.07 428.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,69,"POLYGON ((782.78 410.31, 827.66 409.32, 827.97 423.52, 824.72 427.53, 783.17 428.43, 782.78 410.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,70,"POLYGON ((732.17 412.93, 769.10 413.57, 769.66 421.76, 732.74 421.49, 732.17 412.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,71,"POLYGON ((860.18 401.94, 894.18 400.60, 894.19 410.41, 883.39 410.22, 884.90 423.50, 865.66 423.75, 864.66 421.20, 861.13 420.83, 860.18 401.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,72,"POLYGON ((600.63 395.96, 639.94 394.96, 639.29 415.44, 601.77 415.09, 600.63 395.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,73,"POLYGON ((792.90 395.13, 817.54 394.33, 817.95 406.50, 793.31 407.33, 792.90 395.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,74,"POLYGON ((673.60 390.32, 683.03 389.93, 683.18 393.52, 703.09 400.65, 694.93 401.36, 695.68 409.78, 688.86 410.38, 682.34 410.81, 674.51 411.14, 673.60 390.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,75,"POLYGON ((730.15 382.86, 739.76 382.75, 739.81 386.37, 756.20 386.17, 756.44 404.30, 730.43 404.65, 730.15 382.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,76,"POLYGON ((791.23 376.95, 822.12 376.16, 823.73 386.86, 791.35 387.38, 791.23 376.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,77,"POLYGON ((856.79 370.59, 900.00 367.32, 900.00 376.06, 894.03 376.54, 891.03 378.48, 888.84 384.62, 891.30 388.76, 858.37 391.22, 856.79 370.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,78,"POLYGON ((729.41 353.11, 768.96 350.90, 769.64 362.92, 730.09 365.13, 729.41 353.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,79,"POLYGON ((668.42 352.30, 698.55 351.33, 698.87 361.11, 668.75 362.08, 668.42 352.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,80,"POLYGON ((597.41 319.37, 645.26 319.50, 647.72 329.58, 646.34 388.60, 615.67 387.89, 597.22 387.83, 597.41 319.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,81,"POLYGON ((521.73 724.95, 539.08 737.01, 538.90 740.90, 524.81 757.66, 517.91 761.17, 506.48 750.89, 504.33 742.89, 521.73 724.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,82,"POLYGON ((556.18 726.05, 579.10 726.04, 579.10 743.69, 556.18 743.70, 556.18 726.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,83,"POLYGON ((482.03 722.06, 496.64 703.91, 517.95 718.37, 506.06 734.23, 495.62 740.89, 481.04 726.81, 482.03 722.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,84,"POLYGON ((508.00 668.50, 529.73 669.14, 530.06 690.33, 508.54 689.91, 508.45 686.38, 504.47 685.30, 504.51 671.34, 508.07 671.25, 508.00 668.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,85,"POLYGON ((555.02 666.81, 578.09 666.91, 577.97 691.42, 554.88 691.30, 555.02 666.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,86,"POLYGON ((508.32 636.28, 527.88 636.60, 528.71 658.40, 508.60 658.21, 508.54 655.59, 504.91 655.81, 505.03 638.72, 508.39 639.05, 508.32 636.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,87,"POLYGON ((556.73 635.05, 577.18 635.26, 576.94 659.01, 556.46 658.78, 556.73 635.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,88,"POLYGON ((0.00 613.65, 6.91 615.34, 1.74 636.31, 0.00 635.89, 0.00 613.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,89,"POLYGON ((508.07 604.46, 528.18 604.92, 528.88 626.72, 508.21 626.27, 508.16 624.05, 504.64 623.59, 504.80 607.34, 508.13 606.83, 508.07 604.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,90,"POLYGON ((555.50 603.67, 576.70 603.28, 578.44 627.12, 556.10 627.14, 555.50 603.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,91,"POLYGON ((502.47 576.90, 507.66 571.77, 529.28 572.53, 528.37 594.79, 508.66 593.52, 502.53 591.17, 502.47 576.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,92,"POLYGON ((556.00 570.98, 578.31 570.25, 579.80 588.02, 577.03 595.75, 555.74 595.69, 556.00 570.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,93,"POLYGON ((555.44 538.38, 577.41 538.20, 578.44 563.75, 556.66 564.12, 555.44 538.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,94,"POLYGON ((507.40 539.05, 528.62 538.69, 528.83 561.31, 507.42 561.67, 507.41 557.12, 507.40 539.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,95,"POLYGON ((403.20 416.25, 483.51 383.32, 488.57 384.75, 490.45 514.58, 404.65 515.84, 403.20 416.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,96,"POLYGON ((196.21 390.32, 255.24 364.60, 253.62 353.19, 271.25 345.83, 276.50 358.31, 330.90 335.63, 328.89 330.84, 333.38 328.97, 329.99 320.89, 368.33 304.88, 395.85 370.29, 364.51 392.69, 223.99 453.52, 196.21 390.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,97,"POLYGON ((278.10 186.85, 317.08 277.09, 254.43 302.24, 258.92 316.27, 243.09 321.38, 236.89 310.35, 191.22 328.58, 195.02 338.50, 184.47 342.30, 179.49 332.41, 171.35 337.33, 131.24 249.47, 278.10 186.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,98,"POLYGON ((444.60 101.33, 506.53 76.04, 522.90 115.76, 543.09 107.52, 552.89 131.31, 535.57 138.41, 590.16 270.74, 524.61 297.57, 467.03 157.99, 453.05 163.69, 489.53 252.44, 454.99 266.51, 423.02 188.68, 428.52 186.43, 412.27 146.96, 456.04 129.10, 444.60 101.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,99,"POLYGON ((305.61 164.84, 401.07 127.09, 401.34 137.69, 404.15 145.61, 408.39 156.91, 412.70 167.86, 328.42 200.77, 328.51 204.30, 316.43 208.18, 312.65 196.47, 317.00 193.41, 305.61 164.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,100,"POLYGON ((871.91 890.66, 873.35 889.01, 894.34 888.65, 894.54 900.00, 872.08 900.00, 871.91 890.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,101,"POLYGON ((744.45 886.04, 757.24 885.67, 757.32 888.02, 761.19 887.90, 761.58 900.00, 738.91 900.00, 738.76 892.67, 744.58 892.56, 744.45 886.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,102,"POLYGON ((682.15 885.52, 700.31 884.82, 700.90 900.00, 674.00 900.00, 673.99 899.85, 676.49 898.90, 676.10 888.94, 682.27 888.69, 682.15 885.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,103,"POLYGON ((798.72 878.51, 822.84 878.04, 822.90 886.32, 822.32 894.13, 797.71 893.83, 797.66 885.39, 798.72 878.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,104,"POLYGON ((876.67 865.50, 875.64 864.00, 893.54 863.34, 894.01 875.89, 896.38 875.81, 896.67 883.48, 872.10 884.38, 871.62 871.18, 876.86 871.00, 876.67 865.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,105,"POLYGON ((744.08 860.39, 754.52 859.74, 754.64 861.41, 760.78 861.03, 761.90 879.20, 755.79 879.58, 755.91 881.62, 744.40 882.31, 744.18 878.97, 739.68 879.24, 738.70 863.41, 744.24 863.07, 744.08 860.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,106,"POLYGON ((671.87 857.55, 697.84 857.20, 697.88 859.28, 701.95 859.25, 702.20 878.44, 689.06 878.60, 688.94 868.19, 672.00 868.42, 671.87 857.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,107,"POLYGON ((792.30 855.01, 824.65 853.82, 825.20 868.88, 822.63 871.92, 799.10 873.52, 792.91 871.53, 792.30 855.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,108,"POLYGON ((870.16 841.63, 874.75 841.36, 900.00 840.40, 900.00 861.80, 869.73 862.46, 869.70 860.75, 871.24 859.40, 870.16 841.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,109,"POLYGON ((743.35 834.79, 755.47 834.48, 756.21 840.15, 760.06 840.05, 759.97 852.44, 755.45 852.64, 755.31 855.02, 743.89 856.00, 743.81 852.85, 739.67 853.35, 739.24 836.47, 743.68 836.74, 743.35 834.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,110,"POLYGON ((672.05 836.35, 703.04 835.07, 703.70 851.16, 672.72 852.44, 672.05 836.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,111,"POLYGON ((790.93 834.06, 823.41 832.81, 824.07 850.09, 822.29 852.11, 796.91 852.60, 791.50 848.92, 790.93 834.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,112,"POLYGON ((611.97 824.69, 649.02 824.50, 649.17 839.84, 618.60 841.11, 615.92 833.27, 611.97 824.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,113,"POLYGON ((858.86 785.62, 880.59 784.58, 881.20 814.95, 879.56 816.15, 879.00 820.47, 862.57 820.56, 861.97 816.60, 859.43 814.36, 858.86 785.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,114,"POLYGON ((688.08 787.83, 705.79 788.02, 705.58 808.70, 705.45 813.14, 689.51 812.72, 687.84 809.02, 688.08 787.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,115,"POLYGON ((814.28 788.25, 821.22 787.25, 833.35 787.27, 834.49 813.02, 817.06 812.13, 815.35 810.69, 814.28 788.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,116,"POLYGON ((792.33 786.50, 801.65 787.60, 811.09 787.02, 812.40 812.09, 803.48 813.65, 792.15 812.12, 792.33 786.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,117,"POLYGON ((900.00 818.19, 885.93 818.81, 882.69 815.43, 882.17 781.25, 900.00 780.96, 900.00 818.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,118,"POLYGON ((664.32 786.97, 682.90 787.12, 682.74 806.93, 682.58 812.10, 665.67 811.60, 664.16 807.60, 664.32 786.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,119,"POLYGON ((768.44 786.96, 779.24 787.32, 787.51 786.62, 787.65 811.57, 768.24 811.89, 768.44 786.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,120,"POLYGON ((744.60 786.52, 764.96 785.54, 763.87 810.36, 745.05 809.73, 744.60 786.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,121,"POLYGON ((604.12 780.90, 632.06 780.83, 632.14 810.11, 604.18 810.18, 604.12 780.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,122,"POLYGON ((562.72 772.39, 579.42 772.16, 579.75 798.75, 573.01 803.47, 566.93 799.43, 562.89 785.70, 562.72 772.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,123,"POLYGON ((795.80 747.26, 799.92 745.86, 800.21 740.11, 806.90 739.18, 807.01 733.98, 817.15 733.79, 831.76 733.73, 831.35 747.59, 831.13 753.30, 827.40 753.02, 827.28 757.97, 826.82 759.58, 818.89 760.54, 807.07 760.35, 805.90 758.65, 796.49 759.76, 795.80 747.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,124,"POLYGON ((625.54 743.19, 625.31 740.56, 644.90 740.39, 645.95 743.16, 644.95 748.64, 625.51 748.65, 625.54 743.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,125,"POLYGON ((600.64 729.62, 623.19 729.04, 623.93 757.77, 601.20 757.86, 600.64 729.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,126,"POLYGON ((863.01 730.38, 895.11 729.40, 898.61 737.26, 896.27 743.71, 866.99 744.46, 863.64 742.68, 863.01 730.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,127,"POLYGON ((715.34 716.04, 735.46 715.41, 736.51 750.17, 727.82 750.43, 716.27 746.80, 715.34 716.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,128,"POLYGON ((653.43 717.04, 672.66 716.37, 673.14 748.08, 654.22 747.92, 653.92 736.18, 652.10 736.23, 651.96 730.80, 653.94 730.41, 653.43 717.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,129,"POLYGON ((682.63 716.96, 702.08 717.62, 703.66 747.48, 684.07 747.16, 682.63 716.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,130,"POLYGON ((752.25 712.25, 771.41 712.25, 771.42 746.66, 749.85 746.68, 749.85 718.20, 752.26 718.18, 752.25 712.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,131,"POLYGON ((867.19 716.09, 893.99 716.18, 894.77 728.48, 867.05 728.72, 867.19 716.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,132,"POLYGON ((602.36 704.54, 623.02 704.16, 623.40 724.16, 602.72 724.55, 602.36 704.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,133,"POLYGON ((866.52 706.40, 893.83 705.06, 894.22 712.78, 866.90 714.12, 866.52 706.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,134,"POLYGON ((556.17 695.99, 580.17 695.38, 580.46 717.62, 578.86 721.25, 557.36 720.98, 556.17 695.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,135,"POLYGON ((793.55 675.06, 825.41 675.18, 826.37 683.88, 829.98 693.59, 830.29 698.78, 805.38 700.53, 803.81 689.82, 795.04 689.29, 793.55 675.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,136,"POLYGON ((865.73 672.64, 900.00 670.29, 900.00 681.02, 857.36 683.95, 856.97 678.21, 866.08 677.58, 865.73 672.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,137,"POLYGON ((630.93 656.70, 650.06 655.70, 649.62 687.17, 646.78 692.68, 635.09 692.49, 632.48 687.61, 630.93 656.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,138,"POLYGON ((751.15 655.94, 756.16 656.28, 770.93 657.28, 769.13 681.81, 766.69 681.49, 766.46 687.07, 758.06 686.18, 748.75 686.04, 751.15 655.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,0,"POLYGON ((757.33 46.87, 759.81 121.90, 678.47 124.58, 675.99 49.55, 757.33 46.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,1,"POLYGON ((608.62 77.33, 608.30 66.13, 596.18 66.46, 583.45 59.26, 583.24 51.05, 591.85 50.83, 591.42 33.88, 583.79 34.08, 583.30 15.18, 643.20 13.63, 643.84 38.56, 641.19 38.63, 642.38 89.25, 647.82 89.11, 648.25 107.97, 644.49 108.05, 644.85 125.17, 584.63 126.47, 584.30 110.78, 581.66 108.43, 580.81 78.11, 608.62 77.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,2,"POLYGON ((873.35 15.45, 881.19 15.40, 881.29 28.61, 872.90 30.53, 873.06 51.95, 857.20 52.06, 857.09 36.90, 861.87 36.87, 861.79 26.66, 855.25 26.72, 855.17 15.00, 866.37 14.94, 866.34 11.72, 873.33 11.67, 873.35 15.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,3,"POLYGON ((886.95 16.36, 898.85 16.13, 898.77 11.89, 900.00 11.87, 900.00 33.37, 891.67 33.53, 891.59 29.12, 887.20 29.19, 886.95 16.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,4,"POLYGON ((371.19 20.46, 371.15 13.56, 384.77 13.48, 384.83 24.44, 388.00 24.43, 388.05 35.31, 385.90 35.32, 386.00 52.07, 366.11 52.18, 365.99 30.21, 364.40 30.20, 364.35 20.50, 371.19 20.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,5,"POLYGON ((416.89 13.64, 416.98 18.83, 417.96 19.45, 418.68 47.53, 417.27 47.57, 415.50 50.10, 403.61 50.51, 400.27 46.71, 399.81 19.60, 404.61 19.52, 404.52 13.84, 416.89 13.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,6,"POLYGON ((538.52 12.64, 539.13 48.03, 517.91 48.39, 517.41 19.48, 525.19 19.35, 525.08 12.87, 538.52 12.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,7,"POLYGON ((427.39 26.49, 427.25 23.25, 430.07 23.13, 430.22 26.73, 444.72 26.15, 445.10 35.38, 441.84 36.77, 421.04 37.66, 420.59 26.75, 427.39 26.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,8,"POLYGON ((449.79 17.06, 459.67 17.16, 459.61 23.20, 469.71 23.32, 469.59 33.69, 452.19 33.47, 452.26 27.41, 449.66 27.38, 449.79 17.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,9,"POLYGON ((526.57 507.12, 545.69 506.52, 546.88 544.49, 527.78 545.09, 527.33 530.99, 524.42 531.08, 523.99 517.26, 526.88 517.19, 526.57 507.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,10,"POLYGON ((116.68 497.23, 117.10 554.20, 101.19 554.32, 100.78 497.35, 116.68 497.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,11,"POLYGON ((432.15 502.41, 440.81 502.32, 441.34 547.94, 418.03 548.23, 417.85 534.09, 415.89 534.12, 415.67 514.64, 432.29 514.43, 432.15 502.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,12,"POLYGON ((384.04 499.71, 384.97 517.84, 385.69 520.44, 385.82 525.65, 384.72 527.64, 385.13 540.92, 365.88 541.50, 365.45 527.57, 370.83 527.41, 370.50 516.90, 374.28 510.28, 373.77 500.23, 384.04 499.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,13,"POLYGON ((270.78 500.47, 284.64 500.14, 284.86 511.69, 271.00 511.87, 270.78 500.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,14,"POLYGON ((522.31 484.23, 522.58 501.07, 511.16 501.25, 511.11 497.99, 496.10 498.24, 495.89 484.66, 522.31 484.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,15,"POLYGON ((456.86 469.65, 481.02 469.44, 484.87 474.53, 484.78 482.57, 491.87 482.66, 491.64 499.20, 463.88 498.84, 464.02 487.85, 456.92 487.74, 456.99 483.15, 456.86 469.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,16,"POLYGON ((94.97 481.34, 94.80 456.99, 128.38 456.75, 128.47 468.25, 134.96 468.20, 142.10 472.01, 172.80 471.78, 172.86 480.77, 94.97 481.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,17,"POLYGON ((259.93 453.71, 277.17 453.20, 277.26 456.70, 282.38 456.55, 282.67 466.38, 285.49 468.10, 286.05 477.12, 250.34 479.21, 249.83 469.99, 260.75 469.38, 263.50 464.34, 263.13 459.37, 260.06 458.39, 259.93 453.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,18,"POLYGON ((343.42 453.79, 371.16 452.55, 372.23 476.19, 344.50 477.43, 343.42 453.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,19,"POLYGON ((495.21 456.02, 533.96 455.19, 534.09 461.20, 529.11 461.30, 529.16 464.30, 525.66 464.37, 525.79 469.94, 495.53 470.60, 495.21 456.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,20,"POLYGON ((249.30 428.98, 260.76 428.96, 260.75 427.22, 270.45 427.20, 270.46 429.86, 285.81 429.78, 285.87 446.47, 280.77 446.49, 280.77 448.09, 270.89 448.12, 270.90 450.67, 261.37 450.69, 261.36 448.87, 240.06 448.95, 240.04 439.50, 244.73 439.47, 244.71 434.36, 249.31 434.35, 249.30 428.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,21,"POLYGON ((390.62 430.41, 390.67 434.58, 396.75 434.50, 396.86 442.57, 391.50 442.64, 391.55 445.73, 356.20 446.21, 356.17 444.32, 353.04 444.36, 352.86 430.91, 390.62 430.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,22,"POLYGON ((539.21 426.62, 539.51 434.40, 536.74 436.29, 537.04 444.56, 488.42 446.32, 488.20 439.93, 473.88 440.45, 473.61 432.78, 486.33 432.32, 486.20 428.50, 539.21 426.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,23,"POLYGON ((122.07 447.48, 99.29 448.04, 99.17 443.00, 94.46 443.12, 94.13 429.59, 98.17 429.49, 98.05 424.76, 134.41 423.88, 121.78 435.35, 122.07 447.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,24,"POLYGON ((247.83 398.81, 260.34 398.82, 273.08 408.91, 279.65 400.66, 288.60 400.23, 289.60 421.94, 282.48 422.25, 282.56 423.96, 267.84 424.65, 267.31 423.44, 262.25 423.57, 261.81 424.56, 247.80 424.54, 247.83 398.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,25,"POLYGON ((97.74 412.74, 91.26 412.82, 91.13 401.14, 102.43 401.03, 102.46 403.47, 121.55 403.27, 121.62 411.18, 117.49 411.24, 117.57 418.89, 97.81 419.11, 97.74 412.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,26,"POLYGON ((339.61 404.74, 345.41 404.69, 345.36 399.07, 353.27 398.98, 353.29 400.38, 358.52 400.33, 358.49 397.11, 388.67 397.05, 388.75 422.56, 361.33 422.79, 361.29 418.06, 358.69 418.11, 358.73 421.75, 339.78 421.94, 339.61 404.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,27,"POLYGON ((503.15 398.21, 503.08 394.84, 528.47 394.28, 528.54 397.70, 535.65 397.54, 535.91 408.94, 538.61 408.87, 538.74 414.58, 535.37 416.95, 503.46 418.30, 503.24 413.42, 498.02 413.64, 492.06 409.66, 491.81 398.48, 503.15 398.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,28,"POLYGON ((263.51 371.77, 268.77 376.03, 272.19 371.84, 288.61 371.88, 288.57 386.82, 295.71 386.84, 295.68 393.75, 289.71 393.74, 289.69 395.72, 281.55 395.71, 280.57 394.42, 251.06 395.31, 250.38 373.17, 259.49 372.92, 263.51 371.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,29,"POLYGON ((97.36 385.49, 92.66 385.57, 92.46 373.45, 131.77 372.78, 132.10 393.11, 97.50 393.70, 97.36 385.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,30,"POLYGON ((343.40 369.49, 361.59 369.82, 361.60 368.78, 367.70 368.84, 367.70 370.02, 387.80 370.26, 387.53 393.78, 384.67 393.76, 384.69 391.98, 376.62 391.88, 374.43 394.64, 370.98 394.58, 371.02 392.00, 366.88 391.93, 365.52 393.67, 347.92 393.65, 347.93 387.46, 343.10 384.88, 343.40 369.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,31,"POLYGON ((532.56 368.49, 532.84 388.82, 520.25 388.99, 520.27 390.54, 507.71 390.73, 506.53 389.05, 492.43 389.23, 492.16 369.50, 494.95 369.48, 494.89 365.99, 508.52 365.80, 508.56 368.82, 532.56 368.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,32,"POLYGON ((97.45 361.31, 91.37 361.38, 91.24 350.26, 96.93 350.21, 96.90 347.81, 131.34 347.42, 131.57 367.90, 97.54 368.28, 97.45 361.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,33,"POLYGON ((252.35 345.62, 288.17 345.12, 288.43 364.70, 282.61 364.78, 282.66 367.44, 275.32 367.54, 275.36 369.78, 264.59 369.92, 264.56 367.97, 252.38 368.15, 250.17 366.36, 250.35 347.93, 252.35 345.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,34,"POLYGON ((365.76 344.43, 365.71 352.58, 372.18 352.59, 372.16 357.63, 373.74 357.66, 373.71 360.08, 379.03 360.12, 378.98 366.07, 359.90 365.93, 358.61 363.28, 353.27 363.33, 350.96 366.01, 344.12 365.96, 344.22 344.34, 365.76 344.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,35,"POLYGON ((498.04 347.75, 502.62 347.59, 502.51 344.78, 506.90 344.62, 506.99 347.48, 512.71 347.29, 512.60 344.36, 527.16 343.81, 527.53 353.86, 529.09 355.33, 529.28 362.23, 501.57 363.03, 500.04 362.09, 484.40 362.33, 484.32 356.47, 489.79 356.40, 489.67 347.88, 498.04 347.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,36,"POLYGON ((35.43 446.27, 24.87 446.32, 24.94 457.15, 0.00 457.28, 0.00 208.25, 25.54 208.12, 25.58 217.95, 34.24 217.91, 35.43 446.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,37,"POLYGON ((90.88 323.34, 97.65 323.51, 97.70 321.08, 130.41 321.85, 129.88 343.99, 99.12 343.29, 99.25 338.12, 90.53 337.92, 90.88 323.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,38,"POLYGON ((249.63 320.54, 260.16 320.51, 260.15 317.34, 269.07 317.33, 269.09 318.71, 275.04 318.69, 275.05 322.73, 284.30 322.69, 284.32 325.73, 286.64 325.72, 286.71 340.81, 249.69 340.93, 249.63 320.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,39,"POLYGON ((344.08 316.97, 350.06 316.88, 350.20 325.80, 369.42 325.53, 369.60 339.31, 357.76 339.46, 357.73 337.73, 353.77 337.79, 352.61 338.99, 344.38 339.12, 344.08 316.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,40,"POLYGON ((528.38 312.31, 528.84 326.21, 507.95 326.92, 506.49 329.23, 492.11 329.68, 491.89 323.29, 500.91 323.00, 500.59 313.24, 528.38 312.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,41,"POLYGON ((89.41 317.37, 89.51 303.07, 96.49 303.11, 96.53 296.19, 128.48 296.37, 128.39 309.69, 134.66 309.73, 138.02 314.17, 133.42 317.64, 89.41 317.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,42,"POLYGON ((235.45 290.06, 291.93 289.09, 292.32 312.99, 264.06 313.46, 264.00 309.85, 257.37 309.97, 257.40 311.81, 246.76 312.00, 246.80 314.21, 238.37 314.36, 238.17 303.03, 235.67 303.07, 235.45 290.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,43,"POLYGON ((343.98 289.25, 347.91 289.23, 347.95 294.30, 374.77 294.16, 374.83 310.26, 361.70 310.30, 361.70 307.80, 355.30 307.83, 355.31 311.22, 344.08 311.29, 343.98 289.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,44,"POLYGON ((495.10 284.88, 532.13 282.95, 533.46 308.03, 499.87 309.77, 499.26 307.52, 491.52 307.90, 490.72 291.98, 495.46 291.75, 495.10 284.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,45,"POLYGON ((89.13 288.05, 89.19 272.33, 99.61 275.06, 99.67 268.80, 118.23 268.97, 126.12 271.30, 126.05 288.17, 89.13 288.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,46,"POLYGON ((254.38 264.21, 285.51 263.52, 285.69 271.82, 291.91 271.69, 292.17 283.27, 276.46 283.62, 276.50 285.22, 264.31 285.49, 264.27 283.98, 233.45 284.65, 233.08 267.70, 249.01 267.34, 249.11 272.69, 254.56 272.57, 254.38 264.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,47,"POLYGON ((377.33 261.47, 377.37 263.73, 380.83 263.67, 381.17 281.88, 377.65 284.41, 362.95 285.81, 362.55 281.82, 358.85 282.16, 358.13 283.69, 353.97 283.42, 353.26 284.66, 347.13 284.73, 347.10 282.93, 343.10 282.99, 342.86 262.75, 347.34 262.70, 347.30 258.73, 354.67 258.65, 363.15 265.83, 371.90 265.80, 375.27 261.50, 377.33 261.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,48,"POLYGON ((501.48 260.32, 504.72 270.13, 504.94 279.54, 497.60 279.71, 497.66 281.92, 469.46 282.58, 468.96 261.06, 501.48 260.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,49,"POLYGON ((459.65 234.68, 459.72 249.22, 444.88 249.29, 444.80 234.75, 459.65 234.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,50,"POLYGON ((82.10 245.72, 82.60 193.36, 100.25 193.53, 100.18 200.26, 108.05 200.33, 107.72 232.81, 82.10 245.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,51,"POLYGON ((225.69 239.66, 226.24 195.40, 243.06 195.62, 242.70 222.86, 239.18 222.82, 238.97 239.83, 225.69 239.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,52,"POLYGON ((383.48 188.61, 384.35 241.73, 362.06 242.10, 361.16 188.98, 383.48 188.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,53,"POLYGON ((252.59 191.84, 274.84 192.01, 274.77 202.29, 270.49 207.95, 276.32 207.86, 276.41 213.54, 274.24 213.58, 274.42 225.36, 263.30 225.51, 263.41 232.94, 255.00 233.07, 254.89 225.30, 252.78 225.34, 252.51 201.75, 252.59 191.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,54,"POLYGON ((197.82 196.02, 204.96 196.01, 213.83 205.13, 213.82 211.48, 220.76 211.48, 220.76 219.36, 210.39 219.36, 210.40 226.80, 197.81 226.81, 197.82 196.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,55,"POLYGON ((454.61 194.99, 456.55 193.34, 462.15 193.31, 463.70 195.86, 463.89 216.21, 462.13 216.22, 462.21 226.91, 445.16 227.08, 444.88 195.08, 454.61 194.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,56,"POLYGON ((142.83 205.39, 142.86 196.00, 153.04 196.03, 165.60 204.81, 165.12 222.49, 142.83 205.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,57,"POLYGON ((521.20 191.89, 527.21 193.33, 530.17 197.23, 536.26 197.05, 536.35 200.51, 538.72 200.45, 539.00 211.34, 536.48 211.41, 536.87 226.00, 523.72 226.34, 523.49 218.42, 520.74 217.27, 521.20 191.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,58,"POLYGON ((413.11 195.40, 432.41 194.80, 433.12 217.27, 423.13 217.57, 423.25 221.47, 413.93 221.78, 413.11 195.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,59,"POLYGON ((355.87 192.40, 356.02 217.15, 353.71 217.16, 353.75 222.31, 341.01 222.39, 340.98 216.85, 332.68 216.90, 332.52 192.56, 355.87 192.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,60,"POLYGON ((512.19 195.36, 512.20 198.82, 514.30 198.81, 514.42 213.55, 514.34 218.59, 507.22 218.48, 507.17 222.43, 494.17 222.23, 494.19 220.88, 493.91 191.76, 504.99 191.66, 505.03 195.41, 512.19 195.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,61,"POLYGON ((410.26 191.26, 410.56 220.20, 386.83 220.45, 386.52 191.80, 388.82 191.76, 388.79 188.45, 399.10 188.35, 399.14 191.39, 410.26 191.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,62,"POLYGON ((123.68 201.24, 123.18 194.06, 138.41 193.00, 139.25 204.88, 129.57 205.55, 123.68 201.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,63,"POLYGON ((77.04 122.98, 76.61 99.75, 81.08 99.68, 80.98 94.40, 90.97 94.24, 90.94 92.24, 115.86 91.78, 115.95 97.55, 130.67 97.28, 131.10 121.42, 116.75 121.68, 116.98 134.30, 92.32 134.76, 92.10 122.71, 77.04 122.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,64,"POLYGON ((481.05 96.17, 482.94 118.61, 481.88 121.61, 470.65 122.37, 470.83 125.02, 467.13 126.12, 462.61 126.32, 462.53 124.64, 453.49 125.05, 452.33 97.02, 467.87 96.49, 471.19 99.31, 474.93 98.99, 474.74 96.73, 481.05 96.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,65,"POLYGON ((354.89 96.58, 355.88 109.67, 357.02 122.54, 346.94 123.42, 342.94 122.12, 334.86 122.42, 334.50 112.59, 333.73 97.87, 346.05 97.25, 354.89 96.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,66,"POLYGON ((320.92 103.50, 314.09 95.33, 323.58 95.31, 323.63 123.30, 314.08 123.32, 314.06 118.24, 303.98 118.28, 303.95 98.25, 313.47 109.66, 320.92 103.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,67,"POLYGON ((399.64 120.21, 398.42 118.73, 398.36 112.27, 394.34 112.31, 394.14 89.93, 417.68 89.73, 417.92 118.90, 409.32 118.98, 409.37 123.84, 399.68 123.93, 399.64 120.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,68,"POLYGON ((486.66 93.61, 501.42 93.28, 501.27 86.62, 519.21 86.23, 519.38 93.51, 534.59 93.18, 535.27 124.89, 517.17 125.26, 516.72 104.16, 504.42 104.43, 504.89 125.93, 487.36 126.31, 486.66 93.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,69,"POLYGON ((372.83 92.61, 372.82 89.84, 388.11 89.76, 388.30 118.90, 384.63 118.92, 384.65 122.08, 365.59 122.19, 365.42 92.67, 372.83 92.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,70,"POLYGON ((424.82 65.73, 442.00 65.65, 442.10 83.20, 443.02 85.87, 446.84 90.48, 447.20 117.42, 425.07 117.60, 424.82 65.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,71,"POLYGON ((448.15 51.02, 459.00 50.90, 459.19 67.18, 470.90 67.04, 471.05 79.89, 448.49 80.16, 448.15 51.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,72,"POLYGON ((299.91 57.58, 324.00 57.98, 323.81 69.44, 320.85 71.98, 299.67 71.64, 299.91 57.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,73,"POLYGON ((505.26 42.57, 505.45 49.84, 509.80 49.73, 510.31 68.63, 509.15 71.88, 495.97 72.24, 495.39 50.72, 482.08 51.08, 481.88 43.19, 505.26 42.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,74,"POLYGON ((243.32 25.68, 279.45 24.80, 280.35 60.63, 298.51 60.19, 298.68 71.15, 298.34 75.29, 244.59 76.73, 243.32 25.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,75,"POLYGON ((339.79 59.07, 339.81 38.73, 337.03 38.74, 337.04 26.33, 357.72 26.36, 357.67 56.41, 348.10 56.39, 348.09 59.06, 339.79 59.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,76,"POLYGON ((619.70 835.87, 628.07 837.52, 628.03 840.99, 624.91 845.51, 625.02 849.48, 632.77 856.34, 632.18 862.28, 609.05 861.63, 609.22 848.64, 602.96 847.80, 602.79 841.12, 605.39 840.57, 605.27 835.86, 619.70 835.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,77,"POLYGON ((851.09 832.13, 851.95 852.16, 822.67 853.42, 822.55 850.75, 815.16 851.08, 815.48 858.62, 802.15 859.20, 801.06 834.30, 851.09 832.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,78,"POLYGON ((725.63 836.69, 727.87 836.61, 731.13 838.64, 735.30 840.00, 749.14 838.98, 751.77 837.58, 752.28 849.60, 729.74 850.57, 729.62 847.96, 726.10 848.11, 725.63 836.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,79,"POLYGON ((796.61 813.10, 796.64 829.33, 730.19 829.39, 730.18 813.16, 796.61 813.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,80,"POLYGON ((850.06 805.90, 850.67 828.24, 834.53 828.67, 834.58 830.27, 826.41 830.50, 825.74 806.57, 850.06 805.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,81,"POLYGON ((632.10 808.01, 632.81 822.93, 625.67 826.44, 590.28 827.32, 590.12 821.02, 586.75 821.11, 600.91 809.49, 632.10 808.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,82,"POLYGON ((698.05 806.54, 698.22 816.86, 668.32 817.34, 668.15 807.02, 698.05 806.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,83,"POLYGON ((666.96 786.54, 698.87 785.90, 699.18 801.68, 667.27 802.31, 666.96 786.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,84,"POLYGON ((587.54 799.78, 586.83 776.53, 612.06 775.80, 612.15 779.02, 620.26 778.79, 620.16 774.86, 623.77 774.77, 624.20 791.43, 622.13 791.48, 622.69 796.86, 613.89 797.15, 613.71 798.78, 587.54 799.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,85,"POLYGON ((850.04 778.04, 850.43 793.50, 838.59 792.32, 831.45 790.53, 823.40 791.33, 822.72 779.72, 832.80 779.59, 837.10 778.13, 850.04 778.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,86,"POLYGON ((900.00 792.58, 870.59 792.32, 870.69 783.66, 876.48 783.74, 876.58 773.94, 900.00 774.24, 900.00 792.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,87,"POLYGON ((795.36 771.58, 795.63 788.67, 729.60 789.69, 729.35 772.60, 795.36 771.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,88,"POLYGON ((650.54 774.44, 659.23 774.26, 659.18 771.69, 672.93 771.40, 672.97 773.44, 702.13 772.81, 702.30 780.02, 700.11 780.07, 700.18 783.38, 676.48 783.88, 676.50 785.36, 661.98 785.56, 662.04 783.91, 650.75 784.16, 650.54 774.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,89,"POLYGON ((587.80 743.41, 613.52 742.91, 613.58 745.81, 622.08 745.64, 622.21 751.25, 624.67 751.19, 624.74 757.41, 622.60 759.35, 619.65 760.73, 618.95 759.22, 616.74 759.30, 616.79 761.12, 611.73 761.27, 611.94 768.76, 600.44 769.13, 600.11 768.18, 588.29 768.41, 587.80 743.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,90,"POLYGON ((694.84 746.27, 695.85 763.36, 676.52 764.52, 676.37 762.15, 668.09 762.65, 667.20 747.91, 694.84 746.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,91,"POLYGON ((900.00 751.01, 870.35 751.55, 870.11 738.68, 900.00 738.11, 900.00 751.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,92,"POLYGON ((662.69 722.61, 696.23 721.57, 696.71 736.96, 663.16 738.00, 662.69 722.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,93,"POLYGON ((900.00 733.42, 870.44 733.50, 870.41 721.18, 900.00 721.12, 900.00 733.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,94,"POLYGON ((728.95 717.36, 767.56 716.17, 767.69 720.32, 773.99 720.14, 774.43 734.58, 729.52 735.93, 728.95 717.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,95,"POLYGON ((700.41 709.74, 701.62 717.10, 666.22 717.65, 665.88 710.96, 700.41 709.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,96,"POLYGON ((899.74 705.80, 900.00 713.14, 900.00 718.21, 869.89 719.11, 869.41 706.18, 899.74 705.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,97,"POLYGON ((759.82 689.53, 733.86 690.26, 733.44 675.02, 749.57 674.57, 749.34 666.16, 759.16 665.89, 759.82 689.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,98,"POLYGON ((811.13 634.66, 822.52 634.41, 833.86 633.99, 835.77 689.21, 812.13 689.68, 811.13 634.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,99,"POLYGON ((889.46 628.13, 858.95 628.93, 857.70 580.77, 900.00 579.66, 900.00 705.27, 899.72 705.27, 890.58 705.39, 889.46 628.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,100,"POLYGON ((601.17 415.38, 601.12 413.47, 609.61 413.25, 609.65 415.03, 638.33 414.14, 638.08 412.24, 646.22 412.25, 646.40 414.42, 659.36 414.33, 660.32 433.27, 655.73 433.21, 646.79 433.19, 646.98 436.94, 659.53 436.95, 660.57 455.30, 646.46 455.33, 646.52 457.62, 638.89 457.97, 638.83 455.68, 610.40 456.65, 610.45 458.45, 602.26 458.84, 602.13 457.35, 589.11 457.93, 588.79 438.67, 602.04 437.93, 601.95 434.18, 588.68 434.43, 588.85 415.65, 601.17 415.38), (610.81 433.60, 610.75 437.75, 639.11 437.36, 638.93 433.19, 610.81 433.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,101,"POLYGON ((616.49 367.73, 616.68 373.78, 613.42 373.86, 613.79 385.46, 576.87 386.63, 576.60 378.27, 580.56 378.14, 580.27 368.87, 616.49 367.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,102,"POLYGON ((578.40 347.12, 613.85 345.11, 614.09 349.36, 617.18 349.20, 617.72 358.55, 613.57 358.77, 613.83 363.02, 579.43 364.99, 578.40 347.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,103,"POLYGON ((740.90 338.97, 760.24 338.24, 761.26 367.98, 747.44 368.40, 747.60 373.79, 739.39 374.04, 739.34 372.09, 734.76 372.23, 734.65 368.59, 727.44 368.82, 726.08 330.10, 740.55 329.55, 740.90 338.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,104,"POLYGON ((822.06 329.02, 841.27 328.35, 841.99 349.20, 809.61 350.34, 808.69 323.92, 821.86 323.45, 822.06 329.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,105,"POLYGON ((591.99 331.24, 598.52 331.16, 598.40 322.82, 622.32 322.47, 622.53 337.07, 592.08 337.52, 591.99 331.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,106,"POLYGON ((805.59 293.21, 851.59 292.75, 851.76 308.02, 848.48 308.05, 848.45 305.44, 805.73 305.86, 805.59 293.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,107,"POLYGON ((618.77 273.39, 618.82 291.42, 586.69 291.53, 586.64 273.50, 618.77 273.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,108,"POLYGON ((619.29 271.36, 638.49 271.09, 638.54 274.97, 654.07 274.75, 654.26 288.76, 622.77 289.18, 622.78 291.16, 619.56 291.20, 619.29 271.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,109,"POLYGON ((755.60 262.64, 755.71 269.71, 762.05 269.62, 762.12 274.86, 720.46 275.50, 720.28 263.18, 755.60 262.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,110,"POLYGON ((832.70 258.65, 833.30 278.50, 806.31 279.32, 806.26 277.41, 802.67 277.53, 802.18 261.31, 804.77 261.22, 804.71 259.52, 832.70 258.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,111,"POLYGON ((615.49 250.35, 616.33 253.34, 616.37 259.25, 574.72 259.80, 574.54 249.02, 598.39 248.58, 598.42 250.65, 615.49 250.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,112,"POLYGON ((637.66 244.23, 637.78 251.15, 619.75 251.46, 619.63 244.51, 637.66 244.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,113,"POLYGON ((724.68 235.06, 753.03 234.55, 753.09 237.55, 757.89 237.47, 758.22 256.98, 754.28 258.79, 725.09 259.29, 724.68 235.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,114,"POLYGON ((837.17 236.20, 837.85 248.19, 834.36 251.68, 832.92 256.97, 806.37 256.94, 804.96 252.58, 802.73 248.93, 802.00 238.41, 837.17 236.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,115,"POLYGON ((634.12 216.93, 634.32 228.95, 614.99 229.27, 614.79 217.22, 634.12 216.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,116,"POLYGON ((580.62 244.53, 580.89 183.30, 602.21 183.47, 601.72 244.57, 580.62 244.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,117,"POLYGON ((644.54 183.94, 644.47 178.46, 664.21 178.22, 664.45 198.64, 660.11 198.68, 660.18 204.14, 641.13 204.38, 640.88 183.99, 644.54 183.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,118,"POLYGON ((827.68 178.08, 827.68 194.15, 822.08 194.16, 822.08 198.47, 815.11 198.47, 814.14 197.65, 804.68 197.63, 804.74 178.60, 813.84 178.61, 815.10 180.18, 819.85 180.17, 819.85 178.08, 827.68 178.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,119,"POLYGON ((610.40 180.15, 617.71 180.41, 617.83 176.96, 623.65 177.17, 623.56 180.10, 635.99 180.52, 635.35 196.58, 609.77 195.44, 610.40 180.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,120,"POLYGON ((830.04 115.98, 830.11 111.60, 851.48 111.99, 852.84 116.77, 853.07 122.72, 836.94 123.37, 830.12 123.52, 824.39 123.56, 824.36 119.63, 824.43 115.88, 830.04 115.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,121,"POLYGON ((798.81 114.47, 798.55 106.44, 820.28 105.71, 820.82 121.63, 812.98 121.90, 813.04 124.07, 805.66 124.30, 805.62 122.46, 794.69 122.81, 794.42 114.60, 798.81 114.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,122,"POLYGON ((900.00 122.85, 889.76 122.69, 890.01 99.20, 900.00 99.25, 900.00 122.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,123,"POLYGON ((867.18 99.42, 867.03 93.70, 878.72 93.45, 879.19 114.01, 879.25 122.87, 872.21 122.91, 872.23 127.57, 865.13 127.62, 865.11 122.25, 858.01 122.28, 857.94 107.14, 865.10 99.45, 867.18 99.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,124,"POLYGON ((539.31 886.87, 539.64 892.39, 536.42 892.58, 536.83 900.00, 509.41 900.00, 509.30 897.96, 528.81 896.86, 530.84 892.24, 530.94 887.35, 539.31 886.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,125,"POLYGON ((317.22 874.30, 329.72 899.84, 329.39 900.00, 265.51 900.00, 265.26 899.49, 317.22 874.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,126,"POLYGON ((328.44 872.17, 358.53 858.97, 369.72 884.21, 339.63 897.43, 328.44 872.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,127,"POLYGON ((496.28 825.95, 496.30 844.75, 535.45 844.68, 535.49 864.19, 487.60 864.26, 487.58 849.79, 476.16 849.82, 476.13 825.98, 496.28 825.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,128,"POLYGON ((344.81 661.71, 357.47 661.43, 357.63 668.98, 360.79 668.92, 360.88 673.09, 357.60 673.15, 357.71 678.83, 345.16 679.08, 344.81 661.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,129,"POLYGON ((286.98 618.86, 308.04 618.61, 308.10 623.76, 309.25 624.59, 309.54 635.84, 307.78 635.89, 308.17 650.35, 309.94 651.55, 309.66 660.34, 308.33 661.73, 308.41 667.97, 287.60 668.21, 286.98 618.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,130,"POLYGON ((318.98 618.71, 340.91 618.41, 341.55 666.61, 319.62 666.91, 318.98 618.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,131,"POLYGON ((380.35 639.78, 376.12 631.65, 387.19 625.93, 394.08 639.19, 396.64 637.86, 398.88 642.15, 395.49 643.90, 399.08 650.82, 388.83 656.10, 383.53 645.89, 378.59 648.44, 375.41 642.31, 380.35 639.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,132,"POLYGON ((354.82 636.95, 349.55 635.11, 348.76 609.98, 368.82 609.35, 370.29 655.91, 355.43 656.38, 354.82 636.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,133,"POLYGON ((13.82 609.23, 13.75 650.05, 4.76 650.04, 4.75 652.86, 0.00 652.85, 0.00 609.21, 13.82 609.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,134,"POLYGON ((408.08 606.15, 407.99 603.96, 414.58 603.70, 414.70 606.67, 422.16 606.39, 422.44 613.73, 424.35 613.66, 425.05 632.27, 423.23 634.36, 404.56 635.06, 403.47 606.34, 408.08 606.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,135,"POLYGON ((467.19 538.05, 466.94 550.71, 470.45 550.77, 470.35 556.28, 464.22 557.95, 450.14 557.69, 450.52 537.74, 467.19 538.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,136,"POLYGON ((251.64 552.57, 233.30 553.19, 232.69 535.22, 236.36 535.09, 235.85 520.25, 250.51 519.76, 251.64 552.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,137,"POLYGON ((255.24 517.78, 269.31 517.84, 269.14 548.90, 266.35 551.44, 259.51 551.23, 258.37 550.15, 255.08 548.37, 255.24 517.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,138,"POLYGON ((491.50 528.59, 496.08 528.59, 496.08 533.74, 491.63 533.76, 491.65 548.41, 475.02 548.44, 474.98 520.00, 491.50 519.98, 491.50 528.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,139,"POLYGON ((125.57 513.41, 148.99 513.54, 148.75 553.57, 125.33 553.44, 125.57 513.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,140,"POLYGON ((203.38 522.79, 207.62 522.61, 207.23 512.94, 223.51 512.26, 223.89 521.66, 231.04 521.37, 231.56 533.52, 227.98 533.68, 228.68 550.11, 204.57 551.10, 203.38 522.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,141,"POLYGON ((159.50 521.02, 167.64 520.88, 167.50 512.47, 183.59 512.22, 183.78 522.56, 186.30 522.51, 186.75 549.69, 163.42 550.09, 163.17 535.34, 159.74 535.38, 159.50 521.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,142,"POLYGON ((497.87 514.45, 521.03 514.54, 520.90 547.15, 501.39 547.08, 501.40 542.35, 497.75 542.33, 497.87 514.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,143,"POLYGON ((388.33 512.94, 400.33 513.03, 400.32 516.92, 413.87 516.97, 413.85 523.41, 410.85 529.21, 414.52 529.25, 414.33 540.93, 411.76 540.89, 411.67 545.66, 405.02 545.56, 404.98 547.63, 395.79 547.49, 395.83 544.56, 393.77 544.54, 393.86 538.59, 391.15 538.57, 391.24 532.53, 388.07 532.50, 388.33 512.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,144,"POLYGON ((338.39 522.16, 337.91 510.41, 356.50 509.51, 357.96 543.37, 354.57 543.50, 354.77 549.00, 336.13 549.68, 335.15 522.29, 338.39 522.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,145,"POLYGON ((806.95 881.19, 804.86 877.05, 804.46 864.80, 822.19 864.22, 825.97 876.00, 828.02 900.00, 801.76 900.00, 801.25 881.36, 806.95 881.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,146,"POLYGON ((705.65 886.57, 706.17 900.00, 682.00 900.00, 681.52 887.52, 705.65 886.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,147,"POLYGON ((730.28 884.52, 750.76 884.36, 752.52 890.86, 752.57 896.32, 730.38 896.51, 730.28 884.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,148,"POLYGON ((788.91 875.21, 790.49 895.82, 777.65 895.46, 775.83 893.22, 771.93 892.87, 772.25 897.35, 759.06 896.97, 758.17 876.00, 776.09 875.54, 788.91 875.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,149,"POLYGON ((900.00 888.72, 897.54 888.82, 895.10 892.12, 886.84 892.49, 884.90 886.34, 878.80 884.88, 879.57 880.02, 900.00 879.28, 900.00 888.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,150,"POLYGON ((851.36 877.46, 851.94 887.98, 837.65 888.79, 837.78 890.92, 829.43 891.38, 828.73 878.72, 851.36 877.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,151,"POLYGON ((624.03 872.54, 624.75 888.55, 611.60 889.15, 611.32 883.26, 608.47 883.37, 608.21 877.28, 611.28 877.15, 611.15 874.18, 615.71 872.91, 624.03 872.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,152,"POLYGON ((703.37 870.38, 704.02 881.44, 698.41 881.76, 698.19 878.31, 681.81 879.28, 681.51 874.09, 697.45 873.15, 697.29 870.74, 703.37 870.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,153,"POLYGON ((746.25 864.56, 746.22 870.62, 751.28 870.65, 753.61 871.74, 753.57 880.20, 730.07 880.07, 730.14 868.14, 735.57 868.16, 735.59 864.50, 746.25 864.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,0,"POLYGON ((53.80 281.37, 53.19 241.43, 70.40 241.17, 70.53 249.05, 76.24 248.97, 76.44 262.04, 79.37 261.99, 79.52 272.37, 76.47 274.22, 72.89 273.14, 68.99 276.48, 69.07 281.14, 53.80 281.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,1,"POLYGON ((453.93 250.58, 476.75 250.02, 476.79 251.59, 496.36 251.11, 496.40 252.78, 501.07 252.66, 501.46 268.79, 497.05 268.90, 497.09 270.21, 463.20 271.03, 463.16 269.45, 457.27 269.60, 457.07 261.20, 454.18 261.27, 453.93 250.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,2,"POLYGON ((139.23 238.86, 138.56 255.28, 142.27 255.43, 142.00 262.10, 137.93 261.93, 137.26 278.31, 113.65 277.34, 114.11 266.14, 111.04 266.02, 111.96 244.39, 114.42 244.51, 114.70 237.84, 139.23 238.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,3,"POLYGON ((14.16 242.85, 14.46 254.47, 12.30 256.44, 13.55 261.76, 8.78 262.86, 3.54 260.92, 2.00 258.17, 5.00 254.27, 7.90 251.85, 7.24 248.91, 7.59 245.37, 10.77 242.94, 14.16 242.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,4,"POLYGON ((498.30 224.82, 498.35 234.88, 494.42 234.91, 494.47 245.54, 467.41 245.70, 467.35 236.78, 465.33 236.79, 465.25 225.03, 498.30 224.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,5,"POLYGON ((0.00 141.45, 9.96 141.51, 9.82 164.22, 0.00 164.16, 0.00 141.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,6,"POLYGON ((0.00 116.41, 10.96 116.49, 10.91 123.51, 17.16 123.55, 17.05 139.42, 0.00 139.30, 0.00 116.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,7,"POLYGON ((261.45 107.32, 262.07 143.67, 198.10 144.74, 197.44 105.91, 229.24 105.37, 229.29 107.88, 261.45 107.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,8,"POLYGON ((131.16 133.13, 93.20 133.37, 93.18 130.64, 90.03 130.68, 89.95 115.67, 131.04 115.45, 131.16 133.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,9,"POLYGON ((423.28 53.91, 515.83 54.18, 516.80 165.99, 329.78 168.84, 328.51 98.13, 344.49 83.56, 344.49 77.07, 423.59 75.92, 423.28 53.91), (426.65 98.73, 427.00 137.01, 491.19 137.23, 490.78 112.69, 437.94 98.73, 426.65 98.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,10,"POLYGON ((0.00 94.68, 20.52 94.80, 20.44 107.66, 13.90 107.63, 13.86 114.93, 0.00 114.85, 0.00 94.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,11,"POLYGON ((125.03 106.01, 95.48 106.79, 95.02 89.17, 129.53 88.27, 129.78 98.16, 124.84 98.29, 125.03 106.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,12,"POLYGON ((0.00 70.67, 23.02 70.65, 23.02 78.51, 19.59 78.51, 19.60 93.34, 0.00 93.35, 0.00 70.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,13,"POLYGON ((94.64 86.81, 93.60 58.33, 116.49 57.48, 116.60 60.36, 131.97 59.79, 132.48 73.65, 122.05 74.05, 122.16 77.11, 124.92 77.02, 125.24 85.67, 94.64 86.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,14,"POLYGON ((0.00 49.06, 27.93 49.29, 27.80 62.54, 25.97 62.52, 25.90 69.56, 0.00 69.35, 0.00 49.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,15,"POLYGON ((95.96 50.79, 95.35 31.65, 140.67 30.18, 141.29 49.34, 95.96 50.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,16,"POLYGON ((0.00 24.87, 32.37 24.44, 32.56 38.31, 28.22 38.36, 28.31 45.55, 0.00 45.92, 0.00 24.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,17,"POLYGON ((116.67 24.64, 116.58 20.38, 109.43 20.52, 109.01 0.00, 144.77 0.00, 145.26 24.07, 116.67 24.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,18,"POLYGON ((42.74 0.00, 42.46 21.12, 22.58 21.39, 22.48 17.44, 31.28 11.51, 32.30 2.58, 33.74 0.00, 42.74 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,19,"POLYGON ((337.45 0.00, 310.43 55.70, 268.18 36.74, 284.93 0.00, 337.45 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,20,"POLYGON ((327.06 666.97, 327.37 686.83, 309.60 687.13, 309.29 667.25, 327.06 666.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,21,"POLYGON ((521.51 654.39, 521.37 665.54, 526.65 665.40, 527.23 679.48, 495.78 680.29, 495.97 655.04, 521.51 654.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,22,"POLYGON ((471.37 645.79, 472.31 677.51, 453.75 678.05, 452.82 646.33, 471.37 645.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,23,"POLYGON ((39.92 640.45, 65.14 637.81, 68.03 666.13, 62.91 666.64, 63.36 670.98, 49.15 672.34, 48.72 667.66, 42.71 668.24, 39.92 640.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,24,"POLYGON ((68.04 643.97, 88.97 640.38, 91.83 657.26, 70.88 660.70, 68.04 643.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,25,"POLYGON ((0.00 627.92, 9.87 628.94, 9.16 635.78, 9.98 638.35, 7.26 664.59, 0.00 663.84, 0.00 627.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,26,"POLYGON ((20.08 621.18, 39.04 621.78, 37.64 667.08, 22.57 666.64, 20.40 662.95, 15.50 662.83, 15.74 653.97, 14.58 652.89, 15.09 639.46, 17.87 634.22, 19.67 634.29, 20.08 621.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,27,"POLYGON ((99.62 625.41, 121.01 625.00, 121.51 658.59, 113.67 658.68, 113.65 656.31, 100.19 656.45, 99.62 625.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,28,"POLYGON ((519.80 628.59, 519.43 649.74, 488.57 649.22, 488.94 628.07, 519.80 628.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,29,"POLYGON ((125.83 614.08, 132.34 613.72, 132.61 618.42, 139.85 618.01, 139.71 615.51, 149.71 614.94, 151.34 648.35, 146.97 648.51, 147.07 651.59, 143.62 651.70, 143.68 653.25, 130.51 653.70, 130.37 649.62, 127.51 649.71, 126.98 635.17, 125.83 614.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,30,"POLYGON ((161.89 616.56, 182.17 616.35, 182.36 637.08, 178.73 637.11, 178.78 641.92, 170.72 642.00, 167.12 638.20, 162.09 638.26, 161.89 616.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,31,"POLYGON ((243.83 604.70, 245.41 623.91, 220.25 624.37, 219.85 616.21, 223.09 614.31, 225.89 609.33, 227.08 606.04, 243.83 604.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,32,"POLYGON ((281.73 586.04, 282.95 612.69, 250.10 614.17, 248.88 587.55, 281.73 586.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,33,"POLYGON ((344.60 589.12, 357.51 590.03, 356.62 602.42, 353.03 602.18, 343.42 605.40, 344.60 589.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,34,"POLYGON ((442.36 574.03, 441.49 592.93, 431.20 592.46, 431.06 595.68, 422.06 595.26, 423.09 573.15, 442.36 574.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,35,"POLYGON ((391.98 561.36, 392.57 602.48, 375.05 602.72, 374.47 561.61, 391.98 561.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,36,"POLYGON ((334.00 601.15, 327.97 600.91, 326.62 604.76, 313.41 604.28, 313.52 601.26, 307.04 601.00, 307.79 581.60, 314.88 581.86, 315.82 557.00, 333.08 557.65, 332.11 583.00, 334.69 583.09, 334.00 601.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,37,"POLYGON ((447.70 559.54, 468.03 558.57, 469.64 591.78, 452.81 592.59, 452.03 576.94, 448.55 577.12, 447.70 559.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,38,"POLYGON ((498.49 556.48, 500.49 589.02, 476.93 590.47, 475.35 564.98, 478.66 564.78, 478.22 557.73, 498.49 556.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,39,"POLYGON ((292.49 513.15, 293.29 551.14, 275.36 551.51, 274.55 513.52, 292.49 513.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,40,"POLYGON ((256.28 510.73, 256.90 552.62, 238.74 552.91, 238.12 510.99, 256.28 510.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,41,"POLYGON ((0.00 504.55, 4.60 504.43, 4.68 508.12, 11.38 507.97, 11.93 540.39, 6.06 540.45, 0.00 540.40, 0.00 504.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,42,"POLYGON ((21.90 519.93, 29.02 518.24, 32.66 513.00, 49.13 512.38, 49.55 523.64, 37.43 524.09, 37.61 528.72, 30.14 529.00, 29.33 530.42, 21.89 530.43, 21.90 519.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,43,"POLYGON ((304.33 507.95, 319.47 507.56, 319.61 513.04, 324.02 512.93, 324.55 533.78, 305.01 534.28, 304.33 507.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,44,"POLYGON ((340.19 507.39, 340.33 513.53, 344.74 513.42, 345.21 533.14, 327.64 533.57, 327.04 507.70, 340.19 507.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,45,"POLYGON ((89.67 503.31, 99.51 503.19, 99.57 505.76, 106.71 505.71, 107.74 534.41, 86.61 534.51, 82.43 521.96, 82.24 503.50, 89.67 503.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,46,"POLYGON ((179.08 498.96, 200.88 498.38, 201.47 520.01, 203.31 519.96, 203.54 528.35, 199.64 536.61, 191.28 536.07, 191.43 533.83, 189.25 533.68, 189.59 528.66, 182.87 528.23, 178.95 523.67, 178.47 506.50, 176.58 506.55, 176.50 503.13, 179.18 503.06, 179.08 498.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,47,"POLYGON ((76.07 503.76, 77.09 527.09, 74.01 528.52, 53.52 528.80, 51.80 524.89, 50.87 504.94, 76.07 503.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,48,"POLYGON ((231.70 498.50, 231.80 533.02, 211.30 533.08, 211.22 505.29, 214.78 505.28, 214.76 498.56, 231.70 498.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,49,"POLYGON ((153.86 501.49, 163.15 501.14, 163.43 508.24, 164.95 508.18, 165.10 511.88, 173.28 511.56, 173.68 522.14, 166.13 522.42, 166.33 527.65, 150.85 528.25, 150.57 520.73, 145.42 520.93, 145.20 515.50, 154.39 515.13, 153.86 501.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,50,"POLYGON ((138.76 494.93, 139.50 528.07, 137.71 529.74, 137.80 534.02, 128.68 534.23, 128.56 528.88, 122.56 529.02, 122.33 519.21, 118.79 519.28, 118.27 495.38, 138.76 494.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,51,"POLYGON ((451.13 491.32, 452.01 517.85, 443.25 518.14, 442.95 509.29, 407.70 510.46, 407.99 519.15, 400.68 519.40, 399.80 493.03, 451.13 491.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,52,"POLYGON ((521.97 489.49, 522.49 508.12, 514.08 508.45, 506.66 512.17, 503.78 514.59, 500.84 515.65, 489.22 516.72, 487.68 518.14, 487.13 519.53, 473.80 518.76, 474.55 506.04, 480.10 506.36, 484.78 504.67, 492.46 496.24, 492.31 490.31, 521.97 489.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,53,"POLYGON ((396.13 514.21, 373.77 514.83, 373.25 495.93, 380.31 495.73, 380.26 493.69, 387.85 493.49, 387.92 496.16, 395.63 495.96, 396.13 514.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,54,"POLYGON ((265.82 417.50, 292.83 416.94, 292.99 424.62, 297.12 424.53, 297.28 432.08, 294.44 434.30, 289.69 441.08, 289.70 443.72, 274.59 443.78, 274.61 449.53, 270.08 449.53, 269.43 447.19, 268.34 446.00, 266.40 445.56, 265.82 417.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,55,"POLYGON ((39.23 411.58, 39.70 435.70, 38.72 437.92, 35.62 441.40, 39.79 445.11, 40.92 448.61, 27.22 448.76, 27.16 443.52, 18.29 443.62, 17.96 411.99, 39.23 411.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,56,"POLYGON ((165.89 412.92, 167.03 442.53, 165.25 442.60, 165.45 447.36, 158.57 444.30, 153.45 446.49, 153.25 441.41, 143.23 441.80, 142.14 413.84, 165.89 412.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,57,"POLYGON ((109.95 412.06, 123.34 411.97, 123.39 420.53, 129.49 420.49, 129.50 422.53, 127.06 424.21, 127.29 428.16, 130.92 429.42, 131.47 441.39, 127.95 441.55, 128.21 447.56, 121.82 447.86, 117.30 446.59, 113.08 446.64, 113.03 440.40, 110.14 440.43, 109.95 412.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,58,"POLYGON ((235.40 413.34, 245.18 413.05, 245.29 416.53, 256.40 416.18, 257.31 445.84, 233.76 446.55, 232.87 417.25, 235.52 417.18, 235.40 413.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,59,"POLYGON ((73.32 443.85, 69.94 443.92, 70.01 448.07, 53.13 448.37, 52.95 437.76, 49.38 437.83, 48.92 411.22, 50.92 411.20, 52.95 412.88, 56.64 412.18, 60.16 414.22, 68.26 412.84, 72.38 412.09, 72.67 429.27, 77.45 429.19, 77.55 435.33, 73.17 435.40, 73.32 443.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,60,"POLYGON ((15.98 442.50, 10.31 442.49, 10.30 450.10, 0.01 450.10, 0.01 443.15, 0.00 443.14, 0.00 415.58, 3.74 415.62, 4.88 413.66, 7.57 412.50, 11.45 412.49, 11.45 408.05, 14.06 408.05, 14.06 413.87, 15.99 413.86, 15.98 442.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,61,"POLYGON ((100.09 440.91, 91.64 441.10, 91.69 443.23, 82.13 443.45, 82.06 440.61, 79.20 440.69, 78.62 414.82, 97.02 414.41, 97.18 421.60, 101.63 421.51, 101.84 430.52, 99.86 430.57, 100.09 440.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,62,"POLYGON ((204.03 405.18, 214.81 404.81, 215.01 410.36, 224.91 410.02, 225.24 419.91, 227.19 419.84, 227.63 432.97, 222.93 433.11, 223.16 439.85, 215.86 440.08, 215.98 443.63, 205.35 443.99, 204.03 405.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,63,"POLYGON ((194.94 410.72, 195.72 439.11, 174.82 439.66, 174.12 413.80, 177.81 413.71, 177.63 407.36, 184.63 407.18, 184.73 411.00, 194.94 410.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,64,"POLYGON ((324.22 395.27, 360.59 394.21, 360.95 408.30, 344.18 408.86, 345.23 449.90, 326.09 450.25, 324.22 395.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,65,"POLYGON ((397.54 393.00, 398.93 447.99, 379.81 448.77, 378.41 407.74, 361.65 408.17, 361.18 394.08, 397.54 393.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,66,"POLYGON ((219.75 376.03, 226.40 375.42, 227.74 390.08, 221.08 390.69, 219.75 376.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,67,"POLYGON ((468.59 348.98, 496.50 348.89, 496.53 354.31, 501.02 354.28, 501.06 366.69, 475.07 366.78, 475.06 357.25, 473.22 354.17, 469.60 351.56, 468.59 348.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,68,"POLYGON ((465.04 325.43, 496.33 324.77, 496.47 330.80, 503.82 330.66, 504.16 346.08, 465.51 346.93, 465.04 325.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,69,"POLYGON ((496.67 300.43, 496.81 308.08, 501.91 307.98, 502.07 317.32, 497.44 317.41, 497.54 322.56, 466.42 323.16, 466.29 315.61, 460.73 315.71, 460.66 312.36, 463.94 312.30, 465.64 310.50, 467.32 307.97, 468.69 305.92, 469.38 303.83, 469.32 300.95, 496.67 300.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,70,"POLYGON ((336.73 244.76, 410.93 244.33, 413.20 333.44, 337.59 335.66, 336.73 244.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,71,"POLYGON ((462.80 277.14, 502.30 276.95, 502.35 289.56, 497.55 289.60, 497.58 296.72, 462.88 296.90, 462.80 277.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,72,"POLYGON ((281.62 250.27, 282.17 271.64, 270.57 271.91, 270.63 274.19, 258.57 274.52, 258.49 272.33, 238.52 272.86, 238.58 274.95, 228.63 275.22, 228.57 273.05, 210.90 273.52, 210.96 275.72, 198.69 276.05, 198.63 273.77, 179.94 274.27, 179.99 276.35, 171.75 276.56, 172.33 298.55, 180.46 298.34, 180.52 300.69, 199.88 300.17, 199.83 297.98, 210.27 297.71, 210.33 299.71, 229.45 299.24, 229.39 296.98, 240.02 296.71, 240.10 299.59, 258.92 299.13, 258.87 296.34, 270.11 296.07, 270.19 299.22, 281.70 298.93, 282.19 318.96, 159.69 321.96, 159.11 297.82, 152.90 297.98, 152.40 277.57, 157.87 277.43, 157.27 253.38, 281.62 250.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,73,"POLYGON ((28.73 246.90, 42.82 247.65, 42.53 252.58, 45.24 252.73, 44.09 274.16, 39.78 278.98, 38.57 283.87, 35.12 281.76, 32.18 276.98, 31.91 271.54, 28.41 267.79, 24.83 266.26, 25.62 251.55, 28.48 251.70, 28.73 246.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,74,"POLYGON ((84.63 287.40, 84.49 248.49, 92.50 248.46, 92.48 243.16, 107.94 243.10, 108.07 282.56, 95.97 282.61, 96.00 287.36, 84.63 287.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,75,"POLYGON ((652.61 851.78, 650.40 888.71, 605.71 900.00, 604.18 900.00, 602.32 892.73, 573.59 900.00, 562.62 900.00, 556.53 876.10, 652.61 851.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,76,"POLYGON ((900.00 840.31, 882.29 845.40, 873.65 815.52, 900.00 807.96, 900.00 840.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,77,"POLYGON ((666.02 762.40, 645.99 684.93, 662.21 679.30, 657.16 651.21, 646.70 613.61, 665.39 608.47, 667.96 611.20, 691.09 604.93, 700.57 640.67, 723.62 634.75, 714.92 601.14, 732.95 596.29, 734.22 600.87, 758.64 593.39, 763.20 608.48, 760.88 609.91, 765.72 623.00, 788.06 616.34, 782.64 592.13, 801.10 587.09, 803.28 599.06, 809.67 597.21, 807.49 589.76, 815.50 587.45, 815.22 582.44, 900.00 567.79, 900.00 742.58, 883.87 742.62, 885.70 619.00, 826.92 626.43, 857.79 733.90, 852.98 735.09, 858.16 755.87, 830.34 764.01, 829.87 762.12, 809.16 767.24, 809.19 768.48, 801.40 770.55, 799.49 769.04, 771.20 777.87, 771.24 779.42, 713.70 795.56, 711.34 787.14, 707.77 788.15, 709.23 793.33, 702.16 795.30, 702.96 798.17, 667.10 808.19, 666.08 804.53, 581.84 830.72, 574.98 807.55, 583.87 804.81, 579.19 787.77, 666.02 762.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,78,"POLYGON ((619.77 620.80, 619.38 737.93, 596.54 737.85, 596.51 748.06, 569.70 747.97, 570.12 626.69, 599.60 626.78, 599.61 620.74, 619.77 620.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,79,"POLYGON ((766.33 457.29, 765.81 530.56, 717.13 530.21, 717.65 456.96, 766.33 457.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,80,"POLYGON ((701.73 453.51, 703.97 513.43, 683.09 514.21, 680.86 454.28, 701.73 453.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,81,"POLYGON ((704.04 332.93, 705.85 425.99, 584.55 428.35, 585.54 480.14, 563.96 480.56, 562.91 426.49, 570.17 426.35, 568.41 335.58, 704.04 332.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,82,"POLYGON ((657.72 248.25, 686.50 247.67, 687.80 312.55, 690.77 312.49, 690.92 320.81, 687.92 320.87, 687.97 322.73, 659.23 323.31, 657.72 248.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,83,"POLYGON ((563.75 234.69, 578.73 234.53, 578.76 237.17, 602.95 236.93, 603.45 285.82, 648.21 285.37, 648.60 323.78, 568.37 324.61, 567.75 264.18, 564.04 264.21, 563.75 234.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,84,"POLYGON ((803.94 0.00, 839.37 2.85, 832.62 139.65, 604.35 128.02, 601.48 164.16, 571.56 162.95, 574.81 75.96, 582.70 76.38, 583.37 59.85, 556.04 58.53, 555.46 0.00, 803.94 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,85,"POLYGON ((42.72 897.30, 45.33 897.30, 45.36 893.95, 54.48 894.03, 54.46 897.09, 58.21 897.11, 58.18 900.00, 42.69 900.00, 42.72 897.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,86,"POLYGON ((449.27 883.17, 449.68 900.00, 406.41 900.00, 406.03 884.23, 449.27 883.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,87,"POLYGON ((61.97 888.31, 42.08 889.24, 41.09 866.35, 38.65 866.46, 38.17 855.66, 41.68 847.96, 53.85 847.54, 60.31 856.65, 61.97 888.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,88,"POLYGON ((90.68 854.46, 91.14 873.38, 72.26 873.86, 71.99 863.10, 90.68 854.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,89,"POLYGON ((6.90 835.57, 27.90 834.82, 29.15 868.57, 5.43 869.44, 5.17 861.43, 2.63 860.03, 2.95 852.43, 5.07 849.78, 7.19 843.47, 6.90 835.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,90,"POLYGON ((106.80 828.92, 106.62 825.95, 117.94 825.21, 119.47 848.66, 121.40 851.74, 121.92 867.67, 107.97 868.00, 104.93 866.81, 97.99 867.26, 95.55 829.63, 106.80 828.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,91,"POLYGON ((124.71 823.75, 132.86 823.43, 133.11 829.78, 152.23 829.02, 153.64 864.59, 128.06 865.53, 126.24 862.74, 124.71 823.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,92,"POLYGON ((168.09 824.24, 167.59 817.95, 176.36 817.22, 178.97 845.32, 172.62 845.90, 173.59 856.18, 168.30 858.67, 163.66 857.05, 158.09 853.25, 155.87 825.20, 168.09 824.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,93,"POLYGON ((216.09 816.45, 218.50 857.81, 193.82 859.06, 193.13 843.09, 191.06 843.19, 190.61 832.81, 189.93 831.54, 189.62 826.11, 188.06 826.20, 187.63 818.55, 192.76 818.26, 192.69 817.07, 203.98 816.42, 204.15 819.26, 208.11 819.05, 208.00 816.92, 216.09 816.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,94,"POLYGON ((302.81 796.10, 303.11 800.51, 304.65 800.42, 305.81 818.44, 301.50 818.71, 304.75 869.10, 289.95 870.06, 286.73 820.28, 282.80 820.54, 282.57 817.04, 279.18 817.26, 277.92 797.71, 302.81 796.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,95,"POLYGON ((242.25 808.70, 243.40 834.57, 221.66 835.53, 220.53 809.65, 242.25 808.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,96,"POLYGON ((265.39 797.03, 265.88 804.54, 273.19 804.05, 275.17 833.65, 265.64 834.29, 266.26 843.42, 254.75 844.18, 254.08 834.08, 251.66 834.25, 249.23 798.11, 265.39 797.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,97,"POLYGON ((331.91 789.85, 334.12 823.47, 328.83 823.82, 329.50 834.22, 324.23 834.55, 324.83 843.50, 312.20 844.34, 308.70 791.37, 331.91 789.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,98,"POLYGON ((492.47 767.92, 493.40 860.73, 462.04 861.07, 461.11 768.23, 492.47 767.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,99,"POLYGON ((363.46 788.22, 365.27 827.15, 341.79 828.15, 339.70 789.38, 363.46 788.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,100,"POLYGON ((366.84 788.00, 387.90 787.20, 388.13 812.10, 381.31 811.32, 377.50 816.28, 373.86 823.27, 372.46 826.57, 368.92 826.53, 366.84 788.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,101,"POLYGON ((413.66 786.14, 414.14 800.25, 415.91 800.20, 416.69 822.62, 397.19 823.28, 395.94 786.73, 413.66 786.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,102,"POLYGON ((12.94 765.78, 13.79 782.34, 11.09 782.48, 11.44 789.91, 8.65 790.02, 8.81 793.44, 3.09 793.69, 3.22 796.71, 0.00 796.86, 0.00 766.48, 12.94 765.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,103,"POLYGON ((27.18 758.03, 33.63 767.30, 37.31 784.12, 37.23 791.96, 32.04 791.91, 30.78 789.28, 26.21 789.40, 26.16 787.64, 19.81 787.81, 17.78 783.28, 19.31 779.87, 18.59 769.30, 21.37 767.74, 20.42 758.76, 27.18 758.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,104,"POLYGON ((148.26 758.44, 149.04 777.67, 123.06 778.58, 122.29 759.64, 148.26 758.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,105,"POLYGON ((97.28 750.89, 100.01 750.73, 99.76 746.76, 109.32 746.16, 109.64 751.06, 120.50 750.36, 122.23 777.40, 99.07 778.86, 97.28 750.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,106,"POLYGON ((91.29 738.32, 91.47 750.59, 93.07 750.55, 93.24 761.60, 91.16 761.63, 91.38 775.17, 82.33 775.31, 82.38 777.97, 73.10 778.12, 73.06 775.75, 67.76 775.84, 67.38 751.50, 72.51 751.41, 72.31 738.61, 91.29 738.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,107,"POLYGON ((160.68 745.61, 160.61 739.97, 179.44 739.78, 179.95 771.13, 163.51 771.31, 163.55 774.95, 153.65 775.04, 153.34 745.68, 160.68 745.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,108,"POLYGON ((182.48 743.12, 208.87 742.16, 209.70 765.11, 203.12 765.35, 203.24 768.41, 190.61 768.86, 190.48 765.58, 183.30 765.85, 182.48 743.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,109,"POLYGON ((236.50 737.46, 237.38 763.23, 218.19 763.87, 217.31 738.12, 236.50 737.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,110,"POLYGON ((269.86 756.40, 246.63 757.13, 245.73 728.63, 258.51 728.21, 258.75 736.04, 269.20 735.71, 269.86 756.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,111,"POLYGON ((296.63 715.79, 297.40 744.62, 299.61 744.55, 299.88 755.46, 278.86 756.02, 277.79 716.29, 296.63 715.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,112,"POLYGON ((356.94 721.15, 357.49 748.39, 333.55 748.87, 333.02 721.63, 356.94 721.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,113,"POLYGON ((395.73 729.90, 403.03 729.81, 405.53 730.41, 407.66 729.69, 408.31 729.00, 412.79 728.73, 414.21 726.21, 416.34 724.85, 419.20 724.80, 419.39 738.09, 395.85 738.40, 395.73 729.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,114,"POLYGON ((363.78 717.40, 372.10 720.30, 379.05 723.65, 384.74 723.50, 384.31 744.60, 375.29 744.83, 374.62 741.74, 364.60 741.40, 363.78 717.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,115,"POLYGON ((327.95 708.22, 329.35 746.36, 310.59 747.06, 309.90 727.94, 306.30 728.08, 305.61 709.05, 327.95 708.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,116,"POLYGON ((52.81 705.07, 55.55 729.78, 24.79 733.18, 23.30 719.83, 21.04 720.09, 20.30 713.41, 30.60 712.26, 30.09 707.59, 52.81 705.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,117,"POLYGON ((430.05 733.24, 430.26 709.93, 434.08 708.08, 434.73 705.95, 434.32 703.34, 435.31 701.06, 437.37 699.58, 442.12 699.15, 442.53 703.58, 447.14 703.17, 447.22 704.10, 447.49 710.33, 447.58 714.02, 448.86 716.09, 450.85 717.62, 453.98 717.65, 453.81 733.48, 430.05 733.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,118,"POLYGON ((153.00 694.64, 180.67 694.28, 180.84 708.13, 179.81 710.53, 179.90 716.92, 171.89 717.02, 171.94 720.52, 165.57 720.60, 165.51 716.23, 153.26 716.36, 153.00 694.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,119,"POLYGON ((183.73 687.04, 208.71 685.89, 209.83 709.86, 204.21 710.11, 204.30 712.13, 186.25 712.97, 186.04 708.40, 184.60 705.95, 183.73 687.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,120,"POLYGON ((521.07 693.56, 521.27 704.23, 501.48 704.60, 498.96 702.74, 498.04 700.85, 498.09 695.94, 501.72 693.92, 521.07 693.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,121,"POLYGON ((270.21 670.82, 270.42 700.96, 256.58 701.14, 254.19 693.03, 251.70 688.54, 244.72 686.72, 245.24 671.66, 270.21 670.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,0,"POLYGON ((898.18 343.50, 900.00 350.90, 900.00 356.43, 895.59 357.51, 900.00 375.46, 900.00 475.43, 851.84 475.59, 851.59 388.22, 848.13 373.26, 842.29 374.59, 838.49 358.26, 843.69 357.06, 790.94 128.83, 786.44 129.86, 779.22 98.36, 784.32 97.21, 776.64 63.58, 769.92 65.10, 758.09 13.15, 764.23 11.75, 761.97 1.75, 769.79 0.00, 853.57 0.00, 858.55 22.02, 879.75 17.26, 885.52 42.84, 867.26 46.94, 868.05 50.41, 883.33 47.00, 898.30 113.39, 900.00 113.01, 900.00 343.06, 898.18 343.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,1,"POLYGON ((596.21 217.65, 595.96 228.49, 584.58 228.25, 584.82 217.41, 596.21 217.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,2,"POLYGON ((557.57 208.87, 555.93 175.82, 580.96 174.58, 581.46 184.62, 583.09 184.54, 583.72 197.06, 580.80 197.20, 581.33 207.71, 557.57 208.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,3,"POLYGON ((593.86 196.14, 593.75 193.43, 588.75 193.63, 587.99 174.04, 590.68 173.95, 590.55 170.54, 599.46 170.18, 599.66 175.28, 606.00 175.05, 606.80 195.63, 593.86 196.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,4,"POLYGON ((332.13 5.53, 372.58 4.69, 373.58 53.48, 341.18 54.13, 340.55 23.71, 332.51 23.90, 332.13 5.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,5,"POLYGON ((429.37 10.88, 436.83 10.73, 437.32 36.14, 429.81 34.22, 429.37 10.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,6,"POLYGON ((272.30 7.97, 272.99 11.81, 271.56 16.71, 256.70 19.40, 260.68 13.92, 260.01 10.14, 272.30 7.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,7,"POLYGON ((3.76 22.90, 3.54 11.37, 7.96 11.28, 7.82 3.36, 43.59 2.62, 43.85 15.64, 35.68 15.83, 35.83 22.26, 3.76 22.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,8,"POLYGON ((283.44 439.95, 310.08 439.18, 311.23 479.00, 284.59 479.76, 283.44 439.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,9,"POLYGON ((314.09 417.63, 330.96 417.56, 331.64 430.48, 340.66 430.23, 347.87 437.39, 350.69 498.92, 314.29 498.78, 314.09 417.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,10,"POLYGON ((534.26 437.05, 560.71 436.75, 561.06 470.15, 534.62 470.42, 534.26 437.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,11,"POLYGON ((30.58 427.27, 41.17 456.72, 24.05 462.84, 23.45 461.17, 21.08 462.01, 22.52 465.97, 3.18 472.88, 0.00 464.05, 0.00 432.96, 8.84 429.80, 12.55 440.12, 17.37 438.40, 15.33 432.72, 30.58 427.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,12,"POLYGON ((465.53 401.58, 465.08 424.70, 436.18 423.35, 432.81 422.19, 432.50 418.03, 429.71 415.19, 425.46 413.01, 422.11 412.48, 419.52 409.21, 416.35 407.63, 416.55 399.30, 465.53 401.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,13,"POLYGON ((351.39 393.68, 379.03 393.64, 379.05 428.71, 351.43 428.75, 351.39 393.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,14,"POLYGON ((178.77 364.17, 178.85 367.79, 183.66 367.69, 185.06 429.52, 184.45 431.93, 184.73 445.22, 166.08 445.61, 165.75 429.95, 156.65 430.13, 156.38 416.62, 151.77 416.72, 150.76 369.28, 158.58 369.11, 158.48 364.62, 178.77 364.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,15,"POLYGON ((122.09 357.90, 124.40 445.43, 81.53 446.55, 79.22 359.03, 122.09 357.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,16,"POLYGON ((249.05 379.36, 252.62 379.25, 253.16 396.77, 255.29 397.42, 255.76 401.96, 253.76 402.70, 254.88 428.45, 253.24 431.64, 250.17 433.54, 230.63 433.48, 228.72 431.33, 227.94 428.29, 227.66 417.29, 226.06 416.53, 225.83 410.94, 227.56 409.94, 229.79 409.86, 228.56 370.60, 248.75 369.98, 249.05 379.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,17,"POLYGON ((456.37 365.56, 454.47 392.65, 433.60 391.18, 434.09 384.27, 433.04 377.37, 428.26 370.30, 424.00 368.94, 424.39 363.31, 456.37 365.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,18,"POLYGON ((370.62 363.51, 367.27 380.47, 361.18 379.27, 356.97 380.07, 354.40 382.24, 348.38 381.06, 347.64 384.81, 329.85 381.31, 330.97 375.71, 323.42 374.24, 324.67 367.95, 330.61 369.13, 333.19 356.17, 370.62 363.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,19,"POLYGON ((295.95 351.97, 297.19 344.46, 308.00 346.22, 306.79 353.73, 295.95 351.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,20,"POLYGON ((426.08 332.08, 447.07 332.81, 446.19 358.01, 425.39 357.30, 425.77 346.72, 418.72 346.48, 419.06 336.75, 425.91 336.99, 426.08 332.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,21,"POLYGON ((372.99 337.10, 369.66 353.50, 364.82 352.54, 363.73 357.92, 337.77 352.72, 338.91 347.12, 336.20 343.86, 333.69 342.17, 336.21 329.74, 372.99 337.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,22,"POLYGON ((27.23 355.01, 0.00 355.71, 0.00 331.64, 26.62 330.97, 27.23 355.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,23,"POLYGON ((286.64 324.97, 283.87 323.04, 285.78 309.90, 299.09 310.67, 299.30 324.27, 286.64 324.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,24,"POLYGON ((337.76 323.97, 339.22 311.86, 333.39 310.79, 335.28 302.15, 367.95 308.15, 370.19 320.68, 366.16 327.62, 337.76 323.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,25,"POLYGON ((308.90 301.56, 322.93 302.22, 321.80 316.59, 318.86 316.26, 318.71 317.64, 307.45 314.96, 308.90 301.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,26,"POLYGON ((422.97 321.44, 422.59 295.46, 459.02 294.90, 459.19 306.11, 456.88 313.47, 458.74 320.90, 422.97 321.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,27,"POLYGON ((422.97 321.44, 422.59 295.46, 459.02 294.90, 459.19 306.11, 456.88 313.47, 458.74 320.90, 422.97 321.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,28,"POLYGON ((522.78 293.76, 523.47 318.56, 494.03 319.38, 498.70 306.14, 498.38 294.45, 522.78 293.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,29,"POLYGON ((522.78 293.76, 523.47 318.56, 494.03 319.38, 498.70 306.14, 498.38 294.45, 522.78 293.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,30,"POLYGON ((0.00 263.50, 31.23 262.74, 34.49 266.88, 33.92 271.95, 34.34 274.92, 32.86 278.46, 26.88 277.90, 23.78 280.63, 27.36 283.15, 21.07 284.89, 19.90 287.18, 0.00 287.68, 0.00 263.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,31,"POLYGON ((180.90 267.47, 192.27 267.82, 192.09 274.35, 185.91 274.18, 185.73 280.09, 180.51 279.93, 180.90 267.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,32,"POLYGON ((151.15 260.37, 158.82 259.46, 160.83 267.11, 160.89 274.99, 156.45 275.48, 148.83 274.83, 149.08 272.16, 145.08 271.80, 145.79 263.84, 150.81 264.28, 151.15 260.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,33,"POLYGON ((555.48 227.13, 561.94 226.83, 562.66 240.42, 559.55 240.63, 560.05 235.67, 558.77 233.97, 557.39 233.52, 557.02 229.31, 555.39 228.73, 555.48 227.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,34,"POLYGON ((75.68 203.56, 79.93 202.45, 87.03 202.23, 87.12 205.27, 91.25 205.14, 94.31 207.86, 95.83 212.19, 92.73 214.89, 88.00 215.54, 86.51 219.07, 76.57 219.45, 75.68 203.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,35,"POLYGON ((265.52 181.82, 265.86 194.35, 269.47 194.24, 270.45 230.28, 250.35 230.84, 249.02 182.26, 265.52 181.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,36,"POLYGON ((127.86 221.56, 122.14 221.84, 121.89 216.65, 117.68 214.10, 117.30 211.02, 114.87 208.00, 110.39 206.20, 107.92 202.43, 108.44 199.62, 113.65 195.09, 114.12 190.21, 122.93 188.21, 125.52 190.81, 129.10 192.34, 132.28 195.19, 131.91 203.88, 128.89 207.48, 127.38 211.36, 127.86 221.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,37,"POLYGON ((214.71 218.88, 202.85 218.98, 202.89 224.20, 198.44 224.24, 198.08 185.23, 214.39 185.06, 214.71 218.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,38,"POLYGON ((284.91 217.95, 282.24 213.65, 278.27 199.05, 277.55 189.52, 298.34 189.90, 299.36 212.34, 297.55 216.94, 284.91 217.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,39,"POLYGON ((172.51 185.08, 184.81 184.86, 184.85 187.19, 188.43 187.12, 188.96 215.65, 173.08 215.95, 172.51 185.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,40,"POLYGON ((226.00 215.79, 222.15 210.12, 220.55 195.46, 226.09 192.53, 225.51 183.79, 238.17 183.47, 240.72 214.37, 226.00 215.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,41,"POLYGON ((311.06 181.23, 318.33 180.89, 318.49 184.15, 322.16 183.99, 329.96 184.59, 334.62 198.01, 337.47 198.72, 338.24 215.70, 316.37 216.68, 315.96 207.59, 308.38 207.94, 307.67 192.33, 311.56 192.16, 311.06 181.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,42,"POLYGON ((157.70 183.80, 165.20 183.49, 165.69 195.22, 162.47 199.10, 167.52 203.12, 167.71 208.91, 143.18 209.74, 143.04 205.66, 146.11 204.69, 153.06 204.51, 152.25 201.74, 150.44 200.03, 153.63 197.88, 153.05 192.31, 157.70 183.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,43,"POLYGON ((361.27 178.11, 362.71 192.34, 369.38 195.15, 376.40 194.26, 381.25 179.59, 393.17 179.36, 393.58 199.90, 376.85 200.24, 377.04 209.69, 365.07 209.93, 344.84 209.96, 344.81 183.92, 347.01 179.51, 361.27 178.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,44,"POLYGON ((49.84 202.33, 49.27 184.81, 50.83 182.86, 54.03 185.40, 53.97 190.64, 57.56 193.17, 61.45 193.05, 61.75 201.94, 49.84 202.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,45,"POLYGON ((411.65 206.92, 410.96 204.54, 407.57 203.27, 400.50 204.61, 399.79 176.86, 421.61 175.99, 422.52 187.09, 422.91 205.81, 419.59 207.14, 411.65 206.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,46,"POLYGON ((548.33 170.84, 549.78 209.94, 533.68 210.53, 533.56 207.05, 524.89 207.36, 522.96 172.38, 548.33 170.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,47,"POLYGON ((465.82 171.73, 477.29 171.32, 479.18 175.12, 484.94 178.54, 488.83 179.73, 492.40 178.11, 494.59 173.70, 493.23 170.65, 511.66 169.87, 512.33 175.89, 514.12 198.73, 508.24 199.17, 509.09 210.22, 492.35 211.50, 491.66 202.52, 482.74 203.19, 481.53 206.04, 464.99 207.38, 463.63 190.36, 466.40 188.25, 465.82 171.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,48,"POLYGON ((44.19 200.83, 27.00 201.50, 25.78 169.61, 42.97 168.94, 44.19 200.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,49,"POLYGON ((456.31 172.28, 456.40 177.07, 445.80 177.26, 444.34 181.13, 440.56 184.05, 440.36 172.58, 456.31 172.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,50,"POLYGON ((0.00 110.48, 17.97 109.80, 18.67 128.63, 0.00 129.33, 0.00 110.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,51,"POLYGON ((73.76 119.61, 54.21 114.92, 64.12 74.06, 83.67 78.76, 73.76 119.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,52,"POLYGON ((0.00 84.98, 14.88 84.55, 14.81 82.02, 30.98 81.57, 31.13 86.60, 26.67 86.72, 23.88 88.10, 21.84 90.79, 15.65 95.92, 15.77 100.49, 0.00 100.94, 0.00 84.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,53,"POLYGON ((381.77 102.71, 381.62 90.81, 379.04 90.84, 378.93 82.09, 380.89 82.06, 380.74 70.32, 398.64 70.09, 399.02 98.63, 390.61 98.73, 390.67 102.59, 381.77 102.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,54,"POLYGON ((125.53 80.42, 126.42 104.41, 98.33 105.44, 98.07 98.17, 89.27 98.50, 88.70 83.25, 99.81 82.85, 99.25 67.53, 121.01 66.73, 121.51 80.57, 125.53 80.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,55,"POLYGON ((172.67 68.03, 189.43 66.42, 192.39 97.47, 188.63 97.83, 189.04 101.93, 181.66 102.63, 181.10 96.87, 170.39 97.87, 169.15 84.90, 174.24 84.41, 172.67 68.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,56,"POLYGON ((373.37 55.97, 374.33 108.98, 324.47 109.90, 323.83 74.84, 341.70 74.50, 341.37 56.57, 373.37 55.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,57,"POLYGON ((484.77 62.96, 492.65 66.64, 491.96 73.54, 493.70 82.33, 490.39 88.23, 491.40 93.80, 496.37 101.87, 479.19 101.26, 479.64 88.64, 475.34 88.49, 476.27 62.64, 484.77 62.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,58,"POLYGON ((239.80 64.36, 263.89 64.19, 264.01 81.92, 270.05 81.88, 270.18 98.39, 240.00 98.61, 239.80 64.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,59,"POLYGON ((133.05 98.30, 132.84 79.74, 141.08 79.64, 140.99 69.95, 138.21 69.99, 138.09 59.50, 151.21 59.36, 151.25 64.13, 156.89 64.06, 157.32 103.31, 148.66 103.40, 148.60 98.12, 133.05 98.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,60,"POLYGON ((222.65 60.16, 223.19 72.66, 225.94 74.75, 226.91 101.12, 203.46 101.98, 202.65 79.71, 204.42 78.63, 203.65 60.98, 222.65 60.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,61,"POLYGON ((450.76 55.19, 472.10 55.20, 472.05 101.16, 452.05 101.16, 452.06 76.94, 447.68 76.94, 447.68 68.17, 450.74 68.16, 450.76 55.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,62,"POLYGON ((303.16 91.29, 291.13 91.37, 291.05 78.79, 284.19 78.85, 284.11 66.29, 292.40 66.23, 292.38 63.28, 306.80 63.20, 306.91 79.76, 303.07 79.79, 303.16 91.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,63,"POLYGON ((422.69 54.94, 437.28 54.32, 438.71 86.96, 429.77 84.54, 423.60 80.57, 422.69 54.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,64,"POLYGON ((0.00 60.93, 1.33 60.90, 2.12 58.29, 3.24 57.21, 26.05 56.70, 26.50 76.78, 0.00 77.37, 0.00 60.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,65,"POLYGON ((6.08 31.79, 37.74 30.98, 38.28 52.21, 10.81 52.91, 10.57 43.66, 6.38 43.77, 6.08 31.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,66,"POLYGON ((36.09 895.80, 42.80 895.63, 42.74 900.00, 33.74 900.00, 36.09 895.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,67,"POLYGON ((117.43 887.73, 116.54 868.57, 156.92 866.66, 158.05 890.45, 137.94 891.38, 137.73 886.77, 117.43 887.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,68,"POLYGON ((43.31 859.17, 57.85 859.48, 61.08 866.95, 68.42 867.45, 67.55 879.73, 58.60 879.11, 57.59 893.28, 39.18 891.99, 39.84 882.79, 42.92 876.45, 43.31 859.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,69,"POLYGON ((513.48 824.87, 513.92 892.92, 401.83 894.30, 401.04 873.54, 432.65 825.78, 513.48 824.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,70,"POLYGON ((380.10 768.59, 356.72 855.56, 351.85 854.24, 345.06 872.35, 349.84 874.45, 337.45 900.00, 284.93 900.00, 305.62 854.62, 308.64 855.79, 315.19 840.26, 311.84 839.06, 337.47 755.89, 380.10 768.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,71,"POLYGON ((130.55 861.42, 129.91 841.13, 153.19 840.40, 153.84 860.67, 130.55 861.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,72,"POLYGON ((59.83 814.88, 59.69 818.26, 64.98 818.46, 64.58 828.06, 60.74 828.80, 61.34 831.80, 63.31 833.68, 57.68 833.83, 57.76 836.98, 54.34 833.56, 41.61 837.39, 36.65 836.47, 31.23 837.66, 28.51 834.22, 26.97 826.82, 30.80 826.03, 30.30 823.67, 28.98 817.76, 32.92 813.82, 59.83 814.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,73,"POLYGON ((139.21 835.12, 138.57 816.05, 177.14 814.77, 177.36 821.32, 168.99 821.60, 169.40 834.11, 139.21 835.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,74,"POLYGON ((150.72 811.01, 150.69 790.99, 174.51 790.95, 174.54 810.98, 150.72 811.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,75,"POLYGON ((63.10 785.43, 63.06 794.80, 74.48 794.84, 74.43 805.87, 39.05 805.71, 39.15 785.31, 63.10 785.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,76,"POLYGON ((158.26 765.89, 180.82 765.31, 181.36 786.38, 158.63 786.96, 158.46 780.31, 152.31 780.44, 152.05 770.26, 158.37 770.10, 158.26 765.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,77,"POLYGON ((510.33 739.44, 511.56 810.22, 466.20 811.01, 465.73 784.62, 401.26 785.74, 401.06 774.00, 403.69 762.48, 403.59 756.18, 398.81 756.28, 398.56 741.39, 510.33 739.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,78,"POLYGON ((42.72 775.87, 42.53 755.77, 79.78 755.39, 79.99 775.50, 42.72 775.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,79,"POLYGON ((166.31 760.35, 166.15 755.63, 158.63 755.89, 158.23 743.75, 166.68 743.49, 166.58 740.23, 193.10 739.33, 193.86 761.87, 185.15 762.16, 185.07 759.74, 166.31 760.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,80,"POLYGON ((51.13 729.06, 88.91 729.12, 88.89 740.58, 82.03 740.57, 82.01 749.92, 51.09 749.87, 51.13 729.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,81,"POLYGON ((174.28 735.69, 172.66 734.66, 170.48 730.48, 173.92 728.68, 173.40 721.55, 177.42 721.24, 175.80 719.53, 175.46 717.37, 197.46 713.92, 202.90 715.84, 201.31 720.30, 206.20 726.13, 206.21 732.94, 199.16 732.94, 197.99 734.90, 194.15 736.22, 191.80 733.49, 189.17 733.38, 187.30 729.40, 184.44 734.18, 182.89 735.80, 174.28 735.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,82,"POLYGON ((72.33 727.92, 72.16 719.98, 64.75 720.13, 64.49 707.79, 93.79 707.20, 93.88 711.57, 91.47 714.47, 91.22 719.29, 93.38 721.10, 93.51 727.49, 72.33 727.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,83,"POLYGON ((166.59 687.09, 194.98 686.14, 196.61 688.21, 195.44 690.35, 196.96 694.33, 199.98 697.40, 197.79 699.55, 196.46 702.40, 192.96 702.51, 193.12 707.59, 163.64 708.59, 163.15 694.10, 166.80 693.99, 166.59 687.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,84,"POLYGON ((72.39 705.83, 72.28 694.76, 66.11 694.83, 68.25 692.27, 72.19 692.14, 72.09 688.79, 82.15 688.47, 88.61 691.75, 92.45 695.40, 86.05 702.11, 86.09 705.71, 72.39 705.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,85,"POLYGON ((166.99 683.17, 166.70 676.83, 162.05 677.04, 161.36 662.23, 199.26 660.51, 196.53 664.06, 193.24 665.54, 195.58 667.95, 193.40 671.25, 196.59 673.34, 197.56 676.64, 200.51 681.63, 166.99 683.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,86,"POLYGON ((66.06 661.62, 91.05 658.21, 96.45 663.33, 104.05 664.71, 106.71 672.33, 102.32 672.62, 102.02 674.56, 68.47 679.14, 66.06 661.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,87,"POLYGON ((412.20 613.62, 445.28 619.13, 443.19 631.59, 467.07 635.55, 464.68 649.82, 461.60 649.30, 460.38 656.66, 467.64 657.85, 467.18 660.63, 472.71 661.53, 469.89 678.30, 466.65 677.76, 463.51 696.35, 400.06 685.81, 412.20 613.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,88,"POLYGON ((167.67 656.79, 167.42 650.58, 161.36 650.82, 160.94 640.13, 164.77 638.99, 167.59 639.25, 171.78 637.75, 174.19 636.11, 188.01 635.22, 188.38 640.83, 187.78 644.86, 190.71 649.52, 188.97 651.65, 194.04 655.73, 167.67 656.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,89,"POLYGON ((75.56 631.48, 112.18 625.53, 113.31 632.45, 110.69 632.89, 112.81 645.87, 96.48 648.53, 95.49 642.53, 86.57 643.98, 85.87 639.69, 77.14 641.12, 75.56 631.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,90,"POLYGON ((292.00 580.40, 320.06 582.17, 312.96 681.00, 284.90 679.21, 292.00 580.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,91,"POLYGON ((206.68 557.56, 223.47 557.49, 223.49 561.59, 249.29 561.47, 249.62 636.80, 224.59 636.91, 224.49 616.18, 206.94 616.27, 206.68 557.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,92,"POLYGON ((386.49 578.96, 404.84 581.85, 403.05 593.06, 400.97 592.73, 399.21 603.77, 396.72 603.39, 394.99 614.26, 381.22 612.08, 386.49 578.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,93,"POLYGON ((282.86 573.11, 279.50 620.10, 254.34 618.32, 257.71 571.33, 282.86 573.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,94,"POLYGON ((181.55 553.65, 182.30 581.36, 173.80 589.61, 185.74 601.56, 186.01 611.48, 156.49 612.25, 155.76 585.46, 154.06 585.50, 153.78 575.47, 155.40 575.41, 154.82 554.38, 181.55 553.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,95,"POLYGON ((0.00 557.26, 12.17 553.65, 25.53 598.33, 0.47 605.74, 0.00 604.17, 0.00 557.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,96,"POLYGON ((70.87 553.82, 95.95 551.07, 101.35 599.90, 93.70 600.74, 93.19 596.09, 89.56 591.99, 75.26 593.57, 70.87 553.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,97,"POLYGON ((96.31 532.32, 123.77 533.37, 125.26 578.49, 103.87 580.25, 96.31 532.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,98,"POLYGON ((563.78 527.37, 540.85 527.80, 540.31 498.69, 563.23 498.26, 563.78 527.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,99,"POLYGON ((499.91 458.84, 500.09 490.62, 429.42 491.05, 429.33 475.09, 467.66 474.87, 467.57 459.02, 499.91 458.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,100,"POLYGON ((710.08 632.27, 783.23 635.77, 783.30 634.19, 821.59 636.03, 821.49 637.95, 900.00 641.69, 900.00 835.28, 797.60 831.48, 794.73 899.26, 803.94 900.00, 555.46 900.00, 552.66 621.55, 710.08 632.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,101,"POLYGON ((871.74 516.72, 871.54 522.30, 881.15 522.65, 879.94 554.38, 832.84 552.61, 834.10 519.71, 844.43 520.11, 844.60 515.71, 871.74 516.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,102,"POLYGON ((625.27 500.29, 625.95 548.74, 624.25 551.04, 621.46 552.18, 592.26 550.48, 590.61 494.81, 600.14 494.68, 600.22 500.67, 625.27 500.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,103,"POLYGON ((664.56 467.26, 700.91 466.33, 702.84 541.89, 690.76 542.19, 690.37 526.93, 681.48 527.16, 665.23 492.83, 664.56 467.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,104,"POLYGON ((607.42 430.09, 607.45 437.79, 595.75 437.85, 595.79 443.64, 579.37 443.73, 579.32 433.16, 590.48 433.10, 590.46 425.84, 599.64 425.81, 599.65 430.11, 607.42 430.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,105,"POLYGON ((576.07 431.54, 560.68 430.62, 561.35 419.62, 576.74 420.56, 576.07 431.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,106,"POLYGON ((571.57 392.27, 604.57 391.27, 605.38 417.58, 572.37 418.58, 571.57 392.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,107,"POLYGON ((570.39 384.58, 569.59 365.66, 607.72 364.07, 608.50 382.98, 570.39 384.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,108,"POLYGON ((608.02 328.65, 608.86 352.38, 569.13 353.64, 568.53 329.91, 608.02 328.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,109,"POLYGON ((606.90 304.66, 607.57 322.09, 568.14 323.61, 568.01 319.93, 558.64 320.28, 558.27 310.75, 567.79 310.39, 567.62 306.18, 606.90 304.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,110,"POLYGON ((606.91 281.89, 606.90 287.86, 603.77 287.85, 603.76 291.07, 593.76 291.04, 593.76 293.03, 579.72 292.97, 579.73 291.20, 575.15 291.18, 575.15 293.80, 570.16 293.77, 570.22 284.31, 574.61 280.60, 573.18 276.36, 569.30 272.44, 569.25 271.29, 604.99 271.88, 605.28 281.89, 606.91 281.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,111,"POLYGON ((616.20 231.95, 615.94 254.86, 585.80 254.50, 586.09 231.59, 616.20 231.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,0,"POLYGON ((207.24 477.12, 225.76 478.24, 223.58 514.01, 219.74 513.80, 218.62 532.17, 198.27 530.93, 200.32 497.29, 205.98 497.64, 207.24 477.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,1,"POLYGON ((434.38 489.74, 441.26 496.24, 448.09 501.26, 446.78 508.49, 436.42 511.71, 419.55 512.89, 410.38 504.71, 410.51 490.10, 434.38 489.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,2,"POLYGON ((318.77 482.17, 325.68 481.95, 329.53 486.80, 331.91 492.18, 341.72 492.99, 340.31 509.52, 337.73 516.03, 323.79 515.38, 321.25 512.98, 319.68 509.56, 318.77 482.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,3,"POLYGON ((265.41 478.01, 279.76 479.61, 278.67 489.28, 282.52 489.71, 279.17 519.54, 256.45 516.42, 258.97 484.83, 264.68 484.40, 265.41 478.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,4,"POLYGON ((300.71 473.86, 304.53 487.13, 311.26 487.69, 312.37 491.88, 312.26 497.81, 314.82 510.13, 311.07 509.47, 303.08 498.55, 298.07 496.68, 287.67 508.34, 288.70 474.50, 300.71 473.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,5,"POLYGON ((126.17 477.59, 189.70 475.83, 190.32 497.59, 126.78 499.35, 126.17 477.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,6,"POLYGON ((62.03 485.04, 0.00 487.14, 0.00 469.25, 52.32 467.49, 52.91 469.83, 50.97 472.21, 50.31 474.40, 48.92 476.79, 50.62 479.25, 54.47 480.62, 61.88 480.41, 62.03 485.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,7,"POLYGON ((417.06 481.39, 417.03 473.87, 411.77 473.89, 411.74 466.90, 448.27 466.78, 448.29 473.58, 438.67 481.30, 417.06 481.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,8,"POLYGON ((443.38 435.12, 444.03 449.67, 438.70 455.00, 424.00 455.66, 423.76 450.52, 414.94 450.92, 414.28 436.44, 443.38 435.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,9,"POLYGON ((14.77 452.71, 14.30 440.64, 10.26 440.81, 9.66 425.87, 19.31 425.49, 19.51 430.54, 50.34 429.31, 50.70 438.21, 62.07 437.74, 62.41 445.92, 58.24 446.09, 58.44 450.95, 14.77 452.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,10,"POLYGON ((138.15 443.99, 137.83 435.58, 143.51 435.35, 143.16 426.54, 154.51 426.10, 156.79 428.08, 159.91 429.05, 163.32 428.96, 169.23 434.98, 169.54 442.74, 138.15 443.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,11,"POLYGON ((404.63 413.11, 423.47 413.05, 427.85 409.75, 457.16 408.49, 460.93 410.12, 462.71 421.95, 458.69 429.74, 409.14 429.87, 409.12 423.50, 404.68 423.52, 404.63 413.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,12,"POLYGON ((350.20 401.28, 368.19 400.11, 368.53 413.46, 365.74 415.28, 364.95 425.49, 353.31 425.79, 354.16 417.69, 351.18 412.15, 351.75 405.81, 350.20 401.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,13,"POLYGON ((43.94 394.38, 38.78 413.58, 26.87 410.40, 25.69 414.83, 13.97 411.68, 19.19 407.84, 13.98 400.76, 17.66 401.89, 20.06 399.85, 25.93 398.90, 25.45 395.92, 29.89 390.63, 43.94 394.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,14,"POLYGON ((145.00 393.95, 171.23 392.13, 172.42 408.88, 146.17 410.73, 145.00 393.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,15,"POLYGON ((59.09 385.63, 71.08 388.47, 70.54 390.68, 82.63 393.55, 77.47 415.12, 65.33 412.26, 66.23 408.46, 54.32 405.66, 59.09 385.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,16,"POLYGON ((302.63 389.28, 297.87 420.59, 288.54 421.43, 285.06 424.41, 271.29 424.47, 267.63 421.79, 258.58 422.51, 247.00 418.39, 242.00 426.75, 238.70 428.65, 218.36 423.38, 232.14 370.77, 248.39 374.97, 246.13 383.62, 254.90 385.89, 261.66 381.16, 266.56 381.48, 269.42 385.23, 265.39 395.07, 267.77 398.52, 271.47 399.65, 282.09 396.02, 283.97 391.69, 295.19 393.56, 298.12 388.60, 302.63 389.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,17,"POLYGON ((451.41 382.86, 451.54 395.44, 444.38 403.89, 408.56 404.29, 408.32 383.36, 451.41 382.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,18,"POLYGON ((348.78 393.65, 348.39 401.41, 318.86 399.95, 319.25 391.88, 326.82 384.29, 328.13 365.15, 342.35 366.09, 341.11 384.37, 340.67 393.26, 348.78 393.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,19,"POLYGON ((451.23 362.89, 451.35 374.94, 406.93 375.41, 406.84 366.09, 417.29 365.97, 417.26 363.27, 451.23 362.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,20,"POLYGON ((199.49 358.02, 218.44 357.07, 219.53 378.69, 200.58 379.64, 199.49 358.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,21,"POLYGON ((19.79 351.00, 16.57 362.07, 4.49 358.58, 5.73 354.29, 1.77 354.52, 1.34 347.65, 1.87 345.84, 19.79 351.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,22,"POLYGON ((153.04 340.03, 152.85 325.47, 174.99 325.19, 173.18 331.92, 174.51 335.11, 186.85 341.47, 193.76 341.39, 193.90 352.77, 166.03 353.13, 165.86 339.88, 153.04 340.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,23,"POLYGON ((447.30 323.83, 447.52 336.61, 433.96 336.85, 434.03 341.40, 418.42 341.68, 419.73 344.62, 407.17 344.81, 407.49 332.82, 421.20 324.30, 447.30 323.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,24,"POLYGON ((10.79 323.66, 26.42 328.08, 21.74 344.36, 6.13 339.94, 10.79 323.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,25,"POLYGON ((96.07 342.79, 76.79 338.56, 73.26 335.38, 72.06 323.69, 79.54 325.66, 81.84 323.44, 79.15 318.52, 82.13 316.89, 84.89 321.90, 94.33 323.95, 92.35 332.79, 98.01 334.02, 96.07 342.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,26,"POLYGON ((451.78 318.23, 405.73 320.32, 404.80 300.10, 450.85 298.01, 451.78 318.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,27,"POLYGON ((197.93 297.33, 198.11 304.74, 174.25 305.41, 172.60 309.18, 172.80 318.57, 167.15 318.69, 166.68 298.04, 197.93 297.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,28,"POLYGON ((282.70 284.86, 301.41 284.49, 301.12 294.69, 298.17 296.25, 296.53 300.25, 301.12 324.64, 280.45 324.61, 282.70 284.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,29,"POLYGON ((366.72 286.02, 368.06 303.77, 362.90 304.12, 364.17 321.80, 350.20 322.80, 346.81 310.77, 341.49 291.75, 353.93 291.61, 355.29 286.22, 366.72 286.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,30,"POLYGON ((277.13 283.69, 269.87 321.55, 246.62 317.13, 253.90 279.27, 277.13 283.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,31,"POLYGON ((338.93 288.06, 339.90 299.78, 334.93 301.97, 335.39 309.66, 310.92 311.11, 317.64 282.77, 326.23 282.06, 326.81 289.06, 338.93 288.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,32,"POLYGON ((69.48 308.44, 68.25 280.66, 102.14 279.20, 102.69 291.97, 106.93 291.79, 107.59 306.78, 69.48 308.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,33,"POLYGON ((220.51 271.00, 216.28 294.19, 190.06 289.47, 194.27 266.28, 220.51 271.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,34,"POLYGON ((436.40 264.88, 436.60 290.32, 410.81 290.53, 410.78 287.89, 407.92 287.92, 407.83 278.71, 412.47 278.68, 412.36 265.08, 436.40 264.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,35,"POLYGON ((64.35 268.77, 64.02 258.50, 106.61 257.11, 107.04 270.32, 100.58 270.53, 100.77 276.54, 68.28 277.62, 68.00 268.66, 64.35 268.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,36,"POLYGON ((409.17 216.37, 414.57 220.78, 411.83 224.00, 420.85 233.05, 400.68 255.61, 396.18 251.17, 400.09 246.34, 389.53 238.47, 409.17 216.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,37,"POLYGON ((224.14 217.39, 224.22 226.95, 226.65 226.93, 226.72 234.92, 188.16 235.24, 188.02 216.93, 200.40 216.84, 200.40 215.37, 214.06 215.27, 215.12 217.46, 224.14 217.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,38,"POLYGON ((115.20 231.69, 123.04 204.39, 136.77 208.30, 128.92 235.60, 115.20 231.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,39,"POLYGON ((16.53 196.87, 13.57 220.70, 11.39 220.18, 10.02 235.91, 5.01 241.14, 0.00 239.22, 0.00 195.53, 16.53 196.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,40,"POLYGON ((363.55 199.77, 383.66 205.41, 375.65 233.67, 369.70 232.00, 368.90 234.80, 364.32 233.52, 363.56 236.16, 360.10 235.18, 361.65 229.77, 355.55 228.06, 363.55 199.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,41,"POLYGON ((333.94 202.37, 339.66 200.56, 351.33 203.22, 352.60 207.65, 344.72 235.28, 335.83 232.78, 337.04 228.53, 327.25 225.76, 333.94 202.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,42,"POLYGON ((306.18 188.16, 316.21 190.95, 313.34 201.28, 324.56 204.37, 315.54 236.67, 294.30 230.80, 306.18 188.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,43,"POLYGON ((274.91 190.43, 297.50 195.16, 289.60 232.61, 267.03 227.90, 274.91 190.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,44,"POLYGON ((259.48 196.71, 258.35 224.90, 250.87 224.61, 250.76 227.38, 239.64 226.94, 240.34 209.85, 243.71 208.41, 245.21 204.73, 245.72 196.24, 244.10 190.86, 250.12 191.42, 250.98 194.86, 254.60 196.52, 259.48 196.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,45,"POLYGON ((26.86 187.20, 29.25 188.60, 32.88 191.15, 35.51 190.35, 37.10 188.84, 41.35 187.72, 52.05 190.08, 46.71 211.71, 44.21 212.50, 41.44 214.33, 37.90 214.42, 30.41 210.79, 26.92 212.79, 25.37 209.90, 21.29 206.45, 26.86 187.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,46,"POLYGON ((102.94 166.39, 141.00 164.81, 145.06 169.22, 138.05 196.12, 102.54 196.23, 102.94 166.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,47,"POLYGON ((77.23 170.62, 99.18 169.37, 100.23 187.77, 78.29 189.01, 77.23 170.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,48,"POLYGON ((438.17 148.48, 469.46 146.92, 470.18 161.35, 463.94 161.67, 464.22 167.54, 439.19 168.78, 438.17 148.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,49,"POLYGON ((540.29 0.00, 541.61 33.29, 565.59 33.23, 567.23 140.96, 437.27 143.73, 433.39 35.50, 453.46 34.43, 451.62 0.00, 540.29 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,50,"POLYGON ((804.89 505.37, 867.90 760.33, 832.67 769.42, 841.48 803.02, 748.62 826.16, 758.04 861.59, 736.25 867.03, 716.14 782.50, 708.80 784.15, 664.95 608.84, 642.57 613.94, 630.52 568.77, 661.61 560.98, 653.82 531.08, 713.57 516.61, 716.32 527.73, 804.89 505.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,51,"POLYGON ((705.44 291.64, 611.36 292.15, 611.40 299.39, 596.29 299.47, 596.32 305.08, 586.08 305.14, 586.03 298.37, 582.21 298.41, 582.12 283.16, 583.76 283.56, 582.95 269.00, 585.24 268.87, 584.29 259.31, 591.58 259.98, 597.77 258.54, 602.56 254.64, 614.83 253.29, 615.59 224.03, 612.47 220.80, 602.15 220.49, 599.04 218.48, 594.65 216.46, 589.05 215.74, 584.69 216.16, 579.61 218.31, 575.35 221.93, 574.58 204.43, 559.71 204.77, 559.54 188.75, 544.70 188.91, 544.53 173.73, 526.41 173.92, 526.09 145.61, 538.45 145.47, 538.55 154.32, 599.76 153.62, 600.07 180.23, 614.89 180.07, 615.80 182.96, 675.01 181.56, 675.50 202.83, 688.43 202.52, 690.41 206.02, 690.96 226.34, 700.97 226.09, 701.47 246.52, 705.20 245.38, 705.44 291.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,52,"POLYGON ((695.33 46.25, 703.33 40.36, 713.68 54.28, 705.68 60.17, 695.33 46.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,53,"POLYGON ((16.75 898.29, 16.49 874.85, 58.30 874.39, 58.34 879.60, 64.49 879.53, 64.68 897.75, 16.75 898.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,54,"POLYGON ((282.33 868.11, 288.36 867.73, 292.33 872.94, 293.64 877.61, 282.94 878.26, 282.33 868.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,55,"POLYGON ((393.99 849.01, 396.29 876.50, 369.93 878.66, 367.65 851.19, 393.99 849.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,56,"POLYGON ((25.07 849.53, 64.70 849.32, 64.76 859.48, 68.31 859.46, 68.33 864.54, 66.03 867.28, 61.24 870.80, 25.18 870.99, 25.07 849.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,57,"POLYGON ((91.25 851.08, 107.30 850.91, 107.45 866.80, 91.42 866.97, 91.25 851.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,58,"POLYGON ((34.68 815.63, 69.81 815.00, 70.25 838.67, 30.38 839.40, 30.04 821.08, 34.78 821.00, 34.68 815.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,59,"POLYGON ((368.04 811.31, 382.06 810.66, 383.30 838.14, 369.28 838.76, 368.04 811.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,60,"POLYGON ((202.39 764.29, 254.64 765.44, 255.72 771.25, 271.08 772.43, 269.69 766.56, 276.28 766.44, 287.60 765.42, 289.11 772.97, 294.06 771.45, 296.90 779.63, 301.22 778.08, 302.15 808.24, 278.94 808.95, 274.51 803.73, 269.81 804.94, 271.57 812.80, 261.11 812.60, 259.36 803.61, 254.48 798.43, 250.42 801.28, 242.74 800.37, 245.29 812.89, 230.32 811.70, 224.77 815.79, 229.39 826.40, 230.00 836.37, 229.48 842.86, 215.64 834.43, 203.94 826.47, 199.11 821.40, 200.76 818.00, 206.00 811.23, 203.26 805.58, 201.19 799.86, 195.41 797.12, 189.33 799.32, 184.87 798.90, 181.17 792.53, 178.46 790.98, 179.12 782.22, 181.91 781.61, 201.07 781.48, 202.39 764.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,61,"POLYGON ((38.05 811.17, 37.33 783.73, 69.05 782.92, 69.24 790.35, 74.42 793.73, 74.85 796.78, 81.69 795.81, 86.86 799.00, 86.52 806.36, 83.20 808.82, 80.04 804.59, 76.34 807.33, 71.38 805.17, 71.51 810.32, 38.05 811.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,62,"POLYGON ((383.96 770.59, 385.43 800.68, 370.14 801.40, 368.67 771.34, 383.96 770.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,63,"POLYGON ((397.86 760.22, 419.32 760.23, 419.29 809.51, 397.82 809.48, 397.86 760.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,64,"POLYGON ((443.80 809.22, 422.60 809.27, 422.45 760.02, 443.68 759.96, 443.80 809.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,65,"POLYGON ((363.16 766.93, 364.27 802.24, 311.01 803.91, 310.92 800.85, 307.25 800.97, 306.57 779.32, 316.00 779.01, 315.74 770.65, 337.25 769.99, 337.05 763.60, 348.40 763.24, 348.53 767.39, 363.16 766.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,66,"POLYGON ((469.60 758.92, 473.91 795.37, 452.34 797.90, 451.11 787.52, 455.30 778.98, 453.15 760.85, 469.60 758.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,67,"POLYGON ((499.80 764.90, 500.25 775.83, 501.84 775.77, 502.40 789.54, 479.75 790.47, 479.17 775.82, 483.56 775.64, 483.14 765.57, 499.80 764.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,68,"POLYGON ((48.57 778.07, 48.32 754.37, 75.37 754.06, 75.41 756.79, 72.55 761.74, 73.38 766.63, 68.37 770.26, 77.67 776.48, 83.28 776.16, 80.23 780.10, 54.54 780.38, 54.52 778.01, 48.57 778.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,69,"POLYGON ((109.13 751.24, 120.06 750.98, 120.14 753.98, 139.94 753.49, 140.47 774.90, 109.72 775.64, 109.13 751.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,70,"POLYGON ((82.32 691.55, 114.36 690.91, 114.82 713.20, 109.39 713.30, 109.45 715.65, 98.16 715.87, 98.14 714.25, 82.78 714.55, 82.32 691.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,71,"POLYGON ((286.57 698.03, 286.15 688.18, 288.57 681.57, 291.22 680.66, 292.39 677.21, 301.62 670.58, 302.62 697.44, 286.57 698.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,72,"POLYGON ((212.60 696.65, 219.74 665.26, 242.88 670.93, 236.57 702.30, 212.60 696.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,73,"POLYGON ((195.21 663.22, 196.45 666.43, 203.53 669.47, 206.33 680.43, 203.89 691.73, 198.24 691.87, 194.37 704.11, 179.62 699.43, 178.99 690.70, 187.91 661.03, 195.21 663.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,74,"POLYGON ((486.09 693.31, 486.18 683.71, 492.94 683.52, 492.19 662.25, 505.03 662.03, 505.14 668.45, 508.85 670.68, 509.51 675.53, 503.31 681.61, 503.52 684.76, 512.85 685.10, 512.77 691.83, 507.12 694.30, 498.93 694.40, 498.93 695.93, 495.04 695.99, 495.00 693.19, 486.09 693.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,75,"POLYGON ((445.09 656.55, 445.20 665.89, 447.57 667.12, 448.40 690.12, 431.97 690.72, 432.22 698.21, 429.51 698.93, 423.29 694.69, 423.84 665.77, 426.96 665.05, 426.91 661.85, 432.58 661.80, 432.53 656.69, 445.09 656.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,76,"POLYGON ((250.80 663.71, 255.51 664.61, 257.09 668.28, 267.03 677.93, 265.26 686.14, 265.39 691.33, 246.19 687.67, 250.80 663.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,77,"POLYGON ((404.59 657.19, 404.50 663.83, 412.95 663.94, 412.87 670.12, 420.23 670.22, 419.84 697.04, 396.18 696.71, 396.74 657.08, 404.59 657.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,78,"POLYGON ((342.25 667.59, 341.15 658.67, 351.99 658.55, 352.86 660.64, 356.02 667.51, 362.14 667.46, 362.93 675.23, 357.97 675.36, 355.31 678.65, 353.62 683.02, 356.95 684.31, 357.53 692.48, 334.98 694.10, 334.35 685.62, 338.78 683.53, 342.39 678.98, 341.77 674.04, 331.94 668.85, 342.25 667.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,79,"POLYGON ((466.66 657.44, 474.78 664.07, 473.65 671.45, 475.64 676.79, 473.90 699.37, 456.48 699.68, 453.01 687.85, 449.36 687.87, 449.17 665.26, 457.84 665.19, 457.73 652.79, 466.66 657.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,80,"POLYGON ((84.35 683.72, 83.63 661.59, 128.01 660.17, 128.73 682.28, 84.35 683.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,81,"POLYGON ((245.87 638.18, 241.59 660.24, 221.19 656.32, 225.49 634.24, 245.87 638.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,82,"POLYGON ((94.14 658.70, 93.63 634.01, 124.90 633.34, 124.94 635.83, 128.59 639.40, 125.12 642.90, 130.49 648.16, 130.62 653.26, 128.80 655.29, 130.33 657.95, 94.14 658.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,83,"POLYGON ((324.03 624.22, 354.69 622.46, 355.86 642.96, 325.21 644.72, 324.03 624.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,84,"POLYGON ((319.49 624.80, 319.12 643.70, 308.80 639.01, 309.56 629.58, 307.54 627.90, 310.48 627.09, 310.61 621.27, 316.47 621.39, 316.41 624.72, 319.49 624.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,85,"POLYGON ((500.55 618.48, 512.55 618.00, 513.36 638.05, 510.15 640.26, 501.11 640.33, 500.55 618.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,86,"POLYGON ((107.70 626.12, 107.46 616.87, 109.19 610.59, 111.03 608.66, 113.48 610.97, 116.03 608.33, 125.99 610.16, 130.67 608.89, 133.54 606.64, 136.04 606.58, 136.52 625.39, 107.70 626.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,87,"POLYGON ((442.52 592.33, 443.36 617.17, 413.04 618.19, 412.61 605.41, 414.87 596.94, 418.76 593.14, 442.52 592.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,88,"POLYGON ((0.00 580.10, 6.05 582.06, 6.65 580.20, 18.93 584.19, 18.12 586.63, 24.04 588.55, 18.26 606.14, 0.00 600.19, 0.00 580.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,89,"POLYGON ((168.98 592.73, 168.31 587.53, 180.36 585.98, 181.05 591.15, 168.98 592.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,90,"POLYGON ((338.67 571.68, 341.93 578.25, 342.71 588.95, 330.90 589.81, 327.59 591.43, 322.32 586.97, 322.03 571.95, 338.67 571.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,91,"POLYGON ((111.43 593.88, 111.51 583.76, 113.07 579.66, 107.96 579.79, 106.61 575.52, 117.07 572.28, 118.45 569.00, 139.50 569.15, 139.32 594.08, 111.43 593.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,92,"POLYGON ((436.69 588.79, 408.25 589.50, 407.92 576.65, 417.96 576.40, 417.55 560.40, 432.84 560.01, 433.13 571.35, 436.24 571.27, 436.69 588.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,93,"POLYGON ((0.00 557.47, 12.28 561.10, 13.88 564.48, 16.98 563.02, 21.26 566.02, 20.78 569.78, 15.19 569.08, 17.62 575.95, 14.85 576.93, 0.00 572.52, 0.00 557.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,94,"POLYGON ((115.37 534.45, 146.92 533.73, 147.40 554.01, 115.85 554.73, 115.37 534.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,95,"POLYGON ((0.00 532.85, 3.37 537.42, 6.63 539.05, 9.93 537.59, 15.11 541.52, 18.60 538.74, 15.85 532.82, 25.69 533.41, 27.04 539.50, 22.82 543.58, 28.47 544.84, 28.54 547.72, 31.15 548.74, 31.15 550.32, 13.22 550.24, 13.23 548.24, 0.00 548.19, 0.00 532.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,96,"POLYGON ((295.51 532.89, 295.22 541.06, 301.47 542.13, 301.59 546.83, 288.95 538.74, 287.00 540.28, 281.71 547.34, 282.08 532.74, 295.51 532.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,97,"POLYGON ((367.50 528.32, 368.60 533.00, 360.64 532.47, 355.48 535.31, 353.42 541.80, 344.30 535.10, 346.83 527.60, 367.50 528.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,98,"POLYGON ((415.10 522.53, 442.32 523.50, 443.35 544.87, 435.67 545.51, 427.81 545.51, 422.38 545.23, 415.10 522.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,99,"POLYGON ((122.33 513.89, 125.36 515.68, 128.66 513.62, 126.89 507.23, 134.76 513.46, 134.25 518.29, 130.22 520.88, 133.91 523.87, 122.13 523.64, 122.33 513.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,100,"POLYGON ((255.13 496.49, 253.69 515.44, 245.08 509.49, 234.15 509.77, 227.85 515.61, 229.44 494.55, 255.13 496.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,0,"POLYGON ((562.67 420.30, 584.94 421.66, 584.77 435.41, 573.23 434.82, 573.16 455.99, 560.42 455.79, 559.46 452.93, 561.39 421.76, 562.67 420.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,1,"POLYGON ((863.38 432.28, 869.34 422.42, 871.21 414.58, 878.89 414.68, 894.53 436.34, 889.73 440.15, 884.66 438.21, 876.80 442.68, 871.59 446.94, 866.95 444.99, 858.83 450.95, 857.36 457.29, 858.55 462.86, 847.05 469.92, 831.64 450.89, 838.44 445.72, 824.85 428.27, 824.05 425.65, 831.17 421.05, 827.70 412.61, 830.13 409.31, 836.84 405.74, 841.53 404.02, 863.38 432.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,2,"POLYGON ((788.41 332.35, 849.47 342.22, 844.96 369.39, 836.31 366.71, 825.44 366.56, 824.11 371.59, 823.73 373.67, 817.86 372.99, 816.08 384.69, 798.42 380.97, 800.39 368.25, 784.03 365.74, 786.51 356.53, 788.44 342.34, 787.46 336.75, 788.41 332.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,3,"POLYGON ((628.37 360.93, 610.14 356.13, 607.11 354.48, 621.82 303.89, 643.46 311.73, 640.32 326.50, 650.61 330.44, 645.82 348.76, 633.94 345.20, 628.37 360.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,4,"POLYGON ((792.14 323.31, 801.58 275.83, 816.90 278.14, 816.41 283.15, 838.21 286.12, 841.41 264.00, 838.25 263.04, 841.99 245.47, 887.99 254.06, 885.58 266.20, 881.15 272.75, 878.83 279.89, 876.97 289.10, 878.21 297.17, 880.21 301.27, 880.91 304.81, 888.70 306.47, 887.65 322.73, 879.40 368.09, 857.36 364.50, 862.67 335.02, 792.14 323.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,5,"POLYGON ((667.73 122.29, 668.73 103.97, 676.26 104.49, 676.59 101.26, 692.51 102.63, 692.50 105.96, 706.73 107.26, 707.07 103.81, 720.78 104.39, 720.41 106.38, 719.97 109.61, 724.35 113.67, 728.61 116.27, 732.92 117.40, 735.11 117.34, 737.52 114.06, 740.66 109.92, 740.89 106.59, 746.56 107.06, 746.95 118.82, 745.67 129.46, 669.95 123.39, 667.73 122.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,6,"POLYGON ((591.98 94.68, 601.93 95.38, 601.91 98.91, 614.26 99.73, 614.93 96.91, 631.87 97.83, 631.45 101.48, 645.79 102.05, 645.90 98.52, 661.82 99.97, 661.58 102.89, 667.35 103.78, 666.58 122.12, 589.51 116.19, 588.84 106.72, 591.98 94.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,7,"POLYGON ((847.64 76.42, 865.41 82.31, 849.50 129.73, 831.74 123.84, 847.64 76.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,8,"POLYGON ((662.47 29.79, 668.24 12.46, 675.78 15.11, 676.71 13.35, 689.88 18.10, 689.08 20.72, 705.13 25.74, 706.18 23.36, 718.73 27.75, 718.78 29.83, 734.10 35.88, 734.91 33.64, 748.82 38.48, 748.64 40.84, 762.56 45.67, 763.73 43.16, 784.56 50.93, 785.41 54.99, 779.46 74.68, 675.91 34.91, 662.47 29.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,9,"POLYGON ((622.80 0.00, 624.09 1.71, 635.31 7.73, 644.75 7.00, 650.14 4.26, 660.16 7.34, 669.95 11.17, 663.26 31.39, 593.89 6.19, 592.15 1.29, 591.35 0.00, 622.80 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,10,"POLYGON ((459.71 859.55, 484.41 858.81, 484.63 876.61, 480.70 883.15, 460.31 883.05, 460.16 872.42, 459.71 859.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,11,"POLYGON ((326.69 846.26, 330.75 843.23, 349.65 843.19, 351.26 847.99, 350.90 851.39, 353.47 858.96, 351.80 869.17, 344.46 870.54, 327.61 871.10, 326.69 846.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,12,"POLYGON ((378.31 845.45, 396.65 845.72, 402.31 858.96, 401.60 866.03, 379.46 867.20, 377.94 865.77, 376.57 847.12, 378.31 845.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,13,"POLYGON ((451.15 830.72, 477.35 830.53, 477.31 852.93, 469.10 853.01, 469.05 855.47, 451.26 854.82, 450.49 839.15, 451.15 830.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,14,"POLYGON ((326.41 840.99, 326.52 839.36, 339.76 831.81, 339.24 828.87, 333.97 824.74, 325.44 820.54, 325.36 817.33, 345.91 817.82, 349.83 820.96, 350.13 838.76, 348.89 841.72, 326.41 840.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,15,"POLYGON ((377.41 815.91, 400.29 815.03, 406.95 832.36, 406.92 836.93, 402.41 839.69, 377.63 841.94, 377.41 815.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,16,"POLYGON ((268.70 773.25, 268.66 797.09, 260.58 797.12, 260.62 818.98, 257.02 822.94, 252.51 832.13, 252.76 842.11, 256.48 849.54, 259.88 852.08, 260.79 860.44, 171.92 862.54, 171.62 857.99, 151.94 858.32, 150.76 846.30, 153.74 846.22, 151.28 798.51, 162.71 798.97, 164.39 796.45, 224.76 795.17, 223.50 774.91, 268.70 773.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,17,"POLYGON ((441.75 804.45, 461.25 804.06, 467.35 804.66, 467.01 811.11, 465.43 812.37, 464.30 826.14, 441.45 826.83, 441.75 804.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,18,"POLYGON ((324.59 792.77, 347.62 792.03, 347.89 796.59, 351.23 812.09, 350.28 815.65, 324.88 815.72, 323.12 805.02, 322.08 793.41, 324.59 792.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,19,"POLYGON ((379.11 789.83, 402.88 789.22, 404.26 802.86, 403.71 810.09, 402.29 811.88, 377.63 813.10, 375.80 811.09, 375.57 790.49, 379.11 789.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,20,"POLYGON ((437.73 788.86, 437.67 768.99, 453.03 768.76, 455.79 766.62, 467.45 766.03, 471.33 767.40, 477.68 767.24, 478.12 772.82, 477.70 774.01, 470.90 773.89, 467.56 776.04, 464.63 777.43, 459.56 781.38, 450.15 788.70, 437.73 788.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,21,"POLYGON ((325.65 759.78, 345.61 759.72, 354.02 765.25, 355.36 782.86, 354.99 785.80, 325.01 786.28, 323.46 783.39, 323.65 762.05, 325.65 759.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,22,"POLYGON ((376.40 758.99, 401.80 758.50, 401.88 784.38, 377.25 786.34, 376.14 772.25, 376.40 758.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,23,"POLYGON ((452.26 767.13, 434.51 767.32, 434.70 745.56, 463.90 745.06, 464.85 748.12, 477.78 748.90, 479.81 755.04, 478.37 766.58, 452.26 767.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,24,"POLYGON ((39.65 900.00, 0.00 896.75, 0.00 799.17, 2.24 772.24, 7.80 772.70, 20.52 619.83, 15.33 619.40, 16.89 600.70, 93.88 607.03, 92.25 626.59, 82.13 625.76, 62.09 866.61, 84.75 868.47, 82.13 900.00, 39.65 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,25,"POLYGON ((321.92 735.01, 344.97 734.74, 348.28 737.29, 348.63 739.48, 352.84 742.33, 352.44 754.83, 347.86 755.68, 347.36 758.65, 324.74 758.78, 321.61 757.69, 320.95 737.55, 321.92 735.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,26,"POLYGON ((376.71 730.75, 399.15 729.73, 401.99 731.41, 403.47 748.60, 401.56 754.53, 383.27 756.02, 377.47 754.55, 375.15 750.95, 373.64 749.65, 373.36 732.88, 376.71 730.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,27,"POLYGON ((436.14 733.89, 436.84 727.06, 439.81 726.74, 443.17 717.51, 447.49 711.45, 477.13 708.96, 478.50 718.82, 477.36 727.27, 472.88 727.38, 472.70 734.80, 465.49 734.87, 464.09 733.80, 458.76 733.94, 450.47 735.26, 449.69 733.79, 442.47 733.60, 436.14 733.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,28,"POLYGON ((323.05 705.17, 347.92 705.76, 349.02 709.81, 349.11 713.16, 351.35 713.84, 351.77 719.89, 347.92 720.37, 347.28 729.42, 324.07 730.63, 322.71 720.88, 323.05 705.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,29,"POLYGON ((374.87 705.05, 394.33 702.77, 396.27 704.06, 399.64 725.59, 398.22 727.98, 375.33 728.72, 373.32 719.80, 372.24 706.74, 374.87 705.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,30,"POLYGON ((118.21 674.85, 150.71 673.84, 154.30 752.48, 121.45 752.97, 118.21 674.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,31,"POLYGON ((429.12 704.30, 427.95 702.11, 427.22 698.18, 428.82 696.90, 426.57 691.52, 426.25 689.17, 427.36 684.19, 434.48 680.55, 454.82 678.54, 465.88 678.50, 466.33 681.33, 464.39 683.87, 464.08 690.80, 458.77 697.00, 470.32 696.95, 470.49 703.49, 429.12 704.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,32,"POLYGON ((375.18 676.65, 398.54 676.63, 399.90 700.88, 382.17 701.33, 379.76 699.77, 375.33 699.89, 372.10 695.11, 371.88 680.99, 375.18 676.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,33,"POLYGON ((322.61 702.45, 321.24 702.49, 319.13 702.68, 315.89 702.00, 314.46 699.69, 314.06 678.92, 319.90 679.15, 322.03 684.78, 334.47 684.95, 343.29 675.44, 347.88 675.21, 352.17 677.81, 352.11 684.74, 346.89 684.87, 347.23 693.28, 349.86 694.08, 351.36 699.74, 346.81 701.59, 322.61 702.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,34,"POLYGON ((374.53 674.44, 370.11 674.71, 365.87 671.14, 362.98 656.20, 366.10 651.42, 376.63 646.89, 396.27 645.94, 404.91 649.12, 406.16 663.05, 399.82 663.65, 399.75 672.78, 374.53 674.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,35,"POLYGON ((541.35 643.52, 567.32 642.85, 567.72 658.47, 553.55 658.83, 549.05 656.00, 541.67 656.19, 541.35 643.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,36,"POLYGON ((374.72 602.81, 379.39 601.44, 398.96 603.30, 398.17 626.07, 373.74 627.32, 374.72 602.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,37,"POLYGON ((320.11 625.60, 319.29 622.65, 317.63 606.35, 316.91 602.04, 338.05 598.17, 342.34 600.53, 343.19 623.63, 341.87 625.53, 336.54 626.16, 333.58 627.10, 328.22 626.99, 325.20 625.47, 320.11 625.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,38,"POLYGON ((346.32 625.04, 345.27 612.95, 346.18 599.81, 362.57 599.28, 370.09 601.55, 369.34 625.70, 356.51 624.91, 346.32 625.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,39,"POLYGON ((429.62 584.82, 428.65 561.23, 465.93 560.76, 467.14 583.62, 429.62 584.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,40,"POLYGON ((54.53 533.72, 76.86 559.47, 55.93 577.46, 33.60 551.70, 54.53 533.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,41,"POLYGON ((437.83 497.53, 466.93 496.92, 473.13 502.51, 473.69 535.74, 462.74 535.14, 461.97 522.64, 438.17 522.51, 437.83 497.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,42,"POLYGON ((15.78 454.02, 50.98 423.53, 154.42 541.89, 119.24 572.35, 15.78 454.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,43,"POLYGON ((433.07 455.71, 437.40 451.94, 451.74 451.42, 463.00 452.60, 465.28 455.78, 465.11 466.53, 465.29 473.58, 461.91 473.96, 462.01 483.37, 460.91 486.79, 450.86 486.76, 448.84 483.28, 441.18 483.48, 441.21 468.03, 433.25 468.36, 433.07 455.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,44,"POLYGON ((455.80 409.76, 460.63 408.02, 472.62 408.44, 476.42 412.76, 475.90 432.49, 476.73 435.71, 473.07 437.56, 454.40 435.99, 455.80 409.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,45,"POLYGON ((75.16 223.03, 86.69 225.40, 83.54 240.56, 72.00 238.17, 75.16 223.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,46,"POLYGON ((374.80 196.33, 375.52 185.32, 379.24 175.57, 408.75 182.48, 404.54 202.63, 374.80 196.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,47,"POLYGON ((316.37 147.23, 376.35 159.69, 367.91 194.42, 362.47 195.18, 307.48 183.09, 316.37 147.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,48,"POLYGON ((196.94 143.85, 206.68 144.95, 207.94 137.13, 220.57 140.03, 217.55 156.95, 216.09 157.10, 195.63 154.29, 196.94 143.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,49,"POLYGON ((407.44 155.60, 388.66 140.88, 391.69 124.69, 393.96 123.70, 440.66 130.71, 442.32 134.51, 441.99 137.54, 436.37 159.13, 433.53 158.27, 430.02 152.21, 427.03 149.89, 421.32 146.81, 418.59 146.88, 411.23 148.85, 410.77 151.15, 409.64 156.17, 407.44 155.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,50,"POLYGON ((281.02 108.05, 307.37 112.17, 299.25 163.32, 281.85 160.59, 283.20 151.97, 274.29 150.57, 281.02 108.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,51,"POLYGON ((234.65 101.49, 257.85 105.58, 260.80 106.54, 261.45 107.88, 260.76 113.71, 276.94 116.74, 278.98 123.15, 273.80 157.49, 250.94 154.54, 244.87 145.86, 227.32 142.45, 233.47 104.53, 234.65 101.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,52,"POLYGON ((213.81 100.35, 231.75 103.22, 232.13 106.01, 227.33 139.23, 225.46 139.28, 208.55 136.27, 212.60 102.56, 213.81 100.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,53,"POLYGON ((178.83 141.58, 186.19 92.93, 194.64 93.07, 205.60 95.41, 207.95 97.99, 211.33 99.82, 206.49 144.20, 178.83 141.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,54,"POLYGON ((158.82 87.35, 184.78 92.46, 176.47 139.02, 153.46 139.43, 143.38 136.72, 146.58 110.93, 154.14 110.91, 156.93 89.49, 158.82 87.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,55,"POLYGON ((117.70 81.23, 148.23 85.69, 148.97 87.94, 142.13 136.24, 110.37 131.61, 117.70 81.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,56,"POLYGON ((488.31 55.91, 514.78 55.96, 516.87 60.37, 516.51 65.58, 509.95 66.37, 509.76 78.62, 506.42 84.26, 488.91 84.46, 488.31 55.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,57,"POLYGON ((40.48 19.10, 83.89 25.82, 78.10 62.67, 73.69 61.98, 71.90 73.26, 76.96 74.06, 70.97 112.26, 41.07 107.61, 46.66 71.93, 32.52 69.71, 40.48 19.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,58,"POLYGON ((477.93 25.52, 485.75 25.32, 486.90 29.13, 490.45 33.10, 493.93 34.45, 497.57 33.74, 500.92 30.24, 503.94 25.16, 511.98 25.05, 512.87 47.51, 487.08 48.57, 479.80 45.74, 477.93 25.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,59,"POLYGON ((415.21 0.00, 422.54 38.19, 396.11 43.19, 387.86 0.00, 415.21 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,60,"POLYGON ((467.27 0.00, 470.73 17.98, 459.72 19.92, 459.72 16.48, 455.17 0.00, 467.27 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,61,"POLYGON ((326.29 0.00, 331.60 36.03, 334.62 49.87, 326.78 52.00, 317.85 53.81, 313.65 47.28, 303.77 0.00, 326.29 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,62,"POLYGON ((343.72 0.00, 347.71 33.27, 332.80 35.93, 327.22 0.00, 343.72 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,63,"POLYGON ((205.22 0.00, 206.99 13.31, 189.84 13.13, 187.28 10.91, 186.96 6.54, 188.47 0.26, 188.35 0.00, 205.22 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,64,"POLYGON ((289.94 0.00, 291.76 12.74, 273.67 16.16, 270.74 11.24, 268.88 0.00, 289.94 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,65,"POLYGON ((567.46 860.77, 599.75 860.88, 605.52 857.40, 608.26 866.59, 608.58 871.26, 610.61 872.26, 610.17 879.75, 607.68 880.85, 571.26 878.88, 566.50 876.82, 567.46 860.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,66,"POLYGON ((558.19 833.75, 591.36 834.88, 595.93 833.72, 605.14 834.41, 606.46 836.69, 606.64 852.49, 574.64 851.44, 569.63 852.19, 571.36 850.06, 573.86 849.15, 572.00 846.21, 568.24 842.75, 563.56 843.18, 562.05 841.15, 560.28 841.51, 558.24 843.54, 556.41 841.61, 555.54 840.28, 556.54 834.52, 558.19 833.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,67,"POLYGON ((868.05 824.11, 879.64 823.82, 891.94 823.50, 900.00 826.95, 900.00 833.11, 868.31 834.25, 868.05 824.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,68,"POLYGON ((582.56 796.88, 588.85 809.10, 584.36 814.32, 583.02 814.76, 577.61 815.32, 575.59 818.59, 569.77 819.69, 569.83 818.02, 558.44 818.20, 556.85 816.89, 556.32 804.12, 558.04 802.41, 571.02 802.70, 573.61 802.01, 573.58 796.91, 582.56 796.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,69,"POLYGON ((880.49 798.82, 866.97 799.92, 864.30 797.26, 861.25 775.70, 864.92 772.63, 900.00 770.44, 900.00 793.52, 896.51 793.35, 885.76 795.36, 883.22 798.26, 880.49 798.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,70,"POLYGON ((573.60 735.39, 583.83 735.44, 584.56 737.25, 584.12 760.65, 577.06 760.48, 578.14 757.83, 577.71 753.45, 577.74 752.51, 577.71 751.34, 577.58 750.61, 576.99 749.31, 576.66 748.66, 575.90 747.81, 575.30 747.07, 574.91 746.28, 574.65 745.49, 573.99 744.33, 573.48 743.10, 573.14 742.24, 572.86 741.45, 572.60 740.46, 572.34 739.73, 572.19 739.00, 572.11 738.14, 572.01 737.28, 573.60 735.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,71,"POLYGON ((549.66 699.47, 555.49 699.21, 554.42 690.82, 588.08 690.21, 588.61 701.34, 590.11 701.30, 590.36 706.60, 588.51 706.78, 589.18 713.31, 587.73 715.45, 555.10 717.40, 554.13 709.14, 550.16 709.00, 549.66 699.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,72,"POLYGON ((895.53 669.69, 895.96 674.96, 887.99 675.47, 889.17 692.80, 862.96 696.85, 861.36 680.13, 869.75 679.03, 869.74 666.95, 889.06 665.15, 889.48 669.84, 895.53 669.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,73,"POLYGON ((784.40 605.42, 791.43 598.07, 805.05 588.09, 843.56 637.14, 822.32 651.51, 784.40 605.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,74,"POLYGON ((872.18 580.85, 893.09 573.67, 899.31 589.96, 900.00 589.80, 900.00 626.72, 888.07 631.54, 881.93 611.23, 878.08 612.20, 871.74 597.48, 876.60 595.43, 872.18 580.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,75,"POLYGON ((740.29 598.67, 776.76 567.66, 793.66 588.92, 757.34 618.88, 740.29 598.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,76,"POLYGON ((857.87 553.66, 851.05 539.23, 823.92 548.21, 816.10 528.87, 844.17 518.12, 838.28 501.58, 856.00 495.06, 863.54 513.19, 868.04 513.94, 873.43 525.30, 869.38 527.02, 876.57 545.88, 857.87 553.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,77,"POLYGON ((825.38 498.84, 801.48 454.42, 821.61 444.27, 845.11 488.44, 825.38 498.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,0,"POLYGON ((715.74 0.00, 715.74 0.45, 690.67 0.61, 690.71 6.42, 677.89 6.51, 677.85 0.00, 715.74 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,1,"POLYGON ((45.30 513.27, 31.30 490.18, 47.61 480.38, 48.85 482.41, 56.35 477.89, 64.94 492.03, 51.51 500.12, 55.69 507.03, 45.30 513.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,2,"POLYGON ((349.59 476.72, 352.45 477.93, 350.84 481.70, 363.62 487.11, 355.61 505.82, 327.66 494.02, 330.44 487.51, 342.75 492.70, 349.59 476.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,3,"POLYGON ((368.60 405.80, 384.32 416.14, 371.65 435.27, 364.60 428.25, 361.32 432.82, 357.29 429.97, 359.08 422.73, 368.60 405.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,4,"POLYGON ((396.51 368.59, 412.61 379.54, 404.98 390.64, 401.51 390.24, 399.63 394.99, 409.11 401.43, 401.27 412.87, 377.30 396.56, 396.51 368.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,5,"POLYGON ((0.00 372.65, 15.08 393.81, 0.00 404.47, 0.00 372.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,6,"POLYGON ((101.00 330.57, 114.32 331.85, 117.88 329.90, 120.52 324.97, 123.35 324.94, 126.65 333.00, 135.55 345.52, 134.72 352.11, 135.72 357.03, 150.16 373.82, 146.51 376.94, 157.84 390.12, 150.85 396.07, 138.95 382.21, 142.65 379.08, 101.00 330.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,7,"POLYGON ((13.46 332.59, 30.33 321.90, 62.76 372.66, 60.92 375.95, 58.17 378.66, 58.89 383.35, 61.18 386.71, 57.55 389.13, 54.33 384.40, 51.18 386.52, 48.85 388.00, 13.46 332.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,8,"POLYGON ((419.44 348.80, 413.69 354.83, 407.06 348.79, 416.08 338.99, 421.29 343.47, 419.44 348.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,9,"POLYGON ((473.17 332.09, 483.19 344.15, 472.06 353.18, 463.10 339.81, 466.05 337.95, 473.17 332.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,10,"POLYGON ((255.01 305.68, 276.88 310.56, 275.50 318.16, 282.56 320.38, 280.61 328.96, 275.60 327.84, 273.59 336.57, 249.70 331.19, 255.01 305.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,11,"POLYGON ((462.20 285.78, 467.56 281.69, 474.80 292.38, 478.23 290.07, 483.86 296.61, 462.15 312.99, 457.51 306.18, 469.66 295.48, 462.20 285.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,12,"POLYGON ((477.33 271.00, 498.14 262.10, 503.11 262.22, 508.63 259.86, 514.09 275.06, 489.35 285.75, 485.49 278.23, 481.25 280.05, 477.33 271.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,13,"POLYGON ((290.79 261.10, 287.23 271.89, 264.87 266.54, 261.52 265.31, 255.27 263.63, 259.97 251.66, 267.51 253.53, 282.24 252.29, 285.84 253.66, 290.79 261.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,14,"POLYGON ((518.02 261.84, 541.39 252.58, 544.79 259.42, 532.97 263.92, 529.67 261.03, 525.98 262.12, 523.12 267.15, 519.44 268.73, 518.02 261.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,15,"POLYGON ((242.03 205.05, 249.45 206.33, 249.67 214.87, 247.65 228.44, 243.56 241.80, 241.35 246.20, 241.41 249.84, 239.90 264.05, 237.57 273.72, 219.30 269.41, 225.93 234.30, 232.44 222.95, 234.59 214.37, 239.56 212.18, 242.03 205.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,16,"POLYGON ((147.88 180.51, 156.35 188.46, 142.18 198.84, 135.38 204.14, 131.54 210.90, 123.82 216.56, 119.24 215.43, 107.72 232.04, 95.42 237.06, 88.63 243.49, 78.50 232.92, 125.00 188.80, 134.98 187.72, 142.48 186.64, 147.88 180.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,17,"POLYGON ((0.97 176.08, 20.35 166.21, 30.65 186.48, 26.63 194.62, 37.07 204.17, 41.98 203.33, 56.65 229.28, 52.89 231.40, 45.17 231.60, 41.22 228.55, 33.11 227.69, 25.83 217.40, 30.16 208.54, 25.41 200.96, 10.47 194.35, 0.97 176.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,18,"POLYGON ((397.18 160.33, 418.63 163.49, 418.84 171.65, 416.00 177.16, 394.75 172.51, 397.18 160.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,19,"POLYGON ((233.59 137.41, 240.28 155.00, 159.89 185.33, 153.19 167.75, 233.59 137.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,20,"POLYGON ((438.52 143.93, 453.42 143.80, 452.29 148.29, 454.96 155.63, 454.03 167.78, 446.83 168.21, 445.24 163.81, 433.07 164.36, 433.13 147.05, 438.52 143.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,21,"POLYGON ((471.23 128.69, 494.89 130.06, 494.15 142.44, 490.48 142.23, 489.97 150.81, 470.00 149.65, 471.23 128.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,22,"POLYGON ((507.65 116.68, 532.53 117.78, 535.85 130.81, 531.23 134.88, 507.59 133.75, 507.65 116.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,23,"POLYGON ((544.42 108.10, 563.04 107.98, 567.81 115.21, 569.53 127.42, 543.81 125.61, 544.42 108.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,24,"POLYGON ((400.83 94.29, 426.78 94.51, 426.69 104.68, 421.15 104.64, 421.08 113.66, 412.80 113.60, 412.75 117.71, 400.61 117.60, 400.83 94.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,25,"POLYGON ((296.24 82.09, 313.38 102.32, 303.64 110.51, 297.77 103.60, 274.98 122.77, 263.69 109.45, 296.24 82.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,26,"POLYGON ((469.57 92.99, 481.12 93.90, 479.78 110.91, 468.23 110.01, 469.57 92.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,27,"POLYGON ((169.24 76.23, 179.10 77.05, 179.16 120.78, 164.08 121.85, 162.57 104.04, 166.39 96.77, 167.36 94.15, 170.92 87.05, 169.24 76.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,28,"POLYGON ((518.29 82.00, 517.65 97.45, 509.41 97.10, 510.05 81.68, 518.29 82.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,29,"POLYGON ((38.17 54.39, 46.82 62.91, 60.27 66.08, 59.79 108.58, 37.59 108.35, 38.17 54.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,30,"POLYGON ((552.79 66.36, 552.62 85.74, 536.45 85.60, 536.62 66.22, 552.79 66.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,31,"POLYGON ((81.05 75.74, 80.98 66.50, 82.89 64.34, 88.72 61.49, 118.95 61.47, 119.03 75.50, 81.05 75.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,32,"POLYGON ((0.00 54.07, 20.09 53.01, 21.16 73.34, 0.00 74.45, 0.00 54.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,33,"POLYGON ((168.81 59.46, 168.89 56.24, 216.82 56.44, 217.97 59.45, 258.95 58.98, 259.07 69.50, 218.15 69.99, 218.12 67.26, 168.91 67.83, 168.81 59.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,34,"POLYGON ((447.19 26.46, 461.35 25.23, 461.71 39.34, 466.76 40.10, 466.05 47.47, 449.25 49.07, 437.91 56.42, 430.46 42.78, 446.54 35.89, 447.19 26.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,35,"POLYGON ((492.13 4.13, 508.39 2.16, 508.12 0.00, 534.22 0.00, 534.41 1.56, 530.72 2.01, 532.26 14.54, 523.86 15.58, 524.25 18.83, 511.75 20.37, 510.65 11.41, 496.50 13.15, 492.13 4.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,36,"POLYGON ((660.11 634.04, 666.52 628.35, 674.90 637.73, 668.48 643.42, 660.11 634.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,37,"POLYGON ((664.74 601.63, 667.37 605.93, 654.25 616.04, 650.41 613.00, 659.13 610.29, 663.61 605.81, 664.74 601.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,38,"POLYGON ((687.12 569.29, 692.63 565.77, 697.89 573.94, 682.86 592.77, 677.55 588.58, 689.72 573.31, 687.12 569.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,39,"POLYGON ((719.23 545.02, 730.81 560.11, 719.57 568.66, 714.20 561.67, 709.53 565.21, 705.89 560.46, 702.98 562.67, 697.84 555.96, 696.46 558.50, 691.97 554.02, 697.20 548.83, 700.33 549.75, 704.38 544.74, 711.79 550.68, 719.23 545.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,40,"POLYGON ((774.58 528.05, 785.98 523.05, 791.75 534.98, 780.22 540.56, 774.58 528.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,41,"POLYGON ((733.46 511.54, 747.37 504.04, 762.44 531.80, 755.84 535.36, 739.00 535.48, 727.36 542.82, 723.83 537.25, 729.48 533.69, 732.32 528.60, 736.93 524.08, 737.07 518.20, 733.46 511.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,42,"POLYGON ((767.28 495.78, 776.60 491.12, 781.56 500.96, 788.77 499.58, 791.39 504.24, 791.48 507.90, 789.95 510.89, 772.78 519.46, 767.06 508.13, 772.22 505.53, 767.28 495.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,43,"POLYGON ((816.55 497.67, 815.88 487.30, 846.94 484.42, 849.42 499.34, 842.31 499.52, 840.97 496.00, 834.87 494.29, 829.22 494.44, 816.55 497.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,44,"POLYGON ((576.54 483.50, 581.90 488.71, 573.88 496.84, 568.62 491.21, 573.64 486.46, 576.54 483.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,45,"POLYGON ((861.73 482.86, 872.56 480.19, 876.48 495.85, 865.63 498.52, 861.73 482.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,46,"POLYGON ((622.70 443.89, 635.72 433.39, 646.66 446.81, 654.96 440.12, 672.56 461.71, 651.24 478.93, 622.70 443.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,47,"POLYGON ((676.92 428.50, 695.38 415.53, 700.75 429.13, 704.86 426.12, 707.46 429.78, 705.50 435.25, 689.86 444.82, 676.92 428.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,48,"POLYGON ((701.28 390.43, 706.34 387.08, 712.18 395.81, 717.43 392.35, 731.10 412.80, 735.96 409.59, 737.14 416.48, 733.26 420.55, 719.24 426.11, 714.06 418.56, 719.01 416.95, 701.28 390.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,49,"POLYGON ((865.54 361.03, 884.07 360.87, 884.15 370.70, 876.65 370.76, 876.71 377.55, 887.59 377.47, 887.64 382.95, 893.23 382.90, 893.29 390.15, 861.59 390.43, 861.48 377.49, 865.68 377.45, 865.54 361.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,50,"POLYGON ((779.23 354.89, 793.63 350.86, 795.77 358.44, 798.46 357.68, 804.16 377.83, 806.13 377.29, 809.07 387.60, 784.04 394.64, 782.37 388.75, 785.97 384.46, 786.53 372.20, 783.50 364.24, 783.54 358.29, 779.23 354.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,51,"POLYGON ((816.63 334.80, 828.07 333.77, 829.50 349.56, 839.21 348.69, 839.98 357.35, 846.75 356.76, 849.26 384.60, 824.69 386.80, 822.66 383.53, 823.81 379.99, 820.13 373.80, 816.63 334.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,52,"POLYGON ((880.16 336.35, 893.98 336.64, 893.70 349.74, 879.89 349.45, 880.16 336.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,53,"POLYGON ((756.61 287.28, 769.04 286.40, 769.93 298.86, 775.59 298.47, 774.09 304.94, 757.96 306.09, 756.61 287.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,54,"POLYGON ((653.16 286.48, 670.62 280.84, 676.57 299.11, 659.13 304.78, 653.16 286.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,55,"POLYGON ((636.57 276.87, 641.92 297.87, 627.56 301.52, 622.20 280.53, 636.57 276.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,56,"POLYGON ((585.76 251.18, 591.33 268.20, 565.96 276.44, 563.15 267.85, 570.06 256.29, 585.76 251.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,57,"POLYGON ((900.00 256.44, 888.86 257.05, 887.80 237.92, 900.00 237.24, 900.00 256.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,58,"POLYGON ((737.87 219.94, 738.14 227.39, 742.91 227.23, 744.05 257.70, 719.14 258.62, 718.12 230.75, 710.45 231.03, 710.19 223.87, 719.41 223.54, 719.48 225.38, 723.24 225.24, 723.06 220.50, 737.87 219.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,59,"POLYGON ((607.56 229.62, 622.61 225.51, 630.38 247.33, 609.90 253.05, 602.84 239.13, 607.20 235.07, 607.56 229.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,60,"POLYGON ((810.25 225.92, 822.94 225.89, 822.95 237.67, 827.39 237.56, 829.07 257.52, 817.55 257.81, 818.22 249.25, 815.75 244.61, 805.09 244.01, 801.81 219.97, 810.39 219.75, 810.25 225.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,61,"POLYGON ((644.67 230.69, 667.86 229.37, 668.56 241.69, 645.37 243.00, 644.67 230.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,62,"POLYGON ((790.22 231.83, 790.54 241.72, 773.64 250.99, 768.62 251.12, 762.61 247.45, 762.12 232.68, 775.66 232.23, 775.29 221.25, 780.01 221.08, 780.36 232.15, 790.22 231.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,63,"POLYGON ((864.52 226.13, 864.80 240.24, 846.95 240.61, 846.64 226.50, 864.52 226.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,64,"POLYGON ((633.32 107.23, 651.00 107.33, 650.94 116.70, 636.38 116.61, 633.32 107.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,65,"POLYGON ((801.44 106.28, 828.41 106.72, 829.72 109.28, 827.19 111.57, 823.88 108.92, 821.27 109.37, 820.34 111.25, 820.04 113.86, 820.10 116.32, 814.64 116.46, 812.24 115.41, 810.42 116.70, 807.05 116.28, 805.02 115.11, 800.74 117.44, 801.44 106.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,66,"POLYGON ((842.94 100.00, 867.09 102.33, 866.15 111.92, 871.93 112.64, 871.64 118.40, 864.69 118.42, 864.30 120.78, 850.23 118.95, 847.98 118.12, 847.37 117.24, 844.69 116.14, 841.61 117.39, 840.27 116.69, 842.94 100.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,67,"POLYGON ((674.77 103.19, 696.33 104.51, 695.78 115.55, 678.21 114.54, 678.50 109.96, 675.12 108.38, 674.77 103.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,68,"POLYGON ((712.79 100.93, 735.51 105.12, 734.34 111.44, 735.92 115.26, 731.35 117.13, 710.49 113.29, 712.79 100.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,69,"POLYGON ((602.63 89.12, 614.39 89.02, 614.59 109.57, 609.83 109.63, 609.94 121.41, 591.15 121.58, 590.91 95.44, 602.69 95.31, 602.63 89.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,70,"POLYGON ((892.07 85.49, 900.00 86.28, 900.00 122.38, 887.35 121.30, 892.07 85.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,71,"POLYGON ((755.81 94.42, 766.04 94.00, 766.25 99.16, 780.37 98.58, 780.85 110.02, 756.51 111.02, 755.81 94.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,72,"POLYGON ((626.81 64.53, 626.57 68.73, 629.45 71.10, 633.75 74.14, 639.83 78.54, 618.01 77.34, 619.06 63.33, 626.81 64.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,73,"POLYGON ((592.03 0.00, 592.98 10.50, 569.20 12.37, 564.88 12.33, 561.18 12.69, 557.57 8.45, 555.99 0.00, 592.03 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,74,"POLYGON ((656.75 0.00, 657.12 8.13, 640.59 8.88, 625.47 6.34, 623.56 0.00, 656.75 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,75,"POLYGON ((816.38 867.21, 828.38 866.35, 828.73 871.34, 840.48 870.50, 842.11 893.59, 818.76 895.23, 821.78 874.13, 816.38 867.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,76,"POLYGON ((751.02 859.07, 759.23 859.57, 757.57 885.14, 761.15 885.39, 760.12 900.00, 754.29 900.00, 743.56 899.27, 743.88 894.77, 738.64 894.40, 742.26 886.07, 746.12 880.98, 745.69 870.24, 740.92 868.46, 740.77 862.42, 749.94 862.19, 751.02 859.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,77,"POLYGON ((777.59 863.21, 794.29 863.80, 793.52 885.22, 799.01 885.42, 798.68 894.77, 774.21 893.89, 774.45 887.20, 779.56 882.72, 781.42 873.72, 777.59 863.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,78,"POLYGON ((635.04 863.13, 656.60 862.13, 657.70 885.61, 649.17 886.01, 645.57 891.54, 631.29 891.33, 631.42 882.76, 635.86 880.82, 635.04 863.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,79,"POLYGON ((897.37 860.01, 900.00 859.90, 900.00 887.32, 898.08 887.87, 898.16 890.98, 887.92 891.24, 887.48 873.58, 895.03 866.51, 893.67 862.59, 897.37 860.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,80,"POLYGON ((598.05 856.11, 605.70 855.16, 606.68 863.17, 610.93 868.43, 617.96 870.74, 617.56 892.19, 593.27 891.77, 593.53 877.80, 604.08 869.29, 598.89 862.90, 598.05 856.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,81,"POLYGON ((863.56 862.14, 853.29 864.18, 850.90 869.44, 853.17 876.24, 847.50 876.14, 847.78 861.84, 863.56 862.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,82,"POLYGON ((804.98 839.87, 814.50 838.85, 815.52 848.41, 812.63 847.55, 811.31 851.94, 806.12 850.40, 804.98 839.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,83,"POLYGON ((797.05 800.76, 807.06 797.26, 813.04 814.18, 806.99 816.29, 804.61 813.02, 801.49 813.30, 797.05 800.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,84,"POLYGON ((727.05 814.56, 745.10 792.03, 747.01 797.00, 754.33 794.75, 765.42 811.82, 752.03 808.63, 740.78 807.75, 727.05 814.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,85,"POLYGON ((757.27 792.58, 761.24 790.64, 764.45 797.13, 776.93 791.02, 783.21 803.69, 766.75 811.74, 757.27 792.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,86,"POLYGON ((650.56 768.17, 664.47 777.78, 659.02 785.62, 665.47 790.07, 656.17 803.38, 635.82 789.30, 650.56 768.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,87,"POLYGON ((809.24 763.68, 814.39 770.23, 818.32 768.65, 829.58 766.87, 834.13 795.35, 814.80 798.42, 809.24 763.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,88,"POLYGON ((846.83 759.50, 852.77 762.88, 858.61 762.31, 868.31 757.49, 867.46 765.41, 864.19 768.00, 867.66 781.10, 853.13 784.94, 849.18 777.33, 849.38 769.21, 846.83 759.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,89,"POLYGON ((879.36 748.06, 893.96 747.26, 895.51 750.55, 900.00 750.62, 900.00 780.34, 889.02 780.20, 889.17 770.67, 879.60 770.54, 879.72 762.19, 887.11 756.81, 888.58 750.07, 879.36 748.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,90,"POLYGON ((690.53 741.75, 696.86 748.49, 689.24 755.57, 682.91 748.83, 690.53 741.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,91,"POLYGON ((686.59 713.19, 703.90 701.32, 711.77 712.68, 717.80 708.53, 725.24 719.31, 720.71 722.42, 723.29 726.15, 704.49 739.04, 686.59 713.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,92,"POLYGON ((721.71 691.20, 738.59 680.43, 753.63 703.75, 736.74 714.53, 721.71 691.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,93,"POLYGON ((760.34 656.72, 788.67 646.36, 804.64 689.64, 790.56 694.79, 783.47 675.57, 769.22 680.78, 760.34 656.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,94,"POLYGON ((859.30 655.30, 880.60 654.53, 880.76 660.98, 878.30 662.49, 879.19 673.30, 886.10 673.32, 886.68 679.97, 872.48 680.75, 864.88 677.82, 859.79 674.42, 859.30 655.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,95,"POLYGON ((896.78 657.69, 900.00 657.67, 900.00 675.22, 896.90 675.24, 896.78 657.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,96,"POLYGON ((803.07 657.13, 811.20 654.77, 810.16 651.25, 821.97 647.82, 823.21 652.02, 827.37 650.81, 834.82 676.14, 810.72 683.18, 803.07 657.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,97,"POLYGON ((631.05 643.69, 639.99 637.20, 650.36 651.39, 641.42 657.87, 631.05 643.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,98,"POLYGON ((162.56 900.00, 185.97 881.72, 171.38 863.20, 197.74 842.59, 242.98 900.00, 162.56 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,99,"POLYGON ((145.24 894.19, 150.06 900.00, 138.16 900.00, 145.24 894.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,100,"POLYGON ((127.72 874.39, 142.36 885.03, 131.36 900.00, 108.92 900.00, 127.72 874.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,101,"POLYGON ((383.78 877.57, 403.76 876.73, 404.12 885.00, 401.99 885.10, 402.62 900.00, 384.73 900.00, 383.78 877.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,102,"POLYGON ((484.78 874.32, 510.05 873.43, 510.69 891.66, 505.30 891.87, 505.59 900.00, 485.68 900.00, 484.78 874.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,103,"POLYGON ((446.97 870.65, 458.54 870.29, 458.66 874.14, 475.05 873.64, 475.84 899.46, 460.62 899.91, 460.58 900.00, 447.87 900.00, 446.97 870.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,104,"POLYGON ((409.47 873.94, 437.46 872.84, 438.48 898.77, 410.48 899.86, 409.47 873.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,105,"POLYGON ((524.14 866.21, 537.31 866.85, 538.09 877.73, 543.93 882.02, 544.84 898.09, 539.82 898.06, 540.27 894.90, 520.96 895.75, 519.46 892.12, 522.86 887.31, 525.86 881.46, 530.52 878.01, 524.14 866.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,106,"POLYGON ((105.27 844.44, 131.02 863.68, 116.59 882.80, 90.84 863.57, 105.27 844.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,107,"POLYGON ((369.22 837.87, 381.68 837.38, 382.69 849.43, 370.41 850.45, 369.22 837.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,108,"POLYGON ((489.19 807.08, 518.10 806.38, 518.74 832.52, 511.72 832.70, 512.07 846.63, 494.18 847.06, 493.70 827.45, 489.70 827.56, 489.19 807.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,109,"POLYGON ((158.62 815.84, 168.33 827.27, 151.09 841.76, 132.92 829.93, 141.73 816.52, 142.39 812.55, 145.20 812.10, 144.50 807.79, 159.09 808.75, 158.62 815.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,110,"POLYGON ((332.38 782.37, 339.34 778.95, 344.81 789.99, 349.97 787.47, 355.62 798.86, 330.32 811.28, 324.64 799.81, 337.81 793.35, 332.38 782.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,111,"POLYGON ((370.38 774.62, 390.03 766.22, 399.80 788.85, 380.15 797.26, 370.38 774.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,112,"POLYGON ((430.74 736.21, 440.33 762.36, 434.43 764.48, 436.84 771.08, 420.46 777.03, 417.85 769.92, 411.04 772.41, 401.66 746.79, 430.74 736.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,113,"POLYGON ((77.98 718.70, 98.29 734.61, 62.77 779.53, 42.45 763.63, 77.98 718.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,114,"POLYGON ((454.20 764.33, 442.58 738.37, 444.88 737.36, 442.75 732.62, 446.91 730.78, 451.51 726.35, 455.27 733.74, 459.45 734.59, 462.38 739.37, 463.04 748.50, 466.19 748.29, 468.63 753.71, 460.47 757.31, 462.05 760.87, 454.20 764.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,115,"POLYGON ((479.15 710.20, 496.16 699.97, 507.83 719.19, 494.69 727.09, 500.53 736.71, 487.59 739.39, 474.31 717.07, 479.15 710.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,116,"POLYGON ((534.26 678.80, 551.51 704.70, 540.10 712.23, 535.77 705.75, 523.96 713.56, 513.44 697.75, 528.73 687.66, 526.32 684.04, 534.26 678.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,117,"POLYGON ((324.79 665.86, 340.57 665.57, 340.82 678.08, 347.93 677.95, 348.15 689.44, 340.50 689.59, 340.66 698.84, 330.01 699.05, 329.84 690.15, 322.96 687.11, 321.90 680.65, 329.66 677.41, 324.79 665.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,118,"POLYGON ((547.85 664.29, 557.76 657.00, 565.27 667.10, 567.65 665.36, 584.75 688.43, 576.66 694.36, 573.34 689.87, 564.31 696.50, 550.11 677.35, 554.91 673.81, 547.85 664.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,119,"POLYGON ((379.66 653.27, 387.82 668.20, 394.90 664.36, 397.15 668.47, 375.23 680.36, 371.17 672.91, 376.58 669.98, 370.22 658.37, 379.66 653.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,120,"POLYGON ((5.02 659.19, 4.53 651.61, 20.22 650.61, 20.69 658.19, 5.02 659.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,121,"POLYGON ((278.85 647.95, 291.26 645.54, 300.03 644.03, 304.49 659.17, 302.56 661.92, 303.62 663.65, 301.46 664.00, 294.56 665.19, 290.24 665.21, 284.86 659.67, 280.53 655.76, 278.85 647.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,122,"POLYGON ((403.84 626.50, 411.33 638.05, 424.87 644.37, 429.22 643.21, 431.36 646.53, 410.19 660.13, 392.94 633.51, 403.84 626.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,123,"POLYGON ((429.61 617.21, 447.94 607.37, 454.63 615.10, 450.75 618.33, 458.81 631.45, 440.68 641.48, 429.61 617.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,124,"POLYGON ((463.20 606.07, 481.57 593.70, 493.39 611.12, 475.02 623.46, 463.20 606.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,125,"POLYGON ((301.52 595.98, 311.34 595.17, 311.85 601.11, 325.90 599.93, 327.35 617.34, 315.86 618.30, 306.50 607.44, 296.91 615.56, 276.10 617.29, 275.01 604.20, 302.03 601.96, 301.52 595.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,126,"POLYGON ((457.93 580.50, 461.78 583.93, 459.31 585.66, 466.62 593.38, 456.18 601.75, 444.99 589.78, 457.93 580.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,127,"POLYGON ((508.91 570.25, 518.09 578.92, 516.43 582.42, 522.07 588.96, 507.45 599.97, 504.34 595.10, 496.04 581.21, 502.52 582.78, 507.16 579.20, 508.91 570.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,128,"POLYGON ((184.10 497.51, 215.95 514.57, 244.83 528.48, 175.14 662.32, 139.95 641.62, 193.94 546.74, 180.97 539.44, 189.69 524.05, 174.89 516.53, 184.10 497.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,129,"POLYGON ((511.91 548.33, 531.31 533.10, 544.49 549.74, 539.80 553.43, 546.28 561.61, 549.51 559.07, 556.06 567.34, 538.13 581.42, 511.91 548.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,130,"POLYGON ((564.14 515.21, 586.99 540.28, 569.90 555.70, 547.06 530.65, 564.14 515.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,131,"POLYGON ((327.02 515.50, 326.09 520.34, 335.85 522.22, 333.23 535.70, 316.67 532.50, 317.67 527.30, 311.97 526.21, 313.77 516.93, 316.91 513.63, 327.02 515.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,132,"POLYGON ((518.58 496.70, 522.50 493.52, 525.52 497.22, 529.46 494.01, 541.63 508.81, 524.40 522.82, 520.94 518.62, 526.13 514.38, 523.59 502.81, 518.58 496.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,133,"POLYGON ((202.26 503.91, 204.47 497.52, 201.41 493.92, 205.70 486.39, 215.12 491.70, 217.32 487.83, 221.31 490.08, 223.58 486.09, 228.73 489.00, 230.29 486.27, 241.75 492.73, 228.37 516.18, 224.38 513.93, 220.88 515.33, 205.76 507.48, 202.26 503.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,0,"POLYGON ((662.45 673.23, 714.26 671.50, 714.79 686.98, 688.82 687.86, 686.01 691.88, 678.88 692.06, 677.36 689.82, 663.01 690.29, 662.45 673.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,1,"POLYGON ((888.45 663.90, 889.02 673.92, 898.18 673.42, 898.56 680.11, 900.00 679.54, 900.00 688.87, 854.84 691.41, 853.94 675.63, 868.35 674.82, 867.81 665.04, 888.45 663.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,2,"POLYGON ((792.03 667.55, 823.33 667.06, 824.49 662.86, 834.69 662.87, 834.62 682.20, 824.22 684.97, 792.30 685.45, 792.03 667.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,3,"POLYGON ((773.34 661.67, 773.70 675.82, 763.75 679.83, 760.70 682.61, 728.85 682.69, 724.10 676.46, 723.50 664.54, 773.34 661.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,4,"POLYGON ((900.00 576.11, 856.31 573.17, 858.07 547.42, 870.60 548.28, 874.03 551.94, 887.80 549.93, 890.86 554.85, 898.87 556.31, 900.00 554.45, 900.00 576.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,5,"POLYGON ((587.53 452.10, 592.93 467.39, 564.93 478.11, 558.75 457.82, 565.38 454.55, 571.52 457.50, 587.53 452.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,6,"POLYGON ((754.21 450.37, 757.98 458.40, 755.85 465.13, 738.24 473.28, 735.18 467.75, 731.35 457.19, 754.21 450.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,7,"POLYGON ((677.80 447.07, 694.85 448.53, 695.62 453.81, 696.39 459.12, 693.36 463.37, 689.01 465.57, 678.93 465.00, 676.54 461.71, 677.80 447.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,8,"POLYGON ((654.84 428.48, 657.53 445.28, 649.39 446.57, 646.16 451.65, 639.59 452.68, 636.19 431.44, 654.84 428.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,9,"POLYGON ((623.11 427.71, 626.66 442.56, 624.00 445.33, 599.46 448.04, 594.73 442.44, 593.80 432.76, 604.30 431.65, 603.25 428.17, 615.34 426.15, 617.52 429.03, 623.11 427.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,10,"POLYGON ((776.57 412.27, 795.99 419.90, 784.24 449.45, 764.82 441.80, 776.57 412.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,11,"POLYGON ((764.64 372.41, 779.89 366.68, 789.88 393.10, 788.02 395.59, 779.98 398.08, 774.33 398.05, 764.64 372.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,12,"POLYGON ((828.86 340.90, 842.48 346.09, 838.10 357.78, 844.99 361.47, 840.30 371.98, 820.05 361.53, 828.86 340.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,13,"POLYGON ((864.03 345.52, 889.11 336.80, 894.77 352.13, 870.63 361.60, 864.03 345.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,14,"POLYGON ((666.72 342.94, 660.07 343.57, 660.68 350.82, 648.82 350.27, 647.69 342.44, 639.93 341.69, 640.04 326.19, 667.00 326.84, 666.72 342.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,15,"POLYGON ((745.34 314.84, 769.49 338.95, 752.13 356.17, 742.37 346.45, 740.83 341.41, 737.63 339.74, 728.98 331.10, 745.34 314.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,16,"POLYGON ((620.93 322.19, 623.87 334.04, 624.07 341.91, 622.19 344.07, 612.37 345.72, 611.04 342.24, 605.42 342.39, 604.61 345.20, 594.53 343.71, 590.40 326.81, 588.32 320.73, 606.93 318.51, 608.64 323.72, 620.93 322.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,17,"POLYGON ((679.61 320.08, 711.18 319.19, 711.04 341.19, 699.05 340.78, 699.14 344.62, 690.70 344.10, 690.01 340.44, 680.49 340.86, 679.61 320.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,18,"POLYGON ((759.30 309.93, 766.12 315.95, 761.35 321.38, 754.40 315.36, 759.30 309.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,19,"POLYGON ((613.74 305.97, 624.68 305.40, 626.29 315.97, 615.77 315.51, 613.74 305.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,20,"POLYGON ((656.09 303.90, 666.91 303.38, 667.48 314.79, 661.82 315.07, 660.52 316.72, 656.74 316.91, 656.09 303.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,21,"POLYGON ((798.71 296.88, 809.69 293.87, 811.37 299.17, 813.73 298.47, 815.87 298.88, 820.37 315.15, 805.64 319.32, 798.71 296.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,22,"POLYGON ((675.61 283.33, 688.28 282.74, 688.70 291.72, 676.01 292.31, 675.61 283.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,23,"POLYGON ((674.94 256.02, 691.22 255.61, 691.00 270.42, 700.12 269.92, 700.38 280.64, 675.33 280.81, 674.94 256.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,24,"POLYGON ((745.10 254.33, 746.25 275.86, 715.49 276.13, 714.11 251.63, 731.49 250.66, 731.62 254.43, 745.10 254.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,25,"POLYGON ((614.69 254.23, 616.33 270.50, 590.99 271.83, 590.00 267.31, 588.63 254.71, 602.85 253.13, 610.07 252.94, 614.69 254.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,26,"POLYGON ((658.04 253.43, 658.36 269.74, 635.18 270.20, 631.75 267.11, 630.99 253.70, 658.04 253.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,27,"POLYGON ((800.68 251.56, 818.30 252.67, 817.21 269.55, 799.61 268.44, 800.68 251.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,28,"POLYGON ((790.66 275.77, 770.21 276.84, 769.35 273.47, 759.08 274.40, 755.02 245.27, 761.42 244.37, 760.63 238.69, 772.64 237.03, 776.78 254.70, 788.57 253.74, 790.66 275.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,29,"POLYGON ((609.00 164.65, 629.96 166.36, 633.91 174.43, 632.62 182.65, 606.97 182.81, 606.08 167.45, 609.00 164.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,30,"POLYGON ((675.99 164.30, 676.33 179.98, 652.65 180.63, 652.12 165.86, 654.47 164.76, 675.99 164.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,31,"POLYGON ((590.06 161.98, 591.58 177.99, 559.37 180.71, 558.13 164.83, 568.49 164.01, 590.06 161.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,32,"POLYGON ((734.16 164.80, 759.85 162.24, 761.90 178.41, 735.72 179.65, 734.16 164.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,33,"POLYGON ((779.43 162.78, 802.22 162.34, 802.46 177.38, 796.68 177.53, 780.54 177.34, 779.43 162.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,34,"POLYGON ((689.09 157.11, 714.07 155.87, 713.41 158.84, 720.27 160.73, 721.64 179.43, 702.65 178.58, 693.45 177.64, 689.90 177.29, 689.09 157.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,35,"POLYGON ((815.82 152.29, 829.54 152.52, 829.47 155.76, 833.71 155.83, 833.85 161.55, 830.87 164.85, 830.95 171.71, 833.70 171.41, 833.55 177.94, 828.83 177.84, 828.07 179.44, 817.84 179.70, 815.47 177.80, 815.82 152.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,36,"POLYGON ((689.30 118.01, 707.43 116.49, 708.37 129.56, 690.65 130.87, 689.30 118.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,37,"POLYGON ((726.77 107.43, 747.70 107.27, 747.85 130.07, 726.91 130.22, 726.77 107.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,38,"POLYGON ((803.42 93.12, 820.94 92.76, 823.39 99.18, 825.76 102.45, 825.84 108.66, 832.64 108.55, 832.77 118.14, 803.92 118.56, 803.42 93.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,39,"POLYGON ((695.77 60.47, 702.29 69.89, 703.00 73.20, 700.06 81.00, 696.39 92.97, 673.52 92.09, 671.81 82.34, 666.14 81.44, 665.65 62.08, 695.77 60.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,40,"POLYGON ((729.02 58.38, 756.92 59.76, 759.48 61.98, 759.29 79.50, 757.58 85.80, 756.21 89.59, 730.24 90.25, 729.97 79.82, 725.79 79.73, 724.48 61.20, 729.02 58.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,41,"POLYGON ((608.28 63.11, 638.41 61.92, 640.00 83.35, 608.16 83.34, 605.53 62.34, 608.28 63.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,42,"POLYGON ((833.11 65.61, 832.40 72.13, 831.85 78.27, 829.58 79.55, 807.98 75.37, 809.22 61.31, 833.11 65.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,43,"POLYGON ((486.71 810.88, 499.42 811.05, 502.59 808.50, 509.81 808.08, 510.88 811.02, 522.84 810.96, 532.31 811.21, 531.50 828.35, 524.29 829.02, 523.68 834.50, 504.55 836.71, 504.62 829.03, 487.16 828.47, 486.71 810.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,44,"POLYGON ((274.00 816.35, 290.40 815.96, 290.23 809.46, 306.42 809.05, 306.36 806.09, 335.04 805.43, 336.37 808.62, 341.69 808.39, 342.58 828.77, 323.79 829.58, 324.15 837.90, 310.82 838.48, 310.41 828.68, 299.78 829.15, 274.35 829.77, 274.00 816.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,45,"POLYGON ((362.32 807.07, 376.48 806.07, 379.56 802.13, 385.90 802.32, 387.14 804.67, 400.31 803.60, 401.56 806.36, 413.91 805.98, 414.40 821.82, 412.81 829.39, 393.95 829.91, 393.78 823.74, 365.73 824.52, 363.25 820.32, 362.32 807.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,46,"POLYGON ((194.68 793.19, 224.86 793.69, 235.38 795.51, 236.14 825.50, 210.54 824.06, 210.23 811.58, 198.05 810.22, 194.68 793.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,47,"POLYGON ((191.32 792.45, 192.17 809.95, 153.16 807.61, 152.82 794.25, 191.32 792.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,48,"POLYGON ((2.58 718.00, 0.75 704.22, 6.28 703.50, 7.01 697.55, 12.65 698.25, 15.06 716.35, 2.58 718.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,49,"POLYGON ((298.41 690.47, 331.51 689.63, 334.96 687.10, 342.36 686.91, 342.75 702.33, 340.76 707.29, 294.63 708.46, 294.30 695.15, 298.41 690.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,50,"POLYGON ((533.74 676.97, 534.03 696.10, 521.25 696.29, 517.97 698.86, 479.72 699.43, 479.40 677.77, 533.74 676.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,51,"POLYGON ((380.81 667.73, 384.10 673.07, 389.95 673.14, 392.28 682.25, 398.94 680.19, 400.42 672.65, 409.79 670.13, 408.18 681.22, 409.95 685.13, 410.70 689.71, 410.67 696.59, 385.18 699.32, 384.40 701.63, 377.70 702.02, 367.19 700.60, 362.34 698.86, 360.70 684.29, 367.83 683.91, 368.25 667.85, 380.81 667.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,52,"POLYGON ((592.96 674.02, 594.13 687.33, 543.34 691.33, 541.86 674.50, 592.96 674.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,53,"POLYGON ((455.33 661.36, 472.44 660.66, 472.77 668.35, 473.49 680.10, 466.41 680.54, 467.29 694.77, 423.63 697.48, 423.14 689.57, 422.79 680.92, 456.09 679.54, 455.33 661.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,54,"POLYGON ((358.50 531.52, 366.72 529.87, 366.83 523.36, 377.87 524.06, 379.88 528.13, 390.04 527.88, 390.05 553.43, 360.71 552.73, 358.50 531.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,55,"POLYGON ((326.96 519.25, 331.37 520.95, 333.29 518.86, 338.69 520.97, 344.93 525.40, 343.36 528.68, 347.87 532.19, 339.81 546.20, 317.44 533.52, 326.96 519.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,56,"POLYGON ((409.55 513.02, 423.54 510.82, 427.38 520.14, 436.19 519.78, 436.51 527.23, 427.64 532.54, 425.43 536.28, 413.53 538.16, 409.55 513.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,57,"POLYGON ((445.29 514.47, 456.48 512.58, 458.25 523.04, 477.13 519.89, 478.75 529.38, 448.66 534.42, 445.29 514.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,58,"POLYGON ((487.44 494.88, 510.08 487.63, 514.08 496.69, 515.73 503.75, 493.51 511.40, 491.34 508.95, 487.44 494.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,59,"POLYGON ((280.70 476.36, 300.65 469.75, 310.14 498.14, 290.17 504.73, 280.70 476.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,60,"POLYGON ((52.73 475.33, 55.71 488.24, 55.80 498.96, 0.00 498.21, 0.00 474.70, 52.73 475.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,61,"POLYGON ((524.64 477.31, 551.62 468.17, 558.38 487.93, 531.42 497.07, 524.64 477.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,62,"POLYGON ((294.20 417.66, 311.54 427.74, 308.20 435.19, 307.83 440.80, 305.67 445.93, 302.29 444.44, 297.27 455.09, 281.23 447.44, 294.20 417.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,63,"POLYGON ((466.33 399.44, 484.54 399.04, 490.93 399.01, 491.19 410.86, 484.68 411.16, 484.75 419.52, 466.75 419.71, 466.33 399.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,64,"POLYGON ((345.51 403.17, 338.60 409.16, 341.96 414.09, 332.75 419.63, 329.70 414.20, 319.35 421.55, 312.42 407.29, 338.02 391.37, 345.51 403.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,65,"POLYGON ((390.90 397.09, 392.43 412.92, 368.03 415.25, 366.82 402.65, 367.72 399.32, 390.90 397.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,66,"POLYGON ((356.02 379.73, 367.69 379.37, 367.93 386.64, 356.26 387.03, 356.02 379.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,67,"POLYGON ((489.85 357.35, 489.79 389.36, 465.24 389.32, 465.25 380.88, 468.83 380.88, 468.61 357.47, 489.85 357.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,68,"POLYGON ((520.03 351.06, 536.34 351.38, 536.73 361.53, 543.92 361.59, 543.87 367.57, 535.12 367.48, 535.02 377.58, 519.08 377.43, 520.03 351.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,69,"POLYGON ((362.11 338.09, 386.49 340.44, 386.04 343.96, 388.58 346.72, 387.98 351.10, 385.54 352.23, 385.44 355.03, 386.61 359.22, 389.58 358.43, 383.05 364.72, 388.56 366.52, 387.46 372.34, 372.27 370.26, 366.07 367.31, 359.24 350.08, 362.11 338.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,70,"POLYGON ((577.59 342.23, 581.28 356.14, 577.43 357.30, 556.77 362.20, 553.56 347.03, 555.64 345.58, 577.59 342.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,71,"POLYGON ((468.43 309.68, 488.50 307.71, 490.24 310.37, 488.03 339.00, 480.16 341.90, 467.93 338.88, 467.61 334.94, 467.03 328.28, 470.13 326.53, 468.93 320.30, 467.42 318.88, 468.43 309.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,72,"POLYGON ((372.42 302.84, 393.81 304.07, 392.24 325.07, 372.76 325.25, 370.78 325.30, 370.51 302.38, 372.42 302.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,73,"POLYGON ((370.96 264.99, 387.89 266.22, 392.75 271.32, 391.44 288.97, 369.31 287.36, 370.96 264.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,74,"POLYGON ((471.68 261.90, 489.29 261.98, 490.45 272.63, 489.12 283.19, 480.65 282.36, 479.48 277.99, 471.73 277.68, 469.17 267.04, 471.68 261.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,75,"POLYGON ((546.81 258.92, 562.13 258.71, 563.33 264.30, 570.37 264.47, 571.17 281.81, 546.15 281.23, 546.81 258.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,76,"POLYGON ((531.52 257.31, 532.54 279.26, 502.43 280.65, 501.41 258.70, 531.52 257.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,77,"POLYGON ((54.42 228.00, 60.93 230.39, 56.44 242.54, 49.92 240.15, 54.42 228.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,78,"POLYGON ((69.28 202.94, 88.26 208.10, 79.49 240.11, 69.23 259.79, 60.78 255.41, 63.44 250.31, 57.75 247.36, 57.80 244.77, 69.28 202.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,79,"POLYGON ((365.89 204.54, 386.10 206.02, 386.13 236.52, 370.71 237.16, 361.16 233.92, 356.64 232.30, 357.91 204.01, 365.89 204.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,80,"POLYGON ((0.00 188.99, 1.59 190.25, 7.53 199.03, 6.46 206.00, 5.01 207.15, 7.76 217.49, 1.85 225.59, 0.00 231.93, 0.00 188.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,81,"POLYGON ((403.45 165.69, 412.69 179.18, 394.96 191.21, 385.74 177.73, 403.45 165.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,82,"POLYGON ((474.41 160.23, 505.57 161.26, 504.70 182.10, 498.83 181.83, 494.03 178.73, 485.55 178.19, 487.37 182.12, 473.67 181.67, 474.41 160.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,83,"POLYGON ((547.55 162.30, 545.98 177.26, 526.91 175.28, 527.24 172.18, 522.19 170.09, 520.18 158.98, 542.62 161.78, 547.55 162.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,84,"POLYGON ((436.09 153.43, 458.08 156.58, 458.42 169.71, 457.05 174.21, 431.79 170.63, 431.89 155.03, 436.09 153.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,85,"POLYGON ((372.98 68.86, 380.28 67.64, 392.18 74.64, 395.07 88.95, 393.24 91.30, 384.40 89.44, 363.01 88.94, 358.73 76.75, 360.21 69.41, 372.98 68.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,86,"POLYGON ((577.26 62.43, 574.65 75.22, 571.26 81.99, 563.39 85.29, 557.65 90.46, 552.61 89.96, 549.49 82.74, 546.33 73.23, 544.61 63.26, 577.26 62.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,87,"POLYGON ((440.50 63.40, 450.57 64.19, 451.27 66.88, 457.35 66.94, 458.09 79.42, 457.35 83.84, 427.88 86.25, 424.40 80.92, 422.78 66.98, 428.02 66.85, 433.01 65.25, 440.50 63.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,88,"POLYGON ((481.79 64.66, 495.80 63.26, 515.54 65.25, 516.54 80.03, 510.08 81.45, 509.52 84.18, 483.73 83.17, 481.79 64.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,89,"POLYGON ((548.42 808.67, 561.45 808.41, 561.63 805.92, 571.35 806.18, 571.56 809.26, 586.48 808.02, 586.56 811.12, 592.27 810.73, 592.55 825.98, 586.43 825.75, 586.70 836.54, 564.65 836.47, 563.65 826.20, 548.22 827.10, 548.42 808.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,90,"POLYGON ((708.83 803.58, 712.95 812.77, 713.14 820.14, 710.00 821.62, 671.09 822.60, 668.94 807.41, 708.83 803.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,91,"POLYGON ((733.07 800.70, 741.09 801.23, 742.91 799.69, 755.49 799.00, 755.78 800.85, 771.24 801.08, 771.92 803.79, 776.65 803.43, 778.86 816.63, 733.45 819.16, 733.20 809.24, 732.31 803.56, 733.07 800.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,92,"POLYGON ((786.87 798.49, 800.18 798.39, 800.14 795.46, 810.32 795.38, 810.34 798.45, 824.75 798.32, 826.05 799.60, 839.14 800.22, 838.53 812.82, 834.76 820.64, 831.86 822.16, 810.29 822.51, 808.67 816.51, 787.01 816.66, 786.87 798.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,93,"POLYGON ((622.34 797.94, 630.94 798.10, 631.60 799.43, 646.54 799.43, 646.60 801.65, 652.94 801.38, 653.63 803.58, 651.23 807.13, 651.82 810.82, 652.11 817.63, 645.10 816.07, 638.85 815.50, 638.42 817.73, 608.81 818.86, 607.60 800.80, 622.82 801.52, 622.34 797.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,94,"POLYGON ((863.10 792.20, 865.70 787.76, 873.27 788.61, 874.15 790.45, 896.37 790.73, 897.95 803.83, 894.99 810.99, 881.01 812.81, 877.32 815.61, 872.42 821.15, 860.27 821.04, 856.44 818.65, 853.74 811.19, 845.23 805.79, 845.74 793.06, 863.10 792.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,95,"POLYGON ((651.96 679.21, 653.37 693.98, 649.44 695.74, 612.58 696.26, 608.76 694.27, 609.24 680.07, 651.96 679.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,0,"POLYGON ((540.89 847.71, 562.80 847.29, 563.05 860.89, 573.88 860.68, 574.27 881.36, 541.56 882.01, 540.89 847.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,1,"POLYGON ((477.53 832.60, 498.95 832.08, 499.24 844.28, 502.93 844.19, 503.35 861.38, 514.97 861.11, 515.50 882.47, 478.76 883.36, 477.53 832.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,2,"POLYGON ((1.50 805.75, 25.65 799.83, 38.82 840.19, 30.94 845.40, 25.13 849.68, 18.51 846.01, 9.24 818.54, 3.32 818.98, 1.50 805.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,3,"POLYGON ((542.48 815.08, 542.69 823.27, 537.23 823.41, 537.02 815.22, 542.48 815.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,4,"POLYGON ((293.46 795.37, 294.12 808.31, 288.09 808.62, 288.41 814.47, 294.18 814.19, 295.19 833.61, 272.15 834.80, 271.25 817.35, 266.54 817.58, 266.06 808.34, 270.97 808.08, 270.37 796.58, 293.46 795.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,5,"POLYGON ((0.00 765.92, 2.16 766.69, 1.22 769.20, 14.70 774.56, 10.11 790.79, 0.00 787.56, 0.00 765.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,6,"POLYGON ((397.39 749.67, 399.48 780.60, 392.38 785.73, 375.79 786.33, 375.47 777.24, 382.85 774.07, 381.30 750.74, 397.39 749.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,7,"POLYGON ((424.21 746.50, 442.01 746.31, 442.12 756.30, 462.23 756.10, 462.42 774.76, 424.49 775.15, 424.21 746.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,8,"POLYGON ((532.04 743.21, 563.83 742.22, 563.92 745.28, 581.48 744.75, 582.07 763.38, 532.70 764.92, 532.04 743.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,9,"POLYGON ((471.97 743.84, 482.23 743.56, 482.17 741.23, 500.55 740.72, 500.64 743.78, 520.09 743.24, 520.61 761.21, 472.50 762.56, 471.97 743.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,10,"POLYGON ((265.40 719.30, 274.09 718.70, 283.79 718.08, 284.90 742.11, 287.66 742.53, 288.65 762.59, 274.72 763.19, 268.60 757.15, 265.40 719.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,11,"POLYGON ((63.31 676.68, 61.97 681.93, 59.69 682.61, 57.28 686.42, 56.31 689.98, 47.11 690.43, 18.85 673.90, 25.26 663.04, 33.74 659.07, 63.31 676.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,12,"POLYGON ((307.59 647.99, 316.26 657.27, 308.85 664.12, 312.36 667.87, 291.44 687.21, 279.28 674.15, 307.59 647.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,13,"POLYGON ((450.99 641.98, 451.66 661.92, 439.73 662.33, 439.86 666.03, 426.50 666.48, 426.40 663.18, 410.01 663.71, 409.33 643.37, 450.99 641.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,14,"POLYGON ((397.75 641.64, 398.09 661.59, 374.10 662.00, 374.16 665.90, 362.36 666.12, 362.30 662.45, 353.41 662.61, 353.06 642.40, 397.75 641.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,15,"POLYGON ((517.19 639.03, 517.69 658.84, 469.52 660.06, 469.02 640.25, 517.19 639.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,16,"POLYGON ((578.41 635.30, 579.17 658.17, 562.62 658.74, 562.74 661.87, 545.75 662.46, 545.65 658.95, 530.69 659.44, 530.09 641.55, 567.76 640.28, 567.60 635.66, 578.41 635.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,17,"POLYGON ((282.17 622.62, 288.37 630.06, 277.33 639.20, 271.12 631.76, 282.17 622.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,18,"POLYGON ((512.39 386.59, 524.54 440.82, 422.20 463.57, 410.05 409.32, 512.39 386.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,19,"POLYGON ((207.09 304.40, 218.84 354.49, 245.87 348.21, 249.70 364.54, 220.49 371.34, 231.70 419.09, 195.95 427.41, 169.15 313.24, 207.09 304.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,20,"POLYGON ((374.96 291.32, 392.07 367.49, 339.64 379.17, 322.53 303.00, 374.96 291.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,21,"POLYGON ((516.27 134.99, 516.17 167.62, 497.19 167.57, 497.27 134.94, 516.27 134.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,22,"POLYGON ((345.21 53.10, 357.28 117.30, 369.91 114.96, 373.39 133.47, 337.82 140.10, 345.72 182.16, 274.05 195.53, 271.52 182.07, 219.59 191.76, 207.01 124.84, 271.53 112.82, 263.17 68.39, 345.21 53.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,23,"POLYGON ((499.58 97.01, 517.53 103.93, 517.77 113.67, 499.99 114.12, 499.58 97.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,24,"POLYGON ((511.39 22.57, 535.67 20.54, 536.76 33.54, 531.48 33.98, 532.39 44.77, 513.39 46.36, 511.39 22.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,25,"POLYGON ((877.93 750.22, 888.43 745.83, 896.02 753.71, 900.00 767.57, 900.00 788.70, 894.28 789.72, 882.06 787.10, 878.28 777.76, 885.27 772.92, 888.05 766.30, 884.26 760.14, 878.57 755.04, 877.93 750.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,26,"POLYGON ((656.00 741.94, 688.26 741.12, 688.32 743.45, 700.74 743.14, 701.21 761.55, 656.53 762.68, 656.00 741.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,27,"POLYGON ((775.55 741.46, 820.86 740.82, 821.15 761.76, 775.84 762.38, 775.55 741.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,28,"POLYGON ((592.78 743.57, 609.45 742.95, 609.35 739.82, 623.38 739.31, 623.49 742.19, 640.36 741.56, 641.03 760.02, 593.45 761.76, 592.78 743.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,29,"POLYGON ((717.69 740.58, 727.22 740.53, 727.21 737.89, 746.13 737.79, 746.15 741.34, 765.55 741.27, 765.62 759.16, 717.78 759.37, 717.69 740.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,30,"POLYGON ((867.88 664.71, 887.54 697.77, 873.25 706.19, 853.57 673.13, 867.88 664.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,31,"POLYGON ((835.54 640.53, 835.89 660.68, 787.90 661.50, 787.53 641.38, 835.54 640.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,32,"POLYGON ((652.77 639.63, 668.09 639.46, 668.02 633.65, 680.72 633.52, 680.74 635.88, 700.98 635.65, 701.25 658.57, 680.71 658.81, 680.73 660.80, 663.12 661.01, 663.09 658.32, 652.97 658.45, 652.77 639.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,33,"POLYGON ((759.52 631.12, 760.31 659.74, 724.11 660.75, 724.02 657.64, 713.58 657.93, 713.11 640.65, 721.65 640.41, 721.42 632.16, 759.52 631.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,34,"POLYGON ((590.54 637.10, 606.41 636.70, 607.03 627.94, 619.33 627.43, 619.56 636.56, 628.33 635.92, 628.32 627.40, 639.38 627.34, 639.67 655.39, 590.85 657.25, 590.54 637.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,35,"POLYGON ((684.72 590.76, 685.31 600.64, 673.79 601.34, 673.21 591.45, 684.72 590.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,36,"POLYGON ((596.89 589.44, 604.58 589.42, 604.60 594.72, 596.91 594.74, 596.89 589.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,37,"POLYGON ((612.58 157.67, 613.37 176.43, 576.65 177.99, 575.84 159.25, 612.58 157.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,38,"POLYGON ((624.62 159.37, 639.28 159.61, 639.37 154.46, 655.43 154.76, 655.33 159.83, 666.24 160.04, 665.86 180.05, 633.52 179.47, 633.57 176.19, 624.32 176.00, 624.62 159.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,39,"POLYGON ((729.36 152.46, 733.42 171.78, 698.91 178.96, 694.85 159.67, 729.36 152.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,40,"POLYGON ((752.35 148.44, 765.36 148.04, 765.28 144.87, 773.39 144.64, 773.51 148.72, 782.98 148.46, 783.54 167.80, 775.36 168.05, 775.51 173.40, 763.78 173.74, 763.60 167.51, 752.91 167.83, 752.35 148.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,41,"POLYGON ((863.26 145.91, 864.44 167.54, 848.88 168.41, 847.70 146.75, 863.26 145.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,42,"POLYGON ((900.00 169.09, 884.00 169.11, 883.96 148.69, 887.94 148.68, 887.93 141.84, 900.00 141.83, 900.00 169.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,43,"POLYGON ((725.07 110.84, 731.74 130.52, 714.36 136.37, 707.67 116.70, 725.07 110.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,44,"POLYGON ((689.12 37.28, 713.30 36.45, 714.86 80.82, 685.98 81.82, 685.30 62.28, 690.00 62.12, 689.12 37.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,45,"POLYGON ((639.84 38.25, 650.20 33.83, 661.05 33.38, 661.81 52.36, 640.44 53.24, 639.84 38.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,46,"POLYGON ((775.40 19.20, 775.67 29.00, 779.97 28.87, 780.29 40.63, 758.52 41.22, 758.20 29.40, 753.69 29.51, 753.42 19.80, 775.40 19.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,47,"POLYGON ((896.20 865.19, 900.00 866.90, 900.00 890.17, 887.40 884.48, 896.20 865.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,48,"POLYGON ((634.70 858.02, 635.59 876.62, 626.30 877.08, 626.46 880.23, 603.14 881.36, 602.10 859.58, 634.70 858.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,49,"POLYGON ((661.48 860.23, 697.55 859.38, 697.99 878.19, 661.94 879.04, 661.48 860.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,50,"POLYGON ((725.29 857.85, 757.74 856.78, 757.95 863.08, 765.02 862.86, 765.48 876.48, 725.95 877.77, 725.29 857.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,51,"POLYGON ((779.62 858.51, 818.17 856.56, 823.01 860.65, 823.74 874.91, 780.56 877.11, 779.62 858.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,52,"POLYGON ((899.09 816.90, 900.00 816.85, 900.00 849.67, 896.03 849.90, 895.01 831.90, 899.91 831.62, 899.09 816.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,53,"POLYGON ((642.98 813.51, 644.02 823.05, 636.06 823.92, 635.02 814.35, 642.98 813.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,54,"POLYGON ((601.66 789.49, 601.64 801.39, 593.92 801.39, 593.93 789.51, 601.66 789.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,55,"POLYGON ((705.14 784.65, 705.61 795.07, 697.04 795.44, 696.57 785.04, 705.14 784.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,56,"POLYGON ((28.39 861.10, 47.31 871.23, 43.13 881.94, 33.61 900.00, 8.58 900.00, 0.00 895.54, 0.00 893.07, 8.80 873.78, 13.04 875.34, 20.04 862.86, 27.67 866.42, 28.39 861.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,57,"POLYGON ((356.43 857.68, 378.17 858.08, 382.35 861.83, 387.57 868.47, 391.20 873.60, 391.70 886.32, 356.79 887.21, 356.43 857.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,58,"POLYGON ((425.24 855.33, 464.57 854.69, 465.03 883.91, 418.67 884.66, 418.36 865.18, 425.40 865.07, 425.24 855.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,0,"POLYGON ((186.86 438.87, 200.96 436.91, 202.05 444.79, 205.03 444.38, 205.58 448.25, 205.10 454.52, 189.35 456.70, 186.86 438.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,1,"POLYGON ((517.58 377.80, 521.28 383.32, 528.77 386.37, 537.89 385.87, 538.23 392.45, 532.78 392.75, 530.60 394.87, 518.21 395.29, 517.58 377.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,2,"POLYGON ((401.81 375.96, 401.35 379.31, 404.02 387.12, 407.80 390.35, 401.91 394.01, 390.89 394.95, 390.65 392.01, 385.60 392.42, 384.78 382.81, 390.99 382.28, 390.52 376.92, 401.81 375.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,3,"POLYGON ((476.96 375.83, 493.88 374.98, 494.82 393.49, 477.92 394.36, 476.96 375.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,4,"POLYGON ((432.53 374.56, 452.62 374.41, 452.74 390.81, 446.59 390.85, 443.03 393.05, 437.89 394.71, 434.15 394.54, 432.63 390.32, 432.53 374.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,5,"POLYGON ((150.91 366.11, 173.58 365.00, 174.82 390.59, 156.44 391.47, 155.45 371.57, 151.19 371.78, 150.91 366.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,6,"POLYGON ((146.00 347.59, 167.85 346.41, 168.63 360.98, 142.33 362.40, 142.05 357.08, 146.50 356.85, 146.00 347.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,7,"POLYGON ((425.12 286.27, 453.17 286.31, 456.39 297.88, 453.61 303.79, 425.08 303.76, 425.12 286.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,8,"POLYGON ((468.38 286.35, 475.80 286.98, 479.54 302.31, 467.10 301.25, 468.38 286.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,9,"POLYGON ((546.88 283.42, 571.10 279.92, 574.68 304.38, 550.44 307.88, 546.88 283.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,10,"POLYGON ((384.89 285.96, 402.48 286.34, 403.57 279.23, 411.56 281.11, 410.92 288.63, 411.34 305.29, 401.34 307.21, 385.70 301.77, 384.89 285.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,11,"POLYGON ((511.25 275.80, 518.49 275.66, 518.67 284.87, 529.49 284.66, 529.86 302.74, 523.12 302.87, 511.69 298.17, 511.25 275.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,12,"POLYGON ((114.91 83.98, 153.64 94.70, 123.44 202.70, 110.59 245.20, 120.54 248.17, 125.82 230.74, 202.42 253.68, 191.85 288.58, 178.82 288.33, 66.55 257.31, 114.91 83.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,13,"POLYGON ((570.27 155.28, 574.09 163.73, 572.09 167.62, 574.73 175.10, 573.48 180.75, 552.48 179.17, 551.77 172.00, 555.28 164.36, 554.62 159.83, 553.47 156.17, 570.27 155.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,14,"POLYGON ((428.30 154.78, 440.62 155.16, 447.94 165.49, 445.63 171.88, 440.01 172.71, 430.49 172.26, 424.15 172.07, 424.10 155.93, 428.30 154.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,15,"POLYGON ((506.35 149.29, 534.48 147.53, 537.29 161.11, 537.94 172.68, 533.13 177.00, 520.03 174.54, 517.66 178.79, 510.22 176.89, 506.96 173.47, 502.98 169.35, 506.35 149.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,16,"POLYGON ((490.11 147.59, 494.60 172.39, 485.00 168.77, 476.76 149.33, 490.11 147.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,17,"POLYGON ((334.65 128.81, 340.40 140.74, 349.40 148.44, 339.14 157.04, 271.33 159.19, 262.52 158.15, 264.39 133.08, 334.65 128.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,18,"POLYGON ((432.96 64.69, 447.94 65.80, 454.68 76.77, 453.64 84.75, 427.98 84.40, 426.36 69.57, 432.96 64.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,19,"POLYGON ((536.58 55.62, 539.51 82.56, 532.06 83.00, 529.29 91.75, 509.82 90.00, 510.02 78.34, 518.90 75.41, 520.51 59.98, 528.50 60.78, 529.59 54.80, 536.58 55.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,20,"POLYGON ((550.62 59.22, 572.54 58.91, 573.43 64.59, 579.67 65.43, 582.24 68.34, 582.35 73.04, 580.83 81.76, 552.75 83.96, 550.62 59.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,21,"POLYGON ((495.16 52.46, 493.94 82.72, 481.76 83.54, 478.61 77.16, 467.32 74.48, 464.40 66.87, 468.80 53.86, 495.16 52.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,22,"POLYGON ((379.65 0.00, 379.12 20.14, 368.27 24.38, 372.63 39.63, 363.51 82.51, 351.39 80.84, 349.95 29.31, 352.59 0.00, 379.65 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,23,"POLYGON ((255.38 0.00, 256.23 57.26, 250.88 62.35, 244.32 78.88, 235.94 79.00, 235.77 65.46, 240.11 60.14, 245.55 49.34, 239.54 37.84, 241.57 29.11, 239.16 22.98, 227.58 23.16, 227.24 0.00, 255.38 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,24,"POLYGON ((819.46 372.63, 832.78 372.96, 836.68 377.88, 835.95 390.97, 828.63 390.58, 822.86 394.48, 819.00 391.25, 819.46 372.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,25,"POLYGON ((773.33 373.58, 794.43 372.05, 795.77 390.35, 774.67 391.88, 773.33 373.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,26,"POLYGON ((729.73 373.09, 750.22 371.68, 751.54 390.01, 724.34 391.99, 722.06 384.12, 729.73 373.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,27,"POLYGON ((625.34 369.49, 621.11 384.18, 616.26 391.40, 608.78 393.66, 603.64 388.79, 602.78 370.55, 625.34 369.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,28,"POLYGON ((708.82 379.82, 710.14 389.95, 688.67 390.50, 687.86 372.80, 693.48 372.48, 696.25 377.67, 701.43 380.36, 708.82 379.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,29,"POLYGON ((863.10 371.72, 878.87 372.08, 878.95 388.79, 859.43 390.33, 857.35 387.50, 856.90 384.13, 863.14 382.04, 866.12 379.39, 864.53 375.37, 863.10 371.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,30,"POLYGON ((740.49 285.67, 743.34 298.52, 740.53 303.18, 722.46 301.14, 722.15 289.07, 740.49 285.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,31,"POLYGON ((595.59 286.02, 602.21 283.77, 616.88 283.62, 617.10 303.84, 602.11 303.99, 593.96 300.96, 595.59 286.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,32,"POLYGON ((786.80 277.83, 794.58 276.57, 798.72 302.13, 791.83 310.63, 779.09 304.29, 763.22 302.98, 765.19 279.09, 781.83 280.45, 786.80 277.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,33,"POLYGON ((852.25 285.34, 871.91 285.17, 872.05 300.77, 852.39 300.96, 852.25 285.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,34,"POLYGON ((811.50 283.67, 829.24 283.84, 829.06 301.56, 811.32 301.38, 811.50 283.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,35,"POLYGON ((700.63 280.38, 700.19 302.37, 681.59 302.00, 681.70 296.33, 692.61 280.23, 700.63 280.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,36,"POLYGON ((634.31 276.69, 653.83 275.91, 654.34 284.15, 658.32 288.76, 657.00 295.58, 634.43 293.21, 634.31 276.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,37,"POLYGON ((784.29 216.43, 769.45 211.35, 769.29 205.16, 767.66 199.50, 771.86 197.90, 780.54 196.93, 798.90 193.49, 800.88 202.87, 799.59 211.32, 786.90 211.64, 784.29 216.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,38,"POLYGON ((654.58 197.42, 654.94 211.29, 645.68 209.55, 636.40 206.81, 632.52 200.96, 633.92 197.70, 654.58 197.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,39,"POLYGON ((678.98 194.14, 687.77 194.42, 687.13 213.71, 679.59 210.93, 678.51 207.98, 678.98 194.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,40,"POLYGON ((616.68 165.91, 621.30 171.74, 618.22 177.52, 607.49 177.04, 599.17 172.79, 599.75 164.72, 616.68 165.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,41,"POLYGON ((772.50 155.22, 793.43 155.93, 793.22 167.59, 793.28 179.49, 771.42 181.53, 772.50 155.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,42,"POLYGON ((871.55 152.46, 873.31 162.83, 873.47 169.02, 872.86 174.98, 871.34 183.94, 853.15 183.43, 853.62 172.01, 853.39 163.09, 854.63 153.38, 871.55 152.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,43,"POLYGON ((706.49 155.17, 707.49 175.23, 679.82 173.94, 677.40 167.07, 679.13 157.11, 685.07 155.47, 706.49 155.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,44,"POLYGON ((653.73 155.00, 662.93 156.88, 665.68 167.68, 663.01 173.70, 646.13 174.48, 645.98 168.87, 653.73 155.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,45,"POLYGON ((830.04 151.96, 836.96 169.63, 815.63 172.91, 806.55 168.92, 805.94 152.86, 830.04 151.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,46,"POLYGON ((750.19 152.90, 751.19 178.12, 743.96 171.64, 739.17 162.66, 734.50 159.61, 728.20 174.86, 726.94 152.81, 725.03 147.24, 745.45 146.72, 750.19 152.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,47,"POLYGON ((838.00 74.25, 864.07 74.63, 864.22 94.62, 845.55 94.39, 846.12 88.76, 839.78 88.58, 838.00 74.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,48,"POLYGON ((703.52 68.23, 704.15 82.60, 681.25 83.18, 678.94 70.83, 689.81 67.34, 703.52 68.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,49,"POLYGON ((637.46 65.69, 665.82 64.48, 668.17 78.05, 665.29 82.34, 637.90 83.28, 637.46 65.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,50,"POLYGON ((593.64 56.15, 617.04 55.80, 616.59 67.47, 622.80 67.06, 622.65 80.94, 615.47 82.61, 614.57 86.12, 594.09 83.91, 593.64 56.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,51,"POLYGON ((701.66 45.46, 710.13 45.95, 710.57 63.46, 700.70 63.02, 701.66 45.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,52,"POLYGON ((860.41 32.85, 862.79 56.57, 843.08 58.53, 838.46 56.21, 841.53 39.28, 860.41 32.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,53,"POLYGON ((859.09 0.00, 854.20 5.11, 849.29 5.23, 844.30 3.28, 840.14 0.00, 859.09 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,54,"POLYGON ((746.43 887.65, 764.71 892.12, 762.76 900.00, 743.38 900.00, 746.43 887.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,55,"POLYGON ((612.13 793.71, 646.49 796.08, 645.13 815.67, 610.77 813.32, 612.13 793.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,56,"POLYGON ((730.33 697.83, 759.18 700.25, 758.22 711.53, 753.68 711.13, 753.14 717.47, 728.85 715.45, 730.33 697.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,57,"POLYGON ((876.13 668.11, 886.80 658.38, 896.86 665.83, 898.19 670.17, 899.96 676.80, 897.85 677.19, 896.16 673.55, 894.06 673.44, 890.72 674.40, 883.99 679.12, 876.07 686.87, 871.40 682.94, 880.67 673.61, 876.13 668.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,58,"POLYGON ((724.82 649.73, 738.57 649.30, 738.99 662.14, 725.69 662.56, 725.53 657.82, 722.36 657.92, 722.15 651.29, 724.89 651.84, 724.82 649.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,59,"POLYGON ((686.03 641.91, 700.00 641.91, 703.70 648.27, 702.46 665.00, 691.63 667.85, 686.04 667.86, 686.03 641.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,60,"POLYGON ((754.71 625.02, 763.25 625.72, 763.94 629.67, 773.08 628.09, 775.36 641.23, 772.34 645.71, 761.47 646.38, 758.26 652.10, 752.45 653.72, 754.71 625.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,61,"POLYGON ((623.87 622.15, 640.79 620.04, 643.13 638.14, 640.69 641.13, 639.62 648.44, 636.95 650.19, 635.62 655.42, 619.47 657.45, 617.43 641.30, 623.87 640.49, 621.85 624.51, 623.87 622.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,62,"POLYGON ((811.21 612.93, 826.35 613.72, 828.28 619.58, 830.86 647.15, 812.13 648.89, 808.84 647.49, 808.78 613.55, 811.21 612.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,63,"POLYGON ((730.85 620.08, 732.59 630.87, 731.27 636.74, 732.05 644.67, 714.58 646.38, 711.45 614.73, 730.85 620.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,64,"POLYGON ((836.70 614.75, 858.95 615.51, 858.03 642.04, 839.50 641.40, 835.87 638.85, 836.70 614.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,65,"POLYGON ((613.97 513.33, 672.18 520.66, 667.87 554.47, 609.67 547.13, 613.97 513.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,66,"POLYGON ((766.45 467.32, 814.99 461.91, 823.68 539.52, 832.22 538.57, 835.36 566.55, 801.73 570.31, 798.61 542.46, 786.43 543.82, 781.67 501.27, 770.39 502.51, 766.45 467.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,67,"POLYGON ((648.01 371.42, 663.97 373.08, 667.57 382.57, 664.03 391.43, 663.41 400.19, 647.95 401.85, 643.54 393.62, 638.40 388.75, 636.55 381.70, 648.01 371.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,68,"POLYGON ((562.50 373.28, 578.56 374.85, 581.33 387.27, 580.65 394.15, 560.64 392.20, 562.50 373.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,69,"POLYGON ((438.75 881.95, 438.86 891.52, 440.12 891.49, 440.22 900.00, 422.01 900.00, 421.92 892.84, 420.08 892.86, 419.94 882.21, 438.75 881.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,70,"POLYGON ((538.90 886.00, 562.94 885.48, 563.05 890.47, 565.64 890.41, 565.79 897.66, 563.04 897.71, 563.09 900.00, 539.20 900.00, 538.90 886.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,71,"POLYGON ((492.90 875.80, 512.36 875.11, 512.58 881.36, 522.75 881.00, 523.41 900.00, 491.77 900.00, 491.44 890.80, 493.43 890.73, 492.90 875.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,72,"POLYGON ((315.66 836.16, 356.97 835.62, 357.30 861.02, 320.56 861.51, 320.39 848.27, 315.82 848.32, 315.66 836.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,73,"POLYGON ((384.80 849.23, 370.07 849.67, 369.70 837.40, 384.43 836.96, 384.80 849.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,74,"POLYGON ((401.82 854.52, 388.16 854.87, 387.46 827.34, 405.93 826.87, 406.25 839.83, 401.45 839.95, 401.82 854.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,75,"POLYGON ((320.66 791.14, 336.35 790.97, 336.75 825.48, 321.06 825.65, 320.66 791.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,76,"POLYGON ((420.98 768.77, 429.24 768.45, 433.03 777.33, 440.18 779.01, 441.10 802.09, 422.33 802.84, 420.98 768.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,77,"POLYGON ((490.01 754.97, 512.47 754.40, 512.79 766.26, 520.14 766.08, 520.62 784.76, 511.45 785.01, 511.53 788.21, 491.73 788.71, 491.38 775.91, 488.06 775.99, 487.89 769.52, 490.38 769.45, 490.01 754.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,78,"POLYGON ((564.87 755.93, 566.63 781.48, 558.94 776.52, 557.06 771.84, 554.14 768.08, 552.86 764.29, 554.52 759.68, 552.52 756.78, 564.87 755.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,79,"POLYGON ((425.27 704.63, 440.87 705.85, 444.05 713.21, 437.51 743.23, 426.81 740.92, 423.09 732.09, 425.27 704.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,80,"POLYGON ((330.05 700.83, 351.48 701.26, 352.58 725.05, 341.72 728.79, 328.99 717.70, 330.05 700.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,81,"POLYGON ((490.20 697.36, 505.15 696.63, 505.75 708.46, 518.12 707.86, 524.95 714.12, 515.55 724.29, 491.60 725.47, 490.20 697.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,82,"POLYGON ((187.56 691.90, 185.25 717.78, 168.72 716.31, 167.13 712.89, 169.15 690.26, 187.56 691.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,83,"POLYGON ((370.15 638.79, 385.11 638.30, 387.72 642.38, 392.00 645.63, 395.10 668.90, 391.54 677.31, 381.13 679.66, 371.37 674.92, 370.15 638.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,84,"POLYGON ((119.98 643.48, 121.43 672.07, 106.51 672.83, 105.06 644.23, 119.98 643.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,85,"POLYGON ((413.71 636.24, 420.37 635.18, 419.40 637.92, 430.29 638.28, 430.47 645.36, 424.97 651.11, 423.25 665.76, 420.50 664.99, 418.18 672.55, 410.81 671.07, 409.06 667.99, 404.85 666.65, 404.47 651.86, 411.86 646.03, 413.71 636.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,86,"POLYGON ((147.76 636.76, 153.26 637.61, 154.21 645.03, 160.42 644.62, 161.79 649.81, 159.18 655.07, 151.57 658.99, 143.87 660.16, 144.30 647.76, 145.45 644.01, 145.81 638.56, 147.76 636.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,87,"POLYGON ((487.22 632.37, 499.08 628.61, 505.16 632.41, 505.90 661.64, 500.58 667.73, 487.75 667.92, 487.22 632.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,88,"POLYGON ((249.27 545.39, 252.43 571.17, 244.09 572.62, 237.29 568.62, 235.05 546.17, 249.27 545.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,89,"POLYGON ((547.83 474.85, 550.16 484.80, 549.43 523.51, 513.01 522.84, 509.00 513.06, 502.78 515.60, 502.51 525.46, 455.20 524.15, 456.65 472.33, 547.83 474.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,90,"POLYGON ((196.58 486.90, 198.31 495.54, 201.97 502.37, 181.85 504.88, 183.65 496.64, 183.16 487.73, 196.58 486.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,0,"POLYGON ((597.93 392.55, 596.54 395.72, 599.01 402.23, 599.54 406.28, 598.11 407.87, 597.66 410.59, 595.12 413.58, 588.19 412.71, 588.05 411.36, 585.89 409.13, 582.96 408.63, 585.35 394.65, 590.43 395.50, 591.12 391.42, 597.93 392.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,1,"POLYGON ((837.33 356.26, 863.54 360.16, 860.40 380.42, 834.28 376.53, 837.33 356.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,2,"POLYGON ((755.88 354.11, 761.92 360.90, 759.43 373.67, 757.50 373.29, 756.20 370.97, 752.24 366.99, 750.10 366.42, 746.01 366.64, 743.29 368.08, 742.48 370.32, 742.91 372.66, 732.64 371.24, 735.46 351.26, 755.88 354.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,3,"POLYGON ((723.79 353.24, 723.40 356.25, 728.45 356.89, 726.80 369.57, 719.57 368.64, 719.05 372.67, 704.92 370.86, 701.93 369.31, 699.94 367.01, 695.43 366.26, 697.39 354.40, 699.67 356.41, 702.67 357.35, 705.91 357.13, 709.57 355.86, 713.49 351.93, 723.79 353.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,4,"POLYGON ((573.80 348.59, 586.33 349.01, 586.32 351.69, 587.10 353.65, 589.39 356.83, 588.07 374.23, 572.05 373.01, 572.87 362.18, 569.41 361.91, 569.88 355.69, 571.67 355.82, 573.82 349.81, 573.80 348.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,5,"POLYGON ((683.93 346.55, 683.21 351.83, 690.98 352.87, 688.30 372.79, 670.41 370.40, 676.09 361.13, 676.31 357.73, 675.46 353.78, 667.36 349.26, 668.00 344.42, 683.93 346.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,6,"POLYGON ((862.40 263.23, 863.39 283.90, 837.27 285.14, 836.16 261.73, 847.45 261.19, 847.60 263.94, 862.40 263.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,7,"POLYGON ((816.62 260.25, 817.75 263.24, 819.15 264.75, 818.70 267.70, 815.78 267.97, 814.21 268.54, 813.41 269.92, 822.28 280.64, 822.97 283.44, 807.17 283.84, 807.82 281.00, 807.12 277.47, 804.73 273.98, 800.98 271.28, 794.39 270.64, 794.71 267.15, 797.09 266.58, 799.58 265.05, 800.58 263.25, 800.46 261.01, 816.62 260.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,8,"POLYGON ((876.72 254.81, 900.00 253.54, 900.00 289.45, 878.67 290.61, 876.72 254.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,9,"POLYGON ((712.66 261.60, 732.58 262.16, 732.14 277.07, 712.22 276.51, 712.66 261.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,10,"POLYGON ((753.31 255.97, 749.56 264.48, 750.26 268.19, 753.09 271.23, 749.13 276.90, 749.21 279.27, 742.83 279.53, 741.99 258.17, 747.50 257.96, 747.43 256.19, 753.31 255.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,11,"POLYGON ((671.17 251.40, 674.60 253.91, 677.19 253.65, 677.87 259.91, 675.81 262.18, 677.53 266.25, 677.38 270.07, 677.26 271.98, 663.37 271.16, 663.16 274.71, 653.16 274.12, 654.03 259.54, 660.11 259.90, 660.59 251.76, 662.94 251.90, 663.14 248.25, 671.33 248.73, 671.17 251.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,12,"POLYGON ((597.81 235.91, 610.35 236.12, 610.20 245.49, 620.08 245.66, 619.70 268.69, 611.86 268.56, 611.78 274.07, 598.14 273.86, 598.25 267.64, 600.92 265.33, 601.59 262.72, 600.04 250.37, 601.45 246.85, 601.72 242.89, 601.15 240.04, 597.81 235.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,13,"POLYGON ((679.45 222.07, 679.12 230.42, 667.10 229.95, 667.40 221.60, 679.45 222.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,14,"POLYGON ((573.94 138.22, 608.36 131.62, 615.00 166.01, 580.58 172.62, 573.94 138.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,15,"POLYGON ((646.93 162.81, 644.26 119.23, 656.48 118.50, 656.90 125.59, 694.06 123.41, 730.46 121.95, 730.24 116.23, 741.15 115.82, 741.35 121.49, 752.93 121.11, 752.74 115.25, 764.49 114.91, 764.66 120.66, 800.57 119.70, 838.16 119.36, 838.13 113.51, 848.39 113.44, 848.64 155.97, 752.59 156.57, 744.83 156.83, 646.93 162.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,16,"POLYGON ((880.60 116.47, 900.00 119.72, 900.00 155.19, 874.75 150.95, 880.60 116.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,17,"POLYGON ((322.42 865.06, 326.29 880.32, 317.18 893.45, 277.30 893.91, 276.98 865.57, 322.42 865.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,18,"POLYGON ((16.81 793.69, 31.00 825.00, 21.36 841.09, 0.00 847.93, 0.00 803.31, 16.81 793.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,19,"POLYGON ((266.83 796.86, 371.00 805.89, 369.18 826.45, 265.01 817.40, 266.83 796.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,20,"POLYGON ((544.50 770.23, 543.66 785.43, 524.02 784.36, 524.44 776.78, 513.04 776.16, 513.62 766.02, 526.78 766.75, 525.63 762.43, 530.87 761.03, 539.20 769.94, 544.50 770.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,21,"POLYGON ((387.98 727.35, 409.65 729.88, 399.53 804.77, 378.89 801.98, 380.80 788.04, 387.98 727.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,22,"POLYGON ((250.53 732.83, 261.12 749.86, 227.00 770.86, 216.43 753.99, 250.53 732.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,23,"POLYGON ((338.23 644.04, 348.08 659.64, 331.98 672.94, 292.64 692.28, 259.67 708.98, 248.82 713.23, 240.42 696.09, 338.23 644.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,24,"POLYGON ((572.23 665.39, 583.95 662.23, 573.29 683.66, 557.88 683.16, 549.57 682.49, 552.77 668.26, 557.98 663.40, 572.23 665.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,25,"POLYGON ((514.70 651.36, 529.48 650.63, 531.26 665.30, 532.51 673.35, 533.38 679.99, 525.67 681.23, 521.41 679.58, 519.43 670.87, 516.73 661.83, 514.15 657.68, 514.70 651.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,26,"POLYGON ((486.87 650.47, 486.04 669.47, 457.72 668.24, 451.64 664.89, 452.35 648.97, 486.87 650.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,27,"POLYGON ((151.64 541.25, 151.53 584.38, 74.06 584.02, 73.87 563.49, 61.23 563.59, 62.63 721.40, 21.86 721.77, 21.23 540.34, 151.64 541.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,28,"POLYGON ((311.39 594.64, 312.00 616.67, 229.44 618.92, 228.83 595.78, 238.28 595.54, 238.22 593.44, 263.52 592.77, 263.58 595.21, 277.31 594.84, 277.25 592.24, 302.69 591.55, 302.78 594.88, 311.39 594.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,29,"POLYGON ((171.03 566.22, 189.55 565.75, 189.90 579.59, 171.38 580.06, 171.03 566.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,30,"POLYGON ((350.24 528.10, 350.71 551.91, 343.29 552.05, 343.36 555.60, 317.58 556.13, 317.50 552.27, 303.08 552.57, 303.13 555.47, 277.28 556.00, 277.19 552.69, 264.29 552.95, 264.38 557.79, 236.55 558.36, 236.48 554.68, 227.36 554.87, 226.86 530.64, 350.24 528.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,31,"POLYGON ((379.20 493.36, 382.53 496.38, 388.35 498.15, 395.02 498.27, 401.34 496.64, 408.58 489.69, 409.21 485.32, 425.44 487.66, 422.50 507.80, 433.59 509.41, 427.27 553.01, 414.28 551.14, 413.49 549.41, 409.71 545.95, 405.57 546.79, 401.68 550.88, 401.81 561.93, 394.87 568.44, 371.18 565.73, 373.84 542.55, 384.10 543.71, 384.37 541.35, 403.69 543.57, 407.26 512.94, 385.89 510.48, 386.44 505.74, 377.87 504.76, 379.20 493.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,32,"POLYGON ((334.73 482.57, 333.95 486.19, 335.36 487.88, 352.45 493.66, 357.10 493.46, 357.67 506.94, 313.64 508.74, 312.69 485.91, 322.84 485.47, 322.74 483.05, 334.73 482.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,33,"POLYGON ((296.58 414.64, 297.01 424.82, 298.70 424.73, 301.36 488.59, 297.98 488.72, 298.40 498.77, 276.79 499.65, 273.28 415.61, 296.58 414.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,34,"POLYGON ((455.44 437.97, 462.31 444.79, 462.79 455.61, 461.60 458.15, 402.57 455.68, 402.56 452.41, 402.52 447.86, 402.36 444.34, 402.09 440.59, 397.72 437.46, 394.30 437.31, 394.49 433.51, 401.20 433.82, 401.29 432.16, 419.06 432.99, 418.93 435.90, 436.17 436.71, 436.30 433.82, 453.44 434.63, 453.28 437.87, 455.44 437.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,35,"POLYGON ((251.94 378.21, 252.33 394.28, 255.04 394.21, 255.31 405.72, 253.12 405.78, 254.88 475.63, 256.66 475.59, 256.91 485.21, 254.83 485.27, 255.32 503.37, 231.51 504.02, 231.25 495.23, 228.90 495.31, 228.15 468.96, 231.01 468.89, 230.57 452.96, 236.53 452.81, 240.14 449.32, 245.76 431.78, 235.39 401.99, 226.16 402.20, 225.81 386.74, 228.63 386.69, 228.45 378.77, 251.94 378.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,36,"POLYGON ((442.79 387.66, 446.46 393.01, 447.38 395.09, 449.98 399.93, 452.72 404.06, 451.45 409.71, 454.40 414.54, 444.71 414.07, 444.79 403.39, 442.32 403.46, 442.79 387.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,37,"POLYGON ((367.36 374.66, 368.05 397.97, 358.10 398.25, 358.19 401.00, 293.46 402.86, 293.38 400.27, 285.25 400.50, 284.58 377.05, 367.36 374.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,38,"POLYGON ((425.54 340.15, 423.75 355.29, 425.02 355.44, 423.20 370.93, 421.58 370.75, 420.80 377.37, 400.80 375.03, 405.20 337.78, 425.54 340.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,39,"POLYGON ((529.88 340.56, 529.87 348.91, 524.12 357.98, 512.20 359.28, 507.43 357.16, 500.27 350.42, 500.18 346.69, 511.81 340.98, 511.81 334.48, 519.90 334.47, 519.89 340.55, 529.88 340.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,40,"POLYGON ((263.66 328.66, 264.02 334.75, 265.86 338.42, 268.04 340.96, 271.58 342.62, 274.71 343.03, 275.28 346.61, 272.29 350.90, 272.56 355.54, 217.15 358.81, 215.72 334.87, 222.01 334.51, 221.76 330.41, 247.37 328.90, 247.76 335.50, 254.80 335.08, 254.45 329.20, 263.66 328.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,41,"POLYGON ((367.48 333.84, 367.86 355.54, 300.48 356.69, 300.06 333.06, 305.74 332.96, 305.67 328.72, 329.73 328.31, 329.81 332.93, 336.59 332.80, 336.49 327.54, 362.09 327.11, 362.21 333.93, 367.48 333.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,42,"POLYGON ((549.46 244.88, 549.47 259.31, 542.71 259.33, 542.70 264.83, 528.65 264.84, 528.66 258.64, 523.23 258.65, 523.23 252.61, 526.89 252.61, 526.89 244.88, 549.46 244.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,43,"POLYGON ((306.51 222.11, 339.73 220.62, 342.33 278.94, 309.11 280.40, 306.51 222.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,44,"POLYGON ((481.99 174.06, 496.56 213.71, 448.00 231.39, 433.43 191.74, 481.99 174.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,45,"POLYGON ((499.64 164.60, 530.07 158.56, 536.85 192.44, 506.40 198.48, 499.64 164.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,46,"POLYGON ((569.41 144.33, 575.61 177.83, 542.89 183.83, 536.68 150.36, 569.41 144.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,47,"POLYGON ((255.19 65.81, 270.49 91.37, 144.02 166.44, 128.71 140.88, 255.19 65.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,48,"POLYGON ((10.99 82.98, 48.27 86.18, 47.00 104.79, 42.84 107.72, 37.17 106.11, 10.73 105.71, 10.99 82.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,49,"POLYGON ((276.59 89.00, 268.38 25.81, 366.81 13.12, 370.07 38.08, 296.19 47.61, 301.16 85.84, 276.59 89.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,50,"POLYGON ((187.74 32.87, 187.81 37.93, 190.34 37.89, 190.70 59.55, 168.31 59.92, 168.17 51.37, 161.64 51.47, 161.32 33.30, 187.74 32.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,51,"POLYGON ((373.39 10.89, 459.96 0.00, 470.20 0.00, 473.59 26.86, 376.94 38.99, 373.39 10.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,52,"POLYGON ((535.28 0.00, 489.59 8.85, 487.86 0.00, 535.28 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,53,"POLYGON ((836.30 879.28, 860.23 878.32, 859.14 890.98, 859.36 899.72, 859.09 900.00, 840.14 900.00, 837.83 898.18, 836.30 879.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,54,"POLYGON ((793.87 877.65, 793.37 892.85, 777.46 892.34, 773.89 877.01, 793.87 877.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,55,"POLYGON ((842.05 814.99, 848.42 816.22, 848.62 824.27, 851.28 831.91, 846.89 838.70, 843.10 842.28, 831.50 843.29, 826.62 831.14, 829.21 819.62, 842.95 822.67, 842.05 814.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,56,"POLYGON ((711.56 771.64, 714.24 769.58, 718.24 769.74, 721.85 774.60, 726.17 777.71, 726.16 787.88, 729.27 792.50, 727.90 805.75, 711.89 804.11, 709.53 799.95, 708.31 790.55, 701.04 789.42, 704.03 770.46, 711.56 771.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,57,"POLYGON ((634.89 779.43, 638.45 794.41, 606.35 792.76, 605.07 784.05, 609.23 781.84, 626.22 784.56, 627.09 777.16, 634.89 779.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,58,"POLYGON ((847.77 756.42, 850.36 779.64, 845.55 785.95, 827.37 784.61, 828.97 763.00, 842.26 763.97, 839.34 757.36, 847.77 756.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,59,"POLYGON ((874.21 683.78, 871.37 703.85, 860.22 701.85, 845.81 702.92, 846.21 690.64, 846.99 680.45, 874.21 683.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,60,"POLYGON ((833.17 681.03, 831.76 699.46, 804.56 697.43, 805.95 679.01, 833.17 681.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,61,"POLYGON ((770.37 675.41, 774.89 675.76, 789.48 676.48, 792.57 680.08, 786.30 690.23, 786.76 694.26, 785.38 695.69, 760.44 693.18, 762.36 674.57, 770.37 675.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,62,"POLYGON ((747.16 667.74, 747.11 692.80, 741.13 693.49, 720.12 691.03, 718.65 688.98, 722.93 683.96, 737.05 684.82, 738.52 680.57, 741.23 676.30, 740.26 673.18, 739.99 668.97, 747.16 667.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,63,"POLYGON ((622.31 674.60, 621.94 687.57, 593.15 684.62, 593.22 673.41, 622.31 674.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,64,"POLYGON ((666.76 670.30, 666.84 687.30, 657.38 689.47, 638.69 688.90, 634.71 684.45, 634.39 679.38, 636.20 673.54, 639.98 670.47, 666.76 670.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,65,"POLYGON ((677.64 668.80, 682.35 660.44, 701.18 659.61, 709.51 661.68, 709.80 672.89, 705.92 673.16, 703.00 675.88, 703.72 683.56, 699.44 688.58, 691.57 690.71, 683.72 686.53, 676.91 681.80, 677.64 668.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,66,"POLYGON ((826.70 623.83, 830.65 672.06, 810.40 668.62, 805.83 665.52, 808.02 623.80, 826.70 623.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,67,"POLYGON ((757.09 619.14, 758.36 630.52, 761.75 636.14, 760.61 640.14, 736.64 638.02, 736.48 631.83, 737.39 618.91, 757.09 619.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,68,"POLYGON ((872.41 510.24, 872.02 519.04, 872.76 523.99, 874.10 527.91, 875.95 531.70, 877.76 534.39, 878.85 537.71, 866.52 537.89, 866.45 510.39, 872.41 510.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,69,"POLYGON ((899.81 471.94, 898.51 498.63, 870.09 497.25, 871.40 470.57, 899.81 471.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,70,"POLYGON ((900.00 457.18, 893.08 456.84, 893.50 448.59, 887.83 448.31, 888.75 429.80, 900.00 430.35, 900.00 457.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,0,"POLYGON ((330.28 263.96, 341.66 270.38, 358.21 282.23, 349.26 294.16, 348.33 301.15, 319.32 284.33, 330.28 263.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,1,"POLYGON ((185.79 274.78, 202.15 267.37, 210.54 285.70, 194.20 293.13, 185.79 274.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,2,"POLYGON ((170.86 241.80, 180.52 236.27, 187.91 250.87, 193.35 255.74, 181.13 260.23, 170.86 241.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,3,"POLYGON ((75.16 211.51, 88.02 231.72, 71.94 241.87, 59.08 221.64, 75.16 211.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,4,"POLYGON ((153.82 206.91, 166.45 199.25, 177.73 217.65, 165.09 225.32, 153.82 206.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,5,"POLYGON ((501.05 188.35, 515.45 188.65, 522.51 201.23, 522.08 220.02, 499.83 220.14, 501.05 188.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,6,"POLYGON ((393.05 183.03, 426.72 190.68, 423.12 208.46, 389.24 201.49, 393.05 183.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,7,"POLYGON ((46.11 188.23, 57.60 183.03, 58.47 189.67, 62.10 194.48, 67.25 202.76, 57.52 207.92, 52.09 202.44, 55.82 196.40, 52.83 189.81, 46.11 188.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,8,"POLYGON ((351.73 150.96, 370.87 144.09, 381.99 174.79, 362.84 181.67, 351.73 150.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,9,"POLYGON ((503.15 129.25, 522.48 129.42, 521.27 169.94, 497.64 168.97, 497.79 166.05, 503.96 161.21, 507.20 156.20, 506.35 149.08, 502.62 144.24, 503.15 129.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,10,"POLYGON ((137.36 134.37, 142.50 143.01, 148.42 140.06, 153.17 147.29, 130.26 159.79, 119.65 143.94, 137.36 134.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,11,"POLYGON ((38.24 138.93, 31.91 143.26, 21.16 138.81, 12.50 140.14, 4.95 140.33, 1.94 132.32, 16.64 126.09, 18.38 128.84, 31.12 123.22, 38.24 138.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,12,"POLYGON ((120.68 99.92, 132.04 120.87, 115.61 129.70, 104.25 108.75, 120.68 99.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,13,"POLYGON ((501.82 76.92, 520.47 77.13, 520.80 116.70, 505.04 115.33, 505.13 110.17, 502.01 101.97, 501.82 76.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,14,"POLYGON ((342.80 77.70, 362.22 77.16, 363.25 114.71, 343.82 115.25, 342.80 77.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,15,"POLYGON ((98.96 80.23, 111.19 76.30, 110.69 80.13, 119.45 87.28, 123.52 86.06, 125.61 92.98, 104.22 99.38, 98.71 101.14, 95.20 90.31, 101.58 88.29, 98.96 80.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,16,"POLYGON ((4.55 25.18, 28.65 25.94, 28.68 37.95, 26.84 43.01, 28.97 49.37, 28.15 61.09, 31.24 61.30, 31.17 69.67, 14.08 70.11, 15.00 61.72, 20.02 61.03, 20.18 45.40, 6.16 44.65, 11.15 42.28, 15.21 37.16, 10.55 30.31, 4.55 25.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,17,"POLYGON ((502.81 23.95, 524.35 23.74, 524.63 52.47, 506.14 52.66, 503.01 44.01, 502.81 23.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,18,"POLYGON ((92.51 18.59, 96.37 33.88, 83.70 34.20, 83.84 30.47, 88.80 29.61, 92.43 25.54, 92.51 18.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,19,"POLYGON ((75.51 6.64, 69.59 8.76, 66.43 11.82, 64.78 15.59, 61.31 16.41, 61.60 7.99, 75.51 6.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,20,"POLYGON ((524.06 0.00, 524.01 6.92, 502.64 6.77, 502.69 0.00, 524.06 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,21,"POLYGON ((264.10 0.00, 264.88 5.45, 243.04 6.00, 241.88 0.00, 264.10 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,22,"POLYGON ((601.25 292.75, 625.52 292.97, 625.17 328.66, 600.87 328.41, 601.25 292.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,23,"POLYGON ((761.57 323.43, 760.49 285.95, 779.79 285.39, 780.87 322.88, 761.57 323.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,24,"POLYGON ((600.87 240.88, 620.12 241.08, 619.74 274.63, 600.52 274.43, 600.87 240.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,25,"POLYGON ((761.34 210.03, 763.09 270.48, 749.74 270.86, 749.26 254.18, 739.17 254.48, 737.91 210.72, 761.34 210.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,26,"POLYGON ((600.77 194.98, 620.12 195.15, 619.79 231.06, 600.43 230.88, 600.77 194.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,27,"POLYGON ((861.15 164.65, 876.99 164.85, 876.83 176.79, 885.47 176.91, 885.09 206.62, 863.58 206.34, 863.89 184.09, 861.01 173.93, 861.15 164.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,28,"POLYGON ((603.38 147.90, 622.15 147.84, 622.26 184.95, 603.50 185.03, 603.38 147.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,29,"POLYGON ((769.55 147.11, 771.12 175.22, 765.72 183.46, 761.22 183.29, 757.01 171.69, 751.59 167.37, 751.91 147.27, 769.55 147.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,30,"POLYGON ((758.48 102.36, 777.64 101.71, 778.84 137.93, 759.67 138.55, 758.48 102.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,31,"POLYGON ((600.34 97.70, 619.66 97.65, 613.75 103.84, 610.39 113.31, 614.77 126.63, 617.93 135.50, 619.43 141.94, 600.31 141.10, 600.34 97.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,32,"POLYGON ((875.17 112.02, 872.64 66.84, 891.09 65.82, 893.64 110.99, 875.17 112.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,33,"POLYGON ((599.99 57.66, 610.34 66.79, 613.04 76.11, 612.87 86.64, 613.24 92.66, 600.65 92.54, 599.99 57.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,34,"POLYGON ((744.61 30.43, 745.10 38.59, 741.98 38.76, 742.93 54.63, 727.76 55.52, 727.01 42.82, 723.25 43.05, 722.57 31.73, 744.61 30.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,35,"POLYGON ((861.71 18.73, 881.86 18.84, 881.70 45.46, 861.55 45.35, 861.71 18.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,36,"POLYGON ((600.48 13.61, 618.35 13.64, 618.29 50.09, 600.42 50.08, 600.48 13.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,37,"POLYGON ((154.29 776.98, 178.57 776.59, 179.02 804.35, 173.19 804.45, 173.05 796.11, 170.22 789.34, 164.68 788.08, 161.57 801.66, 163.75 806.51, 160.83 807.80, 157.18 807.87, 157.05 800.00, 154.67 800.04, 154.29 776.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,38,"POLYGON ((508.74 761.41, 520.41 751.45, 533.02 766.09, 521.35 776.05, 508.74 761.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,39,"POLYGON ((276.85 738.33, 277.10 748.16, 264.43 748.48, 262.11 747.67, 259.12 747.90, 255.69 751.85, 255.48 757.12, 252.90 759.47, 252.38 738.95, 276.85 738.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,40,"POLYGON ((70.84 735.97, 73.09 760.02, 57.62 761.46, 60.52 750.86, 54.07 746.83, 49.52 747.48, 46.79 743.86, 46.26 738.26, 70.84 735.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,41,"POLYGON ((172.21 734.57, 173.23 754.28, 157.08 755.11, 156.04 735.41, 172.21 734.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,42,"POLYGON ((292.05 738.37, 282.35 738.32, 282.40 731.31, 292.09 731.35, 292.05 738.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,43,"POLYGON ((170.75 691.04, 171.56 711.68, 145.44 712.70, 148.60 708.49, 143.40 708.62, 140.79 698.68, 143.37 695.79, 141.96 692.15, 170.75 691.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,44,"POLYGON ((372.39 658.46, 372.65 676.05, 347.13 676.43, 346.87 658.84, 372.39 658.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,45,"POLYGON ((244.35 655.94, 254.68 646.26, 266.71 659.01, 268.76 657.09, 281.96 671.10, 264.53 687.39, 253.92 676.12, 258.94 671.42, 244.35 655.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,46,"POLYGON ((58.39 653.62, 80.27 656.09, 79.33 664.39, 75.92 664.01, 74.53 676.34, 56.03 674.26, 58.39 653.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,47,"POLYGON ((332.46 675.12, 316.72 675.41, 316.80 679.78, 313.58 680.57, 311.43 671.70, 306.57 672.87, 302.46 670.33, 298.21 668.86, 298.04 659.12, 318.60 658.76, 318.41 647.62, 331.96 647.36, 332.46 675.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,48,"POLYGON ((407.17 653.29, 407.55 671.77, 392.49 672.08, 392.12 653.60, 407.17 653.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,49,"POLYGON ((168.94 675.03, 155.84 675.39, 155.11 649.66, 168.23 649.28, 168.94 675.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,50,"POLYGON ((461.80 674.39, 434.02 675.18, 433.24 648.23, 456.60 647.57, 457.25 669.91, 461.66 669.77, 461.80 674.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,51,"POLYGON ((512.83 672.40, 502.01 662.49, 505.86 658.30, 500.96 653.83, 512.38 641.47, 512.06 645.65, 513.40 648.86, 515.87 651.59, 520.00 653.48, 525.17 654.60, 529.77 654.06, 512.83 672.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,52,"POLYGON ((453.05 630.55, 464.45 630.26, 464.86 646.69, 453.47 646.98, 453.05 630.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,53,"POLYGON ((59.43 611.33, 61.00 615.27, 67.94 620.11, 70.92 620.61, 74.23 623.19, 75.95 632.29, 58.40 631.40, 59.43 611.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,54,"POLYGON ((494.18 623.66, 493.91 611.93, 507.28 611.61, 507.62 626.47, 501.62 626.60, 501.56 623.50, 494.18 623.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,55,"POLYGON ((164.85 626.17, 159.33 626.64, 155.02 623.60, 155.11 618.27, 150.99 618.22, 151.06 613.13, 151.04 608.61, 150.94 602.48, 153.43 602.42, 159.30 604.73, 166.01 605.79, 169.09 608.50, 165.22 609.13, 162.34 613.60, 164.24 618.97, 164.85 626.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,56,"POLYGON ((255.73 622.24, 243.43 622.73, 242.74 606.10, 247.73 604.11, 250.75 599.64, 254.80 599.49, 255.73 622.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,57,"POLYGON ((529.18 594.45, 529.35 624.59, 515.61 624.67, 515.51 606.92, 511.74 606.95, 510.60 602.98, 510.79 598.71, 513.98 596.28, 516.18 595.33, 519.15 595.84, 520.16 594.50, 529.18 594.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,58,"POLYGON ((140.38 582.22, 151.80 581.64, 152.46 594.52, 141.04 595.10, 140.38 582.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,59,"POLYGON ((258.91 541.25, 268.58 550.22, 248.79 571.38, 239.11 562.42, 258.91 541.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,60,"POLYGON ((373.31 546.89, 373.72 555.07, 361.77 555.66, 353.69 566.92, 345.98 566.90, 346.00 561.06, 338.74 561.04, 338.76 552.50, 341.66 551.18, 347.63 542.88, 349.21 547.24, 348.86 550.35, 352.99 551.82, 358.43 547.62, 373.31 546.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,61,"POLYGON ((325.09 539.97, 324.85 553.83, 311.73 553.60, 311.59 561.20, 304.50 561.07, 304.64 553.47, 303.24 547.45, 300.67 543.50, 306.61 539.66, 325.09 539.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,62,"POLYGON ((390.83 539.54, 415.47 538.67, 416.03 554.13, 391.39 555.02, 390.83 539.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,63,"POLYGON ((459.29 538.44, 459.75 552.20, 428.41 553.26, 427.95 539.51, 459.29 538.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,64,"POLYGON ((169.17 521.91, 168.96 540.83, 166.38 540.80, 162.94 537.78, 159.46 537.49, 157.43 535.55, 153.65 539.44, 150.20 536.13, 150.04 534.03, 152.35 532.48, 154.12 528.22, 156.62 527.04, 155.38 524.46, 155.43 521.77, 169.17 521.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,65,"POLYGON ((70.72 503.10, 89.10 501.52, 89.31 503.94, 87.17 506.65, 84.19 506.13, 81.14 508.27, 77.58 508.36, 75.26 509.91, 71.33 510.23, 70.72 503.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,66,"POLYGON ((38.67 489.95, 55.05 487.65, 58.25 510.10, 39.20 512.80, 37.62 501.79, 40.30 501.41, 38.67 489.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,67,"POLYGON ((228.40 468.13, 227.96 442.24, 250.59 441.86, 250.88 458.52, 258.79 458.39, 258.87 463.29, 262.33 463.23, 262.58 478.45, 255.81 478.58, 255.68 471.19, 250.03 471.29, 249.96 467.76, 228.40 468.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,68,"POLYGON ((167.05 448.53, 168.53 470.36, 147.40 471.81, 145.91 449.96, 167.05 448.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,69,"POLYGON ((300.81 452.02, 331.44 451.37, 330.19 455.75, 332.25 458.41, 336.77 460.16, 336.92 466.70, 301.15 467.46, 300.81 452.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,70,"POLYGON ((372.18 451.98, 391.86 451.19, 392.19 459.04, 396.53 458.86, 396.80 465.98, 372.78 466.95, 372.18 451.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,71,"POLYGON ((25.02 450.41, 40.33 447.13, 44.60 466.76, 26.32 470.69, 24.32 461.48, 27.29 460.83, 25.02 450.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,72,"POLYGON ((415.31 445.93, 434.97 445.94, 434.95 450.36, 438.72 450.35, 438.72 462.50, 442.32 462.49, 442.32 467.04, 415.29 467.02, 415.31 445.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,73,"POLYGON ((497.81 453.87, 521.68 429.64, 534.40 442.06, 510.53 466.29, 497.81 453.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,74,"POLYGON ((390.52 423.28, 391.53 441.83, 377.75 442.58, 376.72 424.03, 390.52 423.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,75,"POLYGON ((143.13 415.71, 150.61 414.08, 150.00 411.34, 154.34 410.41, 155.10 413.94, 159.26 413.04, 163.58 432.84, 155.85 434.52, 150.66 428.59, 149.09 425.10, 145.08 424.61, 143.13 415.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,76,"POLYGON ((292.29 425.80, 291.91 415.62, 295.11 415.49, 297.14 417.17, 301.05 418.29, 298.97 425.56, 292.29 425.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,77,"POLYGON ((30.08 406.82, 36.66 428.51, 20.95 433.26, 20.16 430.66, 13.87 432.56, 8.06 413.44, 30.08 406.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,78,"POLYGON ((132.48 379.40, 150.39 374.11, 157.03 396.40, 139.10 401.70, 132.48 379.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,79,"POLYGON ((14.88 376.50, 19.14 375.37, 24.50 393.37, 16.86 396.08, 15.50 395.22, 9.77 396.70, 5.18 379.12, 14.88 376.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,80,"POLYGON ((506.79 374.11, 528.09 374.12, 529.19 384.41, 515.18 384.77, 513.61 388.98, 507.43 388.32, 506.79 374.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,81,"POLYGON ((115.58 348.62, 140.12 341.18, 145.77 359.66, 121.25 367.10, 115.58 348.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,82,"POLYGON ((0.00 341.09, 13.55 337.14, 20.11 359.35, 0.72 365.01, 0.00 362.58, 0.00 341.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,83,"POLYGON ((124.54 307.44, 134.28 326.81, 115.71 336.07, 105.97 316.70, 124.54 307.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,84,"POLYGON ((444.64 293.00, 459.27 292.81, 459.35 298.77, 489.19 298.39, 489.39 313.31, 444.89 313.86, 444.64 293.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,85,"POLYGON ((497.56 295.25, 504.75 295.20, 504.72 291.34, 521.73 291.22, 521.75 294.92, 535.51 294.82, 535.61 312.71, 497.68 312.96, 497.56 295.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,86,"POLYGON ((387.84 287.16, 428.81 289.83, 427.57 308.68, 386.59 306.02, 387.84 287.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,87,"POLYGON ((107.54 307.85, 97.59 291.07, 105.19 286.97, 113.35 282.86, 122.75 299.65, 107.54 307.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,88,"POLYGON ((779.26 894.06, 795.32 894.17, 795.28 900.00, 779.21 900.00, 779.26 894.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,89,"POLYGON ((886.53 895.98, 869.59 896.41, 868.86 867.86, 881.81 867.53, 881.99 874.45, 885.98 874.35, 886.53 895.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,90,"POLYGON ((797.98 842.38, 796.60 868.58, 777.88 867.59, 778.24 860.68, 785.90 848.59, 786.52 843.87, 784.48 841.68, 797.98 842.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,91,"POLYGON ((875.44 841.10, 873.90 809.06, 893.04 808.15, 894.58 840.19, 875.44 841.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,92,"POLYGON ((740.30 786.77, 740.68 795.16, 743.52 799.26, 744.92 804.84, 745.30 824.92, 721.84 825.38, 721.40 802.95, 715.39 803.06, 715.31 798.44, 706.60 798.60, 706.48 792.47, 709.56 790.11, 717.26 787.83, 740.30 786.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,93,"POLYGON ((606.97 784.44, 620.16 784.12, 624.24 788.19, 625.66 794.39, 629.76 799.31, 630.03 810.60, 607.61 811.15, 606.97 784.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,94,"POLYGON ((875.66 788.30, 874.81 758.11, 893.74 757.59, 894.58 787.78, 875.66 788.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,95,"POLYGON ((605.28 750.32, 612.65 749.98, 612.96 756.63, 620.98 756.25, 620.81 752.63, 626.89 752.34, 627.03 754.96, 636.46 754.52, 637.41 774.32, 606.49 775.79, 605.28 750.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,96,"POLYGON ((796.34 746.28, 796.13 757.25, 797.95 757.27, 797.85 762.16, 795.31 762.11, 795.10 771.37, 791.51 771.29, 791.41 776.04, 784.96 775.89, 785.08 770.90, 782.66 766.56, 778.75 764.40, 779.10 745.96, 796.34 746.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,97,"POLYGON ((874.24 710.60, 891.89 710.13, 892.06 716.37, 897.09 716.24, 897.78 740.99, 875.10 741.61, 874.24 710.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,98,"POLYGON ((782.14 702.82, 798.60 702.41, 799.45 735.77, 783.01 736.19, 782.14 702.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,99,"POLYGON ((900.00 678.53, 891.55 678.73, 891.73 685.92, 872.86 686.38, 872.31 664.13, 877.85 660.48, 884.02 660.65, 888.52 657.21, 891.74 652.76, 900.00 652.55, 900.00 678.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,100,"POLYGON ((872.14 604.15, 890.44 603.73, 890.65 613.45, 900.00 613.25, 900.00 625.75, 892.47 625.92, 892.65 633.75, 872.81 634.21, 872.14 604.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,101,"POLYGON ((739.82 613.80, 754.00 606.20, 753.71 601.50, 762.74 600.96, 763.97 602.58, 760.28 608.55, 767.51 608.21, 764.13 620.55, 765.95 627.72, 740.13 628.29, 739.82 613.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,102,"POLYGON ((870.49 548.77, 894.91 548.06, 895.71 576.41, 887.90 576.63, 888.04 581.66, 871.43 582.13, 870.49 548.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,103,"POLYGON ((732.16 532.51, 749.78 532.06, 747.78 536.66, 748.02 546.31, 745.34 551.97, 744.09 558.15, 746.98 561.05, 744.66 566.88, 741.88 561.01, 738.61 557.23, 736.11 541.53, 733.71 537.91, 732.16 532.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,104,"POLYGON ((599.79 487.35, 607.67 487.19, 607.79 493.92, 615.90 493.78, 616.29 515.10, 600.30 515.39, 599.79 487.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,105,"POLYGON ((787.79 443.03, 787.76 455.80, 791.90 455.80, 791.88 468.83, 784.47 468.82, 784.44 482.99, 777.92 482.97, 772.89 478.53, 767.66 467.27, 758.39 467.09, 758.76 449.45, 764.72 453.88, 768.78 448.49, 774.95 448.51, 774.96 443.01, 787.79 443.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,106,"POLYGON ((599.12 439.95, 615.10 439.43, 616.07 469.60, 600.09 470.12, 599.12 439.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,107,"POLYGON ((645.57 443.32, 663.83 443.61, 663.68 453.52, 645.42 453.25, 645.57 443.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,108,"POLYGON ((659.97 421.47, 669.80 421.28, 670.02 432.00, 660.18 432.21, 659.97 421.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,109,"POLYGON ((598.77 401.16, 607.48 400.58, 607.98 407.92, 615.25 407.44, 616.66 428.76, 600.68 429.81, 598.77 401.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,110,"POLYGON ((785.32 399.21, 785.90 406.10, 789.07 405.84, 790.22 419.55, 787.07 419.81, 787.44 424.42, 766.71 426.12, 764.62 400.91, 785.32 399.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,111,"POLYGON ((864.91 400.89, 885.07 399.83, 884.90 396.95, 891.82 396.57, 892.00 400.14, 900.00 399.71, 900.00 415.16, 865.77 416.99, 864.91 400.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,112,"POLYGON ((790.99 384.97, 772.50 387.28, 767.51 347.48, 786.01 345.19, 790.99 384.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,113,"POLYGON ((603.34 385.39, 601.38 341.29, 622.68 340.35, 624.63 384.45, 603.34 385.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,114,"POLYGON ((857.68 323.87, 857.64 309.36, 869.17 309.29, 871.42 307.76, 895.15 307.72, 895.19 323.79, 857.68 323.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,115,"POLYGON ((95.58 900.00, 90.47 891.90, 87.59 882.34, 91.05 872.95, 108.90 869.48, 112.29 886.77, 110.13 887.18, 110.96 891.38, 115.75 890.46, 117.62 900.00, 95.58 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,116,"POLYGON ((263.21 848.05, 263.84 862.55, 274.21 862.11, 274.69 873.30, 263.45 873.79, 263.54 875.92, 249.78 876.51, 249.43 868.60, 241.03 868.96, 240.53 857.57, 247.79 857.27, 247.42 848.73, 263.21 848.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,117,"POLYGON ((89.22 824.96, 94.41 842.19, 92.62 842.72, 96.29 854.93, 83.78 858.66, 81.86 856.29, 83.41 853.08, 80.68 847.78, 77.51 848.08, 73.38 846.41, 68.78 831.07, 89.22 824.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,118,"POLYGON ((512.14 812.66, 533.37 812.63, 533.39 825.04, 537.31 825.03, 537.32 838.68, 524.64 838.72, 524.63 834.21, 512.17 834.24, 512.14 812.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,119,"POLYGON ((267.67 824.69, 251.64 825.61, 256.95 812.33, 255.14 803.97, 258.25 801.87, 251.69 792.31, 266.80 791.44, 267.26 799.40, 271.99 799.12, 272.60 809.72, 266.81 810.04, 267.67 824.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,120,"POLYGON ((85.14 802.62, 58.85 807.97, 57.60 805.50, 59.50 798.39, 57.97 789.35, 64.57 788.23, 66.69 784.65, 70.67 783.50, 74.46 786.96, 75.54 792.21, 82.71 790.76, 85.14 802.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,0,"POLYGON ((586.15 201.05, 597.60 205.78, 591.67 219.98, 580.24 215.25, 586.15 201.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,1,"POLYGON ((601.65 188.31, 638.77 170.39, 663.63 221.37, 657.32 224.41, 663.69 237.46, 638.89 249.45, 632.64 236.61, 626.61 239.51, 601.65 188.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,2,"POLYGON ((866.09 115.08, 867.87 148.71, 847.48 149.81, 845.69 116.16, 866.09 115.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,3,"POLYGON ((99.12 876.20, 104.04 900.00, 91.60 900.00, 89.20 893.73, 80.80 894.23, 79.27 877.53, 99.12 876.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,4,"POLYGON ((506.83 873.02, 524.23 873.13, 524.06 900.00, 502.69 900.00, 502.84 879.71, 506.78 879.72, 506.83 873.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,5,"POLYGON ((239.28 868.18, 244.41 860.24, 258.41 860.44, 264.10 900.00, 241.88 900.00, 240.78 894.34, 239.28 868.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,6,"POLYGON ((349.79 858.43, 362.15 857.89, 364.61 875.05, 372.31 886.71, 371.77 891.88, 359.69 894.18, 351.73 881.42, 349.79 858.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,7,"POLYGON ((88.33 870.89, 80.70 868.02, 80.55 862.45, 78.30 851.34, 93.44 851.24, 96.24 862.05, 95.90 870.69, 88.33 870.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,8,"POLYGON ((503.60 826.33, 523.15 826.27, 522.32 863.88, 507.25 863.15, 502.49 862.38, 504.60 856.29, 503.82 852.71, 503.60 826.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,9,"POLYGON ((76.05 816.83, 98.27 815.60, 99.63 839.97, 77.40 841.20, 76.05 816.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,10,"POLYGON ((237.56 800.72, 246.24 800.24, 252.34 808.72, 260.64 815.76, 263.64 845.52, 240.96 846.10, 237.56 800.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,11,"POLYGON ((349.83 794.74, 370.07 794.74, 370.05 834.98, 349.79 834.96, 349.83 794.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,12,"POLYGON ((72.93 790.52, 91.67 788.98, 93.38 809.58, 74.65 811.12, 72.93 790.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,13,"POLYGON ((501.76 781.82, 521.44 782.39, 520.46 816.09, 500.78 815.52, 501.76 781.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,14,"POLYGON ((233.64 787.33, 234.14 752.22, 242.48 750.25, 246.17 744.26, 251.09 745.53, 251.92 750.37, 256.46 750.94, 256.95 756.15, 261.14 756.04, 261.83 769.94, 254.08 780.57, 252.15 787.21, 233.64 787.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,15,"POLYGON ((349.99 744.93, 369.11 744.77, 369.47 786.45, 349.99 786.61, 349.90 775.69, 346.59 775.71, 346.43 756.85, 350.09 756.80, 349.99 744.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,16,"POLYGON ((484.15 759.03, 484.00 736.19, 521.60 735.95, 521.82 772.50, 499.63 772.64, 499.55 758.93, 484.15 759.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,17,"POLYGON ((66.43 725.05, 86.00 723.71, 87.54 740.67, 82.74 749.45, 74.91 750.49, 70.89 746.69, 67.58 748.73, 66.43 725.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,18,"POLYGON ((347.39 695.16, 362.51 695.22, 362.88 709.77, 368.82 722.38, 369.17 736.27, 348.67 736.79, 347.39 695.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,19,"POLYGON ((504.27 724.22, 504.69 710.72, 497.83 710.51, 498.51 688.99, 522.98 689.74, 521.89 724.77, 504.27 724.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,20,"POLYGON ((228.36 691.92, 242.68 693.80, 252.09 700.24, 260.10 706.19, 260.59 714.54, 249.96 715.08, 241.41 720.31, 235.48 718.51, 234.53 703.20, 228.07 702.54, 228.36 691.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,21,"POLYGON ((295.66 683.53, 274.35 682.96, 272.21 675.47, 270.50 663.82, 295.98 663.17, 295.66 683.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,22,"POLYGON ((348.37 651.76, 353.10 652.30, 361.23 653.67, 369.27 651.67, 367.93 687.29, 348.80 686.42, 348.37 651.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,23,"POLYGON ((501.23 645.34, 521.60 645.67, 521.06 679.00, 502.35 678.70, 502.53 668.68, 482.04 668.34, 482.20 658.15, 501.00 658.45, 501.23 645.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,24,"POLYGON ((56.45 644.37, 71.93 644.31, 79.77 655.27, 79.83 673.76, 56.53 673.85, 56.45 644.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,25,"POLYGON ((500.48 599.75, 519.78 600.23, 518.86 636.17, 499.57 635.68, 500.48 599.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,26,"POLYGON ((66.26 600.95, 68.23 609.13, 76.11 615.72, 75.93 626.80, 55.66 626.50, 56.06 600.78, 66.26 600.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,27,"POLYGON ((387.73 556.13, 427.28 556.03, 428.08 577.92, 402.03 578.59, 397.35 572.00, 388.32 570.43, 387.73 556.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,28,"POLYGON ((54.49 545.95, 67.64 545.42, 67.94 552.93, 72.78 552.74, 73.71 576.05, 70.78 576.17, 71.11 584.22, 59.50 584.69, 59.29 579.30, 55.74 577.17, 54.49 545.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,29,"POLYGON ((375.69 569.67, 338.92 566.43, 337.27 557.26, 326.85 543.87, 329.08 532.09, 340.53 530.40, 350.90 530.98, 349.56 544.67, 376.24 546.50, 375.69 569.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,30,"POLYGON ((449.04 532.48, 483.21 533.48, 482.59 554.87, 448.41 553.88, 449.04 532.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,31,"POLYGON ((217.77 518.63, 251.49 518.44, 251.78 521.34, 264.14 521.02, 264.20 523.49, 269.82 523.79, 270.09 534.52, 259.07 533.92, 251.12 530.30, 245.18 535.60, 234.93 538.99, 226.59 538.54, 218.06 538.98, 217.77 518.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,32,"POLYGON ((18.19 225.18, 32.29 224.89, 32.98 258.28, 4.36 258.85, 7.07 250.86, 11.60 246.99, 19.48 244.71, 19.29 237.21, 14.31 239.02, 18.32 231.82, 18.19 225.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,33,"POLYGON ((37.11 172.38, 39.00 205.03, 19.95 206.12, 18.07 173.47, 37.11 172.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,34,"POLYGON ((34.22 122.27, 36.27 151.96, 14.83 153.44, 12.77 123.72, 34.22 122.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,35,"POLYGON ((30.69 85.60, 31.92 101.13, 25.32 105.20, 25.16 109.93, 12.88 111.09, 11.88 104.70, 14.89 101.85, 19.49 95.89, 17.71 92.32, 15.33 86.25, 30.69 85.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,36,"POLYGON ((771.67 867.77, 787.13 867.55, 787.31 881.69, 789.83 881.65, 789.93 888.48, 787.59 888.52, 787.74 900.00, 772.07 900.00, 771.67 867.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,37,"POLYGON ((599.22 865.52, 607.45 870.90, 615.66 875.82, 619.51 885.58, 619.18 898.99, 601.86 898.55, 602.25 878.63, 599.55 878.47, 599.22 865.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,38,"POLYGON ((883.24 819.69, 900.00 819.78, 900.00 866.10, 882.95 866.00, 883.24 819.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,39,"POLYGON ((600.18 823.65, 619.29 823.83, 619.21 837.93, 616.79 849.62, 609.24 852.94, 598.99 847.61, 600.18 823.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,40,"POLYGON ((760.83 810.06, 788.22 810.12, 788.15 842.53, 761.11 842.49, 761.13 834.54, 754.45 834.53, 754.48 823.26, 760.81 823.27, 760.83 810.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,41,"POLYGON ((596.00 781.76, 620.97 782.32, 620.20 815.13, 599.73 814.67, 599.94 805.30, 595.46 805.21, 596.00 781.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,42,"POLYGON ((781.02 769.44, 786.00 769.82, 787.40 786.15, 786.51 789.88, 783.29 790.96, 784.02 799.86, 770.88 801.44, 770.51 777.65, 776.48 777.26, 780.90 775.15, 781.02 769.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,43,"POLYGON ((599.05 738.40, 618.58 738.28, 618.81 771.90, 599.28 772.04, 599.05 738.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,44,"POLYGON ((768.78 725.49, 787.22 725.60, 787.04 750.62, 778.69 753.74, 768.60 753.66, 768.78 725.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,45,"POLYGON ((846.99 709.29, 877.87 706.04, 880.32 729.16, 849.43 732.38, 846.99 709.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,46,"POLYGON ((596.40 682.39, 620.25 683.09, 619.55 705.58, 626.17 705.76, 625.86 716.36, 595.43 715.47, 596.40 682.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,47,"POLYGON ((761.37 683.23, 785.01 682.41, 786.67 714.89, 771.40 715.28, 771.29 711.11, 761.44 710.74, 761.37 683.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,48,"POLYGON ((767.44 640.86, 777.60 640.98, 778.31 654.41, 783.89 666.99, 783.69 673.74, 767.11 672.30, 767.44 640.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,49,"POLYGON ((598.24 639.24, 616.33 640.06, 614.78 673.42, 596.67 672.59, 598.24 639.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,50,"POLYGON ((599.25 636.41, 598.62 599.23, 617.16 598.89, 617.79 636.10, 599.25 636.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,51,"POLYGON ((765.10 597.06, 785.40 597.12, 785.30 629.66, 765.00 629.60, 765.10 597.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,52,"POLYGON ((848.37 554.14, 859.16 554.20, 859.10 565.63, 870.22 565.68, 870.08 595.08, 851.52 594.99, 851.59 581.58, 848.21 580.63, 847.05 576.06, 844.19 571.14, 843.83 564.69, 848.37 554.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,53,"POLYGON ((596.25 555.05, 616.58 555.15, 616.39 589.83, 596.05 589.75, 596.25 555.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,54,"POLYGON ((783.49 552.68, 784.17 588.07, 766.98 588.42, 766.68 572.93, 759.59 573.07, 759.31 558.36, 765.36 558.25, 765.26 553.04, 783.49 552.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,55,"POLYGON ((594.41 508.39, 616.21 508.50, 616.06 542.60, 594.24 542.49, 594.41 508.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,56,"POLYGON ((785.62 538.58, 758.18 538.90, 758.04 526.89, 764.11 526.83, 764.01 518.24, 756.43 518.32, 756.30 506.49, 763.11 506.41, 762.91 489.08, 789.94 488.77, 790.22 512.09, 785.32 512.15, 785.62 538.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,57,"POLYGON ((872.28 343.74, 864.03 344.33, 859.43 340.69, 853.76 339.73, 854.01 335.24, 853.89 330.38, 855.29 326.22, 872.57 326.15, 872.28 343.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,58,"POLYGON ((582.21 456.60, 579.37 334.50, 585.32 330.33, 586.14 323.27, 580.68 312.33, 600.29 302.64, 607.90 317.90, 644.55 299.79, 640.29 291.24, 685.71 268.75, 692.34 282.01, 768.13 244.46, 759.35 226.92, 694.60 259.05, 662.56 195.07, 685.04 183.89, 690.51 194.78, 708.61 185.77, 714.81 198.09, 764.43 173.30, 784.74 213.60, 770.51 220.71, 799.86 278.77, 675.83 340.88, 678.60 346.36, 644.85 363.29, 642.35 358.33, 623.08 368.01, 625.14 455.60, 582.21 456.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,59,"POLYGON ((597.16 245.65, 611.95 238.69, 620.14 255.92, 605.35 262.89, 597.16 245.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,0,"POLYGON ((857.56 141.86, 876.30 140.70, 878.15 170.51, 875.90 173.23, 858.89 173.17, 858.95 160.10, 860.63 154.42, 857.99 148.69, 857.56 141.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,1,"POLYGON ((601.61 119.36, 617.30 109.00, 627.06 123.60, 634.44 118.73, 640.54 127.87, 631.67 133.76, 634.72 138.34, 620.55 147.72, 601.61 119.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,2,"POLYGON ((855.77 97.96, 873.87 97.74, 874.25 128.83, 856.15 129.05, 855.77 97.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,3,"POLYGON ((576.96 84.67, 600.76 69.97, 607.96 81.51, 601.58 85.45, 613.20 104.08, 595.80 114.85, 576.96 84.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,4,"POLYGON ((853.87 55.41, 872.38 54.63, 873.42 79.31, 875.67 80.18, 876.03 88.50, 855.27 89.36, 853.87 55.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,5,"POLYGON ((556.25 49.97, 569.15 41.10, 581.62 59.03, 591.89 51.95, 598.12 60.89, 574.93 76.87, 556.25 49.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,6,"POLYGON ((850.95 12.97, 870.19 12.37, 871.21 45.71, 851.97 46.31, 850.95 12.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,7,"POLYGON ((511.72 815.90, 517.93 841.54, 489.04 848.47, 487.01 840.15, 480.55 841.71, 476.36 824.42, 511.72 815.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,8,"POLYGON ((373.99 770.86, 386.00 792.60, 369.17 801.81, 357.16 780.05, 373.99 770.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,9,"POLYGON ((472.14 732.71, 477.09 743.40, 488.47 741.33, 491.41 749.98, 478.41 754.38, 482.52 766.44, 464.00 772.70, 460.09 761.19, 456.77 761.27, 451.17 746.28, 456.75 744.63, 453.53 737.72, 472.14 732.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,10,"POLYGON ((356.89 723.19, 359.66 732.27, 361.56 731.68, 366.16 746.82, 364.24 747.38, 367.44 757.86, 355.48 761.47, 344.95 726.78, 356.89 723.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,11,"POLYGON ((341.71 687.71, 347.38 707.89, 336.36 710.97, 330.68 690.78, 341.71 687.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,12,"POLYGON ((455.89 679.25, 467.73 712.49, 449.65 718.88, 440.76 693.87, 444.79 683.18, 455.89 679.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,13,"POLYGON ((330.48 614.94, 335.25 628.87, 309.17 637.74, 304.40 623.81, 330.48 614.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,14,"POLYGON ((439.50 596.36, 444.65 614.01, 426.07 622.92, 417.92 598.31, 424.57 600.49, 431.62 601.25, 439.50 596.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,15,"POLYGON ((413.70 529.25, 435.09 527.82, 437.45 535.26, 442.55 539.59, 444.68 545.00, 441.99 556.96, 429.08 558.27, 424.73 544.02, 413.79 544.78, 413.70 529.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,16,"POLYGON ((521.56 527.37, 525.73 544.36, 514.80 545.64, 511.94 550.66, 507.42 548.80, 505.03 552.57, 493.70 548.15, 490.20 527.90, 521.56 527.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,17,"POLYGON ((227.07 445.69, 237.54 444.29, 237.15 441.39, 247.94 439.96, 248.54 444.41, 255.97 443.45, 259.55 470.17, 252.54 471.10, 235.02 463.49, 230.12 468.61, 227.07 445.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,18,"POLYGON ((474.98 442.70, 476.40 460.53, 437.98 463.59, 436.55 445.76, 474.98 442.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,19,"POLYGON ((423.19 443.95, 424.71 460.04, 401.84 461.47, 400.22 452.88, 405.13 448.58, 405.34 445.24, 423.19 443.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,20,"POLYGON ((527.20 442.30, 527.83 459.95, 489.47 461.31, 488.84 443.65, 527.20 442.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,21,"POLYGON ((317.88 440.72, 319.05 456.83, 282.17 459.45, 281.71 452.82, 288.71 442.92, 317.88 440.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,22,"POLYGON ((364.03 441.68, 364.20 449.75, 333.89 450.41, 333.73 442.34, 364.03 441.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,23,"POLYGON ((295.30 392.37, 295.45 400.98, 288.32 401.09, 288.18 392.48, 295.30 392.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,24,"POLYGON ((232.08 391.51, 234.63 382.52, 235.33 371.12, 240.45 368.70, 263.95 370.10, 266.03 397.04, 231.21 402.10, 232.08 391.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,25,"POLYGON ((105.19 380.53, 113.24 381.06, 114.09 368.45, 127.03 369.32, 125.39 393.93, 123.20 400.54, 103.92 399.27, 105.19 380.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,26,"POLYGON ((116.04 340.83, 127.43 344.76, 129.40 348.44, 133.63 348.33, 134.43 361.43, 115.57 358.62, 116.04 340.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,27,"POLYGON ((262.94 328.04, 263.16 360.69, 243.11 360.82, 242.87 328.19, 262.94 328.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,28,"POLYGON ((283.36 296.49, 285.05 314.09, 270.70 315.46, 269.00 297.85, 283.36 296.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,29,"POLYGON ((260.67 283.90, 261.06 312.55, 242.33 312.80, 241.96 284.18, 260.67 283.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,30,"POLYGON ((490.42 228.56, 553.08 226.17, 554.24 320.61, 491.55 321.76, 490.42 228.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,31,"POLYGON ((148.17 161.23, 156.81 176.91, 135.24 188.69, 126.61 172.99, 148.17 161.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,32,"POLYGON ((299.56 162.62, 305.54 162.47, 312.46 159.99, 314.71 157.29, 323.02 157.08, 324.46 174.24, 301.26 176.81, 299.56 162.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,33,"POLYGON ((355.74 159.55, 357.16 162.83, 363.85 159.92, 368.09 158.17, 374.66 155.57, 377.41 153.05, 385.26 152.52, 386.54 171.47, 356.67 173.45, 355.74 159.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,34,"POLYGON ((423.95 145.37, 439.39 141.33, 442.61 141.67, 451.99 152.71, 456.49 153.57, 460.91 152.20, 460.75 156.93, 429.02 164.95, 423.95 145.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,35,"POLYGON ((518.66 159.79, 508.78 155.44, 505.04 163.88, 494.62 159.31, 497.66 152.44, 490.49 149.29, 493.33 142.89, 497.80 138.21, 519.32 146.49, 522.41 145.24, 526.60 141.45, 518.66 159.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,36,"POLYGON ((97.60 132.17, 110.10 136.81, 109.24 149.24, 104.38 162.55, 91.40 157.84, 92.75 154.12, 92.65 149.99, 90.48 142.94, 80.38 137.74, 83.66 135.99, 94.62 140.06, 97.60 132.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,37,"POLYGON ((0.00 138.93, 1.05 139.02, 2.33 136.50, 18.20 136.34, 18.25 148.84, 16.38 153.17, 0.00 152.84, 0.00 138.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,38,"POLYGON ((35.78 131.83, 59.62 134.71, 58.42 146.98, 58.07 152.78, 54.56 152.05, 51.57 151.79, 48.08 151.88, 44.05 150.65, 40.37 148.77, 37.84 148.01, 33.88 149.26, 34.67 140.96, 35.78 131.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,39,"POLYGON ((229.83 79.34, 273.91 64.36, 275.10 67.80, 278.31 70.18, 279.44 72.95, 284.21 73.71, 286.56 80.36, 281.84 81.99, 260.83 88.85, 256.59 87.73, 253.26 88.86, 249.65 92.46, 235.77 96.99, 229.83 79.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,40,"POLYGON ((217.36 0.00, 217.51 12.77, 198.90 12.97, 186.18 12.94, 163.46 12.43, 157.25 12.08, 145.74 10.99, 135.99 9.89, 119.99 12.47, 119.04 6.57, 120.16 0.00, 217.36 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,41,"POLYGON ((296.39 0.00, 297.59 3.96, 297.75 9.57, 273.45 10.28, 264.23 5.61, 248.13 9.75, 242.17 8.32, 236.10 10.65, 235.65 0.00, 296.39 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,42,"POLYGON ((590.95 832.76, 628.78 828.51, 630.91 847.37, 593.09 851.62, 590.95 832.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,43,"POLYGON ((707.48 823.87, 709.90 845.29, 673.76 849.32, 671.34 827.89, 707.48 823.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,44,"POLYGON ((869.62 772.82, 880.29 800.31, 859.48 804.82, 856.33 798.44, 850.92 801.06, 844.64 789.32, 853.04 786.62, 857.90 782.04, 858.75 776.07, 869.62 772.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,45,"POLYGON ((747.91 729.79, 760.05 726.92, 764.36 745.02, 746.31 749.29, 748.85 744.92, 750.03 738.61, 747.91 729.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,46,"POLYGON ((595.59 714.88, 596.65 737.52, 557.07 739.35, 556.01 716.73, 595.59 714.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,47,"POLYGON ((609.00 722.35, 622.63 722.65, 625.91 720.92, 636.21 720.66, 641.87 721.18, 644.80 718.11, 648.65 713.40, 648.22 734.92, 609.72 737.54, 609.00 722.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,48,"POLYGON ((663.28 713.36, 691.55 713.62, 695.82 712.20, 696.06 721.45, 693.81 724.48, 692.59 728.82, 696.70 733.35, 663.46 733.53, 663.28 713.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,49,"POLYGON ((758.27 709.94, 756.16 718.61, 757.65 725.19, 750.03 726.36, 742.00 723.92, 739.63 709.42, 758.27 709.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,50,"POLYGON ((878.75 628.19, 890.90 622.55, 899.75 641.44, 893.65 648.81, 885.13 641.81, 878.75 628.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,51,"POLYGON ((753.64 535.37, 753.66 550.95, 759.69 552.66, 758.42 561.97, 754.93 571.72, 755.78 575.05, 739.79 576.94, 738.20 544.66, 741.92 543.83, 744.47 541.17, 745.82 535.94, 753.64 535.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,52,"POLYGON ((692.43 534.33, 694.25 562.12, 694.38 566.95, 693.44 574.03, 687.93 577.15, 684.80 571.66, 686.12 564.94, 673.09 566.01, 666.94 559.10, 666.33 535.34, 692.43 534.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,53,"POLYGON ((881.41 524.10, 884.44 531.99, 881.18 533.23, 883.15 551.38, 885.42 553.17, 889.67 559.01, 888.64 562.39, 891.63 576.78, 882.88 569.95, 867.40 529.41, 881.41 524.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,54,"POLYGON ((606.05 539.87, 648.34 538.20, 649.22 560.42, 606.95 562.09, 606.05 539.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,55,"POLYGON ((900.00 483.25, 892.40 475.03, 900.00 468.08, 900.00 483.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,56,"POLYGON ((661.47 435.58, 661.72 457.68, 623.59 458.12, 623.35 436.02, 661.47 435.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,57,"POLYGON ((588.44 436.73, 589.10 455.47, 551.00 456.81, 550.34 438.07, 588.44 436.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,58,"POLYGON ((679.84 435.82, 701.61 433.87, 702.75 446.58, 707.02 449.54, 703.89 457.67, 683.11 459.07, 679.84 435.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,59,"POLYGON ((807.54 422.10, 812.87 428.73, 816.53 427.33, 819.59 423.83, 821.36 420.10, 824.66 417.98, 831.52 428.30, 800.79 448.75, 792.47 436.33, 795.34 434.42, 796.04 431.25, 807.54 422.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,60,"POLYGON ((861.61 365.38, 876.73 373.63, 854.74 413.53, 840.84 405.93, 844.68 398.98, 838.22 395.45, 841.77 389.04, 846.99 391.88, 861.61 365.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,61,"POLYGON ((646.00 347.03, 652.81 357.62, 655.52 355.60, 664.27 365.21, 667.04 373.39, 665.31 374.61, 658.69 363.37, 653.90 361.14, 652.82 365.30, 654.42 374.31, 654.52 378.04, 659.56 382.24, 655.27 384.33, 648.76 376.61, 648.01 370.53, 644.05 370.25, 644.96 375.53, 642.23 376.98, 636.31 369.07, 638.60 365.55, 632.37 355.81, 646.00 347.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,62,"POLYGON ((765.31 326.88, 781.29 354.39, 773.75 358.74, 753.39 361.48, 748.68 352.18, 754.39 341.38, 750.79 335.21, 765.31 326.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,63,"POLYGON ((880.83 310.84, 882.68 344.84, 877.38 345.71, 877.63 349.95, 867.94 350.55, 867.55 344.21, 862.92 344.50, 862.65 339.94, 854.46 340.44, 853.26 321.00, 860.88 320.54, 860.33 311.58, 880.83 310.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,64,"POLYGON ((731.69 291.64, 748.81 318.48, 732.45 330.80, 714.31 303.25, 731.69 291.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,65,"POLYGON ((874.50 269.34, 873.52 272.25, 875.80 279.56, 871.12 281.50, 871.29 288.06, 872.17 292.24, 872.38 300.38, 858.92 299.94, 853.16 291.41, 852.98 284.33, 860.89 283.87, 860.55 270.49, 874.50 269.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,66,"POLYGON ((709.52 256.44, 727.49 284.82, 711.74 294.72, 693.75 266.34, 709.52 256.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,67,"POLYGON ((872.15 228.71, 869.33 232.44, 871.34 238.43, 875.04 238.60, 876.60 248.02, 878.52 251.39, 878.70 258.46, 859.97 258.67, 859.53 251.34, 866.04 248.29, 867.70 240.63, 867.32 235.65, 862.72 231.83, 872.15 228.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,68,"POLYGON ((669.06 229.90, 685.57 219.87, 687.61 220.73, 689.00 219.78, 696.06 230.06, 692.94 232.18, 703.69 247.84, 685.64 260.11, 669.06 229.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,69,"POLYGON ((646.31 191.32, 659.50 183.42, 662.14 187.77, 662.21 198.78, 664.34 204.23, 666.98 207.31, 670.40 209.78, 674.37 210.48, 677.80 213.14, 665.87 221.44, 661.61 215.37, 657.35 209.00, 650.15 197.66, 646.31 191.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,70,"POLYGON ((876.22 212.84, 872.33 215.30, 871.14 210.09, 867.11 207.30, 864.94 199.24, 857.47 201.25, 851.11 201.41, 850.52 189.28, 858.41 189.08, 858.23 182.25, 875.69 181.80, 876.22 212.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,71,"POLYGON ((624.24 156.95, 626.69 154.09, 640.49 145.72, 649.04 159.69, 652.85 158.41, 658.42 165.89, 654.45 168.81, 658.79 174.65, 645.02 184.83, 624.24 156.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,0,"POLYGON ((852.08 871.66, 869.96 871.09, 870.85 900.00, 852.97 900.00, 852.08 871.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,1,"POLYGON ((858.83 855.33, 837.49 844.39, 835.11 841.79, 833.88 839.51, 841.73 822.64, 863.45 832.63, 864.85 835.26, 864.92 850.98, 861.74 856.87, 858.83 855.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,2,"POLYGON ((755.59 831.96, 756.92 818.56, 779.02 820.73, 782.68 824.39, 782.03 834.77, 781.62 839.91, 781.28 843.27, 778.40 842.99, 770.94 844.75, 768.62 847.17, 764.27 846.68, 761.26 845.38, 758.17 840.55, 755.59 831.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,3,"POLYGON ((744.01 816.12, 741.69 829.96, 733.12 834.68, 728.19 834.81, 724.08 836.69, 712.72 834.83, 716.59 811.60, 744.01 816.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,4,"POLYGON ((661.21 806.90, 675.11 810.94, 677.72 802.04, 694.50 806.92, 687.99 829.11, 657.32 820.19, 661.21 806.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,5,"POLYGON ((554.94 796.84, 578.05 769.04, 643.73 822.40, 619.93 851.04, 554.94 796.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,6,"POLYGON ((604.86 656.30, 607.53 657.68, 609.02 659.35, 616.39 663.62, 619.15 663.42, 622.12 666.36, 626.58 665.32, 630.21 662.74, 632.07 658.50, 636.81 658.51, 636.63 674.98, 609.40 674.57, 607.51 672.24, 604.28 670.10, 604.86 656.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,7,"POLYGON ((713.29 657.05, 729.10 658.20, 728.74 662.96, 723.26 665.19, 723.48 667.98, 715.79 668.58, 715.08 666.28, 716.93 663.91, 714.19 661.78, 713.29 657.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,8,"POLYGON ((719.97 628.47, 732.90 628.72, 732.98 624.63, 740.87 624.78, 742.04 628.97, 740.95 632.29, 741.48 634.71, 740.69 645.39, 736.65 653.08, 722.22 653.45, 719.64 644.99, 719.97 628.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,9,"POLYGON ((721.64 584.61, 737.93 583.68, 738.80 598.60, 734.16 598.85, 731.79 606.44, 732.78 610.27, 728.31 609.17, 724.02 609.79, 723.34 605.03, 722.59 598.63, 722.17 593.94, 721.64 584.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,10,"POLYGON ((721.56 534.82, 738.92 534.00, 740.56 568.81, 736.95 568.99, 733.04 570.00, 728.13 571.19, 723.13 562.37, 722.44 553.51, 721.56 534.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,11,"POLYGON ((560.65 521.18, 586.22 526.74, 589.26 535.70, 589.31 537.78, 585.02 539.98, 584.51 541.95, 582.34 543.93, 580.33 547.47, 555.95 541.79, 558.30 531.83, 560.65 521.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,12,"POLYGON ((723.72 498.07, 742.16 498.11, 742.10 523.35, 723.66 523.31, 723.72 498.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,13,"POLYGON ((714.92 414.63, 726.99 413.90, 733.88 416.46, 737.44 433.59, 735.59 434.51, 729.39 435.42, 724.65 435.03, 721.59 436.73, 719.68 435.53, 716.10 435.74, 714.92 414.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,14,"POLYGON ((717.28 368.14, 716.86 362.53, 726.74 361.79, 729.45 363.25, 730.56 377.03, 734.38 387.63, 718.49 388.57, 717.28 368.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,15,"POLYGON ((613.60 337.39, 632.43 335.95, 628.13 341.37, 627.58 346.13, 630.58 354.67, 628.73 360.58, 626.22 361.84, 614.37 362.47, 611.55 356.60, 610.08 354.97, 609.92 353.11, 614.77 352.74, 613.60 337.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,16,"POLYGON ((716.00 320.64, 733.12 320.14, 733.87 344.96, 716.72 345.46, 716.00 320.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,17,"POLYGON ((714.74 283.00, 743.33 281.90, 744.18 303.74, 741.95 303.82, 738.77 304.06, 738.59 301.62, 736.08 301.29, 731.05 309.54, 723.64 310.04, 723.06 308.59, 719.91 307.93, 719.37 294.12, 715.18 294.27, 714.74 283.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,18,"POLYGON ((711.60 241.15, 729.55 239.45, 729.95 243.53, 730.24 245.61, 732.04 255.13, 732.35 256.99, 732.80 260.66, 733.28 265.75, 714.11 267.57, 711.60 241.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,19,"POLYGON ((599.16 215.71, 605.68 214.32, 606.02 215.89, 609.95 233.04, 613.90 236.54, 609.26 245.33, 606.30 246.52, 598.09 246.59, 595.44 245.42, 594.31 244.47, 593.83 241.00, 602.22 237.19, 600.72 233.94, 602.85 232.98, 599.16 215.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,20,"POLYGON ((702.37 203.83, 703.45 200.92, 718.67 196.67, 722.32 195.57, 726.15 208.26, 724.50 221.29, 711.11 223.96, 707.07 222.07, 707.27 220.75, 705.24 218.72, 704.18 216.77, 702.37 203.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,21,"POLYGON ((689.29 142.83, 703.83 138.73, 708.55 155.46, 714.42 156.44, 717.64 160.55, 717.30 166.89, 714.82 172.17, 707.87 173.57, 702.27 173.09, 696.24 170.78, 689.40 147.29, 689.29 142.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,22,"POLYGON ((570.03 106.97, 567.72 98.31, 583.76 94.08, 586.00 102.48, 579.66 112.63, 578.68 113.32, 575.75 109.18, 572.72 108.99, 570.03 106.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,23,"POLYGON ((672.04 70.73, 672.63 69.09, 681.45 66.74, 685.67 82.41, 693.64 87.54, 695.89 97.27, 693.31 98.82, 689.35 100.14, 678.86 98.43, 672.04 70.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,24,"POLYGON ((675.06 24.26, 677.22 26.07, 677.50 27.41, 678.41 28.88, 679.06 30.48, 678.73 32.09, 678.15 33.59, 677.19 35.10, 676.99 36.59, 677.14 38.08, 677.56 39.55, 677.61 41.55, 677.52 43.04, 676.44 44.29, 675.34 45.56, 674.63 47.06, 673.66 48.33, 672.96 49.70, 672.76 51.57, 673.30 53.05, 678.50 57.27, 670.19 58.45, 667.08 24.46, 675.06 24.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,25,"POLYGON ((559.59 28.68, 558.01 10.01, 562.35 4.81, 564.84 4.88, 565.80 8.46, 571.93 9.28, 573.39 8.37, 574.36 7.11, 575.45 5.86, 576.52 4.45, 577.76 3.56, 581.28 9.55, 581.52 24.15, 576.19 24.80, 573.95 25.34, 572.34 25.52, 570.99 26.17, 569.65 27.56, 568.44 28.83, 567.36 30.21, 565.64 31.12, 564.15 31.67, 563.13 30.57, 562.46 28.74, 561.92 27.27, 559.59 28.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,26,"POLYGON ((120.40 898.59, 122.52 895.00, 217.26 891.84, 217.36 900.00, 120.16 900.00, 120.40 898.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,27,"POLYGON ((235.32 892.25, 294.02 889.76, 294.15 892.64, 296.39 900.00, 235.65 900.00, 235.32 892.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,28,"POLYGON ((70.99 887.32, 78.58 841.82, 97.86 845.22, 88.60 900.00, 82.79 900.00, 78.91 891.58, 70.99 887.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,29,"POLYGON ((168.99 824.83, 240.83 823.34, 241.21 842.13, 164.03 843.71, 162.05 839.59, 161.31 835.44, 164.14 831.19, 166.62 830.09, 168.99 824.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,30,"POLYGON ((259.89 820.35, 321.95 812.40, 323.28 815.07, 325.64 817.30, 328.61 816.89, 330.50 830.25, 262.61 839.68, 259.89 820.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,31,"POLYGON ((96.60 842.50, 81.49 840.51, 81.73 838.75, 82.61 837.66, 84.62 835.08, 86.38 832.75, 88.70 829.51, 90.72 826.62, 87.39 819.91, 84.37 818.50, 82.83 817.05, 85.44 801.27, 90.45 796.35, 91.29 796.33, 94.22 794.10, 97.52 793.02, 104.02 787.41, 96.60 842.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,32,"POLYGON ((161.04 783.77, 168.99 783.14, 170.77 787.27, 180.40 787.23, 183.27 784.65, 184.04 782.14, 205.98 780.54, 209.45 785.45, 215.36 787.16, 220.93 784.31, 223.53 779.05, 234.85 778.25, 236.21 798.64, 162.18 803.74, 156.64 799.91, 153.25 798.55, 150.08 798.81, 149.54 792.18, 158.46 789.03, 161.04 783.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,33,"POLYGON ((516.28 791.83, 508.78 785.83, 501.99 794.24, 442.31 746.47, 445.64 742.35, 441.45 738.97, 452.77 724.94, 457.32 728.58, 460.40 724.79, 519.84 772.34, 512.71 781.18, 521.52 788.21, 543.43 761.11, 561.26 775.37, 515.74 831.73, 496.48 816.33, 516.28 791.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,34,"POLYGON ((143.44 717.09, 167.47 715.24, 171.77 719.30, 177.61 719.15, 183.04 718.79, 187.37 715.97, 189.00 713.45, 214.75 713.41, 217.04 721.68, 214.85 734.25, 174.67 734.85, 145.22 737.47, 144.04 732.92, 141.26 730.06, 139.51 727.00, 140.29 724.67, 142.25 720.25, 143.44 717.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,35,"POLYGON ((136.21 679.56, 203.18 670.35, 208.75 675.43, 212.13 676.79, 213.43 678.22, 213.74 682.58, 212.07 690.55, 205.84 692.37, 192.41 691.47, 137.35 699.75, 136.03 697.48, 135.69 692.29, 137.92 689.11, 137.59 685.14, 134.60 682.11, 136.21 679.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,36,"POLYGON ((34.46 661.19, 46.93 655.26, 48.09 657.69, 54.50 660.17, 59.81 668.09, 67.00 686.56, 70.52 694.92, 73.51 701.60, 75.37 705.41, 62.00 711.88, 58.43 704.58, 54.16 702.22, 52.70 700.33, 52.80 690.34, 46.09 682.80, 41.87 669.59, 34.46 661.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,37,"POLYGON ((69.47 611.40, 74.33 612.56, 77.92 610.67, 84.93 611.74, 89.19 614.14, 91.93 617.53, 110.88 621.22, 115.77 620.70, 120.96 621.54, 124.85 620.74, 134.94 621.59, 139.86 623.15, 143.43 620.55, 148.78 620.53, 148.81 628.89, 146.34 636.33, 142.79 639.06, 131.17 638.38, 126.07 635.87, 111.19 633.34, 107.52 631.34, 104.14 630.58, 98.70 631.14, 69.04 624.37, 67.03 622.07, 69.47 611.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,38,"POLYGON ((460.95 618.03, 466.47 601.20, 485.19 607.27, 482.78 614.75, 483.14 618.82, 490.92 623.20, 485.89 632.05, 460.95 618.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,39,"POLYGON ((450.46 517.70, 459.24 505.91, 465.82 510.76, 455.95 524.04, 452.71 525.17, 451.95 522.96, 451.54 521.71, 451.28 520.85, 450.46 517.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,40,"POLYGON ((481.47 484.32, 504.49 497.37, 498.87 507.17, 489.95 523.51, 484.32 520.45, 474.24 515.32, 471.48 511.26, 467.16 508.73, 475.92 493.99, 481.47 484.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,41,"POLYGON ((456.83 487.19, 435.26 464.72, 444.94 455.53, 451.97 460.30, 471.80 474.42, 464.00 485.28, 461.35 485.97, 456.83 487.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,42,"POLYGON ((539.46 404.98, 559.11 416.35, 562.63 431.40, 560.14 437.88, 555.33 440.02, 534.28 426.97, 532.78 423.44, 530.99 420.98, 531.72 418.14, 539.46 404.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,43,"POLYGON ((496.99 389.21, 502.08 384.24, 508.19 390.41, 509.31 392.76, 509.69 397.30, 509.12 399.07, 502.34 398.80, 496.27 392.47, 496.99 389.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,44,"POLYGON ((355.73 253.49, 366.73 252.96, 369.81 254.13, 368.34 257.92, 371.82 261.09, 376.72 261.57, 376.48 264.04, 374.64 278.69, 372.76 280.36, 371.20 282.62, 371.35 285.03, 357.39 285.92, 356.99 279.41, 355.73 253.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,45,"POLYGON ((454.35 177.13, 475.51 177.77, 474.74 203.54, 470.40 205.91, 453.51 205.43, 454.35 177.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,46,"POLYGON ((358.16 168.41, 371.64 167.93, 372.94 169.48, 375.65 170.65, 376.93 190.60, 371.97 193.23, 360.85 193.20, 358.88 190.28, 358.16 168.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,47,"POLYGON ((353.81 124.44, 370.06 123.96, 371.52 125.78, 370.85 149.06, 373.53 155.10, 371.72 158.41, 354.85 158.93, 353.81 124.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,48,"POLYGON ((460.00 99.98, 477.86 99.84, 478.08 126.12, 460.22 126.28, 460.00 99.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,49,"POLYGON ((340.52 86.10, 342.55 78.24, 354.89 78.59, 354.72 84.52, 357.27 84.59, 357.04 92.50, 371.54 92.91, 371.09 111.30, 361.29 111.08, 359.80 110.37, 356.69 110.60, 353.34 112.75, 341.74 113.27, 340.52 86.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,50,"POLYGON ((454.48 53.93, 472.30 53.56, 472.87 80.36, 455.07 80.75, 454.71 64.49, 452.86 63.05, 452.79 60.41, 454.63 60.36, 454.48 53.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,51,"POLYGON ((486.12 51.13, 493.75 50.93, 494.06 62.33, 492.46 65.35, 486.49 65.50, 486.12 51.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,52,"POLYGON ((352.56 39.72, 371.87 38.89, 372.17 46.25, 374.75 46.14, 375.50 64.17, 373.04 64.28, 373.43 73.08, 354.03 73.89, 352.56 39.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,53,"POLYGON ((453.66 7.80, 467.47 7.45, 464.67 12.94, 466.76 20.39, 471.44 23.40, 471.18 38.21, 459.93 40.36, 452.56 38.88, 453.66 7.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,54,"POLYGON ((373.03 0.00, 373.45 31.53, 356.70 31.31, 355.78 28.22, 357.61 26.49, 356.28 23.62, 355.14 19.70, 355.15 11.97, 364.93 9.63, 366.99 8.14, 366.35 0.00, 373.03 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,0,"POLYGON ((557.01 821.29, 575.37 821.11, 576.27 844.97, 570.58 842.16, 558.09 840.42, 557.01 821.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,1,"POLYGON ((344.23 812.16, 364.88 807.08, 371.85 818.12, 371.98 823.37, 348.51 828.52, 344.23 812.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,2,"POLYGON ((439.94 784.89, 457.19 782.38, 462.78 809.94, 442.14 813.86, 442.04 803.99, 440.74 799.16, 439.94 784.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,3,"POLYGON ((333.97 769.31, 351.12 764.68, 353.04 770.58, 356.28 774.00, 357.86 780.62, 354.11 784.91, 355.68 791.89, 342.76 795.72, 337.61 787.44, 333.97 769.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,4,"POLYGON ((446.76 745.22, 448.27 757.70, 429.45 763.04, 429.37 759.96, 429.88 756.84, 429.24 748.91, 446.76 745.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,5,"POLYGON ((322.72 722.65, 340.04 718.75, 342.22 725.62, 346.83 740.37, 344.78 740.16, 344.18 744.19, 341.23 745.84, 340.74 747.78, 336.35 748.25, 332.50 748.70, 330.35 747.18, 322.72 722.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,6,"POLYGON ((417.32 699.40, 433.51 695.59, 439.76 720.34, 422.57 725.64, 419.65 715.10, 417.32 699.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,7,"POLYGON ((336.73 693.01, 338.36 705.85, 320.20 708.11, 318.59 695.27, 336.73 693.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,8,"POLYGON ((430.34 656.54, 441.62 657.43, 437.93 675.50, 414.38 669.33, 417.62 657.15, 420.08 662.04, 428.85 663.59, 430.34 656.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,9,"POLYGON ((452.26 610.94, 458.37 615.98, 444.99 638.14, 436.87 632.15, 430.40 623.13, 436.94 616.04, 444.29 620.80, 452.26 610.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,10,"POLYGON ((554.07 634.70, 552.57 617.38, 555.26 613.12, 558.78 606.01, 559.61 603.88, 559.73 601.79, 570.79 600.27, 575.93 601.38, 577.70 622.89, 574.15 628.75, 572.26 630.20, 569.99 631.65, 568.10 632.95, 566.01 634.22, 563.93 634.81, 559.68 634.38, 554.07 634.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,11,"POLYGON ((409.94 501.76, 420.39 517.12, 418.12 520.71, 398.95 535.65, 390.69 525.25, 399.99 517.93, 395.10 511.86, 392.26 505.45, 398.40 502.05, 402.66 506.96, 409.94 501.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,12,"POLYGON ((420.50 487.11, 433.52 480.10, 445.44 498.13, 446.10 504.57, 446.21 508.76, 441.53 510.88, 436.12 504.07, 430.43 505.21, 420.50 487.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,13,"POLYGON ((488.98 467.79, 490.67 475.18, 481.24 476.66, 482.36 481.10, 475.68 482.75, 473.61 480.32, 472.24 475.40, 472.12 470.44, 470.39 461.07, 477.34 460.65, 481.34 470.20, 488.98 467.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,14,"POLYGON ((549.79 460.05, 577.98 462.30, 581.71 471.87, 581.68 480.06, 576.67 478.94, 573.61 476.04, 572.98 470.84, 568.31 464.04, 565.89 466.81, 567.34 474.96, 567.22 479.43, 560.53 471.41, 559.88 474.91, 556.20 476.98, 559.01 480.12, 550.04 479.11, 548.84 471.46, 549.79 460.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,15,"POLYGON ((505.19 458.94, 536.42 462.61, 535.27 476.78, 528.05 476.69, 523.27 474.35, 505.81 473.06, 505.19 458.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,16,"POLYGON ((573.89 860.15, 581.19 859.54, 582.59 864.92, 584.95 883.00, 580.57 883.95, 577.35 880.90, 577.84 876.10, 574.20 872.24, 571.18 868.76, 567.55 866.35, 569.33 861.93, 573.89 860.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,17,"POLYGON ((561.40 772.54, 580.05 772.35, 582.09 795.01, 582.10 807.09, 569.74 809.46, 569.30 803.59, 558.92 803.55, 561.40 772.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,18,"POLYGON ((562.36 728.61, 580.74 729.92, 582.42 737.82, 581.26 750.53, 577.00 757.40, 576.80 761.25, 568.25 762.35, 562.91 761.89, 561.01 757.23, 560.40 745.15, 562.36 728.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,19,"POLYGON ((557.51 689.82, 574.05 688.22, 574.01 698.26, 582.87 697.14, 584.52 703.58, 583.86 712.72, 582.80 717.77, 562.67 718.28, 562.83 712.97, 557.51 689.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,20,"POLYGON ((678.54 666.23, 678.62 675.15, 653.98 675.38, 653.91 666.48, 678.54 666.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,21,"POLYGON ((559.09 655.59, 561.73 654.95, 564.24 654.29, 566.02 654.40, 567.95 654.93, 569.90 655.76, 571.69 656.16, 573.33 656.85, 575.12 657.10, 577.04 656.91, 578.81 656.27, 580.09 655.06, 584.14 650.70, 586.21 674.22, 561.85 676.30, 559.09 655.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,22,"POLYGON ((662.70 541.55, 668.67 543.17, 672.39 549.27, 669.20 552.30, 666.38 557.68, 666.24 563.86, 667.27 569.14, 662.22 569.26, 662.70 541.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,23,"POLYGON ((659.72 499.49, 677.78 499.41, 677.57 510.56, 676.93 514.79, 673.40 513.15, 670.69 514.71, 665.39 506.47, 665.29 511.87, 666.19 517.22, 659.53 518.17, 659.72 499.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,24,"POLYGON ((676.97 458.05, 676.77 489.51, 670.09 491.17, 659.83 489.21, 657.98 465.22, 668.52 458.76, 676.97 458.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,25,"POLYGON ((446.36 866.57, 449.38 867.20, 450.47 869.10, 454.42 871.80, 458.85 873.08, 463.43 872.79, 465.16 871.53, 466.67 868.69, 470.53 868.64, 470.69 886.61, 446.54 886.83, 446.36 866.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,26,"POLYGON ((354.13 855.10, 372.53 854.64, 373.57 878.58, 382.99 878.76, 382.61 888.76, 362.75 889.69, 359.33 886.87, 356.68 881.72, 355.16 879.67, 354.13 855.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,27,"POLYGON ((444.27 826.15, 463.99 825.47, 464.81 851.20, 445.86 853.44, 444.27 826.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,0,"POLYGON ((493.48 559.87, 509.83 569.78, 514.34 562.41, 523.76 568.12, 509.36 591.63, 483.60 576.02, 493.48 559.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,1,"POLYGON ((191.07 0.00, 197.66 415.25, 0.00 418.42, 0.00 0.00, 191.07 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,2,"POLYGON ((857.57 799.71, 875.80 799.60, 876.02 835.25, 857.79 835.36, 857.57 799.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,0,"POLYGON ((496.81 865.26, 537.59 863.22, 537.86 868.69, 545.35 868.33, 546.78 897.06, 538.83 897.46, 538.96 900.00, 525.14 900.00, 514.38 893.51, 499.95 887.88, 488.57 886.33, 487.82 871.46, 497.11 871.00, 496.81 865.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,1,"POLYGON ((361.74 847.47, 386.63 846.82, 386.76 851.96, 391.36 851.85, 391.54 859.03, 395.69 858.93, 396.79 900.00, 363.20 900.00, 359.18 895.66, 355.47 895.76, 354.51 859.62, 356.89 859.54, 356.67 851.89, 361.85 851.75, 361.74 847.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,2,"POLYGON ((213.84 798.30, 239.17 797.47, 239.30 802.55, 243.69 802.44, 243.99 807.70, 248.38 807.41, 249.51 850.73, 244.37 850.86, 244.78 862.50, 249.69 862.38, 250.74 900.00, 209.61 900.00, 208.44 863.90, 213.54 863.77, 213.22 851.14, 207.34 851.29, 206.29 809.99, 209.53 809.90, 209.30 803.65, 213.95 803.47, 213.84 798.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,3,"POLYGON ((465.00 806.06, 505.48 804.01, 505.77 809.47, 513.27 809.10, 514.72 837.65, 506.77 838.05, 507.17 845.75, 466.30 847.83, 465.90 840.12, 458.38 840.51, 456.94 812.24, 465.29 811.83, 465.00 806.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,4,"POLYGON ((244.88 753.75, 249.76 753.61, 249.64 748.90, 255.59 748.73, 255.49 745.38, 298.29 744.16, 298.44 749.37, 310.26 749.05, 310.11 743.41, 350.86 742.27, 351.02 747.86, 362.96 747.53, 362.80 741.83, 403.89 740.69, 403.98 743.29, 410.64 743.12, 410.76 747.89, 415.56 747.77, 416.16 770.77, 412.46 770.86, 412.59 776.17, 406.97 776.31, 407.06 779.90, 396.45 780.17, 396.44 779.00, 388.17 779.19, 388.33 785.42, 379.56 785.62, 379.47 782.29, 363.54 782.68, 363.39 775.98, 351.94 776.27, 352.13 783.68, 310.40 784.69, 310.25 777.88, 298.92 778.17, 299.11 785.58, 257.36 786.64, 257.27 783.05, 251.16 783.21, 251.08 779.99, 245.59 780.15, 244.88 753.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,5,"POLYGON ((622.81 804.91, 651.88 790.07, 655.71 797.48, 661.02 794.75, 681.34 833.96, 674.34 837.56, 678.60 845.75, 684.37 842.79, 700.52 873.85, 695.51 876.44, 692.39 879.32, 690.75 884.44, 692.99 889.00, 695.02 893.02, 681.13 900.00, 672.07 900.00, 670.26 896.42, 664.48 899.32, 645.16 861.34, 650.04 858.89, 646.17 851.28, 640.23 854.28, 620.37 815.54, 626.64 812.36, 622.81 804.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,6,"POLYGON ((757.95 711.48, 765.63 717.27, 767.86 715.46, 784.08 727.66, 782.36 729.94, 785.06 731.98, 782.94 734.79, 789.62 739.81, 791.85 736.89, 824.32 761.35, 822.09 764.27, 829.07 769.51, 830.89 767.13, 837.59 772.18, 838.88 770.48, 855.85 783.26, 854.22 785.41, 857.23 787.68, 855.65 789.76, 858.43 791.85, 836.61 820.57, 833.22 818.02, 831.45 820.35, 807.09 802.01, 809.54 798.80, 801.23 792.55, 799.20 795.24, 765.18 769.65, 767.89 766.07, 764.20 763.30, 761.19 767.29, 734.39 747.15, 735.76 745.34, 733.53 743.66, 757.95 711.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,7,"POLYGON ((616.50 725.71, 618.34 728.57, 620.51 727.19, 621.65 728.98, 623.74 727.64, 632.22 740.87, 629.24 742.77, 631.79 746.72, 634.66 744.89, 642.78 757.54, 640.79 758.81, 650.22 773.49, 647.51 775.20, 649.74 778.65, 621.25 796.80, 619.18 793.57, 616.59 795.23, 607.14 780.51, 604.56 782.15, 596.17 769.09, 601.62 765.63, 598.41 760.62, 593.22 763.93, 589.98 758.86, 593.06 756.90, 587.81 748.73, 590.09 747.27, 587.92 743.89, 616.50 725.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,8,"POLYGON ((864.19 650.41, 897.47 676.82, 895.05 679.84, 900.00 683.76, 900.00 733.21, 879.78 717.14, 882.85 713.31, 877.38 708.97, 873.58 713.70, 836.84 684.54, 864.19 650.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,9,"POLYGON ((679.47 667.19, 682.36 668.52, 683.92 665.15, 686.89 666.52, 688.09 663.93, 702.23 670.41, 701.21 672.59, 705.39 674.50, 706.29 672.57, 719.68 678.73, 718.61 681.07, 734.93 688.58, 733.36 691.95, 736.19 693.25, 722.14 723.51, 719.10 722.12, 717.81 724.88, 703.16 718.15, 702.06 720.54, 697.77 718.58, 699.22 715.48, 686.34 709.57, 684.84 712.81, 668.39 705.28, 669.64 702.58, 668.03 701.85, 669.03 699.67, 665.25 697.94, 679.47 667.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,10,"POLYGON ((726.44 566.02, 742.15 567.90, 741.94 573.50, 744.77 573.78, 742.97 600.11, 722.67 597.81, 724.51 565.89, 726.44 566.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,11,"POLYGON ((715.01 573.54, 714.14 576.98, 717.41 577.83, 712.74 595.93, 698.29 592.24, 699.43 587.77, 695.92 586.88, 700.33 569.79, 715.01 573.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,12,"POLYGON ((687.92 592.70, 671.80 588.54, 673.31 582.71, 668.93 581.58, 674.26 561.11, 686.31 564.20, 685.26 568.20, 693.70 570.38, 687.92 592.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,13,"POLYGON ((775.19 558.85, 778.14 590.87, 762.52 592.29, 762.03 586.95, 757.10 587.41, 754.66 560.70, 775.19 558.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,14,"POLYGON ((657.19 585.96, 640.54 582.23, 645.46 560.40, 650.48 561.54, 651.38 557.52, 667.69 561.14, 663.39 580.21, 658.72 579.17, 657.19 585.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,15,"POLYGON ((800.35 545.53, 815.16 575.45, 802.26 581.78, 799.42 576.08, 792.48 579.49, 780.48 555.27, 800.35 545.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,16,"POLYGON ((842.85 526.49, 847.90 531.00, 850.63 527.96, 862.95 538.94, 844.05 559.98, 838.83 555.32, 834.90 559.70, 822.75 548.87, 842.85 526.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,17,"POLYGON ((823.97 512.32, 832.51 520.01, 828.02 524.96, 834.29 530.59, 821.36 544.84, 810.99 535.52, 814.64 531.49, 810.19 527.50, 823.97 512.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,0,"POLYGON ((551.21 865.72, 603.40 862.37, 604.22 874.87, 552.01 878.24, 551.21 865.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,1,"POLYGON ((599.96 799.59, 614.13 798.79, 617.11 850.76, 613.37 854.50, 603.16 855.07, 599.96 799.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,2,"POLYGON ((599.15 792.46, 541.55 787.38, 540.56 780.24, 544.53 779.69, 546.81 775.97, 599.67 779.38, 599.24 785.91, 601.13 787.95, 599.15 792.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,3,"POLYGON ((551.86 702.10, 561.79 709.35, 571.85 703.79, 587.89 707.35, 603.21 707.14, 603.36 718.10, 545.37 718.89, 545.15 702.19, 551.86 702.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,4,"POLYGON ((887.49 691.07, 900.00 695.79, 900.00 726.20, 891.40 721.23, 889.61 712.86, 880.99 706.53, 886.44 699.20, 887.49 691.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,5,"POLYGON ((821.70 675.26, 856.88 672.79, 858.82 699.86, 850.75 700.43, 844.17 690.67, 837.62 691.90, 830.68 700.01, 823.50 700.52, 821.70 675.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,6,"POLYGON ((763.90 662.71, 769.73 669.44, 770.27 681.19, 723.04 683.35, 722.17 664.62, 763.90 662.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,7,"POLYGON ((900.00 679.14, 890.25 676.02, 891.72 672.88, 896.76 673.37, 900.00 668.64, 900.00 679.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,8,"POLYGON ((547.21 647.75, 593.34 646.24, 597.01 660.04, 551.48 658.56, 547.21 647.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,9,"POLYGON ((626.59 228.29, 627.82 258.45, 609.70 259.20, 608.46 229.02, 626.59 228.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,10,"POLYGON ((749.86 130.96, 776.92 167.74, 753.32 184.92, 726.28 148.16, 749.86 130.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,11,"POLYGON ((693.80 146.64, 703.74 152.47, 698.20 161.86, 688.25 156.04, 693.80 146.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,12,"POLYGON ((900.00 102.49, 893.77 102.69, 893.07 82.02, 900.00 81.78, 900.00 102.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,13,"POLYGON ((854.12 31.07, 856.26 76.58, 832.44 77.70, 830.29 32.19, 854.12 31.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,14,"POLYGON ((231.78 879.26, 235.75 876.18, 245.30 882.91, 243.87 884.90, 244.97 889.51, 248.28 896.44, 242.84 899.02, 226.11 887.46, 231.78 879.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,15,"POLYGON ((433.61 887.76, 445.26 889.20, 447.79 836.26, 461.01 837.67, 459.35 889.55, 448.82 890.68, 448.12 900.00, 432.70 900.00, 433.61 887.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,16,"POLYGON ((212.31 864.24, 226.63 872.40, 227.87 875.70, 227.98 880.18, 226.71 883.54, 224.19 886.93, 214.78 878.96, 215.68 873.52, 216.01 869.76, 212.31 864.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,17,"POLYGON ((351.68 859.93, 365.41 861.82, 364.13 870.51, 361.32 877.27, 356.46 881.61, 355.77 883.84, 353.35 876.74, 349.02 872.87, 351.68 859.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,18,"POLYGON ((368.72 835.97, 372.25 837.63, 369.83 840.16, 368.04 847.88, 371.00 856.97, 369.36 860.97, 361.88 859.91, 353.60 857.66, 350.62 857.98, 352.24 852.99, 355.13 839.04, 357.45 832.55, 368.72 835.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,19,"POLYGON ((203.57 829.17, 203.44 844.35, 198.00 840.74, 196.47 833.72, 194.07 829.92, 194.31 826.78, 203.57 829.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,20,"POLYGON ((195.98 800.68, 199.60 799.23, 206.12 815.16, 205.76 828.33, 201.71 828.21, 192.11 822.87, 195.98 800.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,21,"POLYGON ((439.09 799.05, 446.01 800.12, 453.13 799.94, 453.91 805.56, 453.86 812.64, 451.69 817.69, 442.01 815.43, 438.04 815.33, 437.25 809.09, 439.09 799.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,22,"POLYGON ((260.15 762.37, 266.74 773.37, 231.65 794.20, 225.07 783.20, 260.15 762.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,23,"POLYGON ((436.79 757.07, 451.34 758.12, 449.40 785.07, 434.85 784.02, 436.79 757.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,24,"POLYGON ((375.67 728.06, 377.19 743.03, 372.07 747.62, 375.85 785.15, 365.62 786.19, 359.93 729.64, 375.67 728.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,25,"POLYGON ((508.80 725.67, 548.14 726.42, 547.53 758.24, 540.26 751.37, 530.55 752.95, 526.06 750.84, 522.46 745.74, 518.03 748.85, 512.89 756.04, 508.32 750.43, 508.80 725.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,26,"POLYGON ((452.88 730.31, 450.50 753.97, 436.86 752.61, 439.25 728.95, 452.88 730.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,27,"POLYGON ((239.66 711.04, 250.00 720.63, 242.20 728.98, 231.86 719.39, 239.66 711.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,28,"POLYGON ((434.69 707.36, 434.67 720.28, 378.01 720.17, 378.02 713.26, 382.40 711.02, 420.95 710.52, 424.49 707.33, 434.69 707.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,29,"POLYGON ((221.83 694.11, 209.71 706.81, 192.61 690.40, 202.39 673.57, 221.83 694.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,30,"POLYGON ((319.76 623.96, 324.77 631.73, 291.98 652.57, 286.98 644.77, 319.76 623.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,31,"POLYGON ((424.18 623.34, 422.07 633.51, 388.94 634.36, 390.21 624.63, 424.18 623.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,32,"POLYGON ((319.09 583.75, 324.92 591.22, 297.05 612.82, 291.22 605.37, 319.09 583.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,33,"POLYGON ((283.83 578.12, 288.94 581.97, 292.71 588.13, 260.45 607.69, 253.58 596.45, 283.83 578.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,34,"POLYGON ((359.22 545.39, 372.19 550.77, 350.80 602.17, 337.73 596.82, 359.22 545.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,35,"POLYGON ((503.92 543.17, 509.29 544.81, 503.66 550.23, 503.48 560.83, 507.21 568.23, 487.71 600.70, 474.19 592.65, 503.92 543.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,36,"POLYGON ((381.41 534.81, 411.93 517.59, 416.40 519.09, 419.59 524.76, 387.79 542.86, 384.27 539.85, 381.41 534.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,37,"POLYGON ((306.33 522.72, 339.99 503.39, 343.28 514.68, 308.69 534.60, 305.94 529.86, 306.33 522.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,38,"POLYGON ((424.94 496.81, 438.75 491.22, 447.91 513.61, 424.28 523.20, 420.58 514.17, 430.40 510.17, 424.94 496.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,39,"POLYGON ((237.11 458.87, 250.79 464.96, 227.82 515.76, 214.14 509.58, 237.11 458.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,40,"POLYGON ((332.86 445.80, 339.53 456.30, 324.96 465.46, 318.29 454.96, 332.86 445.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,41,"POLYGON ((240.89 380.01, 254.89 379.83, 255.58 434.85, 241.57 435.03, 240.89 380.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,42,"POLYGON ((328.46 378.78, 329.37 434.59, 313.12 434.84, 312.22 379.04, 326.42 379.30, 328.46 378.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,43,"POLYGON ((243.24 372.52, 243.53 380.48, 188.05 382.47, 187.75 374.51, 243.24 372.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,44,"POLYGON ((326.23 366.14, 383.55 365.37, 383.71 378.53, 326.42 379.30, 326.23 366.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,0,"POLYGON ((806.63 790.64, 809.25 788.82, 804.88 782.56, 809.82 779.13, 806.47 774.33, 820.81 764.40, 822.44 766.75, 829.16 762.10, 827.45 759.68, 843.34 748.66, 844.72 750.64, 854.38 743.94, 852.68 741.52, 868.31 730.68, 869.41 732.23, 872.56 730.04, 870.81 727.58, 879.88 721.27, 881.55 721.96, 887.22 717.28, 888.78 719.17, 892.96 715.71, 892.92 714.25, 885.32 703.83, 882.57 705.84, 866.66 684.04, 868.92 682.41, 863.71 675.15, 861.25 676.88, 845.78 655.05, 845.42 651.11, 840.48 643.62, 837.51 645.56, 830.72 635.28, 830.40 627.74, 827.75 625.57, 821.51 625.24, 819.05 626.30, 816.89 629.20, 814.12 631.27, 816.67 634.62, 797.24 649.21, 799.14 651.74, 791.16 657.72, 788.70 654.47, 782.30 659.25, 778.79 654.61, 775.26 657.26, 766.85 646.13, 768.87 644.61, 766.34 641.24, 776.67 633.51, 771.51 626.67, 774.42 624.47, 769.49 617.91, 774.16 614.44, 770.84 610.02, 793.69 592.96, 794.88 594.56, 803.58 588.07, 802.81 586.25, 812.88 579.18, 814.09 580.88, 815.84 579.66, 814.17 577.30, 824.19 570.28, 825.24 571.78, 829.69 568.67, 839.46 582.45, 847.60 576.71, 837.10 561.91, 840.25 559.68, 837.98 556.52, 854.30 545.02, 855.84 547.18, 863.81 541.56, 861.81 538.75, 872.74 531.03, 874.12 532.97, 882.56 527.03, 885.12 525.14, 883.41 522.81, 893.97 515.06, 895.73 517.43, 900.00 514.29, 900.00 556.13, 894.72 548.96, 855.17 577.81, 857.86 581.45, 855.02 583.52, 860.67 591.23, 841.91 604.92, 900.00 683.86, 900.00 767.87, 899.70 768.11, 897.63 765.45, 890.77 770.76, 892.07 772.43, 881.69 780.49, 879.99 778.31, 871.70 784.76, 872.70 786.05, 863.66 793.07, 862.30 791.33, 854.45 797.43, 856.00 799.44, 845.75 807.40, 844.12 805.31, 840.19 808.36, 838.26 805.91, 836.33 807.40, 838.49 810.14, 833.15 814.29, 833.92 815.25, 826.79 820.81, 822.49 822.42, 817.88 825.61, 804.20 806.00, 813.07 799.87, 806.63 790.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,1,"POLYGON ((900.00 483.71, 895.47 483.90, 895.59 486.41, 875.62 487.27, 875.44 483.04, 861.28 483.64, 861.47 488.30, 845.13 489.01, 844.92 484.15, 838.99 484.41, 838.64 476.41, 835.32 476.54, 834.35 454.45, 837.76 454.30, 837.40 446.12, 843.49 445.85, 843.27 440.93, 872.13 439.66, 872.22 441.72, 883.38 441.21, 883.28 438.68, 896.30 438.11, 896.39 440.35, 900.00 440.18, 900.00 483.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,2,"POLYGON ((602.83 466.01, 646.91 465.95, 646.87 440.44, 718.86 440.31, 719.16 637.18, 602.74 637.38, 602.62 558.65, 596.29 558.65, 596.20 501.38, 602.88 501.37, 602.83 466.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,3,"POLYGON ((804.19 458.24, 804.69 547.27, 754.30 547.56, 754.15 518.97, 761.34 518.94, 761.00 458.50, 804.19 458.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,4,"POLYGON ((715.96 380.58, 717.14 405.59, 679.25 407.36, 678.07 382.37, 715.96 380.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,5,"POLYGON ((796.26 284.19, 798.19 383.76, 723.88 385.22, 721.95 285.62, 796.26 284.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,6,"POLYGON ((895.08 273.54, 896.24 308.27, 900.00 308.14, 900.00 377.32, 891.51 377.60, 891.64 381.84, 830.19 383.92, 827.58 306.94, 847.86 306.24, 846.80 275.17, 895.08 273.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,7,"POLYGON ((611.69 208.55, 613.89 357.64, 620.77 357.87, 621.43 368.30, 577.33 396.78, 569.15 393.37, 566.64 215.72, 590.48 215.93, 590.70 209.08, 611.69 208.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,8,"POLYGON ((699.91 225.54, 700.74 261.55, 712.74 261.28, 713.56 296.14, 659.51 297.38, 657.87 226.50, 699.91 225.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,9,"POLYGON ((859.73 188.18, 861.42 247.76, 809.67 249.22, 808.00 189.63, 859.73 188.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,10,"POLYGON ((645.82 102.68, 656.63 160.54, 584.39 172.57, 569.05 172.97, 568.42 113.65, 645.82 102.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,11,"POLYGON ((585.42 18.36, 588.89 30.15, 581.14 32.41, 577.69 20.62, 585.42 18.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,12,"POLYGON ((648.31 13.47, 651.52 25.24, 637.34 29.09, 634.13 17.29, 648.31 13.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,13,"POLYGON ((357.68 111.22, 357.47 136.51, 339.60 136.34, 339.82 111.05, 357.68 111.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,14,"POLYGON ((367.14 115.95, 390.25 114.60, 390.51 119.28, 395.25 119.00, 395.81 128.38, 371.26 129.80, 370.56 127.87, 369.73 126.69, 367.74 126.23, 367.14 115.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,15,"POLYGON ((38.85 110.08, 54.29 109.22, 56.10 110.39, 56.92 129.53, 53.40 129.69, 51.41 133.20, 41.21 133.79, 40.96 129.54, 39.90 128.81, 38.85 110.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,16,"POLYGON ((101.74 120.02, 98.78 120.11, 98.08 99.33, 120.72 98.58, 122.09 139.39, 102.42 140.07, 101.74 120.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,17,"POLYGON ((309.61 104.21, 311.07 127.30, 289.79 128.64, 288.08 101.56, 301.24 100.74, 301.49 104.70, 309.61 104.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,18,"POLYGON ((335.43 97.42, 334.98 128.40, 316.82 128.13, 317.27 97.15, 335.43 97.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,19,"POLYGON ((242.77 100.88, 243.43 121.06, 224.41 121.70, 223.74 101.50, 242.77 100.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,20,"POLYGON ((513.89 129.36, 504.79 126.48, 493.09 122.27, 477.95 120.91, 477.08 110.19, 480.40 109.52, 480.83 102.85, 488.05 101.12, 492.74 92.98, 496.22 90.74, 497.21 106.16, 512.32 106.36, 513.89 129.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,21,"POLYGON ((416.88 82.33, 416.31 88.36, 419.21 88.05, 420.37 95.70, 427.40 96.49, 429.13 136.10, 403.07 136.76, 400.90 108.47, 406.07 103.52, 409.07 97.43, 409.06 87.82, 407.48 83.06, 416.88 82.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,22,"POLYGON ((133.79 89.96, 157.38 89.54, 158.03 126.39, 155.96 129.04, 134.48 129.44, 133.79 89.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,23,"POLYGON ((457.30 91.42, 457.88 123.46, 444.48 123.72, 444.39 118.57, 435.62 118.73, 435.14 91.83, 457.30 91.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,24,"POLYGON ((163.97 84.78, 184.26 83.57, 184.62 89.86, 188.03 89.67, 190.24 126.95, 166.55 128.35, 163.97 84.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,25,"POLYGON ((276.15 74.67, 276.18 121.38, 269.36 121.38, 269.36 127.32, 253.05 127.34, 253.02 74.69, 276.15 74.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,26,"POLYGON ((215.23 60.18, 215.48 81.48, 213.20 88.11, 216.73 88.07, 217.03 127.00, 196.13 127.17, 195.82 88.47, 206.54 88.39, 206.50 84.16, 200.50 77.74, 200.30 60.36, 215.23 60.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,27,"POLYGON ((36.92 66.00, 55.29 65.62, 55.68 85.48, 37.32 85.86, 36.92 66.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,28,"POLYGON ((243.19 56.49, 243.77 76.81, 223.87 77.36, 223.29 57.07, 243.19 56.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,29,"POLYGON ((513.75 54.15, 513.93 68.58, 519.33 68.51, 519.40 73.77, 489.68 74.13, 489.49 58.28, 493.66 58.22, 493.62 54.38, 513.75 54.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,30,"POLYGON ((514.43 18.95, 514.84 42.58, 463.48 43.47, 463.23 28.58, 496.02 27.99, 495.88 19.27, 514.43 18.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,31,"POLYGON ((58.18 0.00, 58.03 12.58, 42.56 12.38, 42.69 0.00, 58.18 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,32,"POLYGON ((449.68 0.00, 449.88 8.35, 406.64 9.41, 406.41 0.00, 449.68 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,33,"POLYGON ((6.76 487.14, 13.82 486.96, 13.32 466.77, 20.45 466.61, 20.61 472.84, 36.94 472.42, 41.77 476.52, 41.86 480.09, 38.26 480.18, 38.36 484.00, 42.31 483.90, 42.56 493.97, 6.94 494.83, 6.76 487.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,34,"POLYGON ((333.59 471.71, 362.06 472.27, 361.77 486.91, 333.30 486.35, 333.59 471.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,35,"POLYGON ((110.66 446.59, 111.08 454.31, 112.15 454.81, 113.59 496.93, 105.79 497.17, 101.02 494.56, 95.99 497.42, 79.43 497.84, 78.90 478.04, 82.74 477.92, 82.64 474.01, 76.34 474.17, 76.04 463.30, 79.32 462.29, 78.92 454.86, 86.31 454.47, 85.96 447.93, 110.66 446.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,36,"POLYGON ((267.68 460.76, 267.83 481.34, 238.42 481.55, 238.30 466.68, 245.15 466.64, 245.10 460.92, 267.68 460.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,37,"POLYGON ((339.80 437.41, 361.26 437.44, 361.26 440.26, 378.10 440.27, 378.06 459.08, 359.78 459.03, 359.78 456.83, 339.75 456.79, 339.80 437.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,38,"POLYGON ((220.52 434.20, 220.80 443.73, 216.82 448.90, 216.93 452.56, 208.85 452.81, 208.31 434.57, 220.52 434.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,39,"POLYGON ((91.54 437.80, 91.47 446.57, 82.27 446.49, 82.32 437.72, 91.54 437.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,40,"POLYGON ((191.55 394.36, 191.89 432.66, 190.90 432.98, 191.39 472.28, 174.04 472.48, 173.69 442.96, 179.23 442.89, 179.11 431.44, 170.54 431.52, 170.31 409.24, 173.15 409.21, 173.01 394.52, 191.55 394.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,41,"POLYGON ((233.20 423.77, 248.50 423.58, 248.64 435.54, 233.33 435.73, 233.20 423.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,42,"POLYGON ((467.35 409.20, 469.29 439.94, 458.05 440.65, 457.64 433.89, 449.66 434.38, 448.16 410.40, 467.35 409.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,43,"POLYGON ((415.83 409.43, 416.76 428.39, 408.25 428.80, 408.51 434.08, 392.05 434.88, 390.86 410.64, 415.83 409.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,44,"POLYGON ((437.46 401.75, 437.72 438.26, 419.21 438.40, 418.96 401.89, 437.46 401.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,45,"POLYGON ((126.81 390.91, 126.73 387.71, 135.10 387.50, 135.27 394.17, 142.98 394.00, 144.01 435.68, 119.16 436.30, 118.05 391.13, 126.81 390.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,46,"POLYGON ((89.36 386.40, 90.80 433.34, 75.16 433.80, 73.91 392.79, 84.84 392.44, 84.65 386.54, 89.36 386.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,47,"POLYGON ((196.30 390.22, 198.02 390.15, 207.95 400.15, 212.06 396.12, 207.47 391.51, 207.83 390.19, 219.27 390.23, 219.24 395.05, 221.15 395.76, 221.41 402.21, 223.13 405.61, 223.95 428.87, 197.70 429.81, 196.30 390.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,48,"POLYGON ((363.25 391.24, 363.00 419.01, 352.00 418.92, 352.03 414.99, 343.48 414.92, 343.71 391.07, 363.25 391.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,49,"POLYGON ((381.85 387.46, 381.64 393.14, 383.18 393.19, 382.17 420.35, 364.11 419.67, 365.14 391.70, 367.70 391.79, 367.87 386.95, 381.85 387.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,50,"POLYGON ((23.51 385.13, 34.87 385.15, 34.90 382.44, 40.29 382.44, 40.24 402.26, 44.67 402.28, 44.63 423.10, 23.44 423.05, 23.51 385.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,51,"POLYGON ((5.64 386.14, 19.31 385.61, 20.59 417.46, 1.75 418.20, 0.73 392.55, 5.90 392.35, 5.64 386.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,52,"POLYGON ((339.92 386.48, 340.76 414.85, 322.99 415.37, 322.16 387.00, 339.92 386.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,53,"POLYGON ((294.02 383.13, 293.99 413.81, 262.62 413.79, 262.66 383.11, 294.02 383.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,54,"POLYGON ((250.67 376.04, 252.30 416.00, 232.72 416.79, 231.09 376.83, 250.67 376.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,55,"POLYGON ((535.03 348.71, 536.82 386.09, 523.60 386.72, 523.39 382.29, 515.36 382.67, 513.77 349.72, 535.03 348.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,56,"POLYGON ((475.35 334.68, 486.22 334.87, 487.04 342.37, 490.14 346.20, 495.28 350.62, 497.45 373.91, 485.96 374.21, 485.39 370.14, 476.88 370.20, 475.35 334.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,57,"POLYGON ((446.64 350.44, 447.18 314.33, 461.56 314.54, 461.34 329.26, 464.51 329.29, 464.19 350.70, 446.64 350.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,58,"POLYGON ((408.50 313.74, 408.02 326.16, 414.12 326.41, 413.59 340.47, 393.07 339.69, 394.08 313.18, 408.50 313.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,59,"POLYGON ((376.10 296.81, 376.06 302.56, 384.87 302.65, 384.74 316.92, 375.54 316.85, 375.51 321.47, 354.81 321.29, 355.02 296.64, 376.10 296.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,60,"POLYGON ((66.86 281.31, 67.07 322.48, 54.50 322.56, 54.46 314.88, 47.76 314.92, 47.58 281.69, 59.17 281.62, 62.60 285.26, 66.86 281.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,61,"POLYGON ((96.21 282.38, 102.57 282.31, 106.42 287.14, 109.47 284.73, 117.01 282.81, 116.97 311.02, 114.86 320.73, 106.78 321.05, 103.32 311.72, 98.21 312.01, 97.77 304.21, 96.40 299.29, 96.21 282.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,62,"POLYGON ((43.95 291.02, 44.81 316.79, 32.08 317.23, 31.88 311.48, 25.77 311.68, 24.82 283.29, 34.68 282.98, 34.94 291.32, 43.95 291.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,63,"POLYGON ((3.72 276.80, 16.01 276.50, 16.16 282.43, 23.37 282.24, 24.09 317.28, 19.72 317.30, 19.73 320.65, 17.21 320.67, 17.22 323.18, 4.28 323.24, 3.72 276.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,64,"POLYGON ((89.49 286.88, 90.47 318.05, 78.37 318.44, 76.77 313.89, 71.40 314.03, 70.47 279.22, 81.09 278.88, 81.35 287.13, 89.49 286.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,65,"POLYGON ((496.18 288.15, 527.68 288.28, 527.70 307.72, 505.38 309.07, 502.57 303.66, 501.02 299.15, 496.56 296.44, 496.18 288.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,66,"POLYGON ((147.34 275.68, 148.95 309.69, 141.73 310.03, 138.56 307.83, 126.74 308.31, 126.30 297.64, 121.67 297.83, 121.18 285.83, 138.14 285.13, 144.14 275.83, 147.34 275.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,67,"POLYGON ((155.87 266.12, 174.48 265.60, 175.08 286.70, 183.82 286.45, 184.21 300.27, 156.72 300.99, 155.87 266.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,68,"POLYGON ((209.48 247.35, 229.47 265.37, 233.20 261.93, 236.65 264.97, 250.55 264.83, 254.50 261.16, 257.71 264.21, 266.71 255.72, 268.52 300.78, 222.73 299.95, 209.44 306.55, 209.48 247.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,69,"POLYGON ((522.47 262.55, 522.71 275.00, 528.94 274.88, 529.13 284.80, 499.12 285.37, 498.69 263.02, 522.47 262.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,70,"POLYGON ((369.77 263.85, 371.27 280.31, 350.92 280.12, 351.69 274.70, 352.65 269.86, 351.25 264.50, 369.77 263.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,71,"POLYGON ((371.46 234.24, 372.24 256.80, 348.03 257.64, 347.25 235.10, 371.46 234.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,72,"POLYGON ((525.95 234.31, 526.15 254.35, 499.03 254.60, 498.86 234.56, 525.95 234.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,73,"POLYGON ((334.35 204.05, 345.39 204.08, 345.36 207.45, 374.43 207.51, 374.39 223.87, 358.87 223.84, 358.85 227.57, 334.30 227.53, 334.35 204.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,74,"POLYGON ((472.63 185.53, 474.15 239.99, 456.05 240.49, 455.28 213.39, 451.78 213.50, 451.03 186.13, 472.63 185.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,75,"POLYGON ((24.37 189.34, 31.76 189.17, 31.86 192.86, 35.66 193.74, 36.70 191.65, 40.43 191.62, 40.67 217.92, 37.04 217.94, 37.10 223.98, 30.72 224.03, 30.69 222.14, 24.89 222.20, 24.37 189.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,76,"POLYGON ((435.66 186.83, 437.80 217.65, 417.61 219.03, 415.47 188.21, 435.66 186.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,77,"POLYGON ((50.38 187.68, 58.84 187.37, 58.75 185.42, 71.40 184.97, 72.54 216.33, 67.59 216.50, 67.49 213.95, 62.34 214.12, 62.47 217.91, 58.71 218.03, 58.82 220.54, 54.54 220.69, 54.30 213.64, 51.13 208.35, 50.38 187.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,78,"POLYGON ((9.23 193.28, 20.69 192.72, 21.83 215.64, 15.67 215.96, 15.80 218.86, 11.22 219.09, 11.07 215.90, 7.59 216.05, 7.69 217.96, 3.45 218.18, 1.86 186.39, 8.88 186.05, 9.23 193.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,79,"POLYGON ((119.12 184.21, 127.22 184.05, 127.31 189.04, 133.24 188.91, 133.65 208.37, 127.77 208.48, 127.89 213.71, 124.38 213.78, 119.69 210.70, 119.12 184.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,80,"POLYGON ((84.85 184.36, 105.64 183.85, 105.92 195.05, 104.32 196.38, 104.68 210.35, 94.45 210.62, 94.12 197.55, 85.17 197.76, 84.85 184.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,81,"POLYGON ((509.36 182.04, 510.41 209.43, 496.72 209.93, 496.41 201.95, 482.11 202.49, 481.64 189.72, 496.75 189.15, 496.49 182.52, 509.36 182.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,82,"POLYGON ((158.01 189.84, 174.91 189.52, 176.97 184.93, 179.13 184.88, 182.70 188.67, 182.97 204.60, 158.29 205.06, 158.10 194.47, 158.01 189.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,83,"POLYGON ((221.07 175.51, 277.83 173.66, 278.11 182.33, 278.40 209.67, 231.58 210.16, 221.94 202.17, 221.07 175.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,84,"POLYGON ((352.96 179.91, 354.39 182.76, 362.53 182.75, 362.55 198.95, 341.21 198.14, 340.91 193.51, 343.46 187.68, 347.46 184.29, 348.71 180.39, 352.96 179.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,85,"POLYGON ((371.89 182.02, 380.97 181.99, 380.96 179.48, 388.34 179.45, 388.34 181.62, 399.54 181.58, 399.60 197.70, 371.94 197.80, 371.89 182.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,86,"POLYGON ((12.96 866.90, 12.14 837.57, 33.97 836.95, 35.06 875.53, 19.87 875.96, 19.68 869.08, 12.96 866.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,87,"POLYGON ((39.53 834.12, 43.13 834.92, 54.82 863.57, 54.72 875.56, 39.19 875.44, 39.53 834.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,88,"POLYGON ((100.86 861.53, 100.08 833.22, 115.18 832.79, 115.23 834.94, 122.77 834.75, 123.58 863.59, 116.58 863.79, 113.45 861.18, 100.86 861.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,89,"POLYGON ((133.41 833.68, 153.62 833.34, 154.04 856.95, 133.80 857.29, 133.41 833.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,90,"POLYGON ((187.80 815.33, 187.66 860.62, 178.50 865.09, 163.39 865.06, 163.51 824.74, 169.22 824.75, 169.26 815.27, 187.80 815.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,91,"POLYGON ((195.80 821.03, 195.70 809.05, 209.62 808.91, 209.69 817.39, 228.71 817.22, 228.79 833.35, 251.51 833.26, 251.68 870.46, 224.80 870.59, 224.75 861.23, 226.53 861.23, 226.35 835.81, 206.10 835.98, 206.00 820.95, 195.80 821.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,92,"POLYGON ((254.30 811.68, 290.62 811.02, 290.27 791.36, 316.23 790.90, 316.75 820.61, 321.45 826.75, 322.66 858.17, 286.56 859.54, 286.89 868.30, 284.02 871.79, 255.37 872.30, 254.30 811.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,93,"POLYGON ((342.98 832.08, 342.06 785.07, 380.78 784.30, 381.96 844.29, 387.08 844.21, 387.46 863.49, 344.40 864.34, 344.57 873.39, 329.54 873.69, 329.35 864.13, 326.65 864.17, 326.02 832.42, 342.98 832.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,94,"POLYGON ((73.52 836.54, 72.73 819.53, 95.14 818.50, 95.93 835.50, 73.52 836.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,95,"POLYGON ((101.04 817.28, 117.65 816.43, 117.92 821.80, 107.94 822.30, 101.68 819.57, 101.04 817.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,96,"POLYGON ((136.99 807.79, 151.32 807.65, 151.45 821.34, 137.12 821.49, 136.99 807.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,97,"POLYGON ((165.44 751.55, 176.02 752.10, 174.99 757.50, 178.17 763.61, 175.26 769.61, 171.79 771.54, 166.72 774.96, 165.44 751.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,98,"POLYGON ((44.13 770.92, 44.43 743.01, 54.86 743.12, 54.94 735.66, 68.69 735.80, 68.20 780.60, 55.46 780.48, 55.53 774.53, 45.92 774.42, 44.13 770.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,99,"POLYGON ((254.28 732.63, 265.01 732.43, 265.12 738.71, 278.92 738.44, 279.61 774.67, 275.14 774.77, 275.23 779.60, 269.37 779.71, 269.45 782.79, 255.29 783.09, 254.28 732.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,100,"POLYGON ((15.47 774.60, 15.46 735.02, 37.46 735.02, 37.46 773.55, 27.42 773.56, 27.40 777.45, 16.48 777.46, 15.47 774.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,101,"POLYGON ((105.76 774.12, 104.80 734.90, 128.77 734.31, 129.64 768.85, 128.01 774.13, 125.31 777.88, 109.46 778.04, 105.76 774.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,102,"POLYGON ((133.73 770.94, 133.51 736.01, 151.51 735.88, 151.53 739.10, 159.24 739.06, 159.49 775.50, 135.09 775.66, 133.73 770.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,103,"POLYGON ((75.66 734.44, 93.68 734.38, 93.70 740.99, 100.93 740.97, 101.04 772.17, 95.35 772.21, 95.36 776.38, 76.38 763.95, 75.66 734.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,104,"POLYGON ((0.00 735.23, 10.09 735.36, 9.63 771.31, 0.00 771.19, 0.00 735.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,105,"POLYGON ((227.87 764.14, 229.23 733.92, 247.88 734.73, 247.10 752.04, 252.00 752.25, 251.32 767.43, 249.81 771.77, 229.84 770.88, 230.14 764.26, 227.87 764.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,106,"POLYGON ((346.05 666.43, 346.11 657.12, 390.24 657.42, 390.12 673.02, 373.01 672.91, 375.21 681.51, 343.24 681.30, 343.34 666.41, 346.05 666.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,107,"POLYGON ((0.00 647.34, 3.76 647.28, 7.05 653.28, 13.18 657.28, 20.53 657.71, 20.61 683.26, 15.01 683.27, 15.02 686.73, 3.53 686.76, 3.52 680.45, 0.00 680.47, 0.00 647.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,108,"POLYGON ((521.05 668.68, 524.15 668.65, 524.26 677.99, 485.59 678.40, 485.55 674.45, 481.41 674.51, 481.31 666.06, 492.84 665.94, 492.81 661.79, 513.06 654.28, 520.92 657.10, 521.05 668.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,109,"POLYGON ((22.10 678.47, 22.02 670.55, 25.57 670.52, 30.72 667.79, 34.36 659.78, 37.92 657.71, 41.75 652.53, 41.72 660.59, 38.13 671.18, 38.86 675.00, 41.39 676.65, 40.30 678.32, 22.10 678.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,110,"POLYGON ((107.62 672.60, 106.79 657.60, 112.19 654.97, 119.45 644.33, 127.89 644.40, 127.55 677.62, 116.24 677.51, 116.26 675.47, 115.02 673.28, 112.77 671.58, 109.51 671.67, 107.62 672.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,111,"POLYGON ((76.59 671.49, 76.23 641.42, 99.90 641.12, 100.33 677.54, 78.78 677.80, 78.70 672.12, 76.59 671.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,112,"POLYGON ((427.99 643.49, 427.82 638.99, 433.69 638.77, 433.83 642.94, 437.22 642.81, 438.47 677.34, 421.49 677.95, 420.24 643.77, 427.99 643.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,113,"POLYGON ((250.98 639.46, 263.27 639.55, 263.18 652.49, 269.05 652.54, 268.98 664.00, 270.33 664.03, 270.32 665.14, 269.06 665.11, 268.90 675.68, 245.79 675.36, 246.11 654.17, 250.85 654.25, 250.98 639.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,114,"POLYGON ((218.80 671.67, 217.94 640.28, 237.91 639.75, 238.75 670.30, 236.48 668.73, 234.88 669.22, 231.43 673.41, 218.94 674.11, 218.80 671.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,115,"POLYGON ((162.35 668.41, 161.65 640.74, 171.55 640.80, 172.43 634.45, 184.70 634.56, 185.73 668.83, 184.47 668.98, 184.67 674.12, 175.15 674.30, 175.20 676.36, 167.42 675.96, 167.30 674.21, 169.44 673.49, 169.99 672.08, 168.44 669.32, 167.97 668.53, 166.12 668.64, 163.72 669.51, 162.46 669.54, 162.35 668.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,116,"POLYGON ((189.70 667.78, 188.76 633.93, 206.32 633.28, 206.60 640.55, 210.77 640.40, 211.27 653.48, 213.66 655.04, 213.63 671.07, 206.70 677.00, 202.82 676.05, 197.76 676.25, 197.55 669.95, 190.39 670.20, 189.70 667.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,117,"POLYGON ((135.75 656.72, 138.69 650.61, 141.79 650.09, 144.16 645.03, 148.74 644.76, 150.77 643.24, 151.29 640.28, 147.64 636.55, 147.58 631.31, 158.27 631.15, 158.58 653.48, 160.84 653.44, 161.16 675.96, 136.03 676.34, 135.75 656.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,118,"POLYGON ((272.52 632.92, 285.62 644.79, 296.44 632.95, 296.38 651.91, 291.88 657.64, 297.68 657.54, 297.94 672.94, 289.88 673.10, 288.50 674.20, 269.44 674.33, 269.42 666.07, 272.61 666.08, 272.52 632.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,119,"POLYGON ((336.74 653.90, 335.94 631.08, 373.15 629.77, 373.34 635.18, 374.66 637.99, 374.96 647.13, 372.02 647.23, 372.15 650.82, 345.07 651.76, 345.13 653.60, 336.74 653.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,120,"POLYGON ((521.75 632.75, 517.14 633.20, 513.77 637.52, 523.29 637.44, 523.33 643.16, 513.73 643.23, 507.56 638.41, 498.08 638.15, 498.36 628.15, 507.80 621.71, 520.45 620.28, 521.75 632.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,121,"POLYGON ((337.00 628.54, 336.47 607.18, 375.13 606.30, 375.15 608.12, 379.60 608.03, 379.72 613.29, 376.03 613.38, 376.34 627.67, 337.00 628.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,122,"POLYGON ((19.65 619.57, 19.59 604.90, 43.32 604.83, 43.36 615.00, 47.30 614.98, 47.36 629.37, 21.62 629.45, 21.60 622.65, 19.65 619.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,123,"POLYGON ((265.65 605.79, 260.29 605.59, 260.50 600.08, 293.85 601.32, 293.16 619.96, 265.15 618.92, 265.65 605.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,124,"POLYGON ((503.45 612.66, 503.52 615.96, 496.98 616.11, 496.93 613.27, 490.14 613.40, 489.78 595.60, 510.00 595.20, 509.91 590.87, 519.97 590.66, 520.07 596.25, 525.63 596.13, 525.76 602.74, 515.68 602.96, 515.87 612.41, 503.45 612.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,125,"POLYGON ((10.07 592.20, 12.47 590.57, 22.29 589.27, 25.22 586.40, 37.84 586.46, 37.82 592.21, 32.04 592.20, 32.01 597.46, 16.06 597.38, 16.05 599.93, 10.08 599.91, 10.07 592.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,126,"POLYGON ((335.85 578.94, 377.40 577.65, 377.86 593.02, 383.33 592.86, 383.49 598.52, 376.40 598.72, 376.45 600.85, 336.55 602.07, 335.85 578.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,127,"POLYGON ((78.93 586.63, 78.53 579.40, 102.26 578.11, 102.53 582.76, 100.44 582.88, 100.65 586.85, 97.21 589.40, 86.56 589.99, 86.36 586.22, 78.93 586.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,128,"POLYGON ((245.75 585.74, 229.44 585.87, 229.15 551.52, 274.14 551.12, 274.18 556.40, 279.33 556.36, 279.49 576.34, 272.99 576.39, 273.06 585.20, 259.88 585.29, 259.96 594.10, 245.82 594.22, 245.75 585.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,129,"POLYGON ((4.00 575.40, 5.86 572.56, 5.97 569.73, 4.69 567.32, 5.16 565.56, 31.91 565.19, 31.93 567.03, 37.12 566.96, 37.23 574.93, 4.00 575.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,130,"POLYGON ((519.83 559.07, 520.27 577.00, 522.84 576.93, 522.92 580.15, 489.46 580.98, 489.37 577.15, 446.73 578.21, 446.45 567.30, 457.01 567.03, 456.91 562.92, 467.14 562.66, 467.26 567.63, 483.75 567.21, 483.57 559.98, 519.83 559.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,131,"POLYGON ((82.21 574.18, 82.09 567.92, 88.24 567.77, 87.97 555.08, 121.58 554.33, 122.00 573.45, 99.09 573.95, 95.32 570.72, 92.52 571.14, 90.31 574.00, 82.21 574.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,132,"POLYGON ((335.60 551.84, 373.87 550.51, 374.73 575.48, 336.46 576.79, 335.60 551.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,133,"POLYGON ((0.00 529.10, 31.69 528.48, 31.89 538.30, 35.82 538.23, 36.06 550.59, 0.00 551.29, 0.00 529.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,134,"POLYGON ((511.12 528.15, 512.19 535.12, 514.43 538.10, 519.45 544.43, 525.90 550.00, 486.13 550.66, 486.39 547.08, 482.99 547.34, 483.01 540.88, 469.68 540.87, 468.84 529.06, 511.12 528.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,135,"POLYGON ((115.00 538.36, 117.52 538.27, 117.74 545.30, 114.11 545.44, 114.17 547.81, 96.68 548.39, 96.28 549.47, 84.09 549.87, 83.80 540.66, 82.17 540.71, 81.97 534.27, 83.69 534.21, 83.45 526.84, 115.70 525.82, 114.65 527.47, 115.00 538.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,136,"POLYGON ((375.28 524.39, 376.06 544.94, 336.52 546.47, 335.73 525.91, 375.28 524.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,137,"POLYGON ((279.62 528.89, 281.70 542.14, 248.56 542.03, 262.94 528.54, 279.62 528.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,138,"POLYGON ((442.55 515.52, 456.16 515.33, 456.34 527.40, 442.71 527.59, 442.55 515.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,139,"POLYGON ((0.00 505.14, 1.70 504.65, 30.25 504.49, 36.01 508.74, 36.34 517.95, 30.46 518.14, 30.67 524.08, 0.00 525.14, 0.00 505.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,140,"POLYGON ((117.19 502.67, 117.90 523.30, 93.54 524.14, 93.40 520.17, 88.46 520.35, 86.55 523.30, 82.01 523.37, 81.74 502.58, 114.05 501.96, 117.19 502.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,141,"POLYGON ((396.91 503.21, 397.22 514.59, 399.81 515.70, 399.90 519.25, 384.56 519.67, 384.13 503.56, 396.91 503.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,142,"POLYGON ((355.01 507.43, 354.90 499.07, 373.86 498.81, 373.96 505.35, 383.65 505.22, 384.37 519.67, 337.37 521.03, 337.18 507.67, 355.01 507.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,143,"POLYGON ((478.59 494.95, 504.46 493.03, 508.11 495.09, 512.74 500.16, 517.90 504.69, 517.01 511.89, 513.01 517.89, 487.27 517.49, 487.38 514.62, 479.61 513.91, 478.59 494.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,0,"POLYGON ((791.64 56.12, 793.90 62.46, 796.36 61.64, 803.49 83.04, 799.10 84.48, 799.88 86.79, 796.08 88.04, 794.69 83.86, 791.23 81.31, 790.40 71.76, 786.03 62.75, 783.46 60.91, 783.12 58.47, 791.64 56.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,1,"POLYGON ((636.33 82.30, 635.99 59.47, 640.18 59.40, 653.06 68.26, 650.54 71.79, 650.81 76.65, 653.31 80.75, 651.10 82.08, 636.33 82.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,2,"POLYGON ((889.03 19.31, 894.16 34.94, 890.19 37.84, 888.00 41.23, 883.84 43.78, 883.21 46.94, 876.27 49.19, 873.31 40.12, 864.86 42.86, 857.60 20.61, 880.88 13.06, 883.52 21.10, 889.03 19.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,3,"POLYGON ((801.63 29.88, 781.16 36.79, 777.59 26.32, 762.90 31.29, 759.79 22.20, 763.69 23.83, 772.37 19.95, 778.20 21.02, 782.88 24.92, 796.72 23.52, 798.38 26.98, 801.63 29.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,4,"POLYGON ((876.85 11.10, 866.28 15.28, 865.19 12.58, 852.94 17.42, 846.00 0.00, 877.83 0.00, 878.83 2.50, 874.16 4.35, 876.85 11.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,5,"POLYGON ((632.96 0.00, 632.96 0.30, 602.42 22.77, 597.26 22.67, 597.38 16.90, 589.48 16.75, 589.62 10.00, 570.68 25.00, 564.02 26.21, 564.93 19.02, 558.90 18.28, 559.73 11.63, 553.38 10.83, 554.30 3.37, 558.47 0.00, 632.96 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,6,"POLYGON ((532.06 573.44, 532.75 583.08, 512.94 584.50, 509.53 575.13, 532.06 573.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,7,"POLYGON ((454.96 570.18, 478.97 570.05, 479.05 586.05, 455.03 586.18, 454.96 570.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,8,"POLYGON ((485.65 476.00, 511.77 474.16, 512.62 485.84, 503.18 490.41, 493.03 492.40, 489.75 500.38, 485.08 502.23, 480.17 502.27, 480.10 492.48, 483.54 490.91, 482.67 485.74, 478.54 485.24, 479.06 480.88, 485.65 476.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,9,"POLYGON ((518.94 458.33, 503.01 460.06, 491.03 448.74, 490.08 441.50, 484.12 442.28, 483.54 438.03, 488.46 435.44, 490.73 437.85, 495.85 433.03, 515.94 430.83, 518.94 458.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,10,"POLYGON ((394.99 405.82, 378.47 419.83, 378.54 432.95, 375.43 447.63, 368.46 456.71, 370.93 465.80, 321.66 464.57, 322.96 413.19, 343.64 413.71, 343.88 404.53, 394.99 405.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,11,"POLYGON ((0.00 362.15, 0.68 361.83, 5.26 355.67, 7.28 354.02, 10.89 362.01, 4.83 367.61, 6.85 372.13, 0.00 375.19, 0.00 362.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,12,"POLYGON ((63.15 305.51, 81.58 303.67, 79.17 307.70, 79.56 311.38, 75.76 312.94, 69.25 314.06, 69.99 318.39, 78.29 319.05, 82.23 323.07, 66.10 325.11, 65.57 320.81, 56.35 321.98, 55.96 319.04, 58.87 316.90, 57.38 311.06, 60.39 306.85, 60.48 305.83, 63.15 305.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,13,"POLYGON ((133.67 283.27, 135.26 302.72, 117.92 304.11, 116.34 284.66, 133.67 283.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,14,"POLYGON ((141.44 268.79, 144.63 268.31, 149.74 273.73, 156.16 271.84, 158.84 274.50, 160.78 286.75, 163.72 286.27, 164.47 290.98, 167.77 290.45, 168.63 295.80, 143.64 299.75, 142.78 294.33, 146.89 293.69, 145.41 284.36, 149.29 283.75, 149.09 282.52, 151.10 282.20, 150.26 276.72, 142.84 277.86, 141.44 268.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,15,"POLYGON ((524.86 269.51, 523.01 281.45, 520.23 284.94, 510.83 290.36, 506.24 291.63, 503.60 287.68, 505.24 282.91, 499.67 283.05, 517.34 263.82, 524.86 269.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,16,"POLYGON ((189.41 264.02, 193.00 283.39, 170.42 287.52, 169.23 281.07, 166.76 281.51, 165.13 272.72, 168.34 267.91, 189.41 264.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,17,"POLYGON ((97.55 271.65, 81.19 273.62, 79.82 262.42, 75.74 262.93, 75.34 259.72, 76.76 257.17, 77.57 253.78, 74.51 250.33, 73.78 244.26, 93.94 241.82, 97.55 271.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,18,"POLYGON ((516.27 263.34, 510.30 258.38, 513.19 257.29, 513.56 251.02, 528.81 232.94, 536.85 238.26, 535.73 239.64, 534.99 243.41, 531.06 244.56, 530.70 251.53, 527.73 253.36, 524.63 252.67, 523.55 257.42, 524.27 261.67, 520.18 260.42, 516.27 263.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,19,"POLYGON ((205.79 228.50, 227.81 238.88, 215.67 264.37, 215.14 257.21, 211.94 255.36, 209.68 249.65, 205.93 247.30, 205.47 242.76, 206.05 238.02, 202.80 234.77, 205.79 228.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,20,"POLYGON ((53.47 232.57, 53.11 208.90, 82.71 208.43, 83.07 232.10, 53.47 232.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,21,"POLYGON ((243.36 202.66, 228.18 227.26, 214.68 219.02, 229.86 194.41, 243.36 202.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,22,"POLYGON ((249.68 196.19, 223.17 182.04, 227.73 173.58, 232.37 176.05, 236.53 168.31, 241.42 174.87, 242.32 180.80, 248.41 185.59, 249.68 196.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,23,"POLYGON ((269.58 158.32, 264.84 157.71, 261.10 152.97, 254.41 158.22, 253.54 152.80, 251.68 148.39, 247.60 144.79, 247.20 138.60, 258.28 133.33, 264.48 130.22, 269.05 139.22, 276.70 135.39, 282.65 134.75, 269.58 158.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,24,"POLYGON ((555.91 77.99, 534.01 100.59, 541.17 107.47, 518.64 130.75, 512.12 124.50, 501.76 135.20, 478.98 113.34, 490.84 101.09, 508.40 117.96, 528.20 97.52, 521.19 90.77, 525.31 86.54, 529.58 90.65, 533.61 86.50, 534.56 83.08, 543.18 73.89, 549.40 74.31, 550.70 72.99, 555.91 77.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,25,"POLYGON ((460.98 25.41, 510.28 29.19, 511.13 51.54, 460.73 53.18, 460.98 25.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,26,"POLYGON ((253.84 0.00, 253.54 17.39, 242.60 17.23, 242.71 10.59, 238.80 10.51, 238.89 5.58, 241.86 5.64, 241.95 0.00, 253.84 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,27,"POLYGON ((488.50 0.00, 490.04 10.43, 450.07 16.32, 447.66 0.00, 488.50 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,28,"POLYGON ((291.69 0.00, 291.71 24.50, 260.89 24.53, 260.87 0.00, 291.69 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,29,"POLYGON ((677.74 881.96, 714.08 881.72, 714.12 887.78, 725.13 887.72, 725.16 895.53, 715.71 895.59, 715.74 900.00, 677.85 900.00, 677.74 881.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,30,"POLYGON ((740.63 881.77, 773.05 881.10, 775.61 894.10, 745.15 898.72, 745.32 900.00, 735.17 900.00, 733.20 885.29, 740.63 881.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,31,"POLYGON ((898.58 879.39, 900.00 881.85, 900.00 896.48, 891.18 900.00, 873.69 900.00, 876.19 886.62, 886.90 880.51, 898.58 879.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,32,"POLYGON ((787.26 861.37, 803.03 857.84, 804.41 863.93, 815.69 861.40, 817.61 869.92, 809.63 871.72, 811.89 881.65, 792.82 885.94, 787.26 861.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,33,"POLYGON ((827.47 846.05, 865.66 846.40, 865.54 857.91, 860.37 857.86, 860.28 865.90, 834.85 865.66, 834.95 854.51, 827.39 854.44, 827.47 846.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,34,"POLYGON ((578.80 825.53, 602.50 824.77, 603.09 843.40, 579.39 844.16, 578.80 825.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,35,"POLYGON ((621.95 821.41, 649.37 820.37, 650.24 843.62, 622.84 844.65, 621.95 821.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,36,"POLYGON ((673.99 808.62, 690.54 806.44, 689.74 800.45, 697.05 799.49, 700.50 825.46, 676.64 828.60, 673.99 808.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,37,"POLYGON ((718.59 798.16, 744.69 792.63, 748.31 809.58, 722.21 815.11, 718.59 798.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,38,"POLYGON ((768.40 788.42, 774.32 786.70, 775.42 790.38, 793.33 785.14, 798.83 803.72, 793.40 803.86, 791.00 808.49, 775.64 812.99, 768.40 788.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,39,"POLYGON ((882.54 784.30, 900.00 784.03, 900.00 803.34, 882.85 803.61, 882.54 784.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,40,"POLYGON ((593.48 700.36, 607.28 719.12, 591.58 730.57, 585.71 722.58, 577.06 728.44, 572.36 715.75, 593.48 700.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,41,"POLYGON ((631.88 693.22, 637.18 713.42, 620.10 717.88, 617.22 706.96, 613.28 708.00, 610.84 698.71, 631.88 693.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,42,"POLYGON ((650.41 685.91, 679.19 677.34, 681.93 686.48, 693.81 682.94, 691.85 680.06, 711.91 673.20, 714.13 679.64, 716.93 683.26, 716.77 687.26, 715.00 689.10, 718.86 697.88, 725.66 694.93, 730.23 705.40, 685.53 724.42, 677.05 713.89, 671.73 714.27, 665.92 716.26, 659.72 698.33, 653.03 700.61, 649.03 689.06, 650.41 685.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,43,"POLYGON ((726.55 657.13, 742.02 652.62, 744.93 656.39, 757.93 652.59, 759.96 659.47, 753.65 661.32, 761.12 686.65, 737.30 693.61, 726.55 657.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,44,"POLYGON ((765.75 652.57, 767.34 644.23, 773.82 642.60, 775.55 649.48, 791.41 645.50, 794.91 659.31, 785.59 665.32, 769.02 668.65, 765.75 652.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,45,"POLYGON ((557.32 547.31, 574.17 543.97, 575.31 547.90, 587.57 545.30, 590.63 558.74, 555.33 567.34, 549.69 559.16, 550.42 554.99, 557.89 552.91, 557.32 547.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,46,"POLYGON ((621.31 532.07, 607.03 533.23, 612.94 518.63, 601.96 519.56, 601.05 508.95, 611.43 508.06, 612.74 503.58, 625.62 502.26, 626.04 506.22, 632.73 505.54, 632.41 502.33, 638.82 501.65, 638.94 502.87, 654.86 501.24, 655.25 505.12, 662.15 504.41, 663.08 513.46, 638.11 516.03, 638.68 521.50, 623.73 523.04, 621.31 532.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,47,"POLYGON ((721.95 514.35, 720.09 491.87, 731.37 490.94, 724.86 496.41, 730.46 495.67, 733.35 498.70, 738.12 500.49, 740.57 503.51, 743.66 504.74, 745.65 499.85, 750.80 499.12, 752.06 502.04, 750.96 505.44, 744.67 507.82, 744.96 511.39, 736.32 512.10, 736.86 518.43, 727.22 519.21, 726.79 513.96, 721.95 514.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,48,"POLYGON ((814.07 507.04, 809.44 487.07, 834.07 481.43, 838.68 501.38, 814.07 507.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,49,"POLYGON ((677.46 512.36, 676.92 505.34, 682.12 504.96, 680.33 481.12, 686.13 480.68, 685.83 476.67, 694.32 476.04, 695.04 485.56, 700.20 485.16, 702.70 487.03, 706.54 487.44, 707.59 489.86, 711.00 491.41, 712.40 509.74, 677.46 512.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,50,"POLYGON ((785.16 476.09, 790.94 505.68, 771.36 509.47, 768.86 496.70, 774.34 495.63, 771.05 478.82, 785.16 476.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,51,"POLYGON ((877.69 472.27, 879.18 481.36, 884.23 480.52, 883.97 484.54, 890.19 484.96, 889.89 489.12, 856.75 495.67, 854.62 485.45, 859.34 484.46, 858.16 478.88, 863.69 477.72, 861.94 474.85, 877.69 472.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,52,"POLYGON ((717.05 453.41, 719.70 467.82, 709.90 469.60, 707.24 455.22, 717.05 453.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,53,"POLYGON ((838.90 452.64, 837.29 445.52, 846.92 443.36, 848.12 448.70, 852.85 447.65, 855.17 457.89, 837.10 461.95, 835.19 453.47, 838.90 452.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,54,"POLYGON ((623.62 462.35, 620.96 446.12, 645.01 442.22, 647.65 458.45, 623.62 462.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,55,"POLYGON ((687.27 443.34, 701.12 441.19, 703.40 455.98, 689.24 458.16, 688.36 452.55, 686.29 452.87, 685.38 447.03, 687.78 446.68, 687.27 443.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,56,"POLYGON ((578.60 444.88, 577.60 425.06, 582.90 425.99, 589.14 422.68, 592.72 425.03, 598.18 425.60, 602.13 428.80, 602.87 443.68, 578.60 444.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,57,"POLYGON ((786.33 358.96, 786.99 368.82, 794.80 368.29, 795.42 377.48, 802.90 376.98, 804.03 393.85, 770.42 396.13, 767.98 360.21, 786.33 358.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,58,"POLYGON ((570.57 392.50, 589.13 367.45, 564.16 349.13, 557.32 358.36, 548.69 352.01, 547.01 344.40, 539.82 340.76, 538.63 334.89, 542.76 334.07, 546.68 337.34, 554.94 336.27, 565.87 344.20, 567.59 341.85, 571.86 344.94, 578.52 345.94, 581.72 349.54, 585.20 358.58, 591.00 360.05, 594.85 359.95, 589.53 367.13, 602.60 376.71, 583.76 402.17, 570.57 392.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,59,"POLYGON ((900.00 369.54, 895.32 372.02, 881.15 345.48, 896.27 337.48, 898.86 342.34, 900.00 341.73, 900.00 369.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,60,"POLYGON ((780.85 329.55, 776.42 330.89, 781.85 348.77, 766.36 353.43, 761.24 336.56, 758.24 337.46, 754.26 324.33, 777.17 317.44, 780.85 329.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,61,"POLYGON ((878.06 332.61, 864.00 301.87, 882.71 293.40, 879.34 298.75, 881.53 302.53, 883.34 302.93, 882.19 308.20, 881.12 312.73, 887.18 314.13, 891.21 315.16, 890.25 318.92, 895.34 320.21, 897.09 324.00, 878.06 332.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,62,"POLYGON ((755.76 285.29, 763.15 313.98, 751.86 316.84, 751.71 310.90, 743.77 302.71, 749.72 301.34, 745.72 296.20, 743.07 295.56, 741.36 288.94, 755.76 285.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,63,"POLYGON ((854.84 295.85, 848.83 280.42, 857.20 277.19, 850.62 261.26, 855.32 259.41, 854.22 257.19, 870.52 251.21, 872.27 254.93, 877.72 252.31, 885.74 267.73, 881.22 269.49, 878.32 280.04, 881.98 285.39, 854.84 295.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,64,"POLYGON ((745.41 243.02, 745.58 249.65, 743.38 252.68, 742.98 257.96, 747.63 260.81, 751.84 261.21, 753.52 271.85, 742.73 275.97, 737.64 268.93, 733.06 268.89, 729.63 265.11, 727.10 255.39, 730.03 253.20, 732.16 247.20, 745.41 243.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,65,"POLYGON ((835.63 256.98, 834.20 253.25, 834.27 248.12, 831.60 245.72, 831.14 240.98, 837.22 238.07, 834.91 233.32, 831.38 234.89, 828.64 228.75, 836.51 225.37, 838.78 226.56, 843.79 223.32, 844.92 218.72, 848.06 225.08, 861.13 225.98, 864.22 233.54, 862.13 234.51, 863.36 237.65, 860.73 241.38, 861.05 245.79, 835.63 256.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,66,"POLYGON ((889.35 229.43, 892.40 231.80, 891.50 238.04, 889.20 236.43, 886.12 240.79, 881.06 242.32, 881.96 239.01, 877.97 237.94, 877.86 233.84, 881.90 233.73, 885.50 230.33, 889.35 229.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,67,"POLYGON ((728.08 206.66, 736.85 235.96, 725.95 239.21, 722.93 236.94, 716.89 229.92, 715.53 225.49, 718.90 220.95, 720.47 208.92, 728.08 206.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,68,"POLYGON ((848.69 206.15, 826.45 215.33, 824.14 209.77, 826.65 208.73, 825.34 205.57, 818.53 201.66, 819.49 190.62, 817.70 186.32, 840.07 177.09, 845.58 190.31, 842.61 191.54, 848.69 206.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,69,"POLYGON ((835.32 168.24, 812.80 177.43, 807.41 164.34, 809.59 163.44, 806.55 156.08, 802.82 156.18, 800.51 150.57, 808.58 147.28, 807.69 145.13, 823.17 138.78, 835.32 168.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,70,"POLYGON ((824.21 130.03, 800.38 138.19, 796.88 128.07, 798.26 124.90, 797.35 122.60, 798.85 122.00, 796.79 116.73, 796.77 109.27, 796.13 101.78, 809.43 97.34, 811.28 102.79, 814.36 101.61, 824.21 130.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,71,"POLYGON ((508.12 900.00, 507.53 895.15, 533.24 892.03, 534.22 900.00, 508.12 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,72,"POLYGON ((164.82 865.89, 184.25 866.88, 182.79 895.35, 163.38 894.36, 164.82 865.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,73,"POLYGON ((124.11 868.90, 154.50 870.30, 154.08 879.28, 151.24 879.15, 150.96 885.38, 145.06 885.11, 145.20 881.86, 135.83 881.43, 135.57 886.79, 123.29 886.24, 124.11 868.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,74,"POLYGON ((101.79 862.26, 110.59 861.90, 110.92 870.73, 118.93 872.81, 118.57 883.59, 106.53 883.20, 106.73 877.69, 102.23 873.44, 101.79 862.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,75,"POLYGON ((422.18 858.13, 431.39 858.85, 431.02 863.68, 440.84 864.42, 440.14 873.30, 446.40 873.78, 445.56 884.48, 431.15 883.36, 431.44 879.56, 420.56 878.70, 422.18 858.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,76,"POLYGON ((272.79 854.45, 281.98 855.83, 281.32 858.78, 287.86 860.23, 282.28 879.07, 273.38 877.39, 271.06 885.08, 264.80 883.18, 268.91 870.00, 263.10 868.21, 265.18 863.45, 270.36 863.77, 272.79 854.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,77,"POLYGON ((247.51 859.93, 253.29 860.67, 253.13 871.86, 250.82 873.68, 248.11 877.88, 235.98 877.17, 236.89 872.41, 229.43 869.83, 230.95 865.51, 233.79 866.19, 239.29 867.80, 245.47 866.31, 247.51 859.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,78,"POLYGON ((198.51 856.97, 206.34 856.88, 206.43 866.11, 217.38 866.01, 220.84 868.56, 220.89 874.38, 198.68 874.59, 198.51 856.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,79,"POLYGON ((462.09 822.28, 469.56 820.62, 480.32 823.57, 481.75 829.92, 488.92 828.34, 491.62 840.46, 467.37 845.83, 462.09 822.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,80,"POLYGON ((500.21 820.66, 527.33 818.90, 528.68 839.62, 501.55 841.38, 500.21 820.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,81,"POLYGON ((433.62 774.86, 444.30 775.32, 443.86 784.88, 458.13 785.49, 457.46 800.38, 432.54 799.28, 433.62 774.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,82,"POLYGON ((61.33 762.56, 85.83 762.60, 85.79 779.27, 61.27 779.21, 61.33 762.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,83,"POLYGON ((166.44 754.43, 174.57 753.60, 179.29 758.48, 184.41 762.10, 190.11 756.11, 192.46 774.99, 167.43 776.45, 166.44 754.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,84,"POLYGON ((114.17 753.48, 135.69 753.55, 139.20 759.70, 138.88 763.88, 135.71 770.40, 114.61 770.74, 114.17 753.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,85,"POLYGON ((514.46 749.57, 524.66 742.52, 530.08 750.28, 534.16 747.47, 541.12 757.41, 544.75 754.90, 550.73 763.47, 527.26 779.70, 517.06 765.11, 521.31 759.36, 514.46 749.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,86,"POLYGON ((469.98 752.62, 482.81 743.39, 492.03 756.07, 496.20 753.08, 503.63 763.32, 486.64 775.55, 469.98 752.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,87,"POLYGON ((221.93 750.53, 248.46 750.05, 249.29 765.83, 221.92 766.53, 221.93 750.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,88,"POLYGON ((561.08 725.34, 570.74 743.80, 556.24 751.32, 546.57 732.88, 561.08 725.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,89,"POLYGON ((15.17 722.20, 45.13 708.85, 51.67 723.36, 57.88 720.60, 62.81 731.53, 26.39 747.78, 24.66 743.96, 20.85 745.66, 17.76 738.81, 14.65 740.22, 10.25 730.45, 17.45 727.23, 15.17 722.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,90,"POLYGON ((0.08 690.18, 25.80 677.82, 34.92 696.66, 37.74 695.30, 42.04 704.20, 21.41 714.10, 17.38 705.74, 21.87 703.59, 16.37 692.21, 3.96 698.16, 0.08 690.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,91,"POLYGON ((211.85 659.88, 238.15 676.66, 244.55 669.01, 323.25 669.98, 323.09 679.77, 311.41 679.60, 308.49 686.29, 302.68 686.09, 302.83 681.80, 266.08 680.52, 265.59 694.34, 251.38 693.83, 251.58 688.52, 248.24 688.41, 236.55 706.80, 199.54 683.53, 211.85 659.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,92,"POLYGON ((0.00 642.30, 13.18 636.53, 18.71 648.99, 16.02 650.17, 23.33 666.70, 3.43 675.44, 0.00 667.68, 0.00 642.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,93,"POLYGON ((265.04 595.52, 327.24 597.88, 327.46 606.54, 323.72 605.90, 321.71 625.24, 260.51 622.59, 259.53 613.22, 264.73 612.34, 265.04 595.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,94,"POLYGON ((554.48 891.95, 583.68 890.96, 584.09 898.43, 591.82 897.63, 592.03 900.00, 555.99 900.00, 554.48 891.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,95,"POLYGON ((620.01 888.19, 656.14 886.55, 656.75 900.00, 623.56 900.00, 620.01 888.19))",1 diff --git a/docker/solaris/solaris/data/sample_road_raster_mask.tif b/docker/solaris/solaris/data/sample_road_raster_mask.tif new file mode 100644 index 00000000..72c05919 Binary files /dev/null and b/docker/solaris/solaris/data/sample_road_raster_mask.tif differ diff --git a/docker/solaris/solaris/data/sample_roads.geojson b/docker/solaris/solaris/data/sample_roads.geojson new file mode 100644 index 00000000..57ed3bd4 --- /dev/null +++ b/docker/solaris/solaris/data/sample_roads.geojson @@ -0,0 +1,33 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 14655, "road_type": "5", "origarea": 0, "origlen": 0.0017089240319089305, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.305334270350002, 36.158651982430001 ], [ -115.30533142486, 36.158449259580003 ], [ -115.305334246699999, 36.158405521070001 ], [ -115.305358232339998, 36.158351906109999 ], [ -115.305387861650004, 36.158325098630002 ], [ -115.305568459400007, 36.158171308370001 ], [ -115.305710962310002, 36.1580457365 ], [ -115.305754700820003, 36.158009052579999 ], [ -115.305761755419994, 36.157982245100001 ], [ -115.30576316634, 36.157942739349998 ], [ -115.305764577260007, 36.157628104209998 ], [ -115.30576316634, 36.157506765100003 ], [ -115.305756111739996, 36.157477135779999 ], [ -115.305637880419994, 36.15715224209 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 19941, "road_type": "5", "origarea": 0, "origlen": 0.0021840289174551468, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.3040076, 36.158656103202638 ], [ -115.304718257900007, 36.1586538711 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 16932, "road_type": "5", "origarea": 0, "origlen": 0.00061020691230463111, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304794494109998, 36.158316912030003 ], [ -115.304854175469998, 36.158295077390001 ], [ -115.304859998040001, 36.158216472680003 ], [ -115.304842530320002, 36.158165525180003 ], [ -115.304800316680002, 36.158142234899998 ], [ -115.304727534549997, 36.158140779260002 ], [ -115.304664941910005, 36.158180081609999 ], [ -115.304654752410002, 36.158228117820002 ], [ -115.304664941910005, 36.158274698390002 ], [ -115.304696966050003, 36.15830672253 ], [ -115.304771203830001, 36.158327101529999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 20674, "road_type": "5", "origarea": 0, "origlen": 2.5421704359567551e-05, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304794494109998, 36.158316912030003 ], [ -115.304771203830001, 36.158327101529999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 4759, "road_type": "5", "origarea": 0, "origlen": 0.00036751717873743738, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304777087779996, 36.158654716450002 ], [ -115.304819240040004, 36.158599306729997 ], [ -115.304810506180004, 36.158344569240001 ], [ -115.304771203830001, 36.158327101529999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 277, "road_type": "5", "origarea": 0, "origlen": 0.00038473841342077095, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304777087779996, 36.158654716450002 ], [ -115.304718800689997, 36.158612407509999 ], [ -115.304718800689997, 36.158363492600003 ], [ -115.304771203830001, 36.158327101529999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 21517, "road_type": "5", "origarea": 0, "origlen": 9.5177264571465376e-05, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304813425340001, 36.158655238599998 ], [ -115.304777087779996, 36.158654716450002 ], [ -115.304718257900007, 36.1586538711 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 9198, "road_type": "5", "origarea": 0, "origlen": 0.00028644810990776168, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304777087779996, 36.158654716450002 ], [ -115.304714433759997, 36.158708479929999 ], [ -115.304712978119994, 36.158829298279997 ], [ -115.304763937019999, 36.158894891229998 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 13669, "road_type": "5", "origarea": 0, "origlen": 0.00026815643391558204, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304763937019999, 36.158894891229998 ], [ -115.304816328749993, 36.158806008 ], [ -115.304816328749993, 36.158704113 ], [ -115.304777087779996, 36.158654716450002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "3", "lane_number": "3", "one_way_ty": "2", "paved": "1", "road_id": 15490, "road_type": "2", "origarea": 0, "origlen": 0.0017015904383822285, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.307235461239998, 36.158885233829999 ], [ -115.305533891859994, 36.158893699350003 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "4", "lane_number": "4", "one_way_ty": "2", "paved": "1", "road_id": 22773, "road_type": "2", "origarea": 0, "origlen": 0.00091145533203984097, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.305533891859994, 36.158893699350003 ], [ -115.304812210250006, 36.158894816500002 ], [ -115.304763937019999, 36.158894891229998 ], [ -115.304716265750002, 36.158894965019996 ], [ -115.304622437619997, 36.158895110270002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "3", "lane_number": "3", "one_way_ty": "2", "paved": "1", "road_id": 11013, "road_type": "2", "origarea": 0, "origlen": 0.00075908014504109467, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304622437619997, 36.158895110270002 ], [ -115.3040076, 36.158897395911893 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 13039, "road_type": "2", "origarea": 0, "origlen": 0.00014077602261271154, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304763937019999, 36.158894891229998 ], [ -115.304762782699996, 36.159035662519997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "3", "lane_number": "3", "one_way_ty": "2", "paved": "1", "road_id": 13547, "road_type": "2", "origarea": 0, "origlen": 0.00076778803435859967, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.306466509909995, 36.159031264040003 ], [ -115.307234296139995, 36.159032928590001 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "3", "lane_number": "3", "one_way_ty": "2", "paved": "1", "road_id": 10504, "road_type": "2", "origarea": 0, "origlen": 0.0015294496788786783, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.306466509909995, 36.159031264040003 ], [ -115.304937068170005, 36.159036191939997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "4", "lane_number": "4", "one_way_ty": "2", "paved": "1", "road_id": 22869, "road_type": "2", "origarea": 0, "origlen": 0.00092556313019524479, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304937068170005, 36.159036191939997 ], [ -115.304762782699996, 36.159035662519997 ], [ -115.304011509310001, 36.159033380419999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "3", "lane_number": "3", "one_way_ty": "2", "paved": "1", "road_id": 9733, "road_type": "2", "origarea": 0, "origlen": 0.0010807946163444123, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304011509310001, 36.159033380419999 ], [ -115.3040076, 36.159033399520865 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 2207, "road_type": "5", "origarea": 0, "origlen": 0.0021187288976393381, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304031262180004, 36.159379055789998 ], [ -115.304038316779994, 36.159325440830003 ], [ -115.30406371334, 36.159292989679997 ], [ -115.304101808179993, 36.159271825879998 ], [ -115.304165299570002, 36.159261949440001 ], [ -115.305879567229994, 36.159256305760003 ], [ -115.305933182190003, 36.159261949440001 ], [ -115.30596281151, 36.159295811520003 ], [ -115.305974098869996, 36.159346604630002 ], [ -115.305972513970005, 36.159396974929997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 14360, "road_type": "5", "origarea": 0, "origlen": 0.00060620635352942218, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.306909865430001, 36.159582676630002 ], [ -115.306901073229994, 36.159404452350003 ], [ -115.306918004270003, 36.159325440830003 ], [ -115.306957510030003, 36.159277469560003 ], [ -115.307028056020002, 36.159249251159999 ], [ -115.307236872160004, 36.15924642932 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 18886, "road_type": "5", "origarea": 0, "origlen": 0.00017671102624952563, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304031262180004, 36.159379055789998 ], [ -115.304035910479996, 36.159555705670002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "4", "lane_number": "4", "one_way_ty": "2", "paved": "1", "road_id": 4414, "road_type": "5", "origarea": 0, "origlen": 0.00055551672896427928, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.305960011426549, 36.159887699800002 ], [ -115.305972513970005, 36.159396974929997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "4", "lane_number": "4", "one_way_ty": "2", "paved": "1", "road_id": 8866, "road_type": "5", "origarea": 0, "origlen": 0.00040571587428926529, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.306898346152181, 36.159887699800002 ], [ -115.306908096840004, 36.159764841159998 ], [ -115.306909865430001, 36.159582676630002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 5865, "road_type": "5", "origarea": 0, "origlen": 0.00056254711252776385, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304035910479996, 36.159555705670002 ], [ -115.304028602435167, 36.159887699800002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 4214, "road_type": "5", "origarea": 0, "origlen": 0.0030770784666620775, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304540695628134, 36.156377699799997 ], [ -115.304225969130002, 36.15646409531 ], [ -115.3040076, 36.156523805617901 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 6668, "road_type": "5", "origarea": 0, "origlen": 0.00030445562381666708, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304564589899996, 36.157148391450001 ], [ -115.304675347110006, 36.157431986349998 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 11117, "road_type": "5", "origarea": 0, "origlen": 0.00028852803692290427, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304475211440007, 36.157495147520002 ], [ -115.304364239280005, 36.157228813880003 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 17863, "road_type": "5", "origarea": 0, "origlen": 0.006987667279550927, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.304813425340001, 36.158655238599998 ], [ -115.305334270350002, 36.158651982430001 ], [ -115.305488036970004, 36.158651021129998 ], [ -115.30614834747, 36.158652432049998 ], [ -115.30689472409, 36.158653842969997 ], [ -115.306967386470006, 36.158653137510001 ], [ -115.307004775839999, 36.158647493830003 ], [ -115.307027350560006, 36.158634090089997 ], [ -115.307047808899995, 36.158612220830001 ], [ -115.307061212639994, 36.158561427720002 ], [ -115.307018885039994, 36.157412233469998 ], [ -115.307016063199995, 36.157364262190001 ], [ -115.306996310320002, 36.157331811040002 ], [ -115.306963859169997, 36.15729794896 ], [ -115.30691588789, 36.157268319640004 ], [ -115.30674798842, 36.157173788009999 ], [ -115.306716948190001, 36.157155446049998 ], [ -115.306622416549999, 36.157121583970003 ], [ -115.30652647399999, 36.157103242010002 ], [ -115.306252755540001, 36.15704116154 ], [ -115.306186442309993, 36.157029874179997 ], [ -115.306077801480001, 36.157032696020003 ], [ -115.305988913530001, 36.157051037979997 ], [ -115.305847821539999, 36.157096187409998 ], [ -115.305702496790005, 36.157134282249999 ], [ -115.305637880419994, 36.15715224209 ], [ -115.305235482309996, 36.157264086879998 ], [ -115.304985749490001, 36.157337454710003 ], [ -115.304788220700004, 36.157388247829999 ], [ -115.304675347110006, 36.157431986349998 ], [ -115.304569528119998, 36.15747290302 ], [ -115.304475211440007, 36.157495147520002 ], [ -115.304419970609999, 36.157508176020002 ], [ -115.304277467700004, 36.157543449019997 ], [ -115.3040076, 36.157610573470834 ] ] } } +] +} diff --git a/docker/solaris/solaris/data/sample_roads_for_masking.geojson b/docker/solaris/solaris/data/sample_roads_for_masking.geojson new file mode 100755 index 00000000..ee81863b --- /dev/null +++ b/docker/solaris/solaris/data/sample_roads_for_masking.geojson @@ -0,0 +1,15 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "1", "lane_number": "1", "one_way_ty": "2", "paved": "1", "road_id": 5125, "road_type": "5", "origarea": 0, "origlen": 0.0037521307560270363, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.231131789230005, 36.140381615110002 ], [ -115.230796123749997, 36.140380192800002 ], [ -115.230599844780002, 36.140394415910002 ], [ -115.2302976, 36.140389113375228 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 22455, "road_type": "5", "origarea": 0, "origlen": 0.0092618898636028565, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.231724168650004, 36.140387299929998 ], [ -115.231719669238075, 36.138827699799997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 11989, "road_type": "5", "origarea": 0, "origlen": 0.011700061022758033, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.2338076, 36.140373976815766 ], [ -115.233652988770004, 36.140364626989999 ], [ -115.23203395777, 36.140369445529998 ], [ -115.231724168650004, 36.140387299929998 ], [ -115.231383454249993, 36.140379082620001 ], [ -115.231131789230005, 36.140381615110002 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 17850, "road_type": "5", "origarea": 0, "origlen": 0.00059990379520043864, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.233308168250005, 36.140896600449999 ], [ -115.2338076, 36.140889434306622 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 10103, "road_type": "5", "origarea": 0, "origlen": 0.00053092267667631521, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.233273866260006, 36.141714131150003 ], [ -115.233266745310004, 36.142245006069999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 1183, "road_type": "5", "origarea": 0, "origlen": 0.00082909371579691841, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.232186207409995, 36.141832758859998 ], [ -115.231778871309999, 36.141841334349998 ], [ -115.231784588310006, 36.142262962949999 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 5662, "road_type": "5", "origarea": 0, "origlen": 0.00034446098079607071, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.232777916689997, 36.141907079829998 ], [ -115.232775058190001, 36.142251528949998 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 13901, "road_type": "5", "origarea": 0, "origlen": 0.001700144468269229, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.2302976, 36.142278939225932 ], [ -115.230389342690003, 36.142277556170001 ], [ -115.231114270299997, 36.142271538449997 ] ] } }, +{ "type": "Feature", "properties": { "bridge_typ": "2", "heading": "0", "lane_numbe": "2", "lane_number": "2", "one_way_ty": "2", "paved": "1", "road_id": 21540, "road_type": "5", "origarea": 0, "origlen": 0.011743944577663961, "partialDec": 1, "truncated": 0 }, "geometry": { "type": "LineString", "coordinates": [ [ -115.2338076, 36.14223833925346 ], [ -115.233266745310004, 36.142245006069999 ], [ -115.232775058190001, 36.142251528949998 ], [ -115.231784588310006, 36.142262962949999 ], [ -115.231114270299997, 36.142271538449997 ] ] } } +] +} diff --git a/docker/solaris/solaris/data/sample_truth.csv b/docker/solaris/solaris/data/sample_truth.csv new file mode 100644 index 00000000..bb8b31c3 --- /dev/null +++ b/docker/solaris/solaris/data/sample_truth.csv @@ -0,0 +1,152 @@ +ImageId,BuildingId,PolygonWKT_Pix,PolygonWKT_Geo +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,0,"POLYGON ((131.60 895.50, 164.33 889.06, 165.81 900.00, 133.85 900.00, 131.60 895.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,1,"POLYGON ((403.49 891.48, 417.58 891.12, 432.38 890.75, 436.71 900.00, 402.68 900.00, 403.49 891.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,2,"POLYGON ((199.79 878.96, 208.24 894.84, 198.46 900.00, 174.73 900.00, 171.49 893.90, 199.79 878.96))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,3,"POLYGON ((379.23 875.22, 389.31 875.34, 389.14 887.13, 397.24 887.24, 399.23 891.25, 399.45 900.00, 371.36 900.00, 371.37 899.28, 370.67 896.50, 371.16 890.87, 369.87 884.02, 372.02 881.48, 373.27 882.07, 379.92 877.69, 379.23 875.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,4,"POLYGON ((553.60 882.37, 558.59 884.82, 564.66 883.27, 569.06 880.12, 571.29 884.72, 575.30 885.80, 576.78 898.15, 560.59 898.56, 554.41 895.45, 553.60 882.37))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,5,"POLYGON ((522.61 878.25, 521.63 874.14, 534.96 874.98, 534.47 879.12, 548.38 878.77, 550.65 898.16, 519.85 897.76, 513.58 895.54, 514.88 888.45, 518.31 883.95, 522.61 878.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,6,"POLYGON ((811.87 196.52, 697.91 200.08, 693.30 0.00, 809.85 0.00, 811.87 196.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,7,"POLYGON ((566.25 392.67, 585.96 391.24, 591.79 417.73, 577.05 419.99, 572.01 415.68, 566.25 392.67))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,8,"POLYGON ((551.82 362.93, 583.89 361.24, 586.09 383.87, 555.80 386.12, 551.82 362.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,9,"POLYGON ((65.35 385.12, 64.22 357.89, 72.19 356.80, 73.43 358.83, 77.57 358.88, 77.71 370.22, 78.08 384.93, 65.35 385.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,10,"POLYGON ((345.42 360.87, 363.92 360.56, 363.85 363.96, 358.33 367.91, 360.05 371.42, 360.80 377.73, 346.73 377.64, 345.42 360.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,11,"POLYGON ((0.00 359.94, 11.09 359.58, 11.64 377.77, 0.00 378.13, 0.00 359.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,12,"POLYGON ((56.13 359.45, 55.94 362.09, 55.71 367.74, 56.69 372.31, 57.50 375.42, 56.45 378.00, 49.21 378.20, 48.66 359.79, 56.13 359.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,13,"POLYGON ((128.52 352.36, 140.80 351.99, 143.09 384.14, 123.67 383.90, 123.90 376.85, 122.67 369.80, 126.23 361.79, 128.86 357.77, 128.52 352.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,14,"POLYGON ((171.38 354.77, 173.12 372.37, 153.61 374.26, 151.86 356.33, 160.24 355.52, 159.94 352.24, 169.70 351.28, 170.05 354.94, 171.38 354.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,15,"POLYGON ((299.07 353.93, 299.40 367.04, 289.33 366.72, 289.12 370.10, 281.13 369.72, 281.16 353.96, 299.07 353.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,16,"POLYGON ((28.24 351.54, 45.19 351.13, 45.71 372.49, 35.30 372.76, 35.08 363.95, 22.80 364.24, 22.63 356.45, 28.36 356.30, 28.24 351.54))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,17,"POLYGON ((489.45 352.83, 490.20 368.75, 469.76 367.68, 470.65 353.81, 489.45 352.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,18,"POLYGON ((459.13 352.30, 459.48 364.79, 439.92 366.28, 440.97 353.45, 459.13 352.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,19,"POLYGON ((428.51 351.28, 428.59 366.01, 405.21 366.45, 404.24 351.29, 428.51 351.28))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,20,"POLYGON ((332.70 348.14, 333.37 361.39, 331.96 361.45, 332.22 366.80, 308.50 367.97, 307.58 349.37, 332.70 348.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,21,"POLYGON ((573.39 332.05, 573.62 336.90, 569.23 337.19, 572.96 356.36, 555.98 357.97, 550.95 334.08, 573.39 332.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,22,"POLYGON ((553.64 306.17, 567.30 301.57, 568.67 326.70, 552.85 326.97, 553.64 306.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,23,"POLYGON ((554.00 273.16, 569.99 272.75, 572.30 294.65, 569.09 296.06, 567.96 298.44, 557.01 298.43, 554.00 273.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,24,"POLYGON ((316.50 278.05, 337.20 278.15, 337.10 286.27, 331.46 286.19, 331.37 292.92, 320.86 292.81, 320.91 287.59, 310.93 287.49, 310.99 282.58, 316.50 278.05))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,25,"POLYGON ((392.39 270.59, 393.80 282.56, 391.99 289.29, 371.54 287.96, 370.85 270.37, 392.39 270.59))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,26,"POLYGON ((64.82 272.66, 83.14 272.06, 83.00 267.83, 89.88 267.61, 90.01 271.96, 92.60 271.87, 93.21 290.90, 65.41 291.78, 64.82 272.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,27,"POLYGON ((403.18 267.87, 421.39 267.50, 421.54 274.87, 426.03 274.78, 426.37 291.37, 410.44 291.71, 410.32 286.23, 400.53 286.45, 400.38 279.24, 403.42 279.19, 403.18 267.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,28,"POLYGON ((160.71 274.46, 161.27 263.62, 186.85 265.06, 186.44 274.04, 186.13 294.67, 162.88 293.59, 160.71 274.46))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,29,"POLYGON ((433.54 269.42, 441.62 268.97, 441.90 274.91, 453.46 274.87, 454.52 286.60, 447.91 286.28, 448.22 288.49, 444.87 288.71, 444.41 286.13, 434.11 286.87, 433.54 269.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,30,"POLYGON ((467.59 271.34, 490.45 269.58, 491.69 285.58, 465.26 287.62, 464.32 275.61, 467.90 275.35, 467.59 271.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,31,"POLYGON ((307.24 284.83, 284.59 285.15, 281.36 270.36, 287.39 272.45, 289.15 278.36, 292.14 278.28, 294.84 277.46, 298.58 277.12, 302.25 279.76, 306.37 280.39, 307.24 284.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,32,"POLYGON ((209.74 266.13, 210.98 280.08, 204.23 282.47, 204.08 288.51, 192.99 288.63, 190.80 266.45, 209.74 266.13))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,33,"POLYGON ((244.25 266.61, 245.97 284.70, 223.83 284.66, 223.19 267.36, 244.25 266.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,34,"POLYGON ((260.47 263.52, 260.90 268.66, 272.71 268.23, 273.87 274.52, 279.48 274.76, 279.66 281.93, 276.78 281.75, 277.01 285.72, 252.77 286.33, 252.95 263.91, 260.47 263.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,35,"POLYGON ((133.56 253.33, 138.86 253.68, 138.08 265.40, 148.79 266.08, 149.61 270.64, 158.21 271.20, 156.99 289.83, 131.27 288.17, 133.56 253.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,36,"POLYGON ((118.73 251.79, 119.38 259.19, 116.49 259.26, 119.35 278.48, 117.41 278.08, 115.10 280.65, 116.20 283.26, 119.07 284.97, 119.50 287.31, 101.18 286.51, 99.89 251.42, 118.73 251.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,37,"POLYGON ((552.85 240.11, 570.58 239.46, 571.35 260.47, 576.59 260.27, 576.79 266.01, 553.82 266.86, 552.85 240.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,38,"POLYGON ((197.58 222.39, 198.08 234.19, 188.49 234.61, 187.99 222.79, 197.58 222.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,39,"POLYGON ((547.98 207.58, 562.52 206.31, 564.23 225.68, 569.31 225.25, 570.04 233.60, 562.30 234.26, 562.04 231.31, 550.17 232.37, 547.98 207.58))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,40,"POLYGON ((425.46 194.57, 426.17 208.76, 413.55 209.39, 412.85 195.18, 425.46 194.57))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,41,"POLYGON ((547.58 178.36, 564.48 175.65, 568.47 201.58, 550.11 203.71, 547.58 178.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,42,"POLYGON ((308.55 148.90, 329.14 147.65, 329.96 184.50, 320.73 183.80, 321.10 193.89, 310.20 193.77, 309.00 182.85, 308.55 148.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,43,"POLYGON ((11.39 153.16, 12.45 156.11, 11.07 158.85, 12.22 161.80, 14.28 163.36, 14.09 166.79, 15.62 168.72, 15.59 171.79, 2.18 172.17, 0.00 169.19, 0.00 153.39, 11.39 153.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,44,"POLYGON ((217.75 149.19, 241.15 148.58, 242.11 170.22, 217.33 171.66, 217.75 149.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,45,"POLYGON ((462.20 152.34, 485.74 151.66, 486.22 162.70, 475.44 162.88, 474.94 168.20, 466.89 168.34, 465.66 159.93, 461.43 159.39, 462.20 152.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,46,"POLYGON ((561.51 147.15, 562.29 155.08, 570.25 154.30, 571.44 166.30, 563.26 167.11, 563.61 170.63, 547.72 172.18, 545.41 148.73, 561.51 147.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,47,"POLYGON ((339.04 147.16, 361.97 146.74, 362.78 161.43, 345.02 161.15, 344.57 167.06, 338.50 167.06, 339.04 147.16))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,48,"POLYGON ((81.26 141.17, 85.54 146.90, 86.56 171.60, 69.54 172.30, 69.03 160.15, 63.26 160.38, 62.83 149.76, 70.95 149.42, 70.62 141.57, 81.26 141.17))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,49,"POLYGON ((246.59 147.89, 266.25 147.15, 266.20 165.24, 246.51 164.49, 246.59 147.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,50,"POLYGON ((125.71 148.62, 139.71 147.85, 141.12 161.77, 126.09 163.62, 125.71 148.62))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,51,"POLYGON ((120.19 145.83, 120.25 164.58, 94.51 164.61, 94.14 149.84, 103.54 149.60, 103.46 146.25, 120.19 145.83))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,52,"POLYGON ((186.04 162.88, 185.22 146.43, 207.58 145.47, 209.52 155.61, 205.53 155.51, 206.37 163.84, 186.04 162.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,53,"POLYGON ((369.93 145.43, 392.12 145.73, 392.71 155.20, 388.66 154.94, 389.03 163.17, 370.74 163.45, 369.93 145.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,54,"POLYGON ((172.29 146.94, 180.11 150.07, 179.57 161.74, 161.09 159.92, 161.52 151.78, 161.19 146.60, 172.29 146.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,55,"POLYGON ((429.24 144.22, 431.28 147.86, 435.61 152.48, 442.54 155.10, 451.04 149.98, 455.62 150.22, 455.90 161.42, 430.96 163.27, 429.24 144.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,56,"POLYGON ((302.24 145.15, 302.63 155.38, 297.73 155.57, 298.00 162.38, 286.48 162.82, 286.37 159.87, 279.57 160.13, 279.05 146.36, 280.41 146.30, 280.35 144.62, 283.70 144.49, 283.76 146.09, 285.47 146.02, 285.39 143.80, 289.43 143.63, 289.51 145.96, 290.53 145.94, 290.44 143.72, 294.55 143.57, 294.62 145.46, 302.24 145.15))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,57,"POLYGON ((422.24 145.60, 422.90 162.21, 400.22 163.09, 399.59 146.70, 405.86 146.45, 405.74 143.48, 411.21 143.26, 411.32 146.03, 422.24 145.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,58,"POLYGON ((543.94 117.65, 561.21 116.04, 562.14 129.29, 568.08 130.16, 568.98 142.21, 545.61 142.80, 543.94 117.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,59,"POLYGON ((545.25 87.27, 561.29 86.58, 562.74 111.87, 547.11 113.75, 545.25 87.27))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,60,"POLYGON ((40.58 84.52, 40.02 68.65, 42.08 67.31, 43.98 65.97, 45.05 63.70, 44.50 60.99, 46.56 59.49, 50.26 59.06, 53.80 58.98, 54.51 61.22, 55.05 63.30, 56.97 63.25, 58.62 64.32, 59.18 67.52, 60.24 71.33, 59.41 76.66, 60.43 79.03, 60.82 81.28, 60.41 84.03, 56.08 85.40, 55.66 87.83, 57.52 90.98, 54.64 92.03, 52.05 91.76, 50.56 90.20, 47.15 89.49, 45.82 87.43, 45.11 85.21, 42.35 84.48, 40.58 84.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,61,"POLYGON ((590.16 66.25, 591.66 78.89, 573.05 80.38, 571.85 68.05, 590.16 66.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,62,"POLYGON ((221.11 63.87, 241.02 63.77, 242.32 80.58, 237.09 80.09, 236.66 82.70, 229.42 82.15, 229.13 80.67, 221.54 80.73, 221.11 63.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,63,"POLYGON ((468.76 50.18, 468.91 59.81, 478.03 59.44, 480.55 69.35, 487.41 73.44, 487.72 94.92, 476.35 95.09, 476.11 78.91, 460.62 79.15, 460.19 50.30, 468.76 50.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,64,"POLYGON ((280.22 63.02, 289.05 62.56, 289.00 71.24, 298.48 71.73, 298.69 74.70, 301.49 77.85, 306.48 78.48, 320.72 75.50, 323.09 70.25, 334.07 67.13, 335.42 80.72, 290.39 81.35, 285.17 81.72, 281.17 81.34, 279.86 78.77, 280.22 63.02))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,65,"POLYGON ((369.16 62.87, 384.11 63.36, 383.91 67.21, 374.47 68.33, 374.19 74.95, 378.50 75.60, 381.43 79.94, 370.18 80.07, 369.16 62.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,66,"POLYGON ((63.69 57.84, 109.17 55.36, 110.50 79.61, 85.46 80.98, 76.43 87.09, 67.54 86.33, 64.93 80.85, 63.69 57.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,67,"POLYGON ((248.41 59.87, 272.27 59.14, 273.52 79.17, 265.94 79.50, 266.79 83.18, 248.48 82.65, 248.10 67.81, 248.41 59.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,68,"POLYGON ((401.12 60.85, 403.38 67.45, 419.33 68.93, 423.77 79.66, 401.60 79.99, 401.12 60.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,69,"POLYGON ((157.03 66.35, 168.69 65.05, 169.25 57.85, 182.48 59.00, 183.25 79.29, 157.16 81.70, 157.03 66.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,70,"POLYGON ((542.10 55.86, 559.86 54.72, 560.34 81.32, 544.67 81.21, 542.10 55.86))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,71,"POLYGON ((143.56 51.79, 143.09 61.86, 150.47 62.20, 149.83 75.98, 153.68 76.15, 153.48 80.04, 147.98 79.78, 147.75 84.65, 129.34 84.67, 128.19 80.32, 114.79 78.75, 114.74 62.13, 128.65 61.78, 128.57 51.08, 143.56 51.79))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,72,"POLYGON ((338.57 53.88, 350.29 53.80, 350.70 61.69, 364.30 61.77, 365.53 77.17, 339.80 77.82, 338.57 53.88))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,73,"POLYGON ((187.58 53.66, 214.10 51.52, 214.71 58.59, 205.91 58.41, 206.16 68.62, 211.82 68.47, 212.68 78.44, 187.97 77.40, 187.58 53.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,74,"POLYGON ((560.92 0.00, 562.35 15.92, 552.54 17.57, 548.84 17.31, 547.52 13.83, 548.87 11.87, 549.00 9.78, 548.59 7.68, 547.32 5.60, 546.37 3.36, 544.55 1.12, 542.25 0.67, 541.82 0.00, 560.92 0.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,75,"POLYGON ((831.82 858.92, 845.09 860.56, 846.92 862.09, 849.18 865.96, 847.49 893.13, 842.90 891.29, 839.36 891.38, 837.06 894.57, 829.51 893.38, 831.82 858.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,76,"POLYGON ((870.38 813.51, 878.27 813.11, 879.05 828.43, 877.20 833.20, 876.99 840.38, 881.15 840.45, 880.91 853.04, 857.66 852.61, 858.04 833.49, 860.39 832.63, 864.93 832.51, 867.28 831.28, 871.09 826.28, 871.45 817.01, 870.38 813.51))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,77,"POLYGON ((783.37 762.98, 793.82 762.30, 793.27 773.56, 789.42 770.33, 784.00 770.89, 783.37 762.98))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,78,"POLYGON ((787.56 702.08, 789.04 740.93, 770.36 740.91, 770.26 706.71, 775.48 706.58, 775.87 702.37, 787.56 702.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,79,"POLYGON ((840.64 705.89, 846.53 727.29, 799.67 726.88, 799.87 705.54, 840.64 705.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,80,"POLYGON ((803.37 617.39, 824.09 618.43, 828.00 623.94, 829.21 634.52, 825.78 635.23, 825.89 639.60, 828.15 654.84, 817.47 653.86, 814.20 636.17, 813.65 626.81, 802.97 626.46, 803.37 617.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,81,"POLYGON ((764.00 614.12, 766.58 616.57, 773.33 618.48, 791.42 618.36, 791.50 633.85, 790.72 635.96, 790.84 640.95, 785.26 643.18, 776.65 642.77, 774.63 636.79, 774.63 625.24, 764.00 625.25, 764.00 614.12))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,82,"POLYGON ((845.17 621.34, 862.13 621.84, 864.24 618.35, 868.01 618.56, 867.85 624.83, 862.88 626.51, 856.52 635.41, 850.18 635.22, 845.43 631.32, 845.17 621.34))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,83,"POLYGON ((724.57 610.94, 732.04 616.39, 736.02 616.92, 750.80 614.97, 751.63 621.32, 750.13 629.59, 725.77 625.50, 724.57 610.94))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,84,"POLYGON ((696.37 223.25, 811.13 223.17, 812.93 462.08, 697.54 464.99, 696.37 223.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,85,"POLYGON ((578.97 852.43, 595.04 850.47, 604.87 853.18, 607.15 896.74, 604.50 900.00, 585.78 900.00, 585.79 882.83, 582.46 880.27, 578.97 852.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,86,"POLYGON ((34.56 842.14, 37.53 847.23, 39.21 847.41, 39.01 849.13, 45.39 858.78, 42.53 860.67, 43.28 864.65, 31.03 871.44, 28.52 866.93, 26.64 868.00, 23.16 861.82, 17.46 852.04, 34.56 842.14))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,87,"POLYGON ((64.98 830.41, 72.22 842.72, 71.54 845.31, 78.52 857.19, 65.44 864.78, 50.28 838.94, 64.98 830.41))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,88,"POLYGON ((81.91 841.50, 85.59 838.83, 90.02 837.08, 92.16 837.96, 94.26 837.22, 99.55 832.87, 107.39 845.54, 89.36 857.21, 81.91 841.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,89,"POLYGON ((674.38 824.77, 687.43 828.39, 692.79 823.82, 694.04 834.18, 702.54 835.96, 702.72 842.88, 695.26 843.07, 695.90 848.51, 676.52 849.98, 674.38 824.77))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,90,"POLYGON ((105.68 826.36, 124.73 815.61, 137.24 837.16, 118.29 848.07, 105.68 826.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,91,"POLYGON ((572.69 812.84, 598.35 811.83, 597.80 817.80, 601.55 826.83, 600.77 838.06, 589.87 838.33, 588.23 829.25, 570.01 832.53, 570.95 827.93, 574.77 825.39, 576.35 819.05, 572.69 812.84))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,92,"POLYGON ((129.82 812.93, 146.13 801.68, 159.62 820.61, 160.41 823.85, 150.23 830.19, 145.81 831.70, 140.17 831.62, 132.18 822.46, 129.82 812.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,93,"POLYGON ((369.11 796.89, 402.18 794.64, 403.41 812.44, 399.04 815.19, 395.51 816.47, 386.33 816.71, 378.37 817.06, 370.52 817.59, 369.11 796.89))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,94,"POLYGON ((404.94 796.08, 428.32 793.61, 431.77 797.72, 435.57 799.17, 436.68 813.82, 417.12 815.31, 413.75 813.17, 410.24 814.59, 407.19 817.31, 404.94 796.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,95,"POLYGON ((343.18 791.91, 361.32 789.81, 364.49 816.99, 349.09 818.82, 346.06 815.65, 343.18 791.91))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,96,"POLYGON ((451.55 796.80, 466.37 791.25, 476.06 794.11, 477.69 811.74, 475.34 812.98, 453.10 814.87, 451.55 796.80))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,97,"POLYGON ((508.61 792.01, 517.46 790.45, 520.74 787.04, 531.96 783.63, 540.16 785.73, 541.73 789.84, 540.84 795.92, 542.21 800.26, 540.86 804.67, 533.81 807.98, 528.32 806.03, 511.60 808.98, 508.61 792.01))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,98,"POLYGON ((180.17 784.69, 186.11 777.99, 190.90 778.85, 190.23 782.55, 194.53 785.73, 202.50 784.82, 205.72 788.82, 196.44 796.20, 196.75 798.77, 190.88 802.29, 180.17 784.69))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,99,"POLYGON ((571.49 765.21, 589.72 762.29, 594.26 774.80, 599.05 783.43, 597.23 795.39, 594.55 800.37, 584.00 800.63, 580.85 787.39, 579.88 777.25, 572.40 773.24, 571.49 765.21))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,100,"POLYGON ((230.09 774.87, 213.94 785.68, 208.06 769.72, 214.47 767.09, 218.41 765.50, 224.82 763.10, 230.41 767.42, 231.77 772.34, 230.09 774.87))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,101,"POLYGON ((25.50 789.42, 16.05 785.53, 5.84 771.76, 16.29 764.11, 35.34 757.79, 42.33 778.41, 25.50 789.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,102,"POLYGON ((677.36 756.48, 701.26 755.46, 702.26 779.16, 682.22 780.02, 681.61 766.96, 674.50 764.41, 677.36 756.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,103,"POLYGON ((68.10 743.25, 78.72 761.25, 69.51 766.63, 63.71 775.14, 59.18 772.08, 52.87 764.87, 51.35 756.23, 57.29 749.56, 68.10 743.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,104,"POLYGON ((259.45 744.40, 263.46 746.04, 266.41 744.23, 270.59 752.05, 264.68 754.42, 256.34 759.33, 250.65 751.06, 259.45 744.40))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,105,"POLYGON ((283.78 740.00, 277.65 731.61, 274.03 728.46, 265.07 725.75, 266.13 720.71, 275.15 714.29, 280.63 720.05, 289.36 726.32, 289.24 733.38, 283.78 740.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,106,"POLYGON ((577.72 713.44, 592.39 713.07, 593.91 733.35, 584.95 733.57, 585.07 728.86, 584.72 724.41, 581.92 722.26, 578.55 716.88, 577.72 713.44))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,107,"POLYGON ((688.05 694.48, 704.66 693.62, 705.21 715.67, 704.28 740.64, 686.56 740.65, 688.05 694.48))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,108,"POLYGON ((181.89 703.43, 174.73 700.37, 165.90 702.94, 157.78 709.05, 155.25 714.71, 155.74 722.35, 144.19 710.26, 174.82 692.42, 181.89 703.43))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,109,"POLYGON ((499.76 695.52, 533.07 695.12, 535.39 703.25, 532.18 706.38, 527.03 706.51, 524.97 708.89, 518.41 709.99, 513.33 713.15, 508.44 714.92, 499.99 715.02, 499.76 695.52))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,110,"POLYGON ((441.60 673.47, 452.48 683.25, 443.30 693.12, 448.58 698.14, 421.80 723.29, 406.27 710.23, 441.60 673.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,111,"POLYGON ((282.48 683.07, 290.08 676.84, 297.78 683.31, 309.14 694.28, 314.68 689.54, 321.28 694.39, 306.45 712.26, 302.40 709.03, 303.56 705.05, 303.43 699.84, 302.70 695.90, 297.32 689.98, 294.94 687.13, 282.48 683.07))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,112,"POLYGON ((3.82 660.85, 11.40 662.99, 19.24 659.59, 27.06 677.55, 10.46 695.53, 4.55 690.26, 0.00 681.32, 0.00 672.95, 2.25 671.81, 0.00 667.38, 0.00 662.78, 3.82 660.85))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,113,"POLYGON ((317.67 660.30, 324.60 665.03, 329.57 666.33, 338.27 657.32, 345.25 664.04, 342.42 673.24, 334.95 683.95, 328.60 683.89, 317.42 678.31, 314.21 672.55, 314.80 667.85, 314.20 662.25, 317.67 660.30))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,114,"POLYGON ((36.83 653.31, 49.24 647.87, 54.93 660.85, 60.97 664.32, 62.72 669.55, 65.36 677.06, 58.28 679.52, 41.62 685.78, 37.98 676.19, 41.64 674.81, 39.89 670.17, 35.97 660.55, 36.83 653.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,115,"POLYGON ((185.25 641.90, 202.29 653.21, 182.27 683.11, 165.21 671.79, 185.25 641.90))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,116,"POLYGON ((216.87 639.66, 221.64 643.09, 215.52 651.53, 210.76 648.12, 216.87 639.66))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,117,"POLYGON ((562.44 626.39, 577.37 623.75, 583.69 659.42, 577.40 665.10, 569.80 662.63, 562.44 626.39))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,118,"POLYGON ((108.09 632.92, 114.18 639.54, 118.35 640.03, 120.79 643.50, 115.57 647.76, 111.23 651.40, 107.57 647.67, 109.25 643.50, 104.32 635.66, 108.09 632.92))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,119,"POLYGON ((363.25 636.33, 358.08 642.63, 359.88 647.78, 362.83 650.15, 354.99 659.85, 343.85 650.92, 347.30 646.63, 346.69 639.17, 340.19 630.37, 346.52 622.73, 363.25 636.33))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,120,"POLYGON ((475.42 623.68, 485.45 614.21, 489.37 613.14, 493.92 614.00, 498.77 617.63, 499.93 632.74, 498.66 637.10, 486.73 648.28, 472.70 633.56, 479.10 627.51, 475.42 623.68))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,121,"POLYGON ((216.60 627.26, 207.89 638.93, 191.67 626.62, 195.37 623.62, 189.60 618.55, 197.97 610.42, 202.80 610.72, 211.07 615.52, 214.10 619.60, 210.87 623.85, 216.60 627.26))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,122,"POLYGON ((689.46 593.45, 710.47 591.43, 713.64 624.29, 702.96 625.32, 700.33 619.92, 692.08 620.73, 689.46 593.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,123,"POLYGON ((72.98 602.93, 86.65 593.75, 90.03 598.08, 90.70 601.30, 91.98 605.69, 92.69 610.40, 85.97 613.81, 83.10 617.12, 72.98 602.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,124,"POLYGON ((382.15 588.60, 397.32 599.45, 383.62 618.44, 378.78 614.99, 372.35 610.20, 378.06 602.58, 375.58 597.71, 382.15 588.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,125,"POLYGON ((553.65 582.11, 581.72 580.89, 580.65 587.18, 579.94 592.03, 579.08 599.16, 578.18 607.90, 580.18 617.13, 563.52 616.00, 559.80 591.39, 553.99 589.98, 553.65 582.11))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,126,"POLYGON ((11.31 572.00, 24.63 590.34, 5.98 603.19, 0.82 597.66, 0.00 596.29, 0.00 588.57, 7.24 583.60, 7.47 575.78, 11.31 572.00))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,127,"POLYGON ((227.26 577.47, 239.09 565.85, 242.44 565.59, 244.25 563.12, 253.69 560.67, 262.78 566.19, 264.71 568.36, 251.95 587.06, 247.03 585.78, 244.68 594.86, 230.38 582.59, 227.26 577.47))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,128,"POLYGON ((332.28 572.45, 343.10 571.00, 344.31 580.01, 333.48 581.08, 332.28 572.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,129,"POLYGON ((61.62 559.22, 76.74 581.32, 57.89 594.12, 61.62 588.07, 59.42 577.25, 55.26 572.81, 50.67 572.57, 48.01 571.77, 44.75 566.79, 36.64 572.03, 27.80 558.51, 42.58 548.97, 47.48 556.47, 53.14 552.79, 55.71 556.72, 58.70 554.78, 61.62 559.22))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,130,"POLYGON ((402.32 553.20, 417.37 554.57, 417.60 563.62, 416.96 569.92, 396.17 568.29, 396.27 556.30, 399.80 555.61, 402.32 553.20))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,131,"POLYGON ((680.79 544.19, 700.09 542.50, 702.67 571.87, 683.37 573.56, 680.79 544.19))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,132,"POLYGON ((369.03 541.65, 385.86 542.78, 384.25 565.24, 368.40 563.89, 360.09 563.30, 360.51 548.15, 368.99 548.13, 369.03 541.65))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,133,"POLYGON ((281.86 541.93, 306.16 541.90, 308.56 542.88, 308.93 557.57, 306.88 559.46, 281.89 559.49, 281.86 541.93))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,134,"POLYGON ((323.56 540.42, 347.68 541.19, 348.09 557.69, 323.17 556.37, 323.56 540.42))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,135,"POLYGON ((121.51 554.60, 117.97 538.93, 130.25 536.18, 131.10 539.96, 137.01 538.63, 140.01 551.85, 136.64 554.05, 133.76 554.89, 129.35 557.89, 126.21 558.50, 121.10 554.68, 121.51 554.60))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,136,"POLYGON ((681.04 497.35, 700.18 496.78, 697.49 501.04, 697.90 504.18, 700.11 507.99, 701.63 512.14, 703.14 516.30, 701.88 522.30, 700.21 525.85, 681.56 525.21, 681.04 497.35))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,137,"POLYGON ((138.05 464.18, 152.73 462.79, 155.07 487.17, 140.39 488.58, 138.05 464.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,138,"POLYGON ((241.88 463.78, 259.13 463.04, 259.61 473.24, 263.98 475.88, 261.61 479.76, 256.35 480.16, 247.61 478.80, 242.66 481.54, 241.88 463.78))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,139,"POLYGON ((114.39 458.49, 130.98 457.63, 132.21 481.44, 128.10 481.65, 128.28 485.38, 115.81 486.00, 114.39 458.49))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,140,"POLYGON ((334.76 461.45, 352.98 459.32, 354.97 482.23, 343.47 484.00, 341.24 479.33, 336.69 480.09, 334.76 461.45))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,141,"POLYGON ((71.72 454.50, 79.53 455.33, 79.04 460.05, 83.50 460.51, 83.13 464.05, 91.15 464.89, 94.12 467.70, 94.49 473.66, 88.93 474.00, 89.68 486.35, 78.73 487.02, 78.36 480.95, 71.37 475.35, 68.02 467.56, 68.10 460.48, 71.22 459.09, 71.72 454.50))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,142,"POLYGON ((199.00 461.31, 201.01 464.24, 201.75 475.94, 183.59 477.08, 182.94 466.98, 191.27 462.22, 199.00 461.31))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,143,"POLYGON ((435.58 457.18, 435.97 480.92, 422.82 481.23, 421.48 456.85, 425.45 460.25, 431.76 459.03, 435.58 457.18))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,144,"POLYGON ((392.17 449.08, 411.47 450.48, 409.48 477.08, 403.51 480.12, 389.95 479.06, 392.17 449.08))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,145,"POLYGON ((209.20 446.25, 223.55 444.76, 220.50 449.03, 222.59 458.96, 227.96 463.02, 229.37 470.91, 222.18 472.18, 223.33 478.70, 213.81 480.36, 211.76 468.72, 210.94 462.79, 209.20 446.25))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,146,"POLYGON ((300.29 442.36, 309.71 441.79, 308.75 446.03, 310.18 450.46, 316.86 453.17, 322.91 453.02, 323.04 458.26, 321.15 466.72, 321.93 479.88, 302.56 481.01, 300.29 442.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,147,"POLYGON ((487.12 444.61, 505.68 436.77, 516.18 461.43, 514.73 462.05, 517.84 469.36, 501.44 476.28, 494.16 459.12, 489.11 461.25, 485.19 452.00, 489.50 450.18, 487.12 444.61))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,148,"POLYGON ((23.88 446.03, 26.10 456.18, 24.04 460.59, 26.42 464.57, 22.88 466.70, 6.48 465.80, 2.89 461.54, 3.51 445.21, 23.88 446.03))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,149,"POLYGON ((379.75 420.36, 400.11 420.23, 400.21 436.07, 379.85 436.23, 379.75 420.36))",1 +Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,150,"POLYGON ((222.41 405.96, 242.39 406.57, 242.01 418.72, 222.05 418.14, 222.41 405.96))",1 \ No newline at end of file diff --git a/docker/solaris/solaris/data/sample_truth_competition.csv b/docker/solaris/solaris/data/sample_truth_competition.csv new file mode 100644 index 00000000..cc08c550 --- /dev/null +++ b/docker/solaris/solaris/data/sample_truth_competition.csv @@ -0,0 +1,2320 @@ +ImageId,BuildingId,PolygonWKT_Pix,PolygonWKT_Geo +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,0,"POLYGON ((476.88 884.61, 485.59 877.64, 490.50 883.69, 501.08 875.21, 510.57 886.91, 494.26 900.00, 489.33 900.00, 476.88 884.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,1,"POLYGON ((459.45 858.97, 467.41 853.09, 463.37 847.69, 473.92 839.87, 491.62 863.55, 473.11 877.25, 459.45 858.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,2,"POLYGON ((407.34 754.17, 434.90 780.55, 420.27 795.69, 414.77 790.43, 405.88 792.34, 390.17 783.38, 400.26 772.75, 394.62 767.35, 407.34 754.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,3,"POLYGON ((311.00 760.22, 318.38 746.78, 341.02 759.10, 333.64 772.54, 311.00 760.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,4,"POLYGON ((490.49 742.67, 509.81 731.14, 534.12 771.57, 514.79 783.07, 490.49 742.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,5,"POLYGON ((319.28 723.07, 339.97 698.22, 354.29 708.97, 358.58 713.28, 356.47 722.39, 351.91 738.05, 350.69 740.92, 338.71 730.61, 333.62 734.63, 327.30 731.95, 319.28 723.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,6,"POLYGON ((466.49 709.69, 484.26 696.45, 502.59 720.85, 484.79 734.06, 466.49 709.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,7,"POLYGON ((433.84 673.34, 443.90 663.96, 448.70 666.97, 452.01 665.31, 471.95 686.44, 458.26 699.25, 433.84 673.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,8,"POLYGON ((459.24 649.03, 467.38 641.90, 472.84 645.11, 481.72 649.79, 470.13 661.94, 459.24 649.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,9,"POLYGON ((403.55 643.50, 416.98 630.51, 440.36 654.44, 426.94 667.43, 403.55 643.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,10,"POLYGON ((369.12 614.88, 382.54 600.91, 406.33 623.54, 392.92 637.51, 369.12 614.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,11,"POLYGON ((24.86 585.97, 45.12 583.63, 47.43 603.64, 40.92 602.18, 32.55 604.35, 26.62 601.26, 24.86 585.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,12,"POLYGON ((192.24 526.97, 217.80 533.82, 210.59 560.40, 213.69 599.32, 198.64 600.50, 195.50 561.05, 183.84 557.93, 192.24 526.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,13,"POLYGON ((340.54 582.71, 357.91 563.11, 351.06 557.09, 379.47 525.07, 375.45 521.53, 389.78 505.40, 420.55 532.46, 360.43 600.20, 340.54 582.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,14,"POLYGON ((328.04 500.00, 342.08 505.49, 319.73 562.15, 305.69 556.67, 328.04 500.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,15,"POLYGON ((204.86 506.54, 209.61 490.74, 204.85 489.33, 208.87 476.00, 214.61 477.72, 221.47 454.99, 238.66 460.13, 223.02 511.96, 204.86 506.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,16,"POLYGON ((327.22 455.36, 340.03 425.56, 411.42 455.95, 398.61 485.73, 327.22 455.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,17,"POLYGON ((239.02 426.00, 250.66 401.97, 267.34 409.98, 255.70 434.01, 239.02 426.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,18,"POLYGON ((396.36 315.67, 447.39 329.15, 438.70 361.67, 387.69 348.21, 396.36 315.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,19,"POLYGON ((531.30 281.18, 539.07 274.27, 545.03 280.94, 556.14 271.09, 558.49 273.69, 568.18 265.10, 562.05 258.24, 569.70 251.45, 593.53 278.06, 557.28 310.19, 531.30 281.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,20,"POLYGON ((441.50 261.02, 461.97 220.32, 488.44 233.50, 467.96 274.20, 441.50 261.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,21,"POLYGON ((260.80 197.32, 272.78 166.62, 284.29 171.08, 274.76 195.50, 295.23 203.41, 292.77 209.67, 260.80 197.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,22,"POLYGON ((487.08 158.93, 510.80 136.48, 557.55 185.39, 533.83 207.86, 487.08 158.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,23,"POLYGON ((272.75 138.17, 278.76 125.18, 283.79 124.26, 287.47 117.19, 297.89 122.57, 297.41 126.55, 298.72 130.69, 306.06 133.94, 311.49 132.81, 313.73 136.10, 310.84 141.35, 306.98 142.33, 304.42 145.35, 291.10 138.90, 293.15 134.69, 290.62 130.23, 286.29 128.39, 280.17 130.23, 278.79 136.57, 279.99 141.49, 272.75 138.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,24,"POLYGON ((593.72 470.20, 637.72 519.60, 615.80 538.96, 579.92 498.68, 573.87 504.00, 565.75 494.89, 593.72 470.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,25,"POLYGON ((715.23 492.01, 770.00 451.95, 813.62 414.14, 834.44 431.66, 832.45 433.49, 797.60 470.69, 798.95 474.27, 788.92 482.56, 783.77 481.08, 740.14 519.81, 715.23 492.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,26,"POLYGON ((648.04 452.41, 666.87 435.15, 701.60 472.65, 670.22 470.74, 668.52 463.59, 660.83 457.79, 648.04 452.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,27,"POLYGON ((571.40 394.03, 584.01 397.04, 589.66 443.18, 586.08 457.96, 559.90 451.70, 567.15 421.71, 564.82 421.17, 571.40 394.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,28,"POLYGON ((868.09 363.96, 900.00 364.38, 900.00 372.62, 896.98 372.31, 895.34 402.47, 900.00 402.79, 900.00 453.54, 898.32 453.05, 894.79 453.19, 890.15 453.26, 888.79 455.69, 871.48 453.29, 872.10 442.40, 872.41 416.88, 865.19 415.96, 868.09 363.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,29,"POLYGON ((650.23 407.96, 697.21 368.76, 715.95 391.01, 668.97 430.21, 650.23 407.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,30,"POLYGON ((780.04 377.48, 809.37 342.00, 830.13 356.36, 804.14 385.75, 803.64 390.16, 799.40 394.28, 780.04 377.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,31,"POLYGON ((580.95 330.28, 612.45 336.14, 608.87 355.21, 604.68 358.40, 599.10 365.11, 589.85 363.22, 585.90 366.98, 582.09 387.23, 573.74 385.69, 577.87 363.70, 574.77 363.11, 580.95 330.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,32,"POLYGON ((645.57 304.24, 667.36 285.46, 718.66 344.44, 696.84 363.22, 645.57 304.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,33,"POLYGON ((747.60 290.07, 764.14 269.32, 799.62 297.36, 803.16 294.87, 829.13 315.39, 809.97 339.38, 747.60 290.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,34,"POLYGON ((611.15 259.41, 630.34 244.20, 626.79 239.76, 664.56 209.83, 682.91 232.76, 647.39 260.91, 649.98 264.15, 628.53 281.14, 611.15 259.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,35,"POLYGON ((540.66 204.71, 562.84 184.30, 598.62 222.86, 570.20 248.98, 564.03 242.34, 570.27 236.61, 540.66 204.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,36,"POLYGON ((607.46 49.04, 605.71 66.27, 609.43 68.88, 606.51 81.57, 601.80 81.80, 583.10 74.97, 584.61 67.85, 581.43 67.00, 584.30 60.05, 584.44 57.44, 587.39 58.10, 589.65 44.28, 607.46 49.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,37,"POLYGON ((840.99 891.66, 851.72 890.34, 852.28 887.84, 868.66 885.82, 870.42 900.00, 842.01 900.00, 840.99 891.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,38,"POLYGON ((879.53 888.01, 888.10 887.01, 886.65 874.60, 895.41 873.58, 898.51 900.00, 880.94 900.00, 879.53 888.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,39,"POLYGON ((733.84 869.12, 758.63 871.91, 756.90 887.16, 752.42 886.65, 751.80 892.06, 731.49 889.76, 733.84 869.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,40,"POLYGON ((745.92 841.53, 761.61 842.51, 761.28 847.74, 763.89 847.91, 763.41 855.52, 766.54 856.53, 766.50 862.52, 752.38 862.46, 753.99 858.84, 751.86 854.41, 748.72 853.09, 745.19 852.87, 745.92 841.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,41,"POLYGON ((828.19 815.66, 849.43 813.04, 849.69 815.03, 857.88 814.02, 859.77 829.24, 830.34 832.88, 828.19 815.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,42,"POLYGON ((867.27 816.07, 875.11 815.76, 875.01 813.05, 884.67 812.67, 884.88 818.17, 892.71 817.86, 892.97 824.47, 886.06 829.26, 867.81 829.97, 867.27 816.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,43,"POLYGON ((743.14 805.76, 761.53 807.09, 761.15 812.44, 766.24 812.80, 765.13 828.13, 752.67 827.24, 747.98 823.21, 742.07 820.46, 743.14 805.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,44,"POLYGON ((868.97 790.41, 869.09 796.38, 859.87 796.54, 859.76 790.60, 868.97 790.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,45,"POLYGON ((743.49 774.40, 761.34 774.93, 761.21 779.35, 765.56 779.48, 765.14 793.85, 742.95 793.22, 743.49 774.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,46,"POLYGON ((827.26 772.58, 847.97 770.37, 850.11 790.24, 834.14 791.96, 833.90 789.70, 830.49 790.08, 829.59 781.76, 820.63 782.72, 820.12 777.87, 827.75 777.05, 827.26 772.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,47,"POLYGON ((889.28 755.33, 900.00 754.63, 900.00 772.50, 890.45 773.12, 889.28 755.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,48,"POLYGON ((741.53 741.71, 758.98 742.00, 758.90 747.44, 762.29 747.49, 762.16 755.50, 765.62 755.55, 765.48 763.21, 741.15 762.81, 741.53 741.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,49,"POLYGON ((814.79 742.71, 844.28 739.69, 846.15 757.78, 825.49 759.91, 824.33 748.68, 815.50 749.59, 814.79 742.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,50,"POLYGON ((889.59 723.16, 900.00 722.92, 900.00 741.95, 890.02 742.17, 889.59 723.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,51,"POLYGON ((750.02 710.31, 762.44 710.26, 762.46 714.67, 766.62 714.66, 766.67 728.26, 750.89 728.33, 750.87 721.78, 747.11 721.81, 747.07 715.40, 750.06 715.39, 750.02 710.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,52,"POLYGON ((814.43 708.18, 843.81 706.85, 844.66 725.54, 822.90 726.52, 822.35 714.55, 814.75 714.90, 814.43 708.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,53,"POLYGON ((678.59 673.30, 703.05 674.61, 702.27 689.06, 705.43 689.22, 702.23 748.06, 678.76 746.80, 680.29 718.25, 676.15 718.03, 678.59 673.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,54,"POLYGON ((891.86 694.17, 900.00 693.82, 900.00 713.55, 892.69 713.86, 891.86 694.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,55,"POLYGON ((813.51 682.21, 842.81 680.71, 843.76 698.88, 820.96 700.04, 820.40 689.05, 813.88 689.39, 813.51 682.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,56,"POLYGON ((744.51 673.80, 759.45 674.33, 759.32 677.59, 764.41 677.78, 763.84 693.99, 747.13 693.42, 747.41 685.58, 744.10 685.46, 744.51 673.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,57,"POLYGON ((754.68 635.89, 767.70 639.29, 764.22 657.67, 755.36 654.37, 755.83 648.12, 755.27 642.11, 754.68 635.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,58,"POLYGON ((828.95 633.53, 843.10 620.43, 855.57 633.77, 842.07 646.27, 840.62 644.74, 836.70 648.36, 827.43 638.43, 830.71 635.40, 828.95 633.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,59,"POLYGON ((877.72 617.11, 882.84 617.75, 883.30 614.12, 900.00 616.29, 900.00 632.62, 883.18 630.44, 883.34 629.28, 876.24 628.38, 877.72 617.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,60,"POLYGON ((675.10 589.46, 701.66 590.83, 698.35 653.51, 671.80 652.12, 675.10 589.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,61,"POLYGON ((761.75 598.77, 776.83 606.96, 774.74 610.79, 778.27 612.72, 774.27 620.03, 777.28 621.66, 775.29 625.29, 767.37 620.98, 766.41 622.74, 753.59 615.79, 756.20 613.06, 758.55 612.84, 760.24 615.17, 762.12 613.06, 764.87 605.47, 763.59 601.68, 761.75 598.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,62,"POLYGON ((772.12 587.37, 786.25 574.00, 798.38 586.70, 796.59 588.39, 799.60 591.53, 787.24 603.21, 772.12 587.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,63,"POLYGON ((796.09 565.29, 811.54 554.58, 821.69 569.06, 820.21 570.09, 823.27 574.45, 809.30 584.16, 796.09 565.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,64,"POLYGON ((824.91 549.84, 841.47 542.20, 848.80 557.95, 847.06 558.78, 849.02 562.97, 834.18 569.80, 824.91 549.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,65,"POLYGON ((860.89 547.41, 885.04 546.24, 885.54 556.20, 881.00 556.42, 881.27 562.12, 861.66 563.09, 860.89 547.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3728739,66,"POLYGON ((615.15 543.37, 644.55 518.87, 679.90 560.90, 652.75 583.53, 647.20 576.92, 652.55 572.48, 625.23 540.01, 617.64 546.33, 615.15 543.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,0,"POLYGON ((394.89 770.59, 402.04 768.06, 406.44 769.57, 410.42 769.95, 410.60 771.81, 409.63 773.08, 405.41 773.68, 405.36 781.11, 410.55 784.44, 410.10 786.56, 408.51 787.47, 406.93 789.00, 402.95 789.34, 399.70 802.81, 409.22 805.79, 409.38 807.14, 407.90 807.31, 407.33 809.79, 383.80 804.81, 384.87 797.86, 394.20 797.74, 395.06 786.95, 387.92 785.75, 390.12 778.64, 402.45 779.19, 403.06 773.85, 395.08 773.18, 394.89 770.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,1,"POLYGON ((340.68 613.32, 342.65 616.20, 342.43 619.25, 342.24 622.21, 341.98 628.85, 343.31 632.04, 345.35 634.23, 351.40 637.29, 355.68 636.83, 356.57 644.84, 353.71 649.86, 338.26 648.77, 333.39 643.43, 336.19 616.37, 340.68 613.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,2,"POLYGON ((199.73 437.91, 225.61 438.29, 225.94 457.55, 223.64 457.61, 223.77 462.69, 201.03 461.34, 199.73 437.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,3,"POLYGON ((11.94 347.48, 11.74 339.54, 13.72 339.49, 13.20 332.06, 19.97 330.40, 20.18 338.66, 30.60 337.08, 32.84 346.13, 31.76 349.62, 31.33 352.45, 32.64 364.64, 29.00 365.07, 26.85 365.28, 24.97 363.69, 23.21 359.58, 21.61 355.49, 21.69 352.03, 17.31 349.98, 15.77 347.87, 11.94 347.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,4,"POLYGON ((188.19 347.68, 188.99 338.20, 209.04 337.69, 211.33 344.47, 211.45 349.02, 205.12 349.00, 204.67 345.15, 203.73 343.27, 202.29 341.55, 200.01 342.47, 197.91 342.17, 196.72 344.13, 195.54 345.92, 194.17 347.70, 192.64 349.50, 190.35 350.09, 188.05 349.44, 188.19 347.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,5,"POLYGON ((555.34 211.96, 565.52 175.87, 571.74 173.85, 574.25 173.56, 577.20 174.11, 579.32 175.52, 580.24 178.21, 580.51 180.71, 580.78 183.41, 580.85 186.31, 580.93 189.22, 580.79 191.73, 580.22 194.26, 579.86 196.75, 578.68 199.49, 577.68 201.82, 575.65 203.54, 575.29 206.04, 572.59 207.15, 570.10 207.83, 566.76 207.92, 564.71 209.63, 565.81 212.11, 564.39 214.02, 555.34 211.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,6,"POLYGON ((465.25 194.94, 464.57 188.17, 501.48 185.61, 502.77 187.55, 502.60 190.90, 491.52 195.40, 483.15 193.89, 474.72 195.21, 467.75 195.63, 465.25 194.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,7,"POLYGON ((642.02 892.27, 651.42 870.23, 658.94 872.39, 661.88 865.37, 672.70 870.67, 664.35 889.35, 657.20 887.18, 656.15 889.56, 654.48 892.44, 649.59 891.32, 647.09 896.23, 642.02 892.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,8,"POLYGON ((860.84 719.75, 874.25 718.72, 875.19 730.99, 861.80 732.02, 860.84 719.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,9,"POLYGON ((705.56 692.49, 721.67 696.92, 721.22 698.80, 726.42 700.42, 725.82 704.58, 720.08 702.98, 713.95 722.31, 707.09 718.40, 704.92 713.77, 703.20 711.24, 702.45 704.54, 700.00 702.98, 700.40 700.50, 702.59 701.02, 705.56 692.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,10,"POLYGON ((786.25 613.52, 782.83 636.52, 752.44 632.05, 756.13 607.23, 776.14 610.16, 775.87 611.99, 786.25 613.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,11,"POLYGON ((698.79 609.62, 722.04 611.49, 721.22 625.08, 718.16 625.35, 716.72 623.33, 715.41 622.27, 712.67 623.12, 711.00 623.85, 709.22 623.52, 708.48 621.85, 706.50 621.61, 704.06 622.47, 702.23 620.54, 703.57 619.33, 701.35 617.33, 699.02 614.92, 698.12 614.66, 698.79 609.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,12,"POLYGON ((709.43 585.13, 685.48 577.24, 687.31 576.39, 688.44 572.70, 686.61 569.78, 688.71 565.86, 692.61 556.22, 700.67 558.54, 705.37 559.46, 707.34 557.37, 710.87 558.55, 711.34 560.84, 716.71 563.48, 709.43 585.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,13,"POLYGON ((754.86 555.85, 753.37 562.92, 750.69 562.37, 746.12 559.60, 747.25 554.27, 754.86 555.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,14,"POLYGON ((769.76 211.11, 795.89 219.26, 790.23 237.21, 787.96 236.51, 784.44 236.60, 782.79 240.84, 784.14 245.00, 781.56 247.17, 780.47 250.58, 759.41 244.01, 769.76 211.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,15,"POLYGON ((819.20 146.10, 823.60 139.71, 833.97 146.77, 829.56 153.16, 819.20 146.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,16,"POLYGON ((888.79 140.84, 883.20 148.02, 876.44 142.78, 882.01 135.62, 888.79 140.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,17,"POLYGON ((864.16 130.59, 856.42 141.47, 849.68 128.92, 849.40 121.09, 848.78 112.58, 845.72 102.36, 859.81 112.30, 852.59 122.45, 864.16 130.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,18,"POLYGON ((702.60 88.14, 699.99 95.04, 701.83 97.22, 700.62 102.09, 696.58 100.32, 692.97 110.67, 685.16 108.67, 685.19 105.48, 678.90 102.53, 676.47 107.23, 673.00 105.45, 675.78 101.85, 673.42 100.36, 675.93 94.77, 679.11 93.27, 682.45 85.46, 693.78 89.43, 695.17 86.40, 702.60 88.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,19,"POLYGON ((890.40 80.22, 877.84 95.13, 861.76 81.71, 865.38 77.42, 862.71 75.18, 864.96 72.48, 867.15 75.58, 871.44 78.44, 875.30 77.83, 877.54 76.20, 880.80 72.08, 890.40 80.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729189,20,"POLYGON ((710.48 44.79, 713.47 40.63, 717.15 41.64, 719.18 43.26, 719.47 46.00, 719.40 47.53, 719.70 50.52, 722.80 54.31, 719.96 64.97, 703.94 58.21, 710.48 44.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,0,"POLYGON ((604.65 313.72, 620.87 319.81, 620.72 322.03, 681.40 345.97, 682.11 344.06, 691.84 347.72, 689.91 349.75, 696.82 352.35, 687.66 376.55, 597.35 341.16, 604.65 313.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,1,"POLYGON ((697.92 233.12, 724.60 242.29, 701.17 309.73, 674.49 300.55, 697.92 233.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,2,"POLYGON ((592.94 222.12, 596.18 231.96, 597.73 292.85, 597.06 295.84, 574.21 297.42, 570.92 295.75, 568.98 222.68, 592.94 222.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,3,"POLYGON ((715.14 155.83, 739.51 157.47, 735.89 210.13, 709.20 208.32, 712.60 158.87, 715.14 155.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,4,"POLYGON ((567.21 164.97, 663.90 165.12, 663.96 188.74, 662.23 191.54, 657.16 192.84, 647.46 192.18, 645.01 190.62, 637.11 189.34, 636.15 188.12, 633.54 188.54, 631.35 192.88, 625.90 191.66, 625.23 190.02, 624.57 189.10, 621.76 189.37, 620.01 190.46, 619.88 192.68, 617.05 192.51, 614.99 192.34, 615.25 188.87, 603.52 187.86, 600.02 185.98, 596.70 187.72, 589.73 187.01, 566.22 192.32, 566.70 185.05, 567.02 178.63, 567.17 173.45, 567.21 164.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,5,"POLYGON ((707.78 53.68, 734.23 53.19, 735.88 139.56, 709.41 140.06, 707.78 53.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,6,"POLYGON ((650.16 43.14, 652.47 137.65, 646.29 141.13, 635.60 140.36, 632.63 138.99, 625.57 139.26, 621.97 44.02, 650.16 43.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730089,7,"POLYGON ((812.43 46.49, 812.09 41.83, 819.98 41.23, 822.57 44.61, 875.55 43.88, 878.61 40.98, 886.69 40.55, 888.28 70.24, 813.93 74.24, 812.43 46.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,0,"POLYGON ((730.28 414.30, 729.56 434.03, 696.66 432.85, 697.38 413.12, 730.28 414.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,1,"POLYGON ((692.70 417.03, 692.24 432.69, 667.32 432.00, 667.77 416.20, 656.00 415.86, 656.07 405.67, 670.69 406.07, 670.30 416.40, 692.70 417.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,2,"POLYGON ((773.80 407.37, 803.77 407.34, 803.78 415.95, 799.46 415.95, 799.45 419.35, 773.81 419.38, 773.80 407.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,3,"POLYGON ((625.16 188.72, 624.81 194.43, 627.79 203.81, 631.59 215.90, 633.19 219.83, 632.02 222.61, 620.03 222.65, 614.28 222.56, 613.21 209.66, 612.64 197.49, 611.91 188.81, 625.16 188.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,4,"POLYGON ((625.01 153.65, 626.68 160.06, 625.12 167.56, 625.12 177.53, 625.81 184.97, 611.07 184.83, 610.29 154.02, 625.01 153.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,5,"POLYGON ((617.90 119.51, 631.15 120.17, 632.44 131.33, 630.02 134.87, 627.14 138.92, 625.24 142.94, 624.18 150.18, 616.95 151.37, 611.48 152.26, 609.42 150.07, 608.49 142.37, 609.32 135.64, 610.17 120.46, 617.90 119.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,6,"POLYGON ((614.33 77.58, 625.82 77.78, 623.93 81.31, 622.84 88.31, 623.60 98.23, 616.16 100.66, 609.63 99.32, 609.40 79.93, 614.33 77.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,7,"POLYGON ((610.54 36.63, 626.01 36.24, 626.74 68.45, 609.84 68.77, 609.39 40.90, 610.54 36.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,8,"POLYGON ((626.13 0.00, 627.38 23.82, 631.79 23.59, 632.00 27.47, 623.14 27.94, 623.39 32.44, 609.12 33.18, 607.38 0.00, 626.13 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,9,"POLYGON ((532.50 547.35, 535.57 584.63, 518.81 585.99, 516.33 555.87, 524.29 555.22, 523.70 548.07, 532.50 547.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,10,"POLYGON ((429.64 542.36, 440.18 541.49, 441.66 553.00, 443.98 556.49, 444.76 563.86, 440.77 564.41, 435.26 564.68, 431.37 563.45, 431.64 556.65, 430.80 552.83, 429.64 542.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,11,"POLYGON ((27.89 557.23, 27.45 548.07, 34.33 544.14, 38.35 539.54, 60.35 538.49, 61.18 555.65, 27.89 557.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,12,"POLYGON ((135.78 551.46, 114.32 552.61, 112.66 521.77, 134.12 520.63, 135.78 551.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,13,"POLYGON ((359.92 526.78, 362.00 526.72, 362.95 523.30, 369.18 523.14, 369.53 525.36, 383.94 524.83, 384.77 534.14, 383.83 537.84, 381.47 538.50, 365.72 538.59, 360.09 539.05, 359.92 526.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,14,"POLYGON ((275.20 519.77, 277.21 540.40, 268.91 540.93, 267.87 535.18, 256.15 535.48, 254.96 523.39, 275.20 519.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,15,"POLYGON ((69.34 525.58, 77.38 525.27, 77.02 516.29, 82.29 516.06, 82.59 523.63, 88.58 523.39, 88.76 527.71, 95.59 527.45, 96.13 540.75, 70.01 541.84, 69.34 525.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,16,"POLYGON ((0.00 515.48, 4.52 515.45, 4.55 521.00, 15.83 520.93, 15.92 534.65, 2.51 534.74, 2.53 537.67, 0.00 537.69, 0.00 515.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,17,"POLYGON ((319.33 513.91, 317.66 529.92, 302.41 531.64, 299.97 529.92, 296.00 531.36, 293.17 530.98, 291.11 518.72, 319.33 513.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,18,"POLYGON ((315.60 417.78, 315.77 424.48, 310.90 431.31, 317.05 433.44, 321.38 433.44, 321.42 441.45, 314.10 441.46, 299.90 436.81, 297.31 433.94, 293.29 432.80, 290.65 428.47, 288.07 419.39, 293.11 417.95, 293.10 413.85, 299.52 413.82, 304.05 418.10, 315.60 417.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,19,"POLYGON ((563.50 415.13, 562.19 422.62, 559.59 428.66, 553.42 431.81, 543.65 430.55, 537.76 425.49, 539.11 419.24, 543.75 414.90, 563.50 415.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,20,"POLYGON ((272.99 411.14, 275.87 416.93, 276.75 426.94, 267.57 429.26, 261.89 429.20, 255.00 431.05, 250.60 431.16, 249.65 427.43, 245.46 427.54, 241.53 421.78, 245.41 417.48, 258.42 416.75, 266.85 417.78, 272.99 411.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,21,"POLYGON ((433.18 390.64, 434.30 399.62, 433.94 402.89, 431.86 402.94, 431.65 417.73, 422.33 419.15, 415.24 415.49, 415.68 391.81, 433.18 390.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,22,"POLYGON ((382.45 391.99, 383.25 404.65, 387.76 404.38, 388.57 417.34, 366.23 418.76, 364.31 388.51, 376.57 387.73, 376.87 392.36, 382.45 391.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,23,"POLYGON ((466.13 388.86, 468.17 386.53, 472.39 386.82, 474.19 392.01, 472.00 396.24, 470.63 400.05, 472.78 402.08, 474.30 404.35, 474.01 409.57, 473.45 411.67, 467.95 410.77, 465.39 409.17, 466.13 388.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,24,"POLYGON ((386.42 376.82, 386.29 372.94, 392.61 372.73, 392.75 376.90, 402.30 376.55, 402.78 390.37, 382.48 391.08, 381.99 376.98, 386.42 376.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,25,"POLYGON ((45.19 372.37, 45.92 410.55, 42.20 410.62, 42.27 414.26, 21.92 414.65, 21.87 410.92, 19.32 410.96, 18.20 346.42, 43.28 345.98, 43.32 348.51, 48.63 348.42, 48.57 345.40, 106.87 344.52, 107.30 373.10, 50.60 373.94, 50.11 372.26, 45.19 372.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,26,"POLYGON ((303.95 323.40, 305.67 332.35, 275.40 331.66, 275.50 327.28, 277.31 324.93, 282.57 325.41, 286.92 322.79, 303.95 323.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,27,"POLYGON ((521.46 314.31, 540.69 313.96, 538.75 321.35, 542.28 322.37, 542.61 325.72, 538.83 329.05, 535.40 337.22, 523.51 335.66, 522.15 326.99, 521.46 314.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,28,"POLYGON ((390.10 314.95, 392.72 318.86, 393.92 325.09, 392.77 329.51, 391.64 333.93, 385.99 334.92, 381.34 334.00, 379.12 329.04, 376.31 326.58, 373.59 326.65, 371.66 333.20, 365.74 332.09, 365.61 326.65, 366.94 321.60, 381.90 315.16, 390.10 314.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,29,"POLYGON ((394.21 303.13, 407.21 301.95, 409.26 308.18, 404.92 311.22, 393.39 312.14, 392.46 308.61, 394.21 303.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,30,"POLYGON ((510.35 276.81, 535.84 275.52, 536.11 281.86, 539.15 281.74, 539.64 293.12, 540.90 293.06, 541.12 297.81, 537.39 297.97, 537.67 304.60, 521.71 305.29, 521.62 303.12, 519.62 303.22, 519.45 299.38, 520.89 299.32, 520.75 295.84, 510.32 296.66, 510.35 276.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,31,"POLYGON ((295.20 260.05, 296.94 262.12, 296.77 272.35, 290.38 277.11, 292.78 297.14, 288.80 297.44, 285.81 295.01, 283.83 291.51, 277.33 292.09, 278.02 278.49, 276.99 270.97, 274.64 269.79, 274.53 265.40, 277.90 265.11, 276.56 261.79, 295.20 260.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,32,"POLYGON ((402.04 251.23, 402.96 267.47, 379.98 268.75, 379.84 266.20, 372.26 266.61, 371.85 259.30, 379.89 258.85, 379.55 252.49, 402.04 251.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,33,"POLYGON ((536.20 243.24, 537.60 256.74, 539.14 261.63, 534.94 262.96, 519.44 264.75, 519.79 257.71, 516.93 243.02, 523.81 242.13, 529.84 243.40, 536.20 243.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,34,"POLYGON ((535.34 226.32, 516.78 227.37, 515.92 211.92, 521.98 211.59, 525.06 207.98, 524.75 203.24, 533.89 202.65, 535.34 226.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,35,"POLYGON ((268.89 182.39, 285.10 184.26, 285.63 179.67, 291.57 180.34, 289.48 198.24, 267.34 195.70, 268.89 182.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,36,"POLYGON ((517.14 159.97, 530.84 158.89, 531.25 164.58, 534.76 165.49, 537.09 168.92, 536.01 175.40, 534.90 180.67, 536.03 185.61, 525.67 186.66, 524.64 181.42, 523.55 166.13, 517.14 159.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,37,"POLYGON ((516.25 118.53, 529.51 118.81, 533.88 123.80, 533.77 134.86, 536.73 133.05, 537.16 140.26, 534.72 142.05, 534.20 146.30, 533.12 147.95, 526.87 147.98, 525.20 146.64, 522.74 142.36, 519.50 143.33, 508.57 135.39, 507.39 126.41, 514.18 125.55, 516.25 118.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,38,"POLYGON ((297.03 73.39, 297.43 83.43, 268.49 84.33, 265.77 83.20, 264.53 75.26, 270.46 74.22, 297.03 73.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,39,"POLYGON ((503.67 53.18, 508.74 56.05, 508.84 60.02, 510.35 70.68, 494.37 70.58, 492.68 53.47, 503.67 53.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,40,"POLYGON ((413.66 40.09, 414.06 49.98, 401.56 49.42, 400.15 46.50, 398.91 38.85, 413.66 40.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,41,"POLYGON ((246.25 38.61, 256.36 38.73, 256.30 45.03, 246.17 44.92, 246.25 38.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,42,"POLYGON ((120.80 27.20, 123.62 39.05, 122.31 44.94, 113.08 46.22, 110.89 42.52, 107.49 40.92, 106.12 36.58, 104.17 34.52, 106.42 31.76, 108.32 23.96, 110.80 22.23, 114.85 24.64, 120.80 27.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,43,"POLYGON ((516.55 19.78, 544.41 20.67, 544.06 30.27, 546.04 39.67, 538.50 47.33, 532.66 48.07, 531.01 35.00, 515.47 36.95, 514.26 27.30, 516.55 19.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,44,"POLYGON ((164.57 21.31, 175.70 21.03, 175.80 24.29, 179.72 26.56, 183.44 26.91, 184.75 37.51, 183.93 40.35, 179.09 42.69, 152.50 42.88, 152.15 24.60, 164.66 24.28, 164.57 21.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,45,"POLYGON ((488.34 20.01, 503.54 18.87, 505.96 25.78, 506.10 30.99, 506.25 37.20, 504.18 43.74, 498.70 44.61, 496.99 36.46, 487.97 35.69, 488.34 20.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,46,"POLYGON ((442.60 22.15, 468.48 22.80, 472.04 29.28, 472.69 39.46, 459.29 40.31, 456.58 37.38, 442.37 37.28, 442.45 28.02, 442.60 22.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,47,"POLYGON ((213.84 22.10, 222.82 21.96, 222.79 19.32, 232.58 19.20, 232.63 22.80, 239.00 22.12, 238.33 27.22, 236.01 31.34, 233.96 34.15, 232.79 38.46, 213.09 37.10, 211.57 28.46, 212.58 25.88, 213.84 22.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,48,"POLYGON ((276.38 20.39, 284.66 19.01, 295.95 18.72, 301.38 21.82, 302.40 27.12, 299.82 30.74, 293.35 32.97, 287.43 34.32, 280.86 33.00, 276.29 28.39, 276.38 20.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,49,"POLYGON ((378.36 1.41, 387.76 2.42, 389.37 10.28, 387.33 20.37, 384.57 30.11, 374.00 31.07, 371.07 26.75, 372.57 23.03, 376.93 20.99, 378.14 13.03, 376.07 8.18, 375.01 1.68, 378.36 1.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,50,"POLYGON ((804.92 820.15, 878.90 817.71, 882.04 830.10, 882.37 843.21, 805.75 845.35, 804.92 820.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,51,"POLYGON ((809.26 761.06, 874.68 757.75, 874.47 754.05, 897.80 752.74, 899.74 787.21, 811.00 792.16, 809.26 761.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,52,"POLYGON ((797.29 624.76, 824.75 623.21, 825.10 629.60, 823.02 629.72, 825.30 671.93, 826.80 671.84, 827.29 681.29, 825.60 681.37, 826.22 694.08, 828.37 693.98, 828.76 701.87, 826.22 701.98, 826.66 711.47, 829.05 711.36, 829.41 718.77, 802.65 720.21, 797.29 624.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,53,"POLYGON ((900.00 701.98, 894.53 702.13, 878.43 703.05, 878.24 695.81, 880.65 695.69, 880.09 688.93, 878.33 688.98, 877.74 679.76, 879.15 679.72, 877.94 655.53, 876.66 655.57, 876.30 645.76, 877.69 645.73, 877.29 634.53, 875.42 634.69, 875.09 625.93, 876.36 625.65, 875.78 616.56, 873.31 616.74, 873.16 610.88, 876.09 610.81, 875.77 606.97, 900.00 606.11, 900.00 701.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,54,"POLYGON ((640.34 562.10, 651.00 561.25, 652.15 575.74, 641.50 576.56, 640.34 562.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,55,"POLYGON ((720.27 527.54, 720.94 600.51, 692.71 600.85, 692.60 593.64, 694.51 593.61, 694.38 585.87, 692.84 585.89, 692.74 578.28, 694.08 578.24, 693.87 563.64, 692.00 563.67, 691.89 555.10, 693.14 555.09, 693.01 542.93, 690.60 542.95, 690.52 533.89, 693.89 533.85, 693.83 527.79, 720.27 527.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,56,"POLYGON ((558.71 549.26, 565.63 549.15, 565.61 547.40, 575.10 547.24, 575.14 549.62, 616.54 548.90, 616.51 546.88, 626.01 546.70, 626.05 548.97, 632.70 548.84, 633.14 574.00, 559.15 575.29, 558.71 549.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,57,"POLYGON ((814.63 512.51, 814.93 517.78, 817.25 517.65, 817.80 527.47, 815.04 527.63, 815.60 537.45, 817.38 537.36, 818.09 549.75, 816.03 549.87, 817.22 571.20, 819.61 571.07, 820.19 581.33, 818.43 581.42, 818.87 589.00, 821.24 588.88, 821.64 595.81, 794.19 597.36, 789.52 513.90, 814.63 512.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3730539,58,"POLYGON ((895.66 514.28, 900.00 576.14, 900.00 587.41, 875.02 589.04, 874.68 582.21, 871.05 582.50, 870.75 573.92, 873.18 573.63, 872.13 551.28, 870.35 551.48, 870.14 543.43, 871.83 543.26, 871.38 531.59, 868.73 531.53, 868.10 523.62, 870.19 523.41, 870.00 515.78, 895.66 514.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,0,"POLYGON ((275.97 836.50, 277.61 868.87, 261.10 869.71, 259.46 837.35, 275.97 836.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,1,"POLYGON ((366.05 814.01, 367.56 814.73, 366.41 818.46, 366.39 827.39, 367.07 835.05, 372.33 836.14, 372.96 841.09, 354.82 842.04, 353.86 814.56, 366.05 814.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,2,"POLYGON ((144.24 815.17, 160.41 813.83, 162.44 838.15, 146.29 839.52, 144.24 815.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,3,"POLYGON ((258.61 803.21, 276.17 802.18, 277.37 822.53, 259.79 823.55, 258.61 803.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,4,"POLYGON ((58.66 784.46, 59.46 807.54, 44.18 808.07, 43.68 793.52, 35.91 793.78, 35.79 790.63, 46.32 790.25, 46.15 784.89, 58.66 784.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,5,"POLYGON ((354.43 777.99, 371.34 777.36, 372.24 801.11, 355.33 801.76, 354.43 777.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,6,"POLYGON ((142.23 775.34, 146.50 777.67, 147.60 779.39, 152.25 782.07, 157.52 781.94, 159.74 800.11, 143.94 801.22, 142.23 775.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,7,"POLYGON ((260.30 767.69, 276.69 766.70, 278.48 796.00, 262.08 796.99, 260.30 767.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,8,"POLYGON ((41.16 774.67, 40.95 764.93, 33.03 765.09, 32.78 753.02, 46.78 752.75, 46.69 748.38, 56.07 748.18, 56.38 763.27, 59.61 763.21, 59.83 774.30, 41.16 774.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,9,"POLYGON ((354.95 748.30, 372.49 747.70, 373.34 772.20, 355.80 772.80, 354.95 748.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,10,"POLYGON ((144.58 743.04, 160.75 742.99, 163.16 768.14, 159.99 768.22, 155.38 766.59, 150.41 765.32, 147.24 765.40, 144.44 765.47, 144.58 743.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,11,"POLYGON ((252.16 758.37, 251.94 746.26, 277.53 745.79, 277.75 757.90, 252.16 758.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,12,"POLYGON ((16.56 745.15, 17.28 757.03, 5.42 757.73, 4.71 745.85, 16.56 745.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,13,"POLYGON ((212.34 727.25, 229.23 727.35, 229.12 743.40, 212.23 743.30, 212.34 727.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,14,"POLYGON ((354.51 709.95, 371.68 709.42, 372.45 733.67, 355.28 734.22, 354.51 709.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,15,"POLYGON ((40.17 710.87, 56.13 710.34, 56.88 732.71, 40.92 733.23, 40.17 710.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,16,"POLYGON ((364.81 671.93, 373.74 671.59, 374.85 699.82, 359.60 700.41, 359.50 697.99, 362.97 696.51, 363.21 692.30, 365.22 688.03, 364.34 681.40, 362.59 678.22, 366.95 675.83, 364.81 671.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,17,"POLYGON ((141.68 668.73, 146.80 668.51, 146.97 672.19, 157.40 671.75, 158.58 698.51, 142.05 688.37, 141.68 668.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,18,"POLYGON ((40.32 660.08, 58.21 659.36, 59.49 690.78, 41.60 691.50, 40.32 660.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,19,"POLYGON ((346.03 637.51, 372.27 636.49, 372.55 643.79, 377.13 643.60, 377.45 651.83, 370.56 652.09, 370.92 661.10, 358.61 661.59, 358.14 649.66, 346.51 650.11, 346.03 637.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,20,"POLYGON ((141.13 631.65, 148.10 631.37, 149.58 634.84, 162.11 634.34, 162.44 642.21, 157.90 642.39, 158.59 659.42, 142.30 660.08, 141.13 631.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,21,"POLYGON ((248.50 628.52, 261.99 627.98, 271.04 637.89, 268.35 643.22, 249.13 644.00, 248.50 628.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,22,"POLYGON ((43.90 567.63, 47.69 580.78, 29.29 586.04, 25.50 572.89, 43.90 567.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,23,"POLYGON ((271.12 531.65, 288.25 531.12, 289.58 574.87, 272.45 575.39, 271.12 531.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,24,"POLYGON ((0.00 523.36, 9.93 522.96, 11.13 552.85, 0.00 553.30, 0.00 523.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,25,"POLYGON ((459.66 553.84, 458.67 534.09, 428.37 535.28, 427.06 522.68, 471.39 520.49, 473.02 553.19, 459.66 553.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,26,"POLYGON ((30.79 550.33, 29.75 531.23, 34.75 530.97, 34.44 525.25, 71.27 523.26, 72.63 548.07, 30.79 550.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,27,"POLYGON ((250.47 511.29, 258.07 511.03, 258.24 515.66, 262.39 515.53, 263.62 550.42, 245.71 551.05, 244.51 517.26, 250.67 517.05, 250.47 511.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,28,"POLYGON ((157.82 512.00, 167.00 511.68, 167.22 517.54, 170.82 517.40, 171.41 533.92, 176.58 533.72, 176.77 539.18, 171.80 539.35, 172.07 546.74, 155.88 547.33, 154.79 516.90, 158.00 516.79, 157.82 512.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,29,"POLYGON ((180.17 512.48, 188.37 512.11, 188.61 517.21, 191.26 517.08, 191.02 512.05, 203.31 511.49, 203.52 516.21, 205.80 516.11, 206.09 522.30, 217.61 521.76, 217.87 527.57, 208.14 528.02, 208.61 538.48, 205.07 538.64, 205.39 545.82, 191.75 546.46, 191.44 539.43, 189.64 539.50, 188.97 524.86, 180.76 525.23, 180.17 512.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,30,"POLYGON ((122.62 516.36, 138.12 515.66, 139.12 537.41, 128.44 537.90, 128.20 532.69, 123.38 532.93, 122.62 516.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,31,"POLYGON ((407.25 442.23, 418.91 441.73, 419.69 459.70, 408.34 460.16, 413.23 456.95, 416.58 452.29, 414.61 447.64, 407.25 442.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,32,"POLYGON ((390.45 363.26, 404.78 350.95, 435.78 388.45, 422.74 399.68, 422.03 395.28, 421.30 389.86, 418.61 388.45, 417.60 383.61, 414.45 381.49, 411.81 376.99, 406.42 374.33, 404.24 376.45, 403.89 379.99, 390.45 363.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,33,"POLYGON ((30.83 145.38, 36.18 151.94, 68.03 126.23, 90.44 153.74, 98.51 147.23, 76.49 120.20, 85.65 112.79, 103.48 134.67, 122.08 119.63, 139.87 141.49, 121.75 156.14, 193.22 243.82, 240.80 205.40, 248.44 214.77, 143.62 299.47, 138.23 292.88, 121.07 306.77, 100.18 281.15, 0.00 362.14, 0.00 302.42, 1.97 300.83, 0.00 298.42, 0.00 281.36, 3.93 286.11, 2.02 287.69, 7.96 294.86, 83.53 232.85, 79.09 227.50, 61.88 241.63, 48.55 225.55, 0.00 265.38, 0.00 207.27, 20.55 190.90, 8.47 175.89, 28.08 160.25, 22.64 153.49, 0.00 171.52, 0.00 114.17, 16.54 100.81, 44.11 134.65, 30.83 145.38), (97.39 164.39, 121.39 192.39, 88.13 218.84, 95.18 227.11, 134.58 193.86, 143.19 203.96, 145.75 201.79, 106.86 156.14, 97.39 164.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,34,"POLYGON ((836.69 529.29, 838.00 580.73, 770.08 581.07, 769.63 577.24, 756.36 587.81, 818.03 667.06, 784.95 690.55, 669.89 543.35, 702.32 517.64, 729.63 554.08, 755.51 533.45, 770.62 533.06, 769.12 529.26, 836.69 529.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,35,"POLYGON ((900.00 376.06, 758.85 379.04, 760.48 394.40, 734.12 394.65, 733.71 378.44, 683.52 379.30, 684.76 427.99, 643.33 427.80, 643.43 399.05, 611.85 424.43, 615.30 428.09, 600.53 439.73, 572.95 408.77, 583.21 401.01, 423.11 194.32, 454.29 169.77, 567.20 314.36, 900.00 309.11, 900.00 376.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,36,"POLYGON ((860.32 204.24, 900.00 203.55, 900.00 243.90, 861.34 244.57, 860.32 204.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,37,"POLYGON ((36.54 895.81, 52.78 896.48, 57.28 900.00, 36.36 900.00, 36.54 895.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,38,"POLYGON ((356.58 896.52, 374.32 895.33, 374.63 900.00, 356.81 900.00, 356.58 896.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,39,"POLYGON ((143.94 887.86, 149.22 887.63, 149.33 890.41, 158.19 890.05, 158.60 900.00, 144.44 900.00, 143.94 887.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,40,"POLYGON ((276.04 878.68, 281.29 887.38, 278.85 896.56, 259.35 897.35, 258.89 879.12, 276.04 878.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,41,"POLYGON ((356.23 851.40, 372.12 849.48, 374.87 872.28, 359.00 874.19, 356.23 851.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3731439,42,"POLYGON ((148.74 849.16, 160.42 854.61, 151.30 867.80, 144.91 861.04, 144.71 853.39, 148.74 849.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3732339,0,"POLYGON ((74.14 523.87, 104.12 520.89, 104.91 528.64, 98.51 529.29, 99.52 539.41, 75.93 541.76, 74.14 523.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,0,"POLYGON ((622.25 117.12, 653.93 116.04, 654.63 136.76, 622.95 137.81, 622.25 117.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,1,"POLYGON ((639.82 85.57, 664.37 86.25, 663.90 103.18, 639.34 102.50, 639.82 85.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,2,"POLYGON ((875.90 99.55, 875.66 90.14, 880.44 92.26, 884.89 91.64, 888.11 90.58, 888.53 87.35, 887.96 84.63, 893.45 85.22, 894.31 99.32, 875.90 99.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,3,"POLYGON ((858.48 99.75, 842.32 99.67, 841.95 95.46, 845.19 94.89, 848.82 91.07, 849.43 86.10, 849.09 82.16, 845.02 78.80, 840.75 77.42, 840.64 72.96, 858.08 74.01, 856.53 90.87, 858.36 94.80, 858.48 99.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,4,"POLYGON ((778.91 0.00, 780.70 109.41, 730.82 110.22, 729.01 0.00, 778.91 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,5,"POLYGON ((619.37 6.98, 619.01 0.00, 654.25 0.00, 654.41 3.20, 659.12 2.97, 659.67 13.65, 655.57 13.87, 655.74 17.39, 627.80 18.81, 623.63 6.78, 619.37 6.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,6,"POLYGON ((572.26 0.56, 569.96 0.64, 569.93 0.00, 591.62 0.00, 591.96 10.14, 584.75 10.39, 585.05 19.33, 572.91 19.75, 572.26 0.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,7,"POLYGON ((615.73 0.00, 616.13 5.62, 608.90 6.11, 608.47 0.00, 615.73 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,8,"POLYGON ((238.07 751.25, 263.44 761.82, 268.64 749.47, 311.29 767.26, 273.46 857.19, 205.43 828.84, 238.07 751.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,9,"POLYGON ((534.27 771.29, 533.42 769.01, 533.37 766.57, 534.17 764.24, 535.55 762.47, 537.43 761.22, 539.60 760.61, 541.85 760.69, 543.97 761.48, 545.73 762.88, 546.96 764.78, 547.54 766.94, 547.41 769.18, 546.59 771.27, 545.01 773.13, 542.87 774.36, 540.45 774.78, 538.02 774.37, 535.88 773.16, 534.27 771.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,10,"POLYGON ((295.94 696.84, 300.62 698.67, 302.38 694.25, 358.96 716.56, 349.32 740.78, 291.26 717.91, 293.97 711.11, 290.76 709.84, 295.94 696.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,11,"POLYGON ((366.83 698.71, 365.98 696.43, 365.93 693.96, 366.73 691.66, 368.11 689.89, 369.99 688.62, 372.16 688.01, 374.41 688.11, 376.53 688.90, 378.29 690.30, 379.52 692.17, 380.10 694.36, 379.97 696.58, 379.15 698.69, 377.57 700.55, 375.43 701.76, 373.01 702.20, 370.58 701.77, 368.44 700.56, 366.83 698.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,12,"POLYGON ((119.25 572.86, 97.19 595.90, 52.30 553.32, 70.72 534.11, 68.86 532.34, 79.93 520.76, 81.70 522.42, 90.72 513.00, 105.32 526.86, 88.87 544.04, 119.25 572.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,13,"POLYGON ((28.64 487.13, 36.35 486.97, 36.49 493.21, 28.78 493.38, 28.64 487.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,14,"POLYGON ((244.34 478.90, 269.44 478.11, 270.08 498.27, 253.21 498.81, 253.03 493.00, 244.80 493.25, 244.34 478.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,15,"POLYGON ((294.01 472.55, 306.75 471.85, 307.20 479.96, 294.46 480.69, 294.01 472.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,16,"POLYGON ((79.60 459.83, 83.95 482.68, 74.94 484.37, 70.59 461.53, 79.60 459.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,17,"POLYGON ((250.86 405.86, 267.16 405.55, 267.20 407.60, 298.90 407.01, 298.83 403.57, 317.40 403.23, 317.80 424.91, 308.64 425.07, 308.96 442.58, 284.83 443.02, 284.92 447.06, 262.89 447.48, 262.46 425.67, 251.26 425.89, 250.86 405.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,18,"POLYGON ((226.74 258.32, 226.35 281.64, 197.77 281.17, 198.17 257.85, 226.74 258.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,19,"POLYGON ((230.07 244.99, 255.13 244.77, 255.39 279.19, 244.65 279.29, 244.71 285.99, 232.80 286.09, 232.75 278.26, 230.32 278.28, 230.07 244.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,20,"POLYGON ((132.95 232.50, 89.89 278.68, 65.99 256.60, 77.20 244.60, 70.36 238.29, 84.77 222.85, 91.80 229.35, 109.26 210.62, 132.95 232.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,21,"POLYGON ((474.91 210.31, 487.32 209.55, 497.08 209.88, 501.89 247.76, 493.91 247.52, 495.18 262.65, 479.34 262.47, 478.66 247.75, 471.56 246.91, 474.91 210.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,22,"POLYGON ((202.49 208.31, 261.82 208.86, 261.81 231.86, 226.00 231.55, 225.96 234.35, 202.21 234.09, 202.49 208.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,23,"POLYGON ((148.18 217.77, 147.41 192.13, 161.91 191.72, 162.67 217.34, 148.18 217.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,24,"POLYGON ((56.60 158.88, 56.73 183.81, 30.74 183.96, 30.72 178.08, 25.23 178.11, 25.12 159.06, 56.60 158.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,25,"POLYGON ((79.26 149.34, 92.92 149.43, 92.87 155.72, 79.24 155.64, 79.26 149.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,26,"POLYGON ((398.13 139.19, 398.51 154.81, 394.32 154.89, 394.44 159.69, 385.53 159.89, 385.61 163.22, 374.84 163.47, 374.76 160.50, 366.49 160.71, 366.47 159.09, 361.00 159.23, 360.81 151.11, 366.36 150.97, 366.08 139.96, 398.13 139.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,27,"POLYGON ((117.36 125.64, 118.02 151.68, 114.98 151.76, 115.17 159.23, 111.20 159.33, 111.29 162.88, 100.93 163.15, 99.98 126.08, 117.36 125.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,28,"POLYGON ((449.09 118.14, 472.92 117.91, 473.02 127.45, 476.95 127.41, 477.03 134.14, 457.80 134.34, 457.84 138.91, 449.31 139.00, 449.09 118.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,29,"POLYGON ((203.59 117.47, 204.00 133.33, 195.56 133.54, 195.16 117.68, 203.59 117.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,30,"POLYGON ((79.05 114.67, 92.76 114.96, 92.60 123.05, 78.88 122.78, 79.05 114.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,31,"POLYGON ((499.12 112.20, 499.24 119.74, 486.57 119.93, 486.45 112.39, 499.12 112.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,32,"POLYGON ((211.95 109.80, 218.31 109.61, 218.57 118.44, 212.19 118.62, 211.95 109.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,33,"POLYGON ((362.64 97.39, 376.62 97.19, 376.66 100.45, 387.01 100.32, 387.05 103.27, 398.01 103.12, 398.21 118.46, 386.16 118.63, 386.25 125.11, 363.04 125.44, 362.64 97.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,34,"POLYGON ((74.56 93.50, 74.51 107.04, 68.35 107.02, 68.32 118.21, 40.57 118.11, 40.59 111.72, 36.16 111.72, 36.19 97.89, 42.07 97.90, 42.09 93.39, 74.56 93.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,35,"POLYGON ((476.28 83.10, 476.61 99.68, 470.16 99.82, 470.23 103.08, 442.75 103.62, 442.40 85.50, 448.59 85.38, 448.56 83.65, 476.28 83.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,36,"POLYGON ((139.11 78.76, 158.37 78.38, 158.86 103.18, 150.25 103.33, 150.20 100.67, 142.19 100.83, 142.09 95.37, 139.44 95.42, 139.11 78.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,37,"POLYGON ((295.25 64.39, 314.49 64.21, 314.56 72.20, 317.04 72.18, 317.38 107.13, 295.66 107.35, 295.25 64.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,38,"POLYGON ((213.57 86.78, 215.29 78.52, 214.07 72.16, 209.53 68.13, 216.67 67.86, 216.80 71.40, 221.82 71.23, 222.76 96.58, 217.96 96.77, 218.07 99.52, 213.90 99.67, 213.99 102.44, 207.69 102.67, 207.44 95.95, 203.29 96.10, 208.93 93.20, 213.57 86.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,39,"POLYGON ((281.58 67.33, 281.12 94.87, 254.91 94.43, 255.16 79.30, 260.20 79.40, 260.42 66.70, 261.81 66.73, 261.87 63.33, 278.22 63.60, 278.15 67.29, 281.58 67.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,40,"POLYGON ((249.25 63.74, 249.38 91.26, 226.86 91.37, 226.75 65.89, 238.49 65.83, 238.47 63.79, 249.25 63.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,41,"POLYGON ((520.21 63.49, 521.28 89.26, 508.25 89.79, 508.06 85.16, 502.94 85.35, 502.25 68.45, 505.01 68.34, 504.83 64.13, 520.21 63.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,42,"POLYGON ((366.94 58.99, 389.00 58.18, 389.34 67.23, 396.62 66.97, 397.08 79.06, 385.61 79.49, 385.74 82.99, 374.65 83.38, 374.48 78.66, 367.67 78.90, 366.94 58.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,43,"POLYGON ((336.48 59.72, 342.32 59.44, 343.24 78.17, 337.43 78.47, 336.48 59.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,44,"POLYGON ((479.74 55.16, 480.21 74.72, 448.75 75.46, 448.28 55.89, 479.74 55.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,45,"POLYGON ((20.82 6.12, 40.87 5.36, 41.26 7.00, 45.61 7.35, 46.16 28.96, 39.33 28.07, 39.36 29.35, 32.81 29.99, 31.79 26.86, 28.66 24.25, 26.18 23.14, 22.10 24.42, 22.60 16.46, 21.64 10.87, 20.82 6.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,46,"POLYGON ((487.51 15.42, 469.48 14.82, 469.95 0.00, 492.47 0.00, 492.38 7.86, 487.65 7.83, 487.51 15.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,47,"POLYGON ((541.47 10.41, 541.44 17.89, 529.18 17.84, 529.24 0.00, 548.63 0.00, 548.60 10.45, 541.47 10.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,48,"POLYGON ((517.83 12.92, 498.75 14.16, 499.07 0.00, 518.64 0.00, 517.83 12.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,49,"POLYGON ((403.07 12.58, 398.11 11.91, 398.07 10.33, 395.45 10.84, 393.18 11.17, 389.92 11.34, 386.12 10.73, 382.96 10.47, 382.23 6.72, 382.71 4.27, 383.96 1.77, 384.17 0.00, 404.78 0.00, 404.41 3.62, 402.50 4.27, 403.07 12.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,50,"POLYGON ((466.05 0.00, 466.21 6.75, 459.72 6.90, 459.85 12.51, 439.06 13.00, 438.76 0.00, 466.05 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,51,"POLYGON ((248.37 0.00, 248.34 2.01, 248.44 5.76, 246.36 7.14, 242.26 7.25, 238.86 8.44, 231.89 8.62, 230.21 8.33, 229.81 5.48, 231.34 4.33, 233.77 4.27, 234.49 1.94, 233.75 0.00, 248.37 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,52,"POLYGON ((258.11 6.55, 257.71 0.00, 276.00 0.00, 274.38 1.52, 275.11 3.26, 270.34 2.60, 268.08 2.66, 265.41 2.73, 262.79 3.97, 260.18 2.95, 258.11 6.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,53,"POLYGON ((21.58 2.75, 21.35 0.00, 39.54 0.00, 41.24 2.36, 21.58 2.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,54,"POLYGON ((402.96 766.12, 414.53 739.34, 435.70 748.24, 432.07 756.78, 568.02 814.34, 571.26 806.82, 592.62 815.96, 580.66 843.72, 569.09 838.78, 560.84 857.88, 548.53 852.62, 546.29 857.83, 595.92 879.14, 586.87 900.00, 549.39 900.00, 549.44 899.88, 493.83 875.88, 490.92 882.55, 437.04 859.26, 439.73 853.15, 385.49 829.61, 382.23 837.08, 348.47 822.40, 368.17 777.53, 416.69 798.62, 417.70 796.31, 406.13 791.28, 414.90 771.30, 402.96 766.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,55,"POLYGON ((605.99 653.39, 625.46 661.86, 627.95 656.21, 655.06 667.99, 652.33 674.25, 658.16 676.79, 653.90 686.49, 678.06 696.99, 675.19 703.55, 699.05 713.93, 664.19 793.35, 609.64 769.64, 606.99 775.68, 561.09 755.72, 605.99 653.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,56,"POLYGON ((659.22 537.96, 674.17 544.46, 671.03 551.64, 789.23 602.81, 804.97 566.83, 825.90 575.86, 828.11 570.79, 844.62 577.89, 843.08 581.44, 863.28 590.05, 850.21 620.39, 844.83 618.09, 841.11 626.71, 900.00 651.72, 900.00 693.78, 816.07 657.20, 802.87 687.22, 883.74 722.49, 880.57 729.67, 874.51 727.01, 866.38 745.33, 874.06 748.69, 870.04 757.73, 784.26 719.99, 774.44 742.08, 751.38 731.19, 761.05 708.91, 680.99 673.74, 684.15 666.65, 687.79 668.26, 695.35 651.22, 690.08 648.92, 694.96 637.89, 769.53 670.64, 780.55 645.79, 654.21 590.36, 650.93 597.77, 635.81 591.16, 659.22 537.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,57,"POLYGON ((887.84 311.12, 900.00 310.93, 900.00 347.15, 888.41 347.33, 887.84 311.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,58,"POLYGON ((820.39 281.30, 840.07 281.84, 839.04 323.73, 833.41 323.88, 828.69 319.40, 824.88 317.63, 827.25 312.16, 829.86 307.72, 830.26 299.58, 825.36 295.76, 822.81 293.93, 819.88 294.01, 820.39 281.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,59,"POLYGON ((748.29 299.61, 747.48 260.74, 767.93 260.35, 768.77 299.15, 748.29 299.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,60,"POLYGON ((650.24 217.25, 664.56 216.59, 667.63 284.53, 653.33 285.18, 650.24 217.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,61,"POLYGON ((615.65 221.28, 620.38 221.07, 620.52 224.24, 620.67 226.61, 622.76 226.47, 624.20 225.39, 626.69 228.66, 629.16 228.51, 631.65 226.51, 632.39 224.32, 632.27 221.59, 637.29 221.35, 639.38 266.98, 617.81 267.98, 615.65 221.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,62,"POLYGON ((635.59 191.74, 650.89 191.70, 650.91 199.96, 640.92 199.99, 640.45 198.61, 638.53 197.28, 635.61 198.60, 635.59 191.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,63,"POLYGON ((873.71 175.25, 881.03 175.07, 881.05 167.78, 897.60 168.61, 897.16 191.92, 890.05 192.53, 885.86 191.81, 883.13 191.88, 878.27 189.50, 876.30 186.64, 873.61 187.33, 873.71 175.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,64,"POLYGON ((710.91 152.21, 749.29 151.21, 749.94 176.83, 772.39 176.26, 773.18 207.05, 712.35 208.60, 710.91 152.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,65,"POLYGON ((842.72 176.20, 856.27 175.81, 855.98 165.18, 863.74 164.96, 864.43 189.63, 843.11 190.22, 842.72 176.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3733239,66,"POLYGON ((629.43 140.95, 654.71 140.20, 655.26 158.89, 651.52 159.01, 651.72 165.49, 630.18 166.13, 629.43 140.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,0,"POLYGON ((570.90 367.76, 578.81 402.30, 560.04 406.57, 553.23 376.84, 559.83 375.34, 558.73 370.51, 570.90 367.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,1,"POLYGON ((504.83 148.20, 519.08 147.68, 520.36 183.27, 547.06 182.30, 546.83 175.96, 554.46 175.68, 554.73 182.53, 573.11 181.82, 572.52 166.78, 581.70 166.41, 582.36 183.16, 612.59 181.96, 613.70 209.50, 583.33 210.75, 583.71 219.90, 586.90 219.78, 587.81 241.13, 547.37 242.85, 548.18 262.32, 523.84 270.87, 524.77 281.10, 454.09 309.01, 451.81 243.02, 506.70 241.14, 506.19 226.30, 499.41 226.54, 498.96 213.81, 507.17 213.51, 504.83 148.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,2,"POLYGON ((514.43 18.27, 515.20 28.91, 515.28 56.23, 510.16 56.25, 511.29 72.80, 501.39 73.08, 496.39 71.72, 494.59 69.52, 491.95 63.66, 493.20 18.66, 514.43 18.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,3,"POLYGON ((214.49 26.21, 217.24 23.65, 226.66 23.70, 226.65 28.19, 233.96 28.22, 235.34 32.87, 235.28 43.15, 228.28 50.83, 214.71 50.95, 214.49 26.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,4,"POLYGON ((245.24 26.38, 265.54 25.22, 270.61 27.60, 265.33 43.05, 245.99 44.61, 245.24 26.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,5,"POLYGON ((404.37 28.67, 426.48 27.35, 428.71 36.95, 405.36 38.30, 400.64 38.18, 399.15 28.56, 404.37 28.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,6,"POLYGON ((203.29 25.16, 203.50 37.72, 196.41 42.59, 186.37 42.53, 184.04 36.66, 183.88 26.03, 188.05 25.97, 187.97 21.54, 197.56 21.38, 197.62 25.26, 203.29 25.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,7,"POLYGON ((277.28 25.87, 277.33 22.68, 300.60 23.08, 302.54 29.58, 297.96 30.96, 298.02 38.95, 277.38 39.08, 277.28 25.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,8,"POLYGON ((338.91 25.01, 362.97 24.20, 362.28 36.00, 339.61 37.18, 338.91 25.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,9,"POLYGON ((478.56 12.08, 478.90 17.82, 482.43 17.62, 484.07 46.88, 471.38 47.58, 465.41 37.59, 464.02 12.90, 478.56 12.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,10,"POLYGON ((313.43 19.45, 320.99 19.08, 321.19 23.20, 330.19 22.77, 330.55 30.13, 323.84 37.78, 307.52 38.55, 306.84 24.12, 313.64 23.79, 313.43 19.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,11,"POLYGON ((430.87 21.18, 433.26 19.79, 443.26 20.42, 445.72 21.91, 451.26 21.57, 452.03 34.42, 431.74 35.63, 430.87 21.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,12,"POLYGON ((711.39 667.36, 713.79 772.29, 690.15 772.83, 687.77 667.89, 711.39 667.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,13,"POLYGON ((761.92 666.78, 763.70 765.60, 740.71 766.03, 738.91 667.21, 761.92 666.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,14,"POLYGON ((819.30 702.01, 835.16 702.11, 838.34 699.37, 844.55 699.21, 844.74 706.57, 832.98 709.51, 831.61 713.97, 819.23 713.91, 819.30 702.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,15,"POLYGON ((780.67 690.52, 786.25 690.62, 787.81 689.78, 788.77 688.20, 789.29 686.01, 791.39 682.63, 806.55 681.24, 807.30 679.67, 809.85 679.20, 813.62 680.09, 813.85 681.06, 817.81 681.35, 824.47 679.41, 830.08 679.51, 829.88 691.44, 826.95 691.38, 822.64 692.64, 818.59 696.85, 818.24 698.44, 818.66 699.60, 818.47 707.29, 813.00 709.58, 809.22 708.10, 806.46 708.26, 806.07 708.31, 803.50 708.65, 802.13 709.66, 792.53 712.66, 791.25 708.56, 790.02 706.84, 785.44 705.38, 783.92 707.57, 780.32 707.51, 780.67 690.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,16,"POLYGON ((619.73 655.49, 620.57 680.26, 606.00 680.74, 605.15 655.99, 619.73 655.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,17,"POLYGON ((727.84 624.49, 728.58 647.01, 684.85 648.43, 684.10 625.92, 727.84 624.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,18,"POLYGON ((829.05 630.37, 834.57 639.15, 821.42 642.04, 819.32 632.50, 829.05 630.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,19,"POLYGON ((727.06 569.91, 727.64 594.62, 694.25 595.41, 684.32 592.15, 683.81 570.94, 727.06 569.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,20,"POLYGON ((848.72 568.35, 849.17 583.86, 830.73 584.37, 830.47 575.10, 842.09 574.78, 841.92 568.55, 848.72 568.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,21,"POLYGON ((897.33 548.02, 900.00 547.99, 900.00 579.24, 897.74 579.27, 897.33 548.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,22,"POLYGON ((852.66 528.63, 852.80 536.35, 849.62 538.43, 849.95 556.25, 831.98 556.59, 831.68 540.93, 812.97 541.27, 812.76 529.38, 852.66 528.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,23,"POLYGON ((623.64 511.92, 625.76 538.37, 612.27 539.45, 611.78 533.33, 590.34 535.04, 589.04 518.75, 595.70 518.21, 595.37 514.17, 623.64 511.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,24,"POLYGON ((709.22 461.01, 711.38 549.17, 687.97 549.73, 685.81 461.59, 709.22 461.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,25,"POLYGON ((855.58 499.21, 856.05 509.01, 812.41 511.14, 811.91 501.32, 855.58 499.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,26,"POLYGON ((757.51 453.23, 760.83 479.65, 752.91 476.46, 749.80 478.98, 746.08 480.89, 746.58 488.70, 748.65 494.97, 752.64 517.53, 760.02 529.13, 759.85 545.03, 737.52 545.75, 735.04 454.38, 757.51 453.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,27,"POLYGON ((854.29 461.62, 854.67 481.65, 830.95 482.08, 830.71 468.74, 822.83 468.88, 822.71 462.20, 854.29 461.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,28,"POLYGON ((834.00 421.78, 834.65 432.06, 816.35 433.22, 811.05 430.42, 799.74 431.13, 799.29 423.95, 834.00 421.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,29,"POLYGON ((706.23 373.74, 707.32 454.09, 686.57 454.38, 685.48 374.03, 706.23 373.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,30,"POLYGON ((757.75 444.44, 736.71 444.93, 734.63 372.84, 758.53 371.87, 759.65 378.75, 764.93 378.55, 766.55 416.55, 760.64 419.85, 757.75 444.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,31,"POLYGON ((808.20 371.27, 823.40 370.90, 823.72 384.04, 852.75 383.34, 852.81 385.83, 858.25 385.69, 858.45 394.45, 853.22 394.56, 853.35 399.55, 830.33 400.12, 830.48 406.77, 809.07 407.30, 808.20 371.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,32,"POLYGON ((629.75 335.65, 631.29 355.23, 592.61 355.86, 592.83 350.60, 629.75 335.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,33,"POLYGON ((879.39 200.48, 878.83 320.79, 812.53 320.48, 812.43 342.88, 798.45 342.82, 798.59 312.82, 769.22 312.69, 769.09 342.28, 699.86 341.98, 700.18 273.00, 810.76 227.43, 811.15 313.10, 817.72 313.09, 817.31 225.68, 879.39 200.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,34,"POLYGON ((692.18 176.85, 708.00 176.09, 711.88 177.03, 712.70 195.57, 693.01 195.74, 692.18 176.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,35,"POLYGON ((706.46 73.88, 706.98 84.66, 710.07 84.49, 710.47 92.71, 705.64 97.06, 692.30 99.02, 685.87 94.74, 685.27 85.83, 696.07 85.11, 695.37 74.52, 700.72 74.16, 706.46 73.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,36,"POLYGON ((753.52 49.79, 753.43 64.49, 737.72 64.43, 737.79 49.71, 753.52 49.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,37,"POLYGON ((717.82 45.16, 718.14 53.98, 722.18 53.84, 722.52 63.42, 686.14 64.72, 685.49 46.31, 717.82 45.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,38,"POLYGON ((622.69 37.71, 622.90 53.46, 610.87 50.13, 601.65 52.69, 595.10 56.48, 592.56 61.52, 585.01 61.62, 584.69 38.23, 622.69 37.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,39,"POLYGON ((706.66 17.56, 706.46 35.79, 689.59 35.62, 689.73 25.01, 697.55 18.97, 699.54 14.23, 706.65 14.21, 706.66 17.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,40,"POLYGON ((603.38 5.10, 615.57 14.69, 615.80 27.47, 575.50 28.21, 575.30 16.92, 586.82 16.71, 586.62 5.42, 603.38 5.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,41,"POLYGON ((824.72 0.00, 825.44 30.64, 806.90 31.10, 806.17 0.00, 824.72 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,42,"POLYGON ((846.22 0.00, 846.05 21.15, 842.74 22.34, 842.69 29.09, 827.96 28.98, 828.18 0.00, 846.22 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,43,"POLYGON ((802.91 0.00, 803.89 21.40, 802.43 29.50, 785.33 30.29, 783.93 0.00, 802.91 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,44,"POLYGON ((900.00 20.69, 897.21 20.78, 897.37 26.34, 881.80 26.79, 881.05 0.00, 900.00 0.00, 900.00 20.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,45,"POLYGON ((766.86 790.65, 767.94 869.20, 746.10 869.49, 743.63 866.03, 742.63 790.98, 766.86 790.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,46,"POLYGON ((713.71 775.60, 717.07 871.83, 698.54 872.48, 694.08 871.42, 690.76 776.41, 713.71 775.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,47,"POLYGON ((850.63 784.00, 850.89 795.92, 854.69 795.84, 854.90 804.61, 821.51 805.35, 821.06 784.65, 850.63 784.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,48,"POLYGON ((583.73 764.33, 585.46 796.83, 559.18 798.23, 557.46 765.71, 583.73 764.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,49,"POLYGON ((739.19 769.07, 740.13 785.38, 733.35 785.78, 732.40 769.46, 739.19 769.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,50,"POLYGON ((852.95 746.61, 853.10 766.16, 844.70 766.22, 844.74 772.03, 818.37 772.24, 818.20 751.25, 827.83 751.18, 827.79 746.78, 852.95 746.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734589,51,"POLYGON ((575.21 734.29, 613.08 733.31, 613.10 748.05, 588.28 748.10, 580.43 741.82, 575.21 734.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,0,"POLYGON ((725.02 692.67, 725.14 697.39, 731.48 697.23, 732.07 719.75, 725.88 719.93, 725.95 722.52, 695.74 723.38, 695.88 728.09, 682.75 728.49, 682.46 718.44, 691.34 718.17, 695.57 710.22, 695.29 698.44, 713.54 698.00, 713.43 692.96, 725.02 692.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,1,"POLYGON ((820.90 671.55, 835.12 671.23, 835.18 673.52, 843.21 673.33, 843.42 682.30, 849.81 682.16, 849.91 686.88, 844.61 687.00, 844.70 691.19, 850.43 691.06, 851.05 717.58, 822.00 718.27, 820.90 671.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,2,"POLYGON ((774.38 670.36, 783.67 670.24, 783.77 678.74, 787.46 678.69, 787.81 706.25, 784.34 706.29, 784.47 717.21, 773.76 717.35, 773.64 706.88, 767.32 706.95, 766.96 679.90, 774.49 679.80, 774.38 670.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,3,"POLYGON ((795.69 661.83, 807.23 661.93, 807.14 670.70, 814.74 670.78, 814.63 681.59, 817.82 681.62, 817.62 701.36, 815.23 701.33, 815.08 717.25, 800.11 717.10, 800.27 701.54, 795.28 701.48, 795.69 661.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,4,"POLYGON ((899.75 671.00, 900.00 671.75, 900.00 702.25, 885.42 703.31, 883.90 682.91, 885.23 681.90, 884.74 674.92, 896.27 674.09, 896.07 671.25, 899.75 671.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,5,"POLYGON ((795.93 645.75, 806.44 646.15, 806.07 655.88, 805.31 658.50, 795.47 658.13, 795.93 645.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,6,"POLYGON ((724.58 641.51, 724.62 646.64, 719.97 646.67, 720.05 655.88, 703.64 656.03, 703.70 661.00, 684.76 661.18, 684.63 646.64, 692.14 646.56, 692.12 643.50, 698.52 643.44, 698.55 647.59, 704.82 647.54, 704.75 639.24, 710.93 639.20, 710.95 641.64, 724.58 641.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,7,"POLYGON ((834.80 638.30, 848.86 637.63, 849.32 647.56, 835.26 648.21, 834.80 638.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,8,"POLYGON ((743.31 623.74, 746.72 623.72, 746.69 620.33, 751.05 620.28, 751.08 623.90, 759.28 623.82, 759.36 632.72, 743.39 632.86, 743.31 623.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,9,"POLYGON ((900.00 632.38, 897.25 632.42, 886.06 632.35, 886.15 615.46, 891.84 615.49, 891.87 609.98, 900.00 610.03, 900.00 632.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,10,"POLYGON ((683.48 614.41, 713.00 614.82, 714.53 608.76, 740.04 609.24, 739.87 618.28, 725.62 618.02, 725.38 630.73, 709.37 630.42, 707.85 632.53, 698.79 632.16, 698.25 630.69, 683.37 630.60, 683.48 614.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,11,"POLYGON ((823.74 618.49, 823.92 630.78, 806.81 631.04, 806.43 605.26, 825.81 604.96, 825.89 610.33, 822.40 610.40, 822.47 615.22, 827.07 615.14, 827.13 618.43, 823.74 618.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,12,"POLYGON ((900.00 604.73, 899.73 604.74, 899.55 598.38, 892.10 598.57, 891.91 592.13, 886.48 592.29, 886.19 581.62, 900.00 581.25, 900.00 604.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,13,"POLYGON ((694.18 589.77, 698.19 589.87, 698.51 575.52, 724.95 576.08, 726.76 578.50, 727.54 591.71, 725.99 592.75, 726.05 601.81, 690.38 602.05, 690.37 600.30, 684.21 600.34, 684.17 589.82, 694.18 589.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,14,"POLYGON ((826.90 575.21, 826.65 597.51, 806.60 597.22, 806.87 578.88, 809.13 578.91, 809.23 573.00, 820.82 573.17, 821.32 575.20, 826.90 575.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,15,"POLYGON ((900.00 572.78, 891.07 572.80, 886.80 571.29, 886.77 554.02, 900.00 553.99, 900.00 572.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,16,"POLYGON ((724.19 560.85, 719.64 565.59, 726.07 565.47, 726.17 570.79, 715.55 571.02, 712.50 568.50, 700.70 569.16, 697.41 571.66, 692.32 572.03, 686.13 567.11, 686.07 564.18, 684.03 564.21, 683.78 550.08, 723.97 549.34, 724.19 560.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,17,"POLYGON ((806.18 548.79, 826.08 548.69, 828.61 550.66, 828.82 557.30, 823.98 557.44, 824.23 566.42, 813.78 566.71, 807.01 566.78, 806.93 556.50, 803.93 556.53, 803.89 552.09, 806.21 552.08, 806.18 548.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,18,"POLYGON ((757.19 522.45, 769.57 522.25, 769.80 536.12, 757.41 536.32, 757.19 522.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,19,"POLYGON ((900.00 541.30, 886.04 541.38, 886.00 532.75, 893.31 532.72, 888.72 526.80, 884.63 526.88, 884.41 516.10, 891.38 515.94, 900.00 523.28, 900.00 541.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,20,"POLYGON ((708.24 511.94, 740.30 511.21, 740.53 520.61, 708.46 521.32, 708.24 511.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,21,"POLYGON ((886.59 496.40, 886.57 493.11, 900.00 492.97, 900.00 514.37, 884.63 514.09, 884.92 497.13, 886.58 497.15, 886.59 496.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,22,"POLYGON ((888.79 464.09, 888.71 461.07, 900.00 460.75, 900.00 483.23, 893.29 483.42, 893.17 478.71, 885.18 478.94, 884.79 464.19, 888.79 464.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,23,"POLYGON ((795.15 473.67, 804.80 465.74, 804.59 455.47, 816.12 455.22, 816.36 465.91, 822.16 465.79, 822.49 481.07, 818.05 481.19, 818.16 486.56, 785.39 487.26, 785.12 473.88, 795.15 473.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,24,"POLYGON ((692.97 454.59, 706.09 454.90, 705.44 483.57, 693.50 483.30, 693.60 478.37, 686.90 478.23, 687.13 468.30, 692.64 468.43, 692.97 454.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,25,"POLYGON ((889.22 440.17, 889.23 432.04, 900.00 432.06, 900.00 454.28, 882.35 454.24, 882.38 440.16, 889.22 440.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,26,"POLYGON ((692.69 426.72, 703.10 426.14, 703.29 429.89, 709.62 429.55, 710.07 437.88, 708.09 442.19, 709.28 443.72, 709.51 444.96, 709.03 446.19, 708.38 447.74, 707.85 448.86, 707.30 449.98, 706.63 451.24, 705.87 452.60, 705.14 453.81, 687.78 453.86, 690.41 449.93, 690.78 448.56, 690.74 447.12, 690.30 445.78, 687.37 445.85, 685.22 443.71, 683.90 441.35, 683.87 439.99, 684.57 438.62, 686.21 436.65, 688.60 433.66, 687.56 432.82, 687.41 431.27, 688.33 430.42, 692.19 429.59, 692.79 428.64, 692.69 426.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,27,"POLYGON ((715.40 401.70, 715.54 405.87, 711.30 411.75, 712.59 413.64, 714.71 414.46, 716.16 415.99, 716.05 418.44, 716.63 420.71, 722.44 420.92, 718.50 424.86, 683.33 426.16, 682.47 402.89, 715.40 401.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,28,"POLYGON ((680.50 369.11, 701.76 368.75, 712.53 377.22, 717.99 377.61, 719.89 376.34, 722.18 375.75, 724.13 376.57, 725.93 378.27, 726.34 380.37, 726.67 393.33, 711.87 392.64, 708.42 395.53, 706.33 395.76, 703.86 395.64, 702.06 394.11, 701.60 389.93, 699.27 388.06, 696.10 388.14, 694.56 389.58, 693.19 391.19, 691.46 392.47, 689.00 392.54, 685.83 391.91, 683.86 390.91, 681.90 389.90, 680.28 388.21, 680.94 386.26, 680.50 369.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,29,"POLYGON ((808.35 347.73, 824.41 346.37, 826.05 385.69, 823.26 387.00, 821.69 391.52, 824.91 398.41, 826.46 401.81, 820.70 401.34, 816.95 398.19, 815.74 396.14, 815.88 392.72, 815.15 388.98, 814.78 386.79, 812.61 383.74, 814.83 380.56, 810.72 375.04, 809.61 372.48, 808.35 347.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,30,"POLYGON ((872.15 370.83, 869.11 370.40, 865.99 367.06, 867.24 363.68, 867.14 362.13, 878.20 361.45, 877.88 356.19, 886.95 355.65, 887.27 361.04, 894.79 360.60, 896.48 388.04, 891.72 388.31, 891.94 391.84, 883.55 392.36, 883.33 388.86, 876.66 389.28, 874.74 386.77, 872.15 370.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,31,"POLYGON ((780.21 368.68, 779.64 347.89, 783.33 347.80, 783.13 340.72, 801.32 340.23, 801.50 346.84, 805.04 346.73, 805.83 376.10, 786.78 376.61, 786.67 372.35, 785.59 370.89, 782.57 368.62, 780.21 368.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,32,"POLYGON ((717.84 337.35, 719.15 339.93, 719.36 344.83, 719.75 349.73, 719.45 351.85, 714.11 356.18, 713.70 354.08, 709.28 353.15, 704.72 353.80, 703.71 355.73, 704.19 360.63, 706.41 364.77, 704.00 367.11, 698.33 367.28, 698.14 360.40, 682.53 360.85, 681.90 338.38, 717.84 337.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,33,"POLYGON ((717.08 258.94, 717.98 260.27, 717.99 270.06, 716.50 270.59, 715.03 270.76, 713.68 271.79, 713.22 273.27, 710.54 275.20, 709.95 276.70, 709.99 278.43, 709.04 279.57, 707.55 280.23, 706.77 283.60, 705.68 284.74, 704.20 285.51, 702.71 285.19, 701.21 284.47, 699.70 284.27, 698.48 285.30, 694.80 287.24, 686.09 287.35, 681.49 282.14, 681.76 273.21, 690.45 267.92, 699.78 267.93, 702.34 270.22, 712.48 258.95, 717.08 258.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,34,"POLYGON ((734.97 258.77, 751.38 258.69, 751.44 265.88, 747.49 268.04, 747.52 271.37, 735.05 271.45, 734.97 258.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,35,"POLYGON ((851.13 272.06, 843.33 272.12, 843.09 279.34, 824.40 279.55, 824.15 275.65, 821.10 276.02, 823.01 263.74, 826.86 261.42, 828.87 258.04, 827.18 240.99, 832.67 237.65, 839.91 237.18, 840.14 229.27, 850.17 228.46, 851.57 261.35, 851.13 272.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,36,"POLYGON ((887.28 238.61, 894.14 239.15, 893.04 229.03, 900.00 228.75, 900.00 277.60, 888.40 276.81, 888.67 271.10, 891.98 269.35, 892.29 265.46, 892.04 261.00, 888.37 259.30, 888.20 257.90, 887.54 254.72, 887.06 251.96, 887.89 245.70, 887.28 238.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,37,"POLYGON ((4.85 877.72, 11.18 884.44, 5.02 890.22, 7.57 892.91, 13.85 896.96, 11.87 900.00, 6.64 900.00, 6.91 899.58, 2.41 894.83, 0.00 897.09, 0.00 882.27, 4.85 877.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,38,"POLYGON ((82.01 891.94, 79.02 900.00, 72.95 900.00, 76.66 889.99, 82.01 891.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,39,"POLYGON ((142.02 892.30, 147.05 892.17, 152.22 886.44, 165.35 890.81, 164.96 898.77, 164.54 900.00, 146.94 900.00, 142.02 892.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,40,"POLYGON ((378.39 779.58, 412.74 786.99, 410.48 797.30, 415.66 803.36, 414.06 809.88, 403.35 818.68, 385.40 883.78, 392.80 885.79, 389.96 896.05, 353.00 885.92, 349.36 882.77, 352.68 867.63, 353.41 864.80, 360.35 866.57, 375.72 807.24, 365.35 804.58, 370.25 785.67, 373.69 780.88, 378.39 779.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,41,"POLYGON ((98.62 833.40, 92.16 833.81, 92.57 798.22, 87.12 798.16, 87.43 793.78, 107.94 794.30, 110.71 820.86, 104.76 825.39, 98.62 833.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,42,"POLYGON ((447.81 728.71, 443.27 788.58, 426.73 787.34, 428.85 759.30, 425.21 759.01, 426.01 748.34, 428.69 748.53, 430.30 727.38, 447.81 728.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,43,"POLYGON ((97.04 729.04, 97.93 742.12, 69.12 744.07, 68.21 731.00, 97.04 729.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,44,"POLYGON ((391.53 692.17, 397.70 690.52, 397.61 691.90, 412.81 687.92, 415.47 692.75, 418.24 693.11, 423.66 717.12, 412.60 718.42, 411.33 709.09, 404.62 708.86, 402.93 699.73, 394.41 701.62, 391.53 692.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,45,"POLYGON ((178.11 667.35, 178.44 673.83, 168.09 674.42, 160.88 681.27, 161.87 699.44, 169.36 703.80, 175.66 709.44, 173.56 716.66, 164.87 720.57, 160.74 717.17, 161.28 710.67, 161.38 707.87, 154.97 704.70, 152.35 698.82, 153.33 681.81, 159.69 669.58, 168.08 667.61, 178.11 667.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,46,"POLYGON ((64.37 677.69, 80.59 676.70, 81.14 685.79, 84.91 686.11, 84.75 689.45, 81.06 692.47, 67.28 693.02, 67.78 687.79, 65.17 684.53, 64.37 677.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,47,"POLYGON ((133.51 607.25, 133.77 625.46, 134.10 635.96, 104.04 636.86, 104.50 651.98, 82.09 652.66, 81.93 647.21, 66.57 647.67, 66.13 633.47, 68.59 624.48, 110.51 623.84, 110.26 607.59, 133.51 607.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,48,"POLYGON ((226.92 595.99, 231.84 603.36, 231.31 605.73, 226.23 609.41, 221.99 611.43, 219.73 610.02, 217.09 605.36, 216.13 602.01, 226.92 595.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,49,"POLYGON ((348.55 593.10, 354.04 587.97, 356.47 592.19, 372.06 583.31, 384.41 604.84, 377.11 608.98, 372.57 611.40, 375.90 617.58, 372.35 619.47, 364.16 604.21, 356.64 608.19, 348.55 593.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,50,"POLYGON ((79.25 565.43, 92.12 574.69, 86.87 581.93, 74.00 572.69, 79.25 565.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,51,"POLYGON ((438.33 553.57, 474.43 580.10, 470.36 585.56, 465.98 582.34, 457.93 593.18, 426.23 569.88, 438.33 553.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,52,"POLYGON ((422.70 526.86, 438.07 544.87, 422.31 558.10, 407.09 539.96, 422.70 526.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,53,"POLYGON ((347.45 463.23, 368.96 462.53, 369.94 492.91, 373.26 494.89, 373.36 498.86, 371.94 501.54, 372.50 505.94, 379.55 509.74, 392.91 509.31, 394.43 556.40, 383.22 556.77, 383.01 550.23, 343.75 551.49, 341.58 484.82, 348.14 484.61, 347.45 463.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,54,"POLYGON ((41.64 507.26, 64.17 494.07, 68.97 502.70, 46.08 515.53, 41.64 507.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,55,"POLYGON ((150.80 457.41, 162.10 456.92, 162.57 468.21, 157.93 474.52, 153.07 470.98, 152.94 468.01, 151.25 468.08, 150.80 457.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,56,"POLYGON ((893.36 889.37, 900.00 889.10, 900.00 900.00, 893.81 900.00, 893.36 889.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,57,"POLYGON ((839.25 883.74, 839.42 890.26, 841.12 895.21, 844.14 896.56, 844.23 900.00, 816.56 900.00, 816.35 883.15, 823.30 883.08, 823.27 880.26, 838.15 880.06, 839.25 883.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,58,"POLYGON ((717.46 886.50, 732.04 886.26, 731.92 879.47, 739.04 879.35, 738.98 875.47, 751.95 875.25, 752.37 900.00, 717.70 900.00, 717.46 886.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,59,"POLYGON ((794.64 814.58, 800.71 814.62, 804.92 817.65, 819.82 817.78, 819.73 830.30, 794.52 830.12, 794.64 814.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,60,"POLYGON ((719.87 799.68, 719.91 801.90, 724.24 801.84, 724.64 823.74, 734.78 823.54, 734.90 830.71, 695.90 831.44, 695.59 814.33, 702.73 814.19, 702.82 819.14, 704.51 819.12, 704.16 799.97, 719.87 799.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,61,"POLYGON ((845.57 775.19, 845.04 791.83, 849.09 791.97, 848.84 799.70, 843.98 799.56, 843.75 806.66, 825.04 806.08, 826.10 773.24, 841.63 773.71, 842.95 775.10, 845.57 775.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,62,"POLYGON ((900.00 802.47, 888.35 802.75, 888.17 795.52, 884.62 790.77, 884.34 785.23, 882.56 782.99, 882.39 777.15, 900.00 776.65, 900.00 802.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,63,"POLYGON ((82.45 444.02, 101.45 443.62, 103.48 447.14, 103.49 453.11, 98.91 453.12, 98.92 456.54, 88.29 456.56, 88.29 454.32, 82.69 454.33, 82.45 444.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,64,"POLYGON ((129.17 444.31, 142.32 444.07, 142.51 455.40, 129.39 455.65, 129.17 444.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,65,"POLYGON ((97.36 402.04, 97.74 411.15, 102.30 410.95, 103.21 432.17, 80.87 433.13, 79.58 402.80, 97.36 402.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,66,"POLYGON ((138.38 393.15, 152.00 392.63, 153.34 424.94, 151.87 426.09, 114.22 427.58, 113.51 409.97, 123.93 409.57, 123.78 405.80, 138.86 405.22, 138.38 393.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,67,"POLYGON ((0.00 394.33, 2.16 393.94, 4.81 391.48, 6.21 392.68, 8.04 395.24, 13.06 395.22, 16.17 394.41, 18.95 392.87, 19.64 410.97, 16.71 411.04, 13.06 415.62, 10.66 419.74, 0.00 419.65, 0.00 394.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,68,"POLYGON ((142.52 289.18, 153.71 293.98, 165.28 312.42, 164.63 335.73, 149.10 354.50, 129.33 358.33, 129.24 362.00, 130.21 364.95, 126.31 370.67, 118.99 366.66, 124.25 358.82, 106.29 351.57, 67.05 350.64, 66.81 347.85, 45.66 346.11, 41.83 354.44, 32.15 354.51, 33.91 340.81, 30.72 340.18, 27.86 338.50, 26.06 336.11, 25.78 332.61, 27.27 329.24, 31.63 326.86, 34.37 317.34, 34.45 306.48, 33.62 301.60, 28.82 299.63, 26.94 295.11, 28.08 291.59, 31.02 289.05, 33.98 288.29, 42.01 285.98, 42.43 282.30, 47.52 281.82, 47.58 283.90, 71.41 286.80, 110.16 289.32, 122.04 286.22, 127.47 285.37, 127.70 280.46, 131.40 274.06, 136.84 280.23, 136.82 286.71, 142.52 289.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,69,"POLYGON ((267.71 3.43, 280.69 5.59, 285.69 16.09, 286.24 18.17, 288.60 17.66, 291.16 31.29, 286.46 32.59, 282.55 35.62, 275.55 39.64, 265.23 40.92, 260.12 37.52, 255.75 28.35, 252.56 19.47, 256.72 8.75, 267.71 3.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,70,"POLYGON ((320.69 0.00, 319.42 5.15, 316.98 8.17, 313.49 10.48, 307.43 10.48, 303.32 5.56, 301.59 1.79, 302.02 0.00, 320.69 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,71,"POLYGON ((854.83 225.15, 875.85 223.08, 877.97 267.75, 880.36 268.40, 880.80 274.63, 878.30 274.69, 878.48 276.64, 854.61 276.83, 856.15 260.81, 854.83 225.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,72,"POLYGON ((684.73 247.23, 684.76 235.35, 689.31 235.37, 689.32 232.28, 701.99 232.29, 701.98 243.48, 705.45 243.48, 705.44 246.79, 708.50 246.77, 708.49 253.14, 688.96 253.11, 688.98 247.23, 684.73 247.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,73,"POLYGON ((709.79 207.25, 710.52 227.94, 683.88 229.04, 679.17 224.90, 679.10 217.82, 688.10 214.37, 690.26 212.23, 689.82 207.67, 709.79 207.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,74,"POLYGON ((679.75 177.94, 710.42 179.13, 711.85 197.94, 699.84 198.87, 679.75 198.23, 679.75 177.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,75,"POLYGON ((684.88 122.87, 718.83 120.38, 721.74 124.88, 720.77 126.81, 719.33 128.60, 718.79 130.39, 718.85 132.74, 718.59 134.53, 718.34 136.29, 717.80 138.08, 717.10 139.85, 715.80 141.37, 714.67 142.71, 713.68 144.80, 712.84 146.59, 711.70 148.38, 689.97 148.64, 689.17 146.31, 678.96 146.13, 679.01 124.77, 684.88 122.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,76,"POLYGON ((677.67 95.39, 716.70 94.64, 716.87 106.27, 721.86 106.14, 723.00 107.11, 724.50 108.07, 725.66 109.02, 726.80 110.10, 727.10 112.33, 727.14 114.06, 727.08 116.04, 687.56 117.43, 679.51 118.12, 677.88 113.35, 677.67 95.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,77,"POLYGON ((679.61 64.15, 706.84 63.57, 715.98 71.51, 718.25 72.20, 717.61 76.41, 717.02 78.05, 718.77 78.12, 718.31 79.62, 717.23 81.02, 716.26 82.40, 715.32 83.91, 715.23 85.40, 715.51 87.12, 715.44 88.72, 686.14 91.09, 683.58 88.18, 679.32 86.94, 679.61 64.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,78,"POLYGON ((692.42 32.04, 692.45 33.93, 693.13 36.24, 694.88 36.82, 696.15 37.65, 698.40 37.86, 701.04 39.01, 701.56 40.49, 703.63 43.16, 705.12 43.50, 706.48 42.85, 707.56 41.58, 711.80 36.89, 711.76 40.25, 711.77 41.71, 711.85 43.31, 711.98 44.79, 712.13 46.28, 712.38 48.13, 712.63 49.62, 713.79 55.89, 713.10 58.64, 692.98 59.77, 689.42 56.78, 685.56 56.74, 683.05 50.61, 682.80 32.17, 692.42 32.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3735489,79,"POLYGON ((791.99 8.39, 809.03 8.46, 809.39 27.26, 806.67 27.57, 804.82 28.50, 802.88 30.40, 803.20 33.50, 794.76 34.20, 792.75 28.19, 791.99 8.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,0,"POLYGON ((69.96 571.69, 67.43 574.86, 67.58 577.14, 71.54 580.68, 75.36 582.67, 79.96 582.77, 81.73 582.31, 81.74 587.19, 77.73 585.43, 66.13 590.10, 58.60 597.99, 45.69 598.59, 45.61 597.08, 39.85 597.34, 38.69 572.84, 41.54 572.72, 49.70 576.98, 56.73 577.95, 60.33 575.88, 63.07 572.06, 69.96 571.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,1,"POLYGON ((267.33 570.25, 268.06 585.12, 265.01 589.91, 265.28 595.32, 253.46 595.91, 253.62 599.12, 241.43 599.72, 241.18 594.40, 230.35 594.92, 230.20 592.02, 219.57 592.53, 218.60 572.62, 267.33 570.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,2,"POLYGON ((389.47 552.37, 390.10 568.53, 350.75 570.09, 350.12 553.93, 389.47 552.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,3,"POLYGON ((258.14 529.26, 258.67 540.50, 262.56 540.31, 262.93 548.34, 258.21 548.55, 259.06 566.86, 224.13 568.49, 223.55 556.09, 239.85 555.34, 238.91 535.25, 251.92 534.66, 251.68 529.58, 258.14 529.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,4,"POLYGON ((209.53 529.77, 211.02 556.79, 205.74 559.96, 208.22 564.27, 196.19 564.96, 197.52 559.00, 197.02 554.17, 190.15 538.39, 194.44 530.60, 209.53 529.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,5,"POLYGON ((118.23 525.17, 129.96 524.72, 130.62 542.10, 125.79 542.29, 126.27 554.76, 103.75 555.62, 103.20 541.54, 101.29 541.63, 101.06 535.44, 104.67 535.31, 104.25 524.66, 112.57 524.34, 112.72 528.04, 118.32 527.81, 118.23 525.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,6,"POLYGON ((95.95 526.78, 99.07 527.32, 100.88 544.10, 97.31 546.17, 94.91 549.58, 97.63 553.60, 75.88 554.51, 74.65 525.59, 83.96 525.20, 84.07 527.75, 88.51 527.55, 88.39 525.07, 93.37 524.85, 95.95 526.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,7,"POLYGON ((53.76 525.37, 55.56 527.68, 59.43 528.20, 62.35 525.66, 65.47 525.56, 65.75 534.43, 61.41 534.57, 61.88 549.49, 41.02 550.16, 40.55 535.41, 37.16 535.52, 36.84 525.91, 53.76 525.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,8,"POLYGON ((402.28 545.29, 401.80 529.39, 419.69 528.87, 420.15 544.75, 402.28 545.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,9,"POLYGON ((383.91 520.03, 384.61 544.52, 352.46 545.45, 351.76 520.97, 383.91 520.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,10,"POLYGON ((430.06 513.35, 454.08 512.78, 454.96 550.67, 445.97 550.88, 445.74 540.54, 430.69 540.90, 430.06 513.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,11,"POLYGON ((583.55 434.00, 584.33 474.87, 523.24 476.03, 522.76 450.80, 541.06 450.47, 540.75 434.83, 583.55 434.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,12,"POLYGON ((422.39 427.02, 422.39 465.35, 408.68 465.35, 408.70 458.69, 406.79 452.30, 397.39 445.13, 397.39 427.03, 422.39 427.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,13,"POLYGON ((428.33 425.71, 436.00 425.63, 435.98 423.39, 454.19 423.17, 454.60 459.60, 441.23 459.75, 441.28 465.45, 428.79 465.59, 428.33 425.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,14,"POLYGON ((359.39 460.30, 335.19 460.74, 334.67 431.70, 337.14 430.46, 338.71 428.20, 358.80 427.84, 359.39 460.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,15,"POLYGON ((517.96 463.29, 506.66 463.45, 506.60 458.48, 495.31 458.61, 494.95 429.94, 501.20 429.85, 501.13 424.83, 517.48 424.63, 517.96 463.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,16,"POLYGON ((387.82 428.34, 390.60 428.36, 390.40 452.99, 382.57 448.72, 375.64 450.39, 372.28 454.42, 367.12 457.29, 366.57 444.91, 376.79 446.63, 385.90 442.44, 387.75 437.20, 387.82 428.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,17,"POLYGON ((487.28 419.97, 487.74 451.68, 471.70 451.91, 471.61 445.16, 464.15 445.26, 463.95 430.17, 470.07 430.09, 470.04 427.62, 477.44 427.50, 477.33 420.11, 487.28 419.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,18,"POLYGON ((287.60 389.39, 289.64 465.06, 282.43 465.24, 282.56 469.72, 272.65 469.98, 272.50 464.12, 265.11 464.31, 264.05 425.69, 243.77 426.25, 242.79 389.89, 264.03 389.31, 263.98 387.69, 275.99 387.36, 276.05 389.71, 287.60 389.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,19,"POLYGON ((570.32 368.43, 571.15 384.02, 543.68 385.47, 542.86 369.89, 570.32 368.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,20,"POLYGON ((247.37 352.79, 281.44 352.39, 281.75 377.55, 249.14 377.94, 249.00 365.80, 247.52 365.82, 247.37 352.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,21,"POLYGON ((113.61 252.61, 114.37 312.03, 147.66 311.61, 147.22 275.92, 177.40 275.53, 181.01 286.27, 187.13 296.50, 195.22 301.71, 219.47 301.52, 219.99 343.83, 212.78 343.93, 213.20 377.17, 138.96 378.11, 140.08 466.37, 41.33 467.62, 40.46 398.07, 7.46 398.49, 5.63 253.98, 113.61 252.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,22,"POLYGON ((252.40 326.20, 259.13 325.90, 261.41 329.53, 266.94 332.87, 271.23 332.07, 276.79 325.71, 282.06 325.67, 282.29 346.97, 277.30 347.01, 277.31 348.25, 246.52 348.57, 246.46 343.47, 248.11 342.45, 249.08 339.36, 248.73 331.91, 252.65 331.72, 252.40 326.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,23,"POLYGON ((551.71 316.30, 552.55 347.73, 462.61 350.12, 462.01 327.25, 475.58 326.88, 475.47 322.75, 504.68 321.98, 504.56 317.55, 551.71 316.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,24,"POLYGON ((457.93 256.01, 459.02 298.02, 450.53 298.24, 451.99 353.72, 341.74 356.60, 341.41 344.31, 343.71 344.25, 342.43 294.76, 344.35 294.71, 343.70 269.82, 342.29 269.86, 341.95 256.55, 391.39 255.24, 391.46 257.75, 457.93 256.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,25,"POLYGON ((273.13 265.90, 266.73 269.90, 267.06 276.55, 260.99 286.34, 261.11 288.93, 229.83 290.37, 228.76 267.45, 273.13 265.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,26,"POLYGON ((559.77 257.42, 560.17 275.82, 527.88 276.51, 527.49 258.12, 559.77 257.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,27,"POLYGON ((826.43 26.30, 825.78 43.79, 816.52 43.71, 811.60 47.10, 800.62 45.98, 800.21 24.33, 818.05 23.72, 821.27 26.90, 826.43 26.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,28,"POLYGON ((870.23 20.79, 878.34 18.51, 879.41 11.41, 889.61 9.68, 891.14 12.35, 895.32 11.82, 897.33 17.19, 900.00 16.50, 900.00 33.00, 870.58 34.52, 870.23 20.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,29,"POLYGON ((740.79 14.61, 744.55 14.50, 744.46 11.66, 755.80 11.28, 755.86 12.81, 759.43 12.70, 760.10 32.43, 747.54 32.87, 747.42 29.50, 741.30 29.70, 740.79 14.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,30,"POLYGON ((674.78 10.64, 699.45 9.37, 700.52 30.07, 675.85 31.32, 674.78 10.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,31,"POLYGON ((798.03 5.16, 823.34 4.18, 823.65 16.43, 815.52 15.21, 798.64 14.96, 798.03 5.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,32,"POLYGON ((900.00 7.44, 897.28 7.44, 897.27 0.00, 900.00 0.00, 900.00 7.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,33,"POLYGON ((894.54 0.00, 894.66 7.31, 872.22 7.73, 872.08 0.00, 894.54 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,34,"POLYGON ((761.58 0.00, 761.82 7.40, 739.06 7.98, 738.91 0.00, 761.58 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,35,"POLYGON ((700.90 0.00, 701.05 3.75, 674.19 4.79, 674.00 0.00, 700.90 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,36,"POLYGON ((828.10 466.32, 807.23 466.71, 806.80 433.87, 817.64 433.73, 817.68 436.57, 822.02 436.50, 822.28 454.74, 827.82 454.67, 828.10 466.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,37,"POLYGON ((779.20 462.86, 759.34 462.94, 759.21 433.00, 779.09 432.92, 779.20 462.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,38,"POLYGON ((734.44 431.66, 755.63 431.54, 755.79 462.44, 748.89 462.48, 748.88 456.77, 734.56 456.85, 734.44 431.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,39,"POLYGON ((880.98 424.83, 896.20 424.60, 896.30 440.20, 900.00 440.19, 900.00 458.45, 881.55 458.67, 880.98 424.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,40,"POLYGON ((686.64 407.31, 698.13 407.01, 698.55 423.45, 686.84 423.75, 686.64 407.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,41,"POLYGON ((850.52 407.63, 850.85 418.43, 820.09 419.35, 819.97 415.40, 816.19 415.52, 815.99 408.13, 823.79 407.89, 823.71 405.60, 834.44 405.29, 836.90 408.02, 850.52 407.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,42,"POLYGON ((879.11 392.87, 897.06 392.88, 897.05 395.41, 900.00 395.40, 900.00 408.34, 898.05 408.34, 898.03 416.29, 888.24 416.30, 885.42 409.71, 882.74 407.87, 882.99 403.82, 885.06 402.02, 883.90 398.21, 879.11 392.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,43,"POLYGON ((752.57 374.10, 753.52 403.22, 751.29 403.28, 751.51 409.78, 734.86 410.32, 734.31 393.26, 736.51 393.18, 735.97 376.12, 740.78 375.96, 740.75 374.47, 752.57 374.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,44,"POLYGON ((774.72 373.89, 775.32 405.86, 766.48 406.02, 766.37 401.16, 761.57 401.24, 761.06 374.15, 774.72 373.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,45,"POLYGON ((802.95 373.13, 803.82 402.34, 781.98 402.99, 781.12 373.77, 802.95 373.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,46,"POLYGON ((726.34 373.64, 727.30 399.76, 713.94 400.26, 713.00 374.11, 726.34 373.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,47,"POLYGON ((806.34 370.80, 817.88 370.48, 818.22 382.35, 823.11 382.20, 823.38 391.81, 819.84 391.90, 820.12 401.63, 807.23 402.01, 806.34 370.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,48,"POLYGON ((851.90 376.58, 852.35 394.44, 828.21 395.06, 827.75 377.20, 851.90 376.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,49,"POLYGON ((708.56 395.56, 699.77 395.85, 699.68 393.06, 697.01 390.64, 693.06 390.01, 688.91 390.82, 685.09 391.70, 684.86 379.12, 685.73 379.09, 690.82 379.21, 695.77 377.84, 697.25 376.23, 698.55 374.93, 701.16 374.84, 705.50 374.62, 707.89 375.42, 708.56 395.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,50,"POLYGON ((875.50 373.40, 900.00 373.32, 900.00 390.68, 875.55 390.78, 875.50 373.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,51,"POLYGON ((850.94 314.52, 851.81 348.41, 840.73 348.70, 840.58 342.86, 824.26 343.08, 824.18 315.20, 850.94 314.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,52,"POLYGON ((787.67 344.79, 766.87 345.32, 766.08 314.22, 786.87 313.69, 787.67 344.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,53,"POLYGON ((700.03 309.54, 700.07 339.26, 691.08 339.29, 691.08 344.35, 673.89 344.39, 673.85 315.87, 684.12 315.87, 684.12 309.57, 700.03 309.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,54,"POLYGON ((802.70 300.97, 804.29 346.50, 792.80 346.79, 791.64 301.45, 802.70 300.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,55,"POLYGON ((736.60 301.37, 757.18 301.22, 757.48 346.30, 736.88 346.42, 736.60 301.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,56,"POLYGON ((886.78 301.19, 900.00 301.04, 900.00 327.14, 898.40 327.16, 898.46 330.77, 887.21 330.91, 887.09 321.10, 884.75 321.11, 884.69 315.92, 886.95 315.91, 886.78 301.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,57,"POLYGON ((647.66 268.03, 666.57 267.70, 666.65 271.70, 674.01 271.55, 674.29 287.64, 668.40 287.75, 668.50 293.94, 636.55 294.49, 636.38 284.37, 647.93 284.19, 647.66 268.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,58,"POLYGON ((780.12 239.57, 800.51 239.42, 800.72 271.34, 780.32 271.48, 780.12 239.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,59,"POLYGON ((732.35 235.17, 744.40 234.95, 745.08 272.56, 733.01 272.78, 732.35 235.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,60,"POLYGON ((758.12 233.49, 770.94 233.58, 770.70 262.29, 757.91 262.20, 758.12 233.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,61,"POLYGON ((815.46 182.86, 814.86 160.87, 834.41 160.33, 834.81 175.04, 829.64 175.19, 829.84 182.47, 815.46 182.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,62,"POLYGON ((739.78 157.86, 761.20 157.56, 761.58 184.01, 748.09 184.20, 748.02 179.16, 740.10 179.28, 739.78 157.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,63,"POLYGON ((764.73 150.46, 780.52 150.21, 780.64 156.93, 786.02 156.84, 786.45 183.80, 772.73 184.01, 772.64 179.20, 765.17 179.30, 764.73 150.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,64,"POLYGON ((810.81 182.80, 790.58 182.91, 790.43 150.91, 804.74 150.85, 804.78 159.02, 810.68 159.00, 810.81 182.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,65,"POLYGON ((869.29 184.26, 868.88 165.29, 864.99 165.36, 864.44 140.76, 900.00 139.97, 900.00 187.98, 899.69 187.99, 899.75 191.22, 892.88 191.38, 892.85 189.60, 876.58 189.97, 876.64 193.19, 866.86 193.42, 866.66 184.32, 869.29 184.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,66,"POLYGON ((653.73 136.80, 692.01 136.06, 692.37 154.52, 654.07 155.26, 653.73 136.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,67,"POLYGON ((774.00 130.82, 774.12 148.40, 741.91 148.62, 741.80 131.04, 774.00 130.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,68,"POLYGON ((802.05 123.06, 826.74 122.66, 826.99 136.90, 822.97 136.98, 823.11 145.63, 802.43 145.98, 802.05 123.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,69,"POLYGON ((773.74 109.09, 774.06 126.64, 741.90 127.22, 741.59 109.69, 773.74 109.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,70,"POLYGON ((812.84 102.88, 812.73 99.17, 820.68 98.90, 820.77 101.67, 826.25 101.49, 826.85 119.88, 802.62 120.67, 802.04 103.24, 812.84 102.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,71,"POLYGON ((773.26 87.99, 773.74 105.41, 741.37 106.28, 740.91 88.86, 773.26 87.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,72,"POLYGON ((803.35 79.90, 827.62 78.66, 830.28 91.38, 827.39 94.58, 804.20 94.86, 803.35 79.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,73,"POLYGON ((879.97 63.56, 887.78 62.16, 888.78 66.71, 900.00 66.97, 900.00 83.61, 897.19 83.56, 880.19 84.00, 880.74 82.23, 880.34 78.11, 878.65 75.93, 876.45 75.99, 873.77 75.64, 873.04 70.19, 879.96 69.13, 879.97 63.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,74,"POLYGON ((743.00 66.39, 744.42 63.98, 774.12 62.80, 774.84 80.69, 743.62 81.94, 743.00 66.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,75,"POLYGON ((674.08 61.58, 701.01 60.89, 702.04 83.02, 680.74 83.25, 680.51 74.36, 674.40 74.05, 674.08 61.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,76,"POLYGON ((799.19 51.95, 809.87 52.45, 809.92 48.70, 820.56 47.65, 821.56 50.60, 827.35 49.98, 827.54 57.46, 825.52 64.22, 823.33 64.89, 822.70 70.53, 802.82 71.19, 802.48 58.10, 799.04 58.50, 799.19 51.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,77,"POLYGON ((741.45 38.84, 745.23 38.76, 745.20 36.97, 762.96 36.62, 763.28 56.51, 760.14 59.09, 748.43 59.53, 741.77 55.81, 741.45 38.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,78,"POLYGON ((877.02 36.62, 900.00 36.14, 900.00 53.95, 899.59 54.09, 896.76 56.14, 896.76 59.02, 878.81 58.95, 877.37 56.25, 873.59 54.00, 872.68 52.53, 872.44 39.64, 877.09 39.55, 877.02 36.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,79,"POLYGON ((679.83 35.40, 699.73 35.51, 700.63 58.42, 678.21 57.59, 677.58 39.36, 680.10 39.45, 679.83 35.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,80,"POLYGON ((891.69 813.31, 891.51 804.82, 894.36 804.77, 897.15 803.65, 900.00 803.29, 900.00 813.13, 891.69 813.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,81,"POLYGON ((879.55 705.47, 882.66 705.37, 882.15 691.07, 894.99 690.61, 895.12 694.44, 896.79 697.89, 899.82 699.54, 900.00 704.69, 900.00 730.07, 880.46 730.76, 879.55 705.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,82,"POLYGON ((885.09 655.85, 895.75 655.05, 896.00 658.53, 896.13 659.65, 896.58 662.80, 897.01 665.38, 900.00 667.55, 900.00 674.34, 889.60 675.12, 888.81 664.44, 885.76 664.67, 885.09 655.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,83,"POLYGON ((657.85 530.22, 689.16 529.78, 689.23 534.84, 704.95 534.61, 704.90 529.86, 779.00 528.88, 778.93 524.93, 791.10 524.89, 790.87 513.86, 797.02 513.70, 797.21 524.75, 806.93 524.57, 807.23 548.20, 852.02 547.64, 852.55 587.80, 842.98 587.91, 844.04 669.44, 792.56 670.11, 792.19 641.71, 778.44 641.91, 778.52 648.21, 731.20 648.86, 730.68 611.14, 709.67 611.43, 709.54 601.96, 728.19 601.70, 727.98 586.12, 746.17 585.88, 745.98 572.97, 705.33 573.54, 705.43 581.11, 688.69 581.36, 688.59 573.66, 658.21 574.08, 657.83 547.85, 648.06 548.01, 647.85 533.23, 657.88 533.08, 657.85 530.22), (795.52 553.70, 781.96 553.96, 782.29 571.20, 758.26 571.65, 758.58 587.69, 762.96 587.63, 763.00 589.20, 790.06 588.69, 790.09 590.64, 796.24 590.53, 795.52 553.70), (809.51 602.42, 809.74 617.87, 824.34 617.63, 824.10 602.20, 809.51 602.42), (768.59 594.63, 768.81 609.34, 777.60 609.21, 778.00 635.19, 791.22 634.99, 790.59 594.27, 768.59 594.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,84,"POLYGON ((900.00 588.81, 882.98 589.47, 882.35 572.55, 900.00 571.88, 900.00 588.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,85,"POLYGON ((885.60 547.49, 885.09 530.32, 880.64 530.46, 880.40 522.43, 882.88 522.34, 882.53 510.56, 886.73 510.43, 886.82 513.32, 898.78 512.97, 899.81 547.06, 885.60 547.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,86,"POLYGON ((682.35 469.39, 659.11 469.92, 658.56 446.21, 664.02 446.09, 671.95 445.71, 678.33 447.12, 681.83 447.05, 682.35 469.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,87,"POLYGON ((853.92 466.08, 830.41 465.99, 830.44 451.34, 835.26 449.86, 839.73 444.80, 843.94 443.82, 853.99 443.88, 853.92 466.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,88,"POLYGON ((636.87 469.20, 636.21 439.56, 645.47 439.35, 645.78 453.57, 655.23 453.37, 655.57 468.79, 636.87 469.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,89,"POLYGON ((619.00 433.67, 634.06 433.29, 635.11 474.20, 620.05 474.87, 619.00 433.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,90,"POLYGON ((805.04 466.13, 782.53 466.12, 782.55 440.91, 805.05 440.91, 805.04 466.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,91,"POLYGON ((709.19 439.34, 729.23 438.90, 729.84 466.36, 709.80 466.81, 709.19 439.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,92,"POLYGON ((266.68 829.87, 265.03 833.42, 265.29 837.25, 269.80 841.69, 276.95 842.17, 276.51 848.71, 277.70 854.63, 283.38 856.93, 246.30 858.23, 249.35 853.24, 247.46 848.56, 244.93 845.30, 241.37 843.81, 240.75 831.11, 266.68 829.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,93,"POLYGON ((43.65 822.02, 47.04 821.95, 54.12 817.40, 57.01 812.75, 57.91 807.49, 61.93 807.04, 62.19 809.32, 67.56 812.66, 67.06 828.08, 61.02 832.45, 62.40 824.00, 58.54 824.64, 57.27 830.44, 53.84 833.86, 50.86 840.75, 47.04 842.95, 44.00 840.75, 43.65 822.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,94,"POLYGON ((71.94 806.27, 84.52 805.95, 85.69 808.83, 87.86 811.68, 86.39 819.42, 87.76 824.49, 91.92 827.60, 91.39 836.14, 79.18 836.25, 76.27 832.00, 70.38 835.97, 68.53 812.28, 70.15 810.38, 71.94 806.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,95,"POLYGON ((192.19 805.33, 191.22 815.17, 186.81 821.05, 179.25 828.44, 173.06 834.01, 172.16 805.69, 192.19 805.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,96,"POLYGON ((220.76 801.47, 228.31 807.23, 244.86 808.38, 248.65 804.96, 250.46 800.36, 255.58 800.92, 252.16 805.22, 251.39 809.26, 255.15 811.78, 259.37 811.32, 264.37 807.53, 266.73 804.32, 270.10 805.45, 273.20 809.24, 273.87 821.47, 270.29 821.68, 270.61 827.42, 237.48 828.55, 237.51 826.20, 233.36 826.44, 232.68 814.18, 221.51 814.80, 220.76 801.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,97,"POLYGON ((350.74 800.58, 382.46 799.55, 383.10 819.57, 351.38 820.61, 350.74 800.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,98,"POLYGON ((260.33 731.45, 260.47 735.44, 264.77 736.78, 266.73 737.35, 268.00 737.69, 269.04 740.46, 269.15 743.50, 273.51 743.35, 273.72 749.69, 271.37 749.77, 271.62 756.85, 243.18 757.86, 242.56 740.69, 248.82 740.47, 248.51 731.89, 260.33 731.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,99,"POLYGON ((358.17 729.09, 401.00 727.42, 401.62 743.14, 409.16 742.84, 409.67 755.56, 380.40 756.73, 380.33 754.54, 356.26 755.48, 355.64 739.61, 358.58 739.49, 358.17 729.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,100,"POLYGON ((129.39 732.82, 133.99 727.84, 146.87 723.25, 147.51 719.26, 146.85 716.48, 153.53 716.79, 150.03 730.82, 151.17 734.61, 157.58 736.51, 157.34 755.61, 130.14 755.88, 130.74 740.15, 129.07 740.08, 129.39 732.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,101,"POLYGON ((109.94 718.59, 111.89 725.16, 119.74 731.89, 124.06 731.78, 124.60 752.27, 102.31 752.89, 101.38 718.83, 109.94 718.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,102,"POLYGON ((90.34 721.27, 91.24 742.96, 87.50 740.79, 83.71 737.20, 79.24 734.16, 75.91 735.29, 76.10 728.83, 81.72 728.15, 85.18 725.62, 90.34 721.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,103,"POLYGON ((426.10 734.50, 411.87 734.93, 411.56 724.35, 425.80 723.92, 426.10 734.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,104,"POLYGON ((262.06 700.17, 262.29 706.32, 273.49 705.88, 274.11 721.95, 263.05 722.39, 263.22 726.94, 238.14 727.91, 237.94 723.16, 243.09 721.55, 246.09 718.23, 245.50 715.71, 241.03 712.85, 237.55 712.99, 237.23 704.54, 250.14 704.03, 250.01 700.64, 262.06 700.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,105,"POLYGON ((385.22 699.78, 386.01 725.11, 376.04 725.41, 376.11 727.52, 362.26 727.96, 361.95 718.22, 359.84 718.28, 363.59 714.16, 368.97 703.88, 368.80 698.78, 375.39 698.57, 375.45 700.08, 385.22 699.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,106,"POLYGON ((548.54 635.15, 549.04 654.98, 502.99 656.15, 503.94 693.20, 532.98 692.46, 532.58 676.89, 559.19 676.21, 560.47 726.19, 532.61 726.90, 532.22 711.33, 505.64 712.00, 506.92 761.96, 479.06 762.67, 476.32 655.30, 482.79 655.14, 482.32 636.84, 548.54 635.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,107,"POLYGON ((258.26 669.81, 255.21 673.82, 254.73 679.96, 258.44 683.42, 264.94 676.57, 267.37 675.18, 269.30 675.15, 269.54 686.60, 274.67 686.49, 274.86 695.25, 242.44 695.92, 242.35 691.53, 250.63 691.36, 247.12 684.71, 237.37 684.71, 237.36 675.59, 241.62 675.57, 244.85 675.53, 248.39 675.42, 248.95 673.47, 247.89 669.81, 258.26 669.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,108,"POLYGON ((371.25 669.93, 387.06 669.39, 387.10 671.30, 395.02 671.03, 395.68 691.35, 355.42 692.69, 355.07 682.64, 360.83 680.74, 368.93 674.05, 371.25 669.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,109,"POLYGON ((261.73 638.67, 261.82 642.11, 260.63 644.23, 261.93 646.37, 264.04 647.16, 272.96 647.00, 273.36 668.47, 254.62 668.82, 254.56 665.74, 250.81 665.81, 246.54 669.98, 244.45 670.03, 244.41 668.37, 237.55 668.52, 237.46 664.95, 232.77 665.07, 232.20 642.00, 238.11 641.85, 238.19 644.78, 252.66 644.41, 252.54 638.91, 261.73 638.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,110,"POLYGON ((352.77 639.12, 387.15 638.38, 387.39 649.25, 392.60 649.14, 392.86 661.52, 382.63 661.73, 383.02 666.16, 376.49 666.55, 373.21 666.55, 356.72 666.41, 353.31 663.84, 352.77 639.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,111,"POLYGON ((66.29 637.29, 66.29 640.86, 67.28 644.81, 74.61 650.88, 77.87 651.38, 77.49 653.83, 71.25 658.69, 71.26 662.78, 45.32 662.84, 45.28 642.15, 51.01 642.14, 51.01 644.40, 56.13 644.38, 56.11 637.30, 66.29 637.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,112,"POLYGON ((117.51 656.22, 121.48 653.15, 124.76 650.96, 129.73 645.92, 129.48 642.62, 134.40 642.83, 138.26 642.22, 140.99 639.51, 144.14 638.56, 145.21 645.71, 140.09 652.32, 135.07 655.42, 117.51 656.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,113,"POLYGON ((41.33 617.57, 47.85 619.38, 51.30 618.80, 53.25 617.26, 50.56 613.85, 48.17 612.31, 47.72 606.35, 55.32 606.29, 55.90 608.76, 57.10 611.84, 63.82 616.62, 87.67 615.87, 88.00 628.85, 73.83 628.99, 71.79 627.56, 70.00 625.00, 64.68 626.25, 62.37 628.15, 62.33 626.02, 52.08 626.15, 52.20 635.09, 41.56 635.23, 41.33 617.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,114,"POLYGON ((351.33 627.64, 351.22 618.14, 354.49 615.57, 356.51 612.59, 356.42 609.04, 376.49 608.55, 382.75 616.09, 382.60 619.00, 375.64 624.80, 374.67 627.33, 351.33 627.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,115,"POLYGON ((265.70 601.65, 266.39 628.74, 254.38 629.05, 254.42 630.53, 243.11 630.82, 243.06 628.51, 231.16 628.80, 230.91 618.92, 216.60 619.29, 216.39 610.53, 213.93 610.59, 213.70 601.65, 215.79 601.60, 215.74 599.58, 230.31 599.21, 230.35 600.98, 252.61 600.41, 252.65 601.99, 265.70 601.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3736839,116,"POLYGON ((529.56 578.38, 553.20 577.98, 553.57 600.66, 529.94 601.06, 529.56 578.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,0,"POLYGON ((784.89 334.23, 824.94 333.80, 825.07 345.56, 823.20 350.14, 793.60 350.70, 791.91 346.21, 784.24 347.01, 781.64 345.50, 781.77 335.28, 784.89 334.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,1,"POLYGON ((731.22 326.61, 731.40 322.89, 747.28 323.69, 744.93 329.63, 744.88 333.61, 748.52 343.21, 733.28 342.74, 730.28 341.19, 731.22 326.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,2,"POLYGON ((790.30 323.21, 792.38 324.02, 796.09 323.79, 799.76 323.26, 804.02 322.11, 806.51 320.86, 806.70 316.75, 808.28 315.38, 816.70 315.01, 820.86 315.79, 821.69 319.17, 821.68 324.61, 823.22 332.80, 809.47 333.02, 808.93 329.50, 790.77 329.97, 790.30 323.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,3,"POLYGON ((857.63 320.16, 872.93 319.55, 875.78 307.57, 890.75 307.30, 891.10 325.19, 883.45 329.33, 858.04 330.36, 857.63 320.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,4,"POLYGON ((667.62 309.36, 698.80 308.32, 699.16 318.55, 667.98 319.61, 667.62 309.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,5,"POLYGON ((823.22 297.77, 824.17 311.89, 808.78 311.68, 806.61 307.63, 803.43 304.76, 800.73 303.21, 795.11 303.06, 789.35 297.46, 823.22 297.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,6,"POLYGON ((732.29 291.40, 764.37 291.69, 764.27 303.41, 760.43 303.37, 758.35 297.54, 754.33 296.18, 746.35 296.96, 741.11 300.05, 732.27 300.21, 732.23 297.32, 732.29 291.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,7,"POLYGON ((702.62 289.38, 701.43 300.91, 693.98 301.83, 674.11 303.29, 673.84 299.50, 667.14 299.99, 666.42 289.88, 677.90 289.08, 677.22 279.48, 693.29 278.36, 696.54 288.78, 702.62 289.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,8,"POLYGON ((864.94 276.84, 875.80 276.43, 876.52 296.03, 882.58 295.81, 882.77 300.96, 856.20 301.95, 858.07 293.64, 865.57 293.38, 864.94 276.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,9,"POLYGON ((822.97 270.48, 823.91 288.57, 810.43 289.29, 809.45 285.92, 797.93 286.37, 792.19 275.90, 792.02 272.02, 822.97 270.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,10,"POLYGON ((671.13 264.39, 698.66 264.11, 698.74 271.90, 669.84 272.21, 669.79 267.04, 671.13 264.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,11,"POLYGON ((730.34 262.45, 763.89 262.06, 764.02 268.32, 764.20 272.64, 760.52 272.83, 733.49 273.76, 730.48 273.79, 730.34 262.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,12,"POLYGON ((604.88 255.78, 643.76 255.01, 644.01 272.70, 643.49 277.06, 600.01 277.97, 600.08 256.12, 604.88 255.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,13,"POLYGON ((699.50 248.46, 699.69 256.78, 677.73 257.27, 677.56 248.95, 699.50 248.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,14,"POLYGON ((776.51 246.14, 793.83 247.02, 794.02 248.64, 795.38 250.23, 797.32 250.91, 799.22 249.68, 802.99 247.08, 817.62 247.15, 819.28 254.03, 819.26 258.87, 795.29 258.46, 793.92 256.59, 790.47 255.06, 788.13 255.69, 786.10 257.52, 776.49 257.04, 776.63 250.99, 776.51 246.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,15,"POLYGON ((759.32 240.36, 763.79 240.38, 764.66 248.35, 761.77 249.22, 755.71 254.61, 732.39 253.90, 728.40 252.29, 727.57 241.17, 759.32 240.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,16,"POLYGON ((794.94 222.93, 814.87 222.27, 816.47 226.65, 816.76 235.76, 820.80 235.64, 821.03 242.49, 780.51 243.84, 780.27 236.80, 795.39 236.31, 794.94 222.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,17,"POLYGON ((623.50 215.35, 634.15 214.23, 635.11 243.78, 630.76 245.55, 625.09 245.08, 623.50 215.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,18,"POLYGON ((733.14 215.99, 756.86 215.38, 757.05 222.72, 757.02 227.32, 738.33 227.51, 736.48 226.03, 735.61 224.80, 733.36 224.60, 733.02 221.96, 733.14 215.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,19,"POLYGON ((678.45 217.81, 678.54 214.03, 699.71 214.49, 699.60 219.00, 699.36 225.71, 678.19 224.94, 678.45 217.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,20,"POLYGON ((820.61 198.61, 821.41 201.72, 821.27 208.48, 802.65 208.22, 794.60 208.42, 795.00 199.38, 820.61 198.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,21,"POLYGON ((857.63 186.75, 871.49 181.18, 880.42 203.21, 866.56 208.76, 857.63 186.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,22,"POLYGON ((728.72 176.52, 732.08 179.50, 734.21 181.44, 741.26 188.19, 743.99 194.98, 757.11 194.75, 757.34 207.11, 729.27 207.61, 728.72 176.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,23,"POLYGON ((822.69 181.16, 823.00 193.51, 795.81 193.92, 792.94 185.45, 796.09 181.55, 806.72 180.97, 808.31 185.34, 814.29 187.85, 822.69 181.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,24,"POLYGON ((756.41 168.04, 774.21 165.64, 777.45 189.44, 759.65 191.85, 756.41 168.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,25,"POLYGON ((779.50 158.73, 780.13 154.30, 802.45 144.63, 811.14 164.54, 809.67 164.58, 807.92 165.51, 805.17 168.53, 804.53 171.48, 803.10 173.58, 784.93 180.41, 781.77 172.06, 783.77 169.81, 779.50 158.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,26,"POLYGON ((873.17 146.77, 881.92 143.84, 891.13 171.24, 881.39 174.49, 876.79 160.82, 869.11 163.39, 866.62 155.99, 875.30 153.11, 873.17 146.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,27,"POLYGON ((570.97 110.80, 609.05 95.43, 624.49 132.79, 585.89 148.20, 570.97 110.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,28,"POLYGON ((655.90 0.00, 656.20 59.06, 607.06 59.00, 604.65 0.00, 655.90 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,29,"POLYGON ((654.08 650.54, 663.20 650.13, 674.00 649.68, 672.72 679.56, 663.78 680.35, 655.22 680.57, 654.08 650.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,30,"POLYGON ((715.89 646.34, 715.59 683.82, 697.79 683.23, 699.15 646.35, 715.89 646.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,31,"POLYGON ((744.41 649.76, 744.32 678.91, 739.95 679.87, 733.82 677.52, 733.62 660.87, 724.19 660.27, 723.93 650.09, 744.41 649.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,32,"POLYGON ((797.14 661.27, 826.99 659.66, 827.52 663.62, 830.44 663.13, 830.77 668.11, 797.34 669.17, 797.14 661.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,33,"POLYGON ((676.13 645.36, 689.53 645.19, 691.37 659.07, 693.83 660.11, 693.85 668.26, 690.89 669.27, 690.65 681.50, 684.13 682.05, 675.92 681.53, 676.13 645.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,34,"POLYGON ((873.39 641.52, 900.00 641.35, 900.00 667.98, 878.17 668.10, 874.57 666.64, 873.51 663.71, 873.39 641.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,35,"POLYGON ((603.17 643.58, 624.16 643.49, 624.24 663.22, 603.25 663.29, 603.17 643.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,36,"POLYGON ((897.30 619.46, 888.39 620.47, 886.59 623.18, 887.84 629.38, 886.81 631.90, 883.28 634.65, 862.29 635.34, 859.08 632.45, 858.38 610.80, 861.45 608.06, 877.22 606.41, 888.02 605.83, 897.91 606.82, 898.13 609.01, 897.30 619.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,37,"POLYGON ((600.45 583.34, 640.39 582.10, 640.80 595.07, 620.22 595.71, 621.17 626.74, 601.81 627.35, 600.45 583.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,38,"POLYGON ((862.02 589.04, 862.98 583.07, 900.00 582.85, 900.00 605.90, 863.20 606.44, 862.02 589.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,39,"POLYGON ((668.27 585.11, 701.63 585.04, 703.02 593.26, 701.15 597.62, 669.18 597.66, 667.84 591.61, 668.27 585.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,40,"POLYGON ((825.41 565.72, 828.03 568.02, 828.70 589.81, 826.00 592.43, 820.43 590.79, 818.47 588.20, 803.78 590.04, 801.99 575.86, 803.90 573.64, 809.94 570.55, 810.86 568.35, 814.37 566.31, 825.41 565.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,41,"POLYGON ((673.99 569.63, 701.99 568.91, 703.38 576.74, 700.73 581.13, 674.89 581.59, 673.99 569.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,42,"POLYGON ((862.02 567.53, 864.79 566.35, 898.29 565.30, 898.77 576.78, 862.84 577.90, 862.02 567.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,43,"POLYGON ((748.77 541.01, 764.88 540.11, 768.92 541.57, 770.99 544.82, 771.62 552.28, 777.31 566.48, 776.91 586.42, 760.31 586.09, 756.65 581.79, 753.37 566.65, 750.82 561.87, 739.42 562.50, 738.47 545.70, 749.00 545.09, 748.77 541.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,44,"POLYGON ((668.10 551.02, 702.95 549.78, 703.41 564.70, 668.53 565.51, 668.10 551.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,45,"POLYGON ((794.28 547.93, 796.69 546.12, 821.64 545.12, 823.46 547.54, 824.63 551.88, 824.41 557.13, 824.55 562.56, 822.49 564.70, 794.34 564.02, 794.67 556.49, 794.28 547.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,46,"POLYGON ((601.33 542.25, 632.82 543.18, 632.11 567.01, 600.62 566.09, 601.33 542.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,47,"POLYGON ((860.47 557.72, 860.38 528.53, 898.85 528.21, 900.00 531.92, 900.00 539.98, 898.31 539.99, 898.34 557.66, 860.47 557.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,48,"POLYGON ((797.20 525.77, 803.11 525.44, 815.41 524.97, 823.86 524.82, 824.08 536.36, 824.09 543.77, 797.81 543.82, 797.77 536.92, 797.69 534.59, 797.20 525.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,49,"POLYGON ((666.63 526.82, 703.48 526.99, 705.03 533.34, 705.21 540.28, 666.97 540.15, 666.63 526.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,50,"POLYGON ((732.57 510.68, 758.06 509.83, 760.44 529.13, 733.67 529.17, 732.57 510.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,51,"POLYGON ((859.19 507.34, 893.30 507.95, 894.39 514.23, 900.00 516.40, 900.00 527.03, 859.49 526.24, 859.19 507.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,52,"POLYGON ((668.14 509.64, 701.79 507.85, 703.40 522.31, 668.50 523.66, 668.14 509.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,53,"POLYGON ((599.78 505.49, 629.86 504.47, 629.44 526.26, 600.01 527.01, 599.78 505.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,54,"POLYGON ((824.93 497.69, 826.91 513.47, 825.26 517.88, 825.03 523.90, 793.30 522.65, 793.47 518.87, 801.15 516.92, 805.81 512.92, 803.62 510.38, 802.31 507.10, 807.42 500.85, 809.40 499.46, 824.93 497.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,55,"POLYGON ((673.96 494.44, 702.85 492.84, 703.43 503.39, 674.55 505.00, 673.96 494.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,56,"POLYGON ((599.68 477.74, 642.93 477.88, 648.01 492.42, 642.29 502.22, 599.41 503.63, 599.68 477.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,57,"POLYGON ((788.76 481.17, 795.57 480.04, 803.71 479.96, 817.75 480.09, 819.95 482.68, 820.27 486.62, 819.94 490.16, 820.68 494.51, 821.61 498.44, 814.55 499.89, 809.40 499.46, 806.58 499.25, 802.56 497.49, 797.54 497.61, 795.30 499.96, 791.91 497.96, 789.59 496.97, 788.21 492.44, 787.52 489.95, 788.76 481.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,58,"POLYGON ((749.18 474.28, 753.44 475.57, 751.97 479.20, 753.66 484.13, 754.39 488.02, 758.97 489.61, 762.20 493.12, 762.75 496.84, 737.05 496.10, 734.98 494.75, 732.86 491.52, 733.10 476.24, 736.04 474.61, 749.18 474.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,59,"POLYGON ((674.55 476.01, 702.40 474.61, 702.97 485.82, 675.13 487.24, 674.55 476.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,60,"POLYGON ((853.40 472.55, 897.20 471.56, 898.91 478.62, 898.52 485.00, 880.26 485.76, 854.17 486.42, 853.40 472.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,61,"POLYGON ((598.74 454.92, 629.26 453.39, 636.89 453.39, 639.37 462.79, 640.44 475.54, 599.27 475.66, 599.33 470.67, 598.74 454.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,62,"POLYGON ((802.98 453.52, 828.49 452.56, 829.70 456.26, 829.56 463.14, 825.77 468.06, 825.57 472.44, 801.91 472.73, 801.93 467.42, 802.98 453.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,63,"POLYGON ((857.52 456.55, 888.02 456.50, 889.04 460.18, 888.37 462.80, 888.45 467.39, 884.96 467.43, 857.52 467.47, 857.52 456.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,64,"POLYGON ((732.69 450.12, 767.57 447.86, 767.93 469.27, 733.20 470.16, 732.35 467.43, 728.99 467.11, 729.02 453.17, 732.74 452.28, 732.69 450.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,65,"POLYGON ((673.36 446.93, 676.24 444.06, 699.35 445.83, 702.30 451.03, 702.26 470.46, 678.24 470.36, 673.56 466.35, 673.36 446.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,66,"POLYGON ((857.40 445.96, 857.48 441.14, 882.52 441.52, 882.49 443.86, 886.78 451.54, 886.64 455.43, 857.09 454.36, 857.40 445.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,67,"POLYGON ((734.28 429.77, 764.08 429.59, 764.14 439.91, 734.34 440.07, 734.28 429.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,68,"POLYGON ((858.07 428.72, 860.27 427.73, 882.45 427.72, 885.18 432.29, 885.13 436.24, 858.00 436.00, 858.07 428.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,69,"POLYGON ((782.78 410.31, 827.66 409.32, 827.97 423.52, 824.72 427.53, 783.17 428.43, 782.78 410.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,70,"POLYGON ((732.17 412.93, 769.10 413.57, 769.66 421.76, 732.74 421.49, 732.17 412.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,71,"POLYGON ((860.18 401.94, 894.18 400.60, 894.19 410.41, 883.39 410.22, 884.90 423.50, 865.66 423.75, 864.66 421.20, 861.13 420.83, 860.18 401.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,72,"POLYGON ((600.63 395.96, 639.94 394.96, 639.29 415.44, 601.77 415.09, 600.63 395.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,73,"POLYGON ((792.90 395.13, 817.54 394.33, 817.95 406.50, 793.31 407.33, 792.90 395.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,74,"POLYGON ((673.60 390.32, 683.03 389.93, 683.18 393.52, 703.09 400.65, 694.93 401.36, 695.68 409.78, 688.86 410.38, 682.34 410.81, 674.51 411.14, 673.60 390.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,75,"POLYGON ((730.15 382.86, 739.76 382.75, 739.81 386.37, 756.20 386.17, 756.44 404.30, 730.43 404.65, 730.15 382.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,76,"POLYGON ((791.23 376.95, 822.12 376.16, 823.73 386.86, 791.35 387.38, 791.23 376.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,77,"POLYGON ((856.79 370.59, 900.00 367.32, 900.00 376.06, 894.03 376.54, 891.03 378.48, 888.84 384.62, 891.30 388.76, 858.37 391.22, 856.79 370.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,78,"POLYGON ((729.41 353.11, 768.96 350.90, 769.64 362.92, 730.09 365.13, 729.41 353.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,79,"POLYGON ((668.42 352.30, 698.55 351.33, 698.87 361.11, 668.75 362.08, 668.42 352.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,80,"POLYGON ((597.41 319.37, 645.26 319.50, 647.72 329.58, 646.34 388.60, 615.67 387.89, 597.22 387.83, 597.41 319.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,81,"POLYGON ((521.73 724.95, 539.08 737.01, 538.90 740.90, 524.81 757.66, 517.91 761.17, 506.48 750.89, 504.33 742.89, 521.73 724.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,82,"POLYGON ((556.18 726.05, 579.10 726.04, 579.10 743.69, 556.18 743.70, 556.18 726.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,83,"POLYGON ((482.03 722.06, 496.64 703.91, 517.95 718.37, 506.06 734.23, 495.62 740.89, 481.04 726.81, 482.03 722.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,84,"POLYGON ((508.00 668.50, 529.73 669.14, 530.06 690.33, 508.54 689.91, 508.45 686.38, 504.47 685.30, 504.51 671.34, 508.07 671.25, 508.00 668.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,85,"POLYGON ((555.02 666.81, 578.09 666.91, 577.97 691.42, 554.88 691.30, 555.02 666.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,86,"POLYGON ((508.32 636.28, 527.88 636.60, 528.71 658.40, 508.60 658.21, 508.54 655.59, 504.91 655.81, 505.03 638.72, 508.39 639.05, 508.32 636.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,87,"POLYGON ((556.73 635.05, 577.18 635.26, 576.94 659.01, 556.46 658.78, 556.73 635.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,88,"POLYGON ((0.00 613.65, 6.91 615.34, 1.74 636.31, 0.00 635.89, 0.00 613.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,89,"POLYGON ((508.07 604.46, 528.18 604.92, 528.88 626.72, 508.21 626.27, 508.16 624.05, 504.64 623.59, 504.80 607.34, 508.13 606.83, 508.07 604.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,90,"POLYGON ((555.50 603.67, 576.70 603.28, 578.44 627.12, 556.10 627.14, 555.50 603.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,91,"POLYGON ((502.47 576.90, 507.66 571.77, 529.28 572.53, 528.37 594.79, 508.66 593.52, 502.53 591.17, 502.47 576.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,92,"POLYGON ((556.00 570.98, 578.31 570.25, 579.80 588.02, 577.03 595.75, 555.74 595.69, 556.00 570.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,93,"POLYGON ((555.44 538.38, 577.41 538.20, 578.44 563.75, 556.66 564.12, 555.44 538.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,94,"POLYGON ((507.40 539.05, 528.62 538.69, 528.83 561.31, 507.42 561.67, 507.41 557.12, 507.40 539.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,95,"POLYGON ((403.20 416.25, 483.51 383.32, 488.57 384.75, 490.45 514.58, 404.65 515.84, 403.20 416.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,96,"POLYGON ((196.21 390.32, 255.24 364.60, 253.62 353.19, 271.25 345.83, 276.50 358.31, 330.90 335.63, 328.89 330.84, 333.38 328.97, 329.99 320.89, 368.33 304.88, 395.85 370.29, 364.51 392.69, 223.99 453.52, 196.21 390.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,97,"POLYGON ((278.10 186.85, 317.08 277.09, 254.43 302.24, 258.92 316.27, 243.09 321.38, 236.89 310.35, 191.22 328.58, 195.02 338.50, 184.47 342.30, 179.49 332.41, 171.35 337.33, 131.24 249.47, 278.10 186.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,98,"POLYGON ((444.60 101.33, 506.53 76.04, 522.90 115.76, 543.09 107.52, 552.89 131.31, 535.57 138.41, 590.16 270.74, 524.61 297.57, 467.03 157.99, 453.05 163.69, 489.53 252.44, 454.99 266.51, 423.02 188.68, 428.52 186.43, 412.27 146.96, 456.04 129.10, 444.60 101.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,99,"POLYGON ((305.61 164.84, 401.07 127.09, 401.34 137.69, 404.15 145.61, 408.39 156.91, 412.70 167.86, 328.42 200.77, 328.51 204.30, 316.43 208.18, 312.65 196.47, 317.00 193.41, 305.61 164.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,100,"POLYGON ((871.91 890.66, 873.35 889.01, 894.34 888.65, 894.54 900.00, 872.08 900.00, 871.91 890.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,101,"POLYGON ((744.45 886.04, 757.24 885.67, 757.32 888.02, 761.19 887.90, 761.58 900.00, 738.91 900.00, 738.76 892.67, 744.58 892.56, 744.45 886.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,102,"POLYGON ((682.15 885.52, 700.31 884.82, 700.90 900.00, 674.00 900.00, 673.99 899.85, 676.49 898.90, 676.10 888.94, 682.27 888.69, 682.15 885.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,103,"POLYGON ((798.72 878.51, 822.84 878.04, 822.90 886.32, 822.32 894.13, 797.71 893.83, 797.66 885.39, 798.72 878.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,104,"POLYGON ((876.67 865.50, 875.64 864.00, 893.54 863.34, 894.01 875.89, 896.38 875.81, 896.67 883.48, 872.10 884.38, 871.62 871.18, 876.86 871.00, 876.67 865.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,105,"POLYGON ((744.08 860.39, 754.52 859.74, 754.64 861.41, 760.78 861.03, 761.90 879.20, 755.79 879.58, 755.91 881.62, 744.40 882.31, 744.18 878.97, 739.68 879.24, 738.70 863.41, 744.24 863.07, 744.08 860.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,106,"POLYGON ((671.87 857.55, 697.84 857.20, 697.88 859.28, 701.95 859.25, 702.20 878.44, 689.06 878.60, 688.94 868.19, 672.00 868.42, 671.87 857.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,107,"POLYGON ((792.30 855.01, 824.65 853.82, 825.20 868.88, 822.63 871.92, 799.10 873.52, 792.91 871.53, 792.30 855.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,108,"POLYGON ((870.16 841.63, 874.75 841.36, 900.00 840.40, 900.00 861.80, 869.73 862.46, 869.70 860.75, 871.24 859.40, 870.16 841.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,109,"POLYGON ((743.35 834.79, 755.47 834.48, 756.21 840.15, 760.06 840.05, 759.97 852.44, 755.45 852.64, 755.31 855.02, 743.89 856.00, 743.81 852.85, 739.67 853.35, 739.24 836.47, 743.68 836.74, 743.35 834.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,110,"POLYGON ((672.05 836.35, 703.04 835.07, 703.70 851.16, 672.72 852.44, 672.05 836.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,111,"POLYGON ((790.93 834.06, 823.41 832.81, 824.07 850.09, 822.29 852.11, 796.91 852.60, 791.50 848.92, 790.93 834.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,112,"POLYGON ((611.97 824.69, 649.02 824.50, 649.17 839.84, 618.60 841.11, 615.92 833.27, 611.97 824.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,113,"POLYGON ((858.86 785.62, 880.59 784.58, 881.20 814.95, 879.56 816.15, 879.00 820.47, 862.57 820.56, 861.97 816.60, 859.43 814.36, 858.86 785.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,114,"POLYGON ((688.08 787.83, 705.79 788.02, 705.58 808.70, 705.45 813.14, 689.51 812.72, 687.84 809.02, 688.08 787.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,115,"POLYGON ((814.28 788.25, 821.22 787.25, 833.35 787.27, 834.49 813.02, 817.06 812.13, 815.35 810.69, 814.28 788.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,116,"POLYGON ((792.33 786.50, 801.65 787.60, 811.09 787.02, 812.40 812.09, 803.48 813.65, 792.15 812.12, 792.33 786.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,117,"POLYGON ((900.00 818.19, 885.93 818.81, 882.69 815.43, 882.17 781.25, 900.00 780.96, 900.00 818.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,118,"POLYGON ((664.32 786.97, 682.90 787.12, 682.74 806.93, 682.58 812.10, 665.67 811.60, 664.16 807.60, 664.32 786.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,119,"POLYGON ((768.44 786.96, 779.24 787.32, 787.51 786.62, 787.65 811.57, 768.24 811.89, 768.44 786.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,120,"POLYGON ((744.60 786.52, 764.96 785.54, 763.87 810.36, 745.05 809.73, 744.60 786.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,121,"POLYGON ((604.12 780.90, 632.06 780.83, 632.14 810.11, 604.18 810.18, 604.12 780.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,122,"POLYGON ((562.72 772.39, 579.42 772.16, 579.75 798.75, 573.01 803.47, 566.93 799.43, 562.89 785.70, 562.72 772.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,123,"POLYGON ((795.80 747.26, 799.92 745.86, 800.21 740.11, 806.90 739.18, 807.01 733.98, 817.15 733.79, 831.76 733.73, 831.35 747.59, 831.13 753.30, 827.40 753.02, 827.28 757.97, 826.82 759.58, 818.89 760.54, 807.07 760.35, 805.90 758.65, 796.49 759.76, 795.80 747.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,124,"POLYGON ((625.54 743.19, 625.31 740.56, 644.90 740.39, 645.95 743.16, 644.95 748.64, 625.51 748.65, 625.54 743.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,125,"POLYGON ((600.64 729.62, 623.19 729.04, 623.93 757.77, 601.20 757.86, 600.64 729.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,126,"POLYGON ((863.01 730.38, 895.11 729.40, 898.61 737.26, 896.27 743.71, 866.99 744.46, 863.64 742.68, 863.01 730.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,127,"POLYGON ((715.34 716.04, 735.46 715.41, 736.51 750.17, 727.82 750.43, 716.27 746.80, 715.34 716.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,128,"POLYGON ((653.43 717.04, 672.66 716.37, 673.14 748.08, 654.22 747.92, 653.92 736.18, 652.10 736.23, 651.96 730.80, 653.94 730.41, 653.43 717.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,129,"POLYGON ((682.63 716.96, 702.08 717.62, 703.66 747.48, 684.07 747.16, 682.63 716.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,130,"POLYGON ((752.25 712.25, 771.41 712.25, 771.42 746.66, 749.85 746.68, 749.85 718.20, 752.26 718.18, 752.25 712.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,131,"POLYGON ((867.19 716.09, 893.99 716.18, 894.77 728.48, 867.05 728.72, 867.19 716.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,132,"POLYGON ((602.36 704.54, 623.02 704.16, 623.40 724.16, 602.72 724.55, 602.36 704.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,133,"POLYGON ((866.52 706.40, 893.83 705.06, 894.22 712.78, 866.90 714.12, 866.52 706.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,134,"POLYGON ((556.17 695.99, 580.17 695.38, 580.46 717.62, 578.86 721.25, 557.36 720.98, 556.17 695.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,135,"POLYGON ((793.55 675.06, 825.41 675.18, 826.37 683.88, 829.98 693.59, 830.29 698.78, 805.38 700.53, 803.81 689.82, 795.04 689.29, 793.55 675.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,136,"POLYGON ((865.73 672.64, 900.00 670.29, 900.00 681.02, 857.36 683.95, 856.97 678.21, 866.08 677.58, 865.73 672.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,137,"POLYGON ((630.93 656.70, 650.06 655.70, 649.62 687.17, 646.78 692.68, 635.09 692.49, 632.48 687.61, 630.93 656.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3737289,138,"POLYGON ((751.15 655.94, 756.16 656.28, 770.93 657.28, 769.13 681.81, 766.69 681.49, 766.46 687.07, 758.06 686.18, 748.75 686.04, 751.15 655.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,0,"POLYGON ((757.33 46.87, 759.81 121.90, 678.47 124.58, 675.99 49.55, 757.33 46.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,1,"POLYGON ((608.62 77.33, 608.30 66.13, 596.18 66.46, 583.45 59.26, 583.24 51.05, 591.85 50.83, 591.42 33.88, 583.79 34.08, 583.30 15.18, 643.20 13.63, 643.84 38.56, 641.19 38.63, 642.38 89.25, 647.82 89.11, 648.25 107.97, 644.49 108.05, 644.85 125.17, 584.63 126.47, 584.30 110.78, 581.66 108.43, 580.81 78.11, 608.62 77.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,2,"POLYGON ((873.35 15.45, 881.19 15.40, 881.29 28.61, 872.90 30.53, 873.06 51.95, 857.20 52.06, 857.09 36.90, 861.87 36.87, 861.79 26.66, 855.25 26.72, 855.17 15.00, 866.37 14.94, 866.34 11.72, 873.33 11.67, 873.35 15.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,3,"POLYGON ((886.95 16.36, 898.85 16.13, 898.77 11.89, 900.00 11.87, 900.00 33.37, 891.67 33.53, 891.59 29.12, 887.20 29.19, 886.95 16.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,4,"POLYGON ((371.19 20.46, 371.15 13.56, 384.77 13.48, 384.83 24.44, 388.00 24.43, 388.05 35.31, 385.90 35.32, 386.00 52.07, 366.11 52.18, 365.99 30.21, 364.40 30.20, 364.35 20.50, 371.19 20.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,5,"POLYGON ((416.89 13.64, 416.98 18.83, 417.96 19.45, 418.68 47.53, 417.27 47.57, 415.50 50.10, 403.61 50.51, 400.27 46.71, 399.81 19.60, 404.61 19.52, 404.52 13.84, 416.89 13.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,6,"POLYGON ((538.52 12.64, 539.13 48.03, 517.91 48.39, 517.41 19.48, 525.19 19.35, 525.08 12.87, 538.52 12.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,7,"POLYGON ((427.39 26.49, 427.25 23.25, 430.07 23.13, 430.22 26.73, 444.72 26.15, 445.10 35.38, 441.84 36.77, 421.04 37.66, 420.59 26.75, 427.39 26.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,8,"POLYGON ((449.79 17.06, 459.67 17.16, 459.61 23.20, 469.71 23.32, 469.59 33.69, 452.19 33.47, 452.26 27.41, 449.66 27.38, 449.79 17.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,9,"POLYGON ((526.57 507.12, 545.69 506.52, 546.88 544.49, 527.78 545.09, 527.33 530.99, 524.42 531.08, 523.99 517.26, 526.88 517.19, 526.57 507.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,10,"POLYGON ((116.68 497.23, 117.10 554.20, 101.19 554.32, 100.78 497.35, 116.68 497.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,11,"POLYGON ((432.15 502.41, 440.81 502.32, 441.34 547.94, 418.03 548.23, 417.85 534.09, 415.89 534.12, 415.67 514.64, 432.29 514.43, 432.15 502.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,12,"POLYGON ((384.04 499.71, 384.97 517.84, 385.69 520.44, 385.82 525.65, 384.72 527.64, 385.13 540.92, 365.88 541.50, 365.45 527.57, 370.83 527.41, 370.50 516.90, 374.28 510.28, 373.77 500.23, 384.04 499.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,13,"POLYGON ((270.78 500.47, 284.64 500.14, 284.86 511.69, 271.00 511.87, 270.78 500.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,14,"POLYGON ((522.31 484.23, 522.58 501.07, 511.16 501.25, 511.11 497.99, 496.10 498.24, 495.89 484.66, 522.31 484.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,15,"POLYGON ((456.86 469.65, 481.02 469.44, 484.87 474.53, 484.78 482.57, 491.87 482.66, 491.64 499.20, 463.88 498.84, 464.02 487.85, 456.92 487.74, 456.99 483.15, 456.86 469.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,16,"POLYGON ((94.97 481.34, 94.80 456.99, 128.38 456.75, 128.47 468.25, 134.96 468.20, 142.10 472.01, 172.80 471.78, 172.86 480.77, 94.97 481.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,17,"POLYGON ((259.93 453.71, 277.17 453.20, 277.26 456.70, 282.38 456.55, 282.67 466.38, 285.49 468.10, 286.05 477.12, 250.34 479.21, 249.83 469.99, 260.75 469.38, 263.50 464.34, 263.13 459.37, 260.06 458.39, 259.93 453.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,18,"POLYGON ((343.42 453.79, 371.16 452.55, 372.23 476.19, 344.50 477.43, 343.42 453.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,19,"POLYGON ((495.21 456.02, 533.96 455.19, 534.09 461.20, 529.11 461.30, 529.16 464.30, 525.66 464.37, 525.79 469.94, 495.53 470.60, 495.21 456.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,20,"POLYGON ((249.30 428.98, 260.76 428.96, 260.75 427.22, 270.45 427.20, 270.46 429.86, 285.81 429.78, 285.87 446.47, 280.77 446.49, 280.77 448.09, 270.89 448.12, 270.90 450.67, 261.37 450.69, 261.36 448.87, 240.06 448.95, 240.04 439.50, 244.73 439.47, 244.71 434.36, 249.31 434.35, 249.30 428.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,21,"POLYGON ((390.62 430.41, 390.67 434.58, 396.75 434.50, 396.86 442.57, 391.50 442.64, 391.55 445.73, 356.20 446.21, 356.17 444.32, 353.04 444.36, 352.86 430.91, 390.62 430.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,22,"POLYGON ((539.21 426.62, 539.51 434.40, 536.74 436.29, 537.04 444.56, 488.42 446.32, 488.20 439.93, 473.88 440.45, 473.61 432.78, 486.33 432.32, 486.20 428.50, 539.21 426.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,23,"POLYGON ((122.07 447.48, 99.29 448.04, 99.17 443.00, 94.46 443.12, 94.13 429.59, 98.17 429.49, 98.05 424.76, 134.41 423.88, 121.78 435.35, 122.07 447.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,24,"POLYGON ((247.83 398.81, 260.34 398.82, 273.08 408.91, 279.65 400.66, 288.60 400.23, 289.60 421.94, 282.48 422.25, 282.56 423.96, 267.84 424.65, 267.31 423.44, 262.25 423.57, 261.81 424.56, 247.80 424.54, 247.83 398.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,25,"POLYGON ((97.74 412.74, 91.26 412.82, 91.13 401.14, 102.43 401.03, 102.46 403.47, 121.55 403.27, 121.62 411.18, 117.49 411.24, 117.57 418.89, 97.81 419.11, 97.74 412.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,26,"POLYGON ((339.61 404.74, 345.41 404.69, 345.36 399.07, 353.27 398.98, 353.29 400.38, 358.52 400.33, 358.49 397.11, 388.67 397.05, 388.75 422.56, 361.33 422.79, 361.29 418.06, 358.69 418.11, 358.73 421.75, 339.78 421.94, 339.61 404.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,27,"POLYGON ((503.15 398.21, 503.08 394.84, 528.47 394.28, 528.54 397.70, 535.65 397.54, 535.91 408.94, 538.61 408.87, 538.74 414.58, 535.37 416.95, 503.46 418.30, 503.24 413.42, 498.02 413.64, 492.06 409.66, 491.81 398.48, 503.15 398.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,28,"POLYGON ((263.51 371.77, 268.77 376.03, 272.19 371.84, 288.61 371.88, 288.57 386.82, 295.71 386.84, 295.68 393.75, 289.71 393.74, 289.69 395.72, 281.55 395.71, 280.57 394.42, 251.06 395.31, 250.38 373.17, 259.49 372.92, 263.51 371.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,29,"POLYGON ((97.36 385.49, 92.66 385.57, 92.46 373.45, 131.77 372.78, 132.10 393.11, 97.50 393.70, 97.36 385.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,30,"POLYGON ((343.40 369.49, 361.59 369.82, 361.60 368.78, 367.70 368.84, 367.70 370.02, 387.80 370.26, 387.53 393.78, 384.67 393.76, 384.69 391.98, 376.62 391.88, 374.43 394.64, 370.98 394.58, 371.02 392.00, 366.88 391.93, 365.52 393.67, 347.92 393.65, 347.93 387.46, 343.10 384.88, 343.40 369.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,31,"POLYGON ((532.56 368.49, 532.84 388.82, 520.25 388.99, 520.27 390.54, 507.71 390.73, 506.53 389.05, 492.43 389.23, 492.16 369.50, 494.95 369.48, 494.89 365.99, 508.52 365.80, 508.56 368.82, 532.56 368.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,32,"POLYGON ((97.45 361.31, 91.37 361.38, 91.24 350.26, 96.93 350.21, 96.90 347.81, 131.34 347.42, 131.57 367.90, 97.54 368.28, 97.45 361.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,33,"POLYGON ((252.35 345.62, 288.17 345.12, 288.43 364.70, 282.61 364.78, 282.66 367.44, 275.32 367.54, 275.36 369.78, 264.59 369.92, 264.56 367.97, 252.38 368.15, 250.17 366.36, 250.35 347.93, 252.35 345.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,34,"POLYGON ((365.76 344.43, 365.71 352.58, 372.18 352.59, 372.16 357.63, 373.74 357.66, 373.71 360.08, 379.03 360.12, 378.98 366.07, 359.90 365.93, 358.61 363.28, 353.27 363.33, 350.96 366.01, 344.12 365.96, 344.22 344.34, 365.76 344.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,35,"POLYGON ((498.04 347.75, 502.62 347.59, 502.51 344.78, 506.90 344.62, 506.99 347.48, 512.71 347.29, 512.60 344.36, 527.16 343.81, 527.53 353.86, 529.09 355.33, 529.28 362.23, 501.57 363.03, 500.04 362.09, 484.40 362.33, 484.32 356.47, 489.79 356.40, 489.67 347.88, 498.04 347.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,36,"POLYGON ((35.43 446.27, 24.87 446.32, 24.94 457.15, 0.00 457.28, 0.00 208.25, 25.54 208.12, 25.58 217.95, 34.24 217.91, 35.43 446.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,37,"POLYGON ((90.88 323.34, 97.65 323.51, 97.70 321.08, 130.41 321.85, 129.88 343.99, 99.12 343.29, 99.25 338.12, 90.53 337.92, 90.88 323.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,38,"POLYGON ((249.63 320.54, 260.16 320.51, 260.15 317.34, 269.07 317.33, 269.09 318.71, 275.04 318.69, 275.05 322.73, 284.30 322.69, 284.32 325.73, 286.64 325.72, 286.71 340.81, 249.69 340.93, 249.63 320.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,39,"POLYGON ((344.08 316.97, 350.06 316.88, 350.20 325.80, 369.42 325.53, 369.60 339.31, 357.76 339.46, 357.73 337.73, 353.77 337.79, 352.61 338.99, 344.38 339.12, 344.08 316.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,40,"POLYGON ((528.38 312.31, 528.84 326.21, 507.95 326.92, 506.49 329.23, 492.11 329.68, 491.89 323.29, 500.91 323.00, 500.59 313.24, 528.38 312.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,41,"POLYGON ((89.41 317.37, 89.51 303.07, 96.49 303.11, 96.53 296.19, 128.48 296.37, 128.39 309.69, 134.66 309.73, 138.02 314.17, 133.42 317.64, 89.41 317.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,42,"POLYGON ((235.45 290.06, 291.93 289.09, 292.32 312.99, 264.06 313.46, 264.00 309.85, 257.37 309.97, 257.40 311.81, 246.76 312.00, 246.80 314.21, 238.37 314.36, 238.17 303.03, 235.67 303.07, 235.45 290.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,43,"POLYGON ((343.98 289.25, 347.91 289.23, 347.95 294.30, 374.77 294.16, 374.83 310.26, 361.70 310.30, 361.70 307.80, 355.30 307.83, 355.31 311.22, 344.08 311.29, 343.98 289.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,44,"POLYGON ((495.10 284.88, 532.13 282.95, 533.46 308.03, 499.87 309.77, 499.26 307.52, 491.52 307.90, 490.72 291.98, 495.46 291.75, 495.10 284.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,45,"POLYGON ((89.13 288.05, 89.19 272.33, 99.61 275.06, 99.67 268.80, 118.23 268.97, 126.12 271.30, 126.05 288.17, 89.13 288.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,46,"POLYGON ((254.38 264.21, 285.51 263.52, 285.69 271.82, 291.91 271.69, 292.17 283.27, 276.46 283.62, 276.50 285.22, 264.31 285.49, 264.27 283.98, 233.45 284.65, 233.08 267.70, 249.01 267.34, 249.11 272.69, 254.56 272.57, 254.38 264.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,47,"POLYGON ((377.33 261.47, 377.37 263.73, 380.83 263.67, 381.17 281.88, 377.65 284.41, 362.95 285.81, 362.55 281.82, 358.85 282.16, 358.13 283.69, 353.97 283.42, 353.26 284.66, 347.13 284.73, 347.10 282.93, 343.10 282.99, 342.86 262.75, 347.34 262.70, 347.30 258.73, 354.67 258.65, 363.15 265.83, 371.90 265.80, 375.27 261.50, 377.33 261.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,48,"POLYGON ((501.48 260.32, 504.72 270.13, 504.94 279.54, 497.60 279.71, 497.66 281.92, 469.46 282.58, 468.96 261.06, 501.48 260.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,49,"POLYGON ((459.65 234.68, 459.72 249.22, 444.88 249.29, 444.80 234.75, 459.65 234.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,50,"POLYGON ((82.10 245.72, 82.60 193.36, 100.25 193.53, 100.18 200.26, 108.05 200.33, 107.72 232.81, 82.10 245.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,51,"POLYGON ((225.69 239.66, 226.24 195.40, 243.06 195.62, 242.70 222.86, 239.18 222.82, 238.97 239.83, 225.69 239.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,52,"POLYGON ((383.48 188.61, 384.35 241.73, 362.06 242.10, 361.16 188.98, 383.48 188.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,53,"POLYGON ((252.59 191.84, 274.84 192.01, 274.77 202.29, 270.49 207.95, 276.32 207.86, 276.41 213.54, 274.24 213.58, 274.42 225.36, 263.30 225.51, 263.41 232.94, 255.00 233.07, 254.89 225.30, 252.78 225.34, 252.51 201.75, 252.59 191.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,54,"POLYGON ((197.82 196.02, 204.96 196.01, 213.83 205.13, 213.82 211.48, 220.76 211.48, 220.76 219.36, 210.39 219.36, 210.40 226.80, 197.81 226.81, 197.82 196.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,55,"POLYGON ((454.61 194.99, 456.55 193.34, 462.15 193.31, 463.70 195.86, 463.89 216.21, 462.13 216.22, 462.21 226.91, 445.16 227.08, 444.88 195.08, 454.61 194.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,56,"POLYGON ((142.83 205.39, 142.86 196.00, 153.04 196.03, 165.60 204.81, 165.12 222.49, 142.83 205.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,57,"POLYGON ((521.20 191.89, 527.21 193.33, 530.17 197.23, 536.26 197.05, 536.35 200.51, 538.72 200.45, 539.00 211.34, 536.48 211.41, 536.87 226.00, 523.72 226.34, 523.49 218.42, 520.74 217.27, 521.20 191.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,58,"POLYGON ((413.11 195.40, 432.41 194.80, 433.12 217.27, 423.13 217.57, 423.25 221.47, 413.93 221.78, 413.11 195.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,59,"POLYGON ((355.87 192.40, 356.02 217.15, 353.71 217.16, 353.75 222.31, 341.01 222.39, 340.98 216.85, 332.68 216.90, 332.52 192.56, 355.87 192.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,60,"POLYGON ((512.19 195.36, 512.20 198.82, 514.30 198.81, 514.42 213.55, 514.34 218.59, 507.22 218.48, 507.17 222.43, 494.17 222.23, 494.19 220.88, 493.91 191.76, 504.99 191.66, 505.03 195.41, 512.19 195.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,61,"POLYGON ((410.26 191.26, 410.56 220.20, 386.83 220.45, 386.52 191.80, 388.82 191.76, 388.79 188.45, 399.10 188.35, 399.14 191.39, 410.26 191.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,62,"POLYGON ((123.68 201.24, 123.18 194.06, 138.41 193.00, 139.25 204.88, 129.57 205.55, 123.68 201.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,63,"POLYGON ((77.04 122.98, 76.61 99.75, 81.08 99.68, 80.98 94.40, 90.97 94.24, 90.94 92.24, 115.86 91.78, 115.95 97.55, 130.67 97.28, 131.10 121.42, 116.75 121.68, 116.98 134.30, 92.32 134.76, 92.10 122.71, 77.04 122.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,64,"POLYGON ((481.05 96.17, 482.94 118.61, 481.88 121.61, 470.65 122.37, 470.83 125.02, 467.13 126.12, 462.61 126.32, 462.53 124.64, 453.49 125.05, 452.33 97.02, 467.87 96.49, 471.19 99.31, 474.93 98.99, 474.74 96.73, 481.05 96.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,65,"POLYGON ((354.89 96.58, 355.88 109.67, 357.02 122.54, 346.94 123.42, 342.94 122.12, 334.86 122.42, 334.50 112.59, 333.73 97.87, 346.05 97.25, 354.89 96.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,66,"POLYGON ((320.92 103.50, 314.09 95.33, 323.58 95.31, 323.63 123.30, 314.08 123.32, 314.06 118.24, 303.98 118.28, 303.95 98.25, 313.47 109.66, 320.92 103.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,67,"POLYGON ((399.64 120.21, 398.42 118.73, 398.36 112.27, 394.34 112.31, 394.14 89.93, 417.68 89.73, 417.92 118.90, 409.32 118.98, 409.37 123.84, 399.68 123.93, 399.64 120.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,68,"POLYGON ((486.66 93.61, 501.42 93.28, 501.27 86.62, 519.21 86.23, 519.38 93.51, 534.59 93.18, 535.27 124.89, 517.17 125.26, 516.72 104.16, 504.42 104.43, 504.89 125.93, 487.36 126.31, 486.66 93.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,69,"POLYGON ((372.83 92.61, 372.82 89.84, 388.11 89.76, 388.30 118.90, 384.63 118.92, 384.65 122.08, 365.59 122.19, 365.42 92.67, 372.83 92.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,70,"POLYGON ((424.82 65.73, 442.00 65.65, 442.10 83.20, 443.02 85.87, 446.84 90.48, 447.20 117.42, 425.07 117.60, 424.82 65.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,71,"POLYGON ((448.15 51.02, 459.00 50.90, 459.19 67.18, 470.90 67.04, 471.05 79.89, 448.49 80.16, 448.15 51.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,72,"POLYGON ((299.91 57.58, 324.00 57.98, 323.81 69.44, 320.85 71.98, 299.67 71.64, 299.91 57.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,73,"POLYGON ((505.26 42.57, 505.45 49.84, 509.80 49.73, 510.31 68.63, 509.15 71.88, 495.97 72.24, 495.39 50.72, 482.08 51.08, 481.88 43.19, 505.26 42.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,74,"POLYGON ((243.32 25.68, 279.45 24.80, 280.35 60.63, 298.51 60.19, 298.68 71.15, 298.34 75.29, 244.59 76.73, 243.32 25.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,75,"POLYGON ((339.79 59.07, 339.81 38.73, 337.03 38.74, 337.04 26.33, 357.72 26.36, 357.67 56.41, 348.10 56.39, 348.09 59.06, 339.79 59.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,76,"POLYGON ((619.70 835.87, 628.07 837.52, 628.03 840.99, 624.91 845.51, 625.02 849.48, 632.77 856.34, 632.18 862.28, 609.05 861.63, 609.22 848.64, 602.96 847.80, 602.79 841.12, 605.39 840.57, 605.27 835.86, 619.70 835.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,77,"POLYGON ((851.09 832.13, 851.95 852.16, 822.67 853.42, 822.55 850.75, 815.16 851.08, 815.48 858.62, 802.15 859.20, 801.06 834.30, 851.09 832.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,78,"POLYGON ((725.63 836.69, 727.87 836.61, 731.13 838.64, 735.30 840.00, 749.14 838.98, 751.77 837.58, 752.28 849.60, 729.74 850.57, 729.62 847.96, 726.10 848.11, 725.63 836.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,79,"POLYGON ((796.61 813.10, 796.64 829.33, 730.19 829.39, 730.18 813.16, 796.61 813.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,80,"POLYGON ((850.06 805.90, 850.67 828.24, 834.53 828.67, 834.58 830.27, 826.41 830.50, 825.74 806.57, 850.06 805.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,81,"POLYGON ((632.10 808.01, 632.81 822.93, 625.67 826.44, 590.28 827.32, 590.12 821.02, 586.75 821.11, 600.91 809.49, 632.10 808.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,82,"POLYGON ((698.05 806.54, 698.22 816.86, 668.32 817.34, 668.15 807.02, 698.05 806.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,83,"POLYGON ((666.96 786.54, 698.87 785.90, 699.18 801.68, 667.27 802.31, 666.96 786.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,84,"POLYGON ((587.54 799.78, 586.83 776.53, 612.06 775.80, 612.15 779.02, 620.26 778.79, 620.16 774.86, 623.77 774.77, 624.20 791.43, 622.13 791.48, 622.69 796.86, 613.89 797.15, 613.71 798.78, 587.54 799.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,85,"POLYGON ((850.04 778.04, 850.43 793.50, 838.59 792.32, 831.45 790.53, 823.40 791.33, 822.72 779.72, 832.80 779.59, 837.10 778.13, 850.04 778.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,86,"POLYGON ((900.00 792.58, 870.59 792.32, 870.69 783.66, 876.48 783.74, 876.58 773.94, 900.00 774.24, 900.00 792.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,87,"POLYGON ((795.36 771.58, 795.63 788.67, 729.60 789.69, 729.35 772.60, 795.36 771.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,88,"POLYGON ((650.54 774.44, 659.23 774.26, 659.18 771.69, 672.93 771.40, 672.97 773.44, 702.13 772.81, 702.30 780.02, 700.11 780.07, 700.18 783.38, 676.48 783.88, 676.50 785.36, 661.98 785.56, 662.04 783.91, 650.75 784.16, 650.54 774.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,89,"POLYGON ((587.80 743.41, 613.52 742.91, 613.58 745.81, 622.08 745.64, 622.21 751.25, 624.67 751.19, 624.74 757.41, 622.60 759.35, 619.65 760.73, 618.95 759.22, 616.74 759.30, 616.79 761.12, 611.73 761.27, 611.94 768.76, 600.44 769.13, 600.11 768.18, 588.29 768.41, 587.80 743.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,90,"POLYGON ((694.84 746.27, 695.85 763.36, 676.52 764.52, 676.37 762.15, 668.09 762.65, 667.20 747.91, 694.84 746.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,91,"POLYGON ((900.00 751.01, 870.35 751.55, 870.11 738.68, 900.00 738.11, 900.00 751.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,92,"POLYGON ((662.69 722.61, 696.23 721.57, 696.71 736.96, 663.16 738.00, 662.69 722.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,93,"POLYGON ((900.00 733.42, 870.44 733.50, 870.41 721.18, 900.00 721.12, 900.00 733.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,94,"POLYGON ((728.95 717.36, 767.56 716.17, 767.69 720.32, 773.99 720.14, 774.43 734.58, 729.52 735.93, 728.95 717.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,95,"POLYGON ((700.41 709.74, 701.62 717.10, 666.22 717.65, 665.88 710.96, 700.41 709.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,96,"POLYGON ((899.74 705.80, 900.00 713.14, 900.00 718.21, 869.89 719.11, 869.41 706.18, 899.74 705.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,97,"POLYGON ((759.82 689.53, 733.86 690.26, 733.44 675.02, 749.57 674.57, 749.34 666.16, 759.16 665.89, 759.82 689.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,98,"POLYGON ((811.13 634.66, 822.52 634.41, 833.86 633.99, 835.77 689.21, 812.13 689.68, 811.13 634.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,99,"POLYGON ((889.46 628.13, 858.95 628.93, 857.70 580.77, 900.00 579.66, 900.00 705.27, 899.72 705.27, 890.58 705.39, 889.46 628.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,100,"POLYGON ((601.17 415.38, 601.12 413.47, 609.61 413.25, 609.65 415.03, 638.33 414.14, 638.08 412.24, 646.22 412.25, 646.40 414.42, 659.36 414.33, 660.32 433.27, 655.73 433.21, 646.79 433.19, 646.98 436.94, 659.53 436.95, 660.57 455.30, 646.46 455.33, 646.52 457.62, 638.89 457.97, 638.83 455.68, 610.40 456.65, 610.45 458.45, 602.26 458.84, 602.13 457.35, 589.11 457.93, 588.79 438.67, 602.04 437.93, 601.95 434.18, 588.68 434.43, 588.85 415.65, 601.17 415.38), (610.81 433.60, 610.75 437.75, 639.11 437.36, 638.93 433.19, 610.81 433.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,101,"POLYGON ((616.49 367.73, 616.68 373.78, 613.42 373.86, 613.79 385.46, 576.87 386.63, 576.60 378.27, 580.56 378.14, 580.27 368.87, 616.49 367.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,102,"POLYGON ((578.40 347.12, 613.85 345.11, 614.09 349.36, 617.18 349.20, 617.72 358.55, 613.57 358.77, 613.83 363.02, 579.43 364.99, 578.40 347.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,103,"POLYGON ((740.90 338.97, 760.24 338.24, 761.26 367.98, 747.44 368.40, 747.60 373.79, 739.39 374.04, 739.34 372.09, 734.76 372.23, 734.65 368.59, 727.44 368.82, 726.08 330.10, 740.55 329.55, 740.90 338.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,104,"POLYGON ((822.06 329.02, 841.27 328.35, 841.99 349.20, 809.61 350.34, 808.69 323.92, 821.86 323.45, 822.06 329.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,105,"POLYGON ((591.99 331.24, 598.52 331.16, 598.40 322.82, 622.32 322.47, 622.53 337.07, 592.08 337.52, 591.99 331.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,106,"POLYGON ((805.59 293.21, 851.59 292.75, 851.76 308.02, 848.48 308.05, 848.45 305.44, 805.73 305.86, 805.59 293.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,107,"POLYGON ((618.77 273.39, 618.82 291.42, 586.69 291.53, 586.64 273.50, 618.77 273.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,108,"POLYGON ((619.29 271.36, 638.49 271.09, 638.54 274.97, 654.07 274.75, 654.26 288.76, 622.77 289.18, 622.78 291.16, 619.56 291.20, 619.29 271.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,109,"POLYGON ((755.60 262.64, 755.71 269.71, 762.05 269.62, 762.12 274.86, 720.46 275.50, 720.28 263.18, 755.60 262.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,110,"POLYGON ((832.70 258.65, 833.30 278.50, 806.31 279.32, 806.26 277.41, 802.67 277.53, 802.18 261.31, 804.77 261.22, 804.71 259.52, 832.70 258.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,111,"POLYGON ((615.49 250.35, 616.33 253.34, 616.37 259.25, 574.72 259.80, 574.54 249.02, 598.39 248.58, 598.42 250.65, 615.49 250.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,112,"POLYGON ((637.66 244.23, 637.78 251.15, 619.75 251.46, 619.63 244.51, 637.66 244.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,113,"POLYGON ((724.68 235.06, 753.03 234.55, 753.09 237.55, 757.89 237.47, 758.22 256.98, 754.28 258.79, 725.09 259.29, 724.68 235.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,114,"POLYGON ((837.17 236.20, 837.85 248.19, 834.36 251.68, 832.92 256.97, 806.37 256.94, 804.96 252.58, 802.73 248.93, 802.00 238.41, 837.17 236.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,115,"POLYGON ((634.12 216.93, 634.32 228.95, 614.99 229.27, 614.79 217.22, 634.12 216.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,116,"POLYGON ((580.62 244.53, 580.89 183.30, 602.21 183.47, 601.72 244.57, 580.62 244.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,117,"POLYGON ((644.54 183.94, 644.47 178.46, 664.21 178.22, 664.45 198.64, 660.11 198.68, 660.18 204.14, 641.13 204.38, 640.88 183.99, 644.54 183.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,118,"POLYGON ((827.68 178.08, 827.68 194.15, 822.08 194.16, 822.08 198.47, 815.11 198.47, 814.14 197.65, 804.68 197.63, 804.74 178.60, 813.84 178.61, 815.10 180.18, 819.85 180.17, 819.85 178.08, 827.68 178.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,119,"POLYGON ((610.40 180.15, 617.71 180.41, 617.83 176.96, 623.65 177.17, 623.56 180.10, 635.99 180.52, 635.35 196.58, 609.77 195.44, 610.40 180.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,120,"POLYGON ((830.04 115.98, 830.11 111.60, 851.48 111.99, 852.84 116.77, 853.07 122.72, 836.94 123.37, 830.12 123.52, 824.39 123.56, 824.36 119.63, 824.43 115.88, 830.04 115.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,121,"POLYGON ((798.81 114.47, 798.55 106.44, 820.28 105.71, 820.82 121.63, 812.98 121.90, 813.04 124.07, 805.66 124.30, 805.62 122.46, 794.69 122.81, 794.42 114.60, 798.81 114.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,122,"POLYGON ((900.00 122.85, 889.76 122.69, 890.01 99.20, 900.00 99.25, 900.00 122.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,123,"POLYGON ((867.18 99.42, 867.03 93.70, 878.72 93.45, 879.19 114.01, 879.25 122.87, 872.21 122.91, 872.23 127.57, 865.13 127.62, 865.11 122.25, 858.01 122.28, 857.94 107.14, 865.10 99.45, 867.18 99.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,124,"POLYGON ((539.31 886.87, 539.64 892.39, 536.42 892.58, 536.83 900.00, 509.41 900.00, 509.30 897.96, 528.81 896.86, 530.84 892.24, 530.94 887.35, 539.31 886.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,125,"POLYGON ((317.22 874.30, 329.72 899.84, 329.39 900.00, 265.51 900.00, 265.26 899.49, 317.22 874.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,126,"POLYGON ((328.44 872.17, 358.53 858.97, 369.72 884.21, 339.63 897.43, 328.44 872.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,127,"POLYGON ((496.28 825.95, 496.30 844.75, 535.45 844.68, 535.49 864.19, 487.60 864.26, 487.58 849.79, 476.16 849.82, 476.13 825.98, 496.28 825.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,128,"POLYGON ((344.81 661.71, 357.47 661.43, 357.63 668.98, 360.79 668.92, 360.88 673.09, 357.60 673.15, 357.71 678.83, 345.16 679.08, 344.81 661.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,129,"POLYGON ((286.98 618.86, 308.04 618.61, 308.10 623.76, 309.25 624.59, 309.54 635.84, 307.78 635.89, 308.17 650.35, 309.94 651.55, 309.66 660.34, 308.33 661.73, 308.41 667.97, 287.60 668.21, 286.98 618.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,130,"POLYGON ((318.98 618.71, 340.91 618.41, 341.55 666.61, 319.62 666.91, 318.98 618.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,131,"POLYGON ((380.35 639.78, 376.12 631.65, 387.19 625.93, 394.08 639.19, 396.64 637.86, 398.88 642.15, 395.49 643.90, 399.08 650.82, 388.83 656.10, 383.53 645.89, 378.59 648.44, 375.41 642.31, 380.35 639.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,132,"POLYGON ((354.82 636.95, 349.55 635.11, 348.76 609.98, 368.82 609.35, 370.29 655.91, 355.43 656.38, 354.82 636.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,133,"POLYGON ((13.82 609.23, 13.75 650.05, 4.76 650.04, 4.75 652.86, 0.00 652.85, 0.00 609.21, 13.82 609.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,134,"POLYGON ((408.08 606.15, 407.99 603.96, 414.58 603.70, 414.70 606.67, 422.16 606.39, 422.44 613.73, 424.35 613.66, 425.05 632.27, 423.23 634.36, 404.56 635.06, 403.47 606.34, 408.08 606.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,135,"POLYGON ((467.19 538.05, 466.94 550.71, 470.45 550.77, 470.35 556.28, 464.22 557.95, 450.14 557.69, 450.52 537.74, 467.19 538.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,136,"POLYGON ((251.64 552.57, 233.30 553.19, 232.69 535.22, 236.36 535.09, 235.85 520.25, 250.51 519.76, 251.64 552.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,137,"POLYGON ((255.24 517.78, 269.31 517.84, 269.14 548.90, 266.35 551.44, 259.51 551.23, 258.37 550.15, 255.08 548.37, 255.24 517.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,138,"POLYGON ((491.50 528.59, 496.08 528.59, 496.08 533.74, 491.63 533.76, 491.65 548.41, 475.02 548.44, 474.98 520.00, 491.50 519.98, 491.50 528.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,139,"POLYGON ((125.57 513.41, 148.99 513.54, 148.75 553.57, 125.33 553.44, 125.57 513.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,140,"POLYGON ((203.38 522.79, 207.62 522.61, 207.23 512.94, 223.51 512.26, 223.89 521.66, 231.04 521.37, 231.56 533.52, 227.98 533.68, 228.68 550.11, 204.57 551.10, 203.38 522.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,141,"POLYGON ((159.50 521.02, 167.64 520.88, 167.50 512.47, 183.59 512.22, 183.78 522.56, 186.30 522.51, 186.75 549.69, 163.42 550.09, 163.17 535.34, 159.74 535.38, 159.50 521.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,142,"POLYGON ((497.87 514.45, 521.03 514.54, 520.90 547.15, 501.39 547.08, 501.40 542.35, 497.75 542.33, 497.87 514.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,143,"POLYGON ((388.33 512.94, 400.33 513.03, 400.32 516.92, 413.87 516.97, 413.85 523.41, 410.85 529.21, 414.52 529.25, 414.33 540.93, 411.76 540.89, 411.67 545.66, 405.02 545.56, 404.98 547.63, 395.79 547.49, 395.83 544.56, 393.77 544.54, 393.86 538.59, 391.15 538.57, 391.24 532.53, 388.07 532.50, 388.33 512.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,144,"POLYGON ((338.39 522.16, 337.91 510.41, 356.50 509.51, 357.96 543.37, 354.57 543.50, 354.77 549.00, 336.13 549.68, 335.15 522.29, 338.39 522.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,145,"POLYGON ((806.95 881.19, 804.86 877.05, 804.46 864.80, 822.19 864.22, 825.97 876.00, 828.02 900.00, 801.76 900.00, 801.25 881.36, 806.95 881.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,146,"POLYGON ((705.65 886.57, 706.17 900.00, 682.00 900.00, 681.52 887.52, 705.65 886.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,147,"POLYGON ((730.28 884.52, 750.76 884.36, 752.52 890.86, 752.57 896.32, 730.38 896.51, 730.28 884.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,148,"POLYGON ((788.91 875.21, 790.49 895.82, 777.65 895.46, 775.83 893.22, 771.93 892.87, 772.25 897.35, 759.06 896.97, 758.17 876.00, 776.09 875.54, 788.91 875.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,149,"POLYGON ((900.00 888.72, 897.54 888.82, 895.10 892.12, 886.84 892.49, 884.90 886.34, 878.80 884.88, 879.57 880.02, 900.00 879.28, 900.00 888.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,150,"POLYGON ((851.36 877.46, 851.94 887.98, 837.65 888.79, 837.78 890.92, 829.43 891.38, 828.73 878.72, 851.36 877.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,151,"POLYGON ((624.03 872.54, 624.75 888.55, 611.60 889.15, 611.32 883.26, 608.47 883.37, 608.21 877.28, 611.28 877.15, 611.15 874.18, 615.71 872.91, 624.03 872.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,152,"POLYGON ((703.37 870.38, 704.02 881.44, 698.41 881.76, 698.19 878.31, 681.81 879.28, 681.51 874.09, 697.45 873.15, 697.29 870.74, 703.37 870.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3738639,153,"POLYGON ((746.25 864.56, 746.22 870.62, 751.28 870.65, 753.61 871.74, 753.57 880.20, 730.07 880.07, 730.14 868.14, 735.57 868.16, 735.59 864.50, 746.25 864.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,0,"POLYGON ((53.80 281.37, 53.19 241.43, 70.40 241.17, 70.53 249.05, 76.24 248.97, 76.44 262.04, 79.37 261.99, 79.52 272.37, 76.47 274.22, 72.89 273.14, 68.99 276.48, 69.07 281.14, 53.80 281.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,1,"POLYGON ((453.93 250.58, 476.75 250.02, 476.79 251.59, 496.36 251.11, 496.40 252.78, 501.07 252.66, 501.46 268.79, 497.05 268.90, 497.09 270.21, 463.20 271.03, 463.16 269.45, 457.27 269.60, 457.07 261.20, 454.18 261.27, 453.93 250.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,2,"POLYGON ((139.23 238.86, 138.56 255.28, 142.27 255.43, 142.00 262.10, 137.93 261.93, 137.26 278.31, 113.65 277.34, 114.11 266.14, 111.04 266.02, 111.96 244.39, 114.42 244.51, 114.70 237.84, 139.23 238.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,3,"POLYGON ((14.16 242.85, 14.46 254.47, 12.30 256.44, 13.55 261.76, 8.78 262.86, 3.54 260.92, 2.00 258.17, 5.00 254.27, 7.90 251.85, 7.24 248.91, 7.59 245.37, 10.77 242.94, 14.16 242.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,4,"POLYGON ((498.30 224.82, 498.35 234.88, 494.42 234.91, 494.47 245.54, 467.41 245.70, 467.35 236.78, 465.33 236.79, 465.25 225.03, 498.30 224.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,5,"POLYGON ((0.00 141.45, 9.96 141.51, 9.82 164.22, 0.00 164.16, 0.00 141.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,6,"POLYGON ((0.00 116.41, 10.96 116.49, 10.91 123.51, 17.16 123.55, 17.05 139.42, 0.00 139.30, 0.00 116.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,7,"POLYGON ((261.45 107.32, 262.07 143.67, 198.10 144.74, 197.44 105.91, 229.24 105.37, 229.29 107.88, 261.45 107.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,8,"POLYGON ((131.16 133.13, 93.20 133.37, 93.18 130.64, 90.03 130.68, 89.95 115.67, 131.04 115.45, 131.16 133.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,9,"POLYGON ((423.28 53.91, 515.83 54.18, 516.80 165.99, 329.78 168.84, 328.51 98.13, 344.49 83.56, 344.49 77.07, 423.59 75.92, 423.28 53.91), (426.65 98.73, 427.00 137.01, 491.19 137.23, 490.78 112.69, 437.94 98.73, 426.65 98.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,10,"POLYGON ((0.00 94.68, 20.52 94.80, 20.44 107.66, 13.90 107.63, 13.86 114.93, 0.00 114.85, 0.00 94.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,11,"POLYGON ((125.03 106.01, 95.48 106.79, 95.02 89.17, 129.53 88.27, 129.78 98.16, 124.84 98.29, 125.03 106.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,12,"POLYGON ((0.00 70.67, 23.02 70.65, 23.02 78.51, 19.59 78.51, 19.60 93.34, 0.00 93.35, 0.00 70.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,13,"POLYGON ((94.64 86.81, 93.60 58.33, 116.49 57.48, 116.60 60.36, 131.97 59.79, 132.48 73.65, 122.05 74.05, 122.16 77.11, 124.92 77.02, 125.24 85.67, 94.64 86.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,14,"POLYGON ((0.00 49.06, 27.93 49.29, 27.80 62.54, 25.97 62.52, 25.90 69.56, 0.00 69.35, 0.00 49.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,15,"POLYGON ((95.96 50.79, 95.35 31.65, 140.67 30.18, 141.29 49.34, 95.96 50.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,16,"POLYGON ((0.00 24.87, 32.37 24.44, 32.56 38.31, 28.22 38.36, 28.31 45.55, 0.00 45.92, 0.00 24.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,17,"POLYGON ((116.67 24.64, 116.58 20.38, 109.43 20.52, 109.01 0.00, 144.77 0.00, 145.26 24.07, 116.67 24.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,18,"POLYGON ((42.74 0.00, 42.46 21.12, 22.58 21.39, 22.48 17.44, 31.28 11.51, 32.30 2.58, 33.74 0.00, 42.74 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,19,"POLYGON ((337.45 0.00, 310.43 55.70, 268.18 36.74, 284.93 0.00, 337.45 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,20,"POLYGON ((327.06 666.97, 327.37 686.83, 309.60 687.13, 309.29 667.25, 327.06 666.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,21,"POLYGON ((521.51 654.39, 521.37 665.54, 526.65 665.40, 527.23 679.48, 495.78 680.29, 495.97 655.04, 521.51 654.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,22,"POLYGON ((471.37 645.79, 472.31 677.51, 453.75 678.05, 452.82 646.33, 471.37 645.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,23,"POLYGON ((39.92 640.45, 65.14 637.81, 68.03 666.13, 62.91 666.64, 63.36 670.98, 49.15 672.34, 48.72 667.66, 42.71 668.24, 39.92 640.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,24,"POLYGON ((68.04 643.97, 88.97 640.38, 91.83 657.26, 70.88 660.70, 68.04 643.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,25,"POLYGON ((0.00 627.92, 9.87 628.94, 9.16 635.78, 9.98 638.35, 7.26 664.59, 0.00 663.84, 0.00 627.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,26,"POLYGON ((20.08 621.18, 39.04 621.78, 37.64 667.08, 22.57 666.64, 20.40 662.95, 15.50 662.83, 15.74 653.97, 14.58 652.89, 15.09 639.46, 17.87 634.22, 19.67 634.29, 20.08 621.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,27,"POLYGON ((99.62 625.41, 121.01 625.00, 121.51 658.59, 113.67 658.68, 113.65 656.31, 100.19 656.45, 99.62 625.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,28,"POLYGON ((519.80 628.59, 519.43 649.74, 488.57 649.22, 488.94 628.07, 519.80 628.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,29,"POLYGON ((125.83 614.08, 132.34 613.72, 132.61 618.42, 139.85 618.01, 139.71 615.51, 149.71 614.94, 151.34 648.35, 146.97 648.51, 147.07 651.59, 143.62 651.70, 143.68 653.25, 130.51 653.70, 130.37 649.62, 127.51 649.71, 126.98 635.17, 125.83 614.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,30,"POLYGON ((161.89 616.56, 182.17 616.35, 182.36 637.08, 178.73 637.11, 178.78 641.92, 170.72 642.00, 167.12 638.20, 162.09 638.26, 161.89 616.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,31,"POLYGON ((243.83 604.70, 245.41 623.91, 220.25 624.37, 219.85 616.21, 223.09 614.31, 225.89 609.33, 227.08 606.04, 243.83 604.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,32,"POLYGON ((281.73 586.04, 282.95 612.69, 250.10 614.17, 248.88 587.55, 281.73 586.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,33,"POLYGON ((344.60 589.12, 357.51 590.03, 356.62 602.42, 353.03 602.18, 343.42 605.40, 344.60 589.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,34,"POLYGON ((442.36 574.03, 441.49 592.93, 431.20 592.46, 431.06 595.68, 422.06 595.26, 423.09 573.15, 442.36 574.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,35,"POLYGON ((391.98 561.36, 392.57 602.48, 375.05 602.72, 374.47 561.61, 391.98 561.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,36,"POLYGON ((334.00 601.15, 327.97 600.91, 326.62 604.76, 313.41 604.28, 313.52 601.26, 307.04 601.00, 307.79 581.60, 314.88 581.86, 315.82 557.00, 333.08 557.65, 332.11 583.00, 334.69 583.09, 334.00 601.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,37,"POLYGON ((447.70 559.54, 468.03 558.57, 469.64 591.78, 452.81 592.59, 452.03 576.94, 448.55 577.12, 447.70 559.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,38,"POLYGON ((498.49 556.48, 500.49 589.02, 476.93 590.47, 475.35 564.98, 478.66 564.78, 478.22 557.73, 498.49 556.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,39,"POLYGON ((292.49 513.15, 293.29 551.14, 275.36 551.51, 274.55 513.52, 292.49 513.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,40,"POLYGON ((256.28 510.73, 256.90 552.62, 238.74 552.91, 238.12 510.99, 256.28 510.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,41,"POLYGON ((0.00 504.55, 4.60 504.43, 4.68 508.12, 11.38 507.97, 11.93 540.39, 6.06 540.45, 0.00 540.40, 0.00 504.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,42,"POLYGON ((21.90 519.93, 29.02 518.24, 32.66 513.00, 49.13 512.38, 49.55 523.64, 37.43 524.09, 37.61 528.72, 30.14 529.00, 29.33 530.42, 21.89 530.43, 21.90 519.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,43,"POLYGON ((304.33 507.95, 319.47 507.56, 319.61 513.04, 324.02 512.93, 324.55 533.78, 305.01 534.28, 304.33 507.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,44,"POLYGON ((340.19 507.39, 340.33 513.53, 344.74 513.42, 345.21 533.14, 327.64 533.57, 327.04 507.70, 340.19 507.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,45,"POLYGON ((89.67 503.31, 99.51 503.19, 99.57 505.76, 106.71 505.71, 107.74 534.41, 86.61 534.51, 82.43 521.96, 82.24 503.50, 89.67 503.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,46,"POLYGON ((179.08 498.96, 200.88 498.38, 201.47 520.01, 203.31 519.96, 203.54 528.35, 199.64 536.61, 191.28 536.07, 191.43 533.83, 189.25 533.68, 189.59 528.66, 182.87 528.23, 178.95 523.67, 178.47 506.50, 176.58 506.55, 176.50 503.13, 179.18 503.06, 179.08 498.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,47,"POLYGON ((76.07 503.76, 77.09 527.09, 74.01 528.52, 53.52 528.80, 51.80 524.89, 50.87 504.94, 76.07 503.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,48,"POLYGON ((231.70 498.50, 231.80 533.02, 211.30 533.08, 211.22 505.29, 214.78 505.28, 214.76 498.56, 231.70 498.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,49,"POLYGON ((153.86 501.49, 163.15 501.14, 163.43 508.24, 164.95 508.18, 165.10 511.88, 173.28 511.56, 173.68 522.14, 166.13 522.42, 166.33 527.65, 150.85 528.25, 150.57 520.73, 145.42 520.93, 145.20 515.50, 154.39 515.13, 153.86 501.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,50,"POLYGON ((138.76 494.93, 139.50 528.07, 137.71 529.74, 137.80 534.02, 128.68 534.23, 128.56 528.88, 122.56 529.02, 122.33 519.21, 118.79 519.28, 118.27 495.38, 138.76 494.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,51,"POLYGON ((451.13 491.32, 452.01 517.85, 443.25 518.14, 442.95 509.29, 407.70 510.46, 407.99 519.15, 400.68 519.40, 399.80 493.03, 451.13 491.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,52,"POLYGON ((521.97 489.49, 522.49 508.12, 514.08 508.45, 506.66 512.17, 503.78 514.59, 500.84 515.65, 489.22 516.72, 487.68 518.14, 487.13 519.53, 473.80 518.76, 474.55 506.04, 480.10 506.36, 484.78 504.67, 492.46 496.24, 492.31 490.31, 521.97 489.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,53,"POLYGON ((396.13 514.21, 373.77 514.83, 373.25 495.93, 380.31 495.73, 380.26 493.69, 387.85 493.49, 387.92 496.16, 395.63 495.96, 396.13 514.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,54,"POLYGON ((265.82 417.50, 292.83 416.94, 292.99 424.62, 297.12 424.53, 297.28 432.08, 294.44 434.30, 289.69 441.08, 289.70 443.72, 274.59 443.78, 274.61 449.53, 270.08 449.53, 269.43 447.19, 268.34 446.00, 266.40 445.56, 265.82 417.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,55,"POLYGON ((39.23 411.58, 39.70 435.70, 38.72 437.92, 35.62 441.40, 39.79 445.11, 40.92 448.61, 27.22 448.76, 27.16 443.52, 18.29 443.62, 17.96 411.99, 39.23 411.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,56,"POLYGON ((165.89 412.92, 167.03 442.53, 165.25 442.60, 165.45 447.36, 158.57 444.30, 153.45 446.49, 153.25 441.41, 143.23 441.80, 142.14 413.84, 165.89 412.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,57,"POLYGON ((109.95 412.06, 123.34 411.97, 123.39 420.53, 129.49 420.49, 129.50 422.53, 127.06 424.21, 127.29 428.16, 130.92 429.42, 131.47 441.39, 127.95 441.55, 128.21 447.56, 121.82 447.86, 117.30 446.59, 113.08 446.64, 113.03 440.40, 110.14 440.43, 109.95 412.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,58,"POLYGON ((235.40 413.34, 245.18 413.05, 245.29 416.53, 256.40 416.18, 257.31 445.84, 233.76 446.55, 232.87 417.25, 235.52 417.18, 235.40 413.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,59,"POLYGON ((73.32 443.85, 69.94 443.92, 70.01 448.07, 53.13 448.37, 52.95 437.76, 49.38 437.83, 48.92 411.22, 50.92 411.20, 52.95 412.88, 56.64 412.18, 60.16 414.22, 68.26 412.84, 72.38 412.09, 72.67 429.27, 77.45 429.19, 77.55 435.33, 73.17 435.40, 73.32 443.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,60,"POLYGON ((15.98 442.50, 10.31 442.49, 10.30 450.10, 0.01 450.10, 0.01 443.15, 0.00 443.14, 0.00 415.58, 3.74 415.62, 4.88 413.66, 7.57 412.50, 11.45 412.49, 11.45 408.05, 14.06 408.05, 14.06 413.87, 15.99 413.86, 15.98 442.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,61,"POLYGON ((100.09 440.91, 91.64 441.10, 91.69 443.23, 82.13 443.45, 82.06 440.61, 79.20 440.69, 78.62 414.82, 97.02 414.41, 97.18 421.60, 101.63 421.51, 101.84 430.52, 99.86 430.57, 100.09 440.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,62,"POLYGON ((204.03 405.18, 214.81 404.81, 215.01 410.36, 224.91 410.02, 225.24 419.91, 227.19 419.84, 227.63 432.97, 222.93 433.11, 223.16 439.85, 215.86 440.08, 215.98 443.63, 205.35 443.99, 204.03 405.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,63,"POLYGON ((194.94 410.72, 195.72 439.11, 174.82 439.66, 174.12 413.80, 177.81 413.71, 177.63 407.36, 184.63 407.18, 184.73 411.00, 194.94 410.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,64,"POLYGON ((324.22 395.27, 360.59 394.21, 360.95 408.30, 344.18 408.86, 345.23 449.90, 326.09 450.25, 324.22 395.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,65,"POLYGON ((397.54 393.00, 398.93 447.99, 379.81 448.77, 378.41 407.74, 361.65 408.17, 361.18 394.08, 397.54 393.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,66,"POLYGON ((219.75 376.03, 226.40 375.42, 227.74 390.08, 221.08 390.69, 219.75 376.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,67,"POLYGON ((468.59 348.98, 496.50 348.89, 496.53 354.31, 501.02 354.28, 501.06 366.69, 475.07 366.78, 475.06 357.25, 473.22 354.17, 469.60 351.56, 468.59 348.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,68,"POLYGON ((465.04 325.43, 496.33 324.77, 496.47 330.80, 503.82 330.66, 504.16 346.08, 465.51 346.93, 465.04 325.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,69,"POLYGON ((496.67 300.43, 496.81 308.08, 501.91 307.98, 502.07 317.32, 497.44 317.41, 497.54 322.56, 466.42 323.16, 466.29 315.61, 460.73 315.71, 460.66 312.36, 463.94 312.30, 465.64 310.50, 467.32 307.97, 468.69 305.92, 469.38 303.83, 469.32 300.95, 496.67 300.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,70,"POLYGON ((336.73 244.76, 410.93 244.33, 413.20 333.44, 337.59 335.66, 336.73 244.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,71,"POLYGON ((462.80 277.14, 502.30 276.95, 502.35 289.56, 497.55 289.60, 497.58 296.72, 462.88 296.90, 462.80 277.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,72,"POLYGON ((281.62 250.27, 282.17 271.64, 270.57 271.91, 270.63 274.19, 258.57 274.52, 258.49 272.33, 238.52 272.86, 238.58 274.95, 228.63 275.22, 228.57 273.05, 210.90 273.52, 210.96 275.72, 198.69 276.05, 198.63 273.77, 179.94 274.27, 179.99 276.35, 171.75 276.56, 172.33 298.55, 180.46 298.34, 180.52 300.69, 199.88 300.17, 199.83 297.98, 210.27 297.71, 210.33 299.71, 229.45 299.24, 229.39 296.98, 240.02 296.71, 240.10 299.59, 258.92 299.13, 258.87 296.34, 270.11 296.07, 270.19 299.22, 281.70 298.93, 282.19 318.96, 159.69 321.96, 159.11 297.82, 152.90 297.98, 152.40 277.57, 157.87 277.43, 157.27 253.38, 281.62 250.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,73,"POLYGON ((28.73 246.90, 42.82 247.65, 42.53 252.58, 45.24 252.73, 44.09 274.16, 39.78 278.98, 38.57 283.87, 35.12 281.76, 32.18 276.98, 31.91 271.54, 28.41 267.79, 24.83 266.26, 25.62 251.55, 28.48 251.70, 28.73 246.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,74,"POLYGON ((84.63 287.40, 84.49 248.49, 92.50 248.46, 92.48 243.16, 107.94 243.10, 108.07 282.56, 95.97 282.61, 96.00 287.36, 84.63 287.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,75,"POLYGON ((652.61 851.78, 650.40 888.71, 605.71 900.00, 604.18 900.00, 602.32 892.73, 573.59 900.00, 562.62 900.00, 556.53 876.10, 652.61 851.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,76,"POLYGON ((900.00 840.31, 882.29 845.40, 873.65 815.52, 900.00 807.96, 900.00 840.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,77,"POLYGON ((666.02 762.40, 645.99 684.93, 662.21 679.30, 657.16 651.21, 646.70 613.61, 665.39 608.47, 667.96 611.20, 691.09 604.93, 700.57 640.67, 723.62 634.75, 714.92 601.14, 732.95 596.29, 734.22 600.87, 758.64 593.39, 763.20 608.48, 760.88 609.91, 765.72 623.00, 788.06 616.34, 782.64 592.13, 801.10 587.09, 803.28 599.06, 809.67 597.21, 807.49 589.76, 815.50 587.45, 815.22 582.44, 900.00 567.79, 900.00 742.58, 883.87 742.62, 885.70 619.00, 826.92 626.43, 857.79 733.90, 852.98 735.09, 858.16 755.87, 830.34 764.01, 829.87 762.12, 809.16 767.24, 809.19 768.48, 801.40 770.55, 799.49 769.04, 771.20 777.87, 771.24 779.42, 713.70 795.56, 711.34 787.14, 707.77 788.15, 709.23 793.33, 702.16 795.30, 702.96 798.17, 667.10 808.19, 666.08 804.53, 581.84 830.72, 574.98 807.55, 583.87 804.81, 579.19 787.77, 666.02 762.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,78,"POLYGON ((619.77 620.80, 619.38 737.93, 596.54 737.85, 596.51 748.06, 569.70 747.97, 570.12 626.69, 599.60 626.78, 599.61 620.74, 619.77 620.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,79,"POLYGON ((766.33 457.29, 765.81 530.56, 717.13 530.21, 717.65 456.96, 766.33 457.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,80,"POLYGON ((701.73 453.51, 703.97 513.43, 683.09 514.21, 680.86 454.28, 701.73 453.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,81,"POLYGON ((704.04 332.93, 705.85 425.99, 584.55 428.35, 585.54 480.14, 563.96 480.56, 562.91 426.49, 570.17 426.35, 568.41 335.58, 704.04 332.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,82,"POLYGON ((657.72 248.25, 686.50 247.67, 687.80 312.55, 690.77 312.49, 690.92 320.81, 687.92 320.87, 687.97 322.73, 659.23 323.31, 657.72 248.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,83,"POLYGON ((563.75 234.69, 578.73 234.53, 578.76 237.17, 602.95 236.93, 603.45 285.82, 648.21 285.37, 648.60 323.78, 568.37 324.61, 567.75 264.18, 564.04 264.21, 563.75 234.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,84,"POLYGON ((803.94 0.00, 839.37 2.85, 832.62 139.65, 604.35 128.02, 601.48 164.16, 571.56 162.95, 574.81 75.96, 582.70 76.38, 583.37 59.85, 556.04 58.53, 555.46 0.00, 803.94 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,85,"POLYGON ((42.72 897.30, 45.33 897.30, 45.36 893.95, 54.48 894.03, 54.46 897.09, 58.21 897.11, 58.18 900.00, 42.69 900.00, 42.72 897.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,86,"POLYGON ((449.27 883.17, 449.68 900.00, 406.41 900.00, 406.03 884.23, 449.27 883.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,87,"POLYGON ((61.97 888.31, 42.08 889.24, 41.09 866.35, 38.65 866.46, 38.17 855.66, 41.68 847.96, 53.85 847.54, 60.31 856.65, 61.97 888.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,88,"POLYGON ((90.68 854.46, 91.14 873.38, 72.26 873.86, 71.99 863.10, 90.68 854.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,89,"POLYGON ((6.90 835.57, 27.90 834.82, 29.15 868.57, 5.43 869.44, 5.17 861.43, 2.63 860.03, 2.95 852.43, 5.07 849.78, 7.19 843.47, 6.90 835.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,90,"POLYGON ((106.80 828.92, 106.62 825.95, 117.94 825.21, 119.47 848.66, 121.40 851.74, 121.92 867.67, 107.97 868.00, 104.93 866.81, 97.99 867.26, 95.55 829.63, 106.80 828.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,91,"POLYGON ((124.71 823.75, 132.86 823.43, 133.11 829.78, 152.23 829.02, 153.64 864.59, 128.06 865.53, 126.24 862.74, 124.71 823.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,92,"POLYGON ((168.09 824.24, 167.59 817.95, 176.36 817.22, 178.97 845.32, 172.62 845.90, 173.59 856.18, 168.30 858.67, 163.66 857.05, 158.09 853.25, 155.87 825.20, 168.09 824.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,93,"POLYGON ((216.09 816.45, 218.50 857.81, 193.82 859.06, 193.13 843.09, 191.06 843.19, 190.61 832.81, 189.93 831.54, 189.62 826.11, 188.06 826.20, 187.63 818.55, 192.76 818.26, 192.69 817.07, 203.98 816.42, 204.15 819.26, 208.11 819.05, 208.00 816.92, 216.09 816.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,94,"POLYGON ((302.81 796.10, 303.11 800.51, 304.65 800.42, 305.81 818.44, 301.50 818.71, 304.75 869.10, 289.95 870.06, 286.73 820.28, 282.80 820.54, 282.57 817.04, 279.18 817.26, 277.92 797.71, 302.81 796.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,95,"POLYGON ((242.25 808.70, 243.40 834.57, 221.66 835.53, 220.53 809.65, 242.25 808.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,96,"POLYGON ((265.39 797.03, 265.88 804.54, 273.19 804.05, 275.17 833.65, 265.64 834.29, 266.26 843.42, 254.75 844.18, 254.08 834.08, 251.66 834.25, 249.23 798.11, 265.39 797.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,97,"POLYGON ((331.91 789.85, 334.12 823.47, 328.83 823.82, 329.50 834.22, 324.23 834.55, 324.83 843.50, 312.20 844.34, 308.70 791.37, 331.91 789.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,98,"POLYGON ((492.47 767.92, 493.40 860.73, 462.04 861.07, 461.11 768.23, 492.47 767.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,99,"POLYGON ((363.46 788.22, 365.27 827.15, 341.79 828.15, 339.70 789.38, 363.46 788.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,100,"POLYGON ((366.84 788.00, 387.90 787.20, 388.13 812.10, 381.31 811.32, 377.50 816.28, 373.86 823.27, 372.46 826.57, 368.92 826.53, 366.84 788.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,101,"POLYGON ((413.66 786.14, 414.14 800.25, 415.91 800.20, 416.69 822.62, 397.19 823.28, 395.94 786.73, 413.66 786.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,102,"POLYGON ((12.94 765.78, 13.79 782.34, 11.09 782.48, 11.44 789.91, 8.65 790.02, 8.81 793.44, 3.09 793.69, 3.22 796.71, 0.00 796.86, 0.00 766.48, 12.94 765.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,103,"POLYGON ((27.18 758.03, 33.63 767.30, 37.31 784.12, 37.23 791.96, 32.04 791.91, 30.78 789.28, 26.21 789.40, 26.16 787.64, 19.81 787.81, 17.78 783.28, 19.31 779.87, 18.59 769.30, 21.37 767.74, 20.42 758.76, 27.18 758.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,104,"POLYGON ((148.26 758.44, 149.04 777.67, 123.06 778.58, 122.29 759.64, 148.26 758.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,105,"POLYGON ((97.28 750.89, 100.01 750.73, 99.76 746.76, 109.32 746.16, 109.64 751.06, 120.50 750.36, 122.23 777.40, 99.07 778.86, 97.28 750.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,106,"POLYGON ((91.29 738.32, 91.47 750.59, 93.07 750.55, 93.24 761.60, 91.16 761.63, 91.38 775.17, 82.33 775.31, 82.38 777.97, 73.10 778.12, 73.06 775.75, 67.76 775.84, 67.38 751.50, 72.51 751.41, 72.31 738.61, 91.29 738.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,107,"POLYGON ((160.68 745.61, 160.61 739.97, 179.44 739.78, 179.95 771.13, 163.51 771.31, 163.55 774.95, 153.65 775.04, 153.34 745.68, 160.68 745.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,108,"POLYGON ((182.48 743.12, 208.87 742.16, 209.70 765.11, 203.12 765.35, 203.24 768.41, 190.61 768.86, 190.48 765.58, 183.30 765.85, 182.48 743.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,109,"POLYGON ((236.50 737.46, 237.38 763.23, 218.19 763.87, 217.31 738.12, 236.50 737.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,110,"POLYGON ((269.86 756.40, 246.63 757.13, 245.73 728.63, 258.51 728.21, 258.75 736.04, 269.20 735.71, 269.86 756.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,111,"POLYGON ((296.63 715.79, 297.40 744.62, 299.61 744.55, 299.88 755.46, 278.86 756.02, 277.79 716.29, 296.63 715.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,112,"POLYGON ((356.94 721.15, 357.49 748.39, 333.55 748.87, 333.02 721.63, 356.94 721.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,113,"POLYGON ((395.73 729.90, 403.03 729.81, 405.53 730.41, 407.66 729.69, 408.31 729.00, 412.79 728.73, 414.21 726.21, 416.34 724.85, 419.20 724.80, 419.39 738.09, 395.85 738.40, 395.73 729.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,114,"POLYGON ((363.78 717.40, 372.10 720.30, 379.05 723.65, 384.74 723.50, 384.31 744.60, 375.29 744.83, 374.62 741.74, 364.60 741.40, 363.78 717.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,115,"POLYGON ((327.95 708.22, 329.35 746.36, 310.59 747.06, 309.90 727.94, 306.30 728.08, 305.61 709.05, 327.95 708.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,116,"POLYGON ((52.81 705.07, 55.55 729.78, 24.79 733.18, 23.30 719.83, 21.04 720.09, 20.30 713.41, 30.60 712.26, 30.09 707.59, 52.81 705.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,117,"POLYGON ((430.05 733.24, 430.26 709.93, 434.08 708.08, 434.73 705.95, 434.32 703.34, 435.31 701.06, 437.37 699.58, 442.12 699.15, 442.53 703.58, 447.14 703.17, 447.22 704.10, 447.49 710.33, 447.58 714.02, 448.86 716.09, 450.85 717.62, 453.98 717.65, 453.81 733.48, 430.05 733.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,118,"POLYGON ((153.00 694.64, 180.67 694.28, 180.84 708.13, 179.81 710.53, 179.90 716.92, 171.89 717.02, 171.94 720.52, 165.57 720.60, 165.51 716.23, 153.26 716.36, 153.00 694.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,119,"POLYGON ((183.73 687.04, 208.71 685.89, 209.83 709.86, 204.21 710.11, 204.30 712.13, 186.25 712.97, 186.04 708.40, 184.60 705.95, 183.73 687.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,120,"POLYGON ((521.07 693.56, 521.27 704.23, 501.48 704.60, 498.96 702.74, 498.04 700.85, 498.09 695.94, 501.72 693.92, 521.07 693.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739539,121,"POLYGON ((270.21 670.82, 270.42 700.96, 256.58 701.14, 254.19 693.03, 251.70 688.54, 244.72 686.72, 245.24 671.66, 270.21 670.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,0,"POLYGON ((898.18 343.50, 900.00 350.90, 900.00 356.43, 895.59 357.51, 900.00 375.46, 900.00 475.43, 851.84 475.59, 851.59 388.22, 848.13 373.26, 842.29 374.59, 838.49 358.26, 843.69 357.06, 790.94 128.83, 786.44 129.86, 779.22 98.36, 784.32 97.21, 776.64 63.58, 769.92 65.10, 758.09 13.15, 764.23 11.75, 761.97 1.75, 769.79 0.00, 853.57 0.00, 858.55 22.02, 879.75 17.26, 885.52 42.84, 867.26 46.94, 868.05 50.41, 883.33 47.00, 898.30 113.39, 900.00 113.01, 900.00 343.06, 898.18 343.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,1,"POLYGON ((596.21 217.65, 595.96 228.49, 584.58 228.25, 584.82 217.41, 596.21 217.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,2,"POLYGON ((557.57 208.87, 555.93 175.82, 580.96 174.58, 581.46 184.62, 583.09 184.54, 583.72 197.06, 580.80 197.20, 581.33 207.71, 557.57 208.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,3,"POLYGON ((593.86 196.14, 593.75 193.43, 588.75 193.63, 587.99 174.04, 590.68 173.95, 590.55 170.54, 599.46 170.18, 599.66 175.28, 606.00 175.05, 606.80 195.63, 593.86 196.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,4,"POLYGON ((332.13 5.53, 372.58 4.69, 373.58 53.48, 341.18 54.13, 340.55 23.71, 332.51 23.90, 332.13 5.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,5,"POLYGON ((429.37 10.88, 436.83 10.73, 437.32 36.14, 429.81 34.22, 429.37 10.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,6,"POLYGON ((272.30 7.97, 272.99 11.81, 271.56 16.71, 256.70 19.40, 260.68 13.92, 260.01 10.14, 272.30 7.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,7,"POLYGON ((3.76 22.90, 3.54 11.37, 7.96 11.28, 7.82 3.36, 43.59 2.62, 43.85 15.64, 35.68 15.83, 35.83 22.26, 3.76 22.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,8,"POLYGON ((283.44 439.95, 310.08 439.18, 311.23 479.00, 284.59 479.76, 283.44 439.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,9,"POLYGON ((314.09 417.63, 330.96 417.56, 331.64 430.48, 340.66 430.23, 347.87 437.39, 350.69 498.92, 314.29 498.78, 314.09 417.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,10,"POLYGON ((534.26 437.05, 560.71 436.75, 561.06 470.15, 534.62 470.42, 534.26 437.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,11,"POLYGON ((30.58 427.27, 41.17 456.72, 24.05 462.84, 23.45 461.17, 21.08 462.01, 22.52 465.97, 3.18 472.88, 0.00 464.05, 0.00 432.96, 8.84 429.80, 12.55 440.12, 17.37 438.40, 15.33 432.72, 30.58 427.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,12,"POLYGON ((465.53 401.58, 465.08 424.70, 436.18 423.35, 432.81 422.19, 432.50 418.03, 429.71 415.19, 425.46 413.01, 422.11 412.48, 419.52 409.21, 416.35 407.63, 416.55 399.30, 465.53 401.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,13,"POLYGON ((351.39 393.68, 379.03 393.64, 379.05 428.71, 351.43 428.75, 351.39 393.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,14,"POLYGON ((178.77 364.17, 178.85 367.79, 183.66 367.69, 185.06 429.52, 184.45 431.93, 184.73 445.22, 166.08 445.61, 165.75 429.95, 156.65 430.13, 156.38 416.62, 151.77 416.72, 150.76 369.28, 158.58 369.11, 158.48 364.62, 178.77 364.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,15,"POLYGON ((122.09 357.90, 124.40 445.43, 81.53 446.55, 79.22 359.03, 122.09 357.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,16,"POLYGON ((249.05 379.36, 252.62 379.25, 253.16 396.77, 255.29 397.42, 255.76 401.96, 253.76 402.70, 254.88 428.45, 253.24 431.64, 250.17 433.54, 230.63 433.48, 228.72 431.33, 227.94 428.29, 227.66 417.29, 226.06 416.53, 225.83 410.94, 227.56 409.94, 229.79 409.86, 228.56 370.60, 248.75 369.98, 249.05 379.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,17,"POLYGON ((456.37 365.56, 454.47 392.65, 433.60 391.18, 434.09 384.27, 433.04 377.37, 428.26 370.30, 424.00 368.94, 424.39 363.31, 456.37 365.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,18,"POLYGON ((370.62 363.51, 367.27 380.47, 361.18 379.27, 356.97 380.07, 354.40 382.24, 348.38 381.06, 347.64 384.81, 329.85 381.31, 330.97 375.71, 323.42 374.24, 324.67 367.95, 330.61 369.13, 333.19 356.17, 370.62 363.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,19,"POLYGON ((295.95 351.97, 297.19 344.46, 308.00 346.22, 306.79 353.73, 295.95 351.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,20,"POLYGON ((426.08 332.08, 447.07 332.81, 446.19 358.01, 425.39 357.30, 425.77 346.72, 418.72 346.48, 419.06 336.75, 425.91 336.99, 426.08 332.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,21,"POLYGON ((372.99 337.10, 369.66 353.50, 364.82 352.54, 363.73 357.92, 337.77 352.72, 338.91 347.12, 336.20 343.86, 333.69 342.17, 336.21 329.74, 372.99 337.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,22,"POLYGON ((27.23 355.01, 0.00 355.71, 0.00 331.64, 26.62 330.97, 27.23 355.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,23,"POLYGON ((286.64 324.97, 283.87 323.04, 285.78 309.90, 299.09 310.67, 299.30 324.27, 286.64 324.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,24,"POLYGON ((337.76 323.97, 339.22 311.86, 333.39 310.79, 335.28 302.15, 367.95 308.15, 370.19 320.68, 366.16 327.62, 337.76 323.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,25,"POLYGON ((308.90 301.56, 322.93 302.22, 321.80 316.59, 318.86 316.26, 318.71 317.64, 307.45 314.96, 308.90 301.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,26,"POLYGON ((422.97 321.44, 422.59 295.46, 459.02 294.90, 459.19 306.11, 456.88 313.47, 458.74 320.90, 422.97 321.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,27,"POLYGON ((422.97 321.44, 422.59 295.46, 459.02 294.90, 459.19 306.11, 456.88 313.47, 458.74 320.90, 422.97 321.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,28,"POLYGON ((522.78 293.76, 523.47 318.56, 494.03 319.38, 498.70 306.14, 498.38 294.45, 522.78 293.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,29,"POLYGON ((522.78 293.76, 523.47 318.56, 494.03 319.38, 498.70 306.14, 498.38 294.45, 522.78 293.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,30,"POLYGON ((0.00 263.50, 31.23 262.74, 34.49 266.88, 33.92 271.95, 34.34 274.92, 32.86 278.46, 26.88 277.90, 23.78 280.63, 27.36 283.15, 21.07 284.89, 19.90 287.18, 0.00 287.68, 0.00 263.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,31,"POLYGON ((180.90 267.47, 192.27 267.82, 192.09 274.35, 185.91 274.18, 185.73 280.09, 180.51 279.93, 180.90 267.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,32,"POLYGON ((151.15 260.37, 158.82 259.46, 160.83 267.11, 160.89 274.99, 156.45 275.48, 148.83 274.83, 149.08 272.16, 145.08 271.80, 145.79 263.84, 150.81 264.28, 151.15 260.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,33,"POLYGON ((555.48 227.13, 561.94 226.83, 562.66 240.42, 559.55 240.63, 560.05 235.67, 558.77 233.97, 557.39 233.52, 557.02 229.31, 555.39 228.73, 555.48 227.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,34,"POLYGON ((75.68 203.56, 79.93 202.45, 87.03 202.23, 87.12 205.27, 91.25 205.14, 94.31 207.86, 95.83 212.19, 92.73 214.89, 88.00 215.54, 86.51 219.07, 76.57 219.45, 75.68 203.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,35,"POLYGON ((265.52 181.82, 265.86 194.35, 269.47 194.24, 270.45 230.28, 250.35 230.84, 249.02 182.26, 265.52 181.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,36,"POLYGON ((127.86 221.56, 122.14 221.84, 121.89 216.65, 117.68 214.10, 117.30 211.02, 114.87 208.00, 110.39 206.20, 107.92 202.43, 108.44 199.62, 113.65 195.09, 114.12 190.21, 122.93 188.21, 125.52 190.81, 129.10 192.34, 132.28 195.19, 131.91 203.88, 128.89 207.48, 127.38 211.36, 127.86 221.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,37,"POLYGON ((214.71 218.88, 202.85 218.98, 202.89 224.20, 198.44 224.24, 198.08 185.23, 214.39 185.06, 214.71 218.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,38,"POLYGON ((284.91 217.95, 282.24 213.65, 278.27 199.05, 277.55 189.52, 298.34 189.90, 299.36 212.34, 297.55 216.94, 284.91 217.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,39,"POLYGON ((172.51 185.08, 184.81 184.86, 184.85 187.19, 188.43 187.12, 188.96 215.65, 173.08 215.95, 172.51 185.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,40,"POLYGON ((226.00 215.79, 222.15 210.12, 220.55 195.46, 226.09 192.53, 225.51 183.79, 238.17 183.47, 240.72 214.37, 226.00 215.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,41,"POLYGON ((311.06 181.23, 318.33 180.89, 318.49 184.15, 322.16 183.99, 329.96 184.59, 334.62 198.01, 337.47 198.72, 338.24 215.70, 316.37 216.68, 315.96 207.59, 308.38 207.94, 307.67 192.33, 311.56 192.16, 311.06 181.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,42,"POLYGON ((157.70 183.80, 165.20 183.49, 165.69 195.22, 162.47 199.10, 167.52 203.12, 167.71 208.91, 143.18 209.74, 143.04 205.66, 146.11 204.69, 153.06 204.51, 152.25 201.74, 150.44 200.03, 153.63 197.88, 153.05 192.31, 157.70 183.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,43,"POLYGON ((361.27 178.11, 362.71 192.34, 369.38 195.15, 376.40 194.26, 381.25 179.59, 393.17 179.36, 393.58 199.90, 376.85 200.24, 377.04 209.69, 365.07 209.93, 344.84 209.96, 344.81 183.92, 347.01 179.51, 361.27 178.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,44,"POLYGON ((49.84 202.33, 49.27 184.81, 50.83 182.86, 54.03 185.40, 53.97 190.64, 57.56 193.17, 61.45 193.05, 61.75 201.94, 49.84 202.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,45,"POLYGON ((411.65 206.92, 410.96 204.54, 407.57 203.27, 400.50 204.61, 399.79 176.86, 421.61 175.99, 422.52 187.09, 422.91 205.81, 419.59 207.14, 411.65 206.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,46,"POLYGON ((548.33 170.84, 549.78 209.94, 533.68 210.53, 533.56 207.05, 524.89 207.36, 522.96 172.38, 548.33 170.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,47,"POLYGON ((465.82 171.73, 477.29 171.32, 479.18 175.12, 484.94 178.54, 488.83 179.73, 492.40 178.11, 494.59 173.70, 493.23 170.65, 511.66 169.87, 512.33 175.89, 514.12 198.73, 508.24 199.17, 509.09 210.22, 492.35 211.50, 491.66 202.52, 482.74 203.19, 481.53 206.04, 464.99 207.38, 463.63 190.36, 466.40 188.25, 465.82 171.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,48,"POLYGON ((44.19 200.83, 27.00 201.50, 25.78 169.61, 42.97 168.94, 44.19 200.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,49,"POLYGON ((456.31 172.28, 456.40 177.07, 445.80 177.26, 444.34 181.13, 440.56 184.05, 440.36 172.58, 456.31 172.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,50,"POLYGON ((0.00 110.48, 17.97 109.80, 18.67 128.63, 0.00 129.33, 0.00 110.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,51,"POLYGON ((73.76 119.61, 54.21 114.92, 64.12 74.06, 83.67 78.76, 73.76 119.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,52,"POLYGON ((0.00 84.98, 14.88 84.55, 14.81 82.02, 30.98 81.57, 31.13 86.60, 26.67 86.72, 23.88 88.10, 21.84 90.79, 15.65 95.92, 15.77 100.49, 0.00 100.94, 0.00 84.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,53,"POLYGON ((381.77 102.71, 381.62 90.81, 379.04 90.84, 378.93 82.09, 380.89 82.06, 380.74 70.32, 398.64 70.09, 399.02 98.63, 390.61 98.73, 390.67 102.59, 381.77 102.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,54,"POLYGON ((125.53 80.42, 126.42 104.41, 98.33 105.44, 98.07 98.17, 89.27 98.50, 88.70 83.25, 99.81 82.85, 99.25 67.53, 121.01 66.73, 121.51 80.57, 125.53 80.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,55,"POLYGON ((172.67 68.03, 189.43 66.42, 192.39 97.47, 188.63 97.83, 189.04 101.93, 181.66 102.63, 181.10 96.87, 170.39 97.87, 169.15 84.90, 174.24 84.41, 172.67 68.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,56,"POLYGON ((373.37 55.97, 374.33 108.98, 324.47 109.90, 323.83 74.84, 341.70 74.50, 341.37 56.57, 373.37 55.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,57,"POLYGON ((484.77 62.96, 492.65 66.64, 491.96 73.54, 493.70 82.33, 490.39 88.23, 491.40 93.80, 496.37 101.87, 479.19 101.26, 479.64 88.64, 475.34 88.49, 476.27 62.64, 484.77 62.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,58,"POLYGON ((239.80 64.36, 263.89 64.19, 264.01 81.92, 270.05 81.88, 270.18 98.39, 240.00 98.61, 239.80 64.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,59,"POLYGON ((133.05 98.30, 132.84 79.74, 141.08 79.64, 140.99 69.95, 138.21 69.99, 138.09 59.50, 151.21 59.36, 151.25 64.13, 156.89 64.06, 157.32 103.31, 148.66 103.40, 148.60 98.12, 133.05 98.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,60,"POLYGON ((222.65 60.16, 223.19 72.66, 225.94 74.75, 226.91 101.12, 203.46 101.98, 202.65 79.71, 204.42 78.63, 203.65 60.98, 222.65 60.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,61,"POLYGON ((450.76 55.19, 472.10 55.20, 472.05 101.16, 452.05 101.16, 452.06 76.94, 447.68 76.94, 447.68 68.17, 450.74 68.16, 450.76 55.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,62,"POLYGON ((303.16 91.29, 291.13 91.37, 291.05 78.79, 284.19 78.85, 284.11 66.29, 292.40 66.23, 292.38 63.28, 306.80 63.20, 306.91 79.76, 303.07 79.79, 303.16 91.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,63,"POLYGON ((422.69 54.94, 437.28 54.32, 438.71 86.96, 429.77 84.54, 423.60 80.57, 422.69 54.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,64,"POLYGON ((0.00 60.93, 1.33 60.90, 2.12 58.29, 3.24 57.21, 26.05 56.70, 26.50 76.78, 0.00 77.37, 0.00 60.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,65,"POLYGON ((6.08 31.79, 37.74 30.98, 38.28 52.21, 10.81 52.91, 10.57 43.66, 6.38 43.77, 6.08 31.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,66,"POLYGON ((36.09 895.80, 42.80 895.63, 42.74 900.00, 33.74 900.00, 36.09 895.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,67,"POLYGON ((117.43 887.73, 116.54 868.57, 156.92 866.66, 158.05 890.45, 137.94 891.38, 137.73 886.77, 117.43 887.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,68,"POLYGON ((43.31 859.17, 57.85 859.48, 61.08 866.95, 68.42 867.45, 67.55 879.73, 58.60 879.11, 57.59 893.28, 39.18 891.99, 39.84 882.79, 42.92 876.45, 43.31 859.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,69,"POLYGON ((513.48 824.87, 513.92 892.92, 401.83 894.30, 401.04 873.54, 432.65 825.78, 513.48 824.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,70,"POLYGON ((380.10 768.59, 356.72 855.56, 351.85 854.24, 345.06 872.35, 349.84 874.45, 337.45 900.00, 284.93 900.00, 305.62 854.62, 308.64 855.79, 315.19 840.26, 311.84 839.06, 337.47 755.89, 380.10 768.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,71,"POLYGON ((130.55 861.42, 129.91 841.13, 153.19 840.40, 153.84 860.67, 130.55 861.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,72,"POLYGON ((59.83 814.88, 59.69 818.26, 64.98 818.46, 64.58 828.06, 60.74 828.80, 61.34 831.80, 63.31 833.68, 57.68 833.83, 57.76 836.98, 54.34 833.56, 41.61 837.39, 36.65 836.47, 31.23 837.66, 28.51 834.22, 26.97 826.82, 30.80 826.03, 30.30 823.67, 28.98 817.76, 32.92 813.82, 59.83 814.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,73,"POLYGON ((139.21 835.12, 138.57 816.05, 177.14 814.77, 177.36 821.32, 168.99 821.60, 169.40 834.11, 139.21 835.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,74,"POLYGON ((150.72 811.01, 150.69 790.99, 174.51 790.95, 174.54 810.98, 150.72 811.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,75,"POLYGON ((63.10 785.43, 63.06 794.80, 74.48 794.84, 74.43 805.87, 39.05 805.71, 39.15 785.31, 63.10 785.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,76,"POLYGON ((158.26 765.89, 180.82 765.31, 181.36 786.38, 158.63 786.96, 158.46 780.31, 152.31 780.44, 152.05 770.26, 158.37 770.10, 158.26 765.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,77,"POLYGON ((510.33 739.44, 511.56 810.22, 466.20 811.01, 465.73 784.62, 401.26 785.74, 401.06 774.00, 403.69 762.48, 403.59 756.18, 398.81 756.28, 398.56 741.39, 510.33 739.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,78,"POLYGON ((42.72 775.87, 42.53 755.77, 79.78 755.39, 79.99 775.50, 42.72 775.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,79,"POLYGON ((166.31 760.35, 166.15 755.63, 158.63 755.89, 158.23 743.75, 166.68 743.49, 166.58 740.23, 193.10 739.33, 193.86 761.87, 185.15 762.16, 185.07 759.74, 166.31 760.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,80,"POLYGON ((51.13 729.06, 88.91 729.12, 88.89 740.58, 82.03 740.57, 82.01 749.92, 51.09 749.87, 51.13 729.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,81,"POLYGON ((174.28 735.69, 172.66 734.66, 170.48 730.48, 173.92 728.68, 173.40 721.55, 177.42 721.24, 175.80 719.53, 175.46 717.37, 197.46 713.92, 202.90 715.84, 201.31 720.30, 206.20 726.13, 206.21 732.94, 199.16 732.94, 197.99 734.90, 194.15 736.22, 191.80 733.49, 189.17 733.38, 187.30 729.40, 184.44 734.18, 182.89 735.80, 174.28 735.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,82,"POLYGON ((72.33 727.92, 72.16 719.98, 64.75 720.13, 64.49 707.79, 93.79 707.20, 93.88 711.57, 91.47 714.47, 91.22 719.29, 93.38 721.10, 93.51 727.49, 72.33 727.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,83,"POLYGON ((166.59 687.09, 194.98 686.14, 196.61 688.21, 195.44 690.35, 196.96 694.33, 199.98 697.40, 197.79 699.55, 196.46 702.40, 192.96 702.51, 193.12 707.59, 163.64 708.59, 163.15 694.10, 166.80 693.99, 166.59 687.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,84,"POLYGON ((72.39 705.83, 72.28 694.76, 66.11 694.83, 68.25 692.27, 72.19 692.14, 72.09 688.79, 82.15 688.47, 88.61 691.75, 92.45 695.40, 86.05 702.11, 86.09 705.71, 72.39 705.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,85,"POLYGON ((166.99 683.17, 166.70 676.83, 162.05 677.04, 161.36 662.23, 199.26 660.51, 196.53 664.06, 193.24 665.54, 195.58 667.95, 193.40 671.25, 196.59 673.34, 197.56 676.64, 200.51 681.63, 166.99 683.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,86,"POLYGON ((66.06 661.62, 91.05 658.21, 96.45 663.33, 104.05 664.71, 106.71 672.33, 102.32 672.62, 102.02 674.56, 68.47 679.14, 66.06 661.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,87,"POLYGON ((412.20 613.62, 445.28 619.13, 443.19 631.59, 467.07 635.55, 464.68 649.82, 461.60 649.30, 460.38 656.66, 467.64 657.85, 467.18 660.63, 472.71 661.53, 469.89 678.30, 466.65 677.76, 463.51 696.35, 400.06 685.81, 412.20 613.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,88,"POLYGON ((167.67 656.79, 167.42 650.58, 161.36 650.82, 160.94 640.13, 164.77 638.99, 167.59 639.25, 171.78 637.75, 174.19 636.11, 188.01 635.22, 188.38 640.83, 187.78 644.86, 190.71 649.52, 188.97 651.65, 194.04 655.73, 167.67 656.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,89,"POLYGON ((75.56 631.48, 112.18 625.53, 113.31 632.45, 110.69 632.89, 112.81 645.87, 96.48 648.53, 95.49 642.53, 86.57 643.98, 85.87 639.69, 77.14 641.12, 75.56 631.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,90,"POLYGON ((292.00 580.40, 320.06 582.17, 312.96 681.00, 284.90 679.21, 292.00 580.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,91,"POLYGON ((206.68 557.56, 223.47 557.49, 223.49 561.59, 249.29 561.47, 249.62 636.80, 224.59 636.91, 224.49 616.18, 206.94 616.27, 206.68 557.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,92,"POLYGON ((386.49 578.96, 404.84 581.85, 403.05 593.06, 400.97 592.73, 399.21 603.77, 396.72 603.39, 394.99 614.26, 381.22 612.08, 386.49 578.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,93,"POLYGON ((282.86 573.11, 279.50 620.10, 254.34 618.32, 257.71 571.33, 282.86 573.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,94,"POLYGON ((181.55 553.65, 182.30 581.36, 173.80 589.61, 185.74 601.56, 186.01 611.48, 156.49 612.25, 155.76 585.46, 154.06 585.50, 153.78 575.47, 155.40 575.41, 154.82 554.38, 181.55 553.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,95,"POLYGON ((0.00 557.26, 12.17 553.65, 25.53 598.33, 0.47 605.74, 0.00 604.17, 0.00 557.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,96,"POLYGON ((70.87 553.82, 95.95 551.07, 101.35 599.90, 93.70 600.74, 93.19 596.09, 89.56 591.99, 75.26 593.57, 70.87 553.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,97,"POLYGON ((96.31 532.32, 123.77 533.37, 125.26 578.49, 103.87 580.25, 96.31 532.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,98,"POLYGON ((563.78 527.37, 540.85 527.80, 540.31 498.69, 563.23 498.26, 563.78 527.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,99,"POLYGON ((499.91 458.84, 500.09 490.62, 429.42 491.05, 429.33 475.09, 467.66 474.87, 467.57 459.02, 499.91 458.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,100,"POLYGON ((710.08 632.27, 783.23 635.77, 783.30 634.19, 821.59 636.03, 821.49 637.95, 900.00 641.69, 900.00 835.28, 797.60 831.48, 794.73 899.26, 803.94 900.00, 555.46 900.00, 552.66 621.55, 710.08 632.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,101,"POLYGON ((871.74 516.72, 871.54 522.30, 881.15 522.65, 879.94 554.38, 832.84 552.61, 834.10 519.71, 844.43 520.11, 844.60 515.71, 871.74 516.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,102,"POLYGON ((625.27 500.29, 625.95 548.74, 624.25 551.04, 621.46 552.18, 592.26 550.48, 590.61 494.81, 600.14 494.68, 600.22 500.67, 625.27 500.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,103,"POLYGON ((664.56 467.26, 700.91 466.33, 702.84 541.89, 690.76 542.19, 690.37 526.93, 681.48 527.16, 665.23 492.83, 664.56 467.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,104,"POLYGON ((607.42 430.09, 607.45 437.79, 595.75 437.85, 595.79 443.64, 579.37 443.73, 579.32 433.16, 590.48 433.10, 590.46 425.84, 599.64 425.81, 599.65 430.11, 607.42 430.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,105,"POLYGON ((576.07 431.54, 560.68 430.62, 561.35 419.62, 576.74 420.56, 576.07 431.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,106,"POLYGON ((571.57 392.27, 604.57 391.27, 605.38 417.58, 572.37 418.58, 571.57 392.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,107,"POLYGON ((570.39 384.58, 569.59 365.66, 607.72 364.07, 608.50 382.98, 570.39 384.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,108,"POLYGON ((608.02 328.65, 608.86 352.38, 569.13 353.64, 568.53 329.91, 608.02 328.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,109,"POLYGON ((606.90 304.66, 607.57 322.09, 568.14 323.61, 568.01 319.93, 558.64 320.28, 558.27 310.75, 567.79 310.39, 567.62 306.18, 606.90 304.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,110,"POLYGON ((606.91 281.89, 606.90 287.86, 603.77 287.85, 603.76 291.07, 593.76 291.04, 593.76 293.03, 579.72 292.97, 579.73 291.20, 575.15 291.18, 575.15 293.80, 570.16 293.77, 570.22 284.31, 574.61 280.60, 573.18 276.36, 569.30 272.44, 569.25 271.29, 604.99 271.88, 605.28 281.89, 606.91 281.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739989,111,"POLYGON ((616.20 231.95, 615.94 254.86, 585.80 254.50, 586.09 231.59, 616.20 231.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,0,"POLYGON ((207.24 477.12, 225.76 478.24, 223.58 514.01, 219.74 513.80, 218.62 532.17, 198.27 530.93, 200.32 497.29, 205.98 497.64, 207.24 477.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,1,"POLYGON ((434.38 489.74, 441.26 496.24, 448.09 501.26, 446.78 508.49, 436.42 511.71, 419.55 512.89, 410.38 504.71, 410.51 490.10, 434.38 489.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,2,"POLYGON ((318.77 482.17, 325.68 481.95, 329.53 486.80, 331.91 492.18, 341.72 492.99, 340.31 509.52, 337.73 516.03, 323.79 515.38, 321.25 512.98, 319.68 509.56, 318.77 482.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,3,"POLYGON ((265.41 478.01, 279.76 479.61, 278.67 489.28, 282.52 489.71, 279.17 519.54, 256.45 516.42, 258.97 484.83, 264.68 484.40, 265.41 478.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,4,"POLYGON ((300.71 473.86, 304.53 487.13, 311.26 487.69, 312.37 491.88, 312.26 497.81, 314.82 510.13, 311.07 509.47, 303.08 498.55, 298.07 496.68, 287.67 508.34, 288.70 474.50, 300.71 473.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,5,"POLYGON ((126.17 477.59, 189.70 475.83, 190.32 497.59, 126.78 499.35, 126.17 477.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,6,"POLYGON ((62.03 485.04, 0.00 487.14, 0.00 469.25, 52.32 467.49, 52.91 469.83, 50.97 472.21, 50.31 474.40, 48.92 476.79, 50.62 479.25, 54.47 480.62, 61.88 480.41, 62.03 485.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,7,"POLYGON ((417.06 481.39, 417.03 473.87, 411.77 473.89, 411.74 466.90, 448.27 466.78, 448.29 473.58, 438.67 481.30, 417.06 481.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,8,"POLYGON ((443.38 435.12, 444.03 449.67, 438.70 455.00, 424.00 455.66, 423.76 450.52, 414.94 450.92, 414.28 436.44, 443.38 435.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,9,"POLYGON ((14.77 452.71, 14.30 440.64, 10.26 440.81, 9.66 425.87, 19.31 425.49, 19.51 430.54, 50.34 429.31, 50.70 438.21, 62.07 437.74, 62.41 445.92, 58.24 446.09, 58.44 450.95, 14.77 452.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,10,"POLYGON ((138.15 443.99, 137.83 435.58, 143.51 435.35, 143.16 426.54, 154.51 426.10, 156.79 428.08, 159.91 429.05, 163.32 428.96, 169.23 434.98, 169.54 442.74, 138.15 443.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,11,"POLYGON ((404.63 413.11, 423.47 413.05, 427.85 409.75, 457.16 408.49, 460.93 410.12, 462.71 421.95, 458.69 429.74, 409.14 429.87, 409.12 423.50, 404.68 423.52, 404.63 413.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,12,"POLYGON ((350.20 401.28, 368.19 400.11, 368.53 413.46, 365.74 415.28, 364.95 425.49, 353.31 425.79, 354.16 417.69, 351.18 412.15, 351.75 405.81, 350.20 401.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,13,"POLYGON ((43.94 394.38, 38.78 413.58, 26.87 410.40, 25.69 414.83, 13.97 411.68, 19.19 407.84, 13.98 400.76, 17.66 401.89, 20.06 399.85, 25.93 398.90, 25.45 395.92, 29.89 390.63, 43.94 394.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,14,"POLYGON ((145.00 393.95, 171.23 392.13, 172.42 408.88, 146.17 410.73, 145.00 393.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,15,"POLYGON ((59.09 385.63, 71.08 388.47, 70.54 390.68, 82.63 393.55, 77.47 415.12, 65.33 412.26, 66.23 408.46, 54.32 405.66, 59.09 385.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,16,"POLYGON ((302.63 389.28, 297.87 420.59, 288.54 421.43, 285.06 424.41, 271.29 424.47, 267.63 421.79, 258.58 422.51, 247.00 418.39, 242.00 426.75, 238.70 428.65, 218.36 423.38, 232.14 370.77, 248.39 374.97, 246.13 383.62, 254.90 385.89, 261.66 381.16, 266.56 381.48, 269.42 385.23, 265.39 395.07, 267.77 398.52, 271.47 399.65, 282.09 396.02, 283.97 391.69, 295.19 393.56, 298.12 388.60, 302.63 389.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,17,"POLYGON ((451.41 382.86, 451.54 395.44, 444.38 403.89, 408.56 404.29, 408.32 383.36, 451.41 382.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,18,"POLYGON ((348.78 393.65, 348.39 401.41, 318.86 399.95, 319.25 391.88, 326.82 384.29, 328.13 365.15, 342.35 366.09, 341.11 384.37, 340.67 393.26, 348.78 393.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,19,"POLYGON ((451.23 362.89, 451.35 374.94, 406.93 375.41, 406.84 366.09, 417.29 365.97, 417.26 363.27, 451.23 362.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,20,"POLYGON ((199.49 358.02, 218.44 357.07, 219.53 378.69, 200.58 379.64, 199.49 358.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,21,"POLYGON ((19.79 351.00, 16.57 362.07, 4.49 358.58, 5.73 354.29, 1.77 354.52, 1.34 347.65, 1.87 345.84, 19.79 351.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,22,"POLYGON ((153.04 340.03, 152.85 325.47, 174.99 325.19, 173.18 331.92, 174.51 335.11, 186.85 341.47, 193.76 341.39, 193.90 352.77, 166.03 353.13, 165.86 339.88, 153.04 340.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,23,"POLYGON ((447.30 323.83, 447.52 336.61, 433.96 336.85, 434.03 341.40, 418.42 341.68, 419.73 344.62, 407.17 344.81, 407.49 332.82, 421.20 324.30, 447.30 323.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,24,"POLYGON ((10.79 323.66, 26.42 328.08, 21.74 344.36, 6.13 339.94, 10.79 323.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,25,"POLYGON ((96.07 342.79, 76.79 338.56, 73.26 335.38, 72.06 323.69, 79.54 325.66, 81.84 323.44, 79.15 318.52, 82.13 316.89, 84.89 321.90, 94.33 323.95, 92.35 332.79, 98.01 334.02, 96.07 342.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,26,"POLYGON ((451.78 318.23, 405.73 320.32, 404.80 300.10, 450.85 298.01, 451.78 318.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,27,"POLYGON ((197.93 297.33, 198.11 304.74, 174.25 305.41, 172.60 309.18, 172.80 318.57, 167.15 318.69, 166.68 298.04, 197.93 297.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,28,"POLYGON ((282.70 284.86, 301.41 284.49, 301.12 294.69, 298.17 296.25, 296.53 300.25, 301.12 324.64, 280.45 324.61, 282.70 284.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,29,"POLYGON ((366.72 286.02, 368.06 303.77, 362.90 304.12, 364.17 321.80, 350.20 322.80, 346.81 310.77, 341.49 291.75, 353.93 291.61, 355.29 286.22, 366.72 286.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,30,"POLYGON ((277.13 283.69, 269.87 321.55, 246.62 317.13, 253.90 279.27, 277.13 283.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,31,"POLYGON ((338.93 288.06, 339.90 299.78, 334.93 301.97, 335.39 309.66, 310.92 311.11, 317.64 282.77, 326.23 282.06, 326.81 289.06, 338.93 288.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,32,"POLYGON ((69.48 308.44, 68.25 280.66, 102.14 279.20, 102.69 291.97, 106.93 291.79, 107.59 306.78, 69.48 308.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,33,"POLYGON ((220.51 271.00, 216.28 294.19, 190.06 289.47, 194.27 266.28, 220.51 271.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,34,"POLYGON ((436.40 264.88, 436.60 290.32, 410.81 290.53, 410.78 287.89, 407.92 287.92, 407.83 278.71, 412.47 278.68, 412.36 265.08, 436.40 264.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,35,"POLYGON ((64.35 268.77, 64.02 258.50, 106.61 257.11, 107.04 270.32, 100.58 270.53, 100.77 276.54, 68.28 277.62, 68.00 268.66, 64.35 268.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,36,"POLYGON ((409.17 216.37, 414.57 220.78, 411.83 224.00, 420.85 233.05, 400.68 255.61, 396.18 251.17, 400.09 246.34, 389.53 238.47, 409.17 216.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,37,"POLYGON ((224.14 217.39, 224.22 226.95, 226.65 226.93, 226.72 234.92, 188.16 235.24, 188.02 216.93, 200.40 216.84, 200.40 215.37, 214.06 215.27, 215.12 217.46, 224.14 217.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,38,"POLYGON ((115.20 231.69, 123.04 204.39, 136.77 208.30, 128.92 235.60, 115.20 231.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,39,"POLYGON ((16.53 196.87, 13.57 220.70, 11.39 220.18, 10.02 235.91, 5.01 241.14, 0.00 239.22, 0.00 195.53, 16.53 196.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,40,"POLYGON ((363.55 199.77, 383.66 205.41, 375.65 233.67, 369.70 232.00, 368.90 234.80, 364.32 233.52, 363.56 236.16, 360.10 235.18, 361.65 229.77, 355.55 228.06, 363.55 199.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,41,"POLYGON ((333.94 202.37, 339.66 200.56, 351.33 203.22, 352.60 207.65, 344.72 235.28, 335.83 232.78, 337.04 228.53, 327.25 225.76, 333.94 202.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,42,"POLYGON ((306.18 188.16, 316.21 190.95, 313.34 201.28, 324.56 204.37, 315.54 236.67, 294.30 230.80, 306.18 188.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,43,"POLYGON ((274.91 190.43, 297.50 195.16, 289.60 232.61, 267.03 227.90, 274.91 190.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,44,"POLYGON ((259.48 196.71, 258.35 224.90, 250.87 224.61, 250.76 227.38, 239.64 226.94, 240.34 209.85, 243.71 208.41, 245.21 204.73, 245.72 196.24, 244.10 190.86, 250.12 191.42, 250.98 194.86, 254.60 196.52, 259.48 196.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,45,"POLYGON ((26.86 187.20, 29.25 188.60, 32.88 191.15, 35.51 190.35, 37.10 188.84, 41.35 187.72, 52.05 190.08, 46.71 211.71, 44.21 212.50, 41.44 214.33, 37.90 214.42, 30.41 210.79, 26.92 212.79, 25.37 209.90, 21.29 206.45, 26.86 187.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,46,"POLYGON ((102.94 166.39, 141.00 164.81, 145.06 169.22, 138.05 196.12, 102.54 196.23, 102.94 166.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,47,"POLYGON ((77.23 170.62, 99.18 169.37, 100.23 187.77, 78.29 189.01, 77.23 170.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,48,"POLYGON ((438.17 148.48, 469.46 146.92, 470.18 161.35, 463.94 161.67, 464.22 167.54, 439.19 168.78, 438.17 148.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,49,"POLYGON ((540.29 0.00, 541.61 33.29, 565.59 33.23, 567.23 140.96, 437.27 143.73, 433.39 35.50, 453.46 34.43, 451.62 0.00, 540.29 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,50,"POLYGON ((804.89 505.37, 867.90 760.33, 832.67 769.42, 841.48 803.02, 748.62 826.16, 758.04 861.59, 736.25 867.03, 716.14 782.50, 708.80 784.15, 664.95 608.84, 642.57 613.94, 630.52 568.77, 661.61 560.98, 653.82 531.08, 713.57 516.61, 716.32 527.73, 804.89 505.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,51,"POLYGON ((705.44 291.64, 611.36 292.15, 611.40 299.39, 596.29 299.47, 596.32 305.08, 586.08 305.14, 586.03 298.37, 582.21 298.41, 582.12 283.16, 583.76 283.56, 582.95 269.00, 585.24 268.87, 584.29 259.31, 591.58 259.98, 597.77 258.54, 602.56 254.64, 614.83 253.29, 615.59 224.03, 612.47 220.80, 602.15 220.49, 599.04 218.48, 594.65 216.46, 589.05 215.74, 584.69 216.16, 579.61 218.31, 575.35 221.93, 574.58 204.43, 559.71 204.77, 559.54 188.75, 544.70 188.91, 544.53 173.73, 526.41 173.92, 526.09 145.61, 538.45 145.47, 538.55 154.32, 599.76 153.62, 600.07 180.23, 614.89 180.07, 615.80 182.96, 675.01 181.56, 675.50 202.83, 688.43 202.52, 690.41 206.02, 690.96 226.34, 700.97 226.09, 701.47 246.52, 705.20 245.38, 705.44 291.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,52,"POLYGON ((695.33 46.25, 703.33 40.36, 713.68 54.28, 705.68 60.17, 695.33 46.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,53,"POLYGON ((16.75 898.29, 16.49 874.85, 58.30 874.39, 58.34 879.60, 64.49 879.53, 64.68 897.75, 16.75 898.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,54,"POLYGON ((282.33 868.11, 288.36 867.73, 292.33 872.94, 293.64 877.61, 282.94 878.26, 282.33 868.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,55,"POLYGON ((393.99 849.01, 396.29 876.50, 369.93 878.66, 367.65 851.19, 393.99 849.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,56,"POLYGON ((25.07 849.53, 64.70 849.32, 64.76 859.48, 68.31 859.46, 68.33 864.54, 66.03 867.28, 61.24 870.80, 25.18 870.99, 25.07 849.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,57,"POLYGON ((91.25 851.08, 107.30 850.91, 107.45 866.80, 91.42 866.97, 91.25 851.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,58,"POLYGON ((34.68 815.63, 69.81 815.00, 70.25 838.67, 30.38 839.40, 30.04 821.08, 34.78 821.00, 34.68 815.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,59,"POLYGON ((368.04 811.31, 382.06 810.66, 383.30 838.14, 369.28 838.76, 368.04 811.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,60,"POLYGON ((202.39 764.29, 254.64 765.44, 255.72 771.25, 271.08 772.43, 269.69 766.56, 276.28 766.44, 287.60 765.42, 289.11 772.97, 294.06 771.45, 296.90 779.63, 301.22 778.08, 302.15 808.24, 278.94 808.95, 274.51 803.73, 269.81 804.94, 271.57 812.80, 261.11 812.60, 259.36 803.61, 254.48 798.43, 250.42 801.28, 242.74 800.37, 245.29 812.89, 230.32 811.70, 224.77 815.79, 229.39 826.40, 230.00 836.37, 229.48 842.86, 215.64 834.43, 203.94 826.47, 199.11 821.40, 200.76 818.00, 206.00 811.23, 203.26 805.58, 201.19 799.86, 195.41 797.12, 189.33 799.32, 184.87 798.90, 181.17 792.53, 178.46 790.98, 179.12 782.22, 181.91 781.61, 201.07 781.48, 202.39 764.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,61,"POLYGON ((38.05 811.17, 37.33 783.73, 69.05 782.92, 69.24 790.35, 74.42 793.73, 74.85 796.78, 81.69 795.81, 86.86 799.00, 86.52 806.36, 83.20 808.82, 80.04 804.59, 76.34 807.33, 71.38 805.17, 71.51 810.32, 38.05 811.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,62,"POLYGON ((383.96 770.59, 385.43 800.68, 370.14 801.40, 368.67 771.34, 383.96 770.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,63,"POLYGON ((397.86 760.22, 419.32 760.23, 419.29 809.51, 397.82 809.48, 397.86 760.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,64,"POLYGON ((443.80 809.22, 422.60 809.27, 422.45 760.02, 443.68 759.96, 443.80 809.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,65,"POLYGON ((363.16 766.93, 364.27 802.24, 311.01 803.91, 310.92 800.85, 307.25 800.97, 306.57 779.32, 316.00 779.01, 315.74 770.65, 337.25 769.99, 337.05 763.60, 348.40 763.24, 348.53 767.39, 363.16 766.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,66,"POLYGON ((469.60 758.92, 473.91 795.37, 452.34 797.90, 451.11 787.52, 455.30 778.98, 453.15 760.85, 469.60 758.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,67,"POLYGON ((499.80 764.90, 500.25 775.83, 501.84 775.77, 502.40 789.54, 479.75 790.47, 479.17 775.82, 483.56 775.64, 483.14 765.57, 499.80 764.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,68,"POLYGON ((48.57 778.07, 48.32 754.37, 75.37 754.06, 75.41 756.79, 72.55 761.74, 73.38 766.63, 68.37 770.26, 77.67 776.48, 83.28 776.16, 80.23 780.10, 54.54 780.38, 54.52 778.01, 48.57 778.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,69,"POLYGON ((109.13 751.24, 120.06 750.98, 120.14 753.98, 139.94 753.49, 140.47 774.90, 109.72 775.64, 109.13 751.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,70,"POLYGON ((82.32 691.55, 114.36 690.91, 114.82 713.20, 109.39 713.30, 109.45 715.65, 98.16 715.87, 98.14 714.25, 82.78 714.55, 82.32 691.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,71,"POLYGON ((286.57 698.03, 286.15 688.18, 288.57 681.57, 291.22 680.66, 292.39 677.21, 301.62 670.58, 302.62 697.44, 286.57 698.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,72,"POLYGON ((212.60 696.65, 219.74 665.26, 242.88 670.93, 236.57 702.30, 212.60 696.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,73,"POLYGON ((195.21 663.22, 196.45 666.43, 203.53 669.47, 206.33 680.43, 203.89 691.73, 198.24 691.87, 194.37 704.11, 179.62 699.43, 178.99 690.70, 187.91 661.03, 195.21 663.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,74,"POLYGON ((486.09 693.31, 486.18 683.71, 492.94 683.52, 492.19 662.25, 505.03 662.03, 505.14 668.45, 508.85 670.68, 509.51 675.53, 503.31 681.61, 503.52 684.76, 512.85 685.10, 512.77 691.83, 507.12 694.30, 498.93 694.40, 498.93 695.93, 495.04 695.99, 495.00 693.19, 486.09 693.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,75,"POLYGON ((445.09 656.55, 445.20 665.89, 447.57 667.12, 448.40 690.12, 431.97 690.72, 432.22 698.21, 429.51 698.93, 423.29 694.69, 423.84 665.77, 426.96 665.05, 426.91 661.85, 432.58 661.80, 432.53 656.69, 445.09 656.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,76,"POLYGON ((250.80 663.71, 255.51 664.61, 257.09 668.28, 267.03 677.93, 265.26 686.14, 265.39 691.33, 246.19 687.67, 250.80 663.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,77,"POLYGON ((404.59 657.19, 404.50 663.83, 412.95 663.94, 412.87 670.12, 420.23 670.22, 419.84 697.04, 396.18 696.71, 396.74 657.08, 404.59 657.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,78,"POLYGON ((342.25 667.59, 341.15 658.67, 351.99 658.55, 352.86 660.64, 356.02 667.51, 362.14 667.46, 362.93 675.23, 357.97 675.36, 355.31 678.65, 353.62 683.02, 356.95 684.31, 357.53 692.48, 334.98 694.10, 334.35 685.62, 338.78 683.53, 342.39 678.98, 341.77 674.04, 331.94 668.85, 342.25 667.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,79,"POLYGON ((466.66 657.44, 474.78 664.07, 473.65 671.45, 475.64 676.79, 473.90 699.37, 456.48 699.68, 453.01 687.85, 449.36 687.87, 449.17 665.26, 457.84 665.19, 457.73 652.79, 466.66 657.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,80,"POLYGON ((84.35 683.72, 83.63 661.59, 128.01 660.17, 128.73 682.28, 84.35 683.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,81,"POLYGON ((245.87 638.18, 241.59 660.24, 221.19 656.32, 225.49 634.24, 245.87 638.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,82,"POLYGON ((94.14 658.70, 93.63 634.01, 124.90 633.34, 124.94 635.83, 128.59 639.40, 125.12 642.90, 130.49 648.16, 130.62 653.26, 128.80 655.29, 130.33 657.95, 94.14 658.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,83,"POLYGON ((324.03 624.22, 354.69 622.46, 355.86 642.96, 325.21 644.72, 324.03 624.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,84,"POLYGON ((319.49 624.80, 319.12 643.70, 308.80 639.01, 309.56 629.58, 307.54 627.90, 310.48 627.09, 310.61 621.27, 316.47 621.39, 316.41 624.72, 319.49 624.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,85,"POLYGON ((500.55 618.48, 512.55 618.00, 513.36 638.05, 510.15 640.26, 501.11 640.33, 500.55 618.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,86,"POLYGON ((107.70 626.12, 107.46 616.87, 109.19 610.59, 111.03 608.66, 113.48 610.97, 116.03 608.33, 125.99 610.16, 130.67 608.89, 133.54 606.64, 136.04 606.58, 136.52 625.39, 107.70 626.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,87,"POLYGON ((442.52 592.33, 443.36 617.17, 413.04 618.19, 412.61 605.41, 414.87 596.94, 418.76 593.14, 442.52 592.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,88,"POLYGON ((0.00 580.10, 6.05 582.06, 6.65 580.20, 18.93 584.19, 18.12 586.63, 24.04 588.55, 18.26 606.14, 0.00 600.19, 0.00 580.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,89,"POLYGON ((168.98 592.73, 168.31 587.53, 180.36 585.98, 181.05 591.15, 168.98 592.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,90,"POLYGON ((338.67 571.68, 341.93 578.25, 342.71 588.95, 330.90 589.81, 327.59 591.43, 322.32 586.97, 322.03 571.95, 338.67 571.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,91,"POLYGON ((111.43 593.88, 111.51 583.76, 113.07 579.66, 107.96 579.79, 106.61 575.52, 117.07 572.28, 118.45 569.00, 139.50 569.15, 139.32 594.08, 111.43 593.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,92,"POLYGON ((436.69 588.79, 408.25 589.50, 407.92 576.65, 417.96 576.40, 417.55 560.40, 432.84 560.01, 433.13 571.35, 436.24 571.27, 436.69 588.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,93,"POLYGON ((0.00 557.47, 12.28 561.10, 13.88 564.48, 16.98 563.02, 21.26 566.02, 20.78 569.78, 15.19 569.08, 17.62 575.95, 14.85 576.93, 0.00 572.52, 0.00 557.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,94,"POLYGON ((115.37 534.45, 146.92 533.73, 147.40 554.01, 115.85 554.73, 115.37 534.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,95,"POLYGON ((0.00 532.85, 3.37 537.42, 6.63 539.05, 9.93 537.59, 15.11 541.52, 18.60 538.74, 15.85 532.82, 25.69 533.41, 27.04 539.50, 22.82 543.58, 28.47 544.84, 28.54 547.72, 31.15 548.74, 31.15 550.32, 13.22 550.24, 13.23 548.24, 0.00 548.19, 0.00 532.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,96,"POLYGON ((295.51 532.89, 295.22 541.06, 301.47 542.13, 301.59 546.83, 288.95 538.74, 287.00 540.28, 281.71 547.34, 282.08 532.74, 295.51 532.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,97,"POLYGON ((367.50 528.32, 368.60 533.00, 360.64 532.47, 355.48 535.31, 353.42 541.80, 344.30 535.10, 346.83 527.60, 367.50 528.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,98,"POLYGON ((415.10 522.53, 442.32 523.50, 443.35 544.87, 435.67 545.51, 427.81 545.51, 422.38 545.23, 415.10 522.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,99,"POLYGON ((122.33 513.89, 125.36 515.68, 128.66 513.62, 126.89 507.23, 134.76 513.46, 134.25 518.29, 130.22 520.88, 133.91 523.87, 122.13 523.64, 122.33 513.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3740439,100,"POLYGON ((255.13 496.49, 253.69 515.44, 245.08 509.49, 234.15 509.77, 227.85 515.61, 229.44 494.55, 255.13 496.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,0,"POLYGON ((562.67 420.30, 584.94 421.66, 584.77 435.41, 573.23 434.82, 573.16 455.99, 560.42 455.79, 559.46 452.93, 561.39 421.76, 562.67 420.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,1,"POLYGON ((863.38 432.28, 869.34 422.42, 871.21 414.58, 878.89 414.68, 894.53 436.34, 889.73 440.15, 884.66 438.21, 876.80 442.68, 871.59 446.94, 866.95 444.99, 858.83 450.95, 857.36 457.29, 858.55 462.86, 847.05 469.92, 831.64 450.89, 838.44 445.72, 824.85 428.27, 824.05 425.65, 831.17 421.05, 827.70 412.61, 830.13 409.31, 836.84 405.74, 841.53 404.02, 863.38 432.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,2,"POLYGON ((788.41 332.35, 849.47 342.22, 844.96 369.39, 836.31 366.71, 825.44 366.56, 824.11 371.59, 823.73 373.67, 817.86 372.99, 816.08 384.69, 798.42 380.97, 800.39 368.25, 784.03 365.74, 786.51 356.53, 788.44 342.34, 787.46 336.75, 788.41 332.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,3,"POLYGON ((628.37 360.93, 610.14 356.13, 607.11 354.48, 621.82 303.89, 643.46 311.73, 640.32 326.50, 650.61 330.44, 645.82 348.76, 633.94 345.20, 628.37 360.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,4,"POLYGON ((792.14 323.31, 801.58 275.83, 816.90 278.14, 816.41 283.15, 838.21 286.12, 841.41 264.00, 838.25 263.04, 841.99 245.47, 887.99 254.06, 885.58 266.20, 881.15 272.75, 878.83 279.89, 876.97 289.10, 878.21 297.17, 880.21 301.27, 880.91 304.81, 888.70 306.47, 887.65 322.73, 879.40 368.09, 857.36 364.50, 862.67 335.02, 792.14 323.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,5,"POLYGON ((667.73 122.29, 668.73 103.97, 676.26 104.49, 676.59 101.26, 692.51 102.63, 692.50 105.96, 706.73 107.26, 707.07 103.81, 720.78 104.39, 720.41 106.38, 719.97 109.61, 724.35 113.67, 728.61 116.27, 732.92 117.40, 735.11 117.34, 737.52 114.06, 740.66 109.92, 740.89 106.59, 746.56 107.06, 746.95 118.82, 745.67 129.46, 669.95 123.39, 667.73 122.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,6,"POLYGON ((591.98 94.68, 601.93 95.38, 601.91 98.91, 614.26 99.73, 614.93 96.91, 631.87 97.83, 631.45 101.48, 645.79 102.05, 645.90 98.52, 661.82 99.97, 661.58 102.89, 667.35 103.78, 666.58 122.12, 589.51 116.19, 588.84 106.72, 591.98 94.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,7,"POLYGON ((847.64 76.42, 865.41 82.31, 849.50 129.73, 831.74 123.84, 847.64 76.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,8,"POLYGON ((662.47 29.79, 668.24 12.46, 675.78 15.11, 676.71 13.35, 689.88 18.10, 689.08 20.72, 705.13 25.74, 706.18 23.36, 718.73 27.75, 718.78 29.83, 734.10 35.88, 734.91 33.64, 748.82 38.48, 748.64 40.84, 762.56 45.67, 763.73 43.16, 784.56 50.93, 785.41 54.99, 779.46 74.68, 675.91 34.91, 662.47 29.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,9,"POLYGON ((622.80 0.00, 624.09 1.71, 635.31 7.73, 644.75 7.00, 650.14 4.26, 660.16 7.34, 669.95 11.17, 663.26 31.39, 593.89 6.19, 592.15 1.29, 591.35 0.00, 622.80 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,10,"POLYGON ((459.71 859.55, 484.41 858.81, 484.63 876.61, 480.70 883.15, 460.31 883.05, 460.16 872.42, 459.71 859.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,11,"POLYGON ((326.69 846.26, 330.75 843.23, 349.65 843.19, 351.26 847.99, 350.90 851.39, 353.47 858.96, 351.80 869.17, 344.46 870.54, 327.61 871.10, 326.69 846.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,12,"POLYGON ((378.31 845.45, 396.65 845.72, 402.31 858.96, 401.60 866.03, 379.46 867.20, 377.94 865.77, 376.57 847.12, 378.31 845.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,13,"POLYGON ((451.15 830.72, 477.35 830.53, 477.31 852.93, 469.10 853.01, 469.05 855.47, 451.26 854.82, 450.49 839.15, 451.15 830.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,14,"POLYGON ((326.41 840.99, 326.52 839.36, 339.76 831.81, 339.24 828.87, 333.97 824.74, 325.44 820.54, 325.36 817.33, 345.91 817.82, 349.83 820.96, 350.13 838.76, 348.89 841.72, 326.41 840.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,15,"POLYGON ((377.41 815.91, 400.29 815.03, 406.95 832.36, 406.92 836.93, 402.41 839.69, 377.63 841.94, 377.41 815.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,16,"POLYGON ((268.70 773.25, 268.66 797.09, 260.58 797.12, 260.62 818.98, 257.02 822.94, 252.51 832.13, 252.76 842.11, 256.48 849.54, 259.88 852.08, 260.79 860.44, 171.92 862.54, 171.62 857.99, 151.94 858.32, 150.76 846.30, 153.74 846.22, 151.28 798.51, 162.71 798.97, 164.39 796.45, 224.76 795.17, 223.50 774.91, 268.70 773.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,17,"POLYGON ((441.75 804.45, 461.25 804.06, 467.35 804.66, 467.01 811.11, 465.43 812.37, 464.30 826.14, 441.45 826.83, 441.75 804.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,18,"POLYGON ((324.59 792.77, 347.62 792.03, 347.89 796.59, 351.23 812.09, 350.28 815.65, 324.88 815.72, 323.12 805.02, 322.08 793.41, 324.59 792.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,19,"POLYGON ((379.11 789.83, 402.88 789.22, 404.26 802.86, 403.71 810.09, 402.29 811.88, 377.63 813.10, 375.80 811.09, 375.57 790.49, 379.11 789.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,20,"POLYGON ((437.73 788.86, 437.67 768.99, 453.03 768.76, 455.79 766.62, 467.45 766.03, 471.33 767.40, 477.68 767.24, 478.12 772.82, 477.70 774.01, 470.90 773.89, 467.56 776.04, 464.63 777.43, 459.56 781.38, 450.15 788.70, 437.73 788.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,21,"POLYGON ((325.65 759.78, 345.61 759.72, 354.02 765.25, 355.36 782.86, 354.99 785.80, 325.01 786.28, 323.46 783.39, 323.65 762.05, 325.65 759.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,22,"POLYGON ((376.40 758.99, 401.80 758.50, 401.88 784.38, 377.25 786.34, 376.14 772.25, 376.40 758.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,23,"POLYGON ((452.26 767.13, 434.51 767.32, 434.70 745.56, 463.90 745.06, 464.85 748.12, 477.78 748.90, 479.81 755.04, 478.37 766.58, 452.26 767.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,24,"POLYGON ((39.65 900.00, 0.00 896.75, 0.00 799.17, 2.24 772.24, 7.80 772.70, 20.52 619.83, 15.33 619.40, 16.89 600.70, 93.88 607.03, 92.25 626.59, 82.13 625.76, 62.09 866.61, 84.75 868.47, 82.13 900.00, 39.65 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,25,"POLYGON ((321.92 735.01, 344.97 734.74, 348.28 737.29, 348.63 739.48, 352.84 742.33, 352.44 754.83, 347.86 755.68, 347.36 758.65, 324.74 758.78, 321.61 757.69, 320.95 737.55, 321.92 735.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,26,"POLYGON ((376.71 730.75, 399.15 729.73, 401.99 731.41, 403.47 748.60, 401.56 754.53, 383.27 756.02, 377.47 754.55, 375.15 750.95, 373.64 749.65, 373.36 732.88, 376.71 730.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,27,"POLYGON ((436.14 733.89, 436.84 727.06, 439.81 726.74, 443.17 717.51, 447.49 711.45, 477.13 708.96, 478.50 718.82, 477.36 727.27, 472.88 727.38, 472.70 734.80, 465.49 734.87, 464.09 733.80, 458.76 733.94, 450.47 735.26, 449.69 733.79, 442.47 733.60, 436.14 733.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,28,"POLYGON ((323.05 705.17, 347.92 705.76, 349.02 709.81, 349.11 713.16, 351.35 713.84, 351.77 719.89, 347.92 720.37, 347.28 729.42, 324.07 730.63, 322.71 720.88, 323.05 705.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,29,"POLYGON ((374.87 705.05, 394.33 702.77, 396.27 704.06, 399.64 725.59, 398.22 727.98, 375.33 728.72, 373.32 719.80, 372.24 706.74, 374.87 705.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,30,"POLYGON ((118.21 674.85, 150.71 673.84, 154.30 752.48, 121.45 752.97, 118.21 674.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,31,"POLYGON ((429.12 704.30, 427.95 702.11, 427.22 698.18, 428.82 696.90, 426.57 691.52, 426.25 689.17, 427.36 684.19, 434.48 680.55, 454.82 678.54, 465.88 678.50, 466.33 681.33, 464.39 683.87, 464.08 690.80, 458.77 697.00, 470.32 696.95, 470.49 703.49, 429.12 704.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,32,"POLYGON ((375.18 676.65, 398.54 676.63, 399.90 700.88, 382.17 701.33, 379.76 699.77, 375.33 699.89, 372.10 695.11, 371.88 680.99, 375.18 676.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,33,"POLYGON ((322.61 702.45, 321.24 702.49, 319.13 702.68, 315.89 702.00, 314.46 699.69, 314.06 678.92, 319.90 679.15, 322.03 684.78, 334.47 684.95, 343.29 675.44, 347.88 675.21, 352.17 677.81, 352.11 684.74, 346.89 684.87, 347.23 693.28, 349.86 694.08, 351.36 699.74, 346.81 701.59, 322.61 702.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,34,"POLYGON ((374.53 674.44, 370.11 674.71, 365.87 671.14, 362.98 656.20, 366.10 651.42, 376.63 646.89, 396.27 645.94, 404.91 649.12, 406.16 663.05, 399.82 663.65, 399.75 672.78, 374.53 674.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,35,"POLYGON ((541.35 643.52, 567.32 642.85, 567.72 658.47, 553.55 658.83, 549.05 656.00, 541.67 656.19, 541.35 643.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,36,"POLYGON ((374.72 602.81, 379.39 601.44, 398.96 603.30, 398.17 626.07, 373.74 627.32, 374.72 602.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,37,"POLYGON ((320.11 625.60, 319.29 622.65, 317.63 606.35, 316.91 602.04, 338.05 598.17, 342.34 600.53, 343.19 623.63, 341.87 625.53, 336.54 626.16, 333.58 627.10, 328.22 626.99, 325.20 625.47, 320.11 625.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,38,"POLYGON ((346.32 625.04, 345.27 612.95, 346.18 599.81, 362.57 599.28, 370.09 601.55, 369.34 625.70, 356.51 624.91, 346.32 625.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,39,"POLYGON ((429.62 584.82, 428.65 561.23, 465.93 560.76, 467.14 583.62, 429.62 584.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,40,"POLYGON ((54.53 533.72, 76.86 559.47, 55.93 577.46, 33.60 551.70, 54.53 533.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,41,"POLYGON ((437.83 497.53, 466.93 496.92, 473.13 502.51, 473.69 535.74, 462.74 535.14, 461.97 522.64, 438.17 522.51, 437.83 497.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,42,"POLYGON ((15.78 454.02, 50.98 423.53, 154.42 541.89, 119.24 572.35, 15.78 454.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,43,"POLYGON ((433.07 455.71, 437.40 451.94, 451.74 451.42, 463.00 452.60, 465.28 455.78, 465.11 466.53, 465.29 473.58, 461.91 473.96, 462.01 483.37, 460.91 486.79, 450.86 486.76, 448.84 483.28, 441.18 483.48, 441.21 468.03, 433.25 468.36, 433.07 455.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,44,"POLYGON ((455.80 409.76, 460.63 408.02, 472.62 408.44, 476.42 412.76, 475.90 432.49, 476.73 435.71, 473.07 437.56, 454.40 435.99, 455.80 409.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,45,"POLYGON ((75.16 223.03, 86.69 225.40, 83.54 240.56, 72.00 238.17, 75.16 223.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,46,"POLYGON ((374.80 196.33, 375.52 185.32, 379.24 175.57, 408.75 182.48, 404.54 202.63, 374.80 196.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,47,"POLYGON ((316.37 147.23, 376.35 159.69, 367.91 194.42, 362.47 195.18, 307.48 183.09, 316.37 147.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,48,"POLYGON ((196.94 143.85, 206.68 144.95, 207.94 137.13, 220.57 140.03, 217.55 156.95, 216.09 157.10, 195.63 154.29, 196.94 143.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,49,"POLYGON ((407.44 155.60, 388.66 140.88, 391.69 124.69, 393.96 123.70, 440.66 130.71, 442.32 134.51, 441.99 137.54, 436.37 159.13, 433.53 158.27, 430.02 152.21, 427.03 149.89, 421.32 146.81, 418.59 146.88, 411.23 148.85, 410.77 151.15, 409.64 156.17, 407.44 155.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,50,"POLYGON ((281.02 108.05, 307.37 112.17, 299.25 163.32, 281.85 160.59, 283.20 151.97, 274.29 150.57, 281.02 108.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,51,"POLYGON ((234.65 101.49, 257.85 105.58, 260.80 106.54, 261.45 107.88, 260.76 113.71, 276.94 116.74, 278.98 123.15, 273.80 157.49, 250.94 154.54, 244.87 145.86, 227.32 142.45, 233.47 104.53, 234.65 101.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,52,"POLYGON ((213.81 100.35, 231.75 103.22, 232.13 106.01, 227.33 139.23, 225.46 139.28, 208.55 136.27, 212.60 102.56, 213.81 100.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,53,"POLYGON ((178.83 141.58, 186.19 92.93, 194.64 93.07, 205.60 95.41, 207.95 97.99, 211.33 99.82, 206.49 144.20, 178.83 141.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,54,"POLYGON ((158.82 87.35, 184.78 92.46, 176.47 139.02, 153.46 139.43, 143.38 136.72, 146.58 110.93, 154.14 110.91, 156.93 89.49, 158.82 87.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,55,"POLYGON ((117.70 81.23, 148.23 85.69, 148.97 87.94, 142.13 136.24, 110.37 131.61, 117.70 81.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,56,"POLYGON ((488.31 55.91, 514.78 55.96, 516.87 60.37, 516.51 65.58, 509.95 66.37, 509.76 78.62, 506.42 84.26, 488.91 84.46, 488.31 55.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,57,"POLYGON ((40.48 19.10, 83.89 25.82, 78.10 62.67, 73.69 61.98, 71.90 73.26, 76.96 74.06, 70.97 112.26, 41.07 107.61, 46.66 71.93, 32.52 69.71, 40.48 19.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,58,"POLYGON ((477.93 25.52, 485.75 25.32, 486.90 29.13, 490.45 33.10, 493.93 34.45, 497.57 33.74, 500.92 30.24, 503.94 25.16, 511.98 25.05, 512.87 47.51, 487.08 48.57, 479.80 45.74, 477.93 25.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,59,"POLYGON ((415.21 0.00, 422.54 38.19, 396.11 43.19, 387.86 0.00, 415.21 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,60,"POLYGON ((467.27 0.00, 470.73 17.98, 459.72 19.92, 459.72 16.48, 455.17 0.00, 467.27 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,61,"POLYGON ((326.29 0.00, 331.60 36.03, 334.62 49.87, 326.78 52.00, 317.85 53.81, 313.65 47.28, 303.77 0.00, 326.29 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,62,"POLYGON ((343.72 0.00, 347.71 33.27, 332.80 35.93, 327.22 0.00, 343.72 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,63,"POLYGON ((205.22 0.00, 206.99 13.31, 189.84 13.13, 187.28 10.91, 186.96 6.54, 188.47 0.26, 188.35 0.00, 205.22 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,64,"POLYGON ((289.94 0.00, 291.76 12.74, 273.67 16.16, 270.74 11.24, 268.88 0.00, 289.94 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,65,"POLYGON ((567.46 860.77, 599.75 860.88, 605.52 857.40, 608.26 866.59, 608.58 871.26, 610.61 872.26, 610.17 879.75, 607.68 880.85, 571.26 878.88, 566.50 876.82, 567.46 860.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,66,"POLYGON ((558.19 833.75, 591.36 834.88, 595.93 833.72, 605.14 834.41, 606.46 836.69, 606.64 852.49, 574.64 851.44, 569.63 852.19, 571.36 850.06, 573.86 849.15, 572.00 846.21, 568.24 842.75, 563.56 843.18, 562.05 841.15, 560.28 841.51, 558.24 843.54, 556.41 841.61, 555.54 840.28, 556.54 834.52, 558.19 833.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,67,"POLYGON ((868.05 824.11, 879.64 823.82, 891.94 823.50, 900.00 826.95, 900.00 833.11, 868.31 834.25, 868.05 824.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,68,"POLYGON ((582.56 796.88, 588.85 809.10, 584.36 814.32, 583.02 814.76, 577.61 815.32, 575.59 818.59, 569.77 819.69, 569.83 818.02, 558.44 818.20, 556.85 816.89, 556.32 804.12, 558.04 802.41, 571.02 802.70, 573.61 802.01, 573.58 796.91, 582.56 796.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,69,"POLYGON ((880.49 798.82, 866.97 799.92, 864.30 797.26, 861.25 775.70, 864.92 772.63, 900.00 770.44, 900.00 793.52, 896.51 793.35, 885.76 795.36, 883.22 798.26, 880.49 798.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,70,"POLYGON ((573.60 735.39, 583.83 735.44, 584.56 737.25, 584.12 760.65, 577.06 760.48, 578.14 757.83, 577.71 753.45, 577.74 752.51, 577.71 751.34, 577.58 750.61, 576.99 749.31, 576.66 748.66, 575.90 747.81, 575.30 747.07, 574.91 746.28, 574.65 745.49, 573.99 744.33, 573.48 743.10, 573.14 742.24, 572.86 741.45, 572.60 740.46, 572.34 739.73, 572.19 739.00, 572.11 738.14, 572.01 737.28, 573.60 735.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,71,"POLYGON ((549.66 699.47, 555.49 699.21, 554.42 690.82, 588.08 690.21, 588.61 701.34, 590.11 701.30, 590.36 706.60, 588.51 706.78, 589.18 713.31, 587.73 715.45, 555.10 717.40, 554.13 709.14, 550.16 709.00, 549.66 699.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,72,"POLYGON ((895.53 669.69, 895.96 674.96, 887.99 675.47, 889.17 692.80, 862.96 696.85, 861.36 680.13, 869.75 679.03, 869.74 666.95, 889.06 665.15, 889.48 669.84, 895.53 669.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,73,"POLYGON ((784.40 605.42, 791.43 598.07, 805.05 588.09, 843.56 637.14, 822.32 651.51, 784.40 605.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,74,"POLYGON ((872.18 580.85, 893.09 573.67, 899.31 589.96, 900.00 589.80, 900.00 626.72, 888.07 631.54, 881.93 611.23, 878.08 612.20, 871.74 597.48, 876.60 595.43, 872.18 580.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,75,"POLYGON ((740.29 598.67, 776.76 567.66, 793.66 588.92, 757.34 618.88, 740.29 598.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,76,"POLYGON ((857.87 553.66, 851.05 539.23, 823.92 548.21, 816.10 528.87, 844.17 518.12, 838.28 501.58, 856.00 495.06, 863.54 513.19, 868.04 513.94, 873.43 525.30, 869.38 527.02, 876.57 545.88, 857.87 553.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3741789,77,"POLYGON ((825.38 498.84, 801.48 454.42, 821.61 444.27, 845.11 488.44, 825.38 498.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,0,"POLYGON ((715.74 0.00, 715.74 0.45, 690.67 0.61, 690.71 6.42, 677.89 6.51, 677.85 0.00, 715.74 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,1,"POLYGON ((45.30 513.27, 31.30 490.18, 47.61 480.38, 48.85 482.41, 56.35 477.89, 64.94 492.03, 51.51 500.12, 55.69 507.03, 45.30 513.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,2,"POLYGON ((349.59 476.72, 352.45 477.93, 350.84 481.70, 363.62 487.11, 355.61 505.82, 327.66 494.02, 330.44 487.51, 342.75 492.70, 349.59 476.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,3,"POLYGON ((368.60 405.80, 384.32 416.14, 371.65 435.27, 364.60 428.25, 361.32 432.82, 357.29 429.97, 359.08 422.73, 368.60 405.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,4,"POLYGON ((396.51 368.59, 412.61 379.54, 404.98 390.64, 401.51 390.24, 399.63 394.99, 409.11 401.43, 401.27 412.87, 377.30 396.56, 396.51 368.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,5,"POLYGON ((0.00 372.65, 15.08 393.81, 0.00 404.47, 0.00 372.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,6,"POLYGON ((101.00 330.57, 114.32 331.85, 117.88 329.90, 120.52 324.97, 123.35 324.94, 126.65 333.00, 135.55 345.52, 134.72 352.11, 135.72 357.03, 150.16 373.82, 146.51 376.94, 157.84 390.12, 150.85 396.07, 138.95 382.21, 142.65 379.08, 101.00 330.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,7,"POLYGON ((13.46 332.59, 30.33 321.90, 62.76 372.66, 60.92 375.95, 58.17 378.66, 58.89 383.35, 61.18 386.71, 57.55 389.13, 54.33 384.40, 51.18 386.52, 48.85 388.00, 13.46 332.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,8,"POLYGON ((419.44 348.80, 413.69 354.83, 407.06 348.79, 416.08 338.99, 421.29 343.47, 419.44 348.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,9,"POLYGON ((473.17 332.09, 483.19 344.15, 472.06 353.18, 463.10 339.81, 466.05 337.95, 473.17 332.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,10,"POLYGON ((255.01 305.68, 276.88 310.56, 275.50 318.16, 282.56 320.38, 280.61 328.96, 275.60 327.84, 273.59 336.57, 249.70 331.19, 255.01 305.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,11,"POLYGON ((462.20 285.78, 467.56 281.69, 474.80 292.38, 478.23 290.07, 483.86 296.61, 462.15 312.99, 457.51 306.18, 469.66 295.48, 462.20 285.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,12,"POLYGON ((477.33 271.00, 498.14 262.10, 503.11 262.22, 508.63 259.86, 514.09 275.06, 489.35 285.75, 485.49 278.23, 481.25 280.05, 477.33 271.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,13,"POLYGON ((290.79 261.10, 287.23 271.89, 264.87 266.54, 261.52 265.31, 255.27 263.63, 259.97 251.66, 267.51 253.53, 282.24 252.29, 285.84 253.66, 290.79 261.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,14,"POLYGON ((518.02 261.84, 541.39 252.58, 544.79 259.42, 532.97 263.92, 529.67 261.03, 525.98 262.12, 523.12 267.15, 519.44 268.73, 518.02 261.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,15,"POLYGON ((242.03 205.05, 249.45 206.33, 249.67 214.87, 247.65 228.44, 243.56 241.80, 241.35 246.20, 241.41 249.84, 239.90 264.05, 237.57 273.72, 219.30 269.41, 225.93 234.30, 232.44 222.95, 234.59 214.37, 239.56 212.18, 242.03 205.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,16,"POLYGON ((147.88 180.51, 156.35 188.46, 142.18 198.84, 135.38 204.14, 131.54 210.90, 123.82 216.56, 119.24 215.43, 107.72 232.04, 95.42 237.06, 88.63 243.49, 78.50 232.92, 125.00 188.80, 134.98 187.72, 142.48 186.64, 147.88 180.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,17,"POLYGON ((0.97 176.08, 20.35 166.21, 30.65 186.48, 26.63 194.62, 37.07 204.17, 41.98 203.33, 56.65 229.28, 52.89 231.40, 45.17 231.60, 41.22 228.55, 33.11 227.69, 25.83 217.40, 30.16 208.54, 25.41 200.96, 10.47 194.35, 0.97 176.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,18,"POLYGON ((397.18 160.33, 418.63 163.49, 418.84 171.65, 416.00 177.16, 394.75 172.51, 397.18 160.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,19,"POLYGON ((233.59 137.41, 240.28 155.00, 159.89 185.33, 153.19 167.75, 233.59 137.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,20,"POLYGON ((438.52 143.93, 453.42 143.80, 452.29 148.29, 454.96 155.63, 454.03 167.78, 446.83 168.21, 445.24 163.81, 433.07 164.36, 433.13 147.05, 438.52 143.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,21,"POLYGON ((471.23 128.69, 494.89 130.06, 494.15 142.44, 490.48 142.23, 489.97 150.81, 470.00 149.65, 471.23 128.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,22,"POLYGON ((507.65 116.68, 532.53 117.78, 535.85 130.81, 531.23 134.88, 507.59 133.75, 507.65 116.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,23,"POLYGON ((544.42 108.10, 563.04 107.98, 567.81 115.21, 569.53 127.42, 543.81 125.61, 544.42 108.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,24,"POLYGON ((400.83 94.29, 426.78 94.51, 426.69 104.68, 421.15 104.64, 421.08 113.66, 412.80 113.60, 412.75 117.71, 400.61 117.60, 400.83 94.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,25,"POLYGON ((296.24 82.09, 313.38 102.32, 303.64 110.51, 297.77 103.60, 274.98 122.77, 263.69 109.45, 296.24 82.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,26,"POLYGON ((469.57 92.99, 481.12 93.90, 479.78 110.91, 468.23 110.01, 469.57 92.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,27,"POLYGON ((169.24 76.23, 179.10 77.05, 179.16 120.78, 164.08 121.85, 162.57 104.04, 166.39 96.77, 167.36 94.15, 170.92 87.05, 169.24 76.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,28,"POLYGON ((518.29 82.00, 517.65 97.45, 509.41 97.10, 510.05 81.68, 518.29 82.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,29,"POLYGON ((38.17 54.39, 46.82 62.91, 60.27 66.08, 59.79 108.58, 37.59 108.35, 38.17 54.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,30,"POLYGON ((552.79 66.36, 552.62 85.74, 536.45 85.60, 536.62 66.22, 552.79 66.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,31,"POLYGON ((81.05 75.74, 80.98 66.50, 82.89 64.34, 88.72 61.49, 118.95 61.47, 119.03 75.50, 81.05 75.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,32,"POLYGON ((0.00 54.07, 20.09 53.01, 21.16 73.34, 0.00 74.45, 0.00 54.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,33,"POLYGON ((168.81 59.46, 168.89 56.24, 216.82 56.44, 217.97 59.45, 258.95 58.98, 259.07 69.50, 218.15 69.99, 218.12 67.26, 168.91 67.83, 168.81 59.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,34,"POLYGON ((447.19 26.46, 461.35 25.23, 461.71 39.34, 466.76 40.10, 466.05 47.47, 449.25 49.07, 437.91 56.42, 430.46 42.78, 446.54 35.89, 447.19 26.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,35,"POLYGON ((492.13 4.13, 508.39 2.16, 508.12 0.00, 534.22 0.00, 534.41 1.56, 530.72 2.01, 532.26 14.54, 523.86 15.58, 524.25 18.83, 511.75 20.37, 510.65 11.41, 496.50 13.15, 492.13 4.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,36,"POLYGON ((660.11 634.04, 666.52 628.35, 674.90 637.73, 668.48 643.42, 660.11 634.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,37,"POLYGON ((664.74 601.63, 667.37 605.93, 654.25 616.04, 650.41 613.00, 659.13 610.29, 663.61 605.81, 664.74 601.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,38,"POLYGON ((687.12 569.29, 692.63 565.77, 697.89 573.94, 682.86 592.77, 677.55 588.58, 689.72 573.31, 687.12 569.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,39,"POLYGON ((719.23 545.02, 730.81 560.11, 719.57 568.66, 714.20 561.67, 709.53 565.21, 705.89 560.46, 702.98 562.67, 697.84 555.96, 696.46 558.50, 691.97 554.02, 697.20 548.83, 700.33 549.75, 704.38 544.74, 711.79 550.68, 719.23 545.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,40,"POLYGON ((774.58 528.05, 785.98 523.05, 791.75 534.98, 780.22 540.56, 774.58 528.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,41,"POLYGON ((733.46 511.54, 747.37 504.04, 762.44 531.80, 755.84 535.36, 739.00 535.48, 727.36 542.82, 723.83 537.25, 729.48 533.69, 732.32 528.60, 736.93 524.08, 737.07 518.20, 733.46 511.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,42,"POLYGON ((767.28 495.78, 776.60 491.12, 781.56 500.96, 788.77 499.58, 791.39 504.24, 791.48 507.90, 789.95 510.89, 772.78 519.46, 767.06 508.13, 772.22 505.53, 767.28 495.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,43,"POLYGON ((816.55 497.67, 815.88 487.30, 846.94 484.42, 849.42 499.34, 842.31 499.52, 840.97 496.00, 834.87 494.29, 829.22 494.44, 816.55 497.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,44,"POLYGON ((576.54 483.50, 581.90 488.71, 573.88 496.84, 568.62 491.21, 573.64 486.46, 576.54 483.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,45,"POLYGON ((861.73 482.86, 872.56 480.19, 876.48 495.85, 865.63 498.52, 861.73 482.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,46,"POLYGON ((622.70 443.89, 635.72 433.39, 646.66 446.81, 654.96 440.12, 672.56 461.71, 651.24 478.93, 622.70 443.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,47,"POLYGON ((676.92 428.50, 695.38 415.53, 700.75 429.13, 704.86 426.12, 707.46 429.78, 705.50 435.25, 689.86 444.82, 676.92 428.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,48,"POLYGON ((701.28 390.43, 706.34 387.08, 712.18 395.81, 717.43 392.35, 731.10 412.80, 735.96 409.59, 737.14 416.48, 733.26 420.55, 719.24 426.11, 714.06 418.56, 719.01 416.95, 701.28 390.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,49,"POLYGON ((865.54 361.03, 884.07 360.87, 884.15 370.70, 876.65 370.76, 876.71 377.55, 887.59 377.47, 887.64 382.95, 893.23 382.90, 893.29 390.15, 861.59 390.43, 861.48 377.49, 865.68 377.45, 865.54 361.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,50,"POLYGON ((779.23 354.89, 793.63 350.86, 795.77 358.44, 798.46 357.68, 804.16 377.83, 806.13 377.29, 809.07 387.60, 784.04 394.64, 782.37 388.75, 785.97 384.46, 786.53 372.20, 783.50 364.24, 783.54 358.29, 779.23 354.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,51,"POLYGON ((816.63 334.80, 828.07 333.77, 829.50 349.56, 839.21 348.69, 839.98 357.35, 846.75 356.76, 849.26 384.60, 824.69 386.80, 822.66 383.53, 823.81 379.99, 820.13 373.80, 816.63 334.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,52,"POLYGON ((880.16 336.35, 893.98 336.64, 893.70 349.74, 879.89 349.45, 880.16 336.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,53,"POLYGON ((756.61 287.28, 769.04 286.40, 769.93 298.86, 775.59 298.47, 774.09 304.94, 757.96 306.09, 756.61 287.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,54,"POLYGON ((653.16 286.48, 670.62 280.84, 676.57 299.11, 659.13 304.78, 653.16 286.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,55,"POLYGON ((636.57 276.87, 641.92 297.87, 627.56 301.52, 622.20 280.53, 636.57 276.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,56,"POLYGON ((585.76 251.18, 591.33 268.20, 565.96 276.44, 563.15 267.85, 570.06 256.29, 585.76 251.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,57,"POLYGON ((900.00 256.44, 888.86 257.05, 887.80 237.92, 900.00 237.24, 900.00 256.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,58,"POLYGON ((737.87 219.94, 738.14 227.39, 742.91 227.23, 744.05 257.70, 719.14 258.62, 718.12 230.75, 710.45 231.03, 710.19 223.87, 719.41 223.54, 719.48 225.38, 723.24 225.24, 723.06 220.50, 737.87 219.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,59,"POLYGON ((607.56 229.62, 622.61 225.51, 630.38 247.33, 609.90 253.05, 602.84 239.13, 607.20 235.07, 607.56 229.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,60,"POLYGON ((810.25 225.92, 822.94 225.89, 822.95 237.67, 827.39 237.56, 829.07 257.52, 817.55 257.81, 818.22 249.25, 815.75 244.61, 805.09 244.01, 801.81 219.97, 810.39 219.75, 810.25 225.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,61,"POLYGON ((644.67 230.69, 667.86 229.37, 668.56 241.69, 645.37 243.00, 644.67 230.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,62,"POLYGON ((790.22 231.83, 790.54 241.72, 773.64 250.99, 768.62 251.12, 762.61 247.45, 762.12 232.68, 775.66 232.23, 775.29 221.25, 780.01 221.08, 780.36 232.15, 790.22 231.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,63,"POLYGON ((864.52 226.13, 864.80 240.24, 846.95 240.61, 846.64 226.50, 864.52 226.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,64,"POLYGON ((633.32 107.23, 651.00 107.33, 650.94 116.70, 636.38 116.61, 633.32 107.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,65,"POLYGON ((801.44 106.28, 828.41 106.72, 829.72 109.28, 827.19 111.57, 823.88 108.92, 821.27 109.37, 820.34 111.25, 820.04 113.86, 820.10 116.32, 814.64 116.46, 812.24 115.41, 810.42 116.70, 807.05 116.28, 805.02 115.11, 800.74 117.44, 801.44 106.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,66,"POLYGON ((842.94 100.00, 867.09 102.33, 866.15 111.92, 871.93 112.64, 871.64 118.40, 864.69 118.42, 864.30 120.78, 850.23 118.95, 847.98 118.12, 847.37 117.24, 844.69 116.14, 841.61 117.39, 840.27 116.69, 842.94 100.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,67,"POLYGON ((674.77 103.19, 696.33 104.51, 695.78 115.55, 678.21 114.54, 678.50 109.96, 675.12 108.38, 674.77 103.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,68,"POLYGON ((712.79 100.93, 735.51 105.12, 734.34 111.44, 735.92 115.26, 731.35 117.13, 710.49 113.29, 712.79 100.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,69,"POLYGON ((602.63 89.12, 614.39 89.02, 614.59 109.57, 609.83 109.63, 609.94 121.41, 591.15 121.58, 590.91 95.44, 602.69 95.31, 602.63 89.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,70,"POLYGON ((892.07 85.49, 900.00 86.28, 900.00 122.38, 887.35 121.30, 892.07 85.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,71,"POLYGON ((755.81 94.42, 766.04 94.00, 766.25 99.16, 780.37 98.58, 780.85 110.02, 756.51 111.02, 755.81 94.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,72,"POLYGON ((626.81 64.53, 626.57 68.73, 629.45 71.10, 633.75 74.14, 639.83 78.54, 618.01 77.34, 619.06 63.33, 626.81 64.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,73,"POLYGON ((592.03 0.00, 592.98 10.50, 569.20 12.37, 564.88 12.33, 561.18 12.69, 557.57 8.45, 555.99 0.00, 592.03 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,74,"POLYGON ((656.75 0.00, 657.12 8.13, 640.59 8.88, 625.47 6.34, 623.56 0.00, 656.75 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,75,"POLYGON ((816.38 867.21, 828.38 866.35, 828.73 871.34, 840.48 870.50, 842.11 893.59, 818.76 895.23, 821.78 874.13, 816.38 867.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,76,"POLYGON ((751.02 859.07, 759.23 859.57, 757.57 885.14, 761.15 885.39, 760.12 900.00, 754.29 900.00, 743.56 899.27, 743.88 894.77, 738.64 894.40, 742.26 886.07, 746.12 880.98, 745.69 870.24, 740.92 868.46, 740.77 862.42, 749.94 862.19, 751.02 859.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,77,"POLYGON ((777.59 863.21, 794.29 863.80, 793.52 885.22, 799.01 885.42, 798.68 894.77, 774.21 893.89, 774.45 887.20, 779.56 882.72, 781.42 873.72, 777.59 863.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,78,"POLYGON ((635.04 863.13, 656.60 862.13, 657.70 885.61, 649.17 886.01, 645.57 891.54, 631.29 891.33, 631.42 882.76, 635.86 880.82, 635.04 863.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,79,"POLYGON ((897.37 860.01, 900.00 859.90, 900.00 887.32, 898.08 887.87, 898.16 890.98, 887.92 891.24, 887.48 873.58, 895.03 866.51, 893.67 862.59, 897.37 860.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,80,"POLYGON ((598.05 856.11, 605.70 855.16, 606.68 863.17, 610.93 868.43, 617.96 870.74, 617.56 892.19, 593.27 891.77, 593.53 877.80, 604.08 869.29, 598.89 862.90, 598.05 856.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,81,"POLYGON ((863.56 862.14, 853.29 864.18, 850.90 869.44, 853.17 876.24, 847.50 876.14, 847.78 861.84, 863.56 862.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,82,"POLYGON ((804.98 839.87, 814.50 838.85, 815.52 848.41, 812.63 847.55, 811.31 851.94, 806.12 850.40, 804.98 839.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,83,"POLYGON ((797.05 800.76, 807.06 797.26, 813.04 814.18, 806.99 816.29, 804.61 813.02, 801.49 813.30, 797.05 800.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,84,"POLYGON ((727.05 814.56, 745.10 792.03, 747.01 797.00, 754.33 794.75, 765.42 811.82, 752.03 808.63, 740.78 807.75, 727.05 814.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,85,"POLYGON ((757.27 792.58, 761.24 790.64, 764.45 797.13, 776.93 791.02, 783.21 803.69, 766.75 811.74, 757.27 792.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,86,"POLYGON ((650.56 768.17, 664.47 777.78, 659.02 785.62, 665.47 790.07, 656.17 803.38, 635.82 789.30, 650.56 768.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,87,"POLYGON ((809.24 763.68, 814.39 770.23, 818.32 768.65, 829.58 766.87, 834.13 795.35, 814.80 798.42, 809.24 763.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,88,"POLYGON ((846.83 759.50, 852.77 762.88, 858.61 762.31, 868.31 757.49, 867.46 765.41, 864.19 768.00, 867.66 781.10, 853.13 784.94, 849.18 777.33, 849.38 769.21, 846.83 759.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,89,"POLYGON ((879.36 748.06, 893.96 747.26, 895.51 750.55, 900.00 750.62, 900.00 780.34, 889.02 780.20, 889.17 770.67, 879.60 770.54, 879.72 762.19, 887.11 756.81, 888.58 750.07, 879.36 748.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,90,"POLYGON ((690.53 741.75, 696.86 748.49, 689.24 755.57, 682.91 748.83, 690.53 741.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,91,"POLYGON ((686.59 713.19, 703.90 701.32, 711.77 712.68, 717.80 708.53, 725.24 719.31, 720.71 722.42, 723.29 726.15, 704.49 739.04, 686.59 713.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,92,"POLYGON ((721.71 691.20, 738.59 680.43, 753.63 703.75, 736.74 714.53, 721.71 691.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,93,"POLYGON ((760.34 656.72, 788.67 646.36, 804.64 689.64, 790.56 694.79, 783.47 675.57, 769.22 680.78, 760.34 656.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,94,"POLYGON ((859.30 655.30, 880.60 654.53, 880.76 660.98, 878.30 662.49, 879.19 673.30, 886.10 673.32, 886.68 679.97, 872.48 680.75, 864.88 677.82, 859.79 674.42, 859.30 655.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,95,"POLYGON ((896.78 657.69, 900.00 657.67, 900.00 675.22, 896.90 675.24, 896.78 657.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,96,"POLYGON ((803.07 657.13, 811.20 654.77, 810.16 651.25, 821.97 647.82, 823.21 652.02, 827.37 650.81, 834.82 676.14, 810.72 683.18, 803.07 657.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,97,"POLYGON ((631.05 643.69, 639.99 637.20, 650.36 651.39, 641.42 657.87, 631.05 643.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,98,"POLYGON ((162.56 900.00, 185.97 881.72, 171.38 863.20, 197.74 842.59, 242.98 900.00, 162.56 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,99,"POLYGON ((145.24 894.19, 150.06 900.00, 138.16 900.00, 145.24 894.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,100,"POLYGON ((127.72 874.39, 142.36 885.03, 131.36 900.00, 108.92 900.00, 127.72 874.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,101,"POLYGON ((383.78 877.57, 403.76 876.73, 404.12 885.00, 401.99 885.10, 402.62 900.00, 384.73 900.00, 383.78 877.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,102,"POLYGON ((484.78 874.32, 510.05 873.43, 510.69 891.66, 505.30 891.87, 505.59 900.00, 485.68 900.00, 484.78 874.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,103,"POLYGON ((446.97 870.65, 458.54 870.29, 458.66 874.14, 475.05 873.64, 475.84 899.46, 460.62 899.91, 460.58 900.00, 447.87 900.00, 446.97 870.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,104,"POLYGON ((409.47 873.94, 437.46 872.84, 438.48 898.77, 410.48 899.86, 409.47 873.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,105,"POLYGON ((524.14 866.21, 537.31 866.85, 538.09 877.73, 543.93 882.02, 544.84 898.09, 539.82 898.06, 540.27 894.90, 520.96 895.75, 519.46 892.12, 522.86 887.31, 525.86 881.46, 530.52 878.01, 524.14 866.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,106,"POLYGON ((105.27 844.44, 131.02 863.68, 116.59 882.80, 90.84 863.57, 105.27 844.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,107,"POLYGON ((369.22 837.87, 381.68 837.38, 382.69 849.43, 370.41 850.45, 369.22 837.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,108,"POLYGON ((489.19 807.08, 518.10 806.38, 518.74 832.52, 511.72 832.70, 512.07 846.63, 494.18 847.06, 493.70 827.45, 489.70 827.56, 489.19 807.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,109,"POLYGON ((158.62 815.84, 168.33 827.27, 151.09 841.76, 132.92 829.93, 141.73 816.52, 142.39 812.55, 145.20 812.10, 144.50 807.79, 159.09 808.75, 158.62 815.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,110,"POLYGON ((332.38 782.37, 339.34 778.95, 344.81 789.99, 349.97 787.47, 355.62 798.86, 330.32 811.28, 324.64 799.81, 337.81 793.35, 332.38 782.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,111,"POLYGON ((370.38 774.62, 390.03 766.22, 399.80 788.85, 380.15 797.26, 370.38 774.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,112,"POLYGON ((430.74 736.21, 440.33 762.36, 434.43 764.48, 436.84 771.08, 420.46 777.03, 417.85 769.92, 411.04 772.41, 401.66 746.79, 430.74 736.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,113,"POLYGON ((77.98 718.70, 98.29 734.61, 62.77 779.53, 42.45 763.63, 77.98 718.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,114,"POLYGON ((454.20 764.33, 442.58 738.37, 444.88 737.36, 442.75 732.62, 446.91 730.78, 451.51 726.35, 455.27 733.74, 459.45 734.59, 462.38 739.37, 463.04 748.50, 466.19 748.29, 468.63 753.71, 460.47 757.31, 462.05 760.87, 454.20 764.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,115,"POLYGON ((479.15 710.20, 496.16 699.97, 507.83 719.19, 494.69 727.09, 500.53 736.71, 487.59 739.39, 474.31 717.07, 479.15 710.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,116,"POLYGON ((534.26 678.80, 551.51 704.70, 540.10 712.23, 535.77 705.75, 523.96 713.56, 513.44 697.75, 528.73 687.66, 526.32 684.04, 534.26 678.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,117,"POLYGON ((324.79 665.86, 340.57 665.57, 340.82 678.08, 347.93 677.95, 348.15 689.44, 340.50 689.59, 340.66 698.84, 330.01 699.05, 329.84 690.15, 322.96 687.11, 321.90 680.65, 329.66 677.41, 324.79 665.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,118,"POLYGON ((547.85 664.29, 557.76 657.00, 565.27 667.10, 567.65 665.36, 584.75 688.43, 576.66 694.36, 573.34 689.87, 564.31 696.50, 550.11 677.35, 554.91 673.81, 547.85 664.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,119,"POLYGON ((379.66 653.27, 387.82 668.20, 394.90 664.36, 397.15 668.47, 375.23 680.36, 371.17 672.91, 376.58 669.98, 370.22 658.37, 379.66 653.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,120,"POLYGON ((5.02 659.19, 4.53 651.61, 20.22 650.61, 20.69 658.19, 5.02 659.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,121,"POLYGON ((278.85 647.95, 291.26 645.54, 300.03 644.03, 304.49 659.17, 302.56 661.92, 303.62 663.65, 301.46 664.00, 294.56 665.19, 290.24 665.21, 284.86 659.67, 280.53 655.76, 278.85 647.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,122,"POLYGON ((403.84 626.50, 411.33 638.05, 424.87 644.37, 429.22 643.21, 431.36 646.53, 410.19 660.13, 392.94 633.51, 403.84 626.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,123,"POLYGON ((429.61 617.21, 447.94 607.37, 454.63 615.10, 450.75 618.33, 458.81 631.45, 440.68 641.48, 429.61 617.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,124,"POLYGON ((463.20 606.07, 481.57 593.70, 493.39 611.12, 475.02 623.46, 463.20 606.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,125,"POLYGON ((301.52 595.98, 311.34 595.17, 311.85 601.11, 325.90 599.93, 327.35 617.34, 315.86 618.30, 306.50 607.44, 296.91 615.56, 276.10 617.29, 275.01 604.20, 302.03 601.96, 301.52 595.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,126,"POLYGON ((457.93 580.50, 461.78 583.93, 459.31 585.66, 466.62 593.38, 456.18 601.75, 444.99 589.78, 457.93 580.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,127,"POLYGON ((508.91 570.25, 518.09 578.92, 516.43 582.42, 522.07 588.96, 507.45 599.97, 504.34 595.10, 496.04 581.21, 502.52 582.78, 507.16 579.20, 508.91 570.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,128,"POLYGON ((184.10 497.51, 215.95 514.57, 244.83 528.48, 175.14 662.32, 139.95 641.62, 193.94 546.74, 180.97 539.44, 189.69 524.05, 174.89 516.53, 184.10 497.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,129,"POLYGON ((511.91 548.33, 531.31 533.10, 544.49 549.74, 539.80 553.43, 546.28 561.61, 549.51 559.07, 556.06 567.34, 538.13 581.42, 511.91 548.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,130,"POLYGON ((564.14 515.21, 586.99 540.28, 569.90 555.70, 547.06 530.65, 564.14 515.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,131,"POLYGON ((327.02 515.50, 326.09 520.34, 335.85 522.22, 333.23 535.70, 316.67 532.50, 317.67 527.30, 311.97 526.21, 313.77 516.93, 316.91 513.63, 327.02 515.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,132,"POLYGON ((518.58 496.70, 522.50 493.52, 525.52 497.22, 529.46 494.01, 541.63 508.81, 524.40 522.82, 520.94 518.62, 526.13 514.38, 523.59 502.81, 518.58 496.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3742689,133,"POLYGON ((202.26 503.91, 204.47 497.52, 201.41 493.92, 205.70 486.39, 215.12 491.70, 217.32 487.83, 221.31 490.08, 223.58 486.09, 228.73 489.00, 230.29 486.27, 241.75 492.73, 228.37 516.18, 224.38 513.93, 220.88 515.33, 205.76 507.48, 202.26 503.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726489,0,"POLYGON ((0.00 712.83, 158.37 710.28, 160.59 699.81, 197.58 699.22, 197.93 721.99, 188.25 722.15, 191.07 900.00, 0.00 900.00, 0.00 712.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726489,1,"POLYGON ((665.82 0.00, 676.56 1.50, 591.36 603.57, 368.07 572.29, 366.50 583.45, 338.74 579.56, 342.41 553.70, 351.93 555.03, 430.49 0.00, 665.82 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,0,"POLYGON ((182.62 324.15, 194.25 323.52, 197.97 335.12, 201.28 335.30, 205.87 332.59, 204.91 324.51, 203.32 319.07, 214.47 318.87, 215.14 354.57, 186.94 355.25, 183.99 349.35, 182.62 324.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,1,"POLYGON ((92.99 96.94, 117.20 99.64, 114.72 121.68, 110.90 121.26, 109.86 130.50, 92.34 128.55, 93.47 118.35, 89.25 117.88, 89.87 112.41, 93.74 112.84, 94.46 106.56, 91.94 106.27, 92.99 96.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3727839,2,"POLYGON ((0.82 29.96, 3.48 40.71, 2.80 51.00, 0.00 51.51, 0.00 30.16, 0.82 29.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,0,"POLYGON ((662.45 673.23, 714.26 671.50, 714.79 686.98, 688.82 687.86, 686.01 691.88, 678.88 692.06, 677.36 689.82, 663.01 690.29, 662.45 673.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,1,"POLYGON ((888.45 663.90, 889.02 673.92, 898.18 673.42, 898.56 680.11, 900.00 679.54, 900.00 688.87, 854.84 691.41, 853.94 675.63, 868.35 674.82, 867.81 665.04, 888.45 663.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,2,"POLYGON ((792.03 667.55, 823.33 667.06, 824.49 662.86, 834.69 662.87, 834.62 682.20, 824.22 684.97, 792.30 685.45, 792.03 667.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,3,"POLYGON ((773.34 661.67, 773.70 675.82, 763.75 679.83, 760.70 682.61, 728.85 682.69, 724.10 676.46, 723.50 664.54, 773.34 661.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,4,"POLYGON ((900.00 576.11, 856.31 573.17, 858.07 547.42, 870.60 548.28, 874.03 551.94, 887.80 549.93, 890.86 554.85, 898.87 556.31, 900.00 554.45, 900.00 576.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,5,"POLYGON ((587.53 452.10, 592.93 467.39, 564.93 478.11, 558.75 457.82, 565.38 454.55, 571.52 457.50, 587.53 452.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,6,"POLYGON ((754.21 450.37, 757.98 458.40, 755.85 465.13, 738.24 473.28, 735.18 467.75, 731.35 457.19, 754.21 450.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,7,"POLYGON ((677.80 447.07, 694.85 448.53, 695.62 453.81, 696.39 459.12, 693.36 463.37, 689.01 465.57, 678.93 465.00, 676.54 461.71, 677.80 447.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,8,"POLYGON ((654.84 428.48, 657.53 445.28, 649.39 446.57, 646.16 451.65, 639.59 452.68, 636.19 431.44, 654.84 428.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,9,"POLYGON ((623.11 427.71, 626.66 442.56, 624.00 445.33, 599.46 448.04, 594.73 442.44, 593.80 432.76, 604.30 431.65, 603.25 428.17, 615.34 426.15, 617.52 429.03, 623.11 427.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,10,"POLYGON ((776.57 412.27, 795.99 419.90, 784.24 449.45, 764.82 441.80, 776.57 412.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,11,"POLYGON ((764.64 372.41, 779.89 366.68, 789.88 393.10, 788.02 395.59, 779.98 398.08, 774.33 398.05, 764.64 372.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,12,"POLYGON ((828.86 340.90, 842.48 346.09, 838.10 357.78, 844.99 361.47, 840.30 371.98, 820.05 361.53, 828.86 340.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,13,"POLYGON ((864.03 345.52, 889.11 336.80, 894.77 352.13, 870.63 361.60, 864.03 345.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,14,"POLYGON ((666.72 342.94, 660.07 343.57, 660.68 350.82, 648.82 350.27, 647.69 342.44, 639.93 341.69, 640.04 326.19, 667.00 326.84, 666.72 342.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,15,"POLYGON ((745.34 314.84, 769.49 338.95, 752.13 356.17, 742.37 346.45, 740.83 341.41, 737.63 339.74, 728.98 331.10, 745.34 314.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,16,"POLYGON ((620.93 322.19, 623.87 334.04, 624.07 341.91, 622.19 344.07, 612.37 345.72, 611.04 342.24, 605.42 342.39, 604.61 345.20, 594.53 343.71, 590.40 326.81, 588.32 320.73, 606.93 318.51, 608.64 323.72, 620.93 322.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,17,"POLYGON ((679.61 320.08, 711.18 319.19, 711.04 341.19, 699.05 340.78, 699.14 344.62, 690.70 344.10, 690.01 340.44, 680.49 340.86, 679.61 320.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,18,"POLYGON ((759.30 309.93, 766.12 315.95, 761.35 321.38, 754.40 315.36, 759.30 309.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,19,"POLYGON ((613.74 305.97, 624.68 305.40, 626.29 315.97, 615.77 315.51, 613.74 305.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,20,"POLYGON ((656.09 303.90, 666.91 303.38, 667.48 314.79, 661.82 315.07, 660.52 316.72, 656.74 316.91, 656.09 303.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,21,"POLYGON ((798.71 296.88, 809.69 293.87, 811.37 299.17, 813.73 298.47, 815.87 298.88, 820.37 315.15, 805.64 319.32, 798.71 296.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,22,"POLYGON ((675.61 283.33, 688.28 282.74, 688.70 291.72, 676.01 292.31, 675.61 283.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,23,"POLYGON ((674.94 256.02, 691.22 255.61, 691.00 270.42, 700.12 269.92, 700.38 280.64, 675.33 280.81, 674.94 256.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,24,"POLYGON ((745.10 254.33, 746.25 275.86, 715.49 276.13, 714.11 251.63, 731.49 250.66, 731.62 254.43, 745.10 254.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,25,"POLYGON ((614.69 254.23, 616.33 270.50, 590.99 271.83, 590.00 267.31, 588.63 254.71, 602.85 253.13, 610.07 252.94, 614.69 254.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,26,"POLYGON ((658.04 253.43, 658.36 269.74, 635.18 270.20, 631.75 267.11, 630.99 253.70, 658.04 253.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,27,"POLYGON ((800.68 251.56, 818.30 252.67, 817.21 269.55, 799.61 268.44, 800.68 251.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,28,"POLYGON ((790.66 275.77, 770.21 276.84, 769.35 273.47, 759.08 274.40, 755.02 245.27, 761.42 244.37, 760.63 238.69, 772.64 237.03, 776.78 254.70, 788.57 253.74, 790.66 275.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,29,"POLYGON ((609.00 164.65, 629.96 166.36, 633.91 174.43, 632.62 182.65, 606.97 182.81, 606.08 167.45, 609.00 164.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,30,"POLYGON ((675.99 164.30, 676.33 179.98, 652.65 180.63, 652.12 165.86, 654.47 164.76, 675.99 164.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,31,"POLYGON ((590.06 161.98, 591.58 177.99, 559.37 180.71, 558.13 164.83, 568.49 164.01, 590.06 161.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,32,"POLYGON ((734.16 164.80, 759.85 162.24, 761.90 178.41, 735.72 179.65, 734.16 164.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,33,"POLYGON ((779.43 162.78, 802.22 162.34, 802.46 177.38, 796.68 177.53, 780.54 177.34, 779.43 162.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,34,"POLYGON ((689.09 157.11, 714.07 155.87, 713.41 158.84, 720.27 160.73, 721.64 179.43, 702.65 178.58, 693.45 177.64, 689.90 177.29, 689.09 157.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,35,"POLYGON ((815.82 152.29, 829.54 152.52, 829.47 155.76, 833.71 155.83, 833.85 161.55, 830.87 164.85, 830.95 171.71, 833.70 171.41, 833.55 177.94, 828.83 177.84, 828.07 179.44, 817.84 179.70, 815.47 177.80, 815.82 152.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,36,"POLYGON ((689.30 118.01, 707.43 116.49, 708.37 129.56, 690.65 130.87, 689.30 118.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,37,"POLYGON ((726.77 107.43, 747.70 107.27, 747.85 130.07, 726.91 130.22, 726.77 107.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,38,"POLYGON ((803.42 93.12, 820.94 92.76, 823.39 99.18, 825.76 102.45, 825.84 108.66, 832.64 108.55, 832.77 118.14, 803.92 118.56, 803.42 93.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,39,"POLYGON ((695.77 60.47, 702.29 69.89, 703.00 73.20, 700.06 81.00, 696.39 92.97, 673.52 92.09, 671.81 82.34, 666.14 81.44, 665.65 62.08, 695.77 60.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,40,"POLYGON ((729.02 58.38, 756.92 59.76, 759.48 61.98, 759.29 79.50, 757.58 85.80, 756.21 89.59, 730.24 90.25, 729.97 79.82, 725.79 79.73, 724.48 61.20, 729.02 58.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,41,"POLYGON ((608.28 63.11, 638.41 61.92, 640.00 83.35, 608.16 83.34, 605.53 62.34, 608.28 63.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,42,"POLYGON ((833.11 65.61, 832.40 72.13, 831.85 78.27, 829.58 79.55, 807.98 75.37, 809.22 61.31, 833.11 65.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,43,"POLYGON ((486.71 810.88, 499.42 811.05, 502.59 808.50, 509.81 808.08, 510.88 811.02, 522.84 810.96, 532.31 811.21, 531.50 828.35, 524.29 829.02, 523.68 834.50, 504.55 836.71, 504.62 829.03, 487.16 828.47, 486.71 810.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,44,"POLYGON ((274.00 816.35, 290.40 815.96, 290.23 809.46, 306.42 809.05, 306.36 806.09, 335.04 805.43, 336.37 808.62, 341.69 808.39, 342.58 828.77, 323.79 829.58, 324.15 837.90, 310.82 838.48, 310.41 828.68, 299.78 829.15, 274.35 829.77, 274.00 816.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,45,"POLYGON ((362.32 807.07, 376.48 806.07, 379.56 802.13, 385.90 802.32, 387.14 804.67, 400.31 803.60, 401.56 806.36, 413.91 805.98, 414.40 821.82, 412.81 829.39, 393.95 829.91, 393.78 823.74, 365.73 824.52, 363.25 820.32, 362.32 807.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,46,"POLYGON ((194.68 793.19, 224.86 793.69, 235.38 795.51, 236.14 825.50, 210.54 824.06, 210.23 811.58, 198.05 810.22, 194.68 793.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,47,"POLYGON ((191.32 792.45, 192.17 809.95, 153.16 807.61, 152.82 794.25, 191.32 792.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,48,"POLYGON ((2.58 718.00, 0.75 704.22, 6.28 703.50, 7.01 697.55, 12.65 698.25, 15.06 716.35, 2.58 718.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,49,"POLYGON ((298.41 690.47, 331.51 689.63, 334.96 687.10, 342.36 686.91, 342.75 702.33, 340.76 707.29, 294.63 708.46, 294.30 695.15, 298.41 690.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,50,"POLYGON ((533.74 676.97, 534.03 696.10, 521.25 696.29, 517.97 698.86, 479.72 699.43, 479.40 677.77, 533.74 676.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,51,"POLYGON ((380.81 667.73, 384.10 673.07, 389.95 673.14, 392.28 682.25, 398.94 680.19, 400.42 672.65, 409.79 670.13, 408.18 681.22, 409.95 685.13, 410.70 689.71, 410.67 696.59, 385.18 699.32, 384.40 701.63, 377.70 702.02, 367.19 700.60, 362.34 698.86, 360.70 684.29, 367.83 683.91, 368.25 667.85, 380.81 667.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,52,"POLYGON ((592.96 674.02, 594.13 687.33, 543.34 691.33, 541.86 674.50, 592.96 674.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,53,"POLYGON ((455.33 661.36, 472.44 660.66, 472.77 668.35, 473.49 680.10, 466.41 680.54, 467.29 694.77, 423.63 697.48, 423.14 689.57, 422.79 680.92, 456.09 679.54, 455.33 661.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,54,"POLYGON ((358.50 531.52, 366.72 529.87, 366.83 523.36, 377.87 524.06, 379.88 528.13, 390.04 527.88, 390.05 553.43, 360.71 552.73, 358.50 531.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,55,"POLYGON ((326.96 519.25, 331.37 520.95, 333.29 518.86, 338.69 520.97, 344.93 525.40, 343.36 528.68, 347.87 532.19, 339.81 546.20, 317.44 533.52, 326.96 519.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,56,"POLYGON ((409.55 513.02, 423.54 510.82, 427.38 520.14, 436.19 519.78, 436.51 527.23, 427.64 532.54, 425.43 536.28, 413.53 538.16, 409.55 513.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,57,"POLYGON ((445.29 514.47, 456.48 512.58, 458.25 523.04, 477.13 519.89, 478.75 529.38, 448.66 534.42, 445.29 514.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,58,"POLYGON ((487.44 494.88, 510.08 487.63, 514.08 496.69, 515.73 503.75, 493.51 511.40, 491.34 508.95, 487.44 494.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,59,"POLYGON ((280.70 476.36, 300.65 469.75, 310.14 498.14, 290.17 504.73, 280.70 476.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,60,"POLYGON ((52.73 475.33, 55.71 488.24, 55.80 498.96, 0.00 498.21, 0.00 474.70, 52.73 475.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,61,"POLYGON ((524.64 477.31, 551.62 468.17, 558.38 487.93, 531.42 497.07, 524.64 477.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,62,"POLYGON ((294.20 417.66, 311.54 427.74, 308.20 435.19, 307.83 440.80, 305.67 445.93, 302.29 444.44, 297.27 455.09, 281.23 447.44, 294.20 417.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,63,"POLYGON ((466.33 399.44, 484.54 399.04, 490.93 399.01, 491.19 410.86, 484.68 411.16, 484.75 419.52, 466.75 419.71, 466.33 399.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,64,"POLYGON ((345.51 403.17, 338.60 409.16, 341.96 414.09, 332.75 419.63, 329.70 414.20, 319.35 421.55, 312.42 407.29, 338.02 391.37, 345.51 403.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,65,"POLYGON ((390.90 397.09, 392.43 412.92, 368.03 415.25, 366.82 402.65, 367.72 399.32, 390.90 397.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,66,"POLYGON ((356.02 379.73, 367.69 379.37, 367.93 386.64, 356.26 387.03, 356.02 379.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,67,"POLYGON ((489.85 357.35, 489.79 389.36, 465.24 389.32, 465.25 380.88, 468.83 380.88, 468.61 357.47, 489.85 357.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,68,"POLYGON ((520.03 351.06, 536.34 351.38, 536.73 361.53, 543.92 361.59, 543.87 367.57, 535.12 367.48, 535.02 377.58, 519.08 377.43, 520.03 351.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,69,"POLYGON ((362.11 338.09, 386.49 340.44, 386.04 343.96, 388.58 346.72, 387.98 351.10, 385.54 352.23, 385.44 355.03, 386.61 359.22, 389.58 358.43, 383.05 364.72, 388.56 366.52, 387.46 372.34, 372.27 370.26, 366.07 367.31, 359.24 350.08, 362.11 338.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,70,"POLYGON ((577.59 342.23, 581.28 356.14, 577.43 357.30, 556.77 362.20, 553.56 347.03, 555.64 345.58, 577.59 342.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,71,"POLYGON ((468.43 309.68, 488.50 307.71, 490.24 310.37, 488.03 339.00, 480.16 341.90, 467.93 338.88, 467.61 334.94, 467.03 328.28, 470.13 326.53, 468.93 320.30, 467.42 318.88, 468.43 309.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,72,"POLYGON ((372.42 302.84, 393.81 304.07, 392.24 325.07, 372.76 325.25, 370.78 325.30, 370.51 302.38, 372.42 302.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,73,"POLYGON ((370.96 264.99, 387.89 266.22, 392.75 271.32, 391.44 288.97, 369.31 287.36, 370.96 264.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,74,"POLYGON ((471.68 261.90, 489.29 261.98, 490.45 272.63, 489.12 283.19, 480.65 282.36, 479.48 277.99, 471.73 277.68, 469.17 267.04, 471.68 261.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,75,"POLYGON ((546.81 258.92, 562.13 258.71, 563.33 264.30, 570.37 264.47, 571.17 281.81, 546.15 281.23, 546.81 258.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,76,"POLYGON ((531.52 257.31, 532.54 279.26, 502.43 280.65, 501.41 258.70, 531.52 257.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,77,"POLYGON ((54.42 228.00, 60.93 230.39, 56.44 242.54, 49.92 240.15, 54.42 228.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,78,"POLYGON ((69.28 202.94, 88.26 208.10, 79.49 240.11, 69.23 259.79, 60.78 255.41, 63.44 250.31, 57.75 247.36, 57.80 244.77, 69.28 202.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,79,"POLYGON ((365.89 204.54, 386.10 206.02, 386.13 236.52, 370.71 237.16, 361.16 233.92, 356.64 232.30, 357.91 204.01, 365.89 204.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,80,"POLYGON ((0.00 188.99, 1.59 190.25, 7.53 199.03, 6.46 206.00, 5.01 207.15, 7.76 217.49, 1.85 225.59, 0.00 231.93, 0.00 188.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,81,"POLYGON ((403.45 165.69, 412.69 179.18, 394.96 191.21, 385.74 177.73, 403.45 165.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,82,"POLYGON ((474.41 160.23, 505.57 161.26, 504.70 182.10, 498.83 181.83, 494.03 178.73, 485.55 178.19, 487.37 182.12, 473.67 181.67, 474.41 160.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,83,"POLYGON ((547.55 162.30, 545.98 177.26, 526.91 175.28, 527.24 172.18, 522.19 170.09, 520.18 158.98, 542.62 161.78, 547.55 162.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,84,"POLYGON ((436.09 153.43, 458.08 156.58, 458.42 169.71, 457.05 174.21, 431.79 170.63, 431.89 155.03, 436.09 153.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,85,"POLYGON ((372.98 68.86, 380.28 67.64, 392.18 74.64, 395.07 88.95, 393.24 91.30, 384.40 89.44, 363.01 88.94, 358.73 76.75, 360.21 69.41, 372.98 68.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,86,"POLYGON ((577.26 62.43, 574.65 75.22, 571.26 81.99, 563.39 85.29, 557.65 90.46, 552.61 89.96, 549.49 82.74, 546.33 73.23, 544.61 63.26, 577.26 62.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,87,"POLYGON ((440.50 63.40, 450.57 64.19, 451.27 66.88, 457.35 66.94, 458.09 79.42, 457.35 83.84, 427.88 86.25, 424.40 80.92, 422.78 66.98, 428.02 66.85, 433.01 65.25, 440.50 63.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,88,"POLYGON ((481.79 64.66, 495.80 63.26, 515.54 65.25, 516.54 80.03, 510.08 81.45, 509.52 84.18, 483.73 83.17, 481.79 64.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,89,"POLYGON ((548.42 808.67, 561.45 808.41, 561.63 805.92, 571.35 806.18, 571.56 809.26, 586.48 808.02, 586.56 811.12, 592.27 810.73, 592.55 825.98, 586.43 825.75, 586.70 836.54, 564.65 836.47, 563.65 826.20, 548.22 827.10, 548.42 808.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,90,"POLYGON ((708.83 803.58, 712.95 812.77, 713.14 820.14, 710.00 821.62, 671.09 822.60, 668.94 807.41, 708.83 803.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,91,"POLYGON ((733.07 800.70, 741.09 801.23, 742.91 799.69, 755.49 799.00, 755.78 800.85, 771.24 801.08, 771.92 803.79, 776.65 803.43, 778.86 816.63, 733.45 819.16, 733.20 809.24, 732.31 803.56, 733.07 800.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,92,"POLYGON ((786.87 798.49, 800.18 798.39, 800.14 795.46, 810.32 795.38, 810.34 798.45, 824.75 798.32, 826.05 799.60, 839.14 800.22, 838.53 812.82, 834.76 820.64, 831.86 822.16, 810.29 822.51, 808.67 816.51, 787.01 816.66, 786.87 798.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,93,"POLYGON ((622.34 797.94, 630.94 798.10, 631.60 799.43, 646.54 799.43, 646.60 801.65, 652.94 801.38, 653.63 803.58, 651.23 807.13, 651.82 810.82, 652.11 817.63, 645.10 816.07, 638.85 815.50, 638.42 817.73, 608.81 818.86, 607.60 800.80, 622.82 801.52, 622.34 797.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,94,"POLYGON ((863.10 792.20, 865.70 787.76, 873.27 788.61, 874.15 790.45, 896.37 790.73, 897.95 803.83, 894.99 810.99, 881.01 812.81, 877.32 815.61, 872.42 821.15, 860.27 821.04, 856.44 818.65, 853.74 811.19, 845.23 805.79, 845.74 793.06, 863.10 792.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721089,95,"POLYGON ((651.96 679.21, 653.37 693.98, 649.44 695.74, 612.58 696.26, 608.76 694.27, 609.24 680.07, 651.96 679.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,0,"POLYGON ((540.89 847.71, 562.80 847.29, 563.05 860.89, 573.88 860.68, 574.27 881.36, 541.56 882.01, 540.89 847.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,1,"POLYGON ((477.53 832.60, 498.95 832.08, 499.24 844.28, 502.93 844.19, 503.35 861.38, 514.97 861.11, 515.50 882.47, 478.76 883.36, 477.53 832.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,2,"POLYGON ((1.50 805.75, 25.65 799.83, 38.82 840.19, 30.94 845.40, 25.13 849.68, 18.51 846.01, 9.24 818.54, 3.32 818.98, 1.50 805.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,3,"POLYGON ((542.48 815.08, 542.69 823.27, 537.23 823.41, 537.02 815.22, 542.48 815.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,4,"POLYGON ((293.46 795.37, 294.12 808.31, 288.09 808.62, 288.41 814.47, 294.18 814.19, 295.19 833.61, 272.15 834.80, 271.25 817.35, 266.54 817.58, 266.06 808.34, 270.97 808.08, 270.37 796.58, 293.46 795.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,5,"POLYGON ((0.00 765.92, 2.16 766.69, 1.22 769.20, 14.70 774.56, 10.11 790.79, 0.00 787.56, 0.00 765.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,6,"POLYGON ((397.39 749.67, 399.48 780.60, 392.38 785.73, 375.79 786.33, 375.47 777.24, 382.85 774.07, 381.30 750.74, 397.39 749.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,7,"POLYGON ((424.21 746.50, 442.01 746.31, 442.12 756.30, 462.23 756.10, 462.42 774.76, 424.49 775.15, 424.21 746.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,8,"POLYGON ((532.04 743.21, 563.83 742.22, 563.92 745.28, 581.48 744.75, 582.07 763.38, 532.70 764.92, 532.04 743.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,9,"POLYGON ((471.97 743.84, 482.23 743.56, 482.17 741.23, 500.55 740.72, 500.64 743.78, 520.09 743.24, 520.61 761.21, 472.50 762.56, 471.97 743.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,10,"POLYGON ((265.40 719.30, 274.09 718.70, 283.79 718.08, 284.90 742.11, 287.66 742.53, 288.65 762.59, 274.72 763.19, 268.60 757.15, 265.40 719.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,11,"POLYGON ((63.31 676.68, 61.97 681.93, 59.69 682.61, 57.28 686.42, 56.31 689.98, 47.11 690.43, 18.85 673.90, 25.26 663.04, 33.74 659.07, 63.31 676.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,12,"POLYGON ((307.59 647.99, 316.26 657.27, 308.85 664.12, 312.36 667.87, 291.44 687.21, 279.28 674.15, 307.59 647.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,13,"POLYGON ((450.99 641.98, 451.66 661.92, 439.73 662.33, 439.86 666.03, 426.50 666.48, 426.40 663.18, 410.01 663.71, 409.33 643.37, 450.99 641.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,14,"POLYGON ((397.75 641.64, 398.09 661.59, 374.10 662.00, 374.16 665.90, 362.36 666.12, 362.30 662.45, 353.41 662.61, 353.06 642.40, 397.75 641.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,15,"POLYGON ((517.19 639.03, 517.69 658.84, 469.52 660.06, 469.02 640.25, 517.19 639.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,16,"POLYGON ((578.41 635.30, 579.17 658.17, 562.62 658.74, 562.74 661.87, 545.75 662.46, 545.65 658.95, 530.69 659.44, 530.09 641.55, 567.76 640.28, 567.60 635.66, 578.41 635.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,17,"POLYGON ((282.17 622.62, 288.37 630.06, 277.33 639.20, 271.12 631.76, 282.17 622.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,18,"POLYGON ((512.39 386.59, 524.54 440.82, 422.20 463.57, 410.05 409.32, 512.39 386.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,19,"POLYGON ((207.09 304.40, 218.84 354.49, 245.87 348.21, 249.70 364.54, 220.49 371.34, 231.70 419.09, 195.95 427.41, 169.15 313.24, 207.09 304.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,20,"POLYGON ((374.96 291.32, 392.07 367.49, 339.64 379.17, 322.53 303.00, 374.96 291.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,21,"POLYGON ((516.27 134.99, 516.17 167.62, 497.19 167.57, 497.27 134.94, 516.27 134.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,22,"POLYGON ((345.21 53.10, 357.28 117.30, 369.91 114.96, 373.39 133.47, 337.82 140.10, 345.72 182.16, 274.05 195.53, 271.52 182.07, 219.59 191.76, 207.01 124.84, 271.53 112.82, 263.17 68.39, 345.21 53.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,23,"POLYGON ((499.58 97.01, 517.53 103.93, 517.77 113.67, 499.99 114.12, 499.58 97.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,24,"POLYGON ((511.39 22.57, 535.67 20.54, 536.76 33.54, 531.48 33.98, 532.39 44.77, 513.39 46.36, 511.39 22.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,25,"POLYGON ((877.93 750.22, 888.43 745.83, 896.02 753.71, 900.00 767.57, 900.00 788.70, 894.28 789.72, 882.06 787.10, 878.28 777.76, 885.27 772.92, 888.05 766.30, 884.26 760.14, 878.57 755.04, 877.93 750.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,26,"POLYGON ((656.00 741.94, 688.26 741.12, 688.32 743.45, 700.74 743.14, 701.21 761.55, 656.53 762.68, 656.00 741.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,27,"POLYGON ((775.55 741.46, 820.86 740.82, 821.15 761.76, 775.84 762.38, 775.55 741.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,28,"POLYGON ((592.78 743.57, 609.45 742.95, 609.35 739.82, 623.38 739.31, 623.49 742.19, 640.36 741.56, 641.03 760.02, 593.45 761.76, 592.78 743.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,29,"POLYGON ((717.69 740.58, 727.22 740.53, 727.21 737.89, 746.13 737.79, 746.15 741.34, 765.55 741.27, 765.62 759.16, 717.78 759.37, 717.69 740.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,30,"POLYGON ((867.88 664.71, 887.54 697.77, 873.25 706.19, 853.57 673.13, 867.88 664.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,31,"POLYGON ((835.54 640.53, 835.89 660.68, 787.90 661.50, 787.53 641.38, 835.54 640.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,32,"POLYGON ((652.77 639.63, 668.09 639.46, 668.02 633.65, 680.72 633.52, 680.74 635.88, 700.98 635.65, 701.25 658.57, 680.71 658.81, 680.73 660.80, 663.12 661.01, 663.09 658.32, 652.97 658.45, 652.77 639.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,33,"POLYGON ((759.52 631.12, 760.31 659.74, 724.11 660.75, 724.02 657.64, 713.58 657.93, 713.11 640.65, 721.65 640.41, 721.42 632.16, 759.52 631.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,34,"POLYGON ((590.54 637.10, 606.41 636.70, 607.03 627.94, 619.33 627.43, 619.56 636.56, 628.33 635.92, 628.32 627.40, 639.38 627.34, 639.67 655.39, 590.85 657.25, 590.54 637.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,35,"POLYGON ((684.72 590.76, 685.31 600.64, 673.79 601.34, 673.21 591.45, 684.72 590.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,36,"POLYGON ((596.89 589.44, 604.58 589.42, 604.60 594.72, 596.91 594.74, 596.89 589.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,37,"POLYGON ((612.58 157.67, 613.37 176.43, 576.65 177.99, 575.84 159.25, 612.58 157.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,38,"POLYGON ((624.62 159.37, 639.28 159.61, 639.37 154.46, 655.43 154.76, 655.33 159.83, 666.24 160.04, 665.86 180.05, 633.52 179.47, 633.57 176.19, 624.32 176.00, 624.62 159.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,39,"POLYGON ((729.36 152.46, 733.42 171.78, 698.91 178.96, 694.85 159.67, 729.36 152.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,40,"POLYGON ((752.35 148.44, 765.36 148.04, 765.28 144.87, 773.39 144.64, 773.51 148.72, 782.98 148.46, 783.54 167.80, 775.36 168.05, 775.51 173.40, 763.78 173.74, 763.60 167.51, 752.91 167.83, 752.35 148.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,41,"POLYGON ((863.26 145.91, 864.44 167.54, 848.88 168.41, 847.70 146.75, 863.26 145.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,42,"POLYGON ((900.00 169.09, 884.00 169.11, 883.96 148.69, 887.94 148.68, 887.93 141.84, 900.00 141.83, 900.00 169.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,43,"POLYGON ((725.07 110.84, 731.74 130.52, 714.36 136.37, 707.67 116.70, 725.07 110.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,44,"POLYGON ((689.12 37.28, 713.30 36.45, 714.86 80.82, 685.98 81.82, 685.30 62.28, 690.00 62.12, 689.12 37.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,45,"POLYGON ((639.84 38.25, 650.20 33.83, 661.05 33.38, 661.81 52.36, 640.44 53.24, 639.84 38.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,46,"POLYGON ((775.40 19.20, 775.67 29.00, 779.97 28.87, 780.29 40.63, 758.52 41.22, 758.20 29.40, 753.69 29.51, 753.42 19.80, 775.40 19.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,47,"POLYGON ((896.20 865.19, 900.00 866.90, 900.00 890.17, 887.40 884.48, 896.20 865.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,48,"POLYGON ((634.70 858.02, 635.59 876.62, 626.30 877.08, 626.46 880.23, 603.14 881.36, 602.10 859.58, 634.70 858.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,49,"POLYGON ((661.48 860.23, 697.55 859.38, 697.99 878.19, 661.94 879.04, 661.48 860.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,50,"POLYGON ((725.29 857.85, 757.74 856.78, 757.95 863.08, 765.02 862.86, 765.48 876.48, 725.95 877.77, 725.29 857.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,51,"POLYGON ((779.62 858.51, 818.17 856.56, 823.01 860.65, 823.74 874.91, 780.56 877.11, 779.62 858.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,52,"POLYGON ((899.09 816.90, 900.00 816.85, 900.00 849.67, 896.03 849.90, 895.01 831.90, 899.91 831.62, 899.09 816.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,53,"POLYGON ((642.98 813.51, 644.02 823.05, 636.06 823.92, 635.02 814.35, 642.98 813.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,54,"POLYGON ((601.66 789.49, 601.64 801.39, 593.92 801.39, 593.93 789.51, 601.66 789.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,55,"POLYGON ((705.14 784.65, 705.61 795.07, 697.04 795.44, 696.57 785.04, 705.14 784.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,56,"POLYGON ((28.39 861.10, 47.31 871.23, 43.13 881.94, 33.61 900.00, 8.58 900.00, 0.00 895.54, 0.00 893.07, 8.80 873.78, 13.04 875.34, 20.04 862.86, 27.67 866.42, 28.39 861.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,57,"POLYGON ((356.43 857.68, 378.17 858.08, 382.35 861.83, 387.57 868.47, 391.20 873.60, 391.70 886.32, 356.79 887.21, 356.43 857.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3721539,58,"POLYGON ((425.24 855.33, 464.57 854.69, 465.03 883.91, 418.67 884.66, 418.36 865.18, 425.40 865.07, 425.24 855.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,0,"POLYGON ((186.86 438.87, 200.96 436.91, 202.05 444.79, 205.03 444.38, 205.58 448.25, 205.10 454.52, 189.35 456.70, 186.86 438.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,1,"POLYGON ((517.58 377.80, 521.28 383.32, 528.77 386.37, 537.89 385.87, 538.23 392.45, 532.78 392.75, 530.60 394.87, 518.21 395.29, 517.58 377.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,2,"POLYGON ((401.81 375.96, 401.35 379.31, 404.02 387.12, 407.80 390.35, 401.91 394.01, 390.89 394.95, 390.65 392.01, 385.60 392.42, 384.78 382.81, 390.99 382.28, 390.52 376.92, 401.81 375.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,3,"POLYGON ((476.96 375.83, 493.88 374.98, 494.82 393.49, 477.92 394.36, 476.96 375.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,4,"POLYGON ((432.53 374.56, 452.62 374.41, 452.74 390.81, 446.59 390.85, 443.03 393.05, 437.89 394.71, 434.15 394.54, 432.63 390.32, 432.53 374.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,5,"POLYGON ((150.91 366.11, 173.58 365.00, 174.82 390.59, 156.44 391.47, 155.45 371.57, 151.19 371.78, 150.91 366.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,6,"POLYGON ((146.00 347.59, 167.85 346.41, 168.63 360.98, 142.33 362.40, 142.05 357.08, 146.50 356.85, 146.00 347.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,7,"POLYGON ((425.12 286.27, 453.17 286.31, 456.39 297.88, 453.61 303.79, 425.08 303.76, 425.12 286.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,8,"POLYGON ((468.38 286.35, 475.80 286.98, 479.54 302.31, 467.10 301.25, 468.38 286.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,9,"POLYGON ((546.88 283.42, 571.10 279.92, 574.68 304.38, 550.44 307.88, 546.88 283.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,10,"POLYGON ((384.89 285.96, 402.48 286.34, 403.57 279.23, 411.56 281.11, 410.92 288.63, 411.34 305.29, 401.34 307.21, 385.70 301.77, 384.89 285.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,11,"POLYGON ((511.25 275.80, 518.49 275.66, 518.67 284.87, 529.49 284.66, 529.86 302.74, 523.12 302.87, 511.69 298.17, 511.25 275.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,12,"POLYGON ((114.91 83.98, 153.64 94.70, 123.44 202.70, 110.59 245.20, 120.54 248.17, 125.82 230.74, 202.42 253.68, 191.85 288.58, 178.82 288.33, 66.55 257.31, 114.91 83.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,13,"POLYGON ((570.27 155.28, 574.09 163.73, 572.09 167.62, 574.73 175.10, 573.48 180.75, 552.48 179.17, 551.77 172.00, 555.28 164.36, 554.62 159.83, 553.47 156.17, 570.27 155.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,14,"POLYGON ((428.30 154.78, 440.62 155.16, 447.94 165.49, 445.63 171.88, 440.01 172.71, 430.49 172.26, 424.15 172.07, 424.10 155.93, 428.30 154.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,15,"POLYGON ((506.35 149.29, 534.48 147.53, 537.29 161.11, 537.94 172.68, 533.13 177.00, 520.03 174.54, 517.66 178.79, 510.22 176.89, 506.96 173.47, 502.98 169.35, 506.35 149.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,16,"POLYGON ((490.11 147.59, 494.60 172.39, 485.00 168.77, 476.76 149.33, 490.11 147.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,17,"POLYGON ((334.65 128.81, 340.40 140.74, 349.40 148.44, 339.14 157.04, 271.33 159.19, 262.52 158.15, 264.39 133.08, 334.65 128.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,18,"POLYGON ((432.96 64.69, 447.94 65.80, 454.68 76.77, 453.64 84.75, 427.98 84.40, 426.36 69.57, 432.96 64.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,19,"POLYGON ((536.58 55.62, 539.51 82.56, 532.06 83.00, 529.29 91.75, 509.82 90.00, 510.02 78.34, 518.90 75.41, 520.51 59.98, 528.50 60.78, 529.59 54.80, 536.58 55.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,20,"POLYGON ((550.62 59.22, 572.54 58.91, 573.43 64.59, 579.67 65.43, 582.24 68.34, 582.35 73.04, 580.83 81.76, 552.75 83.96, 550.62 59.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,21,"POLYGON ((495.16 52.46, 493.94 82.72, 481.76 83.54, 478.61 77.16, 467.32 74.48, 464.40 66.87, 468.80 53.86, 495.16 52.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,22,"POLYGON ((379.65 0.00, 379.12 20.14, 368.27 24.38, 372.63 39.63, 363.51 82.51, 351.39 80.84, 349.95 29.31, 352.59 0.00, 379.65 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,23,"POLYGON ((255.38 0.00, 256.23 57.26, 250.88 62.35, 244.32 78.88, 235.94 79.00, 235.77 65.46, 240.11 60.14, 245.55 49.34, 239.54 37.84, 241.57 29.11, 239.16 22.98, 227.58 23.16, 227.24 0.00, 255.38 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,24,"POLYGON ((819.46 372.63, 832.78 372.96, 836.68 377.88, 835.95 390.97, 828.63 390.58, 822.86 394.48, 819.00 391.25, 819.46 372.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,25,"POLYGON ((773.33 373.58, 794.43 372.05, 795.77 390.35, 774.67 391.88, 773.33 373.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,26,"POLYGON ((729.73 373.09, 750.22 371.68, 751.54 390.01, 724.34 391.99, 722.06 384.12, 729.73 373.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,27,"POLYGON ((625.34 369.49, 621.11 384.18, 616.26 391.40, 608.78 393.66, 603.64 388.79, 602.78 370.55, 625.34 369.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,28,"POLYGON ((708.82 379.82, 710.14 389.95, 688.67 390.50, 687.86 372.80, 693.48 372.48, 696.25 377.67, 701.43 380.36, 708.82 379.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,29,"POLYGON ((863.10 371.72, 878.87 372.08, 878.95 388.79, 859.43 390.33, 857.35 387.50, 856.90 384.13, 863.14 382.04, 866.12 379.39, 864.53 375.37, 863.10 371.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,30,"POLYGON ((740.49 285.67, 743.34 298.52, 740.53 303.18, 722.46 301.14, 722.15 289.07, 740.49 285.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,31,"POLYGON ((595.59 286.02, 602.21 283.77, 616.88 283.62, 617.10 303.84, 602.11 303.99, 593.96 300.96, 595.59 286.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,32,"POLYGON ((786.80 277.83, 794.58 276.57, 798.72 302.13, 791.83 310.63, 779.09 304.29, 763.22 302.98, 765.19 279.09, 781.83 280.45, 786.80 277.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,33,"POLYGON ((852.25 285.34, 871.91 285.17, 872.05 300.77, 852.39 300.96, 852.25 285.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,34,"POLYGON ((811.50 283.67, 829.24 283.84, 829.06 301.56, 811.32 301.38, 811.50 283.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,35,"POLYGON ((700.63 280.38, 700.19 302.37, 681.59 302.00, 681.70 296.33, 692.61 280.23, 700.63 280.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,36,"POLYGON ((634.31 276.69, 653.83 275.91, 654.34 284.15, 658.32 288.76, 657.00 295.58, 634.43 293.21, 634.31 276.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,37,"POLYGON ((784.29 216.43, 769.45 211.35, 769.29 205.16, 767.66 199.50, 771.86 197.90, 780.54 196.93, 798.90 193.49, 800.88 202.87, 799.59 211.32, 786.90 211.64, 784.29 216.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,38,"POLYGON ((654.58 197.42, 654.94 211.29, 645.68 209.55, 636.40 206.81, 632.52 200.96, 633.92 197.70, 654.58 197.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,39,"POLYGON ((678.98 194.14, 687.77 194.42, 687.13 213.71, 679.59 210.93, 678.51 207.98, 678.98 194.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,40,"POLYGON ((616.68 165.91, 621.30 171.74, 618.22 177.52, 607.49 177.04, 599.17 172.79, 599.75 164.72, 616.68 165.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,41,"POLYGON ((772.50 155.22, 793.43 155.93, 793.22 167.59, 793.28 179.49, 771.42 181.53, 772.50 155.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,42,"POLYGON ((871.55 152.46, 873.31 162.83, 873.47 169.02, 872.86 174.98, 871.34 183.94, 853.15 183.43, 853.62 172.01, 853.39 163.09, 854.63 153.38, 871.55 152.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,43,"POLYGON ((706.49 155.17, 707.49 175.23, 679.82 173.94, 677.40 167.07, 679.13 157.11, 685.07 155.47, 706.49 155.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,44,"POLYGON ((653.73 155.00, 662.93 156.88, 665.68 167.68, 663.01 173.70, 646.13 174.48, 645.98 168.87, 653.73 155.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,45,"POLYGON ((830.04 151.96, 836.96 169.63, 815.63 172.91, 806.55 168.92, 805.94 152.86, 830.04 151.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,46,"POLYGON ((750.19 152.90, 751.19 178.12, 743.96 171.64, 739.17 162.66, 734.50 159.61, 728.20 174.86, 726.94 152.81, 725.03 147.24, 745.45 146.72, 750.19 152.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,47,"POLYGON ((838.00 74.25, 864.07 74.63, 864.22 94.62, 845.55 94.39, 846.12 88.76, 839.78 88.58, 838.00 74.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,48,"POLYGON ((703.52 68.23, 704.15 82.60, 681.25 83.18, 678.94 70.83, 689.81 67.34, 703.52 68.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,49,"POLYGON ((637.46 65.69, 665.82 64.48, 668.17 78.05, 665.29 82.34, 637.90 83.28, 637.46 65.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,50,"POLYGON ((593.64 56.15, 617.04 55.80, 616.59 67.47, 622.80 67.06, 622.65 80.94, 615.47 82.61, 614.57 86.12, 594.09 83.91, 593.64 56.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,51,"POLYGON ((701.66 45.46, 710.13 45.95, 710.57 63.46, 700.70 63.02, 701.66 45.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,52,"POLYGON ((860.41 32.85, 862.79 56.57, 843.08 58.53, 838.46 56.21, 841.53 39.28, 860.41 32.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,53,"POLYGON ((859.09 0.00, 854.20 5.11, 849.29 5.23, 844.30 3.28, 840.14 0.00, 859.09 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,54,"POLYGON ((746.43 887.65, 764.71 892.12, 762.76 900.00, 743.38 900.00, 746.43 887.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,55,"POLYGON ((612.13 793.71, 646.49 796.08, 645.13 815.67, 610.77 813.32, 612.13 793.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,56,"POLYGON ((730.33 697.83, 759.18 700.25, 758.22 711.53, 753.68 711.13, 753.14 717.47, 728.85 715.45, 730.33 697.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,57,"POLYGON ((876.13 668.11, 886.80 658.38, 896.86 665.83, 898.19 670.17, 899.96 676.80, 897.85 677.19, 896.16 673.55, 894.06 673.44, 890.72 674.40, 883.99 679.12, 876.07 686.87, 871.40 682.94, 880.67 673.61, 876.13 668.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,58,"POLYGON ((724.82 649.73, 738.57 649.30, 738.99 662.14, 725.69 662.56, 725.53 657.82, 722.36 657.92, 722.15 651.29, 724.89 651.84, 724.82 649.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,59,"POLYGON ((686.03 641.91, 700.00 641.91, 703.70 648.27, 702.46 665.00, 691.63 667.85, 686.04 667.86, 686.03 641.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,60,"POLYGON ((754.71 625.02, 763.25 625.72, 763.94 629.67, 773.08 628.09, 775.36 641.23, 772.34 645.71, 761.47 646.38, 758.26 652.10, 752.45 653.72, 754.71 625.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,61,"POLYGON ((623.87 622.15, 640.79 620.04, 643.13 638.14, 640.69 641.13, 639.62 648.44, 636.95 650.19, 635.62 655.42, 619.47 657.45, 617.43 641.30, 623.87 640.49, 621.85 624.51, 623.87 622.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,62,"POLYGON ((811.21 612.93, 826.35 613.72, 828.28 619.58, 830.86 647.15, 812.13 648.89, 808.84 647.49, 808.78 613.55, 811.21 612.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,63,"POLYGON ((730.85 620.08, 732.59 630.87, 731.27 636.74, 732.05 644.67, 714.58 646.38, 711.45 614.73, 730.85 620.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,64,"POLYGON ((836.70 614.75, 858.95 615.51, 858.03 642.04, 839.50 641.40, 835.87 638.85, 836.70 614.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,65,"POLYGON ((613.97 513.33, 672.18 520.66, 667.87 554.47, 609.67 547.13, 613.97 513.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,66,"POLYGON ((766.45 467.32, 814.99 461.91, 823.68 539.52, 832.22 538.57, 835.36 566.55, 801.73 570.31, 798.61 542.46, 786.43 543.82, 781.67 501.27, 770.39 502.51, 766.45 467.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,67,"POLYGON ((648.01 371.42, 663.97 373.08, 667.57 382.57, 664.03 391.43, 663.41 400.19, 647.95 401.85, 643.54 393.62, 638.40 388.75, 636.55 381.70, 648.01 371.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,68,"POLYGON ((562.50 373.28, 578.56 374.85, 581.33 387.27, 580.65 394.15, 560.64 392.20, 562.50 373.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,69,"POLYGON ((438.75 881.95, 438.86 891.52, 440.12 891.49, 440.22 900.00, 422.01 900.00, 421.92 892.84, 420.08 892.86, 419.94 882.21, 438.75 881.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,70,"POLYGON ((538.90 886.00, 562.94 885.48, 563.05 890.47, 565.64 890.41, 565.79 897.66, 563.04 897.71, 563.09 900.00, 539.20 900.00, 538.90 886.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,71,"POLYGON ((492.90 875.80, 512.36 875.11, 512.58 881.36, 522.75 881.00, 523.41 900.00, 491.77 900.00, 491.44 890.80, 493.43 890.73, 492.90 875.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,72,"POLYGON ((315.66 836.16, 356.97 835.62, 357.30 861.02, 320.56 861.51, 320.39 848.27, 315.82 848.32, 315.66 836.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,73,"POLYGON ((384.80 849.23, 370.07 849.67, 369.70 837.40, 384.43 836.96, 384.80 849.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,74,"POLYGON ((401.82 854.52, 388.16 854.87, 387.46 827.34, 405.93 826.87, 406.25 839.83, 401.45 839.95, 401.82 854.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,75,"POLYGON ((320.66 791.14, 336.35 790.97, 336.75 825.48, 321.06 825.65, 320.66 791.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,76,"POLYGON ((420.98 768.77, 429.24 768.45, 433.03 777.33, 440.18 779.01, 441.10 802.09, 422.33 802.84, 420.98 768.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,77,"POLYGON ((490.01 754.97, 512.47 754.40, 512.79 766.26, 520.14 766.08, 520.62 784.76, 511.45 785.01, 511.53 788.21, 491.73 788.71, 491.38 775.91, 488.06 775.99, 487.89 769.52, 490.38 769.45, 490.01 754.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,78,"POLYGON ((564.87 755.93, 566.63 781.48, 558.94 776.52, 557.06 771.84, 554.14 768.08, 552.86 764.29, 554.52 759.68, 552.52 756.78, 564.87 755.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,79,"POLYGON ((425.27 704.63, 440.87 705.85, 444.05 713.21, 437.51 743.23, 426.81 740.92, 423.09 732.09, 425.27 704.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,80,"POLYGON ((330.05 700.83, 351.48 701.26, 352.58 725.05, 341.72 728.79, 328.99 717.70, 330.05 700.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,81,"POLYGON ((490.20 697.36, 505.15 696.63, 505.75 708.46, 518.12 707.86, 524.95 714.12, 515.55 724.29, 491.60 725.47, 490.20 697.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,82,"POLYGON ((187.56 691.90, 185.25 717.78, 168.72 716.31, 167.13 712.89, 169.15 690.26, 187.56 691.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,83,"POLYGON ((370.15 638.79, 385.11 638.30, 387.72 642.38, 392.00 645.63, 395.10 668.90, 391.54 677.31, 381.13 679.66, 371.37 674.92, 370.15 638.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,84,"POLYGON ((119.98 643.48, 121.43 672.07, 106.51 672.83, 105.06 644.23, 119.98 643.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,85,"POLYGON ((413.71 636.24, 420.37 635.18, 419.40 637.92, 430.29 638.28, 430.47 645.36, 424.97 651.11, 423.25 665.76, 420.50 664.99, 418.18 672.55, 410.81 671.07, 409.06 667.99, 404.85 666.65, 404.47 651.86, 411.86 646.03, 413.71 636.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,86,"POLYGON ((147.76 636.76, 153.26 637.61, 154.21 645.03, 160.42 644.62, 161.79 649.81, 159.18 655.07, 151.57 658.99, 143.87 660.16, 144.30 647.76, 145.45 644.01, 145.81 638.56, 147.76 636.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,87,"POLYGON ((487.22 632.37, 499.08 628.61, 505.16 632.41, 505.90 661.64, 500.58 667.73, 487.75 667.92, 487.22 632.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,88,"POLYGON ((249.27 545.39, 252.43 571.17, 244.09 572.62, 237.29 568.62, 235.05 546.17, 249.27 545.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,89,"POLYGON ((547.83 474.85, 550.16 484.80, 549.43 523.51, 513.01 522.84, 509.00 513.06, 502.78 515.60, 502.51 525.46, 455.20 524.15, 456.65 472.33, 547.83 474.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722439,90,"POLYGON ((196.58 486.90, 198.31 495.54, 201.97 502.37, 181.85 504.88, 183.65 496.64, 183.16 487.73, 196.58 486.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,0,"POLYGON ((597.93 392.55, 596.54 395.72, 599.01 402.23, 599.54 406.28, 598.11 407.87, 597.66 410.59, 595.12 413.58, 588.19 412.71, 588.05 411.36, 585.89 409.13, 582.96 408.63, 585.35 394.65, 590.43 395.50, 591.12 391.42, 597.93 392.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,1,"POLYGON ((837.33 356.26, 863.54 360.16, 860.40 380.42, 834.28 376.53, 837.33 356.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,2,"POLYGON ((755.88 354.11, 761.92 360.90, 759.43 373.67, 757.50 373.29, 756.20 370.97, 752.24 366.99, 750.10 366.42, 746.01 366.64, 743.29 368.08, 742.48 370.32, 742.91 372.66, 732.64 371.24, 735.46 351.26, 755.88 354.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,3,"POLYGON ((723.79 353.24, 723.40 356.25, 728.45 356.89, 726.80 369.57, 719.57 368.64, 719.05 372.67, 704.92 370.86, 701.93 369.31, 699.94 367.01, 695.43 366.26, 697.39 354.40, 699.67 356.41, 702.67 357.35, 705.91 357.13, 709.57 355.86, 713.49 351.93, 723.79 353.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,4,"POLYGON ((573.80 348.59, 586.33 349.01, 586.32 351.69, 587.10 353.65, 589.39 356.83, 588.07 374.23, 572.05 373.01, 572.87 362.18, 569.41 361.91, 569.88 355.69, 571.67 355.82, 573.82 349.81, 573.80 348.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,5,"POLYGON ((683.93 346.55, 683.21 351.83, 690.98 352.87, 688.30 372.79, 670.41 370.40, 676.09 361.13, 676.31 357.73, 675.46 353.78, 667.36 349.26, 668.00 344.42, 683.93 346.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,6,"POLYGON ((862.40 263.23, 863.39 283.90, 837.27 285.14, 836.16 261.73, 847.45 261.19, 847.60 263.94, 862.40 263.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,7,"POLYGON ((816.62 260.25, 817.75 263.24, 819.15 264.75, 818.70 267.70, 815.78 267.97, 814.21 268.54, 813.41 269.92, 822.28 280.64, 822.97 283.44, 807.17 283.84, 807.82 281.00, 807.12 277.47, 804.73 273.98, 800.98 271.28, 794.39 270.64, 794.71 267.15, 797.09 266.58, 799.58 265.05, 800.58 263.25, 800.46 261.01, 816.62 260.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,8,"POLYGON ((876.72 254.81, 900.00 253.54, 900.00 289.45, 878.67 290.61, 876.72 254.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,9,"POLYGON ((712.66 261.60, 732.58 262.16, 732.14 277.07, 712.22 276.51, 712.66 261.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,10,"POLYGON ((753.31 255.97, 749.56 264.48, 750.26 268.19, 753.09 271.23, 749.13 276.90, 749.21 279.27, 742.83 279.53, 741.99 258.17, 747.50 257.96, 747.43 256.19, 753.31 255.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,11,"POLYGON ((671.17 251.40, 674.60 253.91, 677.19 253.65, 677.87 259.91, 675.81 262.18, 677.53 266.25, 677.38 270.07, 677.26 271.98, 663.37 271.16, 663.16 274.71, 653.16 274.12, 654.03 259.54, 660.11 259.90, 660.59 251.76, 662.94 251.90, 663.14 248.25, 671.33 248.73, 671.17 251.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,12,"POLYGON ((597.81 235.91, 610.35 236.12, 610.20 245.49, 620.08 245.66, 619.70 268.69, 611.86 268.56, 611.78 274.07, 598.14 273.86, 598.25 267.64, 600.92 265.33, 601.59 262.72, 600.04 250.37, 601.45 246.85, 601.72 242.89, 601.15 240.04, 597.81 235.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,13,"POLYGON ((679.45 222.07, 679.12 230.42, 667.10 229.95, 667.40 221.60, 679.45 222.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,14,"POLYGON ((573.94 138.22, 608.36 131.62, 615.00 166.01, 580.58 172.62, 573.94 138.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,15,"POLYGON ((646.93 162.81, 644.26 119.23, 656.48 118.50, 656.90 125.59, 694.06 123.41, 730.46 121.95, 730.24 116.23, 741.15 115.82, 741.35 121.49, 752.93 121.11, 752.74 115.25, 764.49 114.91, 764.66 120.66, 800.57 119.70, 838.16 119.36, 838.13 113.51, 848.39 113.44, 848.64 155.97, 752.59 156.57, 744.83 156.83, 646.93 162.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,16,"POLYGON ((880.60 116.47, 900.00 119.72, 900.00 155.19, 874.75 150.95, 880.60 116.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,17,"POLYGON ((322.42 865.06, 326.29 880.32, 317.18 893.45, 277.30 893.91, 276.98 865.57, 322.42 865.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,18,"POLYGON ((16.81 793.69, 31.00 825.00, 21.36 841.09, 0.00 847.93, 0.00 803.31, 16.81 793.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,19,"POLYGON ((266.83 796.86, 371.00 805.89, 369.18 826.45, 265.01 817.40, 266.83 796.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,20,"POLYGON ((544.50 770.23, 543.66 785.43, 524.02 784.36, 524.44 776.78, 513.04 776.16, 513.62 766.02, 526.78 766.75, 525.63 762.43, 530.87 761.03, 539.20 769.94, 544.50 770.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,21,"POLYGON ((387.98 727.35, 409.65 729.88, 399.53 804.77, 378.89 801.98, 380.80 788.04, 387.98 727.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,22,"POLYGON ((250.53 732.83, 261.12 749.86, 227.00 770.86, 216.43 753.99, 250.53 732.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,23,"POLYGON ((338.23 644.04, 348.08 659.64, 331.98 672.94, 292.64 692.28, 259.67 708.98, 248.82 713.23, 240.42 696.09, 338.23 644.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,24,"POLYGON ((572.23 665.39, 583.95 662.23, 573.29 683.66, 557.88 683.16, 549.57 682.49, 552.77 668.26, 557.98 663.40, 572.23 665.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,25,"POLYGON ((514.70 651.36, 529.48 650.63, 531.26 665.30, 532.51 673.35, 533.38 679.99, 525.67 681.23, 521.41 679.58, 519.43 670.87, 516.73 661.83, 514.15 657.68, 514.70 651.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,26,"POLYGON ((486.87 650.47, 486.04 669.47, 457.72 668.24, 451.64 664.89, 452.35 648.97, 486.87 650.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,27,"POLYGON ((151.64 541.25, 151.53 584.38, 74.06 584.02, 73.87 563.49, 61.23 563.59, 62.63 721.40, 21.86 721.77, 21.23 540.34, 151.64 541.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,28,"POLYGON ((311.39 594.64, 312.00 616.67, 229.44 618.92, 228.83 595.78, 238.28 595.54, 238.22 593.44, 263.52 592.77, 263.58 595.21, 277.31 594.84, 277.25 592.24, 302.69 591.55, 302.78 594.88, 311.39 594.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,29,"POLYGON ((171.03 566.22, 189.55 565.75, 189.90 579.59, 171.38 580.06, 171.03 566.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,30,"POLYGON ((350.24 528.10, 350.71 551.91, 343.29 552.05, 343.36 555.60, 317.58 556.13, 317.50 552.27, 303.08 552.57, 303.13 555.47, 277.28 556.00, 277.19 552.69, 264.29 552.95, 264.38 557.79, 236.55 558.36, 236.48 554.68, 227.36 554.87, 226.86 530.64, 350.24 528.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,31,"POLYGON ((379.20 493.36, 382.53 496.38, 388.35 498.15, 395.02 498.27, 401.34 496.64, 408.58 489.69, 409.21 485.32, 425.44 487.66, 422.50 507.80, 433.59 509.41, 427.27 553.01, 414.28 551.14, 413.49 549.41, 409.71 545.95, 405.57 546.79, 401.68 550.88, 401.81 561.93, 394.87 568.44, 371.18 565.73, 373.84 542.55, 384.10 543.71, 384.37 541.35, 403.69 543.57, 407.26 512.94, 385.89 510.48, 386.44 505.74, 377.87 504.76, 379.20 493.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,32,"POLYGON ((334.73 482.57, 333.95 486.19, 335.36 487.88, 352.45 493.66, 357.10 493.46, 357.67 506.94, 313.64 508.74, 312.69 485.91, 322.84 485.47, 322.74 483.05, 334.73 482.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,33,"POLYGON ((296.58 414.64, 297.01 424.82, 298.70 424.73, 301.36 488.59, 297.98 488.72, 298.40 498.77, 276.79 499.65, 273.28 415.61, 296.58 414.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,34,"POLYGON ((455.44 437.97, 462.31 444.79, 462.79 455.61, 461.60 458.15, 402.57 455.68, 402.56 452.41, 402.52 447.86, 402.36 444.34, 402.09 440.59, 397.72 437.46, 394.30 437.31, 394.49 433.51, 401.20 433.82, 401.29 432.16, 419.06 432.99, 418.93 435.90, 436.17 436.71, 436.30 433.82, 453.44 434.63, 453.28 437.87, 455.44 437.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,35,"POLYGON ((251.94 378.21, 252.33 394.28, 255.04 394.21, 255.31 405.72, 253.12 405.78, 254.88 475.63, 256.66 475.59, 256.91 485.21, 254.83 485.27, 255.32 503.37, 231.51 504.02, 231.25 495.23, 228.90 495.31, 228.15 468.96, 231.01 468.89, 230.57 452.96, 236.53 452.81, 240.14 449.32, 245.76 431.78, 235.39 401.99, 226.16 402.20, 225.81 386.74, 228.63 386.69, 228.45 378.77, 251.94 378.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,36,"POLYGON ((442.79 387.66, 446.46 393.01, 447.38 395.09, 449.98 399.93, 452.72 404.06, 451.45 409.71, 454.40 414.54, 444.71 414.07, 444.79 403.39, 442.32 403.46, 442.79 387.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,37,"POLYGON ((367.36 374.66, 368.05 397.97, 358.10 398.25, 358.19 401.00, 293.46 402.86, 293.38 400.27, 285.25 400.50, 284.58 377.05, 367.36 374.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,38,"POLYGON ((425.54 340.15, 423.75 355.29, 425.02 355.44, 423.20 370.93, 421.58 370.75, 420.80 377.37, 400.80 375.03, 405.20 337.78, 425.54 340.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,39,"POLYGON ((529.88 340.56, 529.87 348.91, 524.12 357.98, 512.20 359.28, 507.43 357.16, 500.27 350.42, 500.18 346.69, 511.81 340.98, 511.81 334.48, 519.90 334.47, 519.89 340.55, 529.88 340.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,40,"POLYGON ((263.66 328.66, 264.02 334.75, 265.86 338.42, 268.04 340.96, 271.58 342.62, 274.71 343.03, 275.28 346.61, 272.29 350.90, 272.56 355.54, 217.15 358.81, 215.72 334.87, 222.01 334.51, 221.76 330.41, 247.37 328.90, 247.76 335.50, 254.80 335.08, 254.45 329.20, 263.66 328.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,41,"POLYGON ((367.48 333.84, 367.86 355.54, 300.48 356.69, 300.06 333.06, 305.74 332.96, 305.67 328.72, 329.73 328.31, 329.81 332.93, 336.59 332.80, 336.49 327.54, 362.09 327.11, 362.21 333.93, 367.48 333.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,42,"POLYGON ((549.46 244.88, 549.47 259.31, 542.71 259.33, 542.70 264.83, 528.65 264.84, 528.66 258.64, 523.23 258.65, 523.23 252.61, 526.89 252.61, 526.89 244.88, 549.46 244.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,43,"POLYGON ((306.51 222.11, 339.73 220.62, 342.33 278.94, 309.11 280.40, 306.51 222.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,44,"POLYGON ((481.99 174.06, 496.56 213.71, 448.00 231.39, 433.43 191.74, 481.99 174.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,45,"POLYGON ((499.64 164.60, 530.07 158.56, 536.85 192.44, 506.40 198.48, 499.64 164.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,46,"POLYGON ((569.41 144.33, 575.61 177.83, 542.89 183.83, 536.68 150.36, 569.41 144.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,47,"POLYGON ((255.19 65.81, 270.49 91.37, 144.02 166.44, 128.71 140.88, 255.19 65.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,48,"POLYGON ((10.99 82.98, 48.27 86.18, 47.00 104.79, 42.84 107.72, 37.17 106.11, 10.73 105.71, 10.99 82.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,49,"POLYGON ((276.59 89.00, 268.38 25.81, 366.81 13.12, 370.07 38.08, 296.19 47.61, 301.16 85.84, 276.59 89.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,50,"POLYGON ((187.74 32.87, 187.81 37.93, 190.34 37.89, 190.70 59.55, 168.31 59.92, 168.17 51.37, 161.64 51.47, 161.32 33.30, 187.74 32.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,51,"POLYGON ((373.39 10.89, 459.96 0.00, 470.20 0.00, 473.59 26.86, 376.94 38.99, 373.39 10.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,52,"POLYGON ((535.28 0.00, 489.59 8.85, 487.86 0.00, 535.28 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,53,"POLYGON ((836.30 879.28, 860.23 878.32, 859.14 890.98, 859.36 899.72, 859.09 900.00, 840.14 900.00, 837.83 898.18, 836.30 879.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,54,"POLYGON ((793.87 877.65, 793.37 892.85, 777.46 892.34, 773.89 877.01, 793.87 877.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,55,"POLYGON ((842.05 814.99, 848.42 816.22, 848.62 824.27, 851.28 831.91, 846.89 838.70, 843.10 842.28, 831.50 843.29, 826.62 831.14, 829.21 819.62, 842.95 822.67, 842.05 814.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,56,"POLYGON ((711.56 771.64, 714.24 769.58, 718.24 769.74, 721.85 774.60, 726.17 777.71, 726.16 787.88, 729.27 792.50, 727.90 805.75, 711.89 804.11, 709.53 799.95, 708.31 790.55, 701.04 789.42, 704.03 770.46, 711.56 771.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,57,"POLYGON ((634.89 779.43, 638.45 794.41, 606.35 792.76, 605.07 784.05, 609.23 781.84, 626.22 784.56, 627.09 777.16, 634.89 779.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,58,"POLYGON ((847.77 756.42, 850.36 779.64, 845.55 785.95, 827.37 784.61, 828.97 763.00, 842.26 763.97, 839.34 757.36, 847.77 756.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,59,"POLYGON ((874.21 683.78, 871.37 703.85, 860.22 701.85, 845.81 702.92, 846.21 690.64, 846.99 680.45, 874.21 683.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,60,"POLYGON ((833.17 681.03, 831.76 699.46, 804.56 697.43, 805.95 679.01, 833.17 681.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,61,"POLYGON ((770.37 675.41, 774.89 675.76, 789.48 676.48, 792.57 680.08, 786.30 690.23, 786.76 694.26, 785.38 695.69, 760.44 693.18, 762.36 674.57, 770.37 675.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,62,"POLYGON ((747.16 667.74, 747.11 692.80, 741.13 693.49, 720.12 691.03, 718.65 688.98, 722.93 683.96, 737.05 684.82, 738.52 680.57, 741.23 676.30, 740.26 673.18, 739.99 668.97, 747.16 667.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,63,"POLYGON ((622.31 674.60, 621.94 687.57, 593.15 684.62, 593.22 673.41, 622.31 674.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,64,"POLYGON ((666.76 670.30, 666.84 687.30, 657.38 689.47, 638.69 688.90, 634.71 684.45, 634.39 679.38, 636.20 673.54, 639.98 670.47, 666.76 670.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,65,"POLYGON ((677.64 668.80, 682.35 660.44, 701.18 659.61, 709.51 661.68, 709.80 672.89, 705.92 673.16, 703.00 675.88, 703.72 683.56, 699.44 688.58, 691.57 690.71, 683.72 686.53, 676.91 681.80, 677.64 668.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,66,"POLYGON ((826.70 623.83, 830.65 672.06, 810.40 668.62, 805.83 665.52, 808.02 623.80, 826.70 623.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,67,"POLYGON ((757.09 619.14, 758.36 630.52, 761.75 636.14, 760.61 640.14, 736.64 638.02, 736.48 631.83, 737.39 618.91, 757.09 619.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,68,"POLYGON ((872.41 510.24, 872.02 519.04, 872.76 523.99, 874.10 527.91, 875.95 531.70, 877.76 534.39, 878.85 537.71, 866.52 537.89, 866.45 510.39, 872.41 510.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,69,"POLYGON ((899.81 471.94, 898.51 498.63, 870.09 497.25, 871.40 470.57, 899.81 471.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3722889,70,"POLYGON ((900.00 457.18, 893.08 456.84, 893.50 448.59, 887.83 448.31, 888.75 429.80, 900.00 430.35, 900.00 457.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,0,"POLYGON ((330.28 263.96, 341.66 270.38, 358.21 282.23, 349.26 294.16, 348.33 301.15, 319.32 284.33, 330.28 263.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,1,"POLYGON ((185.79 274.78, 202.15 267.37, 210.54 285.70, 194.20 293.13, 185.79 274.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,2,"POLYGON ((170.86 241.80, 180.52 236.27, 187.91 250.87, 193.35 255.74, 181.13 260.23, 170.86 241.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,3,"POLYGON ((75.16 211.51, 88.02 231.72, 71.94 241.87, 59.08 221.64, 75.16 211.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,4,"POLYGON ((153.82 206.91, 166.45 199.25, 177.73 217.65, 165.09 225.32, 153.82 206.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,5,"POLYGON ((501.05 188.35, 515.45 188.65, 522.51 201.23, 522.08 220.02, 499.83 220.14, 501.05 188.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,6,"POLYGON ((393.05 183.03, 426.72 190.68, 423.12 208.46, 389.24 201.49, 393.05 183.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,7,"POLYGON ((46.11 188.23, 57.60 183.03, 58.47 189.67, 62.10 194.48, 67.25 202.76, 57.52 207.92, 52.09 202.44, 55.82 196.40, 52.83 189.81, 46.11 188.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,8,"POLYGON ((351.73 150.96, 370.87 144.09, 381.99 174.79, 362.84 181.67, 351.73 150.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,9,"POLYGON ((503.15 129.25, 522.48 129.42, 521.27 169.94, 497.64 168.97, 497.79 166.05, 503.96 161.21, 507.20 156.20, 506.35 149.08, 502.62 144.24, 503.15 129.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,10,"POLYGON ((137.36 134.37, 142.50 143.01, 148.42 140.06, 153.17 147.29, 130.26 159.79, 119.65 143.94, 137.36 134.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,11,"POLYGON ((38.24 138.93, 31.91 143.26, 21.16 138.81, 12.50 140.14, 4.95 140.33, 1.94 132.32, 16.64 126.09, 18.38 128.84, 31.12 123.22, 38.24 138.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,12,"POLYGON ((120.68 99.92, 132.04 120.87, 115.61 129.70, 104.25 108.75, 120.68 99.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,13,"POLYGON ((501.82 76.92, 520.47 77.13, 520.80 116.70, 505.04 115.33, 505.13 110.17, 502.01 101.97, 501.82 76.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,14,"POLYGON ((342.80 77.70, 362.22 77.16, 363.25 114.71, 343.82 115.25, 342.80 77.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,15,"POLYGON ((98.96 80.23, 111.19 76.30, 110.69 80.13, 119.45 87.28, 123.52 86.06, 125.61 92.98, 104.22 99.38, 98.71 101.14, 95.20 90.31, 101.58 88.29, 98.96 80.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,16,"POLYGON ((4.55 25.18, 28.65 25.94, 28.68 37.95, 26.84 43.01, 28.97 49.37, 28.15 61.09, 31.24 61.30, 31.17 69.67, 14.08 70.11, 15.00 61.72, 20.02 61.03, 20.18 45.40, 6.16 44.65, 11.15 42.28, 15.21 37.16, 10.55 30.31, 4.55 25.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,17,"POLYGON ((502.81 23.95, 524.35 23.74, 524.63 52.47, 506.14 52.66, 503.01 44.01, 502.81 23.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,18,"POLYGON ((92.51 18.59, 96.37 33.88, 83.70 34.20, 83.84 30.47, 88.80 29.61, 92.43 25.54, 92.51 18.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,19,"POLYGON ((75.51 6.64, 69.59 8.76, 66.43 11.82, 64.78 15.59, 61.31 16.41, 61.60 7.99, 75.51 6.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,20,"POLYGON ((524.06 0.00, 524.01 6.92, 502.64 6.77, 502.69 0.00, 524.06 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,21,"POLYGON ((264.10 0.00, 264.88 5.45, 243.04 6.00, 241.88 0.00, 264.10 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,22,"POLYGON ((601.25 292.75, 625.52 292.97, 625.17 328.66, 600.87 328.41, 601.25 292.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,23,"POLYGON ((761.57 323.43, 760.49 285.95, 779.79 285.39, 780.87 322.88, 761.57 323.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,24,"POLYGON ((600.87 240.88, 620.12 241.08, 619.74 274.63, 600.52 274.43, 600.87 240.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,25,"POLYGON ((761.34 210.03, 763.09 270.48, 749.74 270.86, 749.26 254.18, 739.17 254.48, 737.91 210.72, 761.34 210.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,26,"POLYGON ((600.77 194.98, 620.12 195.15, 619.79 231.06, 600.43 230.88, 600.77 194.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,27,"POLYGON ((861.15 164.65, 876.99 164.85, 876.83 176.79, 885.47 176.91, 885.09 206.62, 863.58 206.34, 863.89 184.09, 861.01 173.93, 861.15 164.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,28,"POLYGON ((603.38 147.90, 622.15 147.84, 622.26 184.95, 603.50 185.03, 603.38 147.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,29,"POLYGON ((769.55 147.11, 771.12 175.22, 765.72 183.46, 761.22 183.29, 757.01 171.69, 751.59 167.37, 751.91 147.27, 769.55 147.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,30,"POLYGON ((758.48 102.36, 777.64 101.71, 778.84 137.93, 759.67 138.55, 758.48 102.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,31,"POLYGON ((600.34 97.70, 619.66 97.65, 613.75 103.84, 610.39 113.31, 614.77 126.63, 617.93 135.50, 619.43 141.94, 600.31 141.10, 600.34 97.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,32,"POLYGON ((875.17 112.02, 872.64 66.84, 891.09 65.82, 893.64 110.99, 875.17 112.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,33,"POLYGON ((599.99 57.66, 610.34 66.79, 613.04 76.11, 612.87 86.64, 613.24 92.66, 600.65 92.54, 599.99 57.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,34,"POLYGON ((744.61 30.43, 745.10 38.59, 741.98 38.76, 742.93 54.63, 727.76 55.52, 727.01 42.82, 723.25 43.05, 722.57 31.73, 744.61 30.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,35,"POLYGON ((861.71 18.73, 881.86 18.84, 881.70 45.46, 861.55 45.35, 861.71 18.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,36,"POLYGON ((600.48 13.61, 618.35 13.64, 618.29 50.09, 600.42 50.08, 600.48 13.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,37,"POLYGON ((154.29 776.98, 178.57 776.59, 179.02 804.35, 173.19 804.45, 173.05 796.11, 170.22 789.34, 164.68 788.08, 161.57 801.66, 163.75 806.51, 160.83 807.80, 157.18 807.87, 157.05 800.00, 154.67 800.04, 154.29 776.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,38,"POLYGON ((508.74 761.41, 520.41 751.45, 533.02 766.09, 521.35 776.05, 508.74 761.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,39,"POLYGON ((276.85 738.33, 277.10 748.16, 264.43 748.48, 262.11 747.67, 259.12 747.90, 255.69 751.85, 255.48 757.12, 252.90 759.47, 252.38 738.95, 276.85 738.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,40,"POLYGON ((70.84 735.97, 73.09 760.02, 57.62 761.46, 60.52 750.86, 54.07 746.83, 49.52 747.48, 46.79 743.86, 46.26 738.26, 70.84 735.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,41,"POLYGON ((172.21 734.57, 173.23 754.28, 157.08 755.11, 156.04 735.41, 172.21 734.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,42,"POLYGON ((292.05 738.37, 282.35 738.32, 282.40 731.31, 292.09 731.35, 292.05 738.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,43,"POLYGON ((170.75 691.04, 171.56 711.68, 145.44 712.70, 148.60 708.49, 143.40 708.62, 140.79 698.68, 143.37 695.79, 141.96 692.15, 170.75 691.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,44,"POLYGON ((372.39 658.46, 372.65 676.05, 347.13 676.43, 346.87 658.84, 372.39 658.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,45,"POLYGON ((244.35 655.94, 254.68 646.26, 266.71 659.01, 268.76 657.09, 281.96 671.10, 264.53 687.39, 253.92 676.12, 258.94 671.42, 244.35 655.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,46,"POLYGON ((58.39 653.62, 80.27 656.09, 79.33 664.39, 75.92 664.01, 74.53 676.34, 56.03 674.26, 58.39 653.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,47,"POLYGON ((332.46 675.12, 316.72 675.41, 316.80 679.78, 313.58 680.57, 311.43 671.70, 306.57 672.87, 302.46 670.33, 298.21 668.86, 298.04 659.12, 318.60 658.76, 318.41 647.62, 331.96 647.36, 332.46 675.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,48,"POLYGON ((407.17 653.29, 407.55 671.77, 392.49 672.08, 392.12 653.60, 407.17 653.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,49,"POLYGON ((168.94 675.03, 155.84 675.39, 155.11 649.66, 168.23 649.28, 168.94 675.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,50,"POLYGON ((461.80 674.39, 434.02 675.18, 433.24 648.23, 456.60 647.57, 457.25 669.91, 461.66 669.77, 461.80 674.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,51,"POLYGON ((512.83 672.40, 502.01 662.49, 505.86 658.30, 500.96 653.83, 512.38 641.47, 512.06 645.65, 513.40 648.86, 515.87 651.59, 520.00 653.48, 525.17 654.60, 529.77 654.06, 512.83 672.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,52,"POLYGON ((453.05 630.55, 464.45 630.26, 464.86 646.69, 453.47 646.98, 453.05 630.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,53,"POLYGON ((59.43 611.33, 61.00 615.27, 67.94 620.11, 70.92 620.61, 74.23 623.19, 75.95 632.29, 58.40 631.40, 59.43 611.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,54,"POLYGON ((494.18 623.66, 493.91 611.93, 507.28 611.61, 507.62 626.47, 501.62 626.60, 501.56 623.50, 494.18 623.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,55,"POLYGON ((164.85 626.17, 159.33 626.64, 155.02 623.60, 155.11 618.27, 150.99 618.22, 151.06 613.13, 151.04 608.61, 150.94 602.48, 153.43 602.42, 159.30 604.73, 166.01 605.79, 169.09 608.50, 165.22 609.13, 162.34 613.60, 164.24 618.97, 164.85 626.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,56,"POLYGON ((255.73 622.24, 243.43 622.73, 242.74 606.10, 247.73 604.11, 250.75 599.64, 254.80 599.49, 255.73 622.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,57,"POLYGON ((529.18 594.45, 529.35 624.59, 515.61 624.67, 515.51 606.92, 511.74 606.95, 510.60 602.98, 510.79 598.71, 513.98 596.28, 516.18 595.33, 519.15 595.84, 520.16 594.50, 529.18 594.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,58,"POLYGON ((140.38 582.22, 151.80 581.64, 152.46 594.52, 141.04 595.10, 140.38 582.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,59,"POLYGON ((258.91 541.25, 268.58 550.22, 248.79 571.38, 239.11 562.42, 258.91 541.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,60,"POLYGON ((373.31 546.89, 373.72 555.07, 361.77 555.66, 353.69 566.92, 345.98 566.90, 346.00 561.06, 338.74 561.04, 338.76 552.50, 341.66 551.18, 347.63 542.88, 349.21 547.24, 348.86 550.35, 352.99 551.82, 358.43 547.62, 373.31 546.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,61,"POLYGON ((325.09 539.97, 324.85 553.83, 311.73 553.60, 311.59 561.20, 304.50 561.07, 304.64 553.47, 303.24 547.45, 300.67 543.50, 306.61 539.66, 325.09 539.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,62,"POLYGON ((390.83 539.54, 415.47 538.67, 416.03 554.13, 391.39 555.02, 390.83 539.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,63,"POLYGON ((459.29 538.44, 459.75 552.20, 428.41 553.26, 427.95 539.51, 459.29 538.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,64,"POLYGON ((169.17 521.91, 168.96 540.83, 166.38 540.80, 162.94 537.78, 159.46 537.49, 157.43 535.55, 153.65 539.44, 150.20 536.13, 150.04 534.03, 152.35 532.48, 154.12 528.22, 156.62 527.04, 155.38 524.46, 155.43 521.77, 169.17 521.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,65,"POLYGON ((70.72 503.10, 89.10 501.52, 89.31 503.94, 87.17 506.65, 84.19 506.13, 81.14 508.27, 77.58 508.36, 75.26 509.91, 71.33 510.23, 70.72 503.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,66,"POLYGON ((38.67 489.95, 55.05 487.65, 58.25 510.10, 39.20 512.80, 37.62 501.79, 40.30 501.41, 38.67 489.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,67,"POLYGON ((228.40 468.13, 227.96 442.24, 250.59 441.86, 250.88 458.52, 258.79 458.39, 258.87 463.29, 262.33 463.23, 262.58 478.45, 255.81 478.58, 255.68 471.19, 250.03 471.29, 249.96 467.76, 228.40 468.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,68,"POLYGON ((167.05 448.53, 168.53 470.36, 147.40 471.81, 145.91 449.96, 167.05 448.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,69,"POLYGON ((300.81 452.02, 331.44 451.37, 330.19 455.75, 332.25 458.41, 336.77 460.16, 336.92 466.70, 301.15 467.46, 300.81 452.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,70,"POLYGON ((372.18 451.98, 391.86 451.19, 392.19 459.04, 396.53 458.86, 396.80 465.98, 372.78 466.95, 372.18 451.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,71,"POLYGON ((25.02 450.41, 40.33 447.13, 44.60 466.76, 26.32 470.69, 24.32 461.48, 27.29 460.83, 25.02 450.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,72,"POLYGON ((415.31 445.93, 434.97 445.94, 434.95 450.36, 438.72 450.35, 438.72 462.50, 442.32 462.49, 442.32 467.04, 415.29 467.02, 415.31 445.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,73,"POLYGON ((497.81 453.87, 521.68 429.64, 534.40 442.06, 510.53 466.29, 497.81 453.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,74,"POLYGON ((390.52 423.28, 391.53 441.83, 377.75 442.58, 376.72 424.03, 390.52 423.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,75,"POLYGON ((143.13 415.71, 150.61 414.08, 150.00 411.34, 154.34 410.41, 155.10 413.94, 159.26 413.04, 163.58 432.84, 155.85 434.52, 150.66 428.59, 149.09 425.10, 145.08 424.61, 143.13 415.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,76,"POLYGON ((292.29 425.80, 291.91 415.62, 295.11 415.49, 297.14 417.17, 301.05 418.29, 298.97 425.56, 292.29 425.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,77,"POLYGON ((30.08 406.82, 36.66 428.51, 20.95 433.26, 20.16 430.66, 13.87 432.56, 8.06 413.44, 30.08 406.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,78,"POLYGON ((132.48 379.40, 150.39 374.11, 157.03 396.40, 139.10 401.70, 132.48 379.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,79,"POLYGON ((14.88 376.50, 19.14 375.37, 24.50 393.37, 16.86 396.08, 15.50 395.22, 9.77 396.70, 5.18 379.12, 14.88 376.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,80,"POLYGON ((506.79 374.11, 528.09 374.12, 529.19 384.41, 515.18 384.77, 513.61 388.98, 507.43 388.32, 506.79 374.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,81,"POLYGON ((115.58 348.62, 140.12 341.18, 145.77 359.66, 121.25 367.10, 115.58 348.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,82,"POLYGON ((0.00 341.09, 13.55 337.14, 20.11 359.35, 0.72 365.01, 0.00 362.58, 0.00 341.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,83,"POLYGON ((124.54 307.44, 134.28 326.81, 115.71 336.07, 105.97 316.70, 124.54 307.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,84,"POLYGON ((444.64 293.00, 459.27 292.81, 459.35 298.77, 489.19 298.39, 489.39 313.31, 444.89 313.86, 444.64 293.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,85,"POLYGON ((497.56 295.25, 504.75 295.20, 504.72 291.34, 521.73 291.22, 521.75 294.92, 535.51 294.82, 535.61 312.71, 497.68 312.96, 497.56 295.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,86,"POLYGON ((387.84 287.16, 428.81 289.83, 427.57 308.68, 386.59 306.02, 387.84 287.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,87,"POLYGON ((107.54 307.85, 97.59 291.07, 105.19 286.97, 113.35 282.86, 122.75 299.65, 107.54 307.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,88,"POLYGON ((779.26 894.06, 795.32 894.17, 795.28 900.00, 779.21 900.00, 779.26 894.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,89,"POLYGON ((886.53 895.98, 869.59 896.41, 868.86 867.86, 881.81 867.53, 881.99 874.45, 885.98 874.35, 886.53 895.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,90,"POLYGON ((797.98 842.38, 796.60 868.58, 777.88 867.59, 778.24 860.68, 785.90 848.59, 786.52 843.87, 784.48 841.68, 797.98 842.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,91,"POLYGON ((875.44 841.10, 873.90 809.06, 893.04 808.15, 894.58 840.19, 875.44 841.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,92,"POLYGON ((740.30 786.77, 740.68 795.16, 743.52 799.26, 744.92 804.84, 745.30 824.92, 721.84 825.38, 721.40 802.95, 715.39 803.06, 715.31 798.44, 706.60 798.60, 706.48 792.47, 709.56 790.11, 717.26 787.83, 740.30 786.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,93,"POLYGON ((606.97 784.44, 620.16 784.12, 624.24 788.19, 625.66 794.39, 629.76 799.31, 630.03 810.60, 607.61 811.15, 606.97 784.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,94,"POLYGON ((875.66 788.30, 874.81 758.11, 893.74 757.59, 894.58 787.78, 875.66 788.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,95,"POLYGON ((605.28 750.32, 612.65 749.98, 612.96 756.63, 620.98 756.25, 620.81 752.63, 626.89 752.34, 627.03 754.96, 636.46 754.52, 637.41 774.32, 606.49 775.79, 605.28 750.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,96,"POLYGON ((796.34 746.28, 796.13 757.25, 797.95 757.27, 797.85 762.16, 795.31 762.11, 795.10 771.37, 791.51 771.29, 791.41 776.04, 784.96 775.89, 785.08 770.90, 782.66 766.56, 778.75 764.40, 779.10 745.96, 796.34 746.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,97,"POLYGON ((874.24 710.60, 891.89 710.13, 892.06 716.37, 897.09 716.24, 897.78 740.99, 875.10 741.61, 874.24 710.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,98,"POLYGON ((782.14 702.82, 798.60 702.41, 799.45 735.77, 783.01 736.19, 782.14 702.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,99,"POLYGON ((900.00 678.53, 891.55 678.73, 891.73 685.92, 872.86 686.38, 872.31 664.13, 877.85 660.48, 884.02 660.65, 888.52 657.21, 891.74 652.76, 900.00 652.55, 900.00 678.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,100,"POLYGON ((872.14 604.15, 890.44 603.73, 890.65 613.45, 900.00 613.25, 900.00 625.75, 892.47 625.92, 892.65 633.75, 872.81 634.21, 872.14 604.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,101,"POLYGON ((739.82 613.80, 754.00 606.20, 753.71 601.50, 762.74 600.96, 763.97 602.58, 760.28 608.55, 767.51 608.21, 764.13 620.55, 765.95 627.72, 740.13 628.29, 739.82 613.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,102,"POLYGON ((870.49 548.77, 894.91 548.06, 895.71 576.41, 887.90 576.63, 888.04 581.66, 871.43 582.13, 870.49 548.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,103,"POLYGON ((732.16 532.51, 749.78 532.06, 747.78 536.66, 748.02 546.31, 745.34 551.97, 744.09 558.15, 746.98 561.05, 744.66 566.88, 741.88 561.01, 738.61 557.23, 736.11 541.53, 733.71 537.91, 732.16 532.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,104,"POLYGON ((599.79 487.35, 607.67 487.19, 607.79 493.92, 615.90 493.78, 616.29 515.10, 600.30 515.39, 599.79 487.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,105,"POLYGON ((787.79 443.03, 787.76 455.80, 791.90 455.80, 791.88 468.83, 784.47 468.82, 784.44 482.99, 777.92 482.97, 772.89 478.53, 767.66 467.27, 758.39 467.09, 758.76 449.45, 764.72 453.88, 768.78 448.49, 774.95 448.51, 774.96 443.01, 787.79 443.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,106,"POLYGON ((599.12 439.95, 615.10 439.43, 616.07 469.60, 600.09 470.12, 599.12 439.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,107,"POLYGON ((645.57 443.32, 663.83 443.61, 663.68 453.52, 645.42 453.25, 645.57 443.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,108,"POLYGON ((659.97 421.47, 669.80 421.28, 670.02 432.00, 660.18 432.21, 659.97 421.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,109,"POLYGON ((598.77 401.16, 607.48 400.58, 607.98 407.92, 615.25 407.44, 616.66 428.76, 600.68 429.81, 598.77 401.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,110,"POLYGON ((785.32 399.21, 785.90 406.10, 789.07 405.84, 790.22 419.55, 787.07 419.81, 787.44 424.42, 766.71 426.12, 764.62 400.91, 785.32 399.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,111,"POLYGON ((864.91 400.89, 885.07 399.83, 884.90 396.95, 891.82 396.57, 892.00 400.14, 900.00 399.71, 900.00 415.16, 865.77 416.99, 864.91 400.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,112,"POLYGON ((790.99 384.97, 772.50 387.28, 767.51 347.48, 786.01 345.19, 790.99 384.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,113,"POLYGON ((603.34 385.39, 601.38 341.29, 622.68 340.35, 624.63 384.45, 603.34 385.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,114,"POLYGON ((857.68 323.87, 857.64 309.36, 869.17 309.29, 871.42 307.76, 895.15 307.72, 895.19 323.79, 857.68 323.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,115,"POLYGON ((95.58 900.00, 90.47 891.90, 87.59 882.34, 91.05 872.95, 108.90 869.48, 112.29 886.77, 110.13 887.18, 110.96 891.38, 115.75 890.46, 117.62 900.00, 95.58 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,116,"POLYGON ((263.21 848.05, 263.84 862.55, 274.21 862.11, 274.69 873.30, 263.45 873.79, 263.54 875.92, 249.78 876.51, 249.43 868.60, 241.03 868.96, 240.53 857.57, 247.79 857.27, 247.42 848.73, 263.21 848.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,117,"POLYGON ((89.22 824.96, 94.41 842.19, 92.62 842.72, 96.29 854.93, 83.78 858.66, 81.86 856.29, 83.41 853.08, 80.68 847.78, 77.51 848.08, 73.38 846.41, 68.78 831.07, 89.22 824.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,118,"POLYGON ((512.14 812.66, 533.37 812.63, 533.39 825.04, 537.31 825.03, 537.32 838.68, 524.64 838.72, 524.63 834.21, 512.17 834.24, 512.14 812.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,119,"POLYGON ((267.67 824.69, 251.64 825.61, 256.95 812.33, 255.14 803.97, 258.25 801.87, 251.69 792.31, 266.80 791.44, 267.26 799.40, 271.99 799.12, 272.60 809.72, 266.81 810.04, 267.67 824.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3723789,120,"POLYGON ((85.14 802.62, 58.85 807.97, 57.60 805.50, 59.50 798.39, 57.97 789.35, 64.57 788.23, 66.69 784.65, 70.67 783.50, 74.46 786.96, 75.54 792.21, 82.71 790.76, 85.14 802.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,0,"POLYGON ((586.15 201.05, 597.60 205.78, 591.67 219.98, 580.24 215.25, 586.15 201.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,1,"POLYGON ((601.65 188.31, 638.77 170.39, 663.63 221.37, 657.32 224.41, 663.69 237.46, 638.89 249.45, 632.64 236.61, 626.61 239.51, 601.65 188.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,2,"POLYGON ((866.09 115.08, 867.87 148.71, 847.48 149.81, 845.69 116.16, 866.09 115.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,3,"POLYGON ((99.12 876.20, 104.04 900.00, 91.60 900.00, 89.20 893.73, 80.80 894.23, 79.27 877.53, 99.12 876.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,4,"POLYGON ((506.83 873.02, 524.23 873.13, 524.06 900.00, 502.69 900.00, 502.84 879.71, 506.78 879.72, 506.83 873.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,5,"POLYGON ((239.28 868.18, 244.41 860.24, 258.41 860.44, 264.10 900.00, 241.88 900.00, 240.78 894.34, 239.28 868.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,6,"POLYGON ((349.79 858.43, 362.15 857.89, 364.61 875.05, 372.31 886.71, 371.77 891.88, 359.69 894.18, 351.73 881.42, 349.79 858.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,7,"POLYGON ((88.33 870.89, 80.70 868.02, 80.55 862.45, 78.30 851.34, 93.44 851.24, 96.24 862.05, 95.90 870.69, 88.33 870.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,8,"POLYGON ((503.60 826.33, 523.15 826.27, 522.32 863.88, 507.25 863.15, 502.49 862.38, 504.60 856.29, 503.82 852.71, 503.60 826.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,9,"POLYGON ((76.05 816.83, 98.27 815.60, 99.63 839.97, 77.40 841.20, 76.05 816.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,10,"POLYGON ((237.56 800.72, 246.24 800.24, 252.34 808.72, 260.64 815.76, 263.64 845.52, 240.96 846.10, 237.56 800.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,11,"POLYGON ((349.83 794.74, 370.07 794.74, 370.05 834.98, 349.79 834.96, 349.83 794.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,12,"POLYGON ((72.93 790.52, 91.67 788.98, 93.38 809.58, 74.65 811.12, 72.93 790.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,13,"POLYGON ((501.76 781.82, 521.44 782.39, 520.46 816.09, 500.78 815.52, 501.76 781.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,14,"POLYGON ((233.64 787.33, 234.14 752.22, 242.48 750.25, 246.17 744.26, 251.09 745.53, 251.92 750.37, 256.46 750.94, 256.95 756.15, 261.14 756.04, 261.83 769.94, 254.08 780.57, 252.15 787.21, 233.64 787.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,15,"POLYGON ((349.99 744.93, 369.11 744.77, 369.47 786.45, 349.99 786.61, 349.90 775.69, 346.59 775.71, 346.43 756.85, 350.09 756.80, 349.99 744.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,16,"POLYGON ((484.15 759.03, 484.00 736.19, 521.60 735.95, 521.82 772.50, 499.63 772.64, 499.55 758.93, 484.15 759.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,17,"POLYGON ((66.43 725.05, 86.00 723.71, 87.54 740.67, 82.74 749.45, 74.91 750.49, 70.89 746.69, 67.58 748.73, 66.43 725.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,18,"POLYGON ((347.39 695.16, 362.51 695.22, 362.88 709.77, 368.82 722.38, 369.17 736.27, 348.67 736.79, 347.39 695.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,19,"POLYGON ((504.27 724.22, 504.69 710.72, 497.83 710.51, 498.51 688.99, 522.98 689.74, 521.89 724.77, 504.27 724.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,20,"POLYGON ((228.36 691.92, 242.68 693.80, 252.09 700.24, 260.10 706.19, 260.59 714.54, 249.96 715.08, 241.41 720.31, 235.48 718.51, 234.53 703.20, 228.07 702.54, 228.36 691.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,21,"POLYGON ((295.66 683.53, 274.35 682.96, 272.21 675.47, 270.50 663.82, 295.98 663.17, 295.66 683.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,22,"POLYGON ((348.37 651.76, 353.10 652.30, 361.23 653.67, 369.27 651.67, 367.93 687.29, 348.80 686.42, 348.37 651.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,23,"POLYGON ((501.23 645.34, 521.60 645.67, 521.06 679.00, 502.35 678.70, 502.53 668.68, 482.04 668.34, 482.20 658.15, 501.00 658.45, 501.23 645.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,24,"POLYGON ((56.45 644.37, 71.93 644.31, 79.77 655.27, 79.83 673.76, 56.53 673.85, 56.45 644.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,25,"POLYGON ((500.48 599.75, 519.78 600.23, 518.86 636.17, 499.57 635.68, 500.48 599.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,26,"POLYGON ((66.26 600.95, 68.23 609.13, 76.11 615.72, 75.93 626.80, 55.66 626.50, 56.06 600.78, 66.26 600.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,27,"POLYGON ((387.73 556.13, 427.28 556.03, 428.08 577.92, 402.03 578.59, 397.35 572.00, 388.32 570.43, 387.73 556.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,28,"POLYGON ((54.49 545.95, 67.64 545.42, 67.94 552.93, 72.78 552.74, 73.71 576.05, 70.78 576.17, 71.11 584.22, 59.50 584.69, 59.29 579.30, 55.74 577.17, 54.49 545.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,29,"POLYGON ((375.69 569.67, 338.92 566.43, 337.27 557.26, 326.85 543.87, 329.08 532.09, 340.53 530.40, 350.90 530.98, 349.56 544.67, 376.24 546.50, 375.69 569.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,30,"POLYGON ((449.04 532.48, 483.21 533.48, 482.59 554.87, 448.41 553.88, 449.04 532.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,31,"POLYGON ((217.77 518.63, 251.49 518.44, 251.78 521.34, 264.14 521.02, 264.20 523.49, 269.82 523.79, 270.09 534.52, 259.07 533.92, 251.12 530.30, 245.18 535.60, 234.93 538.99, 226.59 538.54, 218.06 538.98, 217.77 518.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,32,"POLYGON ((18.19 225.18, 32.29 224.89, 32.98 258.28, 4.36 258.85, 7.07 250.86, 11.60 246.99, 19.48 244.71, 19.29 237.21, 14.31 239.02, 18.32 231.82, 18.19 225.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,33,"POLYGON ((37.11 172.38, 39.00 205.03, 19.95 206.12, 18.07 173.47, 37.11 172.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,34,"POLYGON ((34.22 122.27, 36.27 151.96, 14.83 153.44, 12.77 123.72, 34.22 122.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,35,"POLYGON ((30.69 85.60, 31.92 101.13, 25.32 105.20, 25.16 109.93, 12.88 111.09, 11.88 104.70, 14.89 101.85, 19.49 95.89, 17.71 92.32, 15.33 86.25, 30.69 85.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,36,"POLYGON ((771.67 867.77, 787.13 867.55, 787.31 881.69, 789.83 881.65, 789.93 888.48, 787.59 888.52, 787.74 900.00, 772.07 900.00, 771.67 867.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,37,"POLYGON ((599.22 865.52, 607.45 870.90, 615.66 875.82, 619.51 885.58, 619.18 898.99, 601.86 898.55, 602.25 878.63, 599.55 878.47, 599.22 865.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,38,"POLYGON ((883.24 819.69, 900.00 819.78, 900.00 866.10, 882.95 866.00, 883.24 819.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,39,"POLYGON ((600.18 823.65, 619.29 823.83, 619.21 837.93, 616.79 849.62, 609.24 852.94, 598.99 847.61, 600.18 823.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,40,"POLYGON ((760.83 810.06, 788.22 810.12, 788.15 842.53, 761.11 842.49, 761.13 834.54, 754.45 834.53, 754.48 823.26, 760.81 823.27, 760.83 810.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,41,"POLYGON ((596.00 781.76, 620.97 782.32, 620.20 815.13, 599.73 814.67, 599.94 805.30, 595.46 805.21, 596.00 781.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,42,"POLYGON ((781.02 769.44, 786.00 769.82, 787.40 786.15, 786.51 789.88, 783.29 790.96, 784.02 799.86, 770.88 801.44, 770.51 777.65, 776.48 777.26, 780.90 775.15, 781.02 769.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,43,"POLYGON ((599.05 738.40, 618.58 738.28, 618.81 771.90, 599.28 772.04, 599.05 738.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,44,"POLYGON ((768.78 725.49, 787.22 725.60, 787.04 750.62, 778.69 753.74, 768.60 753.66, 768.78 725.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,45,"POLYGON ((846.99 709.29, 877.87 706.04, 880.32 729.16, 849.43 732.38, 846.99 709.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,46,"POLYGON ((596.40 682.39, 620.25 683.09, 619.55 705.58, 626.17 705.76, 625.86 716.36, 595.43 715.47, 596.40 682.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,47,"POLYGON ((761.37 683.23, 785.01 682.41, 786.67 714.89, 771.40 715.28, 771.29 711.11, 761.44 710.74, 761.37 683.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,48,"POLYGON ((767.44 640.86, 777.60 640.98, 778.31 654.41, 783.89 666.99, 783.69 673.74, 767.11 672.30, 767.44 640.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,49,"POLYGON ((598.24 639.24, 616.33 640.06, 614.78 673.42, 596.67 672.59, 598.24 639.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,50,"POLYGON ((599.25 636.41, 598.62 599.23, 617.16 598.89, 617.79 636.10, 599.25 636.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,51,"POLYGON ((765.10 597.06, 785.40 597.12, 785.30 629.66, 765.00 629.60, 765.10 597.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,52,"POLYGON ((848.37 554.14, 859.16 554.20, 859.10 565.63, 870.22 565.68, 870.08 595.08, 851.52 594.99, 851.59 581.58, 848.21 580.63, 847.05 576.06, 844.19 571.14, 843.83 564.69, 848.37 554.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,53,"POLYGON ((596.25 555.05, 616.58 555.15, 616.39 589.83, 596.05 589.75, 596.25 555.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,54,"POLYGON ((783.49 552.68, 784.17 588.07, 766.98 588.42, 766.68 572.93, 759.59 573.07, 759.31 558.36, 765.36 558.25, 765.26 553.04, 783.49 552.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,55,"POLYGON ((594.41 508.39, 616.21 508.50, 616.06 542.60, 594.24 542.49, 594.41 508.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,56,"POLYGON ((785.62 538.58, 758.18 538.90, 758.04 526.89, 764.11 526.83, 764.01 518.24, 756.43 518.32, 756.30 506.49, 763.11 506.41, 762.91 489.08, 789.94 488.77, 790.22 512.09, 785.32 512.15, 785.62 538.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,57,"POLYGON ((872.28 343.74, 864.03 344.33, 859.43 340.69, 853.76 339.73, 854.01 335.24, 853.89 330.38, 855.29 326.22, 872.57 326.15, 872.28 343.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,58,"POLYGON ((582.21 456.60, 579.37 334.50, 585.32 330.33, 586.14 323.27, 580.68 312.33, 600.29 302.64, 607.90 317.90, 644.55 299.79, 640.29 291.24, 685.71 268.75, 692.34 282.01, 768.13 244.46, 759.35 226.92, 694.60 259.05, 662.56 195.07, 685.04 183.89, 690.51 194.78, 708.61 185.77, 714.81 198.09, 764.43 173.30, 784.74 213.60, 770.51 220.71, 799.86 278.77, 675.83 340.88, 678.60 346.36, 644.85 363.29, 642.35 358.33, 623.08 368.01, 625.14 455.60, 582.21 456.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724239,59,"POLYGON ((597.16 245.65, 611.95 238.69, 620.14 255.92, 605.35 262.89, 597.16 245.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,0,"POLYGON ((857.56 141.86, 876.30 140.70, 878.15 170.51, 875.90 173.23, 858.89 173.17, 858.95 160.10, 860.63 154.42, 857.99 148.69, 857.56 141.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,1,"POLYGON ((601.61 119.36, 617.30 109.00, 627.06 123.60, 634.44 118.73, 640.54 127.87, 631.67 133.76, 634.72 138.34, 620.55 147.72, 601.61 119.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,2,"POLYGON ((855.77 97.96, 873.87 97.74, 874.25 128.83, 856.15 129.05, 855.77 97.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,3,"POLYGON ((576.96 84.67, 600.76 69.97, 607.96 81.51, 601.58 85.45, 613.20 104.08, 595.80 114.85, 576.96 84.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,4,"POLYGON ((853.87 55.41, 872.38 54.63, 873.42 79.31, 875.67 80.18, 876.03 88.50, 855.27 89.36, 853.87 55.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,5,"POLYGON ((556.25 49.97, 569.15 41.10, 581.62 59.03, 591.89 51.95, 598.12 60.89, 574.93 76.87, 556.25 49.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,6,"POLYGON ((850.95 12.97, 870.19 12.37, 871.21 45.71, 851.97 46.31, 850.95 12.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,7,"POLYGON ((511.72 815.90, 517.93 841.54, 489.04 848.47, 487.01 840.15, 480.55 841.71, 476.36 824.42, 511.72 815.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,8,"POLYGON ((373.99 770.86, 386.00 792.60, 369.17 801.81, 357.16 780.05, 373.99 770.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,9,"POLYGON ((472.14 732.71, 477.09 743.40, 488.47 741.33, 491.41 749.98, 478.41 754.38, 482.52 766.44, 464.00 772.70, 460.09 761.19, 456.77 761.27, 451.17 746.28, 456.75 744.63, 453.53 737.72, 472.14 732.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,10,"POLYGON ((356.89 723.19, 359.66 732.27, 361.56 731.68, 366.16 746.82, 364.24 747.38, 367.44 757.86, 355.48 761.47, 344.95 726.78, 356.89 723.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,11,"POLYGON ((341.71 687.71, 347.38 707.89, 336.36 710.97, 330.68 690.78, 341.71 687.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,12,"POLYGON ((455.89 679.25, 467.73 712.49, 449.65 718.88, 440.76 693.87, 444.79 683.18, 455.89 679.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,13,"POLYGON ((330.48 614.94, 335.25 628.87, 309.17 637.74, 304.40 623.81, 330.48 614.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,14,"POLYGON ((439.50 596.36, 444.65 614.01, 426.07 622.92, 417.92 598.31, 424.57 600.49, 431.62 601.25, 439.50 596.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,15,"POLYGON ((413.70 529.25, 435.09 527.82, 437.45 535.26, 442.55 539.59, 444.68 545.00, 441.99 556.96, 429.08 558.27, 424.73 544.02, 413.79 544.78, 413.70 529.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,16,"POLYGON ((521.56 527.37, 525.73 544.36, 514.80 545.64, 511.94 550.66, 507.42 548.80, 505.03 552.57, 493.70 548.15, 490.20 527.90, 521.56 527.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,17,"POLYGON ((227.07 445.69, 237.54 444.29, 237.15 441.39, 247.94 439.96, 248.54 444.41, 255.97 443.45, 259.55 470.17, 252.54 471.10, 235.02 463.49, 230.12 468.61, 227.07 445.69))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,18,"POLYGON ((474.98 442.70, 476.40 460.53, 437.98 463.59, 436.55 445.76, 474.98 442.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,19,"POLYGON ((423.19 443.95, 424.71 460.04, 401.84 461.47, 400.22 452.88, 405.13 448.58, 405.34 445.24, 423.19 443.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,20,"POLYGON ((527.20 442.30, 527.83 459.95, 489.47 461.31, 488.84 443.65, 527.20 442.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,21,"POLYGON ((317.88 440.72, 319.05 456.83, 282.17 459.45, 281.71 452.82, 288.71 442.92, 317.88 440.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,22,"POLYGON ((364.03 441.68, 364.20 449.75, 333.89 450.41, 333.73 442.34, 364.03 441.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,23,"POLYGON ((295.30 392.37, 295.45 400.98, 288.32 401.09, 288.18 392.48, 295.30 392.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,24,"POLYGON ((232.08 391.51, 234.63 382.52, 235.33 371.12, 240.45 368.70, 263.95 370.10, 266.03 397.04, 231.21 402.10, 232.08 391.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,25,"POLYGON ((105.19 380.53, 113.24 381.06, 114.09 368.45, 127.03 369.32, 125.39 393.93, 123.20 400.54, 103.92 399.27, 105.19 380.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,26,"POLYGON ((116.04 340.83, 127.43 344.76, 129.40 348.44, 133.63 348.33, 134.43 361.43, 115.57 358.62, 116.04 340.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,27,"POLYGON ((262.94 328.04, 263.16 360.69, 243.11 360.82, 242.87 328.19, 262.94 328.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,28,"POLYGON ((283.36 296.49, 285.05 314.09, 270.70 315.46, 269.00 297.85, 283.36 296.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,29,"POLYGON ((260.67 283.90, 261.06 312.55, 242.33 312.80, 241.96 284.18, 260.67 283.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,30,"POLYGON ((490.42 228.56, 553.08 226.17, 554.24 320.61, 491.55 321.76, 490.42 228.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,31,"POLYGON ((148.17 161.23, 156.81 176.91, 135.24 188.69, 126.61 172.99, 148.17 161.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,32,"POLYGON ((299.56 162.62, 305.54 162.47, 312.46 159.99, 314.71 157.29, 323.02 157.08, 324.46 174.24, 301.26 176.81, 299.56 162.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,33,"POLYGON ((355.74 159.55, 357.16 162.83, 363.85 159.92, 368.09 158.17, 374.66 155.57, 377.41 153.05, 385.26 152.52, 386.54 171.47, 356.67 173.45, 355.74 159.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,34,"POLYGON ((423.95 145.37, 439.39 141.33, 442.61 141.67, 451.99 152.71, 456.49 153.57, 460.91 152.20, 460.75 156.93, 429.02 164.95, 423.95 145.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,35,"POLYGON ((518.66 159.79, 508.78 155.44, 505.04 163.88, 494.62 159.31, 497.66 152.44, 490.49 149.29, 493.33 142.89, 497.80 138.21, 519.32 146.49, 522.41 145.24, 526.60 141.45, 518.66 159.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,36,"POLYGON ((97.60 132.17, 110.10 136.81, 109.24 149.24, 104.38 162.55, 91.40 157.84, 92.75 154.12, 92.65 149.99, 90.48 142.94, 80.38 137.74, 83.66 135.99, 94.62 140.06, 97.60 132.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,37,"POLYGON ((0.00 138.93, 1.05 139.02, 2.33 136.50, 18.20 136.34, 18.25 148.84, 16.38 153.17, 0.00 152.84, 0.00 138.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,38,"POLYGON ((35.78 131.83, 59.62 134.71, 58.42 146.98, 58.07 152.78, 54.56 152.05, 51.57 151.79, 48.08 151.88, 44.05 150.65, 40.37 148.77, 37.84 148.01, 33.88 149.26, 34.67 140.96, 35.78 131.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,39,"POLYGON ((229.83 79.34, 273.91 64.36, 275.10 67.80, 278.31 70.18, 279.44 72.95, 284.21 73.71, 286.56 80.36, 281.84 81.99, 260.83 88.85, 256.59 87.73, 253.26 88.86, 249.65 92.46, 235.77 96.99, 229.83 79.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,40,"POLYGON ((217.36 0.00, 217.51 12.77, 198.90 12.97, 186.18 12.94, 163.46 12.43, 157.25 12.08, 145.74 10.99, 135.99 9.89, 119.99 12.47, 119.04 6.57, 120.16 0.00, 217.36 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,41,"POLYGON ((296.39 0.00, 297.59 3.96, 297.75 9.57, 273.45 10.28, 264.23 5.61, 248.13 9.75, 242.17 8.32, 236.10 10.65, 235.65 0.00, 296.39 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,42,"POLYGON ((590.95 832.76, 628.78 828.51, 630.91 847.37, 593.09 851.62, 590.95 832.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,43,"POLYGON ((707.48 823.87, 709.90 845.29, 673.76 849.32, 671.34 827.89, 707.48 823.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,44,"POLYGON ((869.62 772.82, 880.29 800.31, 859.48 804.82, 856.33 798.44, 850.92 801.06, 844.64 789.32, 853.04 786.62, 857.90 782.04, 858.75 776.07, 869.62 772.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,45,"POLYGON ((747.91 729.79, 760.05 726.92, 764.36 745.02, 746.31 749.29, 748.85 744.92, 750.03 738.61, 747.91 729.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,46,"POLYGON ((595.59 714.88, 596.65 737.52, 557.07 739.35, 556.01 716.73, 595.59 714.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,47,"POLYGON ((609.00 722.35, 622.63 722.65, 625.91 720.92, 636.21 720.66, 641.87 721.18, 644.80 718.11, 648.65 713.40, 648.22 734.92, 609.72 737.54, 609.00 722.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,48,"POLYGON ((663.28 713.36, 691.55 713.62, 695.82 712.20, 696.06 721.45, 693.81 724.48, 692.59 728.82, 696.70 733.35, 663.46 733.53, 663.28 713.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,49,"POLYGON ((758.27 709.94, 756.16 718.61, 757.65 725.19, 750.03 726.36, 742.00 723.92, 739.63 709.42, 758.27 709.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,50,"POLYGON ((878.75 628.19, 890.90 622.55, 899.75 641.44, 893.65 648.81, 885.13 641.81, 878.75 628.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,51,"POLYGON ((753.64 535.37, 753.66 550.95, 759.69 552.66, 758.42 561.97, 754.93 571.72, 755.78 575.05, 739.79 576.94, 738.20 544.66, 741.92 543.83, 744.47 541.17, 745.82 535.94, 753.64 535.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,52,"POLYGON ((692.43 534.33, 694.25 562.12, 694.38 566.95, 693.44 574.03, 687.93 577.15, 684.80 571.66, 686.12 564.94, 673.09 566.01, 666.94 559.10, 666.33 535.34, 692.43 534.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,53,"POLYGON ((881.41 524.10, 884.44 531.99, 881.18 533.23, 883.15 551.38, 885.42 553.17, 889.67 559.01, 888.64 562.39, 891.63 576.78, 882.88 569.95, 867.40 529.41, 881.41 524.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,54,"POLYGON ((606.05 539.87, 648.34 538.20, 649.22 560.42, 606.95 562.09, 606.05 539.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,55,"POLYGON ((900.00 483.25, 892.40 475.03, 900.00 468.08, 900.00 483.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,56,"POLYGON ((661.47 435.58, 661.72 457.68, 623.59 458.12, 623.35 436.02, 661.47 435.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,57,"POLYGON ((588.44 436.73, 589.10 455.47, 551.00 456.81, 550.34 438.07, 588.44 436.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,58,"POLYGON ((679.84 435.82, 701.61 433.87, 702.75 446.58, 707.02 449.54, 703.89 457.67, 683.11 459.07, 679.84 435.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,59,"POLYGON ((807.54 422.10, 812.87 428.73, 816.53 427.33, 819.59 423.83, 821.36 420.10, 824.66 417.98, 831.52 428.30, 800.79 448.75, 792.47 436.33, 795.34 434.42, 796.04 431.25, 807.54 422.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,60,"POLYGON ((861.61 365.38, 876.73 373.63, 854.74 413.53, 840.84 405.93, 844.68 398.98, 838.22 395.45, 841.77 389.04, 846.99 391.88, 861.61 365.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,61,"POLYGON ((646.00 347.03, 652.81 357.62, 655.52 355.60, 664.27 365.21, 667.04 373.39, 665.31 374.61, 658.69 363.37, 653.90 361.14, 652.82 365.30, 654.42 374.31, 654.52 378.04, 659.56 382.24, 655.27 384.33, 648.76 376.61, 648.01 370.53, 644.05 370.25, 644.96 375.53, 642.23 376.98, 636.31 369.07, 638.60 365.55, 632.37 355.81, 646.00 347.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,62,"POLYGON ((765.31 326.88, 781.29 354.39, 773.75 358.74, 753.39 361.48, 748.68 352.18, 754.39 341.38, 750.79 335.21, 765.31 326.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,63,"POLYGON ((880.83 310.84, 882.68 344.84, 877.38 345.71, 877.63 349.95, 867.94 350.55, 867.55 344.21, 862.92 344.50, 862.65 339.94, 854.46 340.44, 853.26 321.00, 860.88 320.54, 860.33 311.58, 880.83 310.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,64,"POLYGON ((731.69 291.64, 748.81 318.48, 732.45 330.80, 714.31 303.25, 731.69 291.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,65,"POLYGON ((874.50 269.34, 873.52 272.25, 875.80 279.56, 871.12 281.50, 871.29 288.06, 872.17 292.24, 872.38 300.38, 858.92 299.94, 853.16 291.41, 852.98 284.33, 860.89 283.87, 860.55 270.49, 874.50 269.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,66,"POLYGON ((709.52 256.44, 727.49 284.82, 711.74 294.72, 693.75 266.34, 709.52 256.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,67,"POLYGON ((872.15 228.71, 869.33 232.44, 871.34 238.43, 875.04 238.60, 876.60 248.02, 878.52 251.39, 878.70 258.46, 859.97 258.67, 859.53 251.34, 866.04 248.29, 867.70 240.63, 867.32 235.65, 862.72 231.83, 872.15 228.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,68,"POLYGON ((669.06 229.90, 685.57 219.87, 687.61 220.73, 689.00 219.78, 696.06 230.06, 692.94 232.18, 703.69 247.84, 685.64 260.11, 669.06 229.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,69,"POLYGON ((646.31 191.32, 659.50 183.42, 662.14 187.77, 662.21 198.78, 664.34 204.23, 666.98 207.31, 670.40 209.78, 674.37 210.48, 677.80 213.14, 665.87 221.44, 661.61 215.37, 657.35 209.00, 650.15 197.66, 646.31 191.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,70,"POLYGON ((876.22 212.84, 872.33 215.30, 871.14 210.09, 867.11 207.30, 864.94 199.24, 857.47 201.25, 851.11 201.41, 850.52 189.28, 858.41 189.08, 858.23 182.25, 875.69 181.80, 876.22 212.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3724689,71,"POLYGON ((624.24 156.95, 626.69 154.09, 640.49 145.72, 649.04 159.69, 652.85 158.41, 658.42 165.89, 654.45 168.81, 658.79 174.65, 645.02 184.83, 624.24 156.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,0,"POLYGON ((852.08 871.66, 869.96 871.09, 870.85 900.00, 852.97 900.00, 852.08 871.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,1,"POLYGON ((858.83 855.33, 837.49 844.39, 835.11 841.79, 833.88 839.51, 841.73 822.64, 863.45 832.63, 864.85 835.26, 864.92 850.98, 861.74 856.87, 858.83 855.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,2,"POLYGON ((755.59 831.96, 756.92 818.56, 779.02 820.73, 782.68 824.39, 782.03 834.77, 781.62 839.91, 781.28 843.27, 778.40 842.99, 770.94 844.75, 768.62 847.17, 764.27 846.68, 761.26 845.38, 758.17 840.55, 755.59 831.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,3,"POLYGON ((744.01 816.12, 741.69 829.96, 733.12 834.68, 728.19 834.81, 724.08 836.69, 712.72 834.83, 716.59 811.60, 744.01 816.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,4,"POLYGON ((661.21 806.90, 675.11 810.94, 677.72 802.04, 694.50 806.92, 687.99 829.11, 657.32 820.19, 661.21 806.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,5,"POLYGON ((554.94 796.84, 578.05 769.04, 643.73 822.40, 619.93 851.04, 554.94 796.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,6,"POLYGON ((604.86 656.30, 607.53 657.68, 609.02 659.35, 616.39 663.62, 619.15 663.42, 622.12 666.36, 626.58 665.32, 630.21 662.74, 632.07 658.50, 636.81 658.51, 636.63 674.98, 609.40 674.57, 607.51 672.24, 604.28 670.10, 604.86 656.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,7,"POLYGON ((713.29 657.05, 729.10 658.20, 728.74 662.96, 723.26 665.19, 723.48 667.98, 715.79 668.58, 715.08 666.28, 716.93 663.91, 714.19 661.78, 713.29 657.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,8,"POLYGON ((719.97 628.47, 732.90 628.72, 732.98 624.63, 740.87 624.78, 742.04 628.97, 740.95 632.29, 741.48 634.71, 740.69 645.39, 736.65 653.08, 722.22 653.45, 719.64 644.99, 719.97 628.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,9,"POLYGON ((721.64 584.61, 737.93 583.68, 738.80 598.60, 734.16 598.85, 731.79 606.44, 732.78 610.27, 728.31 609.17, 724.02 609.79, 723.34 605.03, 722.59 598.63, 722.17 593.94, 721.64 584.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,10,"POLYGON ((721.56 534.82, 738.92 534.00, 740.56 568.81, 736.95 568.99, 733.04 570.00, 728.13 571.19, 723.13 562.37, 722.44 553.51, 721.56 534.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,11,"POLYGON ((560.65 521.18, 586.22 526.74, 589.26 535.70, 589.31 537.78, 585.02 539.98, 584.51 541.95, 582.34 543.93, 580.33 547.47, 555.95 541.79, 558.30 531.83, 560.65 521.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,12,"POLYGON ((723.72 498.07, 742.16 498.11, 742.10 523.35, 723.66 523.31, 723.72 498.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,13,"POLYGON ((714.92 414.63, 726.99 413.90, 733.88 416.46, 737.44 433.59, 735.59 434.51, 729.39 435.42, 724.65 435.03, 721.59 436.73, 719.68 435.53, 716.10 435.74, 714.92 414.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,14,"POLYGON ((717.28 368.14, 716.86 362.53, 726.74 361.79, 729.45 363.25, 730.56 377.03, 734.38 387.63, 718.49 388.57, 717.28 368.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,15,"POLYGON ((613.60 337.39, 632.43 335.95, 628.13 341.37, 627.58 346.13, 630.58 354.67, 628.73 360.58, 626.22 361.84, 614.37 362.47, 611.55 356.60, 610.08 354.97, 609.92 353.11, 614.77 352.74, 613.60 337.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,16,"POLYGON ((716.00 320.64, 733.12 320.14, 733.87 344.96, 716.72 345.46, 716.00 320.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,17,"POLYGON ((714.74 283.00, 743.33 281.90, 744.18 303.74, 741.95 303.82, 738.77 304.06, 738.59 301.62, 736.08 301.29, 731.05 309.54, 723.64 310.04, 723.06 308.59, 719.91 307.93, 719.37 294.12, 715.18 294.27, 714.74 283.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,18,"POLYGON ((711.60 241.15, 729.55 239.45, 729.95 243.53, 730.24 245.61, 732.04 255.13, 732.35 256.99, 732.80 260.66, 733.28 265.75, 714.11 267.57, 711.60 241.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,19,"POLYGON ((599.16 215.71, 605.68 214.32, 606.02 215.89, 609.95 233.04, 613.90 236.54, 609.26 245.33, 606.30 246.52, 598.09 246.59, 595.44 245.42, 594.31 244.47, 593.83 241.00, 602.22 237.19, 600.72 233.94, 602.85 232.98, 599.16 215.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,20,"POLYGON ((702.37 203.83, 703.45 200.92, 718.67 196.67, 722.32 195.57, 726.15 208.26, 724.50 221.29, 711.11 223.96, 707.07 222.07, 707.27 220.75, 705.24 218.72, 704.18 216.77, 702.37 203.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,21,"POLYGON ((689.29 142.83, 703.83 138.73, 708.55 155.46, 714.42 156.44, 717.64 160.55, 717.30 166.89, 714.82 172.17, 707.87 173.57, 702.27 173.09, 696.24 170.78, 689.40 147.29, 689.29 142.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,22,"POLYGON ((570.03 106.97, 567.72 98.31, 583.76 94.08, 586.00 102.48, 579.66 112.63, 578.68 113.32, 575.75 109.18, 572.72 108.99, 570.03 106.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,23,"POLYGON ((672.04 70.73, 672.63 69.09, 681.45 66.74, 685.67 82.41, 693.64 87.54, 695.89 97.27, 693.31 98.82, 689.35 100.14, 678.86 98.43, 672.04 70.73))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,24,"POLYGON ((675.06 24.26, 677.22 26.07, 677.50 27.41, 678.41 28.88, 679.06 30.48, 678.73 32.09, 678.15 33.59, 677.19 35.10, 676.99 36.59, 677.14 38.08, 677.56 39.55, 677.61 41.55, 677.52 43.04, 676.44 44.29, 675.34 45.56, 674.63 47.06, 673.66 48.33, 672.96 49.70, 672.76 51.57, 673.30 53.05, 678.50 57.27, 670.19 58.45, 667.08 24.46, 675.06 24.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,25,"POLYGON ((559.59 28.68, 558.01 10.01, 562.35 4.81, 564.84 4.88, 565.80 8.46, 571.93 9.28, 573.39 8.37, 574.36 7.11, 575.45 5.86, 576.52 4.45, 577.76 3.56, 581.28 9.55, 581.52 24.15, 576.19 24.80, 573.95 25.34, 572.34 25.52, 570.99 26.17, 569.65 27.56, 568.44 28.83, 567.36 30.21, 565.64 31.12, 564.15 31.67, 563.13 30.57, 562.46 28.74, 561.92 27.27, 559.59 28.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,26,"POLYGON ((120.40 898.59, 122.52 895.00, 217.26 891.84, 217.36 900.00, 120.16 900.00, 120.40 898.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,27,"POLYGON ((235.32 892.25, 294.02 889.76, 294.15 892.64, 296.39 900.00, 235.65 900.00, 235.32 892.25))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,28,"POLYGON ((70.99 887.32, 78.58 841.82, 97.86 845.22, 88.60 900.00, 82.79 900.00, 78.91 891.58, 70.99 887.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,29,"POLYGON ((168.99 824.83, 240.83 823.34, 241.21 842.13, 164.03 843.71, 162.05 839.59, 161.31 835.44, 164.14 831.19, 166.62 830.09, 168.99 824.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,30,"POLYGON ((259.89 820.35, 321.95 812.40, 323.28 815.07, 325.64 817.30, 328.61 816.89, 330.50 830.25, 262.61 839.68, 259.89 820.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,31,"POLYGON ((96.60 842.50, 81.49 840.51, 81.73 838.75, 82.61 837.66, 84.62 835.08, 86.38 832.75, 88.70 829.51, 90.72 826.62, 87.39 819.91, 84.37 818.50, 82.83 817.05, 85.44 801.27, 90.45 796.35, 91.29 796.33, 94.22 794.10, 97.52 793.02, 104.02 787.41, 96.60 842.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,32,"POLYGON ((161.04 783.77, 168.99 783.14, 170.77 787.27, 180.40 787.23, 183.27 784.65, 184.04 782.14, 205.98 780.54, 209.45 785.45, 215.36 787.16, 220.93 784.31, 223.53 779.05, 234.85 778.25, 236.21 798.64, 162.18 803.74, 156.64 799.91, 153.25 798.55, 150.08 798.81, 149.54 792.18, 158.46 789.03, 161.04 783.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,33,"POLYGON ((516.28 791.83, 508.78 785.83, 501.99 794.24, 442.31 746.47, 445.64 742.35, 441.45 738.97, 452.77 724.94, 457.32 728.58, 460.40 724.79, 519.84 772.34, 512.71 781.18, 521.52 788.21, 543.43 761.11, 561.26 775.37, 515.74 831.73, 496.48 816.33, 516.28 791.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,34,"POLYGON ((143.44 717.09, 167.47 715.24, 171.77 719.30, 177.61 719.15, 183.04 718.79, 187.37 715.97, 189.00 713.45, 214.75 713.41, 217.04 721.68, 214.85 734.25, 174.67 734.85, 145.22 737.47, 144.04 732.92, 141.26 730.06, 139.51 727.00, 140.29 724.67, 142.25 720.25, 143.44 717.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,35,"POLYGON ((136.21 679.56, 203.18 670.35, 208.75 675.43, 212.13 676.79, 213.43 678.22, 213.74 682.58, 212.07 690.55, 205.84 692.37, 192.41 691.47, 137.35 699.75, 136.03 697.48, 135.69 692.29, 137.92 689.11, 137.59 685.14, 134.60 682.11, 136.21 679.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,36,"POLYGON ((34.46 661.19, 46.93 655.26, 48.09 657.69, 54.50 660.17, 59.81 668.09, 67.00 686.56, 70.52 694.92, 73.51 701.60, 75.37 705.41, 62.00 711.88, 58.43 704.58, 54.16 702.22, 52.70 700.33, 52.80 690.34, 46.09 682.80, 41.87 669.59, 34.46 661.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,37,"POLYGON ((69.47 611.40, 74.33 612.56, 77.92 610.67, 84.93 611.74, 89.19 614.14, 91.93 617.53, 110.88 621.22, 115.77 620.70, 120.96 621.54, 124.85 620.74, 134.94 621.59, 139.86 623.15, 143.43 620.55, 148.78 620.53, 148.81 628.89, 146.34 636.33, 142.79 639.06, 131.17 638.38, 126.07 635.87, 111.19 633.34, 107.52 631.34, 104.14 630.58, 98.70 631.14, 69.04 624.37, 67.03 622.07, 69.47 611.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,38,"POLYGON ((460.95 618.03, 466.47 601.20, 485.19 607.27, 482.78 614.75, 483.14 618.82, 490.92 623.20, 485.89 632.05, 460.95 618.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,39,"POLYGON ((450.46 517.70, 459.24 505.91, 465.82 510.76, 455.95 524.04, 452.71 525.17, 451.95 522.96, 451.54 521.71, 451.28 520.85, 450.46 517.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,40,"POLYGON ((481.47 484.32, 504.49 497.37, 498.87 507.17, 489.95 523.51, 484.32 520.45, 474.24 515.32, 471.48 511.26, 467.16 508.73, 475.92 493.99, 481.47 484.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,41,"POLYGON ((456.83 487.19, 435.26 464.72, 444.94 455.53, 451.97 460.30, 471.80 474.42, 464.00 485.28, 461.35 485.97, 456.83 487.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,42,"POLYGON ((539.46 404.98, 559.11 416.35, 562.63 431.40, 560.14 437.88, 555.33 440.02, 534.28 426.97, 532.78 423.44, 530.99 420.98, 531.72 418.14, 539.46 404.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,43,"POLYGON ((496.99 389.21, 502.08 384.24, 508.19 390.41, 509.31 392.76, 509.69 397.30, 509.12 399.07, 502.34 398.80, 496.27 392.47, 496.99 389.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,44,"POLYGON ((355.73 253.49, 366.73 252.96, 369.81 254.13, 368.34 257.92, 371.82 261.09, 376.72 261.57, 376.48 264.04, 374.64 278.69, 372.76 280.36, 371.20 282.62, 371.35 285.03, 357.39 285.92, 356.99 279.41, 355.73 253.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,45,"POLYGON ((454.35 177.13, 475.51 177.77, 474.74 203.54, 470.40 205.91, 453.51 205.43, 454.35 177.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,46,"POLYGON ((358.16 168.41, 371.64 167.93, 372.94 169.48, 375.65 170.65, 376.93 190.60, 371.97 193.23, 360.85 193.20, 358.88 190.28, 358.16 168.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,47,"POLYGON ((353.81 124.44, 370.06 123.96, 371.52 125.78, 370.85 149.06, 373.53 155.10, 371.72 158.41, 354.85 158.93, 353.81 124.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,48,"POLYGON ((460.00 99.98, 477.86 99.84, 478.08 126.12, 460.22 126.28, 460.00 99.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,49,"POLYGON ((340.52 86.10, 342.55 78.24, 354.89 78.59, 354.72 84.52, 357.27 84.59, 357.04 92.50, 371.54 92.91, 371.09 111.30, 361.29 111.08, 359.80 110.37, 356.69 110.60, 353.34 112.75, 341.74 113.27, 340.52 86.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,50,"POLYGON ((454.48 53.93, 472.30 53.56, 472.87 80.36, 455.07 80.75, 454.71 64.49, 452.86 63.05, 452.79 60.41, 454.63 60.36, 454.48 53.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,51,"POLYGON ((486.12 51.13, 493.75 50.93, 494.06 62.33, 492.46 65.35, 486.49 65.50, 486.12 51.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,52,"POLYGON ((352.56 39.72, 371.87 38.89, 372.17 46.25, 374.75 46.14, 375.50 64.17, 373.04 64.28, 373.43 73.08, 354.03 73.89, 352.56 39.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,53,"POLYGON ((453.66 7.80, 467.47 7.45, 464.67 12.94, 466.76 20.39, 471.44 23.40, 471.18 38.21, 459.93 40.36, 452.56 38.88, 453.66 7.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725139,54,"POLYGON ((373.03 0.00, 373.45 31.53, 356.70 31.31, 355.78 28.22, 357.61 26.49, 356.28 23.62, 355.14 19.70, 355.15 11.97, 364.93 9.63, 366.99 8.14, 366.35 0.00, 373.03 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,0,"POLYGON ((557.01 821.29, 575.37 821.11, 576.27 844.97, 570.58 842.16, 558.09 840.42, 557.01 821.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,1,"POLYGON ((344.23 812.16, 364.88 807.08, 371.85 818.12, 371.98 823.37, 348.51 828.52, 344.23 812.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,2,"POLYGON ((439.94 784.89, 457.19 782.38, 462.78 809.94, 442.14 813.86, 442.04 803.99, 440.74 799.16, 439.94 784.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,3,"POLYGON ((333.97 769.31, 351.12 764.68, 353.04 770.58, 356.28 774.00, 357.86 780.62, 354.11 784.91, 355.68 791.89, 342.76 795.72, 337.61 787.44, 333.97 769.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,4,"POLYGON ((446.76 745.22, 448.27 757.70, 429.45 763.04, 429.37 759.96, 429.88 756.84, 429.24 748.91, 446.76 745.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,5,"POLYGON ((322.72 722.65, 340.04 718.75, 342.22 725.62, 346.83 740.37, 344.78 740.16, 344.18 744.19, 341.23 745.84, 340.74 747.78, 336.35 748.25, 332.50 748.70, 330.35 747.18, 322.72 722.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,6,"POLYGON ((417.32 699.40, 433.51 695.59, 439.76 720.34, 422.57 725.64, 419.65 715.10, 417.32 699.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,7,"POLYGON ((336.73 693.01, 338.36 705.85, 320.20 708.11, 318.59 695.27, 336.73 693.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,8,"POLYGON ((430.34 656.54, 441.62 657.43, 437.93 675.50, 414.38 669.33, 417.62 657.15, 420.08 662.04, 428.85 663.59, 430.34 656.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,9,"POLYGON ((452.26 610.94, 458.37 615.98, 444.99 638.14, 436.87 632.15, 430.40 623.13, 436.94 616.04, 444.29 620.80, 452.26 610.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,10,"POLYGON ((554.07 634.70, 552.57 617.38, 555.26 613.12, 558.78 606.01, 559.61 603.88, 559.73 601.79, 570.79 600.27, 575.93 601.38, 577.70 622.89, 574.15 628.75, 572.26 630.20, 569.99 631.65, 568.10 632.95, 566.01 634.22, 563.93 634.81, 559.68 634.38, 554.07 634.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,11,"POLYGON ((409.94 501.76, 420.39 517.12, 418.12 520.71, 398.95 535.65, 390.69 525.25, 399.99 517.93, 395.10 511.86, 392.26 505.45, 398.40 502.05, 402.66 506.96, 409.94 501.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,12,"POLYGON ((420.50 487.11, 433.52 480.10, 445.44 498.13, 446.10 504.57, 446.21 508.76, 441.53 510.88, 436.12 504.07, 430.43 505.21, 420.50 487.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,13,"POLYGON ((488.98 467.79, 490.67 475.18, 481.24 476.66, 482.36 481.10, 475.68 482.75, 473.61 480.32, 472.24 475.40, 472.12 470.44, 470.39 461.07, 477.34 460.65, 481.34 470.20, 488.98 467.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,14,"POLYGON ((549.79 460.05, 577.98 462.30, 581.71 471.87, 581.68 480.06, 576.67 478.94, 573.61 476.04, 572.98 470.84, 568.31 464.04, 565.89 466.81, 567.34 474.96, 567.22 479.43, 560.53 471.41, 559.88 474.91, 556.20 476.98, 559.01 480.12, 550.04 479.11, 548.84 471.46, 549.79 460.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,15,"POLYGON ((505.19 458.94, 536.42 462.61, 535.27 476.78, 528.05 476.69, 523.27 474.35, 505.81 473.06, 505.19 458.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,16,"POLYGON ((573.89 860.15, 581.19 859.54, 582.59 864.92, 584.95 883.00, 580.57 883.95, 577.35 880.90, 577.84 876.10, 574.20 872.24, 571.18 868.76, 567.55 866.35, 569.33 861.93, 573.89 860.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,17,"POLYGON ((561.40 772.54, 580.05 772.35, 582.09 795.01, 582.10 807.09, 569.74 809.46, 569.30 803.59, 558.92 803.55, 561.40 772.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,18,"POLYGON ((562.36 728.61, 580.74 729.92, 582.42 737.82, 581.26 750.53, 577.00 757.40, 576.80 761.25, 568.25 762.35, 562.91 761.89, 561.01 757.23, 560.40 745.15, 562.36 728.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,19,"POLYGON ((557.51 689.82, 574.05 688.22, 574.01 698.26, 582.87 697.14, 584.52 703.58, 583.86 712.72, 582.80 717.77, 562.67 718.28, 562.83 712.97, 557.51 689.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,20,"POLYGON ((678.54 666.23, 678.62 675.15, 653.98 675.38, 653.91 666.48, 678.54 666.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,21,"POLYGON ((559.09 655.59, 561.73 654.95, 564.24 654.29, 566.02 654.40, 567.95 654.93, 569.90 655.76, 571.69 656.16, 573.33 656.85, 575.12 657.10, 577.04 656.91, 578.81 656.27, 580.09 655.06, 584.14 650.70, 586.21 674.22, 561.85 676.30, 559.09 655.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,22,"POLYGON ((662.70 541.55, 668.67 543.17, 672.39 549.27, 669.20 552.30, 666.38 557.68, 666.24 563.86, 667.27 569.14, 662.22 569.26, 662.70 541.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,23,"POLYGON ((659.72 499.49, 677.78 499.41, 677.57 510.56, 676.93 514.79, 673.40 513.15, 670.69 514.71, 665.39 506.47, 665.29 511.87, 666.19 517.22, 659.53 518.17, 659.72 499.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,24,"POLYGON ((676.97 458.05, 676.77 489.51, 670.09 491.17, 659.83 489.21, 657.98 465.22, 668.52 458.76, 676.97 458.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,25,"POLYGON ((446.36 866.57, 449.38 867.20, 450.47 869.10, 454.42 871.80, 458.85 873.08, 463.43 872.79, 465.16 871.53, 466.67 868.69, 470.53 868.64, 470.69 886.61, 446.54 886.83, 446.36 866.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,26,"POLYGON ((354.13 855.10, 372.53 854.64, 373.57 878.58, 382.99 878.76, 382.61 888.76, 362.75 889.69, 359.33 886.87, 356.68 881.72, 355.16 879.67, 354.13 855.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3725589,27,"POLYGON ((444.27 826.15, 463.99 825.47, 464.81 851.20, 445.86 853.44, 444.27 826.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,0,"POLYGON ((493.48 559.87, 509.83 569.78, 514.34 562.41, 523.76 568.12, 509.36 591.63, 483.60 576.02, 493.48 559.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,1,"POLYGON ((191.07 0.00, 197.66 415.25, 0.00 418.42, 0.00 0.00, 191.07 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3726039,2,"POLYGON ((857.57 799.71, 875.80 799.60, 876.02 835.25, 857.79 835.36, 857.57 799.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,0,"POLYGON ((496.81 865.26, 537.59 863.22, 537.86 868.69, 545.35 868.33, 546.78 897.06, 538.83 897.46, 538.96 900.00, 525.14 900.00, 514.38 893.51, 499.95 887.88, 488.57 886.33, 487.82 871.46, 497.11 871.00, 496.81 865.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,1,"POLYGON ((361.74 847.47, 386.63 846.82, 386.76 851.96, 391.36 851.85, 391.54 859.03, 395.69 858.93, 396.79 900.00, 363.20 900.00, 359.18 895.66, 355.47 895.76, 354.51 859.62, 356.89 859.54, 356.67 851.89, 361.85 851.75, 361.74 847.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,2,"POLYGON ((213.84 798.30, 239.17 797.47, 239.30 802.55, 243.69 802.44, 243.99 807.70, 248.38 807.41, 249.51 850.73, 244.37 850.86, 244.78 862.50, 249.69 862.38, 250.74 900.00, 209.61 900.00, 208.44 863.90, 213.54 863.77, 213.22 851.14, 207.34 851.29, 206.29 809.99, 209.53 809.90, 209.30 803.65, 213.95 803.47, 213.84 798.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,3,"POLYGON ((465.00 806.06, 505.48 804.01, 505.77 809.47, 513.27 809.10, 514.72 837.65, 506.77 838.05, 507.17 845.75, 466.30 847.83, 465.90 840.12, 458.38 840.51, 456.94 812.24, 465.29 811.83, 465.00 806.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,4,"POLYGON ((244.88 753.75, 249.76 753.61, 249.64 748.90, 255.59 748.73, 255.49 745.38, 298.29 744.16, 298.44 749.37, 310.26 749.05, 310.11 743.41, 350.86 742.27, 351.02 747.86, 362.96 747.53, 362.80 741.83, 403.89 740.69, 403.98 743.29, 410.64 743.12, 410.76 747.89, 415.56 747.77, 416.16 770.77, 412.46 770.86, 412.59 776.17, 406.97 776.31, 407.06 779.90, 396.45 780.17, 396.44 779.00, 388.17 779.19, 388.33 785.42, 379.56 785.62, 379.47 782.29, 363.54 782.68, 363.39 775.98, 351.94 776.27, 352.13 783.68, 310.40 784.69, 310.25 777.88, 298.92 778.17, 299.11 785.58, 257.36 786.64, 257.27 783.05, 251.16 783.21, 251.08 779.99, 245.59 780.15, 244.88 753.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,5,"POLYGON ((622.81 804.91, 651.88 790.07, 655.71 797.48, 661.02 794.75, 681.34 833.96, 674.34 837.56, 678.60 845.75, 684.37 842.79, 700.52 873.85, 695.51 876.44, 692.39 879.32, 690.75 884.44, 692.99 889.00, 695.02 893.02, 681.13 900.00, 672.07 900.00, 670.26 896.42, 664.48 899.32, 645.16 861.34, 650.04 858.89, 646.17 851.28, 640.23 854.28, 620.37 815.54, 626.64 812.36, 622.81 804.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,6,"POLYGON ((757.95 711.48, 765.63 717.27, 767.86 715.46, 784.08 727.66, 782.36 729.94, 785.06 731.98, 782.94 734.79, 789.62 739.81, 791.85 736.89, 824.32 761.35, 822.09 764.27, 829.07 769.51, 830.89 767.13, 837.59 772.18, 838.88 770.48, 855.85 783.26, 854.22 785.41, 857.23 787.68, 855.65 789.76, 858.43 791.85, 836.61 820.57, 833.22 818.02, 831.45 820.35, 807.09 802.01, 809.54 798.80, 801.23 792.55, 799.20 795.24, 765.18 769.65, 767.89 766.07, 764.20 763.30, 761.19 767.29, 734.39 747.15, 735.76 745.34, 733.53 743.66, 757.95 711.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,7,"POLYGON ((616.50 725.71, 618.34 728.57, 620.51 727.19, 621.65 728.98, 623.74 727.64, 632.22 740.87, 629.24 742.77, 631.79 746.72, 634.66 744.89, 642.78 757.54, 640.79 758.81, 650.22 773.49, 647.51 775.20, 649.74 778.65, 621.25 796.80, 619.18 793.57, 616.59 795.23, 607.14 780.51, 604.56 782.15, 596.17 769.09, 601.62 765.63, 598.41 760.62, 593.22 763.93, 589.98 758.86, 593.06 756.90, 587.81 748.73, 590.09 747.27, 587.92 743.89, 616.50 725.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,8,"POLYGON ((864.19 650.41, 897.47 676.82, 895.05 679.84, 900.00 683.76, 900.00 733.21, 879.78 717.14, 882.85 713.31, 877.38 708.97, 873.58 713.70, 836.84 684.54, 864.19 650.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,9,"POLYGON ((679.47 667.19, 682.36 668.52, 683.92 665.15, 686.89 666.52, 688.09 663.93, 702.23 670.41, 701.21 672.59, 705.39 674.50, 706.29 672.57, 719.68 678.73, 718.61 681.07, 734.93 688.58, 733.36 691.95, 736.19 693.25, 722.14 723.51, 719.10 722.12, 717.81 724.88, 703.16 718.15, 702.06 720.54, 697.77 718.58, 699.22 715.48, 686.34 709.57, 684.84 712.81, 668.39 705.28, 669.64 702.58, 668.03 701.85, 669.03 699.67, 665.25 697.94, 679.47 667.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,10,"POLYGON ((726.44 566.02, 742.15 567.90, 741.94 573.50, 744.77 573.78, 742.97 600.11, 722.67 597.81, 724.51 565.89, 726.44 566.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,11,"POLYGON ((715.01 573.54, 714.14 576.98, 717.41 577.83, 712.74 595.93, 698.29 592.24, 699.43 587.77, 695.92 586.88, 700.33 569.79, 715.01 573.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,12,"POLYGON ((687.92 592.70, 671.80 588.54, 673.31 582.71, 668.93 581.58, 674.26 561.11, 686.31 564.20, 685.26 568.20, 693.70 570.38, 687.92 592.70))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,13,"POLYGON ((775.19 558.85, 778.14 590.87, 762.52 592.29, 762.03 586.95, 757.10 587.41, 754.66 560.70, 775.19 558.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,14,"POLYGON ((657.19 585.96, 640.54 582.23, 645.46 560.40, 650.48 561.54, 651.38 557.52, 667.69 561.14, 663.39 580.21, 658.72 579.17, 657.19 585.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,15,"POLYGON ((800.35 545.53, 815.16 575.45, 802.26 581.78, 799.42 576.08, 792.48 579.49, 780.48 555.27, 800.35 545.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,16,"POLYGON ((842.85 526.49, 847.90 531.00, 850.63 527.96, 862.95 538.94, 844.05 559.98, 838.83 555.32, 834.90 559.70, 822.75 548.87, 842.85 526.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3729639,17,"POLYGON ((823.97 512.32, 832.51 520.01, 828.02 524.96, 834.29 530.59, 821.36 544.84, 810.99 535.52, 814.64 531.49, 810.19 527.50, 823.97 512.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,0,"POLYGON ((551.21 865.72, 603.40 862.37, 604.22 874.87, 552.01 878.24, 551.21 865.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,1,"POLYGON ((599.96 799.59, 614.13 798.79, 617.11 850.76, 613.37 854.50, 603.16 855.07, 599.96 799.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,2,"POLYGON ((599.15 792.46, 541.55 787.38, 540.56 780.24, 544.53 779.69, 546.81 775.97, 599.67 779.38, 599.24 785.91, 601.13 787.95, 599.15 792.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,3,"POLYGON ((551.86 702.10, 561.79 709.35, 571.85 703.79, 587.89 707.35, 603.21 707.14, 603.36 718.10, 545.37 718.89, 545.15 702.19, 551.86 702.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,4,"POLYGON ((887.49 691.07, 900.00 695.79, 900.00 726.20, 891.40 721.23, 889.61 712.86, 880.99 706.53, 886.44 699.20, 887.49 691.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,5,"POLYGON ((821.70 675.26, 856.88 672.79, 858.82 699.86, 850.75 700.43, 844.17 690.67, 837.62 691.90, 830.68 700.01, 823.50 700.52, 821.70 675.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,6,"POLYGON ((763.90 662.71, 769.73 669.44, 770.27 681.19, 723.04 683.35, 722.17 664.62, 763.90 662.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,7,"POLYGON ((900.00 679.14, 890.25 676.02, 891.72 672.88, 896.76 673.37, 900.00 668.64, 900.00 679.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,8,"POLYGON ((547.21 647.75, 593.34 646.24, 597.01 660.04, 551.48 658.56, 547.21 647.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,9,"POLYGON ((626.59 228.29, 627.82 258.45, 609.70 259.20, 608.46 229.02, 626.59 228.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,10,"POLYGON ((749.86 130.96, 776.92 167.74, 753.32 184.92, 726.28 148.16, 749.86 130.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,11,"POLYGON ((693.80 146.64, 703.74 152.47, 698.20 161.86, 688.25 156.04, 693.80 146.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,12,"POLYGON ((900.00 102.49, 893.77 102.69, 893.07 82.02, 900.00 81.78, 900.00 102.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,13,"POLYGON ((854.12 31.07, 856.26 76.58, 832.44 77.70, 830.29 32.19, 854.12 31.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,14,"POLYGON ((231.78 879.26, 235.75 876.18, 245.30 882.91, 243.87 884.90, 244.97 889.51, 248.28 896.44, 242.84 899.02, 226.11 887.46, 231.78 879.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,15,"POLYGON ((433.61 887.76, 445.26 889.20, 447.79 836.26, 461.01 837.67, 459.35 889.55, 448.82 890.68, 448.12 900.00, 432.70 900.00, 433.61 887.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,16,"POLYGON ((212.31 864.24, 226.63 872.40, 227.87 875.70, 227.98 880.18, 226.71 883.54, 224.19 886.93, 214.78 878.96, 215.68 873.52, 216.01 869.76, 212.31 864.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,17,"POLYGON ((351.68 859.93, 365.41 861.82, 364.13 870.51, 361.32 877.27, 356.46 881.61, 355.77 883.84, 353.35 876.74, 349.02 872.87, 351.68 859.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,18,"POLYGON ((368.72 835.97, 372.25 837.63, 369.83 840.16, 368.04 847.88, 371.00 856.97, 369.36 860.97, 361.88 859.91, 353.60 857.66, 350.62 857.98, 352.24 852.99, 355.13 839.04, 357.45 832.55, 368.72 835.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,19,"POLYGON ((203.57 829.17, 203.44 844.35, 198.00 840.74, 196.47 833.72, 194.07 829.92, 194.31 826.78, 203.57 829.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,20,"POLYGON ((195.98 800.68, 199.60 799.23, 206.12 815.16, 205.76 828.33, 201.71 828.21, 192.11 822.87, 195.98 800.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,21,"POLYGON ((439.09 799.05, 446.01 800.12, 453.13 799.94, 453.91 805.56, 453.86 812.64, 451.69 817.69, 442.01 815.43, 438.04 815.33, 437.25 809.09, 439.09 799.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,22,"POLYGON ((260.15 762.37, 266.74 773.37, 231.65 794.20, 225.07 783.20, 260.15 762.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,23,"POLYGON ((436.79 757.07, 451.34 758.12, 449.40 785.07, 434.85 784.02, 436.79 757.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,24,"POLYGON ((375.67 728.06, 377.19 743.03, 372.07 747.62, 375.85 785.15, 365.62 786.19, 359.93 729.64, 375.67 728.06))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,25,"POLYGON ((508.80 725.67, 548.14 726.42, 547.53 758.24, 540.26 751.37, 530.55 752.95, 526.06 750.84, 522.46 745.74, 518.03 748.85, 512.89 756.04, 508.32 750.43, 508.80 725.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,26,"POLYGON ((452.88 730.31, 450.50 753.97, 436.86 752.61, 439.25 728.95, 452.88 730.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,27,"POLYGON ((239.66 711.04, 250.00 720.63, 242.20 728.98, 231.86 719.39, 239.66 711.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,28,"POLYGON ((434.69 707.36, 434.67 720.28, 378.01 720.17, 378.02 713.26, 382.40 711.02, 420.95 710.52, 424.49 707.33, 434.69 707.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,29,"POLYGON ((221.83 694.11, 209.71 706.81, 192.61 690.40, 202.39 673.57, 221.83 694.11))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,30,"POLYGON ((319.76 623.96, 324.77 631.73, 291.98 652.57, 286.98 644.77, 319.76 623.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,31,"POLYGON ((424.18 623.34, 422.07 633.51, 388.94 634.36, 390.21 624.63, 424.18 623.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,32,"POLYGON ((319.09 583.75, 324.92 591.22, 297.05 612.82, 291.22 605.37, 319.09 583.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,33,"POLYGON ((283.83 578.12, 288.94 581.97, 292.71 588.13, 260.45 607.69, 253.58 596.45, 283.83 578.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,34,"POLYGON ((359.22 545.39, 372.19 550.77, 350.80 602.17, 337.73 596.82, 359.22 545.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,35,"POLYGON ((503.92 543.17, 509.29 544.81, 503.66 550.23, 503.48 560.83, 507.21 568.23, 487.71 600.70, 474.19 592.65, 503.92 543.17))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,36,"POLYGON ((381.41 534.81, 411.93 517.59, 416.40 519.09, 419.59 524.76, 387.79 542.86, 384.27 539.85, 381.41 534.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,37,"POLYGON ((306.33 522.72, 339.99 503.39, 343.28 514.68, 308.69 534.60, 305.94 529.86, 306.33 522.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,38,"POLYGON ((424.94 496.81, 438.75 491.22, 447.91 513.61, 424.28 523.20, 420.58 514.17, 430.40 510.17, 424.94 496.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,39,"POLYGON ((237.11 458.87, 250.79 464.96, 227.82 515.76, 214.14 509.58, 237.11 458.87))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,40,"POLYGON ((332.86 445.80, 339.53 456.30, 324.96 465.46, 318.29 454.96, 332.86 445.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,41,"POLYGON ((240.89 380.01, 254.89 379.83, 255.58 434.85, 241.57 435.03, 240.89 380.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,42,"POLYGON ((328.46 378.78, 329.37 434.59, 313.12 434.84, 312.22 379.04, 326.42 379.30, 328.46 378.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,43,"POLYGON ((243.24 372.52, 243.53 380.48, 188.05 382.47, 187.75 374.51, 243.24 372.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3734139,44,"POLYGON ((326.23 366.14, 383.55 365.37, 383.71 378.53, 326.42 379.30, 326.23 366.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,0,"POLYGON ((806.63 790.64, 809.25 788.82, 804.88 782.56, 809.82 779.13, 806.47 774.33, 820.81 764.40, 822.44 766.75, 829.16 762.10, 827.45 759.68, 843.34 748.66, 844.72 750.64, 854.38 743.94, 852.68 741.52, 868.31 730.68, 869.41 732.23, 872.56 730.04, 870.81 727.58, 879.88 721.27, 881.55 721.96, 887.22 717.28, 888.78 719.17, 892.96 715.71, 892.92 714.25, 885.32 703.83, 882.57 705.84, 866.66 684.04, 868.92 682.41, 863.71 675.15, 861.25 676.88, 845.78 655.05, 845.42 651.11, 840.48 643.62, 837.51 645.56, 830.72 635.28, 830.40 627.74, 827.75 625.57, 821.51 625.24, 819.05 626.30, 816.89 629.20, 814.12 631.27, 816.67 634.62, 797.24 649.21, 799.14 651.74, 791.16 657.72, 788.70 654.47, 782.30 659.25, 778.79 654.61, 775.26 657.26, 766.85 646.13, 768.87 644.61, 766.34 641.24, 776.67 633.51, 771.51 626.67, 774.42 624.47, 769.49 617.91, 774.16 614.44, 770.84 610.02, 793.69 592.96, 794.88 594.56, 803.58 588.07, 802.81 586.25, 812.88 579.18, 814.09 580.88, 815.84 579.66, 814.17 577.30, 824.19 570.28, 825.24 571.78, 829.69 568.67, 839.46 582.45, 847.60 576.71, 837.10 561.91, 840.25 559.68, 837.98 556.52, 854.30 545.02, 855.84 547.18, 863.81 541.56, 861.81 538.75, 872.74 531.03, 874.12 532.97, 882.56 527.03, 885.12 525.14, 883.41 522.81, 893.97 515.06, 895.73 517.43, 900.00 514.29, 900.00 556.13, 894.72 548.96, 855.17 577.81, 857.86 581.45, 855.02 583.52, 860.67 591.23, 841.91 604.92, 900.00 683.86, 900.00 767.87, 899.70 768.11, 897.63 765.45, 890.77 770.76, 892.07 772.43, 881.69 780.49, 879.99 778.31, 871.70 784.76, 872.70 786.05, 863.66 793.07, 862.30 791.33, 854.45 797.43, 856.00 799.44, 845.75 807.40, 844.12 805.31, 840.19 808.36, 838.26 805.91, 836.33 807.40, 838.49 810.14, 833.15 814.29, 833.92 815.25, 826.79 820.81, 822.49 822.42, 817.88 825.61, 804.20 806.00, 813.07 799.87, 806.63 790.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,1,"POLYGON ((900.00 483.71, 895.47 483.90, 895.59 486.41, 875.62 487.27, 875.44 483.04, 861.28 483.64, 861.47 488.30, 845.13 489.01, 844.92 484.15, 838.99 484.41, 838.64 476.41, 835.32 476.54, 834.35 454.45, 837.76 454.30, 837.40 446.12, 843.49 445.85, 843.27 440.93, 872.13 439.66, 872.22 441.72, 883.38 441.21, 883.28 438.68, 896.30 438.11, 896.39 440.35, 900.00 440.18, 900.00 483.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,2,"POLYGON ((602.83 466.01, 646.91 465.95, 646.87 440.44, 718.86 440.31, 719.16 637.18, 602.74 637.38, 602.62 558.65, 596.29 558.65, 596.20 501.38, 602.88 501.37, 602.83 466.01))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,3,"POLYGON ((804.19 458.24, 804.69 547.27, 754.30 547.56, 754.15 518.97, 761.34 518.94, 761.00 458.50, 804.19 458.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,4,"POLYGON ((715.96 380.58, 717.14 405.59, 679.25 407.36, 678.07 382.37, 715.96 380.58))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,5,"POLYGON ((796.26 284.19, 798.19 383.76, 723.88 385.22, 721.95 285.62, 796.26 284.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,6,"POLYGON ((895.08 273.54, 896.24 308.27, 900.00 308.14, 900.00 377.32, 891.51 377.60, 891.64 381.84, 830.19 383.92, 827.58 306.94, 847.86 306.24, 846.80 275.17, 895.08 273.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,7,"POLYGON ((611.69 208.55, 613.89 357.64, 620.77 357.87, 621.43 368.30, 577.33 396.78, 569.15 393.37, 566.64 215.72, 590.48 215.93, 590.70 209.08, 611.69 208.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,8,"POLYGON ((699.91 225.54, 700.74 261.55, 712.74 261.28, 713.56 296.14, 659.51 297.38, 657.87 226.50, 699.91 225.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,9,"POLYGON ((859.73 188.18, 861.42 247.76, 809.67 249.22, 808.00 189.63, 859.73 188.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,10,"POLYGON ((645.82 102.68, 656.63 160.54, 584.39 172.57, 569.05 172.97, 568.42 113.65, 645.82 102.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,11,"POLYGON ((585.42 18.36, 588.89 30.15, 581.14 32.41, 577.69 20.62, 585.42 18.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,12,"POLYGON ((648.31 13.47, 651.52 25.24, 637.34 29.09, 634.13 17.29, 648.31 13.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,13,"POLYGON ((357.68 111.22, 357.47 136.51, 339.60 136.34, 339.82 111.05, 357.68 111.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,14,"POLYGON ((367.14 115.95, 390.25 114.60, 390.51 119.28, 395.25 119.00, 395.81 128.38, 371.26 129.80, 370.56 127.87, 369.73 126.69, 367.74 126.23, 367.14 115.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,15,"POLYGON ((38.85 110.08, 54.29 109.22, 56.10 110.39, 56.92 129.53, 53.40 129.69, 51.41 133.20, 41.21 133.79, 40.96 129.54, 39.90 128.81, 38.85 110.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,16,"POLYGON ((101.74 120.02, 98.78 120.11, 98.08 99.33, 120.72 98.58, 122.09 139.39, 102.42 140.07, 101.74 120.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,17,"POLYGON ((309.61 104.21, 311.07 127.30, 289.79 128.64, 288.08 101.56, 301.24 100.74, 301.49 104.70, 309.61 104.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,18,"POLYGON ((335.43 97.42, 334.98 128.40, 316.82 128.13, 317.27 97.15, 335.43 97.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,19,"POLYGON ((242.77 100.88, 243.43 121.06, 224.41 121.70, 223.74 101.50, 242.77 100.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,20,"POLYGON ((513.89 129.36, 504.79 126.48, 493.09 122.27, 477.95 120.91, 477.08 110.19, 480.40 109.52, 480.83 102.85, 488.05 101.12, 492.74 92.98, 496.22 90.74, 497.21 106.16, 512.32 106.36, 513.89 129.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,21,"POLYGON ((416.88 82.33, 416.31 88.36, 419.21 88.05, 420.37 95.70, 427.40 96.49, 429.13 136.10, 403.07 136.76, 400.90 108.47, 406.07 103.52, 409.07 97.43, 409.06 87.82, 407.48 83.06, 416.88 82.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,22,"POLYGON ((133.79 89.96, 157.38 89.54, 158.03 126.39, 155.96 129.04, 134.48 129.44, 133.79 89.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,23,"POLYGON ((457.30 91.42, 457.88 123.46, 444.48 123.72, 444.39 118.57, 435.62 118.73, 435.14 91.83, 457.30 91.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,24,"POLYGON ((163.97 84.78, 184.26 83.57, 184.62 89.86, 188.03 89.67, 190.24 126.95, 166.55 128.35, 163.97 84.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,25,"POLYGON ((276.15 74.67, 276.18 121.38, 269.36 121.38, 269.36 127.32, 253.05 127.34, 253.02 74.69, 276.15 74.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,26,"POLYGON ((215.23 60.18, 215.48 81.48, 213.20 88.11, 216.73 88.07, 217.03 127.00, 196.13 127.17, 195.82 88.47, 206.54 88.39, 206.50 84.16, 200.50 77.74, 200.30 60.36, 215.23 60.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,27,"POLYGON ((36.92 66.00, 55.29 65.62, 55.68 85.48, 37.32 85.86, 36.92 66.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,28,"POLYGON ((243.19 56.49, 243.77 76.81, 223.87 77.36, 223.29 57.07, 243.19 56.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,29,"POLYGON ((513.75 54.15, 513.93 68.58, 519.33 68.51, 519.40 73.77, 489.68 74.13, 489.49 58.28, 493.66 58.22, 493.62 54.38, 513.75 54.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,30,"POLYGON ((514.43 18.95, 514.84 42.58, 463.48 43.47, 463.23 28.58, 496.02 27.99, 495.88 19.27, 514.43 18.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,31,"POLYGON ((58.18 0.00, 58.03 12.58, 42.56 12.38, 42.69 0.00, 58.18 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,32,"POLYGON ((449.68 0.00, 449.88 8.35, 406.64 9.41, 406.41 0.00, 449.68 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,33,"POLYGON ((6.76 487.14, 13.82 486.96, 13.32 466.77, 20.45 466.61, 20.61 472.84, 36.94 472.42, 41.77 476.52, 41.86 480.09, 38.26 480.18, 38.36 484.00, 42.31 483.90, 42.56 493.97, 6.94 494.83, 6.76 487.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,34,"POLYGON ((333.59 471.71, 362.06 472.27, 361.77 486.91, 333.30 486.35, 333.59 471.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,35,"POLYGON ((110.66 446.59, 111.08 454.31, 112.15 454.81, 113.59 496.93, 105.79 497.17, 101.02 494.56, 95.99 497.42, 79.43 497.84, 78.90 478.04, 82.74 477.92, 82.64 474.01, 76.34 474.17, 76.04 463.30, 79.32 462.29, 78.92 454.86, 86.31 454.47, 85.96 447.93, 110.66 446.59))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,36,"POLYGON ((267.68 460.76, 267.83 481.34, 238.42 481.55, 238.30 466.68, 245.15 466.64, 245.10 460.92, 267.68 460.76))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,37,"POLYGON ((339.80 437.41, 361.26 437.44, 361.26 440.26, 378.10 440.27, 378.06 459.08, 359.78 459.03, 359.78 456.83, 339.75 456.79, 339.80 437.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,38,"POLYGON ((220.52 434.20, 220.80 443.73, 216.82 448.90, 216.93 452.56, 208.85 452.81, 208.31 434.57, 220.52 434.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,39,"POLYGON ((91.54 437.80, 91.47 446.57, 82.27 446.49, 82.32 437.72, 91.54 437.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,40,"POLYGON ((191.55 394.36, 191.89 432.66, 190.90 432.98, 191.39 472.28, 174.04 472.48, 173.69 442.96, 179.23 442.89, 179.11 431.44, 170.54 431.52, 170.31 409.24, 173.15 409.21, 173.01 394.52, 191.55 394.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,41,"POLYGON ((233.20 423.77, 248.50 423.58, 248.64 435.54, 233.33 435.73, 233.20 423.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,42,"POLYGON ((467.35 409.20, 469.29 439.94, 458.05 440.65, 457.64 433.89, 449.66 434.38, 448.16 410.40, 467.35 409.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,43,"POLYGON ((415.83 409.43, 416.76 428.39, 408.25 428.80, 408.51 434.08, 392.05 434.88, 390.86 410.64, 415.83 409.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,44,"POLYGON ((437.46 401.75, 437.72 438.26, 419.21 438.40, 418.96 401.89, 437.46 401.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,45,"POLYGON ((126.81 390.91, 126.73 387.71, 135.10 387.50, 135.27 394.17, 142.98 394.00, 144.01 435.68, 119.16 436.30, 118.05 391.13, 126.81 390.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,46,"POLYGON ((89.36 386.40, 90.80 433.34, 75.16 433.80, 73.91 392.79, 84.84 392.44, 84.65 386.54, 89.36 386.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,47,"POLYGON ((196.30 390.22, 198.02 390.15, 207.95 400.15, 212.06 396.12, 207.47 391.51, 207.83 390.19, 219.27 390.23, 219.24 395.05, 221.15 395.76, 221.41 402.21, 223.13 405.61, 223.95 428.87, 197.70 429.81, 196.30 390.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,48,"POLYGON ((363.25 391.24, 363.00 419.01, 352.00 418.92, 352.03 414.99, 343.48 414.92, 343.71 391.07, 363.25 391.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,49,"POLYGON ((381.85 387.46, 381.64 393.14, 383.18 393.19, 382.17 420.35, 364.11 419.67, 365.14 391.70, 367.70 391.79, 367.87 386.95, 381.85 387.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,50,"POLYGON ((23.51 385.13, 34.87 385.15, 34.90 382.44, 40.29 382.44, 40.24 402.26, 44.67 402.28, 44.63 423.10, 23.44 423.05, 23.51 385.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,51,"POLYGON ((5.64 386.14, 19.31 385.61, 20.59 417.46, 1.75 418.20, 0.73 392.55, 5.90 392.35, 5.64 386.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,52,"POLYGON ((339.92 386.48, 340.76 414.85, 322.99 415.37, 322.16 387.00, 339.92 386.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,53,"POLYGON ((294.02 383.13, 293.99 413.81, 262.62 413.79, 262.66 383.11, 294.02 383.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,54,"POLYGON ((250.67 376.04, 252.30 416.00, 232.72 416.79, 231.09 376.83, 250.67 376.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,55,"POLYGON ((535.03 348.71, 536.82 386.09, 523.60 386.72, 523.39 382.29, 515.36 382.67, 513.77 349.72, 535.03 348.71))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,56,"POLYGON ((475.35 334.68, 486.22 334.87, 487.04 342.37, 490.14 346.20, 495.28 350.62, 497.45 373.91, 485.96 374.21, 485.39 370.14, 476.88 370.20, 475.35 334.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,57,"POLYGON ((446.64 350.44, 447.18 314.33, 461.56 314.54, 461.34 329.26, 464.51 329.29, 464.19 350.70, 446.64 350.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,58,"POLYGON ((408.50 313.74, 408.02 326.16, 414.12 326.41, 413.59 340.47, 393.07 339.69, 394.08 313.18, 408.50 313.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,59,"POLYGON ((376.10 296.81, 376.06 302.56, 384.87 302.65, 384.74 316.92, 375.54 316.85, 375.51 321.47, 354.81 321.29, 355.02 296.64, 376.10 296.81))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,60,"POLYGON ((66.86 281.31, 67.07 322.48, 54.50 322.56, 54.46 314.88, 47.76 314.92, 47.58 281.69, 59.17 281.62, 62.60 285.26, 66.86 281.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,61,"POLYGON ((96.21 282.38, 102.57 282.31, 106.42 287.14, 109.47 284.73, 117.01 282.81, 116.97 311.02, 114.86 320.73, 106.78 321.05, 103.32 311.72, 98.21 312.01, 97.77 304.21, 96.40 299.29, 96.21 282.38))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,62,"POLYGON ((43.95 291.02, 44.81 316.79, 32.08 317.23, 31.88 311.48, 25.77 311.68, 24.82 283.29, 34.68 282.98, 34.94 291.32, 43.95 291.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,63,"POLYGON ((3.72 276.80, 16.01 276.50, 16.16 282.43, 23.37 282.24, 24.09 317.28, 19.72 317.30, 19.73 320.65, 17.21 320.67, 17.22 323.18, 4.28 323.24, 3.72 276.80))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,64,"POLYGON ((89.49 286.88, 90.47 318.05, 78.37 318.44, 76.77 313.89, 71.40 314.03, 70.47 279.22, 81.09 278.88, 81.35 287.13, 89.49 286.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,65,"POLYGON ((496.18 288.15, 527.68 288.28, 527.70 307.72, 505.38 309.07, 502.57 303.66, 501.02 299.15, 496.56 296.44, 496.18 288.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,66,"POLYGON ((147.34 275.68, 148.95 309.69, 141.73 310.03, 138.56 307.83, 126.74 308.31, 126.30 297.64, 121.67 297.83, 121.18 285.83, 138.14 285.13, 144.14 275.83, 147.34 275.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,67,"POLYGON ((155.87 266.12, 174.48 265.60, 175.08 286.70, 183.82 286.45, 184.21 300.27, 156.72 300.99, 155.87 266.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,68,"POLYGON ((209.48 247.35, 229.47 265.37, 233.20 261.93, 236.65 264.97, 250.55 264.83, 254.50 261.16, 257.71 264.21, 266.71 255.72, 268.52 300.78, 222.73 299.95, 209.44 306.55, 209.48 247.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,69,"POLYGON ((522.47 262.55, 522.71 275.00, 528.94 274.88, 529.13 284.80, 499.12 285.37, 498.69 263.02, 522.47 262.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,70,"POLYGON ((369.77 263.85, 371.27 280.31, 350.92 280.12, 351.69 274.70, 352.65 269.86, 351.25 264.50, 369.77 263.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,71,"POLYGON ((371.46 234.24, 372.24 256.80, 348.03 257.64, 347.25 235.10, 371.46 234.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,72,"POLYGON ((525.95 234.31, 526.15 254.35, 499.03 254.60, 498.86 234.56, 525.95 234.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,73,"POLYGON ((334.35 204.05, 345.39 204.08, 345.36 207.45, 374.43 207.51, 374.39 223.87, 358.87 223.84, 358.85 227.57, 334.30 227.53, 334.35 204.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,74,"POLYGON ((472.63 185.53, 474.15 239.99, 456.05 240.49, 455.28 213.39, 451.78 213.50, 451.03 186.13, 472.63 185.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,75,"POLYGON ((24.37 189.34, 31.76 189.17, 31.86 192.86, 35.66 193.74, 36.70 191.65, 40.43 191.62, 40.67 217.92, 37.04 217.94, 37.10 223.98, 30.72 224.03, 30.69 222.14, 24.89 222.20, 24.37 189.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,76,"POLYGON ((435.66 186.83, 437.80 217.65, 417.61 219.03, 415.47 188.21, 435.66 186.83))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,77,"POLYGON ((50.38 187.68, 58.84 187.37, 58.75 185.42, 71.40 184.97, 72.54 216.33, 67.59 216.50, 67.49 213.95, 62.34 214.12, 62.47 217.91, 58.71 218.03, 58.82 220.54, 54.54 220.69, 54.30 213.64, 51.13 208.35, 50.38 187.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,78,"POLYGON ((9.23 193.28, 20.69 192.72, 21.83 215.64, 15.67 215.96, 15.80 218.86, 11.22 219.09, 11.07 215.90, 7.59 216.05, 7.69 217.96, 3.45 218.18, 1.86 186.39, 8.88 186.05, 9.23 193.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,79,"POLYGON ((119.12 184.21, 127.22 184.05, 127.31 189.04, 133.24 188.91, 133.65 208.37, 127.77 208.48, 127.89 213.71, 124.38 213.78, 119.69 210.70, 119.12 184.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,80,"POLYGON ((84.85 184.36, 105.64 183.85, 105.92 195.05, 104.32 196.38, 104.68 210.35, 94.45 210.62, 94.12 197.55, 85.17 197.76, 84.85 184.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,81,"POLYGON ((509.36 182.04, 510.41 209.43, 496.72 209.93, 496.41 201.95, 482.11 202.49, 481.64 189.72, 496.75 189.15, 496.49 182.52, 509.36 182.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,82,"POLYGON ((158.01 189.84, 174.91 189.52, 176.97 184.93, 179.13 184.88, 182.70 188.67, 182.97 204.60, 158.29 205.06, 158.10 194.47, 158.01 189.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,83,"POLYGON ((221.07 175.51, 277.83 173.66, 278.11 182.33, 278.40 209.67, 231.58 210.16, 221.94 202.17, 221.07 175.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,84,"POLYGON ((352.96 179.91, 354.39 182.76, 362.53 182.75, 362.55 198.95, 341.21 198.14, 340.91 193.51, 343.46 187.68, 347.46 184.29, 348.71 180.39, 352.96 179.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,85,"POLYGON ((371.89 182.02, 380.97 181.99, 380.96 179.48, 388.34 179.45, 388.34 181.62, 399.54 181.58, 399.60 197.70, 371.94 197.80, 371.89 182.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,86,"POLYGON ((12.96 866.90, 12.14 837.57, 33.97 836.95, 35.06 875.53, 19.87 875.96, 19.68 869.08, 12.96 866.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,87,"POLYGON ((39.53 834.12, 43.13 834.92, 54.82 863.57, 54.72 875.56, 39.19 875.44, 39.53 834.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,88,"POLYGON ((100.86 861.53, 100.08 833.22, 115.18 832.79, 115.23 834.94, 122.77 834.75, 123.58 863.59, 116.58 863.79, 113.45 861.18, 100.86 861.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,89,"POLYGON ((133.41 833.68, 153.62 833.34, 154.04 856.95, 133.80 857.29, 133.41 833.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,90,"POLYGON ((187.80 815.33, 187.66 860.62, 178.50 865.09, 163.39 865.06, 163.51 824.74, 169.22 824.75, 169.26 815.27, 187.80 815.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,91,"POLYGON ((195.80 821.03, 195.70 809.05, 209.62 808.91, 209.69 817.39, 228.71 817.22, 228.79 833.35, 251.51 833.26, 251.68 870.46, 224.80 870.59, 224.75 861.23, 226.53 861.23, 226.35 835.81, 206.10 835.98, 206.00 820.95, 195.80 821.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,92,"POLYGON ((254.30 811.68, 290.62 811.02, 290.27 791.36, 316.23 790.90, 316.75 820.61, 321.45 826.75, 322.66 858.17, 286.56 859.54, 286.89 868.30, 284.02 871.79, 255.37 872.30, 254.30 811.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,93,"POLYGON ((342.98 832.08, 342.06 785.07, 380.78 784.30, 381.96 844.29, 387.08 844.21, 387.46 863.49, 344.40 864.34, 344.57 873.39, 329.54 873.69, 329.35 864.13, 326.65 864.17, 326.02 832.42, 342.98 832.08))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,94,"POLYGON ((73.52 836.54, 72.73 819.53, 95.14 818.50, 95.93 835.50, 73.52 836.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,95,"POLYGON ((101.04 817.28, 117.65 816.43, 117.92 821.80, 107.94 822.30, 101.68 819.57, 101.04 817.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,96,"POLYGON ((136.99 807.79, 151.32 807.65, 151.45 821.34, 137.12 821.49, 136.99 807.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,97,"POLYGON ((165.44 751.55, 176.02 752.10, 174.99 757.50, 178.17 763.61, 175.26 769.61, 171.79 771.54, 166.72 774.96, 165.44 751.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,98,"POLYGON ((44.13 770.92, 44.43 743.01, 54.86 743.12, 54.94 735.66, 68.69 735.80, 68.20 780.60, 55.46 780.48, 55.53 774.53, 45.92 774.42, 44.13 770.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,99,"POLYGON ((254.28 732.63, 265.01 732.43, 265.12 738.71, 278.92 738.44, 279.61 774.67, 275.14 774.77, 275.23 779.60, 269.37 779.71, 269.45 782.79, 255.29 783.09, 254.28 732.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,100,"POLYGON ((15.47 774.60, 15.46 735.02, 37.46 735.02, 37.46 773.55, 27.42 773.56, 27.40 777.45, 16.48 777.46, 15.47 774.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,101,"POLYGON ((105.76 774.12, 104.80 734.90, 128.77 734.31, 129.64 768.85, 128.01 774.13, 125.31 777.88, 109.46 778.04, 105.76 774.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,102,"POLYGON ((133.73 770.94, 133.51 736.01, 151.51 735.88, 151.53 739.10, 159.24 739.06, 159.49 775.50, 135.09 775.66, 133.73 770.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,103,"POLYGON ((75.66 734.44, 93.68 734.38, 93.70 740.99, 100.93 740.97, 101.04 772.17, 95.35 772.21, 95.36 776.38, 76.38 763.95, 75.66 734.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,104,"POLYGON ((0.00 735.23, 10.09 735.36, 9.63 771.31, 0.00 771.19, 0.00 735.23))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,105,"POLYGON ((227.87 764.14, 229.23 733.92, 247.88 734.73, 247.10 752.04, 252.00 752.25, 251.32 767.43, 249.81 771.77, 229.84 770.88, 230.14 764.26, 227.87 764.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,106,"POLYGON ((346.05 666.43, 346.11 657.12, 390.24 657.42, 390.12 673.02, 373.01 672.91, 375.21 681.51, 343.24 681.30, 343.34 666.41, 346.05 666.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,107,"POLYGON ((0.00 647.34, 3.76 647.28, 7.05 653.28, 13.18 657.28, 20.53 657.71, 20.61 683.26, 15.01 683.27, 15.02 686.73, 3.53 686.76, 3.52 680.45, 0.00 680.47, 0.00 647.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,108,"POLYGON ((521.05 668.68, 524.15 668.65, 524.26 677.99, 485.59 678.40, 485.55 674.45, 481.41 674.51, 481.31 666.06, 492.84 665.94, 492.81 661.79, 513.06 654.28, 520.92 657.10, 521.05 668.68))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,109,"POLYGON ((22.10 678.47, 22.02 670.55, 25.57 670.52, 30.72 667.79, 34.36 659.78, 37.92 657.71, 41.75 652.53, 41.72 660.59, 38.13 671.18, 38.86 675.00, 41.39 676.65, 40.30 678.32, 22.10 678.47))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,110,"POLYGON ((107.62 672.60, 106.79 657.60, 112.19 654.97, 119.45 644.33, 127.89 644.40, 127.55 677.62, 116.24 677.51, 116.26 675.47, 115.02 673.28, 112.77 671.58, 109.51 671.67, 107.62 672.60))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,111,"POLYGON ((76.59 671.49, 76.23 641.42, 99.90 641.12, 100.33 677.54, 78.78 677.80, 78.70 672.12, 76.59 671.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,112,"POLYGON ((427.99 643.49, 427.82 638.99, 433.69 638.77, 433.83 642.94, 437.22 642.81, 438.47 677.34, 421.49 677.95, 420.24 643.77, 427.99 643.49))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,113,"POLYGON ((250.98 639.46, 263.27 639.55, 263.18 652.49, 269.05 652.54, 268.98 664.00, 270.33 664.03, 270.32 665.14, 269.06 665.11, 268.90 675.68, 245.79 675.36, 246.11 654.17, 250.85 654.25, 250.98 639.46))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,114,"POLYGON ((218.80 671.67, 217.94 640.28, 237.91 639.75, 238.75 670.30, 236.48 668.73, 234.88 669.22, 231.43 673.41, 218.94 674.11, 218.80 671.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,115,"POLYGON ((162.35 668.41, 161.65 640.74, 171.55 640.80, 172.43 634.45, 184.70 634.56, 185.73 668.83, 184.47 668.98, 184.67 674.12, 175.15 674.30, 175.20 676.36, 167.42 675.96, 167.30 674.21, 169.44 673.49, 169.99 672.08, 168.44 669.32, 167.97 668.53, 166.12 668.64, 163.72 669.51, 162.46 669.54, 162.35 668.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,116,"POLYGON ((189.70 667.78, 188.76 633.93, 206.32 633.28, 206.60 640.55, 210.77 640.40, 211.27 653.48, 213.66 655.04, 213.63 671.07, 206.70 677.00, 202.82 676.05, 197.76 676.25, 197.55 669.95, 190.39 670.20, 189.70 667.78))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,117,"POLYGON ((135.75 656.72, 138.69 650.61, 141.79 650.09, 144.16 645.03, 148.74 644.76, 150.77 643.24, 151.29 640.28, 147.64 636.55, 147.58 631.31, 158.27 631.15, 158.58 653.48, 160.84 653.44, 161.16 675.96, 136.03 676.34, 135.75 656.72))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,118,"POLYGON ((272.52 632.92, 285.62 644.79, 296.44 632.95, 296.38 651.91, 291.88 657.64, 297.68 657.54, 297.94 672.94, 289.88 673.10, 288.50 674.20, 269.44 674.33, 269.42 666.07, 272.61 666.08, 272.52 632.92))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,119,"POLYGON ((336.74 653.90, 335.94 631.08, 373.15 629.77, 373.34 635.18, 374.66 637.99, 374.96 647.13, 372.02 647.23, 372.15 650.82, 345.07 651.76, 345.13 653.60, 336.74 653.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,120,"POLYGON ((521.75 632.75, 517.14 633.20, 513.77 637.52, 523.29 637.44, 523.33 643.16, 513.73 643.23, 507.56 638.41, 498.08 638.15, 498.36 628.15, 507.80 621.71, 520.45 620.28, 521.75 632.75))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,121,"POLYGON ((337.00 628.54, 336.47 607.18, 375.13 606.30, 375.15 608.12, 379.60 608.03, 379.72 613.29, 376.03 613.38, 376.34 627.67, 337.00 628.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,122,"POLYGON ((19.65 619.57, 19.59 604.90, 43.32 604.83, 43.36 615.00, 47.30 614.98, 47.36 629.37, 21.62 629.45, 21.60 622.65, 19.65 619.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,123,"POLYGON ((265.65 605.79, 260.29 605.59, 260.50 600.08, 293.85 601.32, 293.16 619.96, 265.15 618.92, 265.65 605.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,124,"POLYGON ((503.45 612.66, 503.52 615.96, 496.98 616.11, 496.93 613.27, 490.14 613.40, 489.78 595.60, 510.00 595.20, 509.91 590.87, 519.97 590.66, 520.07 596.25, 525.63 596.13, 525.76 602.74, 515.68 602.96, 515.87 612.41, 503.45 612.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,125,"POLYGON ((10.07 592.20, 12.47 590.57, 22.29 589.27, 25.22 586.40, 37.84 586.46, 37.82 592.21, 32.04 592.20, 32.01 597.46, 16.06 597.38, 16.05 599.93, 10.08 599.91, 10.07 592.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,126,"POLYGON ((335.85 578.94, 377.40 577.65, 377.86 593.02, 383.33 592.86, 383.49 598.52, 376.40 598.72, 376.45 600.85, 336.55 602.07, 335.85 578.94))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,127,"POLYGON ((78.93 586.63, 78.53 579.40, 102.26 578.11, 102.53 582.76, 100.44 582.88, 100.65 586.85, 97.21 589.40, 86.56 589.99, 86.36 586.22, 78.93 586.63))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,128,"POLYGON ((245.75 585.74, 229.44 585.87, 229.15 551.52, 274.14 551.12, 274.18 556.40, 279.33 556.36, 279.49 576.34, 272.99 576.39, 273.06 585.20, 259.88 585.29, 259.96 594.10, 245.82 594.22, 245.75 585.74))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,129,"POLYGON ((4.00 575.40, 5.86 572.56, 5.97 569.73, 4.69 567.32, 5.16 565.56, 31.91 565.19, 31.93 567.03, 37.12 566.96, 37.23 574.93, 4.00 575.40))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,130,"POLYGON ((519.83 559.07, 520.27 577.00, 522.84 576.93, 522.92 580.15, 489.46 580.98, 489.37 577.15, 446.73 578.21, 446.45 567.30, 457.01 567.03, 456.91 562.92, 467.14 562.66, 467.26 567.63, 483.75 567.21, 483.57 559.98, 519.83 559.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,131,"POLYGON ((82.21 574.18, 82.09 567.92, 88.24 567.77, 87.97 555.08, 121.58 554.33, 122.00 573.45, 99.09 573.95, 95.32 570.72, 92.52 571.14, 90.31 574.00, 82.21 574.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,132,"POLYGON ((335.60 551.84, 373.87 550.51, 374.73 575.48, 336.46 576.79, 335.60 551.84))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,133,"POLYGON ((0.00 529.10, 31.69 528.48, 31.89 538.30, 35.82 538.23, 36.06 550.59, 0.00 551.29, 0.00 529.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,134,"POLYGON ((511.12 528.15, 512.19 535.12, 514.43 538.10, 519.45 544.43, 525.90 550.00, 486.13 550.66, 486.39 547.08, 482.99 547.34, 483.01 540.88, 469.68 540.87, 468.84 529.06, 511.12 528.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,135,"POLYGON ((115.00 538.36, 117.52 538.27, 117.74 545.30, 114.11 545.44, 114.17 547.81, 96.68 548.39, 96.28 549.47, 84.09 549.87, 83.80 540.66, 82.17 540.71, 81.97 534.27, 83.69 534.21, 83.45 526.84, 115.70 525.82, 114.65 527.47, 115.00 538.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,136,"POLYGON ((375.28 524.39, 376.06 544.94, 336.52 546.47, 335.73 525.91, 375.28 524.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,137,"POLYGON ((279.62 528.89, 281.70 542.14, 248.56 542.03, 262.94 528.54, 279.62 528.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,138,"POLYGON ((442.55 515.52, 456.16 515.33, 456.34 527.40, 442.71 527.59, 442.55 515.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,139,"POLYGON ((0.00 505.14, 1.70 504.65, 30.25 504.49, 36.01 508.74, 36.34 517.95, 30.46 518.14, 30.67 524.08, 0.00 525.14, 0.00 505.14))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,140,"POLYGON ((117.19 502.67, 117.90 523.30, 93.54 524.14, 93.40 520.17, 88.46 520.35, 86.55 523.30, 82.01 523.37, 81.74 502.58, 114.05 501.96, 117.19 502.67))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,141,"POLYGON ((396.91 503.21, 397.22 514.59, 399.81 515.70, 399.90 519.25, 384.56 519.67, 384.13 503.56, 396.91 503.21))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,142,"POLYGON ((355.01 507.43, 354.90 499.07, 373.86 498.81, 373.96 505.35, 383.65 505.22, 384.37 519.67, 337.37 521.03, 337.18 507.67, 355.01 507.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3739089,143,"POLYGON ((478.59 494.95, 504.46 493.03, 508.11 495.09, 512.74 500.16, 517.90 504.69, 517.01 511.89, 513.01 517.89, 487.27 517.49, 487.38 514.62, 479.61 513.91, 478.59 494.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,0,"POLYGON ((791.64 56.12, 793.90 62.46, 796.36 61.64, 803.49 83.04, 799.10 84.48, 799.88 86.79, 796.08 88.04, 794.69 83.86, 791.23 81.31, 790.40 71.76, 786.03 62.75, 783.46 60.91, 783.12 58.47, 791.64 56.12))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,1,"POLYGON ((636.33 82.30, 635.99 59.47, 640.18 59.40, 653.06 68.26, 650.54 71.79, 650.81 76.65, 653.31 80.75, 651.10 82.08, 636.33 82.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,2,"POLYGON ((889.03 19.31, 894.16 34.94, 890.19 37.84, 888.00 41.23, 883.84 43.78, 883.21 46.94, 876.27 49.19, 873.31 40.12, 864.86 42.86, 857.60 20.61, 880.88 13.06, 883.52 21.10, 889.03 19.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,3,"POLYGON ((801.63 29.88, 781.16 36.79, 777.59 26.32, 762.90 31.29, 759.79 22.20, 763.69 23.83, 772.37 19.95, 778.20 21.02, 782.88 24.92, 796.72 23.52, 798.38 26.98, 801.63 29.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,4,"POLYGON ((876.85 11.10, 866.28 15.28, 865.19 12.58, 852.94 17.42, 846.00 0.00, 877.83 0.00, 878.83 2.50, 874.16 4.35, 876.85 11.10))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,5,"POLYGON ((632.96 0.00, 632.96 0.30, 602.42 22.77, 597.26 22.67, 597.38 16.90, 589.48 16.75, 589.62 10.00, 570.68 25.00, 564.02 26.21, 564.93 19.02, 558.90 18.28, 559.73 11.63, 553.38 10.83, 554.30 3.37, 558.47 0.00, 632.96 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,6,"POLYGON ((532.06 573.44, 532.75 583.08, 512.94 584.50, 509.53 575.13, 532.06 573.44))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,7,"POLYGON ((454.96 570.18, 478.97 570.05, 479.05 586.05, 455.03 586.18, 454.96 570.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,8,"POLYGON ((485.65 476.00, 511.77 474.16, 512.62 485.84, 503.18 490.41, 493.03 492.40, 489.75 500.38, 485.08 502.23, 480.17 502.27, 480.10 492.48, 483.54 490.91, 482.67 485.74, 478.54 485.24, 479.06 480.88, 485.65 476.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,9,"POLYGON ((518.94 458.33, 503.01 460.06, 491.03 448.74, 490.08 441.50, 484.12 442.28, 483.54 438.03, 488.46 435.44, 490.73 437.85, 495.85 433.03, 515.94 430.83, 518.94 458.33))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,10,"POLYGON ((394.99 405.82, 378.47 419.83, 378.54 432.95, 375.43 447.63, 368.46 456.71, 370.93 465.80, 321.66 464.57, 322.96 413.19, 343.64 413.71, 343.88 404.53, 394.99 405.82))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,11,"POLYGON ((0.00 362.15, 0.68 361.83, 5.26 355.67, 7.28 354.02, 10.89 362.01, 4.83 367.61, 6.85 372.13, 0.00 375.19, 0.00 362.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,12,"POLYGON ((63.15 305.51, 81.58 303.67, 79.17 307.70, 79.56 311.38, 75.76 312.94, 69.25 314.06, 69.99 318.39, 78.29 319.05, 82.23 323.07, 66.10 325.11, 65.57 320.81, 56.35 321.98, 55.96 319.04, 58.87 316.90, 57.38 311.06, 60.39 306.85, 60.48 305.83, 63.15 305.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,13,"POLYGON ((133.67 283.27, 135.26 302.72, 117.92 304.11, 116.34 284.66, 133.67 283.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,14,"POLYGON ((141.44 268.79, 144.63 268.31, 149.74 273.73, 156.16 271.84, 158.84 274.50, 160.78 286.75, 163.72 286.27, 164.47 290.98, 167.77 290.45, 168.63 295.80, 143.64 299.75, 142.78 294.33, 146.89 293.69, 145.41 284.36, 149.29 283.75, 149.09 282.52, 151.10 282.20, 150.26 276.72, 142.84 277.86, 141.44 268.79))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,15,"POLYGON ((524.86 269.51, 523.01 281.45, 520.23 284.94, 510.83 290.36, 506.24 291.63, 503.60 287.68, 505.24 282.91, 499.67 283.05, 517.34 263.82, 524.86 269.51))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,16,"POLYGON ((189.41 264.02, 193.00 283.39, 170.42 287.52, 169.23 281.07, 166.76 281.51, 165.13 272.72, 168.34 267.91, 189.41 264.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,17,"POLYGON ((97.55 271.65, 81.19 273.62, 79.82 262.42, 75.74 262.93, 75.34 259.72, 76.76 257.17, 77.57 253.78, 74.51 250.33, 73.78 244.26, 93.94 241.82, 97.55 271.65))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,18,"POLYGON ((516.27 263.34, 510.30 258.38, 513.19 257.29, 513.56 251.02, 528.81 232.94, 536.85 238.26, 535.73 239.64, 534.99 243.41, 531.06 244.56, 530.70 251.53, 527.73 253.36, 524.63 252.67, 523.55 257.42, 524.27 261.67, 520.18 260.42, 516.27 263.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,19,"POLYGON ((205.79 228.50, 227.81 238.88, 215.67 264.37, 215.14 257.21, 211.94 255.36, 209.68 249.65, 205.93 247.30, 205.47 242.76, 206.05 238.02, 202.80 234.77, 205.79 228.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,20,"POLYGON ((53.47 232.57, 53.11 208.90, 82.71 208.43, 83.07 232.10, 53.47 232.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,21,"POLYGON ((243.36 202.66, 228.18 227.26, 214.68 219.02, 229.86 194.41, 243.36 202.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,22,"POLYGON ((249.68 196.19, 223.17 182.04, 227.73 173.58, 232.37 176.05, 236.53 168.31, 241.42 174.87, 242.32 180.80, 248.41 185.59, 249.68 196.19))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,23,"POLYGON ((269.58 158.32, 264.84 157.71, 261.10 152.97, 254.41 158.22, 253.54 152.80, 251.68 148.39, 247.60 144.79, 247.20 138.60, 258.28 133.33, 264.48 130.22, 269.05 139.22, 276.70 135.39, 282.65 134.75, 269.58 158.32))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,24,"POLYGON ((555.91 77.99, 534.01 100.59, 541.17 107.47, 518.64 130.75, 512.12 124.50, 501.76 135.20, 478.98 113.34, 490.84 101.09, 508.40 117.96, 528.20 97.52, 521.19 90.77, 525.31 86.54, 529.58 90.65, 533.61 86.50, 534.56 83.08, 543.18 73.89, 549.40 74.31, 550.70 72.99, 555.91 77.99))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,25,"POLYGON ((460.98 25.41, 510.28 29.19, 511.13 51.54, 460.73 53.18, 460.98 25.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,26,"POLYGON ((253.84 0.00, 253.54 17.39, 242.60 17.23, 242.71 10.59, 238.80 10.51, 238.89 5.58, 241.86 5.64, 241.95 0.00, 253.84 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,27,"POLYGON ((488.50 0.00, 490.04 10.43, 450.07 16.32, 447.66 0.00, 488.50 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,28,"POLYGON ((291.69 0.00, 291.71 24.50, 260.89 24.53, 260.87 0.00, 291.69 0.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,29,"POLYGON ((677.74 881.96, 714.08 881.72, 714.12 887.78, 725.13 887.72, 725.16 895.53, 715.71 895.59, 715.74 900.00, 677.85 900.00, 677.74 881.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,30,"POLYGON ((740.63 881.77, 773.05 881.10, 775.61 894.10, 745.15 898.72, 745.32 900.00, 735.17 900.00, 733.20 885.29, 740.63 881.77))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,31,"POLYGON ((898.58 879.39, 900.00 881.85, 900.00 896.48, 891.18 900.00, 873.69 900.00, 876.19 886.62, 886.90 880.51, 898.58 879.39))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,32,"POLYGON ((787.26 861.37, 803.03 857.84, 804.41 863.93, 815.69 861.40, 817.61 869.92, 809.63 871.72, 811.89 881.65, 792.82 885.94, 787.26 861.37))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,33,"POLYGON ((827.47 846.05, 865.66 846.40, 865.54 857.91, 860.37 857.86, 860.28 865.90, 834.85 865.66, 834.95 854.51, 827.39 854.44, 827.47 846.05))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,34,"POLYGON ((578.80 825.53, 602.50 824.77, 603.09 843.40, 579.39 844.16, 578.80 825.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,35,"POLYGON ((621.95 821.41, 649.37 820.37, 650.24 843.62, 622.84 844.65, 621.95 821.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,36,"POLYGON ((673.99 808.62, 690.54 806.44, 689.74 800.45, 697.05 799.49, 700.50 825.46, 676.64 828.60, 673.99 808.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,37,"POLYGON ((718.59 798.16, 744.69 792.63, 748.31 809.58, 722.21 815.11, 718.59 798.16))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,38,"POLYGON ((768.40 788.42, 774.32 786.70, 775.42 790.38, 793.33 785.14, 798.83 803.72, 793.40 803.86, 791.00 808.49, 775.64 812.99, 768.40 788.42))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,39,"POLYGON ((882.54 784.30, 900.00 784.03, 900.00 803.34, 882.85 803.61, 882.54 784.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,40,"POLYGON ((593.48 700.36, 607.28 719.12, 591.58 730.57, 585.71 722.58, 577.06 728.44, 572.36 715.75, 593.48 700.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,41,"POLYGON ((631.88 693.22, 637.18 713.42, 620.10 717.88, 617.22 706.96, 613.28 708.00, 610.84 698.71, 631.88 693.22))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,42,"POLYGON ((650.41 685.91, 679.19 677.34, 681.93 686.48, 693.81 682.94, 691.85 680.06, 711.91 673.20, 714.13 679.64, 716.93 683.26, 716.77 687.26, 715.00 689.10, 718.86 697.88, 725.66 694.93, 730.23 705.40, 685.53 724.42, 677.05 713.89, 671.73 714.27, 665.92 716.26, 659.72 698.33, 653.03 700.61, 649.03 689.06, 650.41 685.91))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,43,"POLYGON ((726.55 657.13, 742.02 652.62, 744.93 656.39, 757.93 652.59, 759.96 659.47, 753.65 661.32, 761.12 686.65, 737.30 693.61, 726.55 657.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,44,"POLYGON ((765.75 652.57, 767.34 644.23, 773.82 642.60, 775.55 649.48, 791.41 645.50, 794.91 659.31, 785.59 665.32, 769.02 668.65, 765.75 652.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,45,"POLYGON ((557.32 547.31, 574.17 543.97, 575.31 547.90, 587.57 545.30, 590.63 558.74, 555.33 567.34, 549.69 559.16, 550.42 554.99, 557.89 552.91, 557.32 547.31))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,46,"POLYGON ((621.31 532.07, 607.03 533.23, 612.94 518.63, 601.96 519.56, 601.05 508.95, 611.43 508.06, 612.74 503.58, 625.62 502.26, 626.04 506.22, 632.73 505.54, 632.41 502.33, 638.82 501.65, 638.94 502.87, 654.86 501.24, 655.25 505.12, 662.15 504.41, 663.08 513.46, 638.11 516.03, 638.68 521.50, 623.73 523.04, 621.31 532.07))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,47,"POLYGON ((721.95 514.35, 720.09 491.87, 731.37 490.94, 724.86 496.41, 730.46 495.67, 733.35 498.70, 738.12 500.49, 740.57 503.51, 743.66 504.74, 745.65 499.85, 750.80 499.12, 752.06 502.04, 750.96 505.44, 744.67 507.82, 744.96 511.39, 736.32 512.10, 736.86 518.43, 727.22 519.21, 726.79 513.96, 721.95 514.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,48,"POLYGON ((814.07 507.04, 809.44 487.07, 834.07 481.43, 838.68 501.38, 814.07 507.04))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,49,"POLYGON ((677.46 512.36, 676.92 505.34, 682.12 504.96, 680.33 481.12, 686.13 480.68, 685.83 476.67, 694.32 476.04, 695.04 485.56, 700.20 485.16, 702.70 487.03, 706.54 487.44, 707.59 489.86, 711.00 491.41, 712.40 509.74, 677.46 512.36))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,50,"POLYGON ((785.16 476.09, 790.94 505.68, 771.36 509.47, 768.86 496.70, 774.34 495.63, 771.05 478.82, 785.16 476.09))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,51,"POLYGON ((877.69 472.27, 879.18 481.36, 884.23 480.52, 883.97 484.54, 890.19 484.96, 889.89 489.12, 856.75 495.67, 854.62 485.45, 859.34 484.46, 858.16 478.88, 863.69 477.72, 861.94 474.85, 877.69 472.27))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,52,"POLYGON ((717.05 453.41, 719.70 467.82, 709.90 469.60, 707.24 455.22, 717.05 453.41))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,53,"POLYGON ((838.90 452.64, 837.29 445.52, 846.92 443.36, 848.12 448.70, 852.85 447.65, 855.17 457.89, 837.10 461.95, 835.19 453.47, 838.90 452.64))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,54,"POLYGON ((623.62 462.35, 620.96 446.12, 645.01 442.22, 647.65 458.45, 623.62 462.35))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,55,"POLYGON ((687.27 443.34, 701.12 441.19, 703.40 455.98, 689.24 458.16, 688.36 452.55, 686.29 452.87, 685.38 447.03, 687.78 446.68, 687.27 443.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,56,"POLYGON ((578.60 444.88, 577.60 425.06, 582.90 425.99, 589.14 422.68, 592.72 425.03, 598.18 425.60, 602.13 428.80, 602.87 443.68, 578.60 444.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,57,"POLYGON ((786.33 358.96, 786.99 368.82, 794.80 368.29, 795.42 377.48, 802.90 376.98, 804.03 393.85, 770.42 396.13, 767.98 360.21, 786.33 358.96))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,58,"POLYGON ((570.57 392.50, 589.13 367.45, 564.16 349.13, 557.32 358.36, 548.69 352.01, 547.01 344.40, 539.82 340.76, 538.63 334.89, 542.76 334.07, 546.68 337.34, 554.94 336.27, 565.87 344.20, 567.59 341.85, 571.86 344.94, 578.52 345.94, 581.72 349.54, 585.20 358.58, 591.00 360.05, 594.85 359.95, 589.53 367.13, 602.60 376.71, 583.76 402.17, 570.57 392.50))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,59,"POLYGON ((900.00 369.54, 895.32 372.02, 881.15 345.48, 896.27 337.48, 898.86 342.34, 900.00 341.73, 900.00 369.54))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,60,"POLYGON ((780.85 329.55, 776.42 330.89, 781.85 348.77, 766.36 353.43, 761.24 336.56, 758.24 337.46, 754.26 324.33, 777.17 317.44, 780.85 329.55))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,61,"POLYGON ((878.06 332.61, 864.00 301.87, 882.71 293.40, 879.34 298.75, 881.53 302.53, 883.34 302.93, 882.19 308.20, 881.12 312.73, 887.18 314.13, 891.21 315.16, 890.25 318.92, 895.34 320.21, 897.09 324.00, 878.06 332.61))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,62,"POLYGON ((755.76 285.29, 763.15 313.98, 751.86 316.84, 751.71 310.90, 743.77 302.71, 749.72 301.34, 745.72 296.20, 743.07 295.56, 741.36 288.94, 755.76 285.29))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,63,"POLYGON ((854.84 295.85, 848.83 280.42, 857.20 277.19, 850.62 261.26, 855.32 259.41, 854.22 257.19, 870.52 251.21, 872.27 254.93, 877.72 252.31, 885.74 267.73, 881.22 269.49, 878.32 280.04, 881.98 285.39, 854.84 295.85))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,64,"POLYGON ((745.41 243.02, 745.58 249.65, 743.38 252.68, 742.98 257.96, 747.63 260.81, 751.84 261.21, 753.52 271.85, 742.73 275.97, 737.64 268.93, 733.06 268.89, 729.63 265.11, 727.10 255.39, 730.03 253.20, 732.16 247.20, 745.41 243.02))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,65,"POLYGON ((835.63 256.98, 834.20 253.25, 834.27 248.12, 831.60 245.72, 831.14 240.98, 837.22 238.07, 834.91 233.32, 831.38 234.89, 828.64 228.75, 836.51 225.37, 838.78 226.56, 843.79 223.32, 844.92 218.72, 848.06 225.08, 861.13 225.98, 864.22 233.54, 862.13 234.51, 863.36 237.65, 860.73 241.38, 861.05 245.79, 835.63 256.98))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,66,"POLYGON ((889.35 229.43, 892.40 231.80, 891.50 238.04, 889.20 236.43, 886.12 240.79, 881.06 242.32, 881.96 239.01, 877.97 237.94, 877.86 233.84, 881.90 233.73, 885.50 230.33, 889.35 229.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,67,"POLYGON ((728.08 206.66, 736.85 235.96, 725.95 239.21, 722.93 236.94, 716.89 229.92, 715.53 225.49, 718.90 220.95, 720.47 208.92, 728.08 206.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,68,"POLYGON ((848.69 206.15, 826.45 215.33, 824.14 209.77, 826.65 208.73, 825.34 205.57, 818.53 201.66, 819.49 190.62, 817.70 186.32, 840.07 177.09, 845.58 190.31, 842.61 191.54, 848.69 206.15))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,69,"POLYGON ((835.32 168.24, 812.80 177.43, 807.41 164.34, 809.59 163.44, 806.55 156.08, 802.82 156.18, 800.51 150.57, 808.58 147.28, 807.69 145.13, 823.17 138.78, 835.32 168.24))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,70,"POLYGON ((824.21 130.03, 800.38 138.19, 796.88 128.07, 798.26 124.90, 797.35 122.60, 798.85 122.00, 796.79 116.73, 796.77 109.27, 796.13 101.78, 809.43 97.34, 811.28 102.79, 814.36 101.61, 824.21 130.03))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,71,"POLYGON ((508.12 900.00, 507.53 895.15, 533.24 892.03, 534.22 900.00, 508.12 900.00))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,72,"POLYGON ((164.82 865.89, 184.25 866.88, 182.79 895.35, 163.38 894.36, 164.82 865.89))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,73,"POLYGON ((124.11 868.90, 154.50 870.30, 154.08 879.28, 151.24 879.15, 150.96 885.38, 145.06 885.11, 145.20 881.86, 135.83 881.43, 135.57 886.79, 123.29 886.24, 124.11 868.90))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,74,"POLYGON ((101.79 862.26, 110.59 861.90, 110.92 870.73, 118.93 872.81, 118.57 883.59, 106.53 883.20, 106.73 877.69, 102.23 873.44, 101.79 862.26))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,75,"POLYGON ((422.18 858.13, 431.39 858.85, 431.02 863.68, 440.84 864.42, 440.14 873.30, 446.40 873.78, 445.56 884.48, 431.15 883.36, 431.44 879.56, 420.56 878.70, 422.18 858.13))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,76,"POLYGON ((272.79 854.45, 281.98 855.83, 281.32 858.78, 287.86 860.23, 282.28 879.07, 273.38 877.39, 271.06 885.08, 264.80 883.18, 268.91 870.00, 263.10 868.21, 265.18 863.45, 270.36 863.77, 272.79 854.45))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,77,"POLYGON ((247.51 859.93, 253.29 860.67, 253.13 871.86, 250.82 873.68, 248.11 877.88, 235.98 877.17, 236.89 872.41, 229.43 869.83, 230.95 865.51, 233.79 866.19, 239.29 867.80, 245.47 866.31, 247.51 859.93))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,78,"POLYGON ((198.51 856.97, 206.34 856.88, 206.43 866.11, 217.38 866.01, 220.84 868.56, 220.89 874.38, 198.68 874.59, 198.51 856.97))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,79,"POLYGON ((462.09 822.28, 469.56 820.62, 480.32 823.57, 481.75 829.92, 488.92 828.34, 491.62 840.46, 467.37 845.83, 462.09 822.28))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,80,"POLYGON ((500.21 820.66, 527.33 818.90, 528.68 839.62, 501.55 841.38, 500.21 820.66))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,81,"POLYGON ((433.62 774.86, 444.30 775.32, 443.86 784.88, 458.13 785.49, 457.46 800.38, 432.54 799.28, 433.62 774.86))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,82,"POLYGON ((61.33 762.56, 85.83 762.60, 85.79 779.27, 61.27 779.21, 61.33 762.56))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,83,"POLYGON ((166.44 754.43, 174.57 753.60, 179.29 758.48, 184.41 762.10, 190.11 756.11, 192.46 774.99, 167.43 776.45, 166.44 754.43))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,84,"POLYGON ((114.17 753.48, 135.69 753.55, 139.20 759.70, 138.88 763.88, 135.71 770.40, 114.61 770.74, 114.17 753.48))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,85,"POLYGON ((514.46 749.57, 524.66 742.52, 530.08 750.28, 534.16 747.47, 541.12 757.41, 544.75 754.90, 550.73 763.47, 527.26 779.70, 517.06 765.11, 521.31 759.36, 514.46 749.57))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,86,"POLYGON ((469.98 752.62, 482.81 743.39, 492.03 756.07, 496.20 753.08, 503.63 763.32, 486.64 775.55, 469.98 752.62))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,87,"POLYGON ((221.93 750.53, 248.46 750.05, 249.29 765.83, 221.92 766.53, 221.93 750.53))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,88,"POLYGON ((561.08 725.34, 570.74 743.80, 556.24 751.32, 546.57 732.88, 561.08 725.34))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,89,"POLYGON ((15.17 722.20, 45.13 708.85, 51.67 723.36, 57.88 720.60, 62.81 731.53, 26.39 747.78, 24.66 743.96, 20.85 745.66, 17.76 738.81, 14.65 740.22, 10.25 730.45, 17.45 727.23, 15.17 722.20))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,90,"POLYGON ((0.08 690.18, 25.80 677.82, 34.92 696.66, 37.74 695.30, 42.04 704.20, 21.41 714.10, 17.38 705.74, 21.87 703.59, 16.37 692.21, 3.96 698.16, 0.08 690.18))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,91,"POLYGON ((211.85 659.88, 238.15 676.66, 244.55 669.01, 323.25 669.98, 323.09 679.77, 311.41 679.60, 308.49 686.29, 302.68 686.09, 302.83 681.80, 266.08 680.52, 265.59 694.34, 251.38 693.83, 251.58 688.52, 248.24 688.41, 236.55 706.80, 199.54 683.53, 211.85 659.88))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,92,"POLYGON ((0.00 642.30, 13.18 636.53, 18.71 648.99, 16.02 650.17, 23.33 666.70, 3.43 675.44, 0.00 667.68, 0.00 642.30))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,93,"POLYGON ((265.04 595.52, 327.24 597.88, 327.46 606.54, 323.72 605.90, 321.71 625.24, 260.51 622.59, 259.53 613.22, 264.73 612.34, 265.04 595.52))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,94,"POLYGON ((554.48 891.95, 583.68 890.96, 584.09 898.43, 591.82 897.63, 592.03 900.00, 555.99 900.00, 554.48 891.95))",1 +Atlanta_nadir8_catid_10300100023BC100_743501_3743139,95,"POLYGON ((620.01 888.19, 656.14 886.55, 656.75 900.00, 623.56 900.00, 620.01 888.19))",1 diff --git a/docker/solaris/solaris/data/split_multi_grouped_result.json b/docker/solaris/solaris/data/split_multi_grouped_result.json new file mode 100644 index 00000000..c33a7d7a --- /dev/null +++ b/docker/solaris/solaris/data/split_multi_grouped_result.json @@ -0,0 +1,16 @@ +{ +"type": "FeatureCollection", +"features": [ +{ "type": "Feature", "properties": { "field_1": 1, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8086", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "137.5869833444336", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742959.515726114157587, 3739469.858595584053546 ], [ 742964.241294768522494, 3739469.934550551231951 ], [ 742964.283561722608283, 3739467.183061860036105 ], [ 742968.740487219416536, 3739467.252177056856453 ], [ 742968.782755396794528, 3739464.500688320025802 ], [ 742970.181843147147447, 3739464.525222400203347 ], [ 742970.371432902873494, 3739451.621851874049753 ], [ 742963.607010386884212, 3739451.527265816926956 ], [ 742963.460884846746922, 3739461.268512606155127 ], [ 742959.643428543815389, 3739461.20458688493818 ], [ 742959.515726114157587, 3739469.858595584053546 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 2, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8229", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "232.75674536334253", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743020.528540185885504, 3739472.034202538896352 ], [ 743027.752335839904845, 3739473.017358276061714 ], [ 743027.956096813315526, 3739471.568572332151234 ], [ 743030.43167938478291, 3739471.909114650916308 ], [ 743031.089060312719084, 3739467.208761358167976 ], [ 743031.633055638638325, 3739464.048288816120476 ], [ 743033.543277067365125, 3739464.385528093203902 ], [ 743035.432349810143933, 3739453.545475867111236 ], [ 743022.484083668328822, 3739451.306581199169159 ], [ 743020.941473806626163, 3739460.190927300136536 ], [ 743022.074339224491268, 3739460.841333461925387 ], [ 743020.528540185885504, 3739472.034202538896352 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 3, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8228", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "115.83593626400437", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743003.139999651815742, 3739467.617796136066318 ], [ 743015.354240971268155, 3739467.740245839115232 ], [ 743015.397793993353844, 3739462.391609387006611 ], [ 743017.594118545646779, 3739462.414260812103748 ], [ 743017.637329374207184, 3739457.442983566783369 ], [ 743011.270679264096543, 3739457.38069434184581 ], [ 743011.241497556096874, 3739460.709668206982315 ], [ 743005.254658216843382, 3739460.657057802192867 ], [ 743005.279539651703089, 3739457.860729276202619 ], [ 743003.231432163971476, 3739457.841856196988374 ], [ 743003.139999651815742, 3739467.617796136066318 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 4, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "7812", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "1117.803722432596", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742737.216587933013216, 3739529.428720968775451 ], [ 742737.272651979583316, 3739527.953976201824844 ], [ 742741.254279248416424, 3739528.12188399489969 ], [ 742741.999519934062846, 3739510.127116858027875 ], [ 742744.01812799833715, 3739510.2117784707807 ], [ 742744.133029837394133, 3739507.517637256067246 ], [ 742742.058839860954322, 3739507.43156136199832 ], [ 742742.566939047770575, 3739495.113465066067874 ], [ 742744.344979755580425, 3739495.180906137917191 ], [ 742745.081523005734198, 3739477.163720175158232 ], [ 742722.468594856210984, 3739476.244281094986945 ], [ 742720.377090848283842, 3739527.479686407838017 ], [ 742728.516634618747048, 3739527.808876014780253 ], [ 742728.465934158419259, 3739529.072875593788922 ], [ 742737.216587933013216, 3739529.428720968775451 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 5, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120819", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "868.6465770175508", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742779.058966275770217, 3739517.785098479129374 ], [ 742785.726463058381341, 3739518.043576586060226 ], [ 742785.779711607610807, 3739516.679750225041062 ], [ 742787.900217710062861, 3739516.767014551907778 ], [ 742788.018007018021308, 3739513.595687717199326 ], [ 742789.496379706077278, 3739512.689894631039351 ], [ 742794.785022282507271, 3739512.857791290152818 ], [ 742794.738495351164602, 3739514.321680113207549 ], [ 742800.81425687042065, 3739514.520712195895612 ], [ 742800.857961681904271, 3739513.167741883080453 ], [ 742804.840443138731644, 3739513.302400338929147 ], [ 742806.71917948697228, 3739511.962838293984532 ], [ 742809.519332245690748, 3739513.754459811840206 ], [ 742810.821611631661654, 3739515.396966869942844 ], [ 742810.842538548749872, 3739518.216655497904867 ], [ 742815.607673472259194, 3739518.193654652219266 ], [ 742817.155500297667459, 3739514.559269691817462 ], [ 742818.472778508905321, 3739513.427399684209377 ], [ 742820.769857842708007, 3739513.496967575978488 ], [ 742821.015552686876617, 3739505.301030760165304 ], [ 742818.829634130583145, 3739505.234292388893664 ], [ 742818.90829657507129, 3739502.872199133038521 ], [ 742817.049548521987163, 3739503.790504309814423 ], [ 742814.600757060572505, 3739498.755805384833366 ], [ 742814.987357385572977, 3739494.492515358142555 ], [ 742817.030302172759548, 3739493.257025897037238 ], [ 742819.674479807261378, 3739492.436405650805682 ], [ 742805.514425508794375, 3739491.920608292799443 ], [ 742805.30673220846802, 3739497.531435517128557 ], [ 742789.785506339860149, 3739496.958818469196558 ], [ 742787.906234814785421, 3739496.134058064781129 ], [ 742787.213316560024396, 3739494.951024409849197 ], [ 742788.194898375542834, 3739493.533130954019725 ], [ 742788.615302526159212, 3739492.311837214045227 ], [ 742788.623362512211315, 3739490.902464678976685 ], [ 742775.303983747260645, 3739490.863168130163103 ], [ 742776.432470980333164, 3739492.778722804971039 ], [ 742776.425669609801844, 3739495.231436580885202 ], [ 742779.106201368384063, 3739497.352975132875144 ], [ 742778.831371949403547, 3739499.410401630215347 ], [ 742777.248018659069203, 3739502.255856039002538 ], [ 742776.283375865314156, 3739503.008239367976785 ], [ 742773.996404947713017, 3739502.905642527155578 ], [ 742773.739076134283096, 3739508.282126087229699 ], [ 742779.37378019397147, 3739510.878410715144128 ], [ 742779.847732048365287, 3739514.109192994888872 ], [ 742779.137002839474007, 3739515.811456004157662 ], [ 742779.058966275770217, 3739517.785098479129374 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 1, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "7813", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "446.8580438238858", "origlen": "0", "partialDec": "0.4599659863913662", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742693.312217903207056, 3739539.0 ], [ 742694.591137416777201, 3739534.692852943204343 ], [ 742701.859658369095996, 3739536.831200783140957 ], [ 742703.390235378639773, 3739531.686894269194454 ], [ 742687.859493555966765, 3739527.118546354118735 ], [ 742687.464354713680223, 3739528.440377403050661 ], [ 742683.241174345719628, 3739527.200840464793146 ], [ 742681.172764874994755, 3739530.167156973853707 ], [ 742679.185603815829381, 3739536.498545913957059 ], [ 742677.585319220670499, 3739536.002776217181236 ], [ 742676.659588023088872, 3739539.0 ], [ 742693.312217903207056, 3739539.0 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 2, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "122421", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "628.0617074431335", "origlen": "0", "partialDec": "0.6854550148892067", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743051.0, 3739240.429075356107205 ], [ 743019.733907211222686, 3739241.220183459110558 ], [ 743020.095183715224266, 3739255.058807611931115 ], [ 743051.0, 3739254.2796860341914 ], [ 743051.0, 3739240.429075356107205 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 3, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "122449", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "164.34510745720252", "origlen": "0", "partialDec": "0.3268898818394317", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743046.408491920679808, 3739315.608520227950066 ], [ 743051.0, 3739315.560417518950999 ], [ 743051.0, 3739304.003256970085204 ], [ 743046.295042799436487, 3739304.051510713994503 ], [ 743046.408491920679808, 3739315.608520227950066 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 6, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "84910", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "67.0691122589702", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 741360.398388836532831, 3743875.358136298134923 ], [ 741367.488589487154968, 3743871.975018986035138 ], [ 741362.919970723101869, 3743862.502813681960106 ], [ 741357.545847294386476, 3743865.074794827029109 ], [ 741360.072563233552501, 3743870.299843532033265 ], [ 741360.398388836532831, 3743875.358136298134923 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 4, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120818", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "921.0009706238087", "origlen": "0", "partialDec": "0.13212004440894673", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742820.916451984550804, 3739539.0 ], [ 742819.206165015813895, 3739537.419993883930147 ], [ 742819.301538870553486, 3739534.037214165087789 ], [ 742810.503156909137033, 3739533.735573504120111 ], [ 742810.456345002283342, 3739535.210554277058691 ], [ 742808.844772503594868, 3739535.158436355181038 ], [ 742808.796266934135929, 3739536.699968244880438 ], [ 742807.335981759359129, 3739538.716122538782656 ], [ 742806.625273184501566, 3739539.0 ], [ 742820.916451984550804, 3739539.0 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": 5, "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120818", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "921.0009706238087", "origlen": "0", "partialDec": "0.13212004440894673", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742784.753871998284012, 3739539.0 ], [ 742786.527636001817882, 3739535.733633679803461 ], [ 742785.028177058673464, 3739534.918539529200643 ], [ 742785.053791071870364, 3739532.45520516205579 ], [ 742775.240038365940563, 3739532.349736623000354 ], [ 742775.176343481289223, 3739538.130713525228202 ], [ 742775.882974016712978, 3739539.0 ], [ 742784.753871998284012, 3739539.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/split_multi_result.csv b/docker/solaris/solaris/data/split_multi_result.csv new file mode 100644 index 00000000..4226a2ee --- /dev/null +++ b/docker/solaris/solaris/data/split_multi_result.csv @@ -0,0 +1,13 @@ +,field_1,access,addr_house,addr_hou_1,addr_inter,admin_leve,aerialway,aeroway,amenity,area,barrier,bicycle,boundary,brand,bridge,building,constructi,covered,culvert,cutting,denominati,disused,embankment,foot,generator_,harbour,highway,historic,horse,intermitte,junction,landuse,layer,leisure,lock,man_made,military,motorcar,name,natural,office,oneway,operator,osm_id,place,population,power,power_sour,public_tra,railway,ref,religion,route,service,shop,sport,surface,tags,toll,tourism,tower_type,tunnel,water,waterway,wetland,width,wood,z_order,tracktype,way_area,origarea,origlen,partialDec,truncated,geometry +0,660,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8086,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,137.5869833444336,0,1.0,0,"POLYGON ((742959.5157261142 3739469.858595584, 742964.2412947685 3739469.934550551, 742964.2835617226 3739467.18306186, 742968.7404872194 3739467.252177057, 742968.7827553968 3739464.50068832, 742970.1818431471 3739464.5252224, 742970.3714329029 3739451.621851874, 742963.6070103869 3739451.527265817, 742963.4608848467 3739461.268512606, 742959.6434285438 3739461.204586885, 742959.5157261142 3739469.858595584))" +1,661,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8229,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,232.75674536334253,0,1.0,0,"POLYGON ((743020.5285401859 3739472.034202539, 743027.7523358399 3739473.017358276, 743027.9560968133 3739471.568572332, 743030.4316793848 3739471.909114651, 743031.0890603127 3739467.208761358, 743031.6330556386 3739464.048288816, 743033.5432770674 3739464.385528093, 743035.4323498101 3739453.545475867, 743022.4840836683 3739451.306581199, 743020.9414738066 3739460.1909273, 743022.0743392245 3739460.841333462, 743020.5285401859 3739472.034202539))" +2,662,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8228,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,115.83593626400437,0,1.0,0,"POLYGON ((743003.1399996518 3739467.617796136, 743015.3542409713 3739467.740245839, 743015.3977939934 3739462.391609387, 743017.5941185456 3739462.414260812, 743017.6373293742 3739457.442983567, 743011.2706792641 3739457.380694342, 743011.2414975561 3739460.709668207, 743005.2546582168 3739460.657057802, 743005.2795396517 3739457.860729276, 743003.231432164 3739457.841856197, 743003.1399996518 3739467.617796136))" +3,663,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,7812,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,1117.803722432596,0,1.0,0,"POLYGON ((742737.216587933 3739529.428720969, 742737.2726519796 3739527.953976202, 742741.2542792484 3739528.121883995, 742741.9995199341 3739510.127116858, 742744.0181279983 3739510.211778471, 742744.1330298374 3739507.517637256, 742742.058839861 3739507.431561362, 742742.5669390478 3739495.113465066, 742744.3449797556 3739495.180906138, 742745.0815230057 3739477.163720175, 742722.4685948562 3739476.244281095, 742720.3770908483 3739527.479686408, 742728.5166346187 3739527.808876015, 742728.4659341584 3739529.072875594, 742737.216587933 3739529.428720969))" +4,664,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120819,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,868.6465770175508,0,1.0,0,"POLYGON ((742779.0589662758 3739517.785098479, 742785.7264630584 3739518.043576586, 742785.7797116076 3739516.679750225, 742787.9002177101 3739516.767014552, 742788.018007018 3739513.595687717, 742789.4963797061 3739512.689894631, 742794.7850222825 3739512.85779129, 742794.7384953512 3739514.321680113, 742800.8142568704 3739514.520712196, 742800.8579616819 3739513.167741883, 742804.8404431387 3739513.302400339, 742806.719179487 3739511.962838294, 742809.5193322457 3739513.754459812, 742810.8216116317 3739515.39696687, 742810.8425385487 3739518.216655498, 742815.6076734723 3739518.193654652, 742817.1555002977 3739514.559269692, 742818.4727785089 3739513.427399684, 742820.7698578427 3739513.496967576, 742821.0155526869 3739505.30103076, 742818.8296341306 3739505.234292389, 742818.9082965751 3739502.872199133, 742817.049548522 3739503.79050431, 742814.6007570606 3739498.755805385, 742814.9873573856 3739494.492515358, 742817.0303021728 3739493.257025897, 742819.6744798073 3739492.436405651, 742805.5144255088 3739491.920608293, 742805.3067322085 3739497.531435517, 742789.7855063399 3739496.958818469, 742787.9062348148 3739496.134058065, 742787.21331656 3739494.95102441, 742788.1948983755 3739493.533130954, 742788.6153025262 3739492.311837214, 742788.6233625122 3739490.902464679, 742775.3039837473 3739490.86316813, 742776.4324709803 3739492.778722805, 742776.4256696098 3739495.231436581, 742779.1062013684 3739497.352975133, 742778.8313719494 3739499.41040163, 742777.2480186591 3739502.255856039, 742776.2833758653 3739503.008239368, 742773.9964049477 3739502.905642527, 742773.7390761343 3739508.282126087, 742779.373780194 3739510.878410715, 742779.8477320484 3739514.109192995, 742779.1370028395 3739515.811456004, 742779.0589662758 3739517.785098479))" +5,665,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,7813,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,446.8580438238858,0,0.4599659863913662,1,"POLYGON ((742693.3122179032 3739539, 742694.5911374168 3739534.692852943, 742701.8596583691 3739536.831200783, 742703.3902353786 3739531.686894269, 742687.859493556 3739527.118546354, 742687.4643547137 3739528.440377403, 742683.2411743457 3739527.200840465, 742681.172764875 3739530.167156974, 742679.1856038158 3739536.498545914, 742677.5853192207 3739536.002776217, 742676.6595880231 3739539, 742693.3122179032 3739539))" +6,666,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120818,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,921.0009706238087,0,0.13212004440894673,1,"MULTIPOLYGON (((742820.9164519846 3739539, 742819.2061650158 3739537.419993884, 742819.3015388706 3739534.037214165, 742810.5031569091 3739533.735573504, 742810.4563450023 3739535.210554277, 742808.8447725036 3739535.158436355, 742808.7962669341 3739536.699968245, 742807.3359817594 3739538.716122539, 742806.6252731845 3739539, 742820.9164519846 3739539)), ((742784.7538719983 3739539, 742786.5276360018 3739535.73363368, 742785.0281770587 3739534.918539529, 742785.0537910719 3739532.455205162, 742775.2400383659 3739532.349736623, 742775.1763434813 3739538.130713525, 742775.8829740167 3739539, 742784.7538719983 3739539)))" +7,667,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,122421,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,628.0617074431335,0,0.6854550148892067,1,"POLYGON ((743051 3739240.429075356, 743019.7339072112 3739241.220183459, 743020.0951837152 3739255.058807612, 743051 3739254.279686034, 743051 3739240.429075356))" +8,668,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,122449,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,164.34510745720252,0,0.3268898818394317,1,"POLYGON ((743046.4084919207 3739315.608520228, 743051 3739315.560417519, 743051 3739304.00325697, 743046.2950427994 3739304.051510714, 743046.4084919207 3739315.608520228))" +9,669,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,84910,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,67.0691122589702,0,1.0,0,"POLYGON ((741360.3983888365 3743875.358136298, 741367.4885894872 3743871.975018986, 741362.9199707231 3743862.502813682, 741357.5458472944 3743865.074794827, 741360.0725632336 3743870.299843532, 741360.3983888365 3743875.358136298))" +10,666,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120818,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,921.0009706238087,0,0.13212004440894673,1,"POLYGON ((742820.9164519846 3739539, 742819.2061650158 3739537.419993884, 742819.3015388706 3739534.037214165, 742810.5031569091 3739533.735573504, 742810.4563450023 3739535.210554277, 742808.8447725036 3739535.158436355, 742808.7962669341 3739536.699968245, 742807.3359817594 3739538.716122539, 742806.6252731845 3739539, 742820.9164519846 3739539))" +11,666,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120818,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,921.0009706238087,0,0.13212004440894673,1,"POLYGON ((742784.7538719983 3739539, 742786.5276360018 3739535.73363368, 742785.0281770587 3739534.918539529, 742785.0537910719 3739532.455205162, 742775.2400383659 3739532.349736623, 742775.1763434813 3739538.130713525, 742775.8829740167 3739539, 742784.7538719983 3739539))" diff --git a/docker/solaris/solaris/data/split_multi_result.json b/docker/solaris/solaris/data/split_multi_result.json new file mode 100644 index 00000000..f55a4972 --- /dev/null +++ b/docker/solaris/solaris/data/split_multi_result.json @@ -0,0 +1,16 @@ +{ +"type": "FeatureCollection", +"features": [ +{ "type": "Feature", "properties": { "field_1": "660", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8086", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "137.5869833444336", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742959.515726114157587, 3739469.858595584053546 ], [ 742964.241294768522494, 3739469.934550551231951 ], [ 742964.283561722608283, 3739467.183061860036105 ], [ 742968.740487219416536, 3739467.252177056856453 ], [ 742968.782755396794528, 3739464.500688320025802 ], [ 742970.181843147147447, 3739464.525222400203347 ], [ 742970.371432902873494, 3739451.621851874049753 ], [ 742963.607010386884212, 3739451.527265816926956 ], [ 742963.460884846746922, 3739461.268512606155127 ], [ 742959.643428543815389, 3739461.20458688493818 ], [ 742959.515726114157587, 3739469.858595584053546 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "661", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8229", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "232.75674536334253", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743020.528540185885504, 3739472.034202538896352 ], [ 743027.752335839904845, 3739473.017358276061714 ], [ 743027.956096813315526, 3739471.568572332151234 ], [ 743030.43167938478291, 3739471.909114650916308 ], [ 743031.089060312719084, 3739467.208761358167976 ], [ 743031.633055638638325, 3739464.048288816120476 ], [ 743033.543277067365125, 3739464.385528093203902 ], [ 743035.432349810143933, 3739453.545475867111236 ], [ 743022.484083668328822, 3739451.306581199169159 ], [ 743020.941473806626163, 3739460.190927300136536 ], [ 743022.074339224491268, 3739460.841333461925387 ], [ 743020.528540185885504, 3739472.034202538896352 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "662", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "8228", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "115.83593626400437", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743003.139999651815742, 3739467.617796136066318 ], [ 743015.354240971268155, 3739467.740245839115232 ], [ 743015.397793993353844, 3739462.391609387006611 ], [ 743017.594118545646779, 3739462.414260812103748 ], [ 743017.637329374207184, 3739457.442983566783369 ], [ 743011.270679264096543, 3739457.38069434184581 ], [ 743011.241497556096874, 3739460.709668206982315 ], [ 743005.254658216843382, 3739460.657057802192867 ], [ 743005.279539651703089, 3739457.860729276202619 ], [ 743003.231432163971476, 3739457.841856196988374 ], [ 743003.139999651815742, 3739467.617796136066318 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "663", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "7812", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "1117.803722432596", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742737.216587933013216, 3739529.428720968775451 ], [ 742737.272651979583316, 3739527.953976201824844 ], [ 742741.254279248416424, 3739528.12188399489969 ], [ 742741.999519934062846, 3739510.127116858027875 ], [ 742744.01812799833715, 3739510.2117784707807 ], [ 742744.133029837394133, 3739507.517637256067246 ], [ 742742.058839860954322, 3739507.43156136199832 ], [ 742742.566939047770575, 3739495.113465066067874 ], [ 742744.344979755580425, 3739495.180906137917191 ], [ 742745.081523005734198, 3739477.163720175158232 ], [ 742722.468594856210984, 3739476.244281094986945 ], [ 742720.377090848283842, 3739527.479686407838017 ], [ 742728.516634618747048, 3739527.808876014780253 ], [ 742728.465934158419259, 3739529.072875593788922 ], [ 742737.216587933013216, 3739529.428720968775451 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "664", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120819", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "868.6465770175508", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742779.058966275770217, 3739517.785098479129374 ], [ 742785.726463058381341, 3739518.043576586060226 ], [ 742785.779711607610807, 3739516.679750225041062 ], [ 742787.900217710062861, 3739516.767014551907778 ], [ 742788.018007018021308, 3739513.595687717199326 ], [ 742789.496379706077278, 3739512.689894631039351 ], [ 742794.785022282507271, 3739512.857791290152818 ], [ 742794.738495351164602, 3739514.321680113207549 ], [ 742800.81425687042065, 3739514.520712195895612 ], [ 742800.857961681904271, 3739513.167741883080453 ], [ 742804.840443138731644, 3739513.302400338929147 ], [ 742806.71917948697228, 3739511.962838293984532 ], [ 742809.519332245690748, 3739513.754459811840206 ], [ 742810.821611631661654, 3739515.396966869942844 ], [ 742810.842538548749872, 3739518.216655497904867 ], [ 742815.607673472259194, 3739518.193654652219266 ], [ 742817.155500297667459, 3739514.559269691817462 ], [ 742818.472778508905321, 3739513.427399684209377 ], [ 742820.769857842708007, 3739513.496967575978488 ], [ 742821.015552686876617, 3739505.301030760165304 ], [ 742818.829634130583145, 3739505.234292388893664 ], [ 742818.90829657507129, 3739502.872199133038521 ], [ 742817.049548521987163, 3739503.790504309814423 ], [ 742814.600757060572505, 3739498.755805384833366 ], [ 742814.987357385572977, 3739494.492515358142555 ], [ 742817.030302172759548, 3739493.257025897037238 ], [ 742819.674479807261378, 3739492.436405650805682 ], [ 742805.514425508794375, 3739491.920608292799443 ], [ 742805.30673220846802, 3739497.531435517128557 ], [ 742789.785506339860149, 3739496.958818469196558 ], [ 742787.906234814785421, 3739496.134058064781129 ], [ 742787.213316560024396, 3739494.951024409849197 ], [ 742788.194898375542834, 3739493.533130954019725 ], [ 742788.615302526159212, 3739492.311837214045227 ], [ 742788.623362512211315, 3739490.902464678976685 ], [ 742775.303983747260645, 3739490.863168130163103 ], [ 742776.432470980333164, 3739492.778722804971039 ], [ 742776.425669609801844, 3739495.231436580885202 ], [ 742779.106201368384063, 3739497.352975132875144 ], [ 742778.831371949403547, 3739499.410401630215347 ], [ 742777.248018659069203, 3739502.255856039002538 ], [ 742776.283375865314156, 3739503.008239367976785 ], [ 742773.996404947713017, 3739502.905642527155578 ], [ 742773.739076134283096, 3739508.282126087229699 ], [ 742779.37378019397147, 3739510.878410715144128 ], [ 742779.847732048365287, 3739514.109192994888872 ], [ 742779.137002839474007, 3739515.811456004157662 ], [ 742779.058966275770217, 3739517.785098479129374 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "665", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "7813", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "446.8580438238858", "origlen": "0", "partialDec": "0.4599659863913662", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742693.312217903207056, 3739539.0 ], [ 742694.591137416777201, 3739534.692852943204343 ], [ 742701.859658369095996, 3739536.831200783140957 ], [ 742703.390235378639773, 3739531.686894269194454 ], [ 742687.859493555966765, 3739527.118546354118735 ], [ 742687.464354713680223, 3739528.440377403050661 ], [ 742683.241174345719628, 3739527.200840464793146 ], [ 742681.172764874994755, 3739530.167156973853707 ], [ 742679.185603815829381, 3739536.498545913957059 ], [ 742677.585319220670499, 3739536.002776217181236 ], [ 742676.659588023088872, 3739539.0 ], [ 742693.312217903207056, 3739539.0 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "667", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "122421", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "628.0617074431335", "origlen": "0", "partialDec": "0.6854550148892067", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743051.0, 3739240.429075356107205 ], [ 743019.733907211222686, 3739241.220183459110558 ], [ 743020.095183715224266, 3739255.058807611931115 ], [ 743051.0, 3739254.2796860341914 ], [ 743051.0, 3739240.429075356107205 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "668", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "122449", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "164.34510745720252", "origlen": "0", "partialDec": "0.3268898818394317", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 743046.408491920679808, 3739315.608520227950066 ], [ 743051.0, 3739315.560417518950999 ], [ 743051.0, 3739304.003256970085204 ], [ 743046.295042799436487, 3739304.051510713994503 ], [ 743046.408491920679808, 3739315.608520227950066 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "669", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "84910", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "67.0691122589702", "origlen": "0", "partialDec": "1.0", "truncated": "0" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 741360.398388836532831, 3743875.358136298134923 ], [ 741367.488589487154968, 3743871.975018986035138 ], [ 741362.919970723101869, 3743862.502813681960106 ], [ 741357.545847294386476, 3743865.074794827029109 ], [ 741360.072563233552501, 3743870.299843532033265 ], [ 741360.398388836532831, 3743875.358136298134923 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "666", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120818", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "921.0009706238087", "origlen": "0", "partialDec": "0.13212004440894673", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742820.916451984550804, 3739539.0 ], [ 742819.206165015813895, 3739537.419993883930147 ], [ 742819.301538870553486, 3739534.037214165087789 ], [ 742810.503156909137033, 3739533.735573504120111 ], [ 742810.456345002283342, 3739535.210554277058691 ], [ 742808.844772503594868, 3739535.158436355181038 ], [ 742808.796266934135929, 3739536.699968244880438 ], [ 742807.335981759359129, 3739538.716122538782656 ], [ 742806.625273184501566, 3739539.0 ], [ 742820.916451984550804, 3739539.0 ] ] ] } }, +{ "type": "Feature", "properties": { "field_1": "666", "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": "120818", "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": "-999999", "tracktype": "", "way_area": "-999999.0", "origarea": "921.0009706238087", "origlen": "0", "partialDec": "0.13212004440894673", "truncated": "1" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 742784.753871998284012, 3739539.0 ], [ 742786.527636001817882, 3739535.733633679803461 ], [ 742785.028177058673464, 3739534.918539529200643 ], [ 742785.053791071870364, 3739532.45520516205579 ], [ 742775.240038365940563, 3739532.349736623000354 ], [ 742775.176343481289223, 3739538.130713525228202 ], [ 742775.882974016712978, 3739539.0 ], [ 742784.753871998284012, 3739539.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/stitching_conf_output.npy b/docker/solaris/solaris/data/stitching_conf_output.npy new file mode 100644 index 00000000..a8642773 Binary files /dev/null and b/docker/solaris/solaris/data/stitching_conf_output.npy differ diff --git a/docker/solaris/solaris/data/stitching_first_output.npy b/docker/solaris/solaris/data/stitching_first_output.npy new file mode 100644 index 00000000..63a79e37 Binary files /dev/null and b/docker/solaris/solaris/data/stitching_first_output.npy differ diff --git a/docker/solaris/solaris/data/test.yml b/docker/solaris/solaris/data/test.yml new file mode 100644 index 00000000..c2f94330 --- /dev/null +++ b/docker/solaris/solaris/data/test.yml @@ -0,0 +1,12 @@ +training_augmentation: + augmentations: + flip: + CenterCrop: + height: 10 + width: 10 + p: 0.5 + oneof: + VerticalFlip: + RandomBrightnessContrast: + p: 0.2 + p: 1.0 diff --git a/docker/solaris/solaris/data/test_overlap_output.txt b/docker/solaris/solaris/data/test_overlap_output.txt new file mode 100644 index 00000000..1c8e5456 --- /dev/null +++ b/docker/solaris/solaris/data/test_overlap_output.txt @@ -0,0 +1 @@ +MULTIPOLYGON (((48.7117333528138445 58.6471729961977886, 49.9517333528138536 81.6571729961977724, 50.1018143746222506 83.1138266407435822, 50.3932155284892076 84.5489048688554305, 50.8231596135766210 85.9487306569355098, 51.3875490446223750 87.2999629550247391, 53.9975490446223745 92.7799629550247431, 54.6676545217119241 94.0308260522879209, 55.4529646210726170 95.2127684732046049, 56.3464509923921284 96.3152120966096987, 57.3401171318409197 97.3282902978524191, 58.4250699489987682 98.2429362528872190, 59.5915993580313881 99.0509640843276031, 60.8292651807939109 99.7451421232238431, 62.1269905840977117 100.3192576308878188, 63.4731612148980062 100.7681724015191804, 64.8557291461642933 101.0878687480017959, 66.2623207031412988 101.2754854593077312, 68.2425869555531790 101.4447770624385612, 68.6594550424128869 101.0255234486841118, 69.5842274953816258 99.8937206002700151, 70.3944604686409434 98.6772896295645410, 71.0824614959109766 97.3877795040038876, 71.6416985982703238 96.0374330148416391, 72.0668622996863775 94.6390705423595335, 72.3539160359307232 93.2059683373663717, 72.3667310433182536 93.0785148983617461, 73.1667882919999926 91.9207764176341868, 73.9005912108528520 90.5877396091686649, 74.4955637232842207 89.1872177186249928, 74.9455830230790383 87.7336233851697784, 75.2460180039120416 86.2419154114923572, 75.6560180039120524 83.4919154114923572, 75.8071112237841618 81.9016889623621296, 75.7882406562251703 80.3044121478166062, 75.5996203059946197 78.7181991469609272, 75.2096203059946049 76.4681991469609272, 74.8556921369347066 74.9588263902428196, 75.0613714238450882 73.6380184393605077, 75.2227067790573471 72.0500677556931635, 75.2143968428717642 70.4539639321397999, 75.0365357065341243 68.8677792370989721, 74.6911372454240023 67.3094736272573613, 73.8285169748152725 64.2089234093146644, 73.3954570073355512 61.7342950237162640, 73.0521919534343311 60.2319888186031775, 72.5566028214641250 58.7728293881692991, 71.9139202900267946 57.3722173808894880, 71.1309275299953185 56.0449355084564331, 70.2158886117681362 54.8049925221539525, 69.1784612824196756 53.6654753579690436, 68.0295950333381114 52.6384110109720567, 67.0170332500160271 51.9052437097400130, 66.9286232728000812 51.7247087611253065, 66.1476017805792367 50.4631502145313959, 65.2457645181234085 49.2849252403260323, 64.2319355603765985 48.2015622438059097, 63.1160347710170484 47.2236614481718249, 61.9089807410204074 46.3607911760776332, 60.6225839551221455 45.6213942278653235, 59.2694312314978120 45.0127052725333954, 57.8627625653671345 44.5406800597309456, 57.1063770916048981 44.3677227127731157, 55.9118047267642453 45.0142572031319403, 54.6925947834766433 45.8380543506613805, 53.5599644563274566 46.7773430007104096, 52.5248127430378560 47.8230846306375241, 51.5971006307841336 48.9652163493402028, 50.7857552442909039 50.1927477297558511, 50.0985839425053072 51.4938665667923843, 49.5421991904767935 52.8560525430082535, 49.1219549293802444 54.2661977082676401, 48.8418950569766821 55.7107326140273784, 48.7047145142702149 57.1757568884992295, 48.7117333528138445 58.6471729961977886)), ((574.7123890649758096 52.6962996781979811, 570.3824701979272049 53.1219607355812400, 568.9272286676848580 53.3375084455760344, 567.5001001541886581 53.6945699705160138, 566.1148116593959685 54.1897108725034613, 564.7846877421667386 54.8181685810576056, 563.5225223544420032 55.5738982024539396, 562.3404557811251152 56.4496306632391196, 561.2498578673363454 57.4369426286612708, 560.2612186562325860 58.5263375234971406, 559.3840474893045212 59.7073368759678544, 558.6267815396668084 60.9685811061411087, 557.9967046581248269 62.2979387893748324, 557.4998773126073957 63.6826233438392251, 557.1410782948488531 65.1093160197444689, 556.9237587550240960 66.5642940072881117, 556.8500090064584356 68.0335624311006768, 556.9205384197048261 69.5029889615858139, 557.1387103528184070 71.7447055743281794, 544.6053343563098679 72.2838595292902113, 543.1143564804135622 72.4228108129097876, 541.6446621666740384 72.7097273683633176, 540.2108982088986977 73.1417498203450691, 538.8273533233615353 73.7145726859289141, 537.5078157492033597 74.4224872824847097, 536.2654358365273310 75.2584386197112707, 535.1125949916173568 76.2140957088070365, 534.0607822853573907 77.2799345880856094, 533.1204799545558899 78.4453332376133972, 532.3010589372559025 79.6986774369611908, 531.6106854831119790 81.0274765111048936, 531.0562397695449590 82.4184878109651606, 530.6432473347354062 83.8578486880289518, 530.3758240107853226 85.3312146478092473, 530.3494781914779423 85.6611615781516775, 530.8168903893390507 86.9625085062966576, 531.4427789236118542 88.2839396800052896, 532.1943505610483953 89.5381553225421101, 533.0644639788343966 90.7132380619148648, 534.0448514876479749 91.7980224353022152, 535.1261975900237076 92.7822009816340056, 536.2982274948354871 93.6564221815657163, 537.5498047468453251 94.4123793142355368, 538.8690370436587500 95.0428893865004483, 540.2433892346268749 95.5419613846773359, 541.6598024279987840 95.9048532002711056, 543.1048180745845002 96.1281166887899303, 544.5647058489032588 96.2096304335055095, 560.2347058489033316 96.3196304335055089, 561.7288309100426886 96.2555665678711279, 563.2091509062780688 96.0430422493791411, 564.6609513655459978 95.6841699828631249, 566.0698013017394032 95.1825169807862039, 567.4216966595995473 94.5430697049292093, 567.7005979702865943 94.3752971958108020, 568.4952097375822859 94.6717418695337898, 569.9022798692082006 95.0460102951760035, 571.3390074451144756 95.2820973180665476, 572.7918557891485989 95.3777785543861114, 574.2471363372008000 95.3321525069186322, 576.8046979234466107 95.1273826647097991, 576.2654059257794188 85.7213863743621687, 576.1112033561483941 84.2709025408840091, 575.8168461003269840 82.8422543722968072, 575.3851177075200667 81.4489516849719450, 575.3290825464159752 81.3155839066024697, 575.3375584005553947 81.0493673672080064, 574.8575584005553765 54.4493673672080121, 574.7537457595581145 52.9377718300833422, 574.7123890649758096 52.6962996781979811)), ((531.7352707642097585 108.9413095505496756, 532.1468678439413225 114.8010357179104659, 532.3148169872220024 116.2203359322982266, 532.6170814057122698 117.6172096683977344, 533.0509170489656299 118.9789756717672162, 533.6123854220156772 120.2932714068870865, 534.2963893402599069 121.5481652881043289, 535.0967192032867388 122.7322649982796747, 536.0061093675608390 123.8348209117876593, 537.0163041061958893 124.8458236829629158, 538.1181325570119043 125.7560951140548156, 539.3015919784764947 126.5573714777625867, 540.5559385577040530 127.2423785379231589, 541.8697849461333362 127.8048975872906539, 543.2312036374202080 128.2398219028957556, 544.6278352490523957 128.5432031064659100, 546.0470007246692603 128.7122870090319395, 547.4758164384812744 128.7455386143124940, 548.9013111568335717 128.6426560538878903, 564.5313111568335671 126.7626560538878948, 566.0173073873323801 126.5075973536962977, 567.4701920383413380 126.1046507958717768, 568.8752861982034119 125.5578874654248551, 570.2183937963442304 124.8728314695992765, 571.4859450302570849 124.0564041262269797, 572.6651334651226080 123.1168540356627119, 573.7440454209041718 122.0636737428013134, 574.7117803396851059 120.9075038311654140, 575.5585609171415626 119.6600254180285248, 576.2758318854616846 118.3338421367264175, 576.5104548125254951 117.7714526263985277, 576.3615914720240880 117.6740026254421991, 576.1731875800199987 114.9897536264590343, 575.9945861027611045 113.5070148500152101, 575.6694258272970046 112.0493854176458939, 575.2009300497594495 110.6313147306634903, 574.5937429433193984 109.2668600466881941, 573.8538835207504007 107.9695471308174319, 572.9886859682825389 106.7522361754519409, 572.0067269422131631 105.6269943179075455, 570.9177405489788271 104.6049760195392082, 569.7325218514880589 103.6963124921719839, 568.4628198582491905 102.9100112679490024, 567.1212210560881886 102.2538669081551319, 565.7210246409886167 101.7343837361547543, 564.2761106838853493 101.3567113603878198, 562.8008025382758888 101.1245936265814436, 561.3097248535956396 101.0403315052106876, 559.8176586018637408 101.1047602821034133, 542.5476586018637590 102.7147602821034127, 541.1149891027241665 102.9184246113909325, 539.7086113026656449 103.2591921806984487, 538.3416140226727293 103.7338915479626564, 537.0267195786717593 104.3381047999889120, 535.7761653779866720 105.0662086689100505, 534.6015900287236491 105.9114268665920946, 533.5139250220360054 106.8658931499233091, 532.5232929953563143 107.9207245300526807, 531.7352707642097585 108.9413095505496756)), ((114.1020082587105406 47.1539545665867266, 112.9153258646083913 47.2413948168529600, 111.4850395649719701 47.4874171483852621, 110.0852232521227734 47.8705100572488362, 108.7289807365090581 48.3870873751616699, 107.4290079302181908 49.0323133738973738, 106.1974739993651298 49.8001480329860726, 105.0459074473987329 50.6834035809066492, 103.9850881957108157 51.6738117804832342, 103.0249466717912838 52.7621013286209219, 102.1744708495745897 53.9380846458346923, 101.4416221121813919 55.1907532431266645, 100.8332607246720869 56.5083807734735331, 100.3550816144687587 57.8786328032495945, 100.0115610606109584 59.2886822760048844, 99.8059147908920323 60.7253295877313732, 99.7400678791327806 62.1751261495814447, 99.7900678791327920 78.7951261495814492, 99.8645667687493273 80.2438014792729319, 100.0786489928932639 81.6785067091778103, 100.4303124448085640 83.0858244103376506, 100.9162683518017047 84.4525932832160606, 101.5319720319944281 85.7660312427740763, 102.2716653963965854 87.0138549571144608, 103.1284307988178597 88.1843947217635673, 104.0942557300114544 89.2667035952815127, 105.1601077510272404 90.2506597755595834, 106.3160189649940577 91.1270612593765463, 107.5511792373474549 91.8877118999538993, 108.8540372927026425 92.5254980576955859, 110.2124087429105828 93.0344551272709879, 111.6135900360134343 93.4098233188769029, 113.0444772604436707 93.6480921720095409, 115.0542074487652258 93.8835605597457175, 115.6322933551789873 93.7046644130460038, 116.9892106554794964 93.1336882938350641, 118.2836227744683839 92.4324497076329834, 119.5030617143436302 91.6077031038328187, 120.6357816317227645 90.6673925820706756, 121.6708719756050243 89.6205753733060817, 122.5983625796554009 88.4773345990322326, 123.4093196965432782 87.2486821489337956, 124.0959320493170992 85.9464526124916262, 124.6515860709531864 84.5831892862029662, 125.0709296073586927 83.1720233544150176, 125.3499234702306921 81.7265474075228724, 125.4858803432052241 80.2606845158256448, 125.4774906665458332 78.7885541201440844, 124.1474906665458349 54.5385541201440844, 123.9948991664892048 53.0747506255492283, 123.6995929851086942 51.6329465140569184, 123.2644149429250433 50.2270215842406458, 122.6935543632457524 48.8705102369803654, 121.9925067428559657 47.5764711840057259, 121.1680208485845895 46.3573617357504872, 120.2280337490305726 45.2249178787095758, 119.1815944068785740 44.1900412967568386, 118.0387765673570044 43.2626944240339526, 116.8105817814340668 42.4518045397035451, 116.4536395447297252 42.2635299882312978, 115.9113447739093488 43.0327366224928340, 115.2008386742893151 44.2781233878346399, 114.6124855388769106 45.5856583590442739, 114.1516611034973181 46.9433946930365806, 114.1020082587105406 47.1539545665867266)), ((361.4901739393890239 92.2768410883343222, 365.9088142375641155 92.1652158961923220, 367.3384798213897966 92.0605809401656927, 368.7516288229998054 91.8199524820668529, 370.1353551588470054 91.4455281464778409, 371.4770214582956669 90.9407274961989742, 372.7643744788829281 90.3101608018808832, 373.9856570133932223 89.5595869370957445, 375.1297152667157206 88.6958607833855410, 376.1861007218268469 87.7268706256295587, 377.1451655645711298 86.6614661094901493, 377.9981507957393774 85.5093774188906508, 378.7372662257298543 84.2811264116660368, 379.3557616212130483 82.9879305249711905, 379.8479883540268816 81.6416003280648823, 380.2094509892734209 80.2544316581047354, 380.4368483414695561 78.8390933240614089, 380.5281036237919352 77.4085114043355276, 380.4823834150682274 75.9757511947705666, 379.2523834150682660 60.5757511947705609, 379.0610936100775348 59.1035200292280507, 378.7252069928291576 57.6574091847069710, 378.2480138435298045 56.2515844827793856, 377.6341886537884420 54.8998171097721013, 376.8897443362025115 53.6153487168943101, 376.0219733230314318 52.4107617075885983, 375.0393761309434240 51.2978559827453466, 373.9515780916018457 50.2875333511604836, 372.7692350637812524 49.3896907375292784, 371.5039290506404086 48.6131232340901249, 370.1680547446637775 47.9654379456054798, 369.9577547879033546 47.8880923526941658, 369.6513749343885138 47.8780504712077288, 368.1849800628037315 47.9017223394917693, 366.7279058370066878 48.0684825783863090, 365.2940810724266498 48.3767370531881227, 363.8972123326348651 48.8235390233614552, 362.5506529022554787 49.4046173117309593, 361.2672751370258197 50.1144171345489298, 360.0593474112686181 50.9461532021240444, 358.9384168390884042 51.8918745823994101, 357.9151988904128530 52.9425407074202994, 356.9994749570878412 54.0881077961121903, 356.1999988482369304 55.3176248672156561, 355.5244131087375195 56.6193384245481681, 354.9791759607621771 57.9808048138600611, 354.5694995667834064 59.3890091772160318, 354.2993002042128410 60.8304898677647117, 354.1711608279773031 62.2914671355605449, 354.1863063789131729 63.7579748542737406, 355.2063063789131547 80.9579748542737292, 355.3585045477831272 82.3772218272013106, 355.6449147341627963 83.7755763809213079, 356.0629434285730781 85.1403760964184642, 356.6088052859632853 86.4592624013810962, 357.2775574028728442 87.7202924798885277, 358.0631440765480420 88.9120474173297168, 358.9584516407098818 90.0237356012761722, 359.9553728814226474 91.0452904419906730, 361.0448804497628998 91.9674615276904888, 361.4901739393890239 92.2768410883343222)), ((386.1247147109966704 61.2260573113229825, 386.6047147109966886 80.3660573113229759, 386.7126098535523511 81.8245611539148570, 386.9620261691272844 83.2656254557999063, 387.3505926787113935 84.6755513007548899, 387.8746156262002955 86.0409357781631456, 388.5291135916481835 87.3487993925614603, 389.3078648451951267 88.5867094481557444, 390.2034664915178723 89.7428982353987266, 391.2074048425690762 90.8063748961351394, 391.4847054829788249 91.0479479782156602, 392.1325915202401120 90.4497352369545951, 393.1082914383305820 89.3536873265163223, 393.9722269229645235 88.1675478614751000, 394.7161298759793340 86.9026685199433331, 395.3328809432699700 85.5711545421219313, 395.8165776489492487 84.1857488798507774, 396.1625908836512053 82.7597102430849247, 396.3676092063701617 81.3066862104249424, 396.4296705358541431 79.8405826180684528, 396.3481809282572499 78.3754304771657928, 396.1239202613445514 76.9252516932174757, 395.9300853078462410 76.1702125772384875, 396.6806814719000727 75.0782713948661495, 397.4027735253707192 73.7632482474766533, 397.9898956974490147 72.3826721478321247, 398.4361749324744437 70.9503531693078742, 398.7371470440953658 69.4806189756925505, 398.8898013710764872 67.9881714997961382, 399.0898013710764758 64.1381714997961438, 399.0931030599368796 62.6482256710796648, 398.9485748299009060 61.1653025225714089, 398.6576426603827485 59.7040332296625280, 398.2231770167895206 58.2788353212315329, 397.6494645292403902 56.9037704299303471, 396.9421656987787514 55.5924055538998161, 396.1082590483663921 54.3576791987680252, 395.1559722696907784 53.2117737206335804, 394.0947010451210986 52.1659951295544815, 392.9349163457525265 51.2306615394544167, 391.6880611201757461 50.4150013650490934, 390.8244108660358052 49.9654492255191940, 390.2214554046388457 50.5436075320739207, 389.2669999818991187 51.6572642499144621, 388.4258706413489790 52.8588123644958898, 387.7061093886100025 54.1367639200112620, 387.1145978336340931 55.4789004702618556, 386.6569913958925895 56.8723898987564098, 386.3376652331256764 58.3039091060886463, 386.1596724106258307 59.7597713915837616, 386.1247147109966704 61.2260573113229825)), ((190.3249806574076786 92.5123967127015590, 190.3958236804160720 92.4785254645242816, 191.6905214154864154 91.6899031542474319, 192.8990070130808192 90.7746272756897667, 194.0089369079917958 89.7420465273482222, 195.0089741905690346 88.6027077687175080, 195.8889044027900468 87.3682482938527443, 196.6397398695229981 86.0512769671494908, 197.2538114993346881 84.6652454354291280, 197.7248471171824065 83.2243107317796245, 198.0480355288942462 81.7431906745263888, 198.2200756630779210 80.2370135382994789, 198.2392102885186489 78.7211635326683421, 197.4692102885186387 58.4311635326683287, 197.3448448920266287 56.9909240094040186, 197.0824176810481845 55.5693443967353176, 196.6843660289417812 54.1796280521266098, 196.1543869627365382 52.8346823931494782, 195.4974028258575913 51.5469990158759117, 194.7195155603490946 50.3285376753414866, 193.8279500332125167 49.1906152056455568, 192.8309869332342714 48.1438004113772422, 191.7378858615443846 47.1978159065952454, 190.5587993302256109 46.3614478130623553, 189.3046784677378866 45.6424641564398286, 187.9871713069479426 45.0475427183621377, 186.6185146004447404 44.5822090144881855, 185.2114201679399343 44.2507849745767530, 183.7789568313362452 44.0563488012358633, 176.6289943978912902 43.4348479094398172, 175.9798812820166347 44.1501500680246366, 175.0847228387506789 45.3613224750228383, 174.3155305195262770 46.6561546703556331, 173.6800586281371750 48.0215933226862717, 173.1847134195277533 49.4438733100065591, 172.8344885177037327 50.9086564871923528, 172.6329145745151550 52.4011762302616262, 172.5820236768059885 53.9063863001704036, 172.9720236768059749 77.6463863001704055, 173.0729832483034158 79.1546771496977613, 173.3252391811030577 80.6451472319913734, 173.7262295256961409 82.1026591068617080, 174.2718817633316633 83.5124100626720747, 174.9566541671346727 84.8600824549227895, 175.7735920847245552 86.1319891184200799, 176.7143985707194815 87.3152123762014583, 177.7695186517719037 88.3977352334219688, 178.9282363683268784 89.3685634237762088, 180.1787836075325231 90.2178370689342870, 181.5084596219786022 90.9369308169613930, 182.9037600204135572 91.5185414427000978, 184.3505139203898864 91.9567620204325920, 185.8340278698953512 92.2471419155141206, 187.3392350762800049 92.3867319856933591, 190.3249806574076786 92.5123967127015590)), ((156.6880975083111593 96.6785640095595369, 157.0316866829456899 96.4334753922443753, 158.1092884416048037 95.4982783419136751, 159.0931584802891052 94.4649251495386011, 159.7146157096396166 93.6735662269257574, 159.9009034333658974 93.5962531364455543, 161.1954091555604123 92.9036099739651036, 162.4160012712354160 92.0877334499265459, 163.5509865100095794 91.1564396600352751, 164.5894917152358516 90.1186503972062667, 165.5215680088236638 88.9843076808031412, 166.3382861014740399 87.7642785121096978, 167.0318218352556414 86.4702507684761770, 167.5955311390245583 85.1146212334742387, 168.0240136786144944 83.7103768357315090, 168.3131645920286701 82.2709702341775966, 168.4602138140109844 80.8101909415944704, 168.6602138140110014 76.9201909415944556, 168.6632527239196406 75.4413831704266045, 168.5206622979407314 73.9694627821382369, 168.2338284400179589 72.5187360687324940, 167.8055390243872580 71.1033033308397506, 167.2399567989054958 69.7369218302115570, 166.5425789253308437 68.4328720763722487, 165.7201835498017033 67.2038287470513609, 165.2786987885190513 66.6670974840263710, 165.4538482021090431 62.8959116726668768, 165.4498900590089647 61.4235376248622345, 165.3015989439447821 59.9586448817788238, 165.0104036589483201 58.5153478542958254, 164.5791099042443477 57.1075528760767739, 164.0118732450046366 55.7488242144375263, 163.3141590719972385 54.4522533770630233, 162.4926899419147901 53.2303329738177737, 161.5553808047639563 52.0948363490059876, 160.5112627424072116 51.0567041438385516, 159.3703959530454313 50.1259388820905656, 158.3744159024234648 49.4646466749637028, 158.1019792644476638 48.1115711135901591, 158.0403308777336804 47.9100114663370462, 157.4933895950257465 48.5343083034241971, 156.6322960825936264 49.7386777987391255, 155.8941263681386999 51.0220691619091440, 155.7226198902329486 51.4028217595524595, 155.3679141784829199 51.4423687085468870, 153.9190087013888899 51.6761547936484575, 152.4998855461502671 52.0504173824890302, 151.1241303446459767 52.5615735628038863, 149.8049135561517744 53.2047299046797590, 148.5548643831374136 53.9737293066211876, 147.3859498686551888 54.8612099390706760, 146.3093603327511119 55.8586757211012923, 145.3354022446456497 56.9565776555900172, 144.4733995562465338 58.1444052442294037, 143.7316044415529461 59.4107871072379083, 143.1171182964660886 60.7435998445106407, 142.6358237552950641 62.1300840960544960, 142.2923283747819596 63.5569666906313273, 142.0899205247729924 65.0105877132485972, 142.0305379078061776 66.4770312750478922, 142.1605379078061731 81.8270312750479150, 142.2438562348543485 83.2838734726950349, 142.4683363809936054 84.7257263765852571, 142.8318539348292404 86.1389447321555366, 143.3309686774964575 87.5101542734436748, 143.9609571398386549 88.8263782930478953, 144.7158573038738325 90.0751604497137777, 145.5885250255077779 91.2446826513352676, 146.5707016445273041 92.3238768977611670, 147.6530921420325910 93.3025300249608307, 148.8254531056510643 94.1713803592825514, 150.0766896700568509 94.9222053670982149, 151.3949605153803191 95.5478994703448166, 152.7677899298349189 96.0425412915403740, 154.1821858760350210 96.4014496918880326, 155.6247629436611533 96.6212280721408519, 156.6880975083111593 96.6785640095595369)), ((323.5897589750455268 54.6496615062946631, 324.8197589750455450 78.5896615062946609, 324.9637112919307924 80.0300989504585232, 325.2458443377742583 81.4499521979912089, 325.6635304102774739 82.8359971629626983, 326.2128793070381789 84.1753246396142458, 326.8887745577246733 85.4554605348165666, 327.6849210773411301 86.6644820480199201, 328.5939037967552849 87.7911287166310217, 329.6072567244208926 88.8249072925734424, 330.7155417960759110 89.7561894732429550, 331.9084367780332059 90.5763015766209207, 333.1748314053592708 91.2776053253387545, 334.5029308595387079 91.8535689872951622, 335.8803656218652236 92.2988282102435278, 337.2943066794167066 92.6092359837546724, 338.7315850106218136 92.7819012632242561, 340.1788142375641542 92.8152158961923135, 344.4059755775441545 92.7084279105340556, 345.0349696259284542 92.2331385422252907, 346.1090177666816317 91.2435402400316349, 347.0817395198906183 90.1541841920868450, 347.9439139839263930 88.9753969331106873, 348.6873681885702467 87.7183527596795187, 349.3050545705657441 86.3949678036454571, 349.7911177808910566 85.0177870733387380, 350.1409501904548733 83.5998655333448397, 350.3512355680485371 82.1546443501586197, 350.4199805165116572 80.6958234768432163, 350.3465333691108299 79.2372317845253917, 348.9965333691108071 65.6472317845253883, 348.7823349867079514 64.2064406558291694, 348.4293986299587687 62.7932188219905072, 347.9410525044304450 61.4208930278602949, 347.3219017358420047 60.1024043668130901, 346.5777849435239091 58.8501862456600975, 345.7157191819774766 57.6760471370689984, 344.7438337697383872 56.5910592251406968, 343.6712936295406848 55.6054539942199355, 342.5082128626871736 54.7285257455421501, 341.2655593726234429 53.9685439515587575, 339.9550514371200620 53.3326752744435453, 338.5890472043906811 52.8269159841491174, 337.1804281552042539 52.4560354133140763, 335.7424776299474729 52.2235309822438012, 334.2887555661328065 52.1315952180811593, 332.8329706275841318 52.1810950791778509, 331.3888509311259440 52.3715637796385778, 329.9700145898301002 52.7012051911327717, 323.5896431525237062 54.5142069110231162, 323.5897589750455268 54.6496615062946631)), ((213.5486963031745233 93.4147692426985543, 215.0373049653344140 93.2536124325030755, 216.5034879853053269 92.9445144567553854, 217.9315171118834655 92.4906785609661881, 219.3071423013217327 91.8966334912574609, 220.6166364392607022 91.1683071156772229, 221.8469323211139965 90.3129672710791027, 222.9857530473338727 89.3391492386960522, 224.0217345323657980 88.2565705721163880, 224.9445389047979234 87.0760341275783674, 225.7449576671056377 85.8093202642263719, 226.4150035855787735 84.4690692900428672, 226.9479903934787046 83.0686553265067431, 227.3385995120819132 81.6220528506598413, 227.5829331238126940 80.1436972463342414, 227.6785530678581893 78.6483407560741199, 227.6245051701330624 77.1509052711820829, 226.7884873567560078 67.4589313184272044, 227.0510097409890307 67.1163989217496919, 227.8308870848773324 65.8592724605629769, 228.4831375244648655 64.5314377664146406, 229.0014166179547033 63.1458106909800350, 229.3806830631972957 61.7158692325842111, 229.6172477344499043 60.2555224356755943, 229.7088095665129401 58.7789750975128911, 229.6544779371973561 57.3005895980635671, 229.0444779371973425 50.2305895980635668, 228.8543925422878260 48.8309138030259575, 221.0346619246377884 48.8701891953798011, 219.5456932241060031 48.9517915180510954, 218.0721846489944937 49.1808312729005905, 216.6286989613715264 49.5550448480902617, 215.2295022055430138 50.0707338713471302, 213.8884227159930447 50.7228017611692366, 212.6187144512331599 51.5048040966063354, 211.4329260042393912 52.4090123078081263, 210.3427765840536665 53.4264900578829867, 209.3590401942299479 54.5471815611813824, 208.4914391527952944 55.7600109651497036, 207.7485480060740315 57.0529918135584495, 207.1377087859993367 58.4133455092740803, 206.6649584484289335 59.8276276058006928, 206.3349692095955277 61.2818606794456358, 206.1510023703503407 62.7616724689265695, 206.1148760845565846 64.2524379171791367, 206.5448760845565630 81.1124379171791645, 206.6525414966062613 82.5640063548771934, 206.9003900478824391 83.9983054401112668, 207.2860879493127015 85.4018295400487375, 207.8060033961398290 86.7613628049685417, 208.4552407656857440 88.0641036112143922, 209.2276867154651541 89.2977851037165493, 210.1160677475806438 90.4507907030309610, 211.1120186973613784 91.5122634892595386, 212.2061615013437574 92.4722084328716818, 213.3881935028998100 93.3215865098023016, 213.5486963031745233 93.4147692426985543)), ((275.6495641544389628 95.2747629686452768, 275.7968070413986652 95.1748917010122142, 276.9066694903151529 94.2548814180435954, 277.5578766388165377 93.5999046611970584, 278.5443709688212834 93.3034955466673495, 279.9214606547943731 92.7354451266856614, 281.2354160834921686 92.0336058186882440, 282.4732783999663184 91.2048994965764450, 283.6228392158232623 90.2574992572366455, 284.6727610141718401 89.2007488135616455, 285.6126889656206913 88.0450703422989989, 286.4333530525430547 86.8018616955744449, 287.1266594944106600 85.4833839898396661, 287.6857705725165033 84.1026406808954761, 288.1051720668170333 82.6732493176106402, 288.3807276397995452 81.2093072391618023, 288.5097196310148320 79.7252525403527983, 288.4908758599405587 78.2357216762393648, 287.2408758599405587 58.2057216762393566, 287.0819379776889377 56.7721965146796137, 286.7860551287433282 55.3605630711329724, 286.3559629218373743 53.9838727164060046, 285.7956378091434999 52.6548537521605127, 285.1102603216639864 51.3857937303744023, 284.3061671722135770 50.1884258477892473, 283.3907926688306134 49.0738204656939061, 282.3725999802825299 48.0522827580067329, 282.1851254062476073 47.8972860577377233, 279.4396312513386533 48.0403129333048966, 277.9521141473658759 48.1924346651440558, 276.4871332585104824 48.4918994396968301, 275.0592462506338620 48.9357314446790213, 273.6826421838844681 49.5195202757800956, 272.3710005145470063 50.2374647633046649, 271.1373551608774051 51.0824306189928450, 269.9939649837160118 52.0460213301768704, 268.9521919689253764 53.1186615967916183, 268.0223883221676715 54.2896924821168056, 267.2137935979716872 55.5474773317281247, 266.5344428853283603 56.8795174081298782, 265.9910869621847382 58.2725760919957452, 265.5891252122686410 59.7128104158173088, 265.3325519708569686 61.1859086228968110, 265.2239168326548224 62.6772323847463895, 264.8639168326548088 78.4272323847463753, 264.9032961317782338 79.9088632043929579, 265.0887047514389678 81.3793747679930419, 265.4183324602526000 82.8244097808935464, 265.8889609481707339 84.2298596879645629, 266.4959952483530401 85.5820024220457327, 267.8059952483530424 88.1520024220457401, 268.5566239140985090 89.4580504873774203, 269.4344629592194451 90.6822251291898880, 270.4306590650483599 91.8121801112696261, 271.5351652399081672 92.8365194361478672, 272.7368421465929487 93.7449122777840955, 274.0235704465413846 94.5281971716172649, 275.3823730276723154 95.1784744111908765, 275.6495641544389628 95.2747629686452768)), ((233.4214195103589020 59.2848035325203071, 233.1114195103588997 67.2248035325203119, 233.1049152673695630 68.1939711723988893, 233.4849152673695585 83.0339711723988927, 233.6002067311850112 84.5451918840392977, 233.8674096524066215 86.0370641171109867, 234.2837960909042181 87.4943569822396512, 234.8451150554460867 88.9021926192958176, 235.5456359031103943 90.2461980891162767, 236.3782068448239784 91.5126521103637458, 237.3343279597299897 92.6886251434518726, 238.4042379729641539 93.7621113913847495, 239.5770139109025081 94.7221513698817148, 240.8406826164773520 95.5589437954392338, 242.1823429860729107 96.2639456490370549, 242.6425730326801329 96.4492265356118281, 242.7575444298066998 96.4047419503810090, 244.0807937320789733 95.7357899793582732, 244.6351496737599405 95.3828409123533163, 245.2532019397962415 95.2904155746999777, 246.6604141875746734 94.9383008980972534, 248.0270342827823242 94.4519054096813591, 249.3402814056601358 93.8357779471950408, 250.5878738878531351 93.0956806197088582, 251.7581440722680952 92.2385349195819089, 252.8401474306129728 91.2723569917098700, 253.8237649181363338 90.2061826654315979, 254.6997976083303570 89.0499829502058446, 255.4600527225613860 87.8145707853546753, 256.0974202500663637 86.5114999159623466, 256.6059394417550266 85.1529568406566568, 256.9808545559579329 83.7516468417916968, 257.2186593347792041 82.3206751638936112, 257.3171297951041083 80.8734244516049330, 257.2753450275963019 79.4234295933447356, 255.9753450275963189 62.6134295933447405, 255.7854062923654794 61.1275055304931527, 255.4482041511815567 59.6679364983184470, 254.9671017039025287 58.2492795704727513, 254.3468972461930093 56.8856837820003420, 253.5937764135188672 55.5907490133364348, 252.7152504884494419 54.3773903513228589, 251.7200814865624352 53.2577092800415670, 250.6181947681096460 52.2428729861498908, 249.4205800470157044 51.3430029824707219, 248.1391817845000389 50.5670741606550393, 246.7867800604861941 49.9228252797193761, 245.3768631109327316 49.4166817832045027, 243.9234928023396947 49.0536917147417171, 242.4411643851266547 48.8374753711755929, 240.9446619246377850 48.7701891953798068, 238.3139375224390051 48.7834022762547406, 237.3286970266588298 49.7603647734935350, 236.3740690936463977 50.9181081766208621, 235.5398905668232885 52.1654375089784850, 234.8345094758511493 53.4898701401347125, 234.2649849118967325 54.8781518294993162, 233.8370163838781650 56.3163893679463214, 233.5548867807110582 57.7901896139054685, 233.4214195103589020 59.2848035325203071)), ((560.0449880260492819 132.2217134299953614, 543.9449880260492591 133.8017134299953455, 542.4884767804386456 134.0172605515639930, 541.0601145482979746 134.3745656089036515, 539.6736636668363190 134.8701859510011047, 538.3424826554303309 135.4993462525766574, 537.0793975056619729 136.2559845245674808, 535.8965781022874353 137.1328105216771007, 534.8054209658255331 138.1213759842311788, 533.8164394465470650 139.2121560375564968, 532.9391624278516701 140.3946409645958795, 532.1820425150274332 141.6574374675256536, 531.5523745940032541 142.9883784427156002, 531.0562255447788402 144.3746402113438592, 530.8239665868896964 145.3016165290540584, 531.1046347651207498 146.6202590753448760, 531.5355729285598727 147.9875333840577412, 532.0950667046950002 149.3074248380950451, 532.7780056854912800 150.5678775680629542, 533.5781519162053428 151.7573786170110566, 534.4881968727734147 152.8650630996141047, 535.4998282177640476 153.8808134418769384, 536.6038057251475948 154.7953517949085835, 537.7900456803868110 155.6003247786594557, 539.0477129849389257 156.2883797815735250, 540.3653201238890915 156.8532321192356562, 541.7308320927440946 157.2897224385878587, 543.1317763249898007 157.5938638433851793, 544.5553566163320056 157.7628783104483432, 545.9885700050411970 157.7952220640870564, 569.3585700050412015 157.2052220640870530, 570.7837400054381760 157.1011558313242631, 572.1925405497300972 156.8619481031138037, 573.5721863703058716 156.4897697579609996, 574.9101567870051213 155.9879984207263135, 576.1943093358698889 155.3611878097144086, 577.4129899654749352 154.6150264103789596, 578.5551388007761489 153.7562858506911709, 579.6103905146375155 152.7927594466805488, 580.5691683961372291 151.7331914758642881, 581.4227712619573367 150.5871978204295942, 582.1634524221085485 149.3651787003541642, 582.7844899833565933 148.0782242884374682, 583.2437635596330665 146.8366441111887468, 582.7195056499674592 145.9626485713129966, 581.8429515629452453 144.7814142826150885, 580.8549164843215067 143.6917133164414224, 579.7649016295289357 142.7040245351937813, 578.5833888794080622 141.8278458242147622, 577.3217399835754122 141.0716027575257669, 575.9920873027327843 140.4425675752037534, 574.6736758187225860 139.9705812090373627, 573.9837008727798775 138.8189264475453513, 573.1071805097659535 137.6365671693177433, 572.1190044818705474 136.5458015907071001, 571.0286867784052447 135.5571314156239282, 569.8467247912152516 134.6800753909441823, 568.5844982475916822 133.9230776616469996, 567.2541596482083150 133.2934264720352076, 565.8685172649296646 132.7971839957689895, 564.4409118249664061 132.4391279702978750, 562.9850880686416303 132.2227056976238657, 561.5150624173885490 132.1500008542690239, 560.0449880260492819 132.2217134299953614)), ((687.6788828935711990 211.0423457609930438, 688.7182416848989988 211.9337580149407643, 689.9308763417825503 212.7817158544377776, 691.2211572804403659 213.5060710214490882, 692.5765285459050347 214.0997746860766426, 693.9838007775783808 214.5570494115982285, 695.4292795572616797 214.8734453757392373, 696.8988986719932655 215.0458836726904792, 698.3783569948858485 215.0726862744920425, 812.3383569948858849 211.5126862744920402, 813.7861635401866351 211.3971071545263669, 815.2160049679574740 211.1420467361585338, 816.6144757056779326 210.7498963551472286, 817.9684642987991765 210.2243326432290189, 818.3496876406632055 210.0320619353502138, 818.1634367450085392 209.9211975049076955, 816.8368872492126229 209.2980341002158298, 815.4558543242098949 208.8073058110354907, 814.0335226372832267 208.4536976011368665, 812.5834711333790210 208.2405853542600482, 811.1195433974518210 208.1700036446849822, 696.3595433974518301 208.2500036446849947, 694.8854721758158348 208.3236415311964720, 693.4257732190021670 208.5417870336993360, 691.9945784143532137 208.9023282046230179, 690.6057436899354798 209.4017745110833459, 689.2727148701576425 210.0352906280267007, 688.0083975017541889 210.7967432508066850, 687.6788828935711990 211.0423457609930438)), ((578.1697612295963609 213.0093395089720048, 577.4618881425017207 204.9909174639298612, 577.2608073022705639 203.5335634209789362, 576.9179320611627872 202.1029164065660098, 576.4365605960277890 200.7127380642777723, 575.8213233030916172 199.3764007624733097, 575.0781382573659357 198.1067589631365422, 574.2141542856510341 196.9160255725603577, 573.2376822007213377 195.8156544632674070, 572.1581148581660727 194.8162302972089606, 570.9858368048695638 193.9273667100525245, 569.7321243882405497 193.1576138359404524, 568.4090372870567762 192.5143760622560194, 567.0293025073073068 192.0038408055274317, 565.6061919588984210 191.6309189935871586, 564.1533947908336586 191.3991978264991758, 562.6848857128978807 191.3109062706548116, 561.2147905704816822 191.3668936179555828, 546.6747905704817185 192.6368936179555931, 545.2154834743140555 192.8369525409700600, 543.7828336990031630 193.1791738069728694, 542.3906558137082357 193.6602574891930715, 541.0523741305044041 194.2755646563901735, 539.7808932581702948 195.0191621045376849, 538.5884736673025373 195.8838795687522918, 537.4866134666425523 196.8613788637926234, 536.4859375306068614 197.9422342864288282, 535.5960950471287561 199.1160235043893465, 534.8256664737215260 200.3714280554726770, 534.7233116380411957 200.5821403263066713, 535.1841509508275294 205.1996409504696715, 535.4049904789781067 206.6701849581831993, 535.7703488653816066 208.1116366657685148, 536.2766354168950329 209.5098296794731993, 536.9188744172994348 210.8510227461774491, 537.6907540279706836 212.1220348005837479, 538.5846883197812076 213.3103745069560944, 539.5918918265932689 214.4043630222895445, 540.7024658876391641 215.3932487744106083, 541.9054959302300176 216.2673131269841349, 543.1891587367099419 217.0179658929658331, 544.5408386414509323 217.6378297578066565, 545.9472515159200157 218.1208127827090095, 547.3945753233097093 218.4621682753831635, 548.8685859596597538 218.6585414399012848, 550.3547970464395576 218.7080023471812638, 551.8386023007303720 218.6100649020703770, 570.1986023007303857 216.4800649020703815, 571.6847967419896577 216.2314532353515517, 573.1385491451109147 215.8349868074188294, 574.5451890053344641 215.2946665489710085, 575.8905212529078881 214.6159450879152928, 577.1609695022658570 213.8056717243138110, 578.1697612295963609 213.0093395089720048)), ((539.2562009625168002 270.4292777996558357, 539.6337902941536413 271.7335463660202208, 540.1800813137331261 273.1012032679624326, 540.8578562560213641 274.4086972094816019, 541.6605816137320062 275.6434244180551332, 542.5805194024004550 276.7934825663602396, 543.6088017517788558 277.8477855064396635, 544.7355163889235428 278.7961701361903692, 545.9498021889498887 279.6294943680218807, 547.2399538723802834 280.3397252553008911, 548.5935348398382985 280.9200164270748701, 549.9974970564052228 281.3647740846319039, 551.4383068299958950 281.6697109237134100, 552.9020752712943931 281.8318874625890658, 554.3746921776651106 281.8497403776062242, 577.3446921776650242 280.9997403776062015, 578.8092672381313832 280.8734639240323077, 580.2544490240470623 280.6044370552542659, 581.6663578435019417 280.1952435362286451, 583.0314335619784742 279.6498133085247559, 584.3365658354413199 278.9733847467144869, 585.5692200235884002 278.1724543484209562, 585.6392662885835989 278.1164740665602721, 584.9072455415445120 271.1765371141111132, 584.6773983086047792 269.7036364425117085, 584.3024874848670152 268.2608239068258058, 583.7862159664585988 266.8623497890216072, 583.1336828321077519 265.5220264521127547, 582.3513329808764638 264.2530919190628538, 581.4468934775790103 263.0680791242951955, 580.4292972345833732 261.9786921291785688, 579.3085947837735148 260.9956905240746323, 578.0958550100748425 260.1287831586789707, 576.8030558269732637 259.3865322502523441, 575.4429658737979025 258.7762688168407976, 574.0290184032097613 258.3040202707281310, 572.5751786044802429 257.9744508872618098, 571.0958056729698455 257.7908157370253548, 569.6055109881117460 257.7549285363577951, 553.6155109881117369 258.1649285363578201, 552.1521899082760001 258.2742484951238566, 550.7065524485117294 258.5260257200719707, 549.2924334199361738 258.9178506926003251, 547.9233660009583673 259.4459736312893483, 546.6124522241939303 260.1053403774674280, 545.3722375894637935 260.8896407637209904, 544.2145910028345952 261.7913690024614652, 543.1505911906898518 262.8018955166273827, 542.1904206758540568 263.9115495250985646, 541.3432683304218926 265.1097115924771401, 540.6172414378669373 266.3849152575296557, 540.0192881059994079 267.7249567677018831, 539.5551307732821442 269.1170118695426936, 539.2562009625168002 270.4292777996558357)), ((537.8698425080148127 240.9290764589316609, 538.4830412429242870 241.7729248114835343, 539.4615893640972217 242.8738060555631364, 540.5433901669941861 243.8734074570570840, 541.7180127073987705 244.7620906551601649, 542.9741310349496644 245.5312867850308010, 544.2996334002666572 246.1735791005353633, 545.6817390389111324 246.6827744880287412, 547.1071214061258843 247.0539631816205599, 548.5620366741012504 247.2835661041403910, 550.0324562527681564 247.3693693773304290, 551.5042020563287224 247.3105456685118497, 554.4878600129446795 247.0441027507348508, 555.0702490203400430 247.4027052303590324, 556.3908228422217235 248.0470092956373946, 557.7680996297409592 248.5590167156363179, 559.1888633840759439 248.9338143969813189, 560.6394808164976666 249.1678058761583259, 562.1060321699525275 249.2587458302654966, 563.5744447895208395 249.2057616225625338, 571.3144447895208486 248.5457616225625372, 572.7752194974409576 248.3485109180831785, 574.2095808521388562 248.0088374103352464, 575.6036775728873636 247.5300212442390091, 576.9440472047917865 246.9166862317171933, 578.2177461224615627 246.1747552006615081, 579.4124745234705642 245.3113927996158736, 580.5166952045740345 244.3349363104982217, 581.5197449736911040 243.2548151374852239, 582.4119376217710169 242.0814597495363216, 583.1846574601726161 240.8262009558776811, 583.8304425202849188 239.5011604871174029, 584.3430566119542391 238.1191339386246852, 584.7175495448640277 236.6934672065595464, 584.9503049313257179 235.2379276097811669, 584.9510820359341778 235.2250471799023614, 584.7649904659589311 234.5829060414367291, 584.2186424533970239 233.2160083257263921, 583.5409448872188705 231.9092445655765857, 582.7384244633996104 230.6751997992096790, 581.8188100083086738 229.5257587306758751, 580.7909580447696953 228.4719912719385491, 579.6647674976248936 227.5240459319709601, 578.4510843602463410 226.6910520796000696, 577.1615972401235695 225.9810320213750003, 575.8087247894940219 225.4008237412097913, 574.4054961051390364 224.9560150458721921, 572.9654252491759507 224.6508897505422624, 571.5023810992992139 224.4883864227113008, 570.0304537819026791 224.4700700817457175, 552.3004537819026609 225.1200700817457232, 550.8342004288314229 225.2460653900497789, 549.3873504789696653 225.5151358932496919, 547.9738308193761895 225.9246916099108375, 546.6072475110362348 226.4707902955656493, 545.3007548215319957 227.1481753894223061, 544.0669286065225378 227.9503266122367506, 542.9176452588212669 228.8695227283097893, 541.8639673902654295 229.8969158674841538, 540.9160373467650516 231.0226166917427975, 540.0829795815205898 232.2357895866234685, 539.3728128261295751 233.5247569611697145, 538.7923729049955455 234.8771116524623324, 538.3472469359993511 236.2798363527625725, 538.0417195507974384 237.7194289097031970, 537.8787316524096696 239.1820322934290459, 537.8598521070845209 240.6535679796683667, 537.8698425080148127 240.9290764589316609)), ((13.2698707731183543 344.7503083731906486, 12.8355419189063120 345.0891641637730345, 11.7719632319011183 346.1009402580421579, 10.8123751233586916 347.2118323096520385, 9.9659678478917773 348.4112009820888147, 9.2408476943323912 349.6875595724889649, 8.6439593494442786 351.0286840231357814, 8.1810193866364482 352.4217299948185769, 7.8564615166757843 353.8533558808072144, 7.6733941247485511 355.3098505833032732, 7.6335705005507393 356.7772648286144204, 7.8035705005507410 364.5672648286144408, 7.9080611546023736 366.0372638718166058, 8.1562964458461380 367.4899148016619961, 8.5458802798413664 368.9111958857860714, 9.0730521934640223 370.2873881896763919, 9.7327236528268681 371.6052079988693890, 10.5185271704202759 372.8519350401808765, 11.4228777673698776 374.0155352643112678, 12.4370461875457199 375.0847770046663072, 13.5512431568228404 376.0493393911676208, 14.7547138741795525 376.8999119725881428, 16.0358418225569395 377.6282845858060000, 17.3822608974438424 378.2274266045128570, 18.7809747708646029 378.6915548024277882, 20.2184823386072097 379.0161891759653940, 21.6809080398107739 379.1981961875301295, 21.7687419222092160 379.2004392614471726, 22.2540521805774283 380.1629214160967081, 23.0430063061281771 381.4067395929556028, 23.9501468465392051 382.5671827450535147, 24.9667268210317417 383.6330614552119300, 25.3018849114187709 383.9216246711973213, 25.6662660335422359 383.0863766943567157, 26.1196526852900206 381.6865875597736135, 26.4337148912004380 380.2491126876111025, 26.6054307180995480 378.7877835829696096, 26.6331479017414949 377.3166612783970209, 26.0831479017414942 359.1266612783970231, 25.9662498131237669 357.6571917678730301, 25.7056798258050598 356.2062923615768000, 25.3039544735417223 354.7879755609885137, 24.7649535408789916 353.4159391911823604, 24.0938825929539178 352.1034341099224889, 23.2972227011697441 350.8631362334813275, 22.3826678502813330 349.7070241151300820, 21.3590506314016224 348.6462632586242307, 20.2362569385715787 347.6910982839611393, 19.0251304927373255 346.8507539868525100, 17.7373681152231839 346.1333462474593716, 16.3854067621287882 345.5458036488154789, 14.9823034106499673 345.0938005619333353, 13.5416089573576368 344.7817023438435626, 13.2698707731183543 344.7503083731906486)), ((302.4959772748363775 381.6994573910949953, 303.1906408447526360 381.5531334310028910, 304.5607708460765934 381.1242622907373629, 305.8836239047199115 380.5663676226156440, 307.1470815891355528 379.8845602046655472, 308.3395695786827559 379.0850859585098078, 309.4501636940072444 378.1752687315420758, 310.4686899715873665 377.1634432044083383, 311.3858178656809628 376.0588785384172184, 312.1931457238657686 374.8716934623320185, 312.8832777531461034 373.6127635764201500, 313.4498917715526431 372.2936217229313911, 313.8877971245749450 370.9263523356945029, 314.1929822358627575 369.5234807366774135, 314.3626513565942560 368.0978583936458222, 314.3952501768543470 366.6625451900563348, 314.0652501768543630 353.5525451900563212, 313.9587403288804239 352.1064289377242744, 313.7130970842731017 350.6773537264938909, 313.3306159481070949 349.2786740835979344, 312.8148711582593933 347.9234604935246580, 312.1706822846077785 346.6243772559922149, 311.4040691907595715 345.3935641396683991, 310.5221957791869727 344.2425229375667186, 309.5333030454640948 343.1820099842444165, 308.4466320672045185 342.2219356392155305, 307.2723376473592225 341.3712716758935812, 306.0213934188664098 340.6379674415031786, 304.7054892974385325 340.0288755714346962, 303.3369222407774828 339.5496879522328300, 301.9284813350589047 339.2048825316359739, 300.4933282825384708 338.9976814727207852, 299.0448744071075566 338.9300210431953815, 296.8286539824844681 338.9337333052131953, 296.4648607591245764 339.2975782626048158, 295.5308361464663562 340.4359275562205767, 294.7129248324008586 341.6603711742163796, 294.0190087232401766 342.9591096100741083, 293.4557748297478952 344.3196274058226436, 293.0286508269102796 345.7288137588387826, 292.7417527494563956 347.1730888657627361, 292.5978453271665103 348.6385347859988997, 292.5983153422021701 350.1110295637190575, 293.5183153422021860 368.7110295637190802, 293.6630710192688980 370.1757965501699346, 293.9506896081671243 371.6193242007397544, 294.3784016658969449 373.0277129703314358, 294.9420888071730928 374.3874016616866811, 295.6363233598357283 375.6852980044385504, 296.4544206272192355 376.9089047189261237, 297.3885032542501676 378.0464398509191142, 298.4295770775027563 379.0869502185645388, 299.5676177288605118 380.0204168791885877, 300.7916671588883446 380.8378516004281096, 302.0899391505041081 381.5313834067827088, 302.4959772748363775 381.6994573910949953)), ((332.3264094928038617 381.8129877271492774, 332.9589835283225057 381.7817857194951330, 334.4232240501932552 381.6373112046842948, 335.8662659023718788 381.3500771465033949, 337.2742246287706394 380.9228472130066052, 338.6335533311617496 380.3597320647697302, 339.9311730128074487 379.6661498035173850, 341.1545984201018200 378.8487738409978647, 342.2920581714190007 377.9154686896932844, 343.3326080173286527 376.8752142931654134, 344.2662361424278288 375.7380196241039130, 345.0839594956113388 374.5148263814081702, 345.7779102219226388 373.2174037128956456, 346.3414113643684118 371.8582349765790127, 346.7690411073195378 370.4503976300571821, 347.0566849433701577 369.0074374036821609, 347.2015752617232351 367.5432379681660109, 347.2017756338008212 367.1462924480952097, 347.4903124539426926 366.4513018288806734, 347.9202372199242745 365.0356270851433464, 348.2086062663224766 363.5844849220389392, 348.3526141208105287 362.1119931487831991, 348.3508597659919701 360.6324772797573814, 347.6808597659919542 347.3824772797573814, 347.5346943715986754 345.9192247071669613, 347.5173776530621126 345.8327489839217606, 345.1686839295533105 345.8721054733427422, 343.7322550462661752 345.9652518648873070, 342.3113684501377634 346.1956547032856975, 340.9191089543967337 346.5611922301119989, 339.5682977479522151 347.0584982440946078, 338.2713743262513049 347.6829931001526575, 337.0402819371162195 348.4289258829239770, 335.8863575964796269 349.2894273664123830, 334.8202276868539116 350.2565732720548226, 333.8517100999574723 351.3214572426692826, 332.9897238246579150 352.4742728602745387, 332.2422068128289538 353.7044039524860750, 331.6160428794854056 355.0005223558655985, 331.1169983103679328 356.3506922359302393, 330.7496687607508079 357.7424800031481595, 330.5174369344776437 359.1630688127145277, 330.4224414329545993 360.5993765936896693, 330.4655570609679671 362.0381765205803504, 331.7755570609679694 378.8081765205803322, 331.9644961186975820 380.2819491159450536, 332.2983145387524928 381.7297988639560913, 332.3264094928038617 381.8129877271492774)), ((537.8644779579598207 326.7731167333995472, 537.8749404315653919 327.8346334031448919, 538.0259367593582738 329.2606656319624676, 538.3124163157787052 330.6657625227093718, 538.7317608459927669 332.0370823153226638, 539.2801377873662432 333.3620919525718591, 539.9525352968360039 334.6286816248384071, 540.7428080562104924 335.8252754466610099, 541.6437334367678886 336.9409372535276361, 542.6470775098390504 337.9654705519940308, 543.7436703000779517 338.8895117096430454, 544.9234895936490375 339.7046155331839259, 546.1757525353730216 340.4033324525569810, 547.4890141776885457 340.9792766056273194, 548.8512720807487995 341.4271842012129810, 550.2500760076696906 341.7429616270437123, 551.6726417123803685 341.9237228629721130, 553.1059677801217731 341.9678158575020461, 568.9259677801217094 341.6978158575020075, 570.4296008846691848 341.5964359739728025, 571.9154722619886115 341.3446887913899559, 573.3685832596412411 340.9451154910101991, 574.7742659143423225 340.4017494374622856, 576.1183310130152222 339.7200754652403702, 577.3872113212563590 338.9069745137998666, 578.5680985334341813 337.9706541701192464, 579.6490725620265039 336.9205658198436595, 580.6192218611230373 335.7673092433013835, 581.4687535695294400 334.5225256194183316, 582.1890923616660984 333.1987800175677990, 582.7729670084440841 331.8094345635048512, 583.2144837743564949 330.3685135596695659, 583.5091859099036355 328.8905619213608134, 583.6540986388218926 327.3904983577499479, 583.6477591860095799 325.8834647797519324, 583.3742503746053671 320.8664746844316369, 583.2423214587021221 320.7393076306356647, 582.0534898900674534 319.8048400204530708, 580.7766160785262173 318.9948131798714712, 579.4246761844595994 318.3174589624237569, 578.0114092232138319 317.7796609433555091, 576.5511774431058711 317.3868844656669808, 575.0588203698597454 317.1431210988637872, 573.5495040007369880 317.0508480748493980, 572.0385666809170289 317.1110031131915434, 549.5985666809170880 319.1410031131915161, 548.1242220986648590 319.3485717171644751, 546.6777180432284240 319.7012793725643292, 545.2733059654827912 320.1956510833789480, 543.9248226117950935 320.8268161310478490, 542.6455536996844558 321.5885560624411710, 541.4481030224058031 322.4733659561790091, 540.3442682720830135 323.4725283636742574, 539.3449248048269737 324.5761991964067192, 538.4599184930202682 325.7735047132377417, 537.8644779579598207 326.7731167333995472)), ((437.0654813268853331 338.9737498345790527, 435.8487881984240175 339.3513053908030770, 434.5337075076936344 339.9010465734994568, 433.2767437517142071 340.5731289399089405, 432.0892467690267722 341.3614838772263624, 430.9819391432837961 342.2589928816690872, 429.9648193829605702 343.2575518354843211, 429.0470716391527617 344.3481441836236741, 428.2369827766677304 345.5209223493319541, 427.5418675472232053 346.7652966535019914, 426.9680025404057915 348.0700309348922588, 426.5205695087845470 349.4233440077983914, 426.2036085789285380 350.8130160410599387, 426.0199817708129331 352.2264988978451470, 424.9699817708129217 365.0564988978451311, 424.9215485633739036 366.4955330674604852, 425.0113120951615429 367.9325813009114086, 425.2384452789628995 369.3544025392250205, 425.6008552943624181 370.7478960259846872, 426.0952028711290609 372.1002220185798137, 426.7169330574772630 373.3989200944569120, 427.4603171896982303 374.6320239622901909, 428.3185056764525598 375.7881717201939296, 429.2835911113647853 376.8567105450495092, 430.3466811323971797 377.8277948483335535, 431.4979803566709506 378.6924769940322335, 432.7268806357822086 379.4427897427602261, 433.9083708112092950 380.0166047519128938, 434.5439522500835210 379.7777322970678142, 435.8537786280841715 379.1339292912674068, 437.0948371205199123 378.3658789874871218, 438.2553783373340366 377.4808526969965783, 439.3244151642878705 376.4872291700003188, 440.2918267805325172 375.3944152720566763, 441.1484544747792711 374.2127569270883214, 441.8861883529542638 372.9534411701088175, 442.4980441164562990 371.6283902369544307, 442.9782291841397068 370.2501486936980086, 443.3221975320307138 368.8317646743178670, 443.5266927315956309 367.3866663509713817, 443.5897787791095084 365.9285348063592096, 443.5097787791095243 351.1985348063591914, 443.4300516519032840 349.7333718273165459, 443.2075522978457229 348.2830087667645671, 442.8444098468482366 346.8613243678020694, 442.3440992638509215 345.4819229430853511, 441.7114080963580705 344.1580041931062510, 440.9523906616736326 342.9022368962654355, 440.0743101122249072 341.7266376794191842, 439.0855689333599798 340.6424560289627266, 437.9956285386972468 339.6600666427964370, 437.0654813268853331 338.9737498345790527)), ((466.6924701176708368 339.3539698824567381, 465.6051820621555635 339.6837898934638815, 464.2611311602710202 340.2386200057348447, 462.9771160125140455 340.9209794276453067, 461.7652024195202216 341.7244560753807718, 460.6367786486524096 342.6414997350173621, 459.6024484192478781 343.6634930113550013, 458.6719312602087371 344.7808323049775936, 457.8539711762677484 345.9830180566061131, 457.1562544811868065 347.2587534107233296, 456.5853375700034462 348.5960503713361618, 456.1465853090448945 349.9823424523360700, 455.8441206226539180 351.4046027638914893, 455.6807857503567334 352.8494664252211805, 454.7907857503567470 366.7194664252211851, 454.7690983464144665 368.2023673157139001, 454.8939530287815955 369.6801618558326368, 455.1641292927772042 371.1384040102387871, 455.5769860568765353 372.5628388760193275, 456.1284874803438925 373.9395420300897399, 456.8132424151973510 375.2550556460236635, 457.6245571068460549 376.4965200497134674, 458.5545006282306986 377.6517994278503920, 459.5939824078217839 378.7096004603758956, 460.7328410936084992 379.6595827172217241, 460.8237298826639972 379.7212720757792113, 462.0355173124421526 379.5707080772813242, 463.4285668688768283 379.2609647115183407, 464.7858770161354300 378.8202412341929062, 466.0951624202366474 378.2525267373509337, 467.3445724304661439 377.5629597386804903, 468.5227983424085210 376.7577816715769927, 469.6191757556960624 375.8442803924590976, 470.6237811000091256 374.8307242166628725, 471.5275214556497190 373.7262870799712573, 472.3222168557002760 372.5409655031594980, 473.0006743248318344 371.2854881111302348, 473.5567529846185835 369.9712185256011594, 473.9854196360756760 368.6100525102871757, 474.2827943163291025 367.2143102995401023, 474.4461854170741049 365.7966250850039955, 474.4741140469561742 364.3698286696209152, 474.1241140469561515 351.8798286696209061, 474.0065368911238579 350.3794141188089384, 473.7391980872263844 348.8983340481198070, 473.3247889269370035 347.4514984459416951, 472.7674812547699048 346.0534725626313843, 472.0728854702470585 344.7183302825784494, 471.2479940481757126 343.4595124428356030, 470.3011111456151525 342.2896915246176377, 469.2417690041785931 341.2206440798152585, 468.0806319892440115 340.2631321768027419, 466.8293892321098042 339.4267950590200371, 466.6924701176708368 339.3539698824567381)), ((539.8076370055199504 353.9638477909684866, 541.3018184175291481 361.0604668631154368, 541.6709671302317020 362.4701753667929438, 542.1751644212246219 363.8374112386109687, 542.8096516790808437 365.1492705134757557, 543.5684406163389895 366.3933718703294176, 544.4443697869299967 367.5579734869943422, 545.4291721758675067 368.6320838594297697, 546.5135532232934565 369.6055655394906694, 547.6872785464850040 370.4692308121104816, 548.9392705319065726 371.2149284089088610, 550.2577128856686386 371.8356204398201612, 551.6301621556489181 372.3254488166637088, 553.0436651727286517 372.6797905417498669, 554.4848813027363121 372.8953013397081122, 555.9402083552834029 372.9699472207408917, 557.3959109611677150 372.9030236774086120, 574.3759109611677331 371.2930236774085984, 575.8323211360018377 371.0824240969915309, 577.2610870354561712 370.7301304904802919, 578.6484576908515010 370.2395334611576345, 579.9810805365672195 369.6153546953311775, 581.2461299200457461 368.8636015190872968, 582.4314305405913501 367.9915090815747476, 583.5255746289429908 367.0074707212669409, 584.5180317398437637 365.9209571853850207, 585.3992501009195166 364.7424254799416872, 586.1607485424507331 363.4832182276683170, 586.7951981232702110 362.1554545024407616, 587.2964926671893409 360.7719131908533541, 587.6598075310843114 359.3459100035128699, 587.8816460390362408 357.8911693197406976, 587.9598731356271628 356.4216920990969015, 587.8937359344987499 354.9516211309939422, 587.6838709644073333 353.4951049192885080, 586.3130924160049062 346.4501116825928193, 585.9559341249763520 346.3829506229783988, 584.5312016472245205 346.2537109180559014, 583.1006368975088208 346.2607842030223537, 551.0306368975088844 347.9507842030223514, 549.5495172710980114 348.1028320918067038, 548.0907677918048648 348.4009660631768384, 546.6687609276706326 348.8422487238130998, 545.2975071366247448 349.4223322943145149, 543.9905168271153570 350.1355014461091741, 542.7606672455236776 350.9747296122725970, 541.6200756018698712 351.9317482174470797, 540.5799796838474549 352.9971281447670890, 539.8076370055199504 353.9638477909684866)), ((552.8247023797824795 400.8095278518258056, 553.9668360116720578 401.0075622514868883, 555.4372495522823101 401.1156130955916979, 556.9111677313188693 401.0787869251779512, 587.2011677313189466 398.8287869251779512, 588.6830588818976366 398.6441681875837730, 590.1392489528739134 398.3131154159221978, 591.5553049295590426 397.8389098367388215, 592.9171915859764113 397.2262515343330165, 594.2114105948760425 396.4812128659156087, 595.4251343162831063 395.6111782754949786, 596.5463329385347606 394.6247711030274559, 597.5638937116500529 393.5317681142614106, 598.4677310912501298 392.3430025984122835, 599.2488867013400977 391.0702569941145157, 599.8996181251716280 389.7261461078879847, 600.4134756441360423 388.3239920825975560, 600.5495731376064441 387.7947025278783713, 600.2370399231158444 386.6393336315533134, 599.7356271740895863 385.3043369401147515, 599.1097053528847027 384.0229889059032757, 598.3649317660058387 382.8068708132307165, 597.5080379449550492 381.6669743747403345, 596.5467688042300551 380.6136023843552039, 595.4898126400015599 379.6562755969218870, 594.3467226021681427 378.8036466762043801, 593.1278303495521413 378.0634219889973338, 591.8441526686521001 377.4422919522119741, 590.5072918999642297 376.9458705624819572, 589.1293310718574503 376.5786446548431741, 587.7227246898170279 376.3439333491048728, 586.3001861681461833 376.2438580504517063, 584.8745729215551137 376.2793232754203814, 565.1645729215550773 377.7093232754203882, 563.7226891653967868 377.8844428598278000, 562.3045025498445284 378.1981981125080097, 560.9233105953986751 378.6476471308518512, 559.5920639448928569 379.2285756898272098, 558.3232449327620088 379.9355367563443906, 557.1287505453686890 380.7619015629535397, 556.0197808698032986 381.6999217619856495, 555.0067340771055342 382.7408020773538624, 554.0991089245944750 383.8747827728003585, 553.3054156914813575 385.0912311633317699, 552.6330963828752374 386.3787413117933625, 552.0884549503796279 387.7252409757833220, 551.6765981835625325 389.1181048021292668, 551.4013878265274116 390.5442727075655966, 551.2654043685585066 391.9903723356255227, 551.2699228483568277 393.4428444415388526, 551.4149008987352545 394.8880700294747044, 551.6989791438712700 396.3124980500348329, 552.8247023797824795 400.8095278518258056)), ((149.4286873662960318 341.5443605447616164, 148.9594985070080781 341.6131018523211651, 147.5301183519125061 341.9685194038513032, 146.1424869014988133 342.4624103042934280, 144.8099836936710858 343.0900124592016027, 143.5454567199986968 343.8452745306520342, 142.3610985454238005 344.7209142842233405, 141.2683287475900045 345.7084888042768398, 140.2776838093120944 346.7984759005220781, 139.3987155258431585 347.9803659209437114, 138.6398989064961143 349.2427630858325642, 138.0085504586315608 350.5734953658557629, 137.5107576419178201 351.9597318447239331, 137.1513201730646756 353.3881064348441896, 136.9337037469705649 354.8448467530926678, 136.8600066205038388 356.3159069140878614, 136.9309393811166444 357.7871029605714170, 138.6809393811166444 375.7171029605714239, 138.8955165815059445 377.1727268541458784, 139.2516722443572519 378.6003253405175997, 139.7459795438889216 379.9861624839444403, 140.3736823996146370 381.3169041641309036, 141.1287412379613215 382.5797463732395158, 142.0038911032821147 383.7625384123152230, 142.9907115591381910 384.8538998017622816, 144.0797077072790842 385.8433297809958162, 145.2604015447810468 386.7213083436934653, 146.5214327803311960 387.4793878365163664, 147.8506681396332283 388.1102742399623367, 149.2353181082293077 388.6078973492917612, 150.6620599884734020 388.9674691802648567, 152.1171660866374964 389.1855300377278013, 153.5866357967717590 389.2599818037892305, 155.0563303104465831 389.1901081252977974, 157.2825541890175600 388.9744462528990994, 157.3373311809381221 388.8318604221212809, 157.7257720197411288 387.4254493431131436, 157.9757338405743781 385.9879523878007603, 158.0848515842232587 384.5329706949854653, 158.0520928133169605 383.0742708384915431, 155.7620928133169400 350.9242708384915659, 155.5920117950104498 349.5007456826849079, 155.2868072990277426 348.0999596042152575, 154.8492673364568475 346.7347086371194678, 154.2833887890056417 345.4174642059158487, 153.5943408979059654 344.1602592004479106, 152.7884180434369910 342.9745780566967142, 151.8729822464207473 341.8712518476616538, 150.8563959169334510 340.8603593426434486, 150.5117477370756660 340.5776561011794001, 150.4553593035234940 340.6192603136142338, 149.4286873662960318 341.5443605447616164)), ((37.6251165587131666 387.7047388389080993, 46.0989181993124930 387.4849572401605542, 47.5653281576674019 387.3748163383821179, 49.0139064401373759 387.1216165284229191, 50.4307306695298792 386.7277913296185261, 50.4508979334705288 386.7199878748415927, 50.4925826903959205 387.1832864290440170, 50.7605588730900692 388.6054278940223980, 51.1643334169344541 389.9951270336369475, 51.7001479928161700 391.3394485432375518, 52.3630152444237353 392.6258794926663995, 52.6749050103262277 393.1100065690567362, 56.8642066299717541 392.9942800049781226, 58.3053365417087974 392.8848152933450706, 59.7292420206895187 392.6371640617212506, 61.1227039211564218 392.2536254358394672, 62.4727857271386711 391.7377600825234367, 63.7669536514985111 391.0943571534391481, 64.9931929961542636 390.3293898239313080, 66.1401196932237241 389.4499598397111413, 67.1970859915706171 388.4642315862076885, 68.1542793075896327 387.3813562926652025, 69.0028133225261939 386.2113870746548514, 69.7348104806090845 384.9651856037222046, 70.3434751221055166 383.6543212706243366, 71.3934751221055137 381.0743212706243526, 71.8525004578526989 379.7795562397265030, 72.1911484542948472 378.4482267243174078, 72.4065788018362895 377.0914988557115635, 72.4969846423536808 375.7207517863752742, 72.4616077236824481 374.3474822506616420, 72.3007447592318613 372.9832081393622616, 72.0157449393893501 371.6393718968150210, 71.2880752742466228 368.8454797257114706, 70.7748484568534764 366.4521669139903679, 70.9180804416722737 362.9336420695286165, 71.0913029830501699 360.5267604419619829, 71.1243467327233674 359.0382160045992919, 71.0096557535564870 357.5537287494523184, 70.7483600608152443 356.0879248770496019, 70.3430341213305326 354.6552465062430883, 69.7976714880612974 353.2698093804125392, 69.1176454528341964 351.9452637893477345, 68.3096561049368631 350.6946600771029807, 67.3816643171798688 349.5303200609343435, 66.3428133098431090 348.4637156281893340, 65.2033385653127056 347.5053557072967010, 63.9744669809923110 346.6646827264975173, 62.6683062541044222 345.9499795804769633, 61.2977255882394374 345.3682880215275190, 59.8762288970145491 344.9253392793093553, 58.7199555803295468 344.6876148941527163, 58.4129326775532647 344.0772887293945246, 57.9507190066799467 344.2629637915357534, 57.4482388398228281 344.5248169507863167, 56.9368734007106170 344.4717172107338570, 55.4479751695699861 344.4655132843771526, 47.9779751695699730 344.8055132843771844, 46.5270146671447833 344.9424286979377143, 45.0961898516110935 345.2195073101065077, 43.6990079140585053 345.6341334591193117, 42.3486584519922857 346.1823930147732540, 41.0578889576447850 346.8591103283764596, 39.8388844798238537 347.6578970916123694, 38.7031525953077633 348.5712126430851185, 37.6614147756730233 349.5904351532422538, 36.7235051750678423 350.7059430156803046, 35.8982777943892017 351.9072056764862850, 35.1935228982468402 353.1828830441744458, 34.6158934737501340 354.5209325417731065, 34.1708424253573639 355.9087227904752808, 33.8625710986770301 357.3331528516629874, 33.6939896191638439 358.7807759016430396, 33.6666894201178337 360.2379261715880148, 34.2166894201178309 378.6479261715879829, 34.3325216808503910 380.1132796092660442, 34.5912225516810423 381.5602597989621358, 34.9903077249163701 382.9749713718437079, 35.5259447806537807 384.3438288350085941, 36.1929899895576526 385.6536870331429441, 36.9850377081563977 386.8919673816117211, 37.6251165587131666 387.7047388389080993)), ((84.9291528641639957 252.7606450904705468, 86.1901259088027984 287.0610669016142538, 86.3187234414433249 288.5453031838263200, 86.5939188588131259 290.0094623790965898, 87.0129975175291719 291.4391014050904687, 87.5718254493780677 292.8201177009470371, 88.2648901405179771 294.1388883408731090, 89.0853549092344252 295.3824044164149996, 90.0251263458452229 296.5383993618046361, 91.0749341494987874 297.5954699565256760, 92.2244225743222046 298.5431888114784442, 93.4622525828555126 299.3722072291329255, 94.7762136990881032 300.0743474230149559, 96.1533444577315066 300.6426831868325849, 97.5800602615663877 301.0716082174890857, 99.0422873856252153 301.3568914180154934, 100.5256018063366241 301.4957186348913183, 103.6632363884673396 301.6327332454647490, 104.1489228699255420 301.1635260240240655, 105.1024787344666436 300.0416054142726239, 105.9414452998097715 298.8316013879891671, 106.6577387515609843 297.5451728698179181, 107.2444572851017028 296.1947151682272192, 107.6959476075325739 294.7932405412228150, 108.0078594096542162 293.3542528174798463, 108.1771872831228762 291.8916172809730938, 108.2022996788907392 290.4194270728258402, 107.5922996788907255 271.3894270728258675, 107.4726754283444734 269.9197370425713416, 107.2093283354040238 268.4688934758874552, 106.8048032678650827 267.0509166720994472, 106.2630093739375070 265.6795093209265133, 105.5891823059961609 264.3679240858548951, 104.7898336255925358 263.1288355363616915, 103.8726878786541477 261.9742176665791931, 103.6876008539783811 261.7831943292948722, 103.5573218214303921 261.4511959121089149, 102.8868177118809797 260.1385414402608944, 102.0907176013508462 258.8980326182853560, 101.1767087652733323 257.7416480062476580, 100.1536170253994413 256.6805538468126429, 99.0313215261909932 255.7249962421430780, 97.8206593401542506 254.8842022158286511, 96.5333208232604534 254.1662906152071173, 95.1817367309189279 253.5781937144206211, 93.7789581845298272 253.1255902752204747, 92.3385306476793062 252.8128507118965160, 90.8743631288841698 252.6429948898279747, 89.4005938738859811 252.6176629651596954, 84.9291528641639957 252.7606450904705468)), ((124.3788396974043877 301.4799976929740524, 124.7210063406345455 301.3720444029683563, 126.0959185486794070 300.7819656509058746, 127.4052064529561932 300.0578512281929306, 128.6358436483454852 299.2069055150062127, 129.7755862436374912 298.2375947650692183, 130.8130946788090228 297.1595628729554619, 131.7380465449023177 295.9835354247737200, 132.5412392840350435 294.7212129868558463, 133.2146817477556340 293.3851546941439210, 133.7516737028086879 291.9886532964808907, 134.1468724932897487 290.5456029059994307, 134.3963461959521055 289.0703607614189536, 134.4976127398112169 287.5776043845907566, 134.4496646008374228 286.0821855504726159, 134.2529788260430337 284.5989825234193518, 133.8353994386432362 282.3265737640809334, 133.9319290319173206 281.9967237179122890, 134.2020688831897530 280.5814399560745755, 134.3351734035922789 279.1467668740048111, 134.3300144799147802 277.7059417453811534, 134.1866397119103738 276.2722586068645683, 132.6761426314437813 266.1212957094631975, 133.2362817409706111 264.9348634722545057, 133.7237073005094032 263.5784007197740380, 134.0786878560423929 262.1814169402862262, 134.2979456356439698 260.7568114155732246, 134.3794560861850584 259.3177384773900371, 134.3224665673807863 257.8774860447571200, 133.6724665673808090 250.4774860447571143, 133.4735392382219175 249.0281074005293931, 133.1343667417865788 247.6050007445504946, 132.6581753931901062 246.2217031134704825, 132.0494948738244716 244.8913728680147130, 131.3141151436089160 243.6266645265312718, 130.4590313650759299 242.4396083912455708, 129.4923773631923893 241.3414961122539637, 128.4233482538664930 240.3427732778081349, 127.4827294165249043 239.6219943706555853, 127.3748068606722654 239.6645916332803097, 126.0648964101300606 240.3367932296480944, 124.8271961318611574 241.1341754440046543, 123.6736304174693259 242.0490560466948580, 122.6153130798546869 243.0726207918396824, 121.6624402792640751 244.1950083365379101, 120.8241922902941354 245.4054052481280621, 120.1086450562544456 246.6921501841807469, 119.5226923830037293 248.0428462415191291, 119.0719795218680304 249.4444803918615037, 118.7608487815236913 250.8835488534059550, 118.5922976928374339 252.3461871904878819, 116.3022976928374419 287.1861871904878853, 116.2779768703469898 288.6591241968547479, 116.3982547723988716 290.1273435787625203, 116.6619713133210752 291.5766842954940330, 117.0665829354410761 292.9931673918905517, 117.6081871418118112 294.3631308259913339, 118.2815601359669557 295.6733612400308289, 119.0802072056682590 296.9112214038576099, 119.9964253646916887 298.0647721015762954, 121.0213776484693682 299.1228872858122259, 122.1451783470027834 300.0753613889286839, 123.3569883529718254 300.9130077561773646, 124.3788396974043877 301.4799976929740524)), ((217.6880913554448114 253.4248270658517583, 216.9554656340001486 253.7170318024571145, 215.6471236967341838 254.3944681145110565, 214.4115513596277935 255.1969888815167451, 213.2606687578289097 256.1168518161840097, 212.2055789856209742 257.1451825773111182, 211.2564609797609023 258.2720603846399854, 210.4224713185901123 259.4866137291902533, 209.7116558843079872 260.7771252557115531, 209.1308722406422760 262.1311448054036646, 208.6857234747747611 263.5356095283300419, 208.3805041417798236 264.9769699067448983, 208.2181588330745967 266.4413204735284921, 208.2002537685899597 267.9145339646302091, 208.8402537685899745 285.2145339646302205, 208.9619318088448949 286.6450814248190682, 209.2198192999076127 288.0574431431074345, 209.6115536810825120 289.4386801813953980, 210.1335461926035748 290.7761387412203362, 210.7810147530056781 292.0575660878240569, 211.5480277687627222 293.2712227999928700, 212.4275584748440906 294.4059903173254042, 213.4115493083637602 295.4514727996665897, 214.4909857255820214 296.3980923655514630, 215.6559787860070685 297.2371768371600069, 216.0784276019799393 297.4838100133748640, 216.4557181450418852 296.9859424487476076, 217.2107892066283625 295.7613705470822651, 217.8450740104039483 294.4700954260902677, 218.3522665713590811 293.1252450524146980, 218.7337636902393001 292.9205275840179752, 219.9946137899135579 292.0690257410141157, 221.1627216085240661 291.0941808884409170, 222.2260696613341793 290.0060222230467843, 223.1737182313155472 288.8157447132618358, 223.9959179168769765 287.5355939254441751, 224.6842099336372485 286.1787400415821594, 225.2315131383372488 284.7591423645579312, 225.6321968795901398 283.2914057049410985, 225.8821389259820762 281.7906301268039897, 225.9787678755587876 280.2722555983719417, 225.9210896103902826 278.7519031457430856, 224.6810896103903019 264.8019031457430970, 224.4740390257917966 263.3178666485309236, 224.1199601329669520 261.8618919209699811, 223.6223862210905509 260.4485078713004214, 222.9862824901564977 259.0918184030977613, 222.2179965041458445 257.8053616749720618, 221.3251948498349293 256.6019750057339479, 220.3167866333137113 255.4936667731141142, 219.2028345776290337 254.4914965843357209, 217.9944546086926493 253.6054649142959079, 217.6880913554448114 253.4248270658517583)), ((244.3701029324321041 251.6291749510871227, 243.7983864523074828 252.0252211011861618, 242.6496435272762540 253.0057505285866739, 241.6053255367752968 254.0968271530836091, 240.6760197685431990 255.2873896482673501, 239.8711475172956114 256.5653681007379987, 239.1988685717121541 257.9178063747944520, 238.6659984905615204 259.3309934616382861, 238.2779395066193331 260.7906024814752186, 238.0386257588738204 262.2818359293121375, 237.9504834082551099 263.7895756919485848, 237.7704834082551315 286.2095756919485439, 237.8337396720250183 287.7113498524851138, 238.0469776958013881 289.1992532530381368, 238.4080562599345683 290.6583451811512191, 238.9133496108985923 292.0739742334219500, 239.5577838691256432 293.4319254368842280, 240.3348879781307517 294.7185629879951421, 241.2368586833228505 295.9209671759189177, 242.2546388880212476 297.0270641151992663, 243.3780085998685081 298.0257469851162000, 244.5956875544061120 298.9069875583069802, 245.8954484853227882 299.6619368987423968, 245.9758491647461085 299.6984180146775998, 247.4119035500181383 299.6305362982193401, 248.8670471481222251 299.4175785311836080, 250.2943431199830400 299.0631492570624914, 251.6800717010504229 298.5706553979566138, 253.0109126898823604 297.9448310103773565, 254.2740734874801376 297.1916917795200561, 255.4574120650889029 296.3184771939874054, 256.5495536784454202 295.3335809568034165, 257.5400002065669582 294.2464703016371459, 258.4192310640717096 293.0675949898015347, 259.1787947170185475 291.8082868627864741, 259.8113899225785417 290.4806509158693189, 260.3109359116281780 289.0974489398482774, 260.6726308396399077 287.6719768493832134, 260.8929979440155762 286.2179368771152213, 260.9699189641798966 284.7493058620876241, 260.9026545031847490 283.2802008985362932, 259.1826545031847218 265.1902008985363182, 258.9640605380621992 263.6951376563821441, 258.5961680740923043 262.2296505173792980, 258.0827099892570118 260.8086092742249207, 257.4288961657019854 259.4464327424358316, 256.6413606268587273 258.1569424575994276, 255.7280942242963420 256.9532224330175723, 254.6983635573058677 255.8474864007350789, 253.5626169479124030 254.8509538830118402, 252.3323784253541930 253.9737363516991593, 251.0201307957313190 253.2247346306212421, 249.6391889832764264 252.6115485819848061, 248.2035649284094916 252.1403999931978035, 246.7278254134097040 251.8160694465378526, 245.2269442582983459 251.6418478122322142, 244.3701029324321041 251.6291749510871227)), ((281.8809649063688312 299.9033243636500288, 283.2089702752770108 299.3791642323471933, 284.5444849586628493 298.6904100323653779, 285.8043875085324999 297.8714916100768164, 286.9760342046662345 296.9306272006263043, 288.0476670162353798 295.8772588250329818, 289.0085315992239430 294.7219575350267746, 289.8489852213763243 293.4763173275259760, 290.4720253329310822 292.3175615882811940, 290.5811539099860852 292.2124314864917096, 291.5306741816553995 291.0999015519786894, 292.3673274711272256 289.9001837206890286, 293.0831588357856390 288.6246849716227416, 293.6713621141055910 287.2855328126534573, 294.1263446389877458 285.8954599715583527, 294.4437804131256371 284.4676833325857501, 294.6206512408110711 283.0157782696383606, 294.6552754250945441 281.5535495709181077, 294.4752754250945372 274.3835495709180918, 294.3624117659190347 272.8854813872260934, 294.1002645201821792 271.4062162622044525, 293.6914632536874592 269.9605925168733620, 293.1401086001366707 268.5631110198364127, 292.4517311281933303 267.2277897309049308, 291.6332358650489596 265.9680230887195762, 290.6928330329704977 264.7964476528335354, 289.6399556936009390 263.7248153479850430, 288.4851651261135430 262.7638755820294705, 287.2400448883584545 261.9232674199910775, 285.9700945847604316 261.2399459373246486, 285.4002873857100440 260.2327125806253889, 284.5845757941054330 259.0651514082313156, 283.6618038455918054 257.9802198790569037, 282.6402911884551941 256.9876996609906996, 281.5292477104050590 256.0965392479097318, 280.4575092522351838 255.3927942728137168, 279.1473364302164555 255.5240935589714582, 277.7305326017793163 255.8057234324322167, 276.3473884293758260 256.2223295680637420, 275.0107311826794216 256.7700483633899466, 273.7329570119298978 257.4438002780179318, 272.5259159861200260 258.2373369412612192, 271.4008021955361869 259.1432990994873080, 270.3680499378409650 260.1532848657855652, 269.4372369504675362 261.2579276390097789, 268.6169955867472368 262.4469829695734688, 267.9149327595239356 263.7094235664059170, 267.3375593946953472 265.0335415639748931, 266.8902300489307891 266.4070571009519881, 266.5770932515487743 267.8172322035633215, 266.4010530310843592 269.2509889174720570, 266.3637419833481204 270.6950305926381475, 266.4655061307436199 272.1359651963604733, 266.7054017132581976 273.5604295108976203, 269.9354017132581589 288.3504295108975839, 270.3181672263584687 289.7667942645533117, 270.8374728576061443 291.1389896641854307, 271.4883503556650908 292.4538877727617887, 272.2645727073763737 293.6989088224315765, 273.1587137122447757 294.8621415664815686, 274.1622190296763506 295.9324572354800580, 275.2654880192504834 296.8996160073791089, 276.4579655910516180 297.7543649729639128, 277.7282431873196629 298.4885266594041013, 279.0641679293159996 299.0950772649920850, 280.4529588851868880 299.5682138565893524, 281.8809649063688312 299.9033243636500288)), ((193.0405110794272900 251.4100047648095710, 190.5466042739742818 251.4521404658520680, 189.0923720090810889 251.5475167624777839, 187.6542589033001036 251.7835650905704483, 186.2458400292447038 252.1580572712884702, 184.8804101607463508 252.6674582846629846, 183.5708582768004078 253.3069596384285944, 182.3295458960135420 254.0705247577993475, 181.1681903900148427 254.9509459677329346, 180.0977543772941374 255.9399125297991304, 179.1283422415346820 257.0280890914179963, 178.2691047512555258 258.2052038069462583, 177.5281526811070307 259.4601452987924404, 176.9124802501912086 260.7810675432961034, 176.4278991001113468 262.1555016913009126, 176.0789834359650285 263.5704737678902916, 175.8690268481226724 265.0126271402595535, 175.8000112223721203 266.4683485976876796, 175.8725880319022394 267.9238968534776291, 178.0625880319022372 290.1038968534776359, 178.2810042768178960 291.5703137273784478, 178.6431165647844637 293.0080059041004006, 179.1453873257394207 294.4029281911091402, 179.7829097446799835 295.7414532261578870, 180.5494556976280762 297.0105046065688157, 181.4375365958092345 298.1976846360461195, 182.4384765436437021 299.2913954410275323, 183.5424970958492850 300.2809522733579115, 184.7388127856419260 301.1566878924004982, 186.0157364907939552 301.9100470068504478, 187.3607936082011918 302.5336698536224844, 188.7608439215574663 303.0214640973103428, 190.2022099715795207 303.3686643478111478, 191.6708106746995952 303.5718787146735167, 193.1522988848696230 303.6291219433662150, 198.1857908934864554 303.5746567457977676, 198.3742720725333868 303.3347447402551325, 199.1791449678139827 302.0672843401463865, 199.8532784075495101 300.7257105073546199, 200.3899182450818444 299.3234644775244533, 200.7836878829131422 297.8745953613778283, 201.0306421408659219 296.3936193865962423, 201.1283067828650246 294.8953744596552724, 201.4348583069845233 274.4948649674452099, 201.8343901774651670 265.7441425359421601, 201.8298130725518433 264.2820537863033223, 201.6829127994739110 262.8273563017796732, 201.3950850573744731 261.3938711596914573, 200.9690644969425648 259.9952178989441336, 200.4088987385036944 258.6446851208219755, 199.7199099155886017 257.3551042340188815, 198.9086441093554924 256.1387275434551611, 197.9828091542885034 255.0071118411578084, 196.9512014060807701 253.9710086052070892, 195.8236221674800106 253.0402618499689709, 194.6107845661344982 252.2237145981382582, 193.3242117691930275 251.5291248632011047, 193.0405110794272900 251.4100047648095710)), ((152.6787079288204438 304.5829532738974308, 156.0238919074793955 304.7988555058015550, 157.4948670929921661 304.8215012329790738, 158.9609859488661812 304.6999433216597026, 160.4081458398683253 304.4353510405395582, 161.8224264975394249 304.0302695096212346, 163.1902239196169262 303.4885952186383520, 164.4983812272849661 302.8155385466200187, 165.7343152215323130 302.0175736431202154, 166.8861374212693818 301.1023761532052845, 167.9427684189357137 300.0787493852257057, 168.8940444536080747 298.9565396315647945, 169.7308151764800073 297.7465414568974893, 170.4450316683037840 296.4603938649934207, 171.0298238621531652 295.1104683428380326, 171.4795666267749823 293.7097498589791371, 171.7899338748723039 292.2717119607781910, 171.9579401758501263 290.8101871720094778, 173.1779401758501251 272.1801871720094823, 173.2019292867072693 270.7080078635089535, 173.0814703330771067 269.2405689263442241, 172.8177239436131458 267.7920092155067096, 172.4132313293060292 266.3762856836023047, 171.8718897987754985 265.0070389048441371, 171.1989152074872891 263.6974616473685273, 170.4007917026983137 262.4601717601876203, 169.4852092483407944 261.3070905995187445, 168.4609895317960309 260.2493281658608453, 167.3380009664507213 259.2970760585276366, 166.1270636089901984 258.4595092790268041, 164.8398449075564542 257.7446978294150313, 163.4887472852424821 257.1595289573837704, 162.0867886420626576 256.7096407972481984, 160.6474769267689453 256.3993680462127145, 160.1808198831754169 256.3458791581733180, 159.7888786185551737 255.8807515407119126, 158.7722384270360010 254.8837990378072504, 157.6656484371330862 253.9877353311955233, 156.4790801143862495 253.2006348372852358, 155.2232256078740136 252.5295901066353679, 153.9094014034212989 251.9806479130901664, 153.7493000323017611 251.9309803368508653, 153.7335085963326549 251.7361013311340798, 153.5567803903514914 250.7597067587586537, 152.3963861141063489 251.5262422463456176, 151.2504957488128525 252.4571359158842085, 150.2016666703628118 253.4961668670388235, 149.2600591319466048 254.6332697633755231, 148.4347947065571418 255.8574292225335682, 147.7338679241737509 257.1567865248169937, 147.1640688268759618 258.5187544913925990, 146.7309171921137647 259.9301394192427210, 146.4386090613293732 261.3772688916593552, 146.2899760919199821 262.8461242261508346, 145.7299760919199798 273.6861242261508096, 145.7170536127129310 274.9199550281659867, 145.8055837664540491 276.1506734567064427, 147.9755837664540365 295.2806734567064382, 148.2214217942099879 296.7721509996758300, 148.6161220223941939 298.2313128573682661, 149.1556761533633164 299.6433408092258901, 149.8346048552163552 300.9938952934556369, 150.6460134060319263 302.2692610295237046, 151.5816617118101135 303.4564863008885709, 152.6320479870660449 304.5435144835163328, 152.6787079288204438 304.5829532738974308)), ((454.2567886821985326 264.5055662636306124, 453.9625400079446536 264.7599759210878574, 452.9454960798968841 265.8314182740256797, 452.0387781196779997 266.9977070923998212, 451.2511807558021815 268.2475300637296414, 450.5903432173768124 269.5687646436039699, 450.0626752380937319 270.9485956371996167, 449.6732948855992618 272.3736394990997383, 449.4259789192619792 273.8300741457771892, 449.3231261578379758 275.3037730216386763, 449.3657342123464673 276.7804421182676720, 450.3057342123464650 288.7904421182676629, 450.4922895089950998 290.2495868218912278, 450.8208712033166989 291.6834418407049156, 451.2883192066560696 293.0782172738820464, 451.8901379029828718 294.4204990630648240, 452.6205393848525773 295.6973780002014109, 453.4724991177992592 296.8965738800549730, 454.4378234978174191 298.0065536033665694, 455.5072286522062086 299.0166420948301038, 456.6704297259217924 299.9171249691410708, 457.9162397947371232 300.6993419577437976, 458.5759795500643463 301.0283144718869721, 459.7171512218642420 300.6708784081547492, 461.1032988396872270 300.0781369776157703, 462.4229478940347349 299.3492515303515802, 463.6627684771290774 298.4915846115484328, 464.8102370366067930 297.5137996009843278, 465.8537628771802588 296.4257732033591424, 466.7828052394159499 295.2384956827073097, 467.5879797730054861 293.9639598486356817, 468.2611533290315720 292.6150399157482980, 468.7955261137226444 291.2053614599097955, 469.1857003738539333 289.7491637849340123, 469.4277349199950322 288.2611560899445067, 469.5191849368608814 286.7563688902750982, 469.4591266786364940 285.2500021927234002, 468.3991266786364349 273.5200021927233820, 468.1927083340520426 272.0509034880385002, 467.8421805599381287 270.6093800519950037, 467.3509720780488124 269.2095322872692122, 466.7238876922600070 267.8650529418764563, 465.9670612899095090 266.5890931722155983, 465.0878958425775522 265.3941339037443186, 464.0949909931948696 264.2918637475846708, 462.9980589377934734 263.2930646672283501, 461.8078294247096665 262.4075065137047886, 460.5359448004998058 261.6438514608265677, 459.1948461291873400 261.0095692752878449, 457.7976514987764176 260.5108642504097247, 457.3414389207046611 260.3973359559632286, 457.2106073743374850 260.5109553181112005, 456.1989631698411927 261.5806824042883818, 455.2970647723731759 262.7444346614447568, 454.5136014319958235 263.9910000346509946, 454.2567886821985326 264.5055662636306124)), ((418.5479959992054546 269.9097101593382604, 419.1179959992054478 287.3597101593382490, 419.2430090285160986 288.8631330747886636, 419.5184038995828928 290.3463951504816123, 419.9413949520121605 291.7944929682286670, 420.5077035679159394 293.1927788015803458, 421.2116014507731165 294.5271087797746645, 422.0459685680071402 295.7839859551094150, 423.0023651711825892 296.9506968265910132, 424.0711171653255178 298.0154399389023183, 425.2414139638440247 298.9674452558917892, 426.5014178392314648 299.7970831011034534, 427.8383836634546924 300.4959615633972021, 429.2387878268338000 301.0570113823876568, 430.6884650313811562 301.4745574550726701, 432.1727515749131499 301.7443762403503342, 433.6766336765955998 301.8637384807705075, 435.1848993435867783 301.8314368093828648, 437.2568305361307921 301.6825796169088676, 438.1611466687042480 300.6419393999909175, 439.0127498802245327 299.4422286554632819, 439.7427279330332226 298.1648618550397032, 440.3440583282775833 296.8221274597991624, 440.8109561769538800 295.4269427769316962, 441.1389298514110351 293.9927296932966669, 441.3248241954855189 292.5332855548494990, 441.3668508775788268 291.0626504340942233, 441.0268508775787950 274.4726504340941915, 440.9245613045770256 273.0046004550536054, 440.6789109900079211 271.5536387668419707, 440.2922643278635064 270.1337309330870653, 439.7683428075570191 268.7585436228396247, 439.1121891944401341 267.4413130683429927, 438.3301189929868542 266.1947176657745331, 437.4296596598134670 265.0307559451811130, 436.4194781516107469 263.9606310841526806, 435.8090449228677130 263.4294822299182215, 435.6525596497362471 262.8546375558289014, 435.1285692768653348 261.4790603536680464, 434.4722747385885100 260.1614655642908360, 433.6899962345856920 258.9145417927068706, 432.7892672061128678 257.7502970730570837, 431.7787617880065909 256.6799432297212888, 430.6682112756648735 255.7137879060265959, 429.6024135027773809 254.9564292980894891, 428.4550431273760864 255.3081888616820549, 427.1157925724244819 255.8653122895626097, 425.8366148828541782 256.5491858168592785, 424.6294716694959561 257.3534145313893760, 423.5056509476527822 258.2704780777085034, 422.4756615826901225 259.2918009800735035, 421.5491350214693398 260.4078328318519198, 420.7347352285358397 261.6081376015256410, 420.0400776692491718 262.8814912201867742, 419.4716580974460385 264.2159865379840085, 419.0347918135431655 265.5991446680690728, 418.7335639610790281 267.0180316768655757, 418.5707913264718627 268.4593795294863412, 418.5479959992054546 269.9097101593382604)), ((302.5063592590402664 299.8983739792520282, 307.4518993831516127 299.8285032137016515, 308.9069259678481671 299.7370908569617427, 310.3462059654225413 299.5048589260807717, 311.7561432424458872 299.1340011959489971, 313.1234188509878891 298.6280209676547770, 314.4351168458264283 297.9916979746119523, 315.6788462946994400 297.2310432308914301, 316.8428583290303209 296.3532422482812194, 317.9161571294105215 295.3665871584748288, 318.8886037974161241 294.2803983815933861, 319.7510121325350951 293.1049365809992651, 320.4952354094503448 291.8513057361187180, 321.1142433359388519 290.5313482488931527, 321.6021884644043212 289.1575330747356816, 321.9544614296893883 287.7428379347610985, 322.1677344913630918 286.3006267219691381, 322.2399929691628699 284.8445232594622780, 322.1705542746367428 283.3882826032378262, 321.9600743592031904 281.9456611052912649, 321.0900743592031858 277.5056611052912672, 320.7193045284415689 276.0199359787245044, 320.1989926603989716 274.5797537058545004, 319.5345612127711661 273.2001232225300669, 318.7329345930870090 271.8954224209173844, 317.8024669956288335 270.6792483095355806, 316.7528553377539993 269.5642753113118033, 315.5950382029570278 268.5621231764114327, 314.3410818438421757 267.6832358863961758, 313.0040544330328771 266.9367728117169918, 311.5978898725183512 266.3305132568496560, 310.1372425807559807 265.8707753878611015, 308.6373347708729398 265.5623503873071058, 308.2830453368653139 265.5081750612331462, 308.1657163952214091 265.5784436899351704, 306.9739766445523514 266.4631592299080012, 301.4639766445523605 270.9931592299079739, 300.3895658050239490 271.9672343435849484, 299.4142422169174438 273.0405119766622875, 298.5471226892063896 274.2029596975698951, 297.7963125965458175 275.4437115599259300, 297.1688301145172773 276.7511696716558163, 296.6705406174361315 278.1131126059521534, 296.3061018519352956 279.5168096407076064, 296.0789203988132954 280.9491397585709365, 295.9911198301192030 282.3967142952764107, 295.9311198301192007 287.3067142952764357, 295.9855555746970026 288.7797987513681051, 296.1843175281030653 290.2404269555818246, 296.5254861436229703 291.6744928678922975, 297.0057665799465667 293.0681469740288776, 297.6205205211502403 294.4079300373794581, 298.3638109713061226 295.6809030817813095, 299.2284595911146425 296.8747723498880760, 300.2061160228298604 297.9780080303265208, 301.2873385339717061 298.9799556070323092, 302.4616852010071852 299.8709387554078489, 302.5063592590402664 299.8983739792520282)), ((399.6925367794071349 302.1497427222245165, 400.7245788582210366 301.4845533813058864, 401.8766396897096911 300.5707072316347990, 402.9336851266816097 299.5484480233633917, 403.8855564506674796 298.4276001624597257, 404.7231057173488580 297.2189355395802295, 405.4382836727326094 295.9340700069846548, 406.0242171103793112 294.5853517445715966, 406.4752749262448219 293.1857425878905019, 408.2852749262448242 286.4557425878904837, 408.5894873832791632 285.0642090447537953, 408.7603373914940335 283.6500940934919868, 408.7962843311988195 282.2261493479996375, 408.6970040551271381 280.8052150611754882, 407.2870040551271131 268.8352150611754610, 407.0495057352829917 267.4121246724888579, 406.6763847240742962 266.0184344568040160, 406.1710929575115188 264.6670381652060087, 405.5383051518193724 263.3704382657504084, 404.7838755552118641 262.1406302766465615, 403.9147837872059199 260.9889917894964810, 402.9390702665406252 259.9261772092935985, 401.8657618250929886 258.9620191849955972, 400.7047881959742881 258.1054376425888677, 399.4668901484182584 257.3643572622264628, 398.1635201193540183 256.7456341629016947, 396.8067362609715474 256.2549924729346458, 395.4090908845012109 255.8969713730925264, 394.3790988364417558 255.7365105738789737, 393.4261160950736098 256.4743100792791779, 392.3555379882018315 257.4858282874782276, 391.3893224437385925 258.5974640596936069, 390.5367850960267333 259.7984997124482902, 389.8061455667027531 261.0773556256067991, 389.2044482163449857 262.4217018856915615, 388.7374942272393810 263.8185771630480190, 388.4097856720780442 265.2545136767231497, 388.2244821078442101 266.7156670422236857, 388.1833701133782029 268.1879497502463892, 388.2317870975854817 270.4716175053573579, 387.7505183100397517 271.1470282193153025, 387.0162277694500972 272.4275414298519422, 386.4113518019725575 273.7740263886710750, 385.9417480175517312 275.1734437552976260, 385.6119640523074850 276.6122415926000144, 385.4251935292936082 278.0764866035497676, 385.3832451314994501 279.5519990610644641, 385.5332451314994273 286.7619990610644436, 385.6362254065513184 288.2319871939895393, 385.8829451724084834 289.6847774077251074, 386.2710233420407349 291.1063488558926906, 386.7967145815740651 292.4829819837333389, 387.4549454564257189 293.8013909352284259, 388.2393633949094465 295.0488517746039179, 389.1423979967651121 296.2133252847542053, 390.1553340949250241 297.2835731574617171, 391.2683958653994978 298.2492664540619671, 392.4708411735390428 299.1010852898032795, 393.7510652461392624 299.8308087798495762, 395.0967126688518647 300.4313943788588404, 396.4947966280165019 300.8970458484332653, 397.9318242461137061 301.2232691964885589, 398.9585682360574879 301.3522329966626216, 399.0535818367816887 301.4747059050608868, 399.6925367794071349 302.1497427222245165)), ((541.7397632911856817 297.0514739145706358, 542.1152912332203186 300.2041619860706874, 542.3563474898222694 301.6347571063907367, 542.7344779533243582 303.0353740667700890, 543.2461454896672421 304.3929111194443067, 543.8865638230952300 305.6946694976926437, 544.6497423082672640 306.9284722035176287, 545.5285419683648342 308.0827779145590739, 546.5147422750009127 309.1467889447281436, 547.5991180452539311 310.1105522486668065, 548.7715257365097159 310.9650525252113766, 550.0209983318876539 311.7022965489444459, 551.3358479286659986 312.3153879409750857, 552.7037750700682182 312.7985916795221897, 554.1119837976888221 313.1473877468503701, 555.5473013483308478 313.3585134107320300, 556.9963013755753991 313.4299937449216600, 567.9463013755754446 313.4399937449216509, 569.4824470616183589 313.3625384886275356, 571.0025851107523067 313.1281815022768455, 572.4907321639785778 312.7393869119724741, 573.9312412288362566 312.2002426645406672, 575.3089661983402721 311.5164175451708388, 576.6094211034028376 310.6951015734676389, 577.8189324242927114 309.7449304046168663, 578.9247828596662657 308.6758945305414841, 579.8094561636081608 307.6250165849875202, 580.8688388156059546 306.9615799697749594, 582.0217944779658410 306.0730780496410262, 582.5411932651929874 305.5856534001016485, 582.2777591860095754 300.7534647797519369, 582.1284913899357889 299.3081770408364264, 581.8400903290362294 297.8841116100094837, 581.4152620224281236 296.4946302560112485, 580.8579925633516723 295.1527702509492883, 580.1735107183203581 293.8711220437462543, 579.3682388664765313 292.6617111260543425, 578.4497327394770991 291.5358851990706057, 577.4266105273221683 290.5042076999463347, 576.3084720153133276 289.5763586868159223, 575.1058085108678597 288.7610440124238380, 573.8299044053278521 288.0659136385581860, 572.4927312943967763 287.4974898577335125, 571.1068356506499413 287.0611060956067035, 569.6852211020731147 286.7608568683321550, 568.2412264211864112 286.5995593643987718, 566.7884003695637603 286.5787270114196872, 565.3403745720595452 286.6985552758929998, 563.9107356135435793 286.9579198291722264, 562.5128975582379098 287.3543870968545093, 548.8528975582379417 291.9543870968545320, 547.4484968151685962 292.5074494214212564, 546.1067876390296760 293.1988508017013828, 544.8413553980435609 294.0215905086936345, 543.6650131244681461 294.9673379577455421, 542.5896717772702686 296.0265170592176673, 541.7397632911856817 297.0514739145706358)), ((425.0476623431490566 177.0508045810410351, 426.3962538496130037 176.7968505517492304, 427.8100062777793937 176.3836317982430728, 429.1764165199844570 175.8337511599289940, 430.4823096749905176 175.1525105753306377, 431.7150943461491011 174.3464785530437098, 432.8628840474049753 173.4234268383025324, 433.9146118125655107 172.3922554779622658, 434.8601369027758210 171.2629070064184305, 435.6903425833273218 170.0462705798819059, 436.3972240270383622 168.7540769833443903, 436.9739654966434728 167.3987855225753947, 437.4150060620010549 165.9934638917361838, 437.7160932184746116 164.5516621749240755, 437.8743238895004311 163.0872821965228923, 437.8881724179961452 161.6144434800796716, 437.2281724179961770 145.0044434800796580, 437.0976715563811013 143.5385451926602798, 436.8241482216395752 142.0924908197913794, 436.4102353959076481 140.6802003267850694, 435.8599174744367133 139.3152686621039038, 435.1784919111269687 138.0108348900750741, 434.3725182243354084 136.7794557120226671, 433.4497548538365663 135.6329845933254319, 432.4190844767617250 134.5824576599430031, 431.2904285014385550 133.6379874627908464, 430.0746515622269044 132.8086656326051695, 428.7834569347010074 132.1024753623594563, 427.4292738779286083 131.5262145596911978, 426.0251379883091545 131.0854304090869107, 424.5845657167062086 130.7843659737409041, 423.1214242567928636 130.6259193511074841, 422.9425687228684296 130.6241809519918036, 422.1174609519252385 131.0188849899468551, 420.8703012149161964 131.7721832337165893, 419.7021092382739198 132.6429261116039982, 418.6239068647723229 133.6228982007262118, 417.6458668890177250 134.7028535051295819, 416.7772170775543827 135.8726026913775513, 416.0261531052704527 137.1211092244882366, 415.3997612295458453 138.4365934971837646, 414.9039514317082080 139.8066439699953492, 414.5434016566046012 141.2183342736228155, 414.3215136763861892 142.6583451686918806, 414.2403809949292963 144.1130902122244777, 414.3007690957132922 145.5688439451639056, 416.0207690957132627 164.6188439451639169, 416.2221157474118627 166.0619111299100155, 416.5625214357053210 167.4786352883892846, 417.0387742689642323 168.8556489457849352, 417.6463805723141718 170.1799593149849557, 418.3796072876224912 171.4390708897866205, 419.2315360676631144 172.6211033460160706, 420.1941285540550552 173.7149036381092344, 421.2583022230454617 174.7101512334687072, 422.4140160834958806 175.5974554916598152, 423.6503654184701304 176.3684442696261669, 424.9556846764907618 177.0158429168920975, 425.0476623431490566 177.0508045810410351)), ((267.6930823310547680 136.5858571248290616, 267.0788756738474490 137.3216272278801284, 266.2537065506862746 138.5334506991235060, 265.5507801462725865 139.8200410291248090, 264.9768115015746162 141.1691074478672476, 264.5372837268291732 142.5677623525889715, 264.2363956215058920 144.0026444227779905, 264.0770215632658164 145.4600462605821463, 264.0606840490924014 146.9260453372891675, 264.5806840490923832 160.6960453372891493, 264.7084037324260066 162.1629673828251441, 264.9793345530785587 163.6102990986425709, 265.3908657342355468 165.0240935292886491, 265.9390316286385882 166.3907268954127687, 266.6185499328416313 167.6970298769003875, 267.4228725891917975 168.9304145166913145, 268.3442488850258201 170.0789955223959851, 269.3738001410383731 171.1317047968081226, 270.5016052691009349 172.0783980936610078, 271.7167963750683271 172.9099527708579558, 273.0076634853137989 173.6183556991953481, 274.3617673878133587 174.1967804794613812, 275.7660595004072093 174.6396532238216253, 277.2070076111476169 174.9427062676009825, 277.6007645409336533 174.9858322906648027, 277.6199475337535318 174.9654716248784609, 278.5370785109688541 173.7720861349490917, 279.3299998827932882 172.4927996717024996, 279.9907285233504695 171.1404920807715939, 280.5126122220994489 169.7287783839045687, 280.8903966582362841 168.2718717030376467, 281.1202783011524389 166.7844401626997239, 281.1999427043500646 165.2814592114548304, 281.2499427043500759 147.1914592114548270, 281.1781230703186907 145.6833229403106031, 280.9550561338136276 144.1900466048617773, 280.5830019426827562 142.7267596367536271, 280.0657300393204991 141.3082876242820305, 279.4084812687855788 139.9490021039429166, 278.6179146801177922 138.6626749522993691, 277.7020400588339726 137.4623388534248249, 276.6701367741635522 136.3601552556246759, 275.5326597632400762 135.3672911552596076, 274.3011336047876512 134.4938059560601289, 272.9880357555175578 133.7485495502402273, 271.6066701322485528 133.1390726540219305, 270.9752879261708358 132.9334600064100300, 270.4067956514811613 133.3891190335043007, 269.3558249058127103 134.4156816005011308, 268.4103186047270242 135.5401317091396436, 267.6930823310547680 136.5858571248290616)), ((93.7189689248213256 134.8477399603009701, 92.2896587986620034 134.9545629073704731, 90.8399804521271932 135.2075063306469360, 89.4220636597628697 135.6012824773623038, 88.0495553834602021 136.1321013874167534, 86.7356655437468902 136.7948541112050975, 85.4930398788413584 137.5831618815009278, 84.3336382337678430 138.4894375068994918, 83.2686194509577859 139.5049583959276447, 82.3082339702258992 140.6199505089894615, 81.4617251718092490 141.8236824301388310, 80.7372404120088305 143.1045686532701779, 80.1417526076847651 144.4502810886336874, 79.6809931243276139 145.8478677164629573, 79.3593966136350275 147.2838772457144501, 79.1800583315129813 148.7444885781216044, 79.1447043473013281 150.2156438315165019, 79.5147043473013326 164.9856438315165121, 79.6209952858660586 166.4314111623949657, 79.8663522375090054 167.8601661816116746, 80.2484835236592318 169.2585640573257422, 80.7638199685754756 170.6135434989400892, 81.4075482360969716 171.9124487519320894, 82.1736557870789710 173.1431478049035491, 83.0549870376023591 174.2941457047744791, 84.0433101934293632 175.3546919217365598, 85.1293941364596094 176.3148807611573829, 86.3030946450526670 177.1657438845708157, 87.5534491429039434 177.8993340755899624, 88.8687790915020344 178.5087994683542831, 90.2367990698032827 178.9884475452034280, 91.6447315222997076 179.3337983058277985, 93.0794261037122652 179.5416261112867460, 94.5274825056084751 179.6099898120631337, 99.2343928683194747 179.6045039025494816, 99.3647791632782145 179.4126583554943579, 100.0689632058679024 178.1198092842128062, 100.6430208763079719 176.7641575302728540, 101.0814225095219712 175.3587615380532725, 101.3799451601065158 173.9171589180363640, 101.5357132802543276 172.4532360444883352, 101.5472264187418574 170.9810942936389893, 100.5272264187418614 146.2810942936390006, 100.3904696491749888 144.7872881883861851, 100.1051976825728360 143.3146106951162153, 99.6742634403129273 141.8777896300699126, 99.1019765695249930 140.4911942198522468, 98.3940603435723204 139.1686913990061498, 97.5575944252713612 137.9235071308619069, 94.6925638734269910 134.0878470696310103, 93.7189689248213256 134.8477399603009701)), ((532.9860937172347803 174.9573908191262603, 533.0080931088475609 175.1057128406700940, 533.3654457658577712 176.5327890758762237, 533.8608731257639874 177.9179963649194747, 534.4896097333381704 179.2480105681738962, 535.2456078479901862 180.5100384415605106, 536.1215956162466227 181.6919406931987169, 537.1091470188196126 182.7823487497712449, 538.1987629194575220 183.7707741094416178, 539.3799624359746758 184.6477092294714453, 540.6413837545774186 185.4047189781121006, 541.9708934177612036 186.0345217711062844, 543.3557030345563135 186.5310596123568416, 544.7824922904977711 186.8895563650511065, 546.2375370741017377 187.1065636927370974, 547.7068414874167956 187.1799942284504539, 549.1762724708562473 187.1091416528421405, 565.0662724708562337 185.5591416528421291, 566.5242898583325086 185.3441739360937390, 567.9541742999268763 184.9871637049890580, 569.3421224804002350 184.4915573392635224, 570.6747359140335902 183.8621391492443138, 571.9391502862142715 183.1049851907590096, 573.1231596384296836 182.2274046102459408, 574.2153341978537355 181.2378690862884412, 574.4356830191576364 180.9947814863856195, 575.8020692992797649 180.6517368784506914, 577.1879050012548760 180.1550203210443044, 578.5183751468689479 179.5248480248428109, 579.7806642459884188 178.7672899996627791, 580.0997602235509021 178.5303479079257443, 579.3055093601259387 173.3687125974970229, 579.0090425909093028 171.9208015081596557, 578.5715253534697240 170.5090941253004644, 577.9972051411166376 169.1472955493140944, 577.2916575541936481 167.8486263572320638, 576.4617321711822342 166.6256942550655822, 575.5154860518049418 165.4903716800062625, 574.4621055176900200 164.4536805407445854, 573.3118169699665714 163.5256852148663143, 572.0757876095859729 162.7153948421275516, 570.7660170241969126 162.0306758621616439, 569.3952206940643919 161.4781756457172719, 567.9767065479795747 161.0632579608290484, 566.5242457675761898 160.7899509004261063, 565.0519390943086364 160.6609077769065266, 563.5740799370049672 160.6773813633198245, 562.1050156089721668 160.8392117312286871, 545.2050156089721895 163.5492117312286950, 543.7273776881614822 163.8631968585372931, 542.2888139272835133 164.3242118160792415, 540.9039145627839389 164.9275808867132582, 539.5867255547595960 165.6671845659418523, 538.3506061296785674 166.5355216272990333, 537.2080932881091258 167.5237852014722648, 536.1707746516457291 168.6219520975470232, 535.2491709386514458 169.8188844604582357, 534.4526292607719142 171.1024427336151348, 533.7892283224307448 172.4596087810083702, 533.2656964847978998 173.8766179200662521, 532.9860937172347803 174.9573908191262603)), ((454.3729585488960083 176.5744841581644096, 457.0096218165617756 176.3789016784057253, 458.4251934933004122 176.2059189035173858, 459.8179400446771865 175.8992867851394806, 461.1752724890092736 175.4617769590141734, 462.4849219513444609 174.8973440593724433, 463.7350505613423479 174.2110899731496261, 464.9143584553222013 173.4092177242287107, 466.0121859152895354 172.4989754045497818, 467.0186097217103907 171.4885906588899616, 467.9245328491055034 170.3871963155036156, 468.7217666937081049 169.2047478348445679, 469.4031050899336037 167.9519333225476885, 469.9623894466288903 166.6400769200597267, 470.3945644143371965 165.2810364461668087, 470.6957235804045467 163.8870962146337718, 470.8631447788908417 162.4708559967716610, 470.8953146961218295 161.0451171325969710, 470.6153146961218567 149.8451171325969824, 470.5037902007816228 148.3564578729964580, 470.2448471147706073 146.8862563281522000, 469.8410501762562603 145.4490743157745953, 469.2963988487347251 144.0591466069766398, 468.6162877077829876 142.7302399359840308, 467.8074530097273396 141.4755166455823314, 466.8779059714462960 140.3074043188439362, 465.8368534221477830 139.2374726883859921, 464.6946066130360578 138.2763190423285948, 463.4624790880758383 137.4334632619659828, 463.2259129164247611 137.3041073166343438, 461.7668756617838994 137.3462545270487851, 460.3384982771500518 137.4559544701103846, 458.9271002373893680 137.7014164912071124, 457.5455553495879713 138.0804016537543646, 456.2064651208085593 138.5894531144424775, 454.9220438157664148 139.2239276541955633, 453.7040070466647990 139.9780380304387677, 452.5634649113977730 140.8449057643645972, 451.5108206548398471 141.8166238817097451, 450.5556757775652272 142.8843290347633683, 449.7067424575300834 144.0382823477596901, 448.9717640835468728 145.2679582482384149, 448.3574446253906558 146.5621404741138178, 447.8693874847742791 147.9090243807422098, 447.5120443849525600 149.2963246148123062, 447.2886747651489259 150.7113871729311825, 446.5186747651489441 157.7613871729311654, 446.4310355247095004 159.2137482795143342, 446.4845221023380759 160.6677677451315560, 446.6786312408239610 162.1097646462157797, 447.0115365600188966 163.5261711799595332, 447.4801057413159242 164.9036603242793433, 448.0799299997524940 166.2292712322652619, 448.8053655664332382 167.4905311812745197, 449.6495867909626440 168.6755729292455896, 450.6046503642450602 169.7732463740229036, 451.6615700573772187 170.7732234650853513, 452.1778393881290299 171.1744681212325645, 452.3250161376140568 171.9262578112060851, 452.7368127807785640 173.3086307507870458, 453.2794812048068138 174.6450597241971536, 453.9480034598921065 175.9231870315742299, 454.3729585488960083 176.5744841581644096)), ((363.2138389083275456 176.4063309728099966, 363.9907245126515249 176.3810583623522916, 365.4266245907201665 176.1946665480733145, 366.8378634097645090 175.8706975090434810, 368.2112910405564890 175.4121699901256761, 369.5341098786783505 174.8233565526622044, 370.7939938926546688 174.1097437627301474, 371.9792034779649157 173.2779810672957126, 373.0786948467278421 172.3358188346396389, 374.0822229337630347 171.2920361363929374, 374.9804368601512579 170.1563589441107638, 375.7649670647633684 168.9393695026286650, 376.4285032918660932 167.6524077246608897, 376.9648627081153904 166.3074655254462186, 377.3690475142236096 164.9170750820315163, 377.6372915144741000 163.4941920583934802, 377.7670952101487956 162.0520748845075047, 377.7572490898674573 160.6041612142415431, 376.9472490898675119 145.9141612142415454, 376.7994063743995525 144.4841838321976297, 376.5153510939961166 143.0749268012238531, 376.0976923819798685 141.6993345716910255, 375.5502665594411269 140.3700423726271822, 374.8781018974730728 139.0992601533325228, 374.0873724309867043 137.8986604313167561, 373.1853412483404213 136.7792710767095912, 372.1802937776854492 135.7513740179512070, 371.0814616828121757 134.8244107991822034, 369.8989380675373013 134.0068958568164419, 368.6435847675045352 133.3063383118774539, 367.3269325809522456 132.7291729964597380, 365.9610753548578259 132.2807013478576152, 364.5585588993101283 131.9650427132663992, 363.6055502103513959 131.8448077579425046, 363.1947038866291564 132.0271724526049582, 361.9430853255742022 132.7331817377958316, 360.7647688262629799 133.5557194941352748, 359.6705687059014735 134.4872366597064683, 358.6705272809600729 135.5191839846965536, 357.7738227011080312 136.6420904944404811, 356.9886847142714714 137.8456504118435078, 356.3223191359041380 139.1188177414275629, 355.7808417156741712 140.4499076469313081, 355.3692220085233657 141.8267036920440489, 355.0912377652376790 143.2365699600417770, 354.9494402611212536 144.6665670233182368, 354.9451308809798320 146.1035706984687295, 355.7551308809798343 164.1235706984687113, 355.8893750693287075 165.5616200341264914, 356.1613015394966624 166.9800922364319149, 356.5683892220286566 168.3658364453759191, 357.1068639484104210 169.7060052268878962, 357.7717334419279496 170.9881736832464583, 358.5568336017836373 172.2004546460953236, 359.4548856513623605 173.3316088840923328, 360.4575636208173819 174.3711493034415412, 361.5555715383355277 175.3094381752495678, 362.7387296144300421 176.1377764882977317, 363.2138389083275456 176.4063309728099966)), ((395.8984695991463809 132.1697762929840110, 394.6711136088338776 132.5294475523781728, 393.3070615144586668 133.0792553806003582, 392.0034073570072337 133.7599995336441623, 390.7726831741166507 134.5651360126289546, 389.6267199270066612 135.4869250342694045, 388.5765337693568426 136.5165054334730996, 387.6322201489347776 137.6439798457899144, 386.8028567599776579 138.8585098508541478, 386.0964162792462275 140.1484201622044168, 385.5196897246207186 141.5013108619039599, 385.0782211729926985 142.9041766010491017, 384.7762544650103109 144.3435316202900083, 384.6166924090049406 145.8055393885412911, 384.6010688762721657 147.2761456136637719, 385.2310688762722179 163.6661456136637867, 385.3597630502670199 165.1328797805525994, 385.6316353633492895 166.5799308342886036, 386.0440663302450730 167.9933564617217030, 386.5930821853581847 169.3595383301322101, 387.2733931699364689 170.6653132996432021, 388.0784444988103701 171.8981002498570376, 389.0004795156407909 173.0460212987439945, 390.0306144281752836 174.0980162458405971, 391.1589239034407228 175.0439491370995029, 392.3745366981635811 175.8747059246404660, 393.6657404030202656 176.5822822804520058, 394.7217783875971691 177.0326407681896228, 395.0834993862533224 176.8942539025125029, 396.4237354565296982 176.2211561173274958, 397.6900573934174190 175.4175877601556977, 398.8698059546286458 174.4915819908374033, 399.9511873597352292 173.4523959588005653, 400.9233911909327048 172.3104182606401196, 401.7766984632664276 171.0770650867790721, 402.5025787839573468 169.7646660954184199, 403.0937756295386407 168.3863411546785755, 403.5443788883002298 166.9558691851229639, 403.8498839428440874 165.4875504138273925, 403.8796190277746518 165.2057028457929562, 404.7718629006743640 164.1169200604993250, 405.6005145241143168 162.8703738698708321, 406.3008029242186581 161.5474464058429191, 406.8657546573647323 160.1613113267054871, 407.2897439569967446 158.7257717096284750, 407.5685487547823413 157.2551226001765201, 407.6993927237984394 155.7640086628222491, 407.6809729250828127 154.2672783499684783, 407.0909729250828377 144.7972783499684795, 406.9238613420308752 143.3122139536500015, 406.6098071219700500 141.8511484214903362, 406.1519275566794249 140.4285842415349350, 405.5547675435476549 139.0586417386832920, 404.8242544730723580 137.7549189168121302, 403.9676393937783132 136.5303564854395972, 402.9934250385501286 135.3971094106715896, 401.9112814267875819 134.3664262654108654, 400.7319498801136319 133.4485375763954096, 399.4671364043716153 132.6525542763291412, 398.1293954961997770 131.9863772690656845, 396.7668538323892449 131.4698301905602591, 395.8984695991463809 132.1697762929840110)), ((110.7148110177539166 148.9998781208835794, 111.0948110177539263 163.9998781208835794, 111.2041394207437861 165.4669311884706531, 111.4566497828639484 166.9162192619187692, 111.8499132940260665 168.3338021390746064, 112.3801472881435473 169.7060445791336463, 113.0422516272921456 171.0197474553189636, 113.8298577581777522 172.2622747127296066, 114.7353899690540970 173.4216749101948096, 115.7501382578814315 174.4867961770649174, 116.8643421108322400 175.4473934792090404, 118.0672843853075449 176.2942271624651767, 119.3473943944355540 177.0191518256870040, 120.6923592015181441 177.6151946685483551, 122.0892420539225895 178.0766225605048874, 123.5246068172434661 178.3989971857994021, 124.9846472128457862 178.5792177340906051, 125.5553187955405292 178.5933161872889059, 126.0134437466993091 178.4285636864850630, 127.3447683524992158 177.7960607604677818, 128.6075894963382211 177.0359502973758481, 129.7897140218098002 176.1555715358718146, 130.8797279378658232 175.1634249639214147, 131.8671066264884359 174.0690902424204296, 132.7423164630296526 172.8831337088787450, 133.4969068680249507 171.6170063542584501, 134.1235919016752405 170.2829332580498942, 134.6163206131627987 168.8937955491439311, 134.9703354655438829 167.4630060322259624, 135.1822182720971171 166.0043796805800582, 135.2499232005898193 164.5320002457581268, 135.1899232005898170 145.7820002457581268, 135.1108979694622292 144.2915580008013876, 134.8841450342537769 142.8163474466496154, 134.5119094133661974 141.3709742369737228, 133.9978765092306503 139.9697486141090792, 133.3471356201523861 138.6265437272849965, 132.5661295525692367 137.3546582783924066, 131.6625908325981129 136.1666848551966780, 130.6454651484223461 135.0743852555920625, 129.5248227814949757 134.0885740372780219, 128.6165163508340186 133.4374712322243397, 124.8862449886429573 133.6426361571448638, 123.4496179497391779 133.7912888966418166, 122.0339471125996482 134.0774199273637919, 120.6523572866747998 134.4983765032363010, 119.3176573133479508 135.0502558986395627, 118.0422213140347907 135.7279415909228248, 116.8378739686100829 136.5251506961378993, 115.7157808877554572 137.4344922182117443, 114.6863450955959394 138.4475355715240426, 113.7591105823438795 139.5548887416118760, 112.9426738211221277 140.7462853593656575, 112.2446040693019995 142.0106798814445597, 111.6713731932491669 143.3363499944851469, 111.2282956670786120 144.7110052937045452, 110.9194793016953611 146.1219012283293637, 110.7477871609166016 147.5559572574524339, 110.7148110177539166 148.9998781208835794)), ((149.8364717675985673 136.8054412825440238, 148.9991325013985204 137.8602774854379618, 148.2029284329060204 139.0942707142357335, 147.5312086319729588 140.4002081998900735, 146.9904116977086517 141.7655722111680348, 146.5857213014213016 143.1772753990072999, 146.3210164998567109 144.6217862419680955, 146.1988345534874725 146.0852587491865222, 146.2203466062488815 147.5536651775942119, 146.4947819345446476 151.8614682096308002, 146.1108853418643321 159.1287199873466136, 146.1035839225279460 160.5582265686157655, 146.2323952868711956 161.9819365141567289, 146.4961495171979777 163.3869191057218018, 146.8924510893007778 164.7604137145809204, 147.4177006295951173 166.0899456987906717, 148.0671276061081301 167.3634397030142225, 148.8348336564086196 168.5693293318560109, 149.7138461589562723 169.6966622006135594, 150.6961815613131819 170.7351994093319263, 151.7729178900447948 171.6755085366968387, 152.1330564240384433 171.9339905717750696, 152.8428903020992777 171.1280897070367359, 153.6927014334480077 169.9509032915304942, 154.4247232938951697 168.6970728358292320, 155.0320978249970949 167.3783450417847689, 155.5091347458254916 166.0070746118610430, 155.8513648632325044 164.5961085022197778, 156.0555819421680326 163.1586655640522849, 156.1198727437819400 161.7082127007559507, 156.0436349498940274 160.2583387011960951, 154.6336349498940308 146.3383387011960792, 154.4220753983979080 144.9251346232937578, 154.0770019181182704 143.5384740653933306, 153.6015460941526385 142.1909411459118928, 153.0000227477142687 140.8947648950422149, 152.2778907785888123 139.6617082747704615, 151.4417036249341493 138.5029614285215018, 150.4990497900076321 137.4290401292041963, 149.8364717675985673 136.8054412825440238)), ((180.2704520600537705 176.7065601217925916, 180.9591150897117870 176.6755401397986134, 182.3762906689702845 176.4751529575111704, 183.7679160113215744 176.1406076664108298, 185.1213208835726789 175.6749501774355053, 186.4241830357514687 175.0824201295123714, 187.6646403906277953 174.3684122892158257, 188.8313990435321443 173.5394274334165061, 189.9138360891735715 172.6030131621162127, 190.9020963392570707 171.5676951803460497, 191.7871820503218885 170.4428996747803922, 192.5610348448624620 169.2388674917996241, 193.2166090798702669 167.9665608983773666, 193.7479359948031572 166.6375637747011638, 194.1501780549389196 165.2639761472344730, 194.4196729953368106 163.8583040224586966, 194.5539671644030761 162.4333455243168771, 195.0939671644030966 150.7633455243168612, 195.0920979488680018 149.3373738671560318, 194.9548306092969483 147.9180231922064763, 194.6834056755764948 146.5181206361878026, 194.2802761036924153 145.1503175768019673, 193.7490851075632747 143.8269752980909573, 193.0946332341235063 142.5600532774662099, 192.3228349792092899 141.3610011039957897, 191.4406653363233204 140.2406550047197129, 190.4560967613349476 139.2091399141184525, 189.3780271227865910 138.2757779717654785, 188.2161992889426188 137.4490042750998953, 186.9811130782991597 136.7362906486894474, 185.6839303692870828 136.1440781189057532, 178.2496066285368954 133.1684472610351690, 177.5602921163564645 133.5331447578392670, 176.3278529469601210 134.3498625509841986, 175.1818033281741123 135.2839420610808077, 174.1332774338884519 136.3263084596731289, 173.1924619693240857 137.4668348826631359, 172.3684972045087136 138.6944408154626842, 171.6693881741462064 139.9971997431110253, 171.1019269065959634 141.3624550195088716, 170.6716264375287153 142.7769428300687196, 170.3826672493328260 144.2269210531669330, 170.2378566566269171 145.6983027684747185, 170.2386015324589721 147.1767931150993149, 171.0586015324589653 163.6267931150993036, 171.2041139337790128 165.0928001785318315, 171.4927333296103029 166.5374625658465959, 171.9216756965307695 167.9468450600023175, 172.4868034553844041 169.3073527538964242, 173.1826653823802360 170.6058621868731962, 174.0025491916108251 171.8298479336584705, 174.9385462817789971 172.9675034246436383, 175.9816280225861078 174.0078548320857692, 177.1217328449225761 174.9408669236778735, 178.3478632947891356 175.7575398624251193, 179.6481921147657488 176.4499960190951242, 180.2704520600537705 176.7065601217925916)), ((293.5513174966288261 149.0988043336234909, 293.9283849676606337 177.5464502036899148, 298.5724992334482408 177.3690708393716307, 300.0402759707192217 177.2405946705811743, 301.4883674482668425 176.9687385522488512, 302.9028019989012250 176.5561254424320907, 304.2699326886337303 176.0067363693586628, 305.5765689868256345 175.3258720211452442, 306.8101040328058389 174.5201016029989773, 307.9586362710462026 173.5971994553454749, 309.0110842813198815 172.5660700444121289, 309.9572936959213507 171.4366620489824697, 310.7881351723761441 170.2198723722415252, 311.4955924763671078 168.9274410048371635, 312.0728398250236637 167.5718377535538934, 312.3080651746338958 166.8228472212161080, 312.5848268274489214 166.6005803252551232, 313.6362859220346877 165.5712545941322276, 314.5818362873703222 164.4438628277295891, 315.4123793012371948 163.2292534294226130, 316.1199230074196862 161.9391140601371433, 316.6976590188431828 160.5858591730433318, 317.1400280317384386 159.1825105546420502, 317.4427733204173023 157.7425720217450476, 317.6029816979030329 156.2798994800864136, 317.6191115482686200 154.8085675949340612, 317.2291115482686337 144.5785675949340714, 317.1005990614790448 143.1097560111672351, 316.8285052545933809 141.6606570193371510, 316.4154590652353249 140.2452716395464449, 315.8654512959885210 138.8772751550346811, 315.1837960557421070 137.5698849832370172, 314.3770794154102077 136.3357329706874737, 313.4530957741071120 135.1867433456373817, 312.4207725506008728 134.1340175075968659, 311.8675065926323100 133.6709806975304389, 307.6410372614776065 133.9275657710585392, 306.2217297163182366 134.0817964150129171, 304.8235134128664185 134.3702616088274624, 303.4590544570387465 134.7903482155439860, 302.1407131550415670 135.3382507714143799, 300.8804320443235270 136.0090059586833888, 299.6896277089951468 136.7965375670929120, 298.5790873597333643 137.6937115368124580, 297.5588711150288077 138.6924005841749761, 296.6382208689871618 139.7835578247889714, 295.8254765712282506 140.9572987270929616, 295.1280006772843763 142.2029906539545152, 294.5521114538822189 143.5093491811782656, 294.1030257432813073 144.8645403203962871, 293.7848117051520944 146.2562877203286007, 293.6003519640934769 147.6719838753030842, 293.5513174966288261 149.0988043336234909)), ((204.1279017007645677 178.7508404563584463, 205.6624755448687836 178.8233043466850631, 207.0940004591129480 178.8225172562958960, 208.5189312923732814 178.6852717826471348, 209.9242900013452129 178.4128179356752355, 211.2972768025180130 178.0076371816675191, 212.6253867505664630 177.4734198424620502, 213.8965236314030562 176.8150314845834430, 215.0991101325712975 176.0384686044372415, 216.2221932875655170 175.1508040131747066, 217.2555442337069280 174.1601224186542254, 218.1897513749975133 173.0754467912085488, 219.0163061014387438 171.9066561838666303, 219.7276802840976018 170.6643957555105260, 220.3173948401072550 169.3599798164621859, 220.7800787431226581 168.0052887795451113, 221.1115179417741388 166.6126609551748174, 221.3086937405766150 165.1947801759907577, 221.3239906798786478 164.8368086299325626, 221.8639459866499806 164.1321474687233604, 222.6469848294625535 162.8682552508892059, 223.3010540586678871 161.5330523408183581, 223.8197275941829787 160.1396568007037899, 224.1979095875450980 158.7017584219747732, 224.4318844874535728 157.2334842262375503, 224.5193535442086272 155.7492596705299945, 224.4594573943998626 154.2636669205163855, 224.2527845039542740 152.7913015840561002, 222.3127845039542763 142.6513015840560854, 221.9790724474336514 141.2668211252008916, 221.5155671597037212 139.9202281184369667, 220.9264466771759885 138.6236607523948408, 220.2170213315594935 137.3888062846068010, 219.3936858825338163 136.2267956925738304, 218.4638618753886306 135.1481033391405049, 217.4670382014824099 134.1922805184302945, 217.3591071540339499 134.1950941055646354, 215.9328703557440576 134.3004721412676759, 214.5231513138539015 134.5411963779727955, 213.1427644116408828 134.9150786260183850, 211.8042574024588873 135.4187202859071704, 210.5197973503294122 136.0475432416653234, 209.3010600310087170 136.7958314760375629, 208.1591237988780847 137.6567830292345036, 207.1043688844104054 138.6225718289268229, 206.1463830376012254 139.6844188294491289, 205.2938743750684409 140.8326718135564875, 204.5545922230425333 142.0568931313339647, 203.9352566757867464 143.3459545787096090, 203.4414975097628258 144.6881385531230535, 203.0778030088147261 146.0712445668418127, 202.8474791655497995 147.4827001497166634, 202.7526196297789625 148.9096751332669157, 202.3326196297789750 171.3796751332669146, 202.3758693236803197 172.8321671022318071, 202.5594621020996442 174.2736583951789271, 202.8816749643907542 175.6906207543487710, 203.3394839736934046 177.0697561236767399, 203.9285926362911425 178.3981214497758572, 204.1279017007645677 178.7508404563584463)), ((231.5901741877046902 147.8177116828322823, 231.5101741877046777 164.4177116828323051, 231.5724431280188753 165.8572580942636421, 231.7725472983070176 167.2841882301238456, 232.1086402517527745 168.6853351953765241, 232.5776207148394690 170.0477700073347762, 233.1751612041280737 171.3589208969174535, 233.8957479577989034 172.6066893137427769, 234.7327318134916823 173.7795615646304555, 235.6783895629720860 174.8667150553760621, 236.7239952174794837 175.8581181554594082, 237.8599005261596062 176.7446227641925418, 239.0756240046041512 177.5180487241598257, 240.3599476519933944 178.1712593030312348, 241.7010204643902966 178.6982270472458083, 243.0864677890258179 179.0940893999051582, 244.5035055105158506 179.3551935696670228, 245.9390580153651058 179.4791302366141679, 253.6519708100525179 179.7729181795301372, 254.1692302982064007 179.1404800663877381, 254.9789215500545083 177.9265464469204971, 255.6668307795295618 176.6396837317114432, 256.2264481188971672 175.2920698444012544, 256.6524777606510384 173.8964576131066053, 256.9408880731457998 172.4660540870207512, 257.0889497529977916 171.0143955545994459, 257.0952616532082970 169.5552194460683779, 256.1352616532082607 147.9152194460683916, 256.0003666936429454 146.4665646297224555, 255.7257596540434861 145.0377929891721180, 255.3140240342981997 143.6423463916731578, 254.7690334384093092 142.2933531828573734, 254.0959151316707221 141.0035046754070436, 253.3010018034149198 139.7849357493303444, 252.3917719891439617 138.6491106871490047, 251.3767797125531160 137.6067153180594289, 250.2655740093737506 136.6675564857738152, 249.0686090901542968 135.8404697858466648, 247.7971459871669992 135.1332364404928512, 246.4631466107450990 134.5525100929379505, 245.0791612117671718 134.1037542100189626, 243.6582103090404416 133.7911906819506669, 242.2136621924086057 133.6177601028308572, 242.0164451496862625 133.6133310566906687, 241.8119025888779561 133.6713578310082937, 240.4775536199371686 134.1918979690302649, 239.1989351988824239 134.8373312649986815, 237.9877051956910350 135.6017729472572171, 236.8549070629345579 136.4782531789958284, 235.8108691465250502 137.4587806061005324, 234.8651105164841795 138.5344152187401789, 234.0262541763268871 139.6953498623713870, 233.3019484423782046 140.9309996549802406, 232.6987972098513069 142.2300984952947260, 232.2222997414892234 143.5808017820493490, 231.8768005277507598 144.9707944077532886, 231.6654496756926847 146.3874030423237400, 231.5901741877046902 147.8177116828322823)), ((324.0455195397861416 146.7531146005770779, 323.5055195397861212 166.6531146005770836, 323.5396672443972079 168.1501577141122823, 323.7229065766349549 169.6363365380208847, 324.0534114106546895 171.0968401169972992, 324.5278880026309594 172.5171133699947461, 325.1416078155367586 173.8830021431970749, 325.8884546427806299 175.1808942674312561, 326.7609855610745058 176.3978552142716012, 327.7505051050911220 177.5217569989140713, 328.8471519246988919 178.5413990452015867, 330.0399970611683784 179.4466198082834580, 331.3171528629472959 180.2283980425006007, 332.6658914555739557 180.8789427052815881, 334.0727715850800905 181.3917706010850566, 335.5237725707889354 181.7617709916054594, 337.0044340325618464 181.9852565283495380, 338.5000000000000000 182.0600000000000023, 344.5699999999999932 182.0600000000000023, 344.9090412803174672 182.0430926994948777, 344.1362876173546965 147.3162969919611669, 344.0270017340586151 145.8122896392151517, 343.7672591091966297 144.3268552615896283, 343.3596848535653976 142.8750065272000711, 342.8083981508357283 141.4714166680192022, 342.1189706266741268 140.1302711836177650, 341.2983700386375858 138.8651244742137010, 340.3548898559422469 137.6887628519805276, 339.2980654408159467 136.6130753151024635, 338.1385776785535882 135.6489333906091019, 336.8881450302470739 134.8060812603685008, 335.5594050991675772 134.0930372806892876, 334.1657869077596388 133.5170078908324740, 333.3738521263464918 133.2794989565029198, 333.2035227618743534 133.3420575537151365, 331.9146928860450316 133.9603788489281726, 330.6905735823380041 134.6985763856593223, 329.5422820502257650 135.5499460058733803, 328.4802468369719577 136.5067557459748855, 327.5141131277124487 137.5603160569095280, 326.6526551498697017 138.7010587208592938, 325.9036964874260320 139.9186237478269277, 325.2740390287445393 141.2019534629317263, 324.7694011932198350 142.5393929299361275, 324.3943659977701941 143.9187957989770155, 324.1523394348216129 145.3276346172140165, 324.0455195397861416 146.7531146005770779)), ((213.8594009258447954 430.6855590463977137, 214.5957637058823479 431.1566954820130491, 215.9079431875836690 431.8248506792383523, 217.2793115240630755 432.3611297534887399, 218.6966533179717374 432.7603647707616688, 220.1463101418198676 433.0187084427332707, 221.6143121594687102 433.1336712017308628, 233.4129422664344702 433.4765171667629602, 233.1514914367707831 433.2356187936350125, 231.9559724429435050 432.3366499168511154, 230.6768640591677411 431.5612194244262128, 229.3268858000746775 430.9170382340737433, 227.9194619137078917 430.4105121142022199, 226.4685878901619560 430.0466779846494205, 224.9886912897629827 429.8291538293116218, 223.4944882747223005 429.7601027187398017, 222.0008372709191633 429.8402113004631815, 213.8594009258447954 430.6855590463977137)), ((712.6155266849536929 534.2520811757533465, 711.7859274447871485 533.1082333287993151, 710.8092364510006291 532.0072420256855139, 709.7293498363892468 531.0072660027432221, 708.5566638236809922 530.1179321720169355, 707.3024680286673629 529.3478022773587099, 705.9788367734604435 528.7042904692965521, 704.5985128452678055 528.1935919279486598, 703.1747848197600206 527.8206232211384759, 701.7213591300536564 527.5889749718944586, 700.2522281129218982 527.5008772910086918, 698.7815353025687273 527.5571783074422001, 679.4815353025686591 529.2471783074422547, 678.0229272659648814 529.4474320932695264, 676.5909770725947965 529.7897150564607500, 675.1994800605078808 530.2707296597077402, 673.8618418437860100 530.8858418318750410, 672.5909491635133008 531.6291256124143274, 671.3990457375440428 532.4934202417775850, 670.2976143051240570 533.4703991478245371, 669.5920919789756454 534.2322749941350821, 670.3522621561321557 535.1792834458216248, 671.4166505477837745 536.2604507550699964, 672.5848097050609340 537.2285672677584216, 673.8447888644908517 538.0737287414854109, 675.1836979050478931 538.7872888160305820, 676.5878392195562583 539.3619474692856102, 678.0428478470198570 539.7918256995750426, 679.5338384322632237 540.0725256703370860, 681.0455575094268852 540.2011757018551634, 699.6955575094269761 540.8411757018551498, 701.1515775362887553 540.8204185560443875, 702.5987240310849984 540.6585785105565947, 704.0233589233117755 540.3571807640905718, 705.4120562943265895 539.9190657227358088, 706.7517289047083295 539.3483622316673518, 708.0297515298660755 538.6504486644475946, 709.2340799415684387 537.8319022366309810, 710.3533644140978822 536.9004370213496031, 711.3770566853362425 535.8648312510241567, 712.2955093647672129 534.7348445903193124, 712.6155266849536929 534.2520811757533465)), ((266.8756922576520765 551.1280146815192893, 266.8900218904050803 559.5156263862919559, 266.9647152321792305 560.9852841199776776, 267.1830543536338496 562.4405507544918237, 267.5429378882741958 563.8674203319172875, 268.0409022002814936 565.2521601968213645, 268.6721547196670485 566.5814431637841153, 269.4306200675195555 567.8424757825512188, 270.3089985274217497 569.0231214663477886, 271.2988363002876895 570.1120172983240764, 272.3906068664661575 571.0986833919514538, 273.5738026720544553 571.9736237528480842, 274.8370362570040015 572.7284176713073975, 276.1681498517276054 573.3558007659390796, 277.5543323874161388 573.8497348984301425, 278.5579372466675636 574.0997602137971398, 278.9733267706865831 573.0030065079229189, 279.3484324258208744 571.6335754328678149, 279.5923747517705920 570.2348124567168952, 279.7029679757241638 568.8192507799765281, 279.6792211599215534 567.3995741229241503, 279.5213470806578471 565.9885030768966772, 279.2307603217645351 564.5986811252079178, 278.8100645996510139 563.2425613549679611, 278.2630294334761629 561.9322948748928184, 277.5945563694908742 560.6796219389009366, 276.8106350621877709 559.4957667510522015, 275.9182896057794210 558.3913368943987052, 273.9882896057794142 556.2213368943987462, 272.9439789824872378 555.1585208950224342, 271.7993764247215154 554.2045567912915658, 270.5657761711774469 553.3688577181155779, 266.8756922576520765 551.1280146815192893)), ((376.9902686080721423 450.9525723815366405, 377.0364770406739012 450.9637737378224074, 378.4956412623399160 451.1687319545443415, 379.9678745528161130 451.2295368458429152, 400.3278745528160698 451.0695368458428902, 401.7960780382838948 450.9859095081886835, 403.2490367407880285 450.7589160828204058, 404.6727853701346476 450.3907383447960342, 406.0536393923188143 449.8849150794545153, 407.3783265600963546 449.2463080689477124, 408.6341144808106947 448.4810553626271030, 409.5256387103864313 447.8098099063059863, 409.3227319468933274 448.0635995148803659, 408.5270711692591021 449.2855016884290080, 407.8538081424295569 450.5788840338676664, 407.3093048159883551 451.9315248296012442, 406.8987064341759492 453.3306423956554454, 406.6258929162812024 454.7630158732241057, 406.4934421936287663 456.2151101542048650, 406.5026058496063115 457.6732037802103150, 407.8426058496062865 482.0532037802103105, 407.9909626801964464 483.4882409454870071, 408.2764941308618631 484.9023884645206408, 408.6965589196756810 486.2825648982083635, 409.2472712766738709 487.6160030537886882, 409.9235368888062112 488.8903680865620345, 410.7191000244014845 490.0938716022153585, 411.6266014012272763 491.2153807042593030, 412.6376462628406330 492.2445209778436492, 412.6823955296013082 492.2820639391832742, 416.2865387744848249 490.4467883169980382, 417.6186797653946314 489.6800750662984569, 418.8653664697401382 488.7810638930260438, 420.0135089606584415 487.7591942100388565, 421.0510520031159558 486.6251954166054361, 421.9671016311343124 485.3909742421525380, 422.7520395319555746 484.0694897283141245, 423.3976240361388363 482.6746171619415122, 423.8970766532148673 481.2210023877470348, 424.2451532442943858 479.7239080302655339, 424.4381990843338599 478.1990532397678066, 426.4281990843338690 451.5990532397678408, 426.4658077062724146 450.1253857920202108, 426.3585828081716613 448.6551433031013403, 426.1075599967645644 447.2025257680465984, 425.7151637174760594 445.7815629553750227, 425.1851838384807820 444.4059789038334998, 424.5227390471432045 443.0890593719619233, 423.8701992660866722 442.0583051562421701, 423.3461381787861910 441.9665350708352776, 421.8922368822853741 441.8556657115801727, 420.4344401836057159 441.8864842142515954, 418.9865234257124484 442.0586993616694258, 417.5621685917952277 442.3706838194311217, 416.1748350183714251 442.8194895132892270, 414.8376322122732063 443.4008754867624020, 413.5631959733242979 444.1093479757427076, 412.3635689932764876 444.9382123214166995, 412.1589393178338128 445.1112221055722102, 412.7391868989552677 444.3173920514940392, 413.4865670466433016 443.0508858647011152, 414.1063378470967677 441.7172820400034539, 414.5925422974997900 440.3293986735957333, 414.9405071843386281 438.9005755761234582, 415.1468880005646724 437.4445460554622400, 415.2097010917324837 435.9753049173501722, 415.2096316049294842 435.9642982077543820, 414.0184987201791955 435.6980801560404757, 412.5552314653669441 435.5193090845851316, 393.2552314653669328 434.1193090845850975, 391.7850097052160550 434.0849413981497946, 390.3184885285209020 434.1947084250599573, 388.8697643529768015 434.4475550684747986, 387.4527625288810668 434.8410509291917379, 386.0811034866133014 435.3714136670017183, 384.7679718150515100 436.0335453571007065, 383.5259895293601744 436.8210814920944927, 382.3670947463203333 437.7264521585814805, 381.3024269333917005 438.7409548002763131, 380.3422198345094216 439.8548378682633597, 379.4957031018292923 441.0573945543221157, 378.7710135789506580 442.3370657063459248, 378.1751170883766804 443.6815509366157926, 377.7137414750017115 445.0779268549396761, 377.3913215492217432 446.5127712901841619, 377.2109564588837998 447.9722923061614779, 376.9902686080721423 450.9525723815366405)), ((102.8404270866076615 493.5156601816717625, 103.3140056161728211 492.6041099173398266, 103.8612245828567637 491.2380332783829999, 104.2719499574323976 489.8249095585049417, 104.5422285282840278 488.3783400043041638, 104.6694588774036561 486.9122477768964927, 104.6524164189118835 485.4407439421713661, 104.6089787772217932 484.7254707756744665, 104.7003434957729553 484.6485797853192707, 105.7288219943723817 483.5942277092289601, 106.6489369540039007 482.4440908549808569, 107.4518167230537813 481.2092587210906913, 108.1297200192787500 479.9016374290475255, 108.6761105704348296 478.5338349257435198, 109.0857201363264437 477.1190394189767403, 109.3545993046239317 475.6708922181393859, 109.4801555706817169 474.2033562061482144, 109.4611783341951963 472.7305812107966858, 109.0911783341952059 466.7705812107966494, 108.9381178979002129 465.3711844289576334, 108.6545435063838028 463.9922992217498745, 108.2429528058174526 462.6460704353292499, 107.7069709752308881 461.3443552869725295, 107.0513187969203699 460.0986189299302964, 106.2817710770813022 458.9198334714728276, 105.4051057828846751 457.8183813335515424, 104.4290443439843585 456.8039638072478397, 101.4590443439843597 453.9939638072478374, 100.4108983554758936 453.0911285694656954, 100.2540515641297105 453.4725343231451689, 99.8303720518116648 454.8820595888605567, 99.5468704245643465 456.3263215565091286, 99.4062761929674679 457.7914150913695153, 99.4099429769149339 459.2632344955572989, 100.8299429769149356 486.7732344955572898, 100.9776015658022175 488.2360582928911299, 101.2677603804501416 489.6773994680472697, 101.6976317599974493 491.0834105341361919, 102.2630857743518646 492.4405834334268093, 102.8404270866076615 493.5156601816717625)), ((168.0447665110760909 465.3512883307137145, 167.9503660507969585 466.4224391791923381, 167.9709669710894957 467.9433536107715099, 168.6209669710895014 478.0433536107714758, 168.7872334303102093 479.5044797139333923, 169.0957731622783626 480.9423035065449312, 169.5436207106899644 482.3430056869475493, 169.7133790672626787 482.7362345357686877, 168.0447665110760909 465.3512883307137145)), ((123.1185062483646959 465.6119547286404554, 125.4585062483646709 490.0119547286404327, 125.6707879497733984 491.4687361631800968, 126.0248500518041652 492.8976923194977076, 126.5172821092171063 494.2850590113295652, 127.1433408508275562 495.6174726561206398, 127.8969958682892099 496.8820989975461657, 128.7709877030154360 498.0667567293844513, 129.7568977717257894 499.1600348299554071, 130.8398178706766828 500.1464730314724534, 131.9306495523680951 499.9289778969450140, 133.3378326792381188 499.5015554592555986, 134.6963964500081374 498.9383869466955161, 135.9932814180842797 498.2448859211979197, 137.2160210358200061 497.4277187901852244, 138.3528614915212245 496.4947407244896453, 139.3928746951442577 495.4549201490643213, 140.3260633265912247 494.3182525323327923, 140.6680545947487815 493.8067309180265738, 141.1716620182951090 493.4686580244783158, 142.3079138498979432 492.5319852091516850, 143.3468476342191309 491.4884140426331669, 144.2784507083812287 490.3480018802969198, 145.0937448028755625 489.1217393768844204, 145.7848725691036691 487.8214445647201956, 146.3451733041093519 486.4596489581239211, 146.7692471427093608 485.0494767816830972, 147.0530070983746782 483.6045184863148165, 147.1937184513213879 482.1386997720992440, 147.1900251042106333 480.6661473801688658, 145.9600251042106152 456.8561473801688635, 145.8120163232328537 455.3914085260249749, 145.5211345846728364 453.9482334416164235, 145.0901818767018483 452.5405238573705446, 144.5233094547274106 451.1818398737709117, 143.8259778534290092 449.8852693404346610, 143.0049042868469371 448.6633017842591471, 142.9899384012883559 448.6451621215071555, 136.6360246699898653 449.2467929175905965, 135.1780151658681461 449.4575102950446421, 133.7477068301795669 449.8102305696675671, 132.3588954224445899 450.3015516469841941, 131.0249764516750872 450.9267345855641906, 129.7588159726100230 451.6797493055557879, 128.5726264886989156 452.5533327506422552, 127.4778491587867251 453.5390589424321774, 126.4850434436519748 454.6274202515874094, 125.6037852567916673 455.8079191018036909, 124.8425746018190381 457.0691692221313360, 124.2087535873399133 458.3990054710262712, 123.7084356100790501 459.7846011728455551, 123.3464463893067915 461.2125918350401435, 123.1262774213069804 462.6692040527564131, 123.0500523028318582 464.1403883575203508, 123.1185062483646959 465.6119547286404554)), ((197.2462631304243814 446.4128725254735173, 194.2237545801854992 446.7686917726555862, 194.2823193956876366 447.8193327842504914, 196.0223193956876457 464.3593327842505118, 196.0813856363599257 464.8446481919367557, 196.9013856363599189 470.7746481919367625, 196.9873531353503893 471.3217118619014059, 199.0373531353504006 482.9617118619013922, 199.3630722103342521 484.3956260282865287, 199.8276232276740529 485.7907586759788501, 200.4265419496439335 487.1337028731971373, 201.1540728930924331 488.4115532082103641, 202.0032246384268433 489.6120298076340305, 202.9658370156873843 490.7235963434352470, 203.1128361128349411 490.8630378573433291, 204.1530589997826439 490.7462590630977388, 205.5931813068820304 490.4393088608537710, 206.9962695115333986 489.9926387633196896, 208.3488030150021189 489.4105530238813344, 209.6377483795364753 488.6986608035921904, 210.8506849226683926 487.8638221195351434, 211.9759244068117425 486.9140817395430076, 213.0026236707918201 485.8585916602880843, 213.9208891179527257 484.7075229157670151, 214.7218720539631249 483.4719675660222151, 215.3978539556145790 482.1638318105661938, 215.9423208489361343 480.7957212565028158, 216.3500260798938370 479.3808194469369823, 216.6170408727974177 477.9327608202116835, 216.7407921892170179 476.4654993241765624, 216.7200875225898642 474.9931739515626532, 215.9800875225898551 463.2931739515626646, 215.7917734905238660 461.6906525394043115, 215.4324154808421667 460.1176303297783079, 214.9061717241795293 458.5923091964845071, 214.2191315321731508 457.1323390510134459, 213.3792448363585379 455.7546136105527239, 211.3692448363585470 452.8246136105527171, 210.4720115286665418 451.6459971292356386, 209.4629049787327233 450.5616224756473116, 208.3517658340517471 449.5820642999263441, 207.1494297478468525 448.7168750984948815, 205.8676217114930580 447.9744920596240831, 204.5188417142555579 447.3621547853085190, 203.1162428453711755 446.8858346918121356, 201.6735030272020310 446.5501767773614006, 200.2046916302997772 446.3584543248570640, 198.7241322711289797 446.3125369813369048, 197.2462631304243814 446.4128725254735173)), ((329.1187631387693955 493.0245551404663615, 329.7630943622659174 492.6722880170764824, 330.9799344411961215 491.8423863258988717, 332.1095155914309771 490.8971440184747621, 333.1409464588192577 489.8456750648156230, 334.0642820483893161 488.6981176659271568, 334.8706196133979915 487.4655365018790576, 335.5521844950843047 486.1598160468190031, 336.1024050854659890 484.7935459795810971, 336.5159761903923368 483.3798997947525322, 336.7889101819096709 481.9325077846236809, 336.9185754467330867 480.4653256167220547, 336.9037217601043608 478.9924997740970980, 336.2483357817152410 467.9349619848659927, 337.6791322545393541 461.5304444398439614, 337.9687661587818752 459.7201167680734670, 338.0353859215825878 457.8879770668309561, 337.9053859215825923 452.6479770668309470, 337.7967162283473499 451.1799782782097736, 337.5446821948416982 449.7296995492835663, 337.1517109979113229 448.3111075769377294, 336.6215870891121540 446.9378639033894274, 336.2374150953043568 446.1751313945821380, 333.0182937587778156 446.5514611666349083, 331.5784636041345834 446.7912883867037976, 330.1687608736321522 447.1699256554978206, 328.8025346875663217 447.6837874834379249, 327.4927224669370958 448.3280078777100357, 326.2517274230880844 449.0964864205777758, 325.0913011060224562 449.9819460370297861, 324.0224321235975253 450.9760019047333230, 323.0552420853685476 452.0692408537542519, 322.1988897564353351 453.2513105041711015, 321.4614843289043051 454.5110172974979150, 320.8500086322373477 455.8364324936125058, 320.3702530096438181 457.2150051294557329, 320.0267604866709235 458.6336808698445111, 319.8227837512156952 460.0790256249458139, 319.7602543523347549 461.5373527638215592, 319.8397644095221608 462.9948527194003418, 321.7697644095221676 481.6348527194003282, 321.9867392429384836 483.0588588901890148, 322.3393024272359639 484.4554872780782944, 322.8242027474949509 485.8118586623949113, 323.4369686218092284 487.1154650585345394, 324.1719493366568372 488.3542850622405922, 325.0223671558130718 489.5168947068082730, 325.9803798222735622 490.5925728109275497, 327.0371528768176290 491.5713998456838567, 328.1829411263150860 492.4443494089999831, 329.1187631387693955 493.0245551404663615)), ((226.8944456413097157 464.4381493468343365, 227.6744456413097168 482.1981493468343842, 227.8081752167860827 483.6431644274076689, 228.0809147158866494 485.0684943359100885, 228.4901113548631031 486.4607982873127980, 229.0319351401381027 487.8070446125265107, 229.7013147162312237 489.0946327320548903, 230.4919848325684484 490.3115110947405242, 231.3426616649841208 491.3786928661058937, 231.3716332977768957 491.3622325797659300, 232.5754665706957383 490.5119155046989476, 233.6900398491477233 489.5475607545692469, 234.7045925291214985 488.4784786544969961, 235.6093296515516613 487.3149906156956490, 236.3955164673668605 486.0683294878123775, 237.0555627667824012 484.7505311118212603, 237.5418784069826472 483.4818457314883062, 238.6416098936466028 482.7014057677630490, 239.7540840586317188 481.7345460996418183, 240.7662979820239570 480.6631734479049101, 241.6684785522103027 479.4976321125688514, 242.4519150498421993 478.2491756096112567, 243.1090432514328086 476.9298580162042640, 243.6335184633262543 475.5524175863823189, 244.0202767808818010 474.1301537608561034, 244.2655839814047170 472.6767987584666457, 244.3670715787531265 471.2063849890863025, 244.3237596915073766 469.7331095681204829, 244.1360665039054538 468.2711972407469716, 242.7260665039054572 460.3811972407469284, 242.4159049636629391 459.0166499426673568, 241.9799327502930169 457.6869440583037658, 241.4219441580834769 456.4036521037174339, 240.7467953977357524 455.1779426515458340, 239.9603623324499893 454.0204831301944068, 239.0694893399086425 452.9413469845193845, 238.0819297452162004 451.9499260059876633, 237.1115889272702191 451.1424802617784167, 237.2418834441283764 450.8863633382622425, 237.7839672295944808 449.4922486099982279, 237.8216490879872254 449.3566666890586134, 236.9118235807933672 449.6266532909124294, 235.5476105816228483 450.1821750175248553, 234.2444609964324513 450.8688209630181518, 233.0149411342946735 451.6799697807162488, 231.8709072907368522 452.6077995391518698, 230.8233914171411527 453.6433631491775031, 229.8824947393189007 454.7766746410046039, 229.0572903510983735 455.9968054591982423, 228.3557357222192365 457.2919898470529461, 227.7845959642219213 458.6497383041241278, 227.3493785942818306 460.0569580228413997, 227.0542804260622063 461.5000791428302023, 226.9021470997188032 462.9651856054669565, 226.8944456413097157 464.4381493468343365)), ((760.1744658382689295 640.7123012863634131, 760.9655686039936882 639.9625817918272332, 761.9225080643112733 638.8602078484333333, 762.7677613913532468 637.6700363251177350, 763.4933232417930640 636.4033392646596212, 764.0923218630667861 635.0721134785891309, 764.5590841753270297 633.6889669261080371, 764.8891895008409847 632.2669993048683637, 766.3891895008409847 623.9969993048683818, 766.5863646625764432 622.4633092670123915, 766.6245979935200694 620.9174692400272306, 766.5034831866746572 619.3759069220566289, 765.6734831866746163 613.0259069220566062, 765.4110531838756515 611.5763110251594981, 765.0076948475355039 610.1594483560292019, 764.4672986992643473 608.7889850133586833, 763.7950770346899390 607.4781395593303159, 762.9975136491806325 606.2395555223087058, 762.0823012995416548 605.0851794459201756, 761.0582675048880219 604.0261456607756827, 759.9352894023675162 603.0726688902514070, 758.7241984789783373 602.2339457261773532, 757.4366760983739368 601.5180659247351969, 756.5303071222165272 601.1249933484288022, 755.5326540087497733 601.7383986550827331, 754.3721957203739521 602.6175922193127690, 753.3024375427321502 603.6051458653553254, 752.3334572861280094 604.6917562024839299, 751.4743833680095122 605.8671866622100879, 750.7333088174037812 607.1203639333446063, 750.1172150333765103 608.4394822797132747, 749.6319060157609329 609.8121147577875263, 749.2819536877452720 611.2253302864879743, 749.0706548254147492 612.6658154662871993, 749.0000000000000000 614.1200000000000045, 749.0000000000000000 625.2500000000000000, 749.0723155707257774 626.7211347932822036, 749.2885650106674120 628.1780847929388756, 749.6466632262688563 629.6068019762042240, 750.1431574072853437 630.9935105432764431, 750.7732603190418104 632.3248397446277522, 751.5308964613143416 633.5879528027888909, 752.4087606487654512 634.7706706855342418, 753.3983884480970801 635.8615895370372755, 754.4902377927606949 636.8501906347164550, 755.6737809882887404 637.7269418115585040, 756.9376062211260887 638.4833893659985051, 758.2695275922047813 639.1122395731532606, 759.6567026143098929 639.6074290114719361, 759.9252217511339040 639.6744630904547648, 759.9823406907481740 640.0220391025000026, 760.1744658382689295 640.7123012863634131)), ((718.3521615729251835 638.5153161766572794, 719.3521771525006443 638.1597884691312856, 720.6841713609196631 637.5330981963435306, 721.9483239605245899 636.7788651592917404, 723.1324600421958166 635.9043532901024491, 724.2251753267470349 634.9179849125798683, 725.2159459982343606 633.8292596277677831, 726.0952300576240077 632.6486628243791301, 726.8545592206940000 631.3875646952154739, 727.4866204751107261 630.0581107321413583, 727.9853265112145664 628.6731047542401711, 728.3458743482034379 627.2458855956974730, 728.5647915910909660 625.7901986410142854, 728.6399698729460397 624.3200634447803168, 728.5706851603333689 622.8496387109477155, 725.9828646205492078 596.0244705919227499, 725.0897113904020443 595.9490060345991651, 723.6013583677586212 595.9713082272268139, 722.1225440634257211 596.1410149186328908, 720.6678311315499741 596.4564549186983413, 719.2515448875193442 596.9145219255625534, 717.8876322391263329 597.5107051149946074, 716.5895243441076445 598.2391335608270992, 715.3700043465437375 599.0926340490224220, 714.2410814945852735 600.0628017160438503, 713.2138728791362610 601.1400828159222556, 712.2984939580738910 602.3138688009648831, 711.5039589440692680 603.5726007896456622, 710.8380920369442038 604.9038833929286056, 710.3074503747031940 606.2946067781141437, 709.9172594619820984 607.7310757681858604, 709.6713617117846979 609.1991447053463844, 709.5721786072418809 610.6843567506722366, 709.6206868560069552 612.1720862481313361, 710.8206868560068870 626.7320862481312815, 711.0230419651480815 628.2435795447403279, 711.3778189830591145 629.7267156960938337, 711.8813509982228425 631.1661652733956771, 712.5284335941967129 632.5470503844304631, 713.3123786415251288 633.8550984487839060, 714.2250834250446587 635.0767897166703051, 715.2571143920919212 636.1994970066373298, 716.3978046560090434 637.2116162178443801, 717.6353642471675585 638.1026862679644864, 718.3521615729251835 638.5153161766572794)), ((549.0040312059954886 619.7613519770919766, 549.3953417383974056 621.0491612167708126, 549.9484806703986806 622.3885728520740486, 550.6282862841712813 623.6683581990615721, 551.4284137689942327 624.8765726729493508, 552.3413953173923119 626.0019396793812803, 553.3587098242155662 627.0339558619521085, 554.4708624164310322 627.9629891328897884, 555.6674730713560848 628.7803685719513851, 556.9373734962337039 629.4784653544799085, 558.2687113649467392 630.0507639533002475, 559.6490609390017426 630.4919229499068933, 561.0655390403268257 630.7978248873758957, 562.5049252934770720 630.9656146997100450, 579.1649252934770402 632.0956146997100404, 580.6261139050101292 632.1233646118460001, 582.0830677213808713 632.0087880302072563, 583.5219564160654500 631.7529725881271361, 584.9291211480930315 631.3583466474757415, 586.2912042203987539 630.8286562471134857, 587.5952758795259570 630.1689295431227720, 588.8289570530199626 629.3854290783723400, 589.9805368594070387 628.4855923345021438, 591.0390837752840980 627.4779611306460083, 591.9945494042483460 626.3721005390859773, 592.8378638626270458 625.1785080875401945, 592.8522513366710882 625.1532411264661278, 592.1399574296780202 621.1330633317755883, 591.8124184510323857 619.6982658915868569, 591.3458519076258426 618.3024718030917484, 590.7447491255163641 616.9591174484091880, 590.0148965217260866 615.6811344071895746, 589.1633199022503504 614.4808249728907867, 588.1982168292921642 613.3697437267746864, 587.1288777087783046 612.3585863096317325, 585.9655963577959028 611.4570864619552140, 584.7195709128554881 610.6739223236894532, 583.4027960328730842 610.0166328955441486, 582.0279474345592234 609.4915454660442720, 580.6082598717183600 609.1037147029283005, 579.1573997330684733 608.8568739952208944, 577.6893334849997927 608.7533995143779748, 576.2181932256830805 608.7942873404629154, 574.7581406447528707 608.9791438735458087, 559.8281406447529207 611.6191438735457950, 558.3700417730884737 611.9527066237748159, 556.9524130543213687 612.4298284568699273, 555.5893508618788701 613.0457650505411493, 554.2944089799183303 613.7943917600790655, 553.0804638295314817 614.6682645195478472, 551.9595864303961434 615.6586938628113330, 550.9429223710402539 616.7558313283567486, 550.0405809812488087 617.9487673887382471, 549.2615348086436597 619.2256399308655546, 549.0040312059954886 619.7613519770919766)), ((90.3555661008981303 630.2268670317490660, 91.0064816474452840 629.8670603653787339, 92.2446890260873715 629.0101077630170039, 93.3907372336153827 628.0333279611987791, 94.4330765221307047 626.9465648394303798, 95.3780878955885214 625.8566736735260747, 99.4777069976539536 623.7763610041744187, 100.7313697636143672 623.0623999433294102, 101.9106441149657343 622.2313026377127017, 103.0046209215517621 621.2907573310898215, 104.0031801164369369 620.2494647394188405, 104.8970843137548741 619.1170575630128496, 105.5146153220122756 618.1578861569848868, 104.5196092606897764 618.3511184379634642, 103.1249035410740476 618.7655725246987686, 101.7769133379239008 619.3132025517714965, 100.4883215989023881 619.9888559869481242, 99.2712524043974440 620.7861757534957405, 95.5012524043974196 623.5261757534957496, 94.3312161938367666 624.4696381616262215, 93.2614720807180504 625.5254570769835709, 92.3027608001410300 626.6830315800168592, 91.4647082652334973 627.9307390771144810, 90.7557289184464366 629.2560519969217694, 90.3555661008981303 630.2268670317490660)), ((357.5026225386205851 612.3269487610899660, 357.5046674516403300 612.3485114681129744, 357.7873768615322092 613.7958319380865078, 358.2108363639956110 615.2083981145962071, 358.7709531548972564 616.5725573185735584, 359.4623136154528993 617.8751247321173423, 360.2782356357227513 619.1035108318777702, 361.2108331982775553 620.2458430688222961, 362.2510925978245950 621.2910806183296017, 363.3889595601171436 622.2291210915338979, 369.8189595601170936 627.0191210915338615, 370.0733675002106793 627.2045221156463413, 374.9133675002107111 630.6545221156463867, 376.1478955853438606 631.4464466944849619, 377.4538637677578095 632.1140178428080389, 377.6572691611953587 632.1940188671215992, 377.5913209092256579 631.9338068083162625, 377.0896867326448501 630.5452725956831728, 376.4539820297527513 629.2127773283417582, 375.6903651237644794 627.9492294156549406, 374.8062334740635606 626.7668693466557670, 373.8101520140834282 625.6771511115829298, 372.7117701794051072 624.6906312425406895, 357.5026225386205851 612.3269487610899660)), ((39.2007048178026523 586.8027309527966509, 38.8256518489448865 585.4940255279719850, 38.2726447759205399 584.1047579423051275, 37.5840673312543458 582.7774515157040014, 36.7667620811503610 581.5252960239409958, 23.4467620811503608 563.1852960239409640, 22.5502864570908663 562.0674293175160301, 21.5512347505354214 561.0402048018941059, 20.4587240610046166 560.1129966747041635, 19.2827243716518950 559.2942664086772311, 18.0339675656557503 558.5914855343327190, 16.7238494897001786 558.0110674566329863, 15.3643259582879246 557.5583089278314901, 13.9678036479254732 557.2373417106174429, 12.8769772296555765 557.0943474167285103, 12.8011133609573697 558.3272444552183060, 12.8558993760946461 559.8037760789995900, 13.0556861494051191 561.2677543536645999, 13.3985351745623120 562.7049744792645924, 13.8811198299221470 564.1014912868583906, 14.4987576563209046 565.4437545467602604, 15.2454557903968215 566.7187404447405470, 24.0854557903968285 580.2387404447405288, 24.9473146771240906 581.4258027831687059, 25.9210033279719170 582.5230029231412345, 26.9972093079311968 583.5198471560308917, 28.1656396981761326 584.4068015850166375, 29.4151195383512309 585.1753833080243794, 30.7336987047730368 585.8182415488705601, 32.1087662023549996 586.3292279606655484, 33.5271707771585952 586.7034554290877395, 34.9753466960226476 586.9373448131300393, 36.4394434903046118 587.0286591762869648, 37.9054584228544442 586.9765251807912136, 39.2007048178026523 586.8027309527966509)), ((323.6986154219875402 560.1593066140248993, 323.7288141973675124 560.0184889936026593, 323.8938998885229239 558.6100481365125461, 323.9252442989738938 557.1923117501280558, 323.5552442989738893 542.5023117501280012, 323.4490967210529107 541.0593410997303181, 323.2044188957376036 539.6333101154923497, 322.8234873398849913 538.2374867900090294, 322.3098462942010087 536.8848580595746398, 321.6682747471269295 535.5880089719038324, 320.9047419704595541 534.3590055930792460, 320.0263519804080374 533.2092827431780506, 319.0412774408289351 532.1495376050996811, 317.9586836236140925 531.1896301964733311, 316.7886431337128670 530.3384916306689547, 315.5420421922013929 529.6040410204641375, 314.2304793493519810 528.9931117975057759, 313.9868520931242983 528.8936306678795063, 313.2564607167196300 529.5187579497610386, 312.2329764861682975 530.5864585058533294, 311.3196163192406516 531.7497631319669154, 310.5252601245032906 532.9973618990188697, 309.8576308251074352 534.3171253498900342, 309.3232192746611418 535.6962224249419933, 308.9272211515799427 537.1212452086839448, 308.6734864454438707 538.5783392847713458, 308.5644820264670898 540.0533384319951438, 308.1744820264671034 556.0033384319951892, 308.2088078021587307 557.4482986688547044, 308.3820458100616975 558.8832470782408564, 308.6925875634950103 560.2948603979416475, 309.1375497328687629 561.6700320282511711, 309.7128009169296092 562.9959937246129584, 310.4130000021484648 564.2604341486901376, 311.2316457540892998 565.4516131771368919, 312.1611371803119255 566.5584709067302356, 313.1928441043483531 567.5707303437706059, 314.3171872954856099 568.4789928242934138, 315.5237274103619711 569.2748252791329833, 316.8012619205697433 569.9508385335963112, 317.3745613864709867 570.1866992920182611, 317.3745613864710435 570.1866992920182611, 317.4648299710922288 570.1034760603431550, 317.4523323662393182 570.2186949999552326, 318.1379291263063465 570.5007559147458096, 319.5213182903245297 570.9194715292851470, 320.9385848696060179 571.2030976709468177, 322.3765697748507364 571.3490009172113560, 347.2965697748506955 572.6690009172114060, 348.4001460981469904 572.6738930967756005, 348.9736141192731793 573.3710458717441725, 349.9843364494686853 574.3849250878597559, 351.0871106263887214 575.2978324261724765, 352.2718915501743027 576.1014522717839554, 353.5278871267237264 576.7884645026177850, 354.8436565721912075 577.3526111679461792, 356.2072146263597006 577.7887534916653749, 357.6061407256141251 578.0929186810800502, 359.0276921410656428 578.2623361148199592, 359.1983304947956981 578.2744512325938331, 359.1765385043846095 578.0134948290449302, 357.9665385043846300 569.0034948290449393, 357.9620937861209882 568.9794213369362978, 358.0131257582946205 568.9386254798117761, 359.0556203731430287 567.9249973049390974, 359.9950753373017847 566.8151948589149924, 360.8226629594910833 565.6196465217564082, 361.5306067221402486 564.3495863941208199, 362.1122543542851417 563.0169487348510984, 362.5621403403597469 561.6342558194077128, 362.8760372775106475 560.2145002729381531, 363.0509955988500224 558.7710229836544613, 363.0853712893835450 557.3173877437184274, 362.6753712893835768 540.8173877437184274, 362.5615029577928681 539.3082801172326981, 362.2961494035711212 537.8183273272027236, 361.8820117747374070 536.3626962442559716, 361.3233057564333990 534.9562043635942246, 360.6257186576498270 533.6131689713326978, 359.7963515176610940 532.3472614026834435, 358.8436468214902675 531.1713678755515957, 358.5809646498146321 530.9068220759577343, 358.3033523476858591 531.1648185450558231, 357.3478961972693355 532.2410441205080360, 356.4999755228571416 533.4038805124954479, 356.2892114064700309 533.7602683660297771, 356.2028406602528321 533.7816883934809766, 354.8413336375692211 534.2623716325844043, 353.5323501532258206 534.8717319421615457, 352.2880187115004560 535.6041232394238705, 351.1198687773517690 536.4527594868940241, 350.0387239491237779 537.4097775690469234, 349.0546016715214819 538.4663101487143422, 348.1766204180754016 539.6125678281953242, 347.4129152031043759 540.8379298538015973, 346.7705622060084352 542.1310425234067907, 346.2555132062915391 543.4799243851954316, 345.8725404368130967 544.8720772528827183, 345.6251923662348418 546.2946020087844090, 345.5157608203651307 547.7343181217526080, 345.2819254450552648 556.1690941597156552, 344.0458310059251517 556.0298495762991706, 342.5742067445021917 556.0092181173738481, 341.1076443616399274 556.1329048227202065, 330.2876443616398774 557.5829048227202520, 328.8354943409638054 557.8508431488368160, 327.4167258762513484 558.2602655135032137, 326.0450886034712425 558.8072041033578898, 324.7338753985586663 559.4863584013008904, 323.6986154219875402 560.1593066140248993)), ((370.5420537899117335 579.1267568406917690, 382.9770066135802153 580.1858853146319461, 384.4606580455202334 580.2385206999842922, 385.9422459723103884 580.1442377721639332, 386.8041040913310553 580.0028837425691108, 387.9679579550539188 580.8489213825446313, 389.2635270851208702 581.6054283399386122, 390.6281855665991429 582.2287335431074098, 392.0482819495308036 582.7126017178744632, 393.5096102073018187 583.0521924574537707, 394.9975518475539502 583.2441086437750073, 401.1169085644314123 583.7238850482247017, 392.6417276555768581 577.6622032511863836, 392.8198936593437338 577.5508457332362013, 393.9948605341430721 576.6434070860467500, 395.0743714302884086 575.6242661338756079, 396.0478519769411037 574.5034058925530189, 396.9057664256975499 573.2918057712656719, 397.6397110582058758 572.0013340235310579, 398.2424965049590924 570.6446314914809363, 398.7082181689096956 569.2349877822352937, 399.0323140640696238 567.7862110892872352, 399.2116095025362483 566.3124929340642666, 400.8216095025362620 543.8524929340642302, 400.8545746112825441 542.3765991736720480, 400.7423023309647192 540.9046127520527989, 400.4858801304360441 539.4507913237353023, 400.1832061138376844 538.3699416563921432, 399.4305246927873441 538.4809330306229640, 398.0491512603921933 538.8208536051895408, 396.7061915225052644 539.2901058818548563, 395.4137245875786562 539.8844692209517007, 394.1833754119213040 540.5985976846752692, 393.0262102402667210 541.4260681205522587, 392.6360783261053484 541.7652507328747333, 391.9685085483699254 541.9299905604917740, 390.5856952737530037 542.4187651925641376, 389.2572258014464524 543.0402489450177654, 387.9958007317619604 543.7885002310079017, 386.8134796987125128 544.6563655248312443, 385.7215660759878233 545.6355477519588248, 384.7304989130250874 546.7166856117673888, 383.8497531343004994 547.8894430746184980, 383.0877489559687206 549.1426081976646856, 382.4517713858577395 550.4642003146697107, 381.9479005764238764 551.8415845750758990, 381.5809536965165307 553.2615927372851274, 381.3544388776768983 554.7106490613319920, 381.2705216752593742 556.1749000973749162, 381.1705216752594083 568.1649000973749253, 381.2330492398124306 569.6638638898125464, 381.4449995636816766 571.1490841453908160, 381.8042523880045565 572.6057033665957761, 382.1637297283103294 573.6159239918135881, 381.1812100746837473 573.6313178241834976, 379.7223239290805168 573.7977573018584962, 378.2867008641109692 574.1060385061006173, 376.8880976636885975 574.5532073481837188, 375.5399163707859884 575.1349788557612328, 374.2550758630495693 575.8457782333928208, 373.0458880583217365 576.6787942827334064, 371.9239399363232224 577.6260446704902733, 370.8999825070184784 578.6784524187196439, 370.5420537899117335 579.1267568406917690)), ((716.4869672452059604 577.7015809666642099, 716.2410579957321488 577.5846076397273237, 714.8564718591514975 577.0857027140095852, 713.4296589146280212 576.7248845938203203, 711.9743545189844554 576.5056267307061262, 710.5045683048338105 576.4300398323094896, 709.0344493154559586 576.4988515434308738, 688.0244493154559677 578.5188515434309693, 686.5679478754776710 578.7314391155573503, 685.1392948398660110 579.0857559572669970, 683.7522471209180139 579.5783902494580389, 682.4201610013319623 580.2045982743885588, 681.1558635227072500 580.9583500943032277, 679.9715289702603513 581.8323876154439631, 678.8785616431192693 582.8182944783288804, 677.8874860390369577 583.9065771013061976, 677.0078455109655806 585.0867560969909391, 676.2481103713573702 586.3474671813104351, 676.0933051849460753 586.6727597270502201, 676.1575319713325598 586.7121977226412355, 677.4811632265394792 587.3557095307032796, 678.8614871547321172 587.8664080720512857, 680.2852151802400158 588.2393767788614696, 681.7386408699462663 588.4710250281054869, 683.2077718870780245 588.5591227089912536, 684.6784646974311954 588.5028216925577453, 703.9784646974311499 586.8128216925578045, 705.4370727340350413 586.6125679067305327, 706.8690229274050125 586.2702849435393091, 708.2605199394919282 585.7892703402923189, 709.5981581562137990 585.1741581681250182, 710.8690508364866218 584.4308743875857317, 712.0609542624558799 583.5665797582225878, 713.1623856948758657 582.5896008521755220, 714.1627339972567370 581.5093498363893332, 715.0523618630339797 580.3362337962568063, 715.8226986609081450 579.0815544742687280, 716.4663230038869415 577.7573993895794047, 716.4869672452059604 577.7015809666642099)), ((91.6396640378919187 579.6212859303033156, 91.2008676440531758 579.4570085816051233, 89.7518323303669092 579.0742159938798750, 88.2718306179739756 578.8379355739521088, 86.7756377488060195 578.7505261706926376, 85.2781906053228198 578.8128604149028433, 83.7944385917308381 579.0243160076142885, 82.3391943901937111 579.3827819326613735, 80.9269860819746327 579.8846795315042755, 79.5719121098234154 580.5249982299080784, 78.2875005295565671 581.2973455598080363, 64.6175005295565512 590.4773455598079863, 63.4168521263587905 591.3737807762714738, 62.3116291770701380 592.3855292221724085, 61.3128601006798277 593.5024952097762707, 60.4305110708499598 594.7135331426981111, 59.6733865688862934 596.0065587317501468, 59.0490415286056205 597.3686695774944155, 58.5637059497582300 598.7862739162724210, 58.2222227322471326 600.2452262450260605, 58.0279993514626398 601.7309684715828553, 57.9829738569378108 603.2286751819437995, 58.0875955336057359 604.7234015750299250, 58.3408204186294199 606.2002325887285679, 58.7401217185394131 607.6444317291892503, 59.2815150227322363 609.0415881182855173, 59.2830172093564158 609.0445481981745388, 59.5492583155675987 609.0279462650702271, 60.9872567414045292 608.7967503446036517, 62.3960543542110457 608.4271826072395015, 63.7623690662805274 607.9227273228659669, 65.0733193170001698 607.2881404757852124, 66.3165455196907487 606.5294049254821402, 85.1665455196907430 593.7294049254821857, 86.3448203135003496 592.8416069515225217, 87.4301837645410416 591.8423557761722122, 88.4121367096491895 590.7413175634441131, 89.2811803176454504 589.5491431048623099, 90.0289079754014097 588.2773647900130527, 90.6480866084356762 586.9382850490387682, 91.1327266493923815 585.5448573462153945, 91.4781399775680342 584.1105608758117569, 91.6809852690144993 582.6492701723470873, 91.7393003185277678 581.1751208965632713, 91.6525210208589129 579.7023730954139182, 91.6396640378919187 579.6212859303033156)), ((287.7699456973233509 705.7928647171924013, 287.6564277494455268 706.2682836328325493, 287.4563939635299903 707.7305237105753122, 287.4010248814195734 709.2053436403331261, 287.4908565150944924 710.6784661193062220, 287.7250192306360645 712.1356302772221625, 288.1012461668995002 713.5627297316732438, 288.6158951803375885 714.9459491479018425, 289.2639841035330051 716.2718979810383644, 290.0392389761120171 717.5277401060712918, 290.9341547811301325 718.7013180806402488, 291.9400680989584203 719.7812708376997080, 293.0472409753292595 720.7571436687046571, 297.0972409753292709 723.9871436687046753, 298.2764310703712454 724.8374707693004666, 299.5323998742528602 725.5696509533595417, 300.8533493995913091 726.1768064604256097, 302.2268712602983101 726.6532339537682219, 303.6400632301937890 726.9944580948771318, 304.3490050858668496 727.0943369957693676, 304.3578336916989997 726.5749206859779861, 304.3126241156118112 725.1287686802203325, 304.1282953560188389 723.6936998881709542, 303.8065624073137769 722.2830661868705420, 303.3504186721787619 720.9099921093262537, 302.7641081109716197 719.5872527338844975, 302.0530857559278388 718.3271548249286980, 301.2239669575520793 717.1414223307687053, 300.2844658354106855 716.0410873040508477, 299.2433235059795607 715.0363872595630710, 298.1102267553147840 714.1366699244183565, 290.5255191027189312 708.6892338440316053, 287.7699456973233509 705.7928647171924013)), ((801.4203671570833194 749.3696230283200066, 801.3975457206398687 749.3546996615908711, 800.0796805534039322 748.6685510906089576, 798.7003990601132273 748.1161463271067760, 797.2732339444241916 747.7029052436768097, 795.8121877161024713 747.4328823202406511, 794.3315953065832673 747.3087268638623755, 792.8459834230048955 747.3316570152959457, 782.3959834230048500 748.0116570152960094, 780.9201089546818366 748.1814178427097204, 779.4682654587458046 748.4963379088856072, 778.0546940627880304 748.9533281640354971, 776.6932604835097891 749.5479059924024341, 775.3973190179574431 750.2742391822222316, 774.1795815512709851 751.1252031338676716, 773.0519928658490016 752.0924507450203009, 772.0256134750213732 753.1664942873668451, 771.1105111305139417 754.3367984716919636, 770.3156620679081925 755.5918837884918275, 770.1604980993263325 755.9008055453533643, 770.3439400520314848 755.9099914025998714, 789.0239400520314348 755.9299914025998532, 790.5307971351795686 755.8557336135193054, 792.0225730992484614 755.6304849480429766, 793.4841769231848048 755.2565240541950971, 794.9008228116612145 754.7376339743748304, 796.2581797703169286 754.0790638755535156, 797.5425165801664207 753.2874759479431077, 798.7408407045927561 752.3708780093187443, 799.8410297237245459 751.3385424967771087, 800.8319539665853881 750.2009126654245392, 801.4203671570833194 749.3696230283200066)), ((582.1956155616677506 748.3040194052581455, 582.4694120083329381 748.3634675859177605, 583.8889835664084558 748.5324277484521645, 585.3181926005662490 748.5654804594214511, 594.2781926005661717 748.3454804594214238, 595.0594255408984736 748.2889561559255753, 594.6453634486080091 748.1216975293750693, 593.2255692435271612 747.7053853572047046, 591.7716676973836911 747.4309737984108324, 590.2978045059149963 747.3011327328265452, 588.8183195811466248 747.3171254455846793, 587.3476075316059450 747.4787963360195135, 582.1956155616677506 748.3040194052581455)), ((266.5733682274578769 741.8757843688398452, 271.6683397314557737 748.8491629852414917, 272.5707161547950932 749.9675451179128913, 273.5759719733334236 750.9944445985689754, 274.6748808964300110 751.9204364899027269, 275.8573570803370671 752.7370219897554762, 277.1125476966029737 753.4367064335693840, 278.4289325399010977 754.0130680808119905, 279.7944297610787316 754.4608170540473111, 281.1965067550024173 754.7758438897089945, 282.6222951854653616 754.9552572549723664, 284.0587090914523287 754.9974104845583724, 285.3079478592551368 754.9142120645407203, 285.5117909468652329 753.5797564963253308, 285.5899173251430625 752.0998019966644961, 285.5216199069931804 750.6193613478293400, 285.3075653872402313 749.1528860788178008, 284.9498432890960089 747.7146913936139754, 284.4519455669852732 746.3188164313174866, 283.8187325193434276 744.9788872211181570, 279.6387325193434776 737.1588872211182206, 278.9008964874073513 735.9247302909011523, 278.0482804785587518 734.7668912347792229, 277.0887192583735441 733.6960095404997446, 276.0310303216416514 732.7219256367517346, 274.8849328676103028 731.8535904685769538, 273.6609584893838587 731.0989832463274070, 272.3703543981625330 730.4650381239792978, 271.0249800716021582 729.9575804805662074, 269.6371982760019250 729.5812733902441778, 268.2197614637273659 729.3395747728815195, 266.7856945897727883 729.2347056189211116, 265.3481754242726538 729.2676295804982374, 263.9204134607804235 729.4380441163538080, 262.5659795003183490 729.7333822949589148, 262.4277593211255066 729.6985392077708639, 261.0319003846018973 729.4836468541001295, 259.6220180706476981 729.4009863729853578, 258.2106108349266265 729.4512905407356129, 256.8101906513703625 729.6341134162933031, 255.4331720948703435 729.9478342944535143, 254.0917622874013659 730.3896720732423091, 252.7978526831929571 730.9557099080874423, 251.5629136522617841 731.6409299342262784, 251.3535981855463319 731.7843628680421943, 251.8027903635786515 732.7486533321239222, 252.5591491975579572 734.0251804934415532, 253.4379253320758778 735.2207359228223140, 254.4305200392070105 736.3236212417451725, 255.5272208887981549 737.3230448385379532, 256.7172967835302870 738.2092274631124837, 257.9891029615266120 738.9734979158519081, 259.3301949390685195 739.6083778943462903, 260.7274502785010668 740.1076551677601856, 266.5733682274578769 741.8757843688398452)), ((671.7058358003221201 742.6060983245082525, 671.7716640506305339 743.1609997707265620, 672.0971258671011128 744.6280990203772490, 672.5677503043707475 746.0552704331201994, 673.1788137455358765 747.4281896125249887, 673.9241829995186208 748.7330766871499463, 674.7963768593765508 749.9568346179239597, 675.7866411905316681 751.0871806519984375, 676.8850367952649094 752.1127696036787711, 678.0805391715864516 753.0233077250749147, 679.3611491652116001 753.8096560235618426, 680.7140134040384964 754.4639219890601680, 682.1255533063350640 754.9795388104796530, 683.5816013677934961 755.3513312862362454, 685.0675433595497452 755.5755677673037098, 686.5684650099386772 755.6499976114533865, 704.2884650099387045 755.6399976114533956, 705.7234197654950094 755.5703897933234430, 707.1451339327556980 755.3638244877943180, 708.5405660303636068 755.0221965325506517, 709.8969156641849167 754.5486397042066073, 711.2017409462009709 753.9474979719586827, 712.4430726247985604 753.2242856500582775, 713.6095238795339810 752.3856368146305158, 714.6903947732166671 751.4392444488391902, 715.4544429556985961 750.6286117895585903, 715.4173346027711204 750.5031989201523857, 714.8631209454641748 749.1389952900607341, 714.1778213075760959 747.8357037921173287, 713.3680395541599637 746.6058835474736952, 712.4415791176509174 745.4613856742190592, 711.4073678003727537 744.4132390845118152, 710.2753717420957855 743.4715442048685645, 709.0564993816927881 742.6453756437707625, 707.7624963383643717 741.9426947445299447, 706.4058322254066979 741.3702728660925914, 704.9995804872357894 740.9336261310854752, 703.5572924176160541 740.6369622699002093, 702.0928665731097453 740.4831400730528230, 700.6204148401424163 740.4736418425526381, 676.7204148401424391 741.4936418425526199, 675.2497341874069434 741.6291825746795894, 673.7995116038739525 741.9086952409521700, 672.3838063392339564 742.3294700929417331, 671.7058358003221201 742.6060983245082525)), ((54.5630873447988947 787.0628528685565470, 54.8286671385824178 786.7035709895628770, 55.6040568105807864 785.3956578637572647, 56.2430560864145406 784.0159673863021226, 56.7390992744022853 782.5786758209794698, 57.0870895508758593 781.0985512804167001, 57.2834513298371917 779.5908019843268448, 57.3261670019595897 778.0709199956672819, 57.2147976654418571 776.5545220403204212, 56.9504876357065655 775.0571890458674034, 56.5359526876053664 773.5943060481880593, 49.5459526876053715 752.9743060481880548, 49.0080386585158507 751.6106214528225564, 48.3395804524038866 750.3059564364256175, 47.5469625497769641 749.0727719251849521, 46.6377552871242358 747.9228461311147385, 45.6206425522229608 746.8671620577148360, 44.5053388438563289 745.9158026007102080, 43.3024964881097674 745.0778542457678668, 42.0236038974279467 744.3613202829806141, 41.8870530213524646 744.3014946231943441, 40.1481309452966713 746.2541226109540276, 39.2064885143499851 747.4245966135624712, 38.3866427974115965 748.6833868498445099, 37.6968166643190017 750.0178679342529904, 37.1439289174587159 751.4146553189015094, 36.7335248977202085 752.8597395375973065, 36.4697208659625360 754.3386267176654201, 36.3551627178352561 755.8364839502610266, 36.3909994460346979 757.3382880611401333, 36.5768716161632810 758.8289762897491073, 38.0968716161632770 767.4689762897490937, 38.3933819454011740 768.7977894166137958, 38.8091579054447493 770.0942434468527154, 39.3407741256748764 771.3476575427766875, 39.9838508853851664 772.5477054512342647, 40.7330901961149152 773.6845005764567986, 41.5823194491317736 774.7486774307875521, 47.8923194491317759 781.9586774307875885, 48.7817525514504524 782.8909412170603446, 49.7479111796048343 783.7434343348851371, 50.7836663410264251 784.5098664951474348, 54.5630873447988947 787.0628528685565470)), ((246.6755090561052270 770.7799266675946228, 246.5419613616959396 769.7343988162876940, 246.2278091996309115 768.3435324163622226, 244.8678091996308979 763.4235324163621499, 244.4401408325413172 762.1138575010600107, 243.8941111658815259 760.8489463503826755, 243.2343266239006141 759.6394700281076666, 242.4663532915550945 758.4956319378227363, 241.5966699578133898 757.4270817448076514, 240.6326134591923278 756.4428339693735097, 239.5823167846189108 755.5511919384211978, 235.7403959209729578 752.5821225053352919, 235.7687220243383024 752.9434982907041558, 236.0156362464779534 754.3517165020092534, 236.3954984706393532 755.7300306368859992, 236.9048577833016509 757.0659192004834495, 237.5390868358441310 758.3472461192114906, 238.2924238823666201 759.5623709926641141, 243.9824238823665894 767.8323709926642096, 244.8931365423386524 769.0237772298345362, 245.9182591460712217 770.1182953970287599, 246.6755090561052270 770.7799266675946228)), ((842.8050333492089976 845.1634886735652117, 842.6629615839375447 852.3119417051201481, 842.7060628097700601 853.7846329259215281, 842.8934314565976820 855.2459919296162525, 843.2232598940211119 856.6819203224550847, 843.6923661173661912 858.0785650513366818, 844.2962244459271233 859.4224520505003966, 845.0290091842636002 860.7006162319555642, 845.8836508253284592 861.9007265655714036, 846.8519042532128651 863.0112050421228105, 847.9244282875214367 864.0213383716017006, 849.0908758019816105 864.9213813391892245, 850.3399935478668112 865.7026508217675200, 851.6597307191972277 866.3576095579505818, 853.0373552123350009 866.8799388634665775, 854.4595764583684741 867.2645995903733365, 855.9126736432648386 867.5078807420068188, 857.3826280787914129 867.6074352746503564, 864.0987909682756936 867.7316481797031429, 864.1510667363627363 866.8912146773814129, 864.1701046439842457 865.4152406380787852, 864.0439809227788146 863.9445419559649508, 863.7739169334533926 862.4933606274288422, 863.3625279298377109 861.0757496461204710, 862.8137977332100945 859.7054369161456862, 862.1330401536487216 858.3956923133731607, 859.8730401536487307 854.5256923133731561, 859.1713493320705766 853.4352649062161618, 858.3781054325954756 852.4095134473765256, 857.4992362855833790 851.4561032723750031, 856.5413095900931921 850.5821591177327718, 854.7113095900932649 849.0521591177326854, 853.5693454624166634 848.1866132150895510, 852.3500025296241347 847.4339814387661818, 851.0644079830283317 846.8011319777996277, 849.7242935972479927 846.2938399401061815, 848.3418886711539244 845.9167346513688699, 846.9298084286267567 845.6732574098308532, 842.8050333492089976 845.1634886735652117)), ((615.1561315042704337 842.2681733819154033, 615.0899777820429790 842.2003800550953656, 613.9891227652622092 841.2702728833916126, 612.8040888782426237 840.4501047265836178, 611.5458152010222648 839.7474465792314504, 610.2259168921337960 839.1687846978016978, 608.8565779687611439 838.7194607258584256, 599.0265779687611030 836.0094607258585029, 597.5986019350945071 835.6898255714713741, 596.1462761736891025 835.5108505245943888, 594.6834221924524400 835.4742388566913860, 593.2239616943569445 835.5803389940389252, 577.1539616943570081 837.5403389940388479, 575.7002068537302648 837.7907222589155936, 574.2780759252898406 838.1826897809742150, 572.9013230951832156 838.7124506334059788, 571.5832636737843586 839.3748812166656990, 570.3366453162307153 840.1635748115337492, 569.1735247330555012 841.0709035419121165, 568.1051510833152633 842.0880921480794541, 567.1418571779770446 843.2053028569048365, 566.2929595457964069 844.4117305281947665, 565.5666683282023541 845.6957071569624986, 565.1232869845573532 846.6982356819614779, 565.4411693643806984 846.8172595910841665, 566.8540244703909821 847.1942360338514391, 568.2968721554487956 847.4318519986014735, 569.7560004237412841 847.5278493196610725, 571.2175425581418722 847.4813156936195355, 572.6676089015269326 847.2926933493357637, 577.2747043277821604 846.4633150289122341, 577.3519969665708231 846.5943572076149621, 578.2510181192611753 847.8167939819026060, 579.2689448702777781 848.9421454068717594, 580.3953600255302945 849.9588949326314378, 581.6187361491925003 850.8566374125045968, 582.9265535324417442 851.6261855865565167, 584.3054283168595475 852.2596641015924206, 585.7412494613116678 852.7505901054457809, 587.2193231506237225 853.0939395907793141, 588.7245231682130679 853.2861988094515482, 590.2414456938026888 853.3254002312894499, 601.1414456938026660 853.0554002312893545, 602.6134963413712740 852.9462863481577415, 604.0676950705842501 852.6930177072756578, 605.4899598155303693 852.2980468934392775, 606.8665177503368113 851.7651986968386382, 608.1840386614780982 851.0996330748264427, 609.4297640339468671 850.3077951842916491, 610.5916306012513814 849.3973529685090398, 611.6583871628146198 848.3771229028567404, 612.6197035375482756 847.2569846184613880, 613.4662705985227831 846.0477852305306214, 614.1898904200273819 844.7612342978331981, 614.7835556640575305 843.4097904305084512, 615.1561315042704337 842.2681733819154033)), ((565.4976755095300405 865.5529146635794859, 567.5764907601558207 882.1357847430695074, 567.8332310669231902 883.5953015770571710, 568.2327354626006581 885.0223619162325122, 568.7711045978973061 886.4030369858895710, 569.4430837388763393 887.7238507531942560, 570.2421140556099317 888.9719114595515066, 570.7860190990053297 889.6608389243253896, 570.7800025440202489 899.9912638337472117, 570.8514285494436535 901.4621061677027001, 571.0667314054700228 902.9188571847929552, 571.4238360912605685 904.3474771794006983, 571.9193009447852774 905.7341975638071290, 572.5483508324724653 907.0656535654861727, 573.3049231704362683 908.3290130325557357, 574.1817263537434428 909.5121001059992523, 575.1703100305948055 910.6035125667442571, 576.2611465441443670 911.5927317266443879, 577.4437227570426785 912.4702238042688123, 578.4137066123810200 913.0518737442496331, 578.6240006393576323 913.0362238879457664, 580.0705898925249357 912.7846171169323952, 581.4856490579824140 912.3927829774629572, 582.8556191870129624 911.8644759832229738, 584.1673733693243094 911.2047583164988964, 585.4083425137280301 910.4199513228778642, 586.5666357838558724 909.5175749407757166, 587.6311545349070684 908.5062756461753679, 588.5916986597018195 907.3957436029990049, 589.4390643250388848 906.1966198129728127, 590.1651321618619477 904.9203941546624037, 590.7629450642035636 903.5792952886599778, 591.2267748514452705 902.1861734838395250, 591.5521771551450456 900.7543774874259270, 591.7360340045098610 899.2976266186976773, 591.7765837024654729 897.8298793119058701, 591.6734377060530505 896.3651993680194892, 590.1934377060530323 884.0151993680194664, 589.9439853449597422 882.5513551722905277, 589.5510176117520587 881.1193486532822590, 589.0183857060271748 879.7332138969081825, 588.3513095774331987 878.4065354322889334, 587.5563267686246718 877.1523150993637046, 586.6412283454175167 875.9828446270266795, 585.6149825420479829 874.9095851705619680, 584.4876468698346343 873.9430549889434587, 583.2702695506022792 873.0927263627975208, 581.9747812408534173 872.3669327632609338, 581.8727814612558404 872.3224015127137818, 581.8626910777524017 872.3039203453625987, 581.0481817080628844 871.1042637940950044, 580.1216440401727823 869.9888583501902986, 579.0917364712170183 868.9681273603016507, 577.9680833791925352 868.0516094482590006, 576.7611851841572843 867.2478693776315595, 575.4823202229086974 866.5644180149129170, 574.1434393541111376 866.0076421412624086, 572.7570542787780141 865.5827447687065614, 571.3361206197403135 865.2936965185386953, 569.8939168527131187 865.1431985162798810, 568.4439202203352579 865.1326571499452029, 566.9996807887532668 865.2621709274952764, 565.5746948236775324 865.5305295562895935, 565.4976755095300405 865.5529146635794859)), ((555.0507817693414836 912.4923235955484415, 556.3124252132740821 912.0501742503136029, 557.6710919085014666 911.4153486718521435, 558.9595795540419658 910.6480297739494745, 560.1650091220068362 909.7558872626515267, 561.2753317882751389 908.7478385135526651, 562.2794493660433091 907.6339594385153760, 563.1673252373180958 906.4253837722878870, 563.9300846735388859 905.1341917856937016, 564.5601035425805776 903.7732895377564546, 565.0510845154677781 902.3562798736921877, 565.3981200110804366 900.8973264582008369, 565.5977412496894203 899.4110122031103174, 565.6479529250082123 897.9121935044511247, 565.5482531481991373 896.4158517459302402, 563.2782531481991555 877.0258517459302539, 563.0341091004802365 875.5673313516346070, 562.6475344031073291 874.1399393031896352, 562.1222863698079664 872.7575491411413395, 561.4634701501411200 871.4335970100895565, 560.6774891099835259 870.1809510660472142, 559.7719825939996099 869.0117864043662621, 559.3004021921324238 868.5132500016680979, 558.8409704159568037 868.3153840643343528, 557.4192304488099126 867.8643638960960516, 555.9597265467073157 867.5567731191085841, 554.4768900489370935 867.3956531413862194, 552.9853830037319540 867.3825970912937464, 551.4999531922996994 867.5177340649493090, 550.0352883051402841 867.7997278497438174, 548.6058707125300771 868.2257901365952648, 547.2258342651795147 868.7917080902993803, 545.9088245409957381 869.4918860053651315, 544.6678639198079281 870.3194006354484600, 543.5152228201754951 871.2660696492957868, 542.4622983714671136 872.3225325363166576, 541.5195017208828858 873.4783431618033092, 540.6961550897099187 874.7220730566264137, 540.0003995967001629 876.0414244200953817, 539.4391147600014165 877.4233517186290783, 539.0178504735907836 878.8541906778888233, 538.7407721308177315 880.3197933929199053, 538.6106194376658323 881.8056682203551873, 538.6286793229824070 883.2971230694482756, 539.4386793229823525 896.3771230694483165, 539.5936445761902860 897.7900025545599192, 539.8816437557471772 899.1818714929752332, 540.3000909584029614 900.5402324934977969, 540.8452290068529464 901.8528890278902281, 541.5121631848923016 903.1080549417494012, 542.2949051863698742 904.2944602806929879, 543.1864268833289771 905.4014524816601579, 544.1787234305575112 906.4190920207453246, 545.2628851399437053 907.3382416587542139, 546.4291774792850447 908.1506484831621719, 547.6671284772542094 908.8490180098298197, 553.8471284772542731 911.9590180098297196, 555.0507817693414836 912.4923235955484415)), ((172.4342484777618836 876.4396109803365107, 164.4871950427570368 880.6349812389573799, 163.2187417871513162 881.3865557268838984, 162.0302013064647042 882.2590288773750444, 160.9330566865193930 883.2439712896427864, 159.9379079918776370 884.3318669409400172, 159.0543698533004999 885.5122051256305440, 158.2909785759768226 886.7735820043883450, 157.6551096659964912 888.1038107824108465, 157.1529065718862057 889.4900394521564522, 156.7892213296712214 890.9188749630371831, 156.5675676849214142 892.3765126183988059, 156.4900871446925521 893.8488694496202243, 156.5575282873523406 895.3217202787388942, 156.7692395301893384 896.7808351550304451, 157.1231754246802552 898.2121168377013873, 157.6159164185944803 899.6017369964049522, 158.2427018940034316 900.9362698136767449, 161.4827018940034122 907.0362698136767676, 162.2381436247596298 908.3038258833122427, 163.1144726916313346 909.4910234089134065, 164.1032086027466903 910.5863735339002005, 165.1947830724300843 911.5792762357664287, 166.3786326162923501 912.4601229055874683, 167.6433007771489088 913.2203893333306723, 168.9765489924960207 913.8527181991207726, 170.2756524736994379 914.3187667624736150, 171.6683807226745557 913.8086702947168760, 173.0217524485878471 913.1525901107077061, 174.3026202207173583 912.3642792667693584, 175.4981067282547258 911.4516631116159715, 176.5961930480450519 910.4239167077593038, 177.5858394778317688 909.2913725892539105, 178.4570965248225036 908.0654168827049944, 179.2012049337434405 906.7583748358970297, 179.8106837487415817 905.3833869048883116, 180.2794055237974078 903.9542766453424747, 180.6026579255137108 902.4854117362593797, 180.7771911089517403 900.9915595333189913, 180.8012503902179446 899.4877386040406009, 180.6745938873246473 897.9890677373638255, 179.1945938873246291 887.0490677373637709, 178.9336776579499713 885.6347118569829036, 178.5385062607795135 884.2518454852834111, 178.0127126027753661 882.9131816498409080, 177.3611304331775500 881.6310270135425071, 176.5897499056693789 880.4171687364358831, 175.7056625094346316 879.2827661134938353, 174.7169958753692640 878.2382479844960699, 173.6328390567898339 877.2932168591637492, 172.4631589715511666 876.4563606389472170, 172.4342484777618836 876.4396109803365107)), ((388.5573318034598742 890.0603449249767891, 387.7473318034598719 898.5803449249767709, 387.6800763142563255 900.0478479034569546, 387.7566926459293200 901.5148919486024397, 387.9764459326638644 902.9674059022860320, 388.3372284100358911 904.3914579718036748, 388.8355796316427018 905.7733893567135510, 389.4667196599866088 907.0999452572689279, 390.2245949132621945 908.3584020078760659, 391.1019362283076930 909.5366891161778540, 392.0903285828089224 910.6235050372241631, 393.1802918080156246 911.6084255722792022, 394.3613715178132679 912.4820038525610926, 395.6222393820043521 913.2358609489170931, 396.9508017820274972 913.8627662383515826, 398.3343158069422998 914.3567067565722937, 399.7595114771046383 914.7129448713585589, 401.0682183849587545 914.9066732404827462, 402.4225675886027602 914.7025114157816006, 403.8718766414368702 914.3334227234082618, 405.2772278540819002 913.8218455908250917, 406.6246506414165651 913.1728656021998631, 407.9007502843305701 912.3929342623886214, 409.0928410865194564 911.4898048625784668, 410.1890724828900261 910.4724554048815435, 411.1785468459327149 909.3509993520885928, 412.0514278189490369 908.1365850898160943, 412.7990380991946040 906.8412851004947015, 413.4139456988761481 905.4779759509182213, 413.8900378264822280 904.0602102863966820, 414.2225816539950642 902.6020821040176543, 414.4082713658951889 901.1180866443347668, 414.4452610222476210 899.6229762942978141, 414.2252610222476505 890.8729762942978141, 414.0954996766138834 889.2457746223060440, 413.7896896348541986 887.6423085308302916, 413.3114525350086410 886.0815675002667149, 412.6664520291372469 884.5820350279343529, 410.6764520291372378 880.5720350279343620, 409.9461291549289967 879.2679060530959987, 409.0896415402177126 878.0429355026536768, 408.1154936269412588 876.9092866475562005, 407.5509133301176803 876.3713482867362927, 403.1068745002948503 876.4848936365401642, 401.6609298234872085 876.5919342327691766, 400.2320744639195027 876.8380765880518766, 398.8336584629643085 877.2210209520289936, 397.4787474618312899 877.7371894096454525, 396.1800006270344738 878.3817593102263572, 394.9495523736221685 879.1487083263310751, 393.7988989912501552 880.0308707213951038, 392.7387912323700903 881.0200043004388135, 391.7791338661006648 882.1068674183126177, 390.9288931362658559 883.2813053259770868, 390.1960129882369870 884.5323450480701695, 389.5873408472846791 885.8482979053025019, 389.1085636419070966 887.2168687237990525, 388.7641546698786783 888.6252707110227220, 388.5573318034598742 890.0603449249767891)), ((194.1232102403991746 764.2181708119628638, 193.8135864174863343 765.0253541096298022, 193.4295128450408185 766.4110960109767348, 193.1798962538040882 767.8272473742069906, 193.0670306680941337 769.2607934968559675, 193.0919533442767602 770.6985598152859893, 193.2544352381809176 772.1273329814508770, 193.5529831100583635 773.5339822960979745, 193.9848532477433594 774.9055803824103350, 199.8648532477433548 790.8655803824103714, 200.4556608858308095 792.2505858684052100, 201.1823483430266037 793.5693804699305929, 202.0375928903933982 794.8086748762997331, 203.0127763469877209 795.9559808892897763, 204.0980719240764927 796.9997372649875160, 205.2825432483273289 797.9294262148443977, 206.5542545661288329 798.7356793919753954, 207.9003910185281541 799.4103722946948665, 209.3073877747910672 799.9467061359952140, 210.7610667233238644 800.3392763539827683, 212.2467793425419984 800.5841270729001735, 213.7495543119945296 800.6787909659385605, 214.9645879578181962 800.6331861669941645, 215.0564446395790981 800.5601363489558935, 216.1662181906991407 799.5845959288915310, 217.1746273768193873 798.5046017810863077, 218.0718871489445689 797.3306335756944918, 218.8492909909574848 796.0740828555209418, 219.4992954028703309 794.7471424987456885, 220.0155930988062494 793.3626884059292479, 220.3931742094380013 791.9341545593441651, 220.6283748950087897 790.4754026669882023, 220.7189128972214007 789.0005876561816649, 220.6639096850194335 787.5240203219253772, 220.4638989793701853 786.0600284628050076, 220.1208215743295966 784.6228178519007770, 219.6380065046423908 783.2263343917667271, 219.0201387426169788 781.8841287910502160, 218.2732137377268486 780.6092250758564433, 217.4044792400611925 779.4139942117508326, 214.1844792400611937 775.4139942117508326, 213.1800174527579941 774.2873257332820458, 212.0674109791380602 773.2673099601752256, 210.8579259504241463 772.3642754603665708, 209.5638094794710469 771.5873662622389020, 208.1981656478429557 770.9444492631917001, 206.7748228153197658 770.4420345703009616, 205.3081935954412245 770.0852095796878984, 203.8131289149616805 769.8775874621034063, 202.8728765568219501 769.8424817580990975, 202.3215267813212677 769.1263830812006290, 201.3124411732066790 768.0527286403225844, 200.2028876499301759 767.0832552771476003, 199.0035721091184939 766.2273172791075240, 197.7260665471941365 765.4931734497570233, 196.3826974031044870 764.8879074209489772, 194.9864266225598612 764.4173593040485457, 194.1232102403991746 764.2181708119628638)), ((390.0235384568679819 797.6608779308547810, 392.2735384568679819 818.8908779308546855, 392.5049757159849833 820.3677870720324563, 392.8822727173749172 821.8143245893281801, 393.4016819395957327 823.2161226760988484, 394.0580443244243156 824.5592579023452799, 394.8448405194621955 825.8303895098807743, 395.7542556319307891 827.0168919200830260, 396.7772568504872197 828.1069801380882609, 397.9036831640770515 829.0898268078462934, 399.1223462866879004 829.9556697553779259, 400.3488806925065546 830.6547242593801457, 400.6233138842029575 830.5715609462788507, 404.1533138842029871 829.2915609462788780, 405.6288330669310085 828.6654324166654533, 407.0291167428743506 827.8854327877676269, 411.3991167428743552 825.1354327877676269, 412.6030630703132829 824.2927461537503859, 413.7187720281454517 823.3362938319287423, 414.7355347565564330 822.2752560962098869, 415.6435921069378310 821.1198170580415763, 416.4342283126907773 819.8810669167065726, 417.0998546453819245 818.5708955127547597, 417.6340822532989705 817.2018782062738183, 418.0317834832829362 815.7871551753684116, 418.2891410972538324 814.3403052933708750, 418.4036849110378284 812.8752157953440474, 418.3743155038265513 811.4059489848480098, 417.1443155038265331 793.6059489848479416, 416.9710686168702409 792.1451474650720002, 416.6555674399971281 790.7083403898487859, 416.2008463311057085 789.3093463665776426, 415.6112786069678009 787.9616203325949755, 414.8925344825034358 786.6781241512341012, 414.0515265370337374 785.4712019500643692, 413.0963432319941262 784.3524614002524231, 412.0361711195073440 783.3326620788537866, 410.8812064899805705 782.4216119877162328, 409.6425573084641201 781.6280732242342992, 408.3321363829055599 780.9596777111746633, 407.6860052811823039 780.7064203631657620, 403.3640822748737378 781.1630135977928830, 401.9092450854231515 781.3893728980765445, 400.4836080716677884 781.7572708263720642, 399.1009067062711893 782.2631628254824818, 397.7744627931871264 782.9021748176044184, 396.5170561171238433 783.6681501642212879, 395.3408013151590126 784.5537089829838351, 394.2570311567998260 785.5503192500810883, 393.2761873570411240 786.6483790030582668, 392.4077199743963433 787.8373088520886540, 391.6599963631628611 789.1056539083874668, 391.0402205571324998 790.4411941477261507, 390.5543638614546467 791.8310621457383149, 390.2071073213725754 793.2618670506794842, 390.0017966221255961 794.7198235992117361, 389.9404098545389274 796.1908849321951038, 390.0235384568679819 797.6608779308547810)), ((568.0924814741946420 798.5753466115924084, 569.4073157000115089 804.1018244369307695, 569.8134962227351252 805.5026902813177685, 570.3538122023998085 806.8574841302590812, 571.0231548728175994 808.1533962108293281, 571.8151955016694501 809.3781734857891479, 572.7224452295907895 810.5202355078339451, 573.7363258784250775 811.5687839144081863, 574.8472510591475384 812.5139045277940113, 576.0447168125666622 813.3466610951043094, 577.3174009257872967 814.0591797818530040, 578.6532699853798931 814.6447236202060367, 580.0396931550558293 815.0977562079918926, 581.4635616020610769 815.4139940561899493, 582.9114124430974471 815.5904470899421312, 584.3695560378424716 815.6254469201452366, 594.9195560378424261 815.3654469201452457, 596.3504080227390887 815.2615590504035481, 597.7647912346145631 815.0214544437692439, 599.1497679639123817 814.6473293958697468, 600.4926694893724743 814.1426061201241282, 601.7812119619264877 813.5119014439194416, 603.0036087680732635 812.7609845773592951, 604.1486783449180393 811.8967243408872037, 605.2059464606664960 810.9270263345038074, 606.1657420249875940 809.8607606233081242, 607.0192855528446216 808.7076806008396943, 607.7587694725946221 807.4783337724005605, 610.2882558524521528 802.7780195292326653, 609.7344626938289593 802.0630143251453319, 608.7012360380286964 800.9740379290909686, 607.5643393650051394 799.9937864979328879, 606.3351589572506555 799.1320774759238930, 605.0260053401528921 798.3975410777381967, 603.6499899892435224 797.7975338548807258, 602.2208940157446477 797.3380650181256897, 600.7530301455610697 797.0237362538848629, 599.2610993740274807 796.8576956372559152, 597.7600437320483024 796.8416061033243523, 572.1000437320483343 797.8516061033243432, 570.6320996876297613 797.9818357020678832, 569.1840195896883188 798.2554841917014983, 568.0924814741946420 798.5753466115924084)), ((354.1446777354008759 797.9093770238205252, 355.5546777354008441 818.6093770238205707, 355.7265636570276115 820.0707743078369276, 356.0408102218203794 821.5082985296502329, 356.4943933627511115 822.9081160627763438, 357.0829481448462275 824.2567561408761776, 357.8008107699607763 825.5412404900403089, 358.6410730808115659 826.7492082217067946, 359.5956490397649077 827.8690347843370319, 360.6553525426380133 828.8899438291507522, 361.8099858186941447 829.8021109134109565, 363.0484375661377499 830.5967580432981094, 364.3587898787292829 831.2662381465647741, 365.7284329345403648 831.8041086620725082, 366.0871753562191770 831.9057408805267642, 366.2600142907204486 831.8852022279204448, 367.7094882830662073 831.6404230380968556, 369.1280424286152311 831.2549417254458604, 370.5020529856430471 830.7324604382847610, 371.8183240074075115 830.0779970676362609, 373.0642140755347782 829.2978370555878200, 374.2277577077353499 828.3994730301377558, 375.2977802738550963 827.3915328462717298, 376.2640053166111898 826.2836967243617892, 377.1171532463090443 825.0866042816867321, 377.8490304616798312 823.8117523499399795, 378.4526080409249289 822.4713845600814466, 378.9220892472217201 821.0783737549577381, 379.2529652003706246 819.6460983589931857, 379.4420581799139427 818.1883138922955823, 379.4875521438463579 816.7190208631475343, 379.3890101698152080 815.2523303076411594, 376.2190101698151921 788.0723303076410957, 375.9771157994910027 786.6211198767731503, 375.5942183725956625 785.2005868213564099, 375.0740014042411303 783.8243968247023759, 374.4214694385426014 782.5057889850487527, 373.8160862715055828 781.5351315022525114, 368.0917915305148540 781.9245981840556396, 366.6297563966483608 782.0964746707205677, 365.1916097374178776 782.4108350465193098, 363.7912030672251831 782.8646515484318797, 362.4420244076403606 783.4535532412882048, 361.1570683774764348 784.1718681164463760, 359.9487110350742682 785.0126777217619747, 358.8285906782552388 785.9678837966839637, 357.8074957500161872 787.0282862706779952, 356.8952609296044898 788.1836718737372394, 356.1006724097715050 789.4229125055267104, 355.4313832725275688 790.7340724157189698, 354.8938397784568224 792.1045231632142531, 354.4932192795363903 793.5210652470129844, 354.2333803534537537 794.9700552372546554, 354.1168256397022560 796.4375371819580778, 354.1446777354008759 797.9093770238205252)), ((436.6048807218392085 798.0819554444465211, 438.1548807218392199 816.1519554444465712, 438.3523639016997322 817.6099323918867867, 438.6917322129293666 819.0415487982357945, 439.1697206361650387 820.4330312641143337, 439.7817305055750126 821.7709925136800848, 440.5218737520381183 823.0425601919063183, 441.3830295515080024 824.2355007078726885, 442.3569128335554979 825.3383369325874810, 443.4341539909778476 826.3404586189859629, 444.0906877959951089 826.8407636164926089, 444.7337261175068193 826.4745444652974129, 445.9350082386600320 825.6244407958333795, 447.0472055588592752 824.6607125642348137, 448.0596125363765623 823.5926362114805670, 448.9624841691816641 822.4304925894981579, 449.7471297962160293 821.1854680020243222, 450.4059967500264179 819.8695465299693979, 450.9327430555522369 818.4953946777152396, 451.3222984752992488 817.0762394506931514, 451.5709133133045725 815.6257410378149189, 451.6761945081266276 814.1578613242636493, 451.6371286674438466 812.6867295002824676, 450.5271286674438329 798.0367295002823766, 450.3541672069954416 796.6345710430775853, 450.0500561216468896 795.2549042522618947, 449.6174931659766116 793.9099680845226885, 449.0603155848282313 792.6116934028866581, 448.3834660732989619 791.3715971386176307, 447.5929489303329660 790.2006801250815897, 446.6957767948786682 789.1093295098914950, 445.6999084371115600 788.1072266110285227, 444.6141781565726774 787.2032610343429724, 443.4482174135290506 786.4054518142955885, 442.2123693887603508 785.7208762774944262, 441.7108089837752800 785.5019060476208779, 441.2409611398131801 785.9039586188763451, 440.2061557908223222 786.9858674067493212, 439.2843527123861804 788.1655401665273075, 438.4847344919017473 789.4312255291059728, 437.8152665662020127 790.7703153065428978, 437.2826178734592304 792.1694700891513321, 436.8920944202244527 793.6147521266963167, 436.6475864253783357 795.0917641699894602, 436.5515295675211860 796.5857928898054752, 436.6048807218392085 798.0819554444465211)), ((39.9059118867788456 828.1305288869898504, 39.3004480546484132 828.7199491645429816, 38.3488576167046986 829.8489141776390170, 37.5128712711940580 831.0659652006041824, 36.8005891178097784 832.3593098941606740, 36.2189126525907810 833.7164166888496766, 35.7734778974443657 835.1241362068249146, 35.4686007911928698 836.5688286697610465, 35.3072353712653566 838.0364960583857510, 35.2909451512233545 839.5129177431058451, 35.4198879714515584 840.9837882715657997, 35.6928144697992593 842.4348559780772803, 36.1070801869910056 843.8520610718941271, 36.6586711895136617 845.2216718663597703, 37.3422429617130121 846.5304178289641186, 52.5022429617130086 872.3704178289640367, 53.3058958769826674 873.5983624971858035, 54.2256967667732539 874.7418975598648103, 54.2935400977777007 874.8111302617710408, 54.9457770427202377 874.0791911630606137, 55.7977359858283535 872.9147616897946591, 56.5338791643970211 871.6738655379756437, 57.1473956834582637 870.3679836444679268, 57.6326092062693149 869.0091982028939128, 57.9850304725379928 867.6100808775655651, 58.1278835057097538 866.6682588952874084, 58.5831141436752958 865.9173481904664413, 59.2192441485234085 864.5896476898802803, 59.7221555077632189 863.2059822075209468, 60.0870036203869233 861.7796807467351528, 60.3102738639831841 860.3244830279871849, 60.3898154514853118 858.8544071326053881, 60.3248621499421276 857.3836144650470033, 60.1160396617251038 855.9262733345151446, 59.7653595970678850 854.4964224700621571, 59.2762000960014035 853.1078357839523960, 58.6532732863555282 851.7738896860348632, 57.9025798913081076 850.5074342273009051, 53.9070556479071072 844.4640472447273396, 53.6291607965314228 843.2762605399261702, 53.1514318972494166 841.8749772670424818, 52.5378936413144615 840.5276129805270102, 51.7945227631419840 839.2472929230577847, 50.9285607513726788 838.0464892312558050, 49.9484433064834548 836.9368994393521461, 48.8637181650781116 835.9293325285834726, 47.8561301034649773 835.1636795611211710, 47.5157602379638035 834.5803520811881526, 46.7142683580746194 833.3494504904467703, 45.7962301194037451 832.2028408131999413, 44.7704485210941527 831.1515178028042783, 43.6467597052044312 830.2055625159845249, 42.4359386386875244 829.3740456463439159, 41.1495957930487037 828.6649405461706692, 39.9059118867788456 828.1305288869898504)), ((149.1109881978869112 846.3263082202155374, 149.1373518475900539 846.2952623944058814, 149.9772511657898519 845.0822744675746208, 150.6939234904405680 843.7926422120729057, 151.2804353494156828 842.4388422214419734, 151.7311125126853710 841.0339718822797295, 152.0415948878585652 839.5916226631037489, 152.2088787020532834 838.1257486232015026, 152.2313455620052594 836.6505314135829394, 152.1087781112714765 835.1802430760888001, 151.8423621330531148 833.7291079680093162, 151.4346750782932247 832.3111651480296587, 150.8896611300351083 830.9401325548521982, 150.2125930452820626 829.6292742925066932, 137.7025930452820432 808.0792742925067387, 136.9086703636539539 806.8532889636886694, 135.9992747664182957 805.7103310035654431, 134.9830287515914904 804.6612374480474728, 133.8695679269043239 803.7159553511220338, 132.6694496491126642 802.8834474711646862, 131.3940529236804196 802.1716072899179153, 130.0554705139424811 801.5871841698919980, 128.6663942827228482 801.1357193598111053, 127.2399948535485521 800.8214934548765314, 125.7897967324608715 800.6474858100028769, 124.3295500744644499 800.6153462908562233, 122.8731003104700505 800.7253796305373044, 121.4342568708721757 800.9765425402331402, 120.4244547316989440 801.2562630064651330, 120.1036989129046191 801.5022489883218668, 119.0029382696210973 802.5381197311952519, 118.0120165802130714 803.6795120367605705, 117.1409958933634243 804.9148359427961168, 116.3987207551635095 806.2315476838211907, 115.7927283995406071 807.6162770634002754, 115.3291722134363368 809.0549632182190862, 115.0127592538882766 810.5329973953520266, 114.8467024514810788 812.0353712929278345, 114.8326879855039806 813.5468294579084159, 114.9708581620952401 815.0520241934970045, 115.2598099692325775 816.5356714032120635, 117.6198099692325911 826.0656714032121499, 118.0270168448098786 827.4292119907390770, 118.5616043663972761 828.7480283369806102, 119.2187611028567744 830.0102507409477539, 119.9925724672279728 831.2045188623177410, 120.8760739495978669 832.3200839675450879, 128.8660739495978476 841.4800839675450561, 129.8334037509243331 842.4899023907114497, 130.8905682299795217 843.4052512075702452, 132.0283796398500158 844.2181751817948907, 133.2369493364542734 844.9216092446255288, 134.5057737200974941 845.5094398969167742, 135.8238255215653112 845.9765583411555099, 137.1796496393947677 846.3189048816851709, 138.5614626954073287 846.5335042072523493, 139.9572554432730556 846.6184912492418562, 145.5972554432730703 846.6984912492418971, 147.3116357006568364 846.6246470719582931, 149.0063642009259013 846.3554855223237610, 149.1109881978869112 846.3263082202155374)), ((92.7092539085400205 833.8939064124049310, 105.3192539085400341 855.6039064124049673, 106.1180640351852418 856.8360695222231698, 107.0335258602355424 857.9842216003484054, 108.0568659086229104 859.0373591473926354, 109.1782768370425742 859.9853892509488560, 110.3870114241989029 860.8192263126339867, 110.9271152799204998 861.1184666108025567, 115.5405066158053842 858.1324279591233335, 116.7206211019430242 857.2847652105938323, 117.8131852366879286 856.3268999032015927, 118.8079473580263112 855.2678198034985826, 119.6955734921182994 854.1174623887297912, 120.4677349351084814 852.8866216022769322, 121.1171864023660447 851.5868465727762668, 121.6378340118707513 850.2303332472414468, 122.0247924638482573 848.8298099550111147, 122.2744308801313764 847.3984179762882150, 122.3844068731311552 845.9495882359115058, 122.3536885247441006 844.4969152793555622, 122.1825640689651351 843.0540297134596130, 121.8726391873530588 841.6344703087870585, 121.4268219427253257 840.2515569636966575, 120.8492954924513896 838.9182657221178943, 120.1454788373788460 837.6471070177544789, 112.3054788373788426 824.9771070177545198, 111.4511628018687190 823.7397029641190329, 110.4772519982631565 822.5940492524315459, 109.3935332548445558 821.5516585552136348, 108.2108968597265601 820.6230058551014963, 106.9412271243643033 819.8174231817598638, 105.5972829581254047 819.1430058343289602, 104.1925696540279063 818.6065310317756030, 102.7412031740698666 818.2133898086287900, 101.2577682979473508 817.9675328404815673, 99.7571720606239154 817.8714307436576973, 98.2544939515574782 817.9260492479917275, 96.7648343809321148 818.1308394922111802, 95.3031629356576246 818.4837435394426848, 93.8841679500118858 818.9812150574167617, 92.5221089025905741 819.6182549555556989, 92.1914410318473330 819.8154638311565350, 91.6647526525256353 821.0146429689764318, 91.2052368588397400 822.4253803224062267, 90.8873379432913140 823.8746128518858995, 90.7141661365310625 825.3481616873006033, 90.6874157012157127 826.8316100548615850, 90.8073483558342645 828.3104443266328190, 91.0727907141303632 829.7701960176511875, 91.4811457651726130 831.1965833413810287, 92.0284182817550942 832.5756509385647632, 92.7092539085400205 833.8939064124049310)), ((68.3567443710135763 847.9272281626956556, 75.8067443710135791 863.6372281626956919, 76.5172127302786720 864.9600203318884724, 77.3563683530038730 866.2051557675914637, 78.3158027342098535 867.3601579669635839, 79.3859021520157029 868.4134535799830701, 80.5559439990297790 869.3544883766991234, 81.8142042249922810 870.1738330026993253, 83.1480748140730554 870.8632774631018947, 84.1557980167187623 871.2621729140915932, 86.0484431438174937 870.1639178288714902, 87.2879806201836743 869.3605593891195440, 88.4424944842961480 868.4391823351350013, 89.5007893488272828 867.4087213131919043, 90.4526028676212519 866.2791687630739261, 91.2887052502547363 865.0614780208114780, 92.0009887638454842 863.7674571035524878, 92.5825463542041831 862.4096542065408357, 93.0277386239361874 861.0012360225457542, 93.3322485180022881 859.5558600636849178, 93.4931231864496795 858.0875422237439807, 93.5088026183677385 856.6105208652485317, 93.3791347694035636 855.1391187492441759, 93.1053770361447590 853.6876041466589413, 92.6901840630724791 852.2700524780597107, 92.1375820003211743 850.9002098234894902, 91.4529294618693029 849.5913596259388214, 87.2062983884391940 842.3635692029660049, 87.2086529554164542 842.1366625487030433, 87.0785296006039857 840.6647389197537450, 86.8042130481305918 839.2127603779130141, 86.3883653770805040 837.7948175118533527, 85.8350221419157577 836.4246706142455423, 85.1495532098188193 835.1156161462967020, 79.3258420075078732 825.2136983423673655, 78.9303297357748477 825.3894348343608272, 77.8284993970178789 825.9941475394149393, 76.7811619520835222 826.6889797691636659, 73.1011619520835154 829.3589797691636250, 71.9443407596562565 830.2890394744539435, 70.8851053544021994 831.3288792135050471, 69.9338279875021698 832.4683166614930769, 69.0998237627972856 833.6961942123213021, 68.3912594213742011 835.0004882361968157, 67.8150733711548384 836.3684268174885119, 67.3769077445775082 837.7866148199549343, 67.0810531496742897 839.2411650546763440, 66.9304066555538384 840.7178342662680279, 66.9264434237041286 842.2021626057694448, 67.0692022629074387 843.6796152244625091, 67.3572852492167868 845.1357246021029823, 67.7878714147150419 846.5562322158576762, 68.3567443710135763 847.9272281626956556)), ((30.5473143216510792 695.8853207626484618, 38.0811116120676729 687.7252198420646891, 39.0088373263641728 686.6178159745379617, 39.8257299629547461 685.4263023375768853, 40.5242123222679282 684.1617309791678281, 41.0978055354823084 682.8358316042020988, 41.5411891601618919 681.4609027739908242, 41.8502505307489159 680.0496978292748054, 42.0221229061566390 678.6153065948649328, 42.0552120606190911 677.1710339631789566, 41.9492110711505788 675.7302764828889394, 41.7051031644518986 674.3063980973983007, 41.3251525968563413 672.9126061857543846, 40.8128836519098712 671.5618290557941918, 32.9928836519098709 653.6018290557942692, 32.3403842099675032 652.2838427644052217, 31.5619555265010945 651.0360879123684299, 30.6650803540863279 649.8705587144800120, 29.6583800284331858 648.7984589801482116, 28.5515315946129959 647.8300944152376815, 27.3551747851342810 646.9747735570573468, 26.0808097440535214 646.2407182947672482, 24.7406864802617434 645.6349848353409016, 24.0937656003307517 645.4159748685059412, 23.6390837960484461 646.1685904962445193, 23.0241134834196828 647.4450620215294521, 22.5323265331837668 648.7738636003423380, 22.1681109265136698 650.1431389678764390, 21.9347163861281516 651.5406707309488183, 21.0747163861281521 658.7806707309488274, 20.9722177341578799 660.2920715091509010, 21.0226856819601835 661.8061029725789695, 21.2256054927402502 663.3073230884842815, 21.5789075302724456 664.7804204906545920, 22.0789883677023653 666.2103706443457440, 23.9755249387252611 670.8646262089478114, 23.7672561842363876 671.3943860426976471, 23.3651778984085112 672.8125786554187471, 23.1042422397949707 674.2633887107930377, 22.9869691942111700 675.7328050124211813, 23.0144913259565627 677.2066366741028105, 23.1865428400762639 678.6706501683029273, 23.5014621492755253 680.1107067864276132, 23.9562079206971106 681.5128991833851160, 27.5962079206971111 691.1028991833850341, 28.1840809014582163 692.4490387596293886, 28.9008005485022323 693.7312241392768328, 29.7394937230059959 694.9371595269660702, 30.5473143216510792 695.8853207626484618)), ((842.2938843229405848 647.9607449664835030, 841.5596322186335101 643.0094166170856624, 841.7811091095296661 642.7033499104156817, 842.5263375847878251 641.4247196415183225, 843.1419382697831679 640.0788754300623395, 843.6219186041762441 638.6789183866242183, 843.9616062229324598 637.2384763827449206, 844.1576944394258817 635.7715713905151915, 844.2082744343556442 634.2924829857911391, 844.1128538371330023 632.8156093437683012, 842.9028538371329660 622.2356093437683739, 842.6561545476818083 620.7467048563487424, 842.2610895653978105 619.2901264094921316, 841.7216581719220585 617.8806190899626927, 841.0433210842638800 616.5324514788766237, 840.2329451754256979 615.2592712094532317, 836.3229451754257298 609.7492712094531271, 836.0685270580095221 609.4263827223990120, 835.1017137868778946 610.2211145913514656, 834.0856775952039470 611.2336754046518763, 833.1710067409162548 612.3386578351635308, 832.3660658237038206 613.5259568974137210, 831.6782159708120616 614.7847148262069368, 831.1137475200589506 616.1034203701125307, 830.6778224951447100 617.4700140610375456, 830.3744273993133902 618.8719984972024122, 830.2063367590652661 620.2965526309874349, 830.1750877513101159 621.7306490164990009, 830.4350877513101068 631.7106490164990191, 830.5402213235400950 633.1350732674035271, 830.7803580383643975 634.5430405513872074, 831.1533206161967655 635.9217850636561025, 831.6557274674802329 637.2588059574188719, 832.2830233529963380 638.5419806869820150, 833.0295206854596017 639.7596749208520350, 833.8884510979245306 640.9008480282803930, 834.8520268114407372 641.9551531828236648, 835.9115112455473309 642.9130311752949183, 840.6615112455473309 646.8130311752948955, 841.7907629226643849 647.6546572634816812, 842.2938843229405848 647.9607449664835030)), ((788.3845657794452109 616.7291208723019054, 787.9845657794452336 625.7991208723019554, 787.9925245276713213 627.2817228704328727, 788.1468080208369429 628.7562969208868253, 788.4459089626805053 630.2084369462023687, 788.8869052406533910 631.6239560415696133, 789.4654884739463796 632.9890250759731316, 790.1760061048777288 634.2903077980097351, 791.0115166224219365 635.5150911264430533, 791.9638573783678339 636.6514093526013767, 793.0237243335623134 637.6881610412048076, 794.1807629551486798 638.6152174875405763, 795.4236693767642237 639.4235216713983618, 796.7403008333975549 640.1051767410185676, 798.1177942919943007 640.6535231625977076, 799.5426921188324059 641.0632037816257025, 799.8611687619821851 641.1215130852941684, 801.7317361177047133 651.2408820524904058, 802.4610486639541023 650.4342621194277854, 803.3599280692968705 649.2114406213223674, 804.1306436640819584 647.9040485326462431, 804.7653057985498890 646.5254693449857086, 805.2574175729482704 645.0898152769468652, 805.6019413448980231 643.6117828104091814, 805.7953502986541707 642.1065022458017211, 805.8356645483601142 640.5893828164723800, 805.7826210438815906 638.3836570885744095, 805.9792322946761942 637.7681414161316980, 806.2723016418594852 636.4536712929850637, 806.4462917053506317 635.1182130178189027, 806.4997999540727278 633.7725316981067181, 806.4197999540726869 618.2825316981067090, 806.3398364855347609 616.8112975608898978, 806.1159182092576430 615.3550061586074662, 805.7502056122175418 613.9277085935639207, 805.2462272911685659 612.5431762195382817, 804.6088459067959775 611.2147677681693949, 803.8442112660854946 609.9553004565498213, 802.9597009855949636 608.7769263196479415, 801.9638493081422439 607.6910149607758740, 800.8662647597257092 606.7080438513886520, 799.6775374411685107 605.8374972386627633, 798.4091368489870320 605.0877746362514245, 797.0733012113656741 604.4661097811458603, 796.2846683468350193 604.1895350763240913, 795.6428233600814792 604.5334553174075154, 794.4152980164290057 605.3561596991887654, 793.2746794870499798 606.2956544248517048, 792.2320376571145744 607.3428215478937773, 791.2974915322618017 608.4874981439827479, 790.4801110320324824 609.7185749437625191, 789.7878289648498367 611.0241041500878509, 789.2273640388461899 612.3914153933060334, 788.8041556557255944 613.8072386992195106, 788.5223111205017403 615.2578332763069966, 788.3845657794452109 616.7291208723019054)), ((176.9477306067556981 629.4088632867977822, 177.0226986440887345 629.8536609267392805, 177.4215892302362363 631.3085808658996712, 177.9646044372781262 632.7160751692645135, 178.6462515922335683 634.0619068397287492, 179.4596357410351288 635.3324626061403251, 180.3965293919308124 636.5148906233974913, 181.4474557376867381 637.5972304706172054, 182.6017845147867718 638.5685341324256115, 195.7830729180398350 648.5723356271262219, 195.7901564761275779 649.0706760072577026, 195.9560034322712454 650.5369579273697127, 196.2651193801171985 651.9798491959331841, 196.7145127808628047 653.3853859096037695, 197.2998345292439240 654.7399656721063366, 198.0154200429751086 656.0304792344711586, 198.8543440830129612 657.2444373627261029, 199.8084877741077037 658.3700917052547084, 200.8686171770364695 659.3965484901002583, 202.0244726521177938 660.3138739518826696, 206.7844726521178131 663.7238739518826378, 208.0170796826928381 664.5186945730561092, 209.3214166293344078 665.1893398155586965, 210.6850136835215892 665.7293981322965237, 211.7540772087992877 666.0359840772139250, 214.7540550323743958 661.5554977173289899, 215.5138264073845278 660.2902835499264711, 216.1455900007215405 658.9565316262860506, 216.6432302862408790 657.5671527802152241, 217.0019300630010264 656.1355963189350859, 217.2182170861845236 654.6757198326242815, 217.2899976787277581 653.2016550517497535, 217.2165769982957499 651.7276710506948803, 217.0738890955852582 650.7719046037318549, 217.9184650834097283 650.0848145690921456, 218.9686160065462275 649.0425796599827208, 219.9109808309072776 647.9019574153558096, 228.6209808309072855 636.2319574153558506, 229.4453155370089519 635.0058291198990901, 230.1450268012699496 633.7045518814637717, 230.7133261235716759 632.3407505085084495, 231.0024132244138286 631.3937591592213039, 230.3971708965628977 630.9116602983662005, 225.6271708965629159 627.4816602983661369, 224.3929954676898433 626.6829225480804553, 223.0865601298845604 626.0088322788293453, 221.7204134615611792 625.4658642654120513, 220.3076775806666490 625.0592338265629451, 218.8619221040559921 624.7928467307499432, 217.3970338085492813 624.6692616804692761, 215.9270832456022049 624.6896657353859155, 214.4661895907838414 624.8538629103825315, 213.0283850262096905 625.1602760580364020, 211.6274799585647770 625.6059620174431757, 210.2769303673250647 626.1866398838782288, 208.9897085573244624 626.8967321277623341, 207.7781785571159787 627.7294181679720850, 206.6539773599504315 628.6766998849163883, 205.6279031480781896 629.7294784441093043, 204.7098115740017761 630.8776416923345778, 201.8123120893773432 634.8895640556606850, 193.5450977537060737 629.4023460899070415, 192.2840247337467190 628.6515096692098723, 190.9556107592054559 628.0275090245320371, 189.5725735370155860 627.5363180898109476, 188.1481537153537147 627.1826393316538315, 186.6959881229411451 626.9698587297938275, 185.2299792154708769 626.9000133610663852, 183.7641619790271932 626.9737718972463654, 182.3125695647122200 627.1904282034504376, 180.8890989408397729 627.5479080983915310, 179.5073778488892629 628.0427892117651254, 178.1806343369298702 628.6703337486615055, 176.9477306067556981 629.4088632867977822)), ((330.2308374372462367 644.6768973805824317, 329.9338473580801292 645.3217238682977950, 329.4517179845172450 646.7141142427991554, 329.1085288128106754 648.1470904743813435, 328.9075915417271290 649.6068246628367433, 328.8508451710009126 651.0792306996571597, 328.9388372904206790 652.5501001958315328, 329.1707187957097744 654.0052395896219650, 329.5442520821931112 655.4306071112615655, 330.0558326371817657 656.8124482828922055, 330.7005238227143877 658.1374286462005330, 331.4721045130095831 659.3927624369588330, 332.3631291269388726 660.5663359647909374, 333.3649994762194524 661.6468245075753885, 334.4680477360085433 662.6238015924819820, 345.6080477360085297 671.5538015924820456, 346.7972370114761702 672.4149765066980535, 348.0649236384901997 673.1557625631625115, 349.3989615262932489 673.7690620655822613, 350.7865688525378118 674.2489988050747343, 352.2144505298096533 674.5909743619113215, 353.6689255899000273 674.7917121643732798, 355.1360582653192068 674.8492888825815044, 356.6017915121188935 674.7631528565043482, 357.6983681234727328 674.5899862798637514, 359.5870220342957850 668.4501926475061282, 359.9564324874341423 666.9931074299018974, 360.1781536632842062 665.5063656451833367, 360.2499589280075156 664.0048978905490458, 360.1711271775795353 662.5037826486795893, 359.9424500794735309 661.0180948621475636, 359.5662241223437263 659.5627545433800378, 359.0462275535491017 658.1523769405054054, 358.3876824361225886 656.8011257637946301, 357.5972022062279621 655.5225709466693615, 356.6827252577595004 654.3295523697107683, 355.6534352210592829 653.2340509162210083, 348.6734352210592647 646.5140509162210947, 347.5652557568801058 645.5472254581094376, 346.3677256181795201 644.6935658314519060, 345.0923560670950678 643.9612778420325867, 343.7514065973549577 643.3574006103995089, 342.3577670898568499 642.8877389082971376, 340.9248339086484521 642.5568073602797767, 339.4663811283365362 642.3677870468696938, 337.9964281307527472 642.3224949264100587, 336.5291048436026244 642.4213663695481955, 335.0785159164920515 642.6634509742328873, 333.6586051399395956 643.0464217014580299, 332.2830214106473363 643.5665972439315965, 330.9649875314393626 644.2189774126530892, 330.2308374372462367 644.6768973805824317)), ((335.5780652288065653 698.9192069275985659, 335.9398849343108964 697.5661255820202769, 336.1765925533430277 696.1482748077830820, 336.2764944138041301 694.7142765744545159, 336.2386730475045624 693.2773002840571053, 336.0634757943267914 691.8505426881804397, 335.7525116123619568 690.4471066930428833, 335.3086363017301892 689.0798810264009262, 334.7359262777837898 687.7614218714081744, 334.0396411345532215 686.5038375544618248, 333.2261753422409924 685.3186773460297445, 332.3029995223593005 684.2168253956758690, 331.2785918398229228 683.2084007753529704, 330.1623601420702130 682.3026645489354678, 323.5623601420702471 677.4526645489354451, 322.3651896844357339 676.6583130175336009, 321.0975131973814882 675.9821416012175632, 319.7709606912729328 675.4303536812606126, 318.3977023211568280 675.0080115107647316, 316.9903367344341518 674.7189897721672196, 315.5617754874277807 674.5659400298458195, 314.1251245912375225 674.5502664039432830, 312.6935642736147543 674.6721126885847752, 311.2802280599524352 674.9303610326720673, 310.8287292949425478 675.0585055912325743, 308.1997652245567565 672.5197964493195286, 307.4296300612042501 671.8258962177319518, 299.7296300612042614 665.3558962177320382, 299.4218927823092145 665.1222777048401440, 299.6994714195755591 667.7130116526592474, 299.3268082400581420 670.6816844386454477, 299.2128017615777367 672.2600948474669167, 299.2657225576316478 673.8417320641372044, 299.4849815899133318 675.4089915744208383, 299.8681383817356050 676.9444288960631866, 300.4109281819121406 678.4309537457161241, 301.1073094338436249 679.8520202634309726, 304.3173094338436613 685.6120202634309635, 305.1109543041189909 686.8824788746728700, 306.0273579579893521 688.0674436868787325, 307.0573810518686173 689.1550969813932852, 308.1907511093029939 690.1345915326309068, 309.4161649689386309 690.9961587880474099, 310.7214015115656025 691.7312062904472896, 321.9014015115656093 697.3112062904473305, 323.1445238755899823 697.8627513488214618, 324.4324915908786693 698.2994369654022648, 325.7547172470402757 698.6176734773591761, 327.1003318250105281 698.8148449025416085, 328.4582740430535068 698.8893304434940319, 334.8082740430534727 698.9493304434940910, 335.5780652288065653 698.9192069275985659)), ((193.2291463878518982 693.3275967361378207, 187.4417727332225354 684.3150106063684461, 186.6117751871360895 683.1488599441038332, 185.6744041839789077 682.0671303586483418, 184.6381952295225801 681.0796718550571995, 183.5125838274060470 680.1954760254839130, 182.3078195613981052 679.4225941736074219, 181.0348727648152760 678.7680640011330979, 179.7053346269439089 678.2378455239415871, 178.3313116460766992 677.8367668014212768, 176.9253153902522513 677.5684799731620842, 175.5001485695138967 677.4354280033298892, 174.0687884570894823 677.4388224355426473, 172.6442687210353313 677.5786323608052726, 171.2395607423605099 677.8535846989606171, 169.8674555003240414 678.2611757910930237, 168.5404471004328002 678.7976941973270186, 167.2706190057105857 679.4582544924279546, 157.8797183730945903 684.9278485925216273, 173.9765677521279486 695.6087592004148519, 175.2381409140181461 696.3596399118955560, 176.5670758070780835 696.9835956280189748, 177.9506418924966056 697.4746491617145239, 179.3755852916675622 697.8280964630685048, 180.8282557517896123 698.0405516817950229, 182.2947374085348713 698.1099796020067743, 183.7609820931447473 698.0357151385761654, 185.2129439069363457 697.8184697083220271, 186.6367137740631108 697.4603244149882357, 188.0186526835801146 696.9647101132997022, 189.3455223444212550 696.3363745430709741, 190.6046120016783902 695.5813368482083661, 191.7838601993447867 694.7068299162884841, 192.8719703230966616 693.7212310910741735, 193.2291463878518982 693.3275967361378207)), ((797.3764374522721710 690.7608274410104059, 797.0714953859336447 690.8033663299568161, 795.6412363783282444 691.1484205789626003, 794.2516618970020090 691.6319348232616449, 792.9161409037981230 692.2492572190200235, 791.6475223171725020 692.9944485671053371, 790.4580113939906596 693.8603394535477946, 789.3590523039301843 694.8385992257794896, 788.3612180262304037 695.9198161410378134, 787.4741086280799891 697.0935879158331545, 786.7062589032981350 698.3486218053141101, 786.0650562599080331 699.6728432496739742, 785.5566696465974701 701.0535120423247690, 785.1859902018596813 702.4773449021936358, 784.9565841968227460 703.9306432708867760, 784.8706587244988668 705.3994251051967694, 784.6706587244988214 726.7394251051968013, 784.7291576758254905 728.2108758935972901, 784.9316590917401300 729.6694994661970668, 785.2762112280006477 731.1012373526095871, 785.7594932305534030 732.4922902114182079, 786.3768471425073585 733.8292508306534501, 787.1223227983422248 735.0992333488592294, 787.9887351726490579 736.2899974512905601, 788.9677336306681354 737.3900663442182122, 790.0498824131749416 738.3888373702845911, 791.2247515799861048 739.2766841987788666, 792.4810175355560204 740.0450496059041825, 793.8065721677813826 740.6865279508051572, 795.1886395481136560 741.1949365524360474, 796.6138990682046597 741.5653752793273270, 798.0686138262708482 741.7942737779145546, 799.5387630257674800 741.8794258842328873, 804.0048969880122058 741.9185021737019952, 804.0106708013587422 741.8675584020969609, 804.0291274788648934 740.3589856198526604, 802.5491274788648752 701.5089856198527514, 802.4226667953599872 700.0548738977282710, 802.1554775836146973 698.6199372684588980, 801.7500897492676586 697.2177625615640864, 801.2103417443328226 695.8616263972866136, 800.5413442224635219 694.5643694756920468, 799.7494316482579961 693.3382749933182367, 798.8421023187980836 692.1949523385991370, 797.8279473653340119 691.1452271673011865, 797.3764374522721710 690.7608274410104059))) \ No newline at end of file diff --git a/docker/solaris/solaris/data/test_polygon_split.geojson b/docker/solaris/solaris/data/test_polygon_split.geojson new file mode 100644 index 00000000..a91f2258 --- /dev/null +++ b/docker/solaris/solaris/data/test_polygon_split.geojson @@ -0,0 +1 @@ +{"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"Polygon","coordinates":[[[-122.4591064453125,39.02345139405935],[-122.508544921875,38.659777730712534],[-122.16247558593751,38.28131307922966],[-121.22314453124999,38.26406296833961],[-121.2176513671875,37.814123701604466],[-120.11901855468751,37.74031329210266],[-119.42138671875,38.37611542403604],[-119.6246337890625,39.20246222588238],[-121.17370605468749,39.33854604847979],[-122.4591064453125,39.02345139405935]]]}}]} \ No newline at end of file diff --git a/docker/solaris/solaris/data/test_results.csv b/docker/solaris/solaris/data/test_results.csv new file mode 100644 index 00000000..ad3b0a30 --- /dev/null +++ b/docker/solaris/solaris/data/test_results.csv @@ -0,0 +1,2 @@ +nadir-category,F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos +Nadir,1.0,0,0,1.0,1.0,151 diff --git a/docker/solaris/solaris/data/test_results_full.csv b/docker/solaris/solaris/data/test_results_full.csv new file mode 100644 index 00000000..d5101c48 --- /dev/null +++ b/docker/solaris/solaris/data/test_results_full.csv @@ -0,0 +1,2 @@ +F1Score,FalseNeg,FalsePos,Precision,Recall,TruePos,imageID,iou_field,nadir-category +1.0,0,0,1.0,1.0,151,Atlanta_nadir10_catid_1030010003CAF100_740801_3728289,iou_score,Nadir diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2118645.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2118645.0.geojson new file mode 100644 index 00000000..24d6bedd --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2118645.0.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 49388, "origarea": 476145.1255013296, "origlen": 0, "partialDec": 0.0011733302628188385, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659660.154028261080384, 2118645.0 ], [ -659770.753831441979855, 2118636.181507399771363 ], [ -659786.859466610010713, 2118645.0 ], [ -659660.154028261080384, 2118645.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2122485.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2122485.0.geojson new file mode 100644 index 00000000..04580799 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2122485.0.geojson @@ -0,0 +1,11 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 49388, "origarea": 476145.1255013296, "origlen": 0, "partialDec": 0.99882666973718004, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659311.63530308578629, 2118898.249322732910514 ], [ -659324.960144641227089, 2118871.126656083390117 ], [ -659339.996072151698172, 2118844.937861145008355 ], [ -659356.679213025839999, 2118819.794185576494783 ], [ -659374.938697636476718, 2118795.802437491249293 ], [ -659394.696960386587307, 2118773.064531718846411 ], [ -659415.870069225900806, 2118751.677056812215596 ], [ -659438.368082174099982, 2118731.730864941142499 ], [ -659462.095429417560808, 2118713.310685710050166 ], [ -659486.95131926285103, 2118696.494766448158771 ], [ -659512.830166305880994, 2118681.354539667721838 ], [ -659539.622039922629483, 2118667.954319780226797 ], [ -659567.213131252327003, 2118656.351029764860868 ], [ -659582.062515574623831, 2118651.226497760042548 ], [ -659660.154028261080384, 2118645.0 ], [ -659786.859466610010713, 2118645.0 ], [ -660013.512340991874225, 2118769.101699479855597 ], [ -660109.498503531562164, 2118897.351060654502362 ], [ -660131.677862829528749, 2119097.582143804524094 ], [ -659724.850629267864861, 2119090.861519446130842 ], [ -659855.950715026585385, 2119471.2081599999219 ], [ -659443.917992893373594, 2119455.862715763505548 ], [ -659435.41632107400801, 2119449.192769099958241 ], [ -659412.969644859666005, 2119429.060237135272473 ], [ -659391.874188715824857, 2119407.476520008873194 ], [ -659372.220500705880113, 2119384.534261402208358 ], [ -659354.092940157279372, 2119360.331936274189502 ], [ -659337.569315572269261, 2119334.973428127821535 ], [ -659322.720550647587515, 2119308.567583160474896 ], [ -659309.610379873192869, 2119281.227742935530841 ], [ -659298.295074963360094, 2119253.07125798612833 ], [ -659288.823203338659368, 2119224.218984119594097 ], [ -659281.235419691773131, 2119194.794763611163944 ], [ -659279.559241548296995, 2119185.966326669324189 ], [ -659305.303102059056982, 2118913.558468156959862 ], [ -659311.63530308578629, 2118898.249322732910514 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51977, "origarea": 506948.43531563453, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659593.499317524489015, 2121101.25985866971314 ], [ -659621.161475712549873, 2121107.280303706414998 ], [ -659648.337560845422558, 2121115.244646563660353 ], [ -659674.895175419631414, 2121125.114085969515145 ], [ -659700.704934947192669, 2121136.840539240743965 ], [ -659725.641098271706142, 2121150.36687646061182 ], [ -659749.582180132973008, 2121165.627198915462941 ], [ -659772.411543014924973, 2121182.547160051763058 ], [ -659794.01796537428163, 2121201.044327731244266 ], [ -659814.296183471102268, 2121221.028585822787136 ], [ -659833.147404208779335, 2121242.402573185972869 ], [ -659850.479786423034966, 2121265.062158104963601 ], [ -659866.208888332010247, 2121288.896945472806692 ], [ -659880.258078924147412, 2121313.790814733598381 ], [ -659892.558911308413371, 2121339.622485547792166 ], [ -659903.051456188433804, 2121366.266108673531562 ], [ -659911.684593858197331, 2121393.591879102867097 ], [ -659918.416263263789006, 2121421.466668413951993 ], [ -659923.213666951516643, 2121449.75467342697084 ], [ -659926.053430877509527, 2121478.318077754229307 ], [ -659926.921718316501938, 2121507.017723280005157 ], [ -659925.814297307748348, 2121535.713788097724319 ], [ -659922.736561301746406, 2121564.266467738896608 ], [ -659917.703502923017368, 2121592.536656247451901 ], [ -659910.739640963030979, 2121620.386623923666775 ], [ -659901.878900956362486, 2121647.680688340216875 ], [ -659891.164449935546145, 2121674.285875346977264 ], [ -659878.648486159043387, 2121700.072566939517856 ], [ -659864.391984826768748, 2121724.915132720023394 ], [ -659848.464401056640781, 2121748.692541981115937 ], [ -659830.943331506918184, 2121771.288953385315835 ], [ -659811.914136369829066, 2121792.594279269687831 ], [ -659791.469523506239057, 2121812.504722049459815 ], [ -659769.709096802282147, 2121830.923279865179211 ], [ -659746.738870900124311, 2121847.760219188872725 ], [ -659722.670754716498777, 2121862.933512050658464 ], [ -659697.622006225050427, 2121876.369235567748547 ], [ -659671.714661172241904, 2121888.001932152081281 ], [ -659645.074938520323485, 2121897.774928473401815 ], [ -659629.673720736638643, 2121902.221729316748679 ], [ -659343.090929316589609, 2121876.186674251686782 ], [ -659342.073580209165812, 2121875.724451997317374 ], [ -659317.137249083491042, 2121862.198084950447083 ], [ -659293.196026191464625, 2121846.937733049038798 ], [ -659270.366551788640209, 2121830.017743398901075 ], [ -659248.760049596894532, 2121811.520548693370074 ], [ -659228.481784906936809, 2121791.536265584174544 ], [ -659209.630551721085794, 2121770.16225570673123 ], [ -659192.298191446112469, 2121747.502651233691722 ], [ -659176.569145439192653, 2121723.667847617994994 ], [ -659162.520043621538207, 2121698.77396573824808 ], [ -659150.219331156695262, 2121672.942286207340658 ], [ -659139.726935007725842, 2121646.298658392392099 ], [ -659131.09397198271472, 2121618.972887450363487 ], [ -659124.362499741604552, 2121591.098101755138487 ], [ -659123.915658929967321, 2121588.463178135920316 ], [ -659150.505961999180727, 2121341.036335645243526 ], [ -659151.616387362941168, 2121338.279078538063914 ], [ -659164.132531132898293, 2121312.492416725028306 ], [ -659178.389187256689183, 2121287.649880652315915 ], [ -659194.316897609969601, 2121263.872500442434102 ], [ -659211.838063069386408, 2121241.27611686848104 ], [ -659230.867321574594826, 2121219.970817078370601 ], [ -659251.311964030377567, 2121200.060398116707802 ], [ -659273.072385977604426, 2121181.641861391719431 ], [ -659296.042572864680551, 2121164.804940011817962 ], [ -659320.110616532503627, 2121149.631661616731435 ], [ -659337.60418646549806, 2121140.248377581126988 ], [ -659359.625597864855081, 2121160.16248986730352 ], [ -659406.979154465836473, 2121215.917041466571391 ], [ -659441.146196235786192, 2121162.595604790840298 ], [ -659436.128465855610557, 2121104.536155052483082 ], [ -659527.27129629557021, 2121126.892843056004494 ], [ -659566.8505983594805, 2121097.409812904894352 ], [ -659593.499317524489015, 2121101.25985866971314 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51978, "origarea": 336542.34411594522, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659497.381202135933563, 2121065.58143145730719 ], [ -659472.24446488043759, 2121063.652641850057989 ], [ -659447.335413984721527, 2121059.753171474672854 ], [ -659422.803856978425756, 2121053.90647235698998 ], [ -659398.797331044799648, 2121046.147707412019372 ], [ -659375.460215675993823, 2121036.523538936395198 ], [ -659352.93286432872992, 2121025.091847995296121 ], [ -659331.350760281318799, 2121011.921386326197535 ], [ -659310.843701803358272, 2120997.091362826060504 ], [ -659291.535021508578211, 2120980.690967177972198 ], [ -659273.540844593662769, 2120962.818833486642689 ], [ -659256.969390446203761, 2120943.582447006832808 ], [ -659241.920321780722588, 2120923.097497791517526 ], [ -659228.484145266585983, 2120901.487184851430357 ], [ -659216.741667206399143, 2120878.881475246977061 ], [ -659206.763507572119124, 2120855.416322454344481 ], [ -659198.609675305662677, 2120831.232848693616688 ], [ -659192.329207431874238, 2120806.476496257353574 ], [ -659187.959874180727638, 2120781.296152719762176 ], [ -659185.527951854164712, 2120755.843255601357669 ], [ -659185.04806483455468, 2120730.270881538745016 ], [ -659186.523097666911781, 2120704.732825704384595 ], [ -659189.944177747122012, 2120679.382676833774894 ], [ -659195.290728722582571, 2120654.372893572319299 ], [ -659202.530594277428463, 2120629.853887521661818 ], [ -659211.62023155996576, 2120605.973118708003312 ], [ -659222.504973092465661, 2120582.874208681751043 ], [ -659235.119355578557588, 2120560.696076818276197 ], [ -659249.387513631605543, 2120539.572104820981622 ], [ -659265.223636070732027, 2120519.62933454522863 ], [ -659282.532482007867657, 2120500.987703995313495 ], [ -659301.209953660843894, 2120483.75932594994083 ], [ -659321.143722414155491, 2120468.047813758719712 ], [ -659342.213904378353618, 2120453.947658159770072 ], [ -659364.293781381682493, 2120441.543659069109708 ], [ -659387.250563065055758, 2120430.910415486432612 ], [ -659410.946185475564562, 2120422.111876958049834 ], [ -659435.238141383626498, 2120415.200958903413266 ], [ -659459.980337314424105, 2120410.219224413391203 ], [ -659485.023972139810212, 2120407.196634254418314 ], [ -659510.21843195729889, 2120406.151366705074906 ], [ -659535.412195857381448, 2120407.089708221610636 ], [ -659560.453747158870101, 2120410.006015637889504 ], [ -659585.192484604660422, 2120414.882750074379146 ], [ -659609.479628061293624, 2120421.690582444425672 ], [ -659633.169113263254985, 2120430.38856980483979 ], [ -659656.118470219196752, 2120440.924401632044464 ], [ -659678.189680017065257, 2120453.234714389778674 ], [ -659699.250004857312888, 2120467.245472597889602 ], [ -659719.172786321258172, 2120482.872414099983871 ], [ -659737.838207094813697, 2120500.021556816995144 ], [ -659755.134011550107971, 2120518.589763960335404 ], [ -659770.956180849694647, 2120538.465364293195307 ], [ -659785.209558523609303, 2120559.528823764529079 ], [ -659797.808422759757377, 2120581.653464351315051 ], [ -659808.677001946023665, 2120604.706225960981101 ], [ -659817.749930387944914, 2120628.548466613516212 ], [ -659824.97264144080691, 2120653.036796306725591 ], [ -659830.301695706904866, 2120678.023939332924783 ], [ -659833.705042308545671, 2120703.359620058909059 ], [ -659835.162211681832559, 2120728.891466641798615 ], [ -659834.66443871811498, 2120754.465927465353161 ], [ -659832.214715508394875, 2120779.929194598458707 ], [ -659827.827773392898962, 2120805.128128825221211 ], [ -659821.529994401265867, 2120829.911180628929287 ], [ -659813.359252625377849, 2120854.129301641602069 ], [ -659803.364686473272741, 2120877.636841059662402 ], [ -659791.606403183541261, 2120900.292421570513397 ], [ -659778.155117357731797, 2120921.959789655636996 ], [ -659763.091725705424324, 2120942.508635034319013 ], [ -659746.506820542388596, 2120961.815374337136745 ], [ -659728.500144974444993, 2120979.763894383795559 ], [ -659709.179993038182147, 2120996.246250514406711 ], [ -659688.662558421376161, 2121011.163315750192851 ], [ -659667.071235650684685, 2121024.425377008039504 ], [ -659644.535877985181287, 2121035.952674577012658 ], [ -659621.192016443586908, 2121045.675881882663816 ], [ -659597.180044692126103, 2121053.536522374488413 ], [ -659572.644374677794985, 2121059.487321235239506 ], [ -659547.732568088686094, 2121063.492489673662931 ], [ -659522.594448870862834, 2121065.527940206229687 ], [ -659497.381202135933563, 2121065.58143145730719 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51979, "origarea": 581844.4800552876, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658946.965828784857877, 2120371.286821369081736 ], [ -658944.344651411520317, 2120400.81970789982006 ], [ -658939.764597370987758, 2120430.11056350544095 ], [ -658933.246562565560453, 2120459.025740545708686 ], [ -658924.820285517838784, 2120487.433305508457124 ], [ -658914.524211706244387, 2120515.203640935476869 ], [ -658902.405318174976856, 2120542.210036988835782 ], [ -658888.518899226561189, 2120568.329269421752542 ], [ -658872.928314155549742, 2120593.44216194562614 ], [ -658855.704698162153363, 2120617.434129951521754 ], [ -658836.926637807278894, 2120640.195703411474824 ], [ -658816.679812461021356, 2120661.623026246204972 ], [ -658795.056603368720971, 2120681.618330281693488 ], [ -658772.155672146822326, 2120700.090381413232535 ], [ -658748.08151061553508, 2120716.954895707312971 ], [ -658722.943964025354944, 2120732.134924151934683 ], [ -658696.857729850686155, 2120745.561203632503748 ], [ -658489.060621699900366, 2120761.666761284228414 ], [ -658475.741586894029751, 2120784.763515352737159 ], [ -658468.949286051560193, 2120784.276907002553344 ], [ -658440.014508809661493, 2120780.197267948184162 ], [ -658411.422001351136714, 2120774.144583037123084 ], [ -658383.302226528176107, 2120766.146469464991242 ], [ -658355.783490139059722, 2120756.239421003963798 ], [ -658328.991355475969613, 2120744.4686414510943 ], [ -658303.04807036567945, 2120730.887838416732848 ], [ -658278.072009374969639, 2120715.558978194836527 ], [ -658254.1771336508682, 2120698.552003097720444 ], [ -658231.472470933687873, 2120679.944512263871729 ], [ -658210.061618061736226, 2120659.821407643146813 ], [ -658190.042268284596503, 2120638.274506502784789 ], [ -658171.505765492212959, 2120615.402122605126351 ], [ -658154.536687429994345, 2120591.308617565780878 ], [ -658139.212459800532088, 2120566.103924655355513 ], [ -658125.603002985124476, 2120539.903047208674252 ], [ -658113.770413023768924, 2120512.82553390879184 ], [ -658103.768678305554204, 2120484.994933296460658 ], [ -658095.643433243385516, 2120456.538230045232922 ], [ -658089.431750078452751, 2120427.585265572648495 ], [ -658085.161969757988118, 2120398.268145555630326 ], [ -658082.853572644176893, 2120368.720637251622975 ], [ -658082.517089662607759, 2120339.077559052035213 ], [ -658084.154054292128421, 2120309.474165397230536 ], [ -658087.756995575735345, 2120280.04552966170013 ], [ -658093.309472275548615, 2120250.925927776377648 ], [ -658100.786147905746475, 2120222.248225611634552 ], [ -658110.152906366158277, 2120194.143272779881954 ], [ -658121.367007654625922, 2120166.739305472467095 ], [ -658134.377282890025526, 2120140.161361502483487 ], [ -658149.124367825686932, 2120114.530709736514837 ], [ -658158.593653818708844, 2120100.360524518415332 ], [ -658455.335083308862522, 2119913.349948056507856 ], [ -658459.05264056159649, 2119912.718425014521927 ], [ -658488.140612175338902, 2119909.805534073617309 ], [ -658517.351258776616305, 2119908.890994604676962 ], [ -658546.551300692837685, 2119909.978979546111077 ], [ -658575.60750667960383, 2119913.064524822868407 ], [ -658604.387301785987802, 2119918.13355194311589 ], [ -658632.759372225613333, 2119925.162932321429253 ], [ -658660.594264514511451, 2119934.120592666324228 ], [ -658687.764976084465161, 2119944.965661464259028 ], [ -658714.147534753428772, 2119957.648655370343477 ], [ -658739.621564340079203, 2119972.111705029383302 ], [ -658764.070833902922459, 2119988.28881906485185 ], [ -658787.383788040606305, 2120006.106185271404684 ], [ -658809.454055875889026, 2120025.482507315464318 ], [ -658830.180936396471225, 2120046.329375660978258 ], [ -658849.469857916235924, 2120068.551671048160642 ], [ -658867.232809569453821, 2120092.047998384106904 ], [ -658883.388742897077464, 2120116.711149514652789 ], [ -658897.863941647927277, 2120142.428592210635543 ], [ -658910.592358138761483, 2120169.082983812317252 ], [ -658921.515914622345008, 2120196.552706511225551 ], [ -658924.112807311699726, 2120204.61632668832317 ], [ -658931.591654603835195, 2120281.948986494913697 ], [ -658947.582965830923058, 2120343.160043694078922 ], [ -658946.965828784857877, 2120371.286821369081736 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43127, "origarea": 691416.50224937801, "origlen": 0, "partialDec": 0.58022415811008121, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658589.384257903206162, 2119553.812619875650853 ], [ -658577.288778321584687, 2119629.622372117824852 ], [ -658553.285590448882431, 2119702.484052273444831 ], [ -658518.005186571157537, 2119770.483718048315495 ], [ -658472.374295899178833, 2119831.835142846684903 ], [ -658417.591543267364614, 2119884.92673698766157 ], [ -658355.095964830950834, 2119928.363881513476372 ], [ -658286.52920765907038, 2119961.005562471225858 ], [ -658213.692406120127998, 2119981.994343436323106 ], [ -658138.498867915594019, 2119990.778888852801174 ], [ -658065.0, 2119987.228731001727283 ], [ -658065.0, 2119048.788411288522184 ], [ -658082.055502896895632, 2119045.757472535129637 ], [ -658157.721512741176412, 2119044.895466048736125 ], [ -658232.501081775058992, 2119056.445045804604888 ], [ -658304.429921047529206, 2119080.102828956674784 ], [ -658371.61862201790791, 2119115.247373646125197 ], [ -658432.30228501255624, 2119160.955502684693784 ], [ -658484.886877641314641, 2119216.026553315576166 ], [ -658527.991105676628649, 2119279.013915992807597 ], [ -658560.482696639373899, 2119348.263033763039857 ], [ -658581.508143070968799, 2119421.954864094499499 ], [ -658589.384257903206162, 2119553.812619875650853 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2126325.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2126325.0.geojson new file mode 100644 index 00000000..033685b7 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2126325.0.geojson @@ -0,0 +1,13 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 44985, "origarea": 380546.08557477366, "origlen": 0, "partialDec": 0.84434529454741736, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661693.049046312924474, 2125281.44501457316801 ], [ -661666.960651054629125, 2125279.469046130776405 ], [ -661641.099107037181966, 2125275.496953601017594 ], [ -661615.612548492848873, 2125269.55148894013837 ], [ -661590.646961719612591, 2125261.666707387659699 ], [ -661566.345348845352419, 2125251.887772443704307 ], [ -661542.846908688894473, 2125240.270697164814919 ], [ -661520.286239411914721, 2125226.882023290265352 ], [ -661498.792567527620122, 2125211.798440108075738 ], [ -661478.48900767264422, 2125195.106345217209309 ], [ -661459.491857402725145, 2125176.901349597610533 ], [ -661441.909931039670482, 2125157.287729956675321 ], [ -661425.843936376972124, 2125136.377831485122442 ], [ -661411.385897836997174, 2125114.291424304712564 ], [ -661398.618629367672838, 2125091.155017446260899 ], [ -661387.615260096616112, 2125067.101134225260466 ], [ -661378.438815474859439, 2125042.267553140874952 ], [ -661371.141856281319633, 2125016.796518718823791 ], [ -661365.766177598387003, 2124990.833926709834486 ], [ -661362.342569427099079, 2124964.528488450683653 ], [ -661360.89064036833588, 2124938.030879037920386 ], [ -661361.418705336167477, 2124911.492874273564667 ], [ -661363.923737964709289, 2124885.066481336485595 ], [ -661368.391387987649068, 2124858.903068054933101 ], [ -661374.796063468093053, 2124833.152495936490595 ], [ -661383.101077428204007, 2124807.962261753622442 ], [ -661393.25885801948607, 2124783.476652692537755 ], [ -661405.211221050121821, 2124759.835919931530952 ], [ -661418.889703286229633, 2124737.175475241150707 ], [ -661434.215954630053602, 2124715.625115408562124 ], [ -661451.102186925476417, 2124695.308278769254684 ], [ -661469.451676818658598, 2124676.341338146477938 ], [ -661489.159319791593589, 2124658.832934310659766 ], [ -661510.112232200684957, 2124642.883353668265045 ], [ -661532.190397863741964, 2124628.58395386300981 ], [ -661555.267355497344397, 2124616.016640475951135 ], [ -661579.210923062753864, 2124605.253397871740162 ], [ -661603.883954883087426, 2124596.355876897461712 ], [ -661629.145127173513174, 2124589.375041739083827 ], [ -661654.84974751342088, 2124584.350878013297915 ], [ -661680.850583590101451, 2124581.312163736671209 ], [ -661706.998706517973915, 2124580.276304449886084 ], [ -661733.14434383274056, 2124581.249233581591398 ], [ -661759.137737356009893, 2124584.225378401111811 ], [ -661784.830000945483334, 2124589.187691974919289 ], [ -661810.073973272694275, 2124596.107750783208758 ], [ -661834.725060706492513, 2124604.945917534641922 ], [ -661858.642065498512238, 2124615.65156820975244 ], [ -661881.68799451738596, 2124628.163381983991712 ], [ -661903.730843905941583, 2124642.409692525397986 ], [ -661905.0, 2124643.37455078586936 ], [ -661905.0, 2125217.375729425344616 ], [ -661892.112959937076084, 2125226.434750369749963 ], [ -661869.519149279803969, 2125239.877744222991168 ], [ -661845.991942365537398, 2125251.551388973370194 ], [ -661821.666101702605374, 2125261.388819009531289 ], [ -661796.680964516592212, 2125269.333686395082623 ], [ -661771.179644609219395, 2125275.340483661741018 ], [ -661745.308212590985931, 2125279.374804461374879 ], [ -661719.214859174564481, 2125281.413540632463992 ], [ -661693.049046312924474, 2125281.44501457316801 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43432, "origarea": 465146.62732980499, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661405.676939211669378, 2123288.86178601719439 ], [ -661403.782982936012559, 2123337.963227344676852 ], [ -661359.605677559156902, 2123462.322005654685199 ], [ -661267.62126125767827, 2123569.535078656394035 ], [ -661156.119410120882094, 2123632.996182947885245 ], [ -661006.832222920143977, 2123655.098585824482143 ], [ -660883.638621504884213, 2123629.000263110734522 ], [ -660780.015362312318757, 2123566.751423150766641 ], [ -660687.361338537652045, 2123455.738921721931547 ], [ -660643.935131138539873, 2123325.046108578331769 ], [ -660649.618192790658213, 2123177.741813613567501 ], [ -660699.846238650381565, 2123053.608332986477762 ], [ -660797.762410051655024, 2122949.689418350812048 ], [ -660915.195275436271913, 2122889.522383940406144 ], [ -661064.126183984219097, 2122876.626461438834667 ], [ -661181.150594006758183, 2122905.568316395394504 ], [ -661296.281015769927762, 2122983.611613548360765 ], [ -661373.810528091271408, 2123094.061052629258484 ], [ -661408.399509966839105, 2123218.278411462437361 ], [ -661405.676939211669378, 2123288.86178601719439 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44988, "origarea": 255530.19481059647, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658457.540065760724247, 2122953.522529276553541 ], [ -658473.490459658904001, 2122935.695226454176009 ], [ -658490.808612616616301, 2122919.233438171446323 ], [ -658509.380740088527091, 2122904.245322190225124 ], [ -658529.084818860283121, 2122890.82935388199985 ], [ -658549.79138875589706, 2122879.073679281398654 ], [ -658571.364403223269619, 2122869.05553590785712 ], [ -658593.662123170797713, 2122860.840745320543647 ], [ -658616.538048211135902, 2122854.483280651271343 ], [ -658639.841879179119132, 2122850.024911969900131 ], [ -658663.4205055993516, 2122847.494931864086539 ], [ -658687.119011628208682, 2122846.909962984733284 ], [ -658710.781693853903562, 2122848.27384881535545 ], [ -658734.253084263764322, 2122851.577628406696022 ], [ -658757.378971670870669, 2122856.799595279619098 ], [ -658780.007414877065457, 2122863.905440015718341 ], [ -658801.989740930031985, 2122872.84847567928955 ], [ -658823.181521901977248, 2122883.569944557268173 ], [ -658843.443523784750141, 2122895.999404221307486 ], [ -658862.642621265025809, 2122910.055190309882164 ], [ -658880.652672362164594, 2122925.644953129347414 ], [ -658897.355347199714743, 2122942.666264357510954 ], [ -658912.640905439970084, 2122961.007290055509657 ], [ -658926.40891730831936, 2122980.547525425907224 ], [ -658938.568923432962038, 2123001.158586544916034 ], [ -658949.041029194486327, 2123022.70505391061306 ], [ -658957.756429665139876, 2123045.045362114906311 ], [ -658964.657861690968275, 2123068.032730029895902 ], [ -658969.699980147765018, 2123091.516125136986375 ], [ -658972.849655898171477, 2123115.341255904175341 ], [ -658974.086193488794379, 2123139.351585451513529 ], [ -658973.401467161602341, 2123163.389360094908625 ], [ -658970.799974273191765, 2123187.296645811758935 ], [ -658966.298805792117491, 2123210.916365884244442 ], [ -658959.927534040762112, 2123234.093332968652248 ], [ -658951.728018435882404, 2123256.675268687307835 ], [ -658941.754130501183681, 2123278.513804163783789 ], [ -658930.071399945300072, 2123299.465454828925431 ], [ -658916.756584150716662, 2123319.392563161440194 ], [ -658901.897163884015754, 2123338.164203129708767 ], [ -658885.590768542140722, 2123355.657040425110608 ], [ -658867.944534721202217, 2123371.756142770405859 ], [ -658849.074402312631719, 2123386.355735100340098 ], [ -658829.104352750815451, 2123399.359894497785717 ], [ -658808.165594420861453, 2123410.683180436491966 ], [ -658786.39570057974197, 2123420.251196170691401 ], [ -658763.937705448712222, 2123428.001077524386346 ], [ -658740.939164424780756, 2123433.881905917543918 ], [ -658717.551184582174756, 2123437.855042927432805 ], [ -658693.927431826130487, 2123439.894384145271033 ], [ -658670.2231212410843, 2123439.986530691385269 ], [ -658646.473029853543267, 2123438.116326023824513 ], [ -658622.956551702925935, 2123434.290675894357264 ], [ -658599.82978837727569, 2123428.534974757581949 ], [ -658577.246254518977366, 2123420.887428554240614 ], [ -658555.355858770548366, 2123411.398801142349839 ], [ -658534.303908661357127, 2123400.132077276706696 ], [ -658514.230146035901271, 2123387.162044564262033 ], [ -658495.267819437780418, 2123372.574797005392611 ], [ -658477.542799591552466, 2123356.467163515742868 ], [ -658461.172743874136358, 2123338.946065162308514 ], [ -658446.266315299551934, 2123320.12780545046553 ], [ -658432.922461228445172, 2123300.137298292014748 ], [ -658421.229756557382643, 2123279.107238846831024 ], [ -658411.265815782360733, 2123257.177222690545022 ], [ -658403.096777815138921, 2123234.492819210048765 ], [ -658396.776866976753809, 2123211.2046053041704 ], [ -658392.348033092450351, 2123187.467165869195014 ], [ -658389.839673056616448, 2123163.438067727722228 ], [ -658389.268435738747939, 2123139.276813630014658 ], [ -658546.601961093838327, 2123136.563198985997587 ], [ -658457.540065760724247, 2122953.522529276553541 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51975, "origarea": 352518.66225779214, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658921.496904977597296, 2124657.013779395725578 ], [ -658854.282888242392801, 2125590.437160382978618 ], [ -658825.412765480112284, 2125588.400236253626645 ], [ -658795.497885896358639, 2125584.280913686845452 ], [ -658765.917407608008943, 2125578.185100108850747 ], [ -658736.798000635113567, 2125570.138898885343224 ], [ -658708.264360537752509, 2125560.176765322685242 ], [ -658680.438674429082312, 2125548.341358996462077 ], [ -658653.440097719896585, 2125534.683361158240587 ], [ -658627.384243847103789, 2125519.261257669422776 ], [ -658602.382689178804867, 2125502.141088569536805 ], [ -658578.54249520751182, 2125483.396165246609598 ], [ -658555.965750074828975, 2125463.106756548397243 ], [ -658534.749131407472305, 2125441.359745018649846 ], [ -658514.983492311206646, 2125418.248254877515137 ], [ -658496.753472328535281, 2125393.871253212913871 ], [ -658480.1371349922847, 2125368.333126229699701 ], [ -658465.205633551348001, 2125341.743232206441462 ], [ -658452.022906283498742, 2125314.21543324412778 ], [ -658440.645402727532201, 2125285.86760764149949 ], [ -658431.121841961285099, 2125256.82114516897127 ], [ -658423.49300400132779, 2125227.200427231844515 ], [ -658417.791555201401934, 2125197.132294275332242 ], [ -658414.04190839570947, 2125166.745502574369311 ], [ -658412.260118386242539, 2125136.170172972604632 ], [ -658412.453813223401085, 2125105.537233613897115 ], [ -658414.62216157768853, 2125074.977859297767282 ], [ -658418.755876322626136, 2125044.622909801546484 ], [ -658424.837254349142313, 2125014.602369507309049 ], [ -658432.840252396184951, 2124985.0447907904163 ], [ -658442.730598602094688, 2124956.07674353942275 ], [ -658454.465939305373468, 2124927.822273160330951 ], [ -658467.996020425693132, 2124900.402369406074286 ], [ -658483.262902688235044, 2124873.934448328334838 ], [ -658500.201209762366489, 2124848.531849378719926 ], [ -658518.738408219069242, 2124824.30335018504411 ], [ -658538.795118156820536, 2124801.352700711693615 ], [ -658560.285453132237308, 2124779.778178946115077 ], [ -658583.117387934704311, 2124759.672170166857541 ], [ -658607.193152680760249, 2124741.120771209243685 ], [ -658632.40965145919472, 2124724.203421888872981 ], [ -658658.658903812174685, 2124708.99256477644667 ], [ -658685.828507106285542, 2124695.553335041273385 ], [ -658713.802117862040177, 2124683.943281442858279 ], [ -658742.459949916810729, 2124674.212120003532618 ], [ -658771.679287369130179, 2124666.401521049439907 ], [ -658801.335010028094985, 2124660.544930789619684 ], [ -658831.300129182054661, 2124656.66742812236771 ], [ -658861.446331353392452, 2124654.785617183428258 ], [ -658891.644527719821781, 2124654.907556267920882 ], [ -658921.496904977597296, 2124657.013779395725578 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44986, "origarea": 477472.65951497306, "origlen": 0, "partialDec": 0.56462261863835739, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658065.0, 2126086.587371344212443 ], [ -658072.151193896192126, 2126073.393368262331933 ], [ -658087.176348270149902, 2126049.819704017601907 ], [ -658103.818547927658074, 2126027.389292704872787 ], [ -658121.992987278616056, 2126006.216434383764863 ], [ -658141.607053027721122, 2125986.40902090864256 ], [ -658162.560796114616096, 2125968.067986173555255 ], [ -658184.747441042563878, 2125951.286791754886508 ], [ -658208.053929978981614, 2125936.150950652081519 ], [ -658232.361498868558556, 2125922.737591560930014 ], [ -658257.546282618888654, 2125911.115065815858543 ], [ -658283.479946272447705, 2125901.342599121388048 ], [ -658310.030338955344632, 2125893.469989716541022 ], [ -658337.062167264288291, 2125887.537354629021138 ], [ -658364.437684660428204, 2125883.574925265274942 ], [ -658392.017393372021616, 2125881.602893332950771 ], [ -658419.660755204269662, 2125881.631307951174676 ], [ -658447.226907656295225, 2125883.66002444922924 ], [ -658474.575381690519862, 2125887.678705106955022 ], [ -658501.566817496553995, 2125893.66687180660665 ], [ -658528.063674598117359, 2125901.594010395463556 ], [ -658553.930932691902854, 2125911.419726196676493 ], [ -658579.036779655958526, 2125923.093949794303626 ], [ -658603.253283190540969, 2125936.557192228734493 ], [ -658626.457042718422599, 2125951.740848090033978 ], [ -658648.529818170121871, 2125968.567545146681368 ], [ -658669.359132494777441, 2125986.951538590714335 ], [ -658688.838844802579843, 2126006.799147974699736 ], [ -658706.869691220344976, 2126028.009234580677003 ], [ -658723.359790714690462, 2126050.473716807551682 ], [ -658738.225113293272443, 2126074.078120913822204 ], [ -658751.389908211887814, 2126098.702164346817881 ], [ -658762.78708998428192, 2126124.220368698704988 ], [ -658772.358580259489827, 2126150.502699077595025 ], [ -658780.055603784625418, 2126177.415226754266769 ], [ -658785.838936982327141, 2126204.820811631157994 ], [ -658789.679107847972773, 2126232.579801086802036 ], [ -658791.556546163395979, 2126260.550741584505886 ], [ -658791.46168325364124, 2126288.591099532321095 ], [ -658789.39500078279525, 2126316.557987560052425 ], [ -658788.16966440633405, 2126325.0 ], [ -658065.0, 2126325.0 ], [ -658065.0, 2126086.587371344212443 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 51976, "origarea": 214943.00700131315, "origlen": 0, "partialDec": 0.051082272785349055, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658065.0, 2125150.8165995310992 ], [ -658076.238426065654494, 2125173.105205598287284 ], [ -658086.655500992550515, 2125198.126909851562232 ], [ -658095.242362370248884, 2125223.85761469649151 ], [ -658101.953194423113018, 2125250.160035675857216 ], [ -658106.752190634142607, 2125276.89383796742186 ], [ -658109.613744819769636, 2125303.916385096497834 ], [ -658110.522587778861634, 2125331.083500024396926 ], [ -658109.473868797649629, 2125358.250234310980886 ], [ -658106.473181562148966, 2125385.271641551051289 ], [ -658101.536534349550493, 2125412.003550697583705 ], [ -658094.690264659002423, 2125438.303335254080594 ], [ -658085.970898708677851, 2125464.030674356501549 ], [ -658075.424956610891968, 2125489.048301302827895 ], [ -658065.0, 2125509.51048609893769 ], [ -658065.0, 2125150.8165995310992 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44987, "origarea": 899412.06156291557, "origlen": 0, "partialDec": 0.054901979028332097, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658065.0, 2122998.69442291604355 ], [ -658078.037971434532665, 2123022.607748864218593 ], [ -658094.79814860841725, 2123057.434500847477466 ], [ -658109.785944731906056, 2123093.08629096718505 ], [ -658122.962454223888926, 2123129.470576967578381 ], [ -658134.293472913559526, 2123166.492915213573724 ], [ -658143.749586842372082, 2123204.057205812074244 ], [ -658151.30624863167759, 2123242.065942096523941 ], [ -658156.943841221509501, 2123280.420463698450476 ], [ -658160.647728810086846, 2123319.021212670952082 ], [ -658162.408294869586825, 2123357.767991894390434 ], [ -658162.220967128174379, 2123396.560225179884583 ], [ -658160.086229468462989, 2123435.297218301799148 ], [ -658156.009620686760172, 2123473.878420419525355 ], [ -658150.001720148255117, 2123512.203685072716326 ], [ -658142.078120347578079, 2123550.173530114348978 ], [ -658132.259386461810209, 2123587.689395941328257 ], [ -658120.571002987446263, 2123624.653901375830173 ], [ -658107.043307624408044, 2123660.97109637549147 ], [ -658091.711412533768453, 2123696.546711169183254 ], [ -658074.615113232517615, 2123731.28840088378638 ], [ -658065.0, 2123748.569132001139224 ], [ -658065.0, 2122998.69442291604355 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2130165.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2130165.0.geojson new file mode 100644 index 00000000..466108f9 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-661905.0_2130165.0.geojson @@ -0,0 +1,10 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 45023, "origarea": 393571.68178765418, "origlen": 0, "partialDec": 0.046704815578436989, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661905.0, 2129275.789142734836787 ], [ -661893.683730909717269, 2129258.920716643333435 ], [ -661880.478122539469041, 2129235.662895787972957 ], [ -661869.025423728860915, 2129211.464926595333964 ], [ -661859.389681786647998, 2129186.46213395986706 ], [ -661851.624782655620947, 2129160.794343632645905 ], [ -661845.774149572127499, 2129134.605100294575095 ], [ -661841.870500267134048, 2129108.04086477169767 ], [ -661839.935664013843052, 2129081.250194957014173 ], [ -661839.980459591839463, 2129054.382915051653981 ], [ -661842.004634817363694, 2129027.589277653954923 ], [ -661845.996867987909354, 2129001.019123523496091 ], [ -661851.934831241727807, 2128974.821043573319912 ], [ -661859.78531545388978, 2128949.141547931823879 ], [ -661869.504416000796482, 2128924.124246568419039 ], [ -661881.037778317695484, 2128899.909046212211251 ], [ -661894.320901909377426, 2128876.631367898546159 ], [ -661905.0, 2128860.775435580406338 ], [ -661905.0, 2129275.789142734836787 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43140, "origarea": 491870.67373823491, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661153.83058622374665, 2127328.616901022382081 ], [ -661135.572882248554379, 2127396.691046867053956 ], [ -661105.812480075517669, 2127460.480009274091572 ], [ -661065.475744732189924, 2127517.998154572676867 ], [ -661015.818270102958195, 2127567.455048063304275 ], [ -660958.385796234360896, 2127607.311187216080725 ], [ -660894.966093846131116, 2127636.325923815835267 ], [ -660827.533313687890768, 2127653.596083151642233 ], [ -660758.186533141997643, 2127658.584078218322247 ], [ -660689.084413231350482, 2127651.13464369205758 ], [ -660622.378000342752784, 2127631.479668885003775 ], [ -660560.143764850799926, 2127600.23097911849618 ], [ -660504.318961397977546, 2127558.361290441360325 ], [ -660456.641323366085999, 2127507.173930419608951 ], [ -660418.594969000900164, 2127448.262267667800188 ], [ -660391.364203251083381, 2127383.460112993605435 ], [ -660375.796653394470923, 2127314.784636141732335 ], [ -660372.376885888981633, 2127244.373575075063854 ], [ -660381.211325464537367, 2127174.418692280072719 ], [ -660402.024945559096523, 2127107.097549493890256 ], [ -660434.169832728570327, 2127044.505724612157792 ], [ -660476.645357989589684, 2126988.591580656822771 ], [ -660528.129326881375164, 2126941.095617241691798 ], [ -660587.019138261326589, 2126903.496292402967811 ], [ -660651.481670495471917, 2126876.964001296553761 ], [ -660719.510342149878852, 2126862.32464409712702 ], [ -660788.987571105943061, 2126860.033917336724699 ], [ -660857.750688107567839, 2126870.163128829095513 ], [ -660923.659253327758051, 2126892.396977754775435 ], [ -660984.661680917371996, 2126926.043369080871344 ], [ -661038.859098049462773, 2126970.054956822656095 ], [ -661084.564451013226062, 2127023.061745565384626 ], [ -661120.355018777307123, 2127083.41373552242294 ], [ -661145.116699527367018, 2127149.232283691409975 ], [ -661158.078691621427424, 2127218.468582318630069 ], [ -661153.83058622374665, 2127328.616901022382081 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43414, "origarea": 396910.89701719442, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -659420.346183134359308, 2129520.998052256181836 ], [ -659461.383203490171582, 2129399.606295715086162 ], [ -659547.086564990342595, 2129298.315927427727729 ], [ -659661.62028721626848, 2129234.975180191453546 ], [ -659798.579059214680456, 2129218.564515012316406 ], [ -659927.475170377991162, 2129254.094571889843792 ], [ -660027.724878244567662, 2129325.433824636507779 ], [ -660059.225607082247734, 2129372.70192670635879 ], [ -660101.998273776727729, 2129441.900743046309799 ], [ -660127.751540555735119, 2129559.633796967100352 ], [ -660110.56129486777354, 2129691.13266032282263 ], [ -660045.32403637690004, 2129811.623265770729631 ], [ -659942.061952754273079, 2129896.894823632668704 ], [ -659819.280962530174293, 2129938.417467396706343 ], [ -659683.268217500881292, 2129930.279335264116526 ], [ -659567.182027778704651, 2129876.788248814176768 ], [ -659473.929295641952194, 2129781.125478959176689 ], [ -659423.975672627915628, 2129662.491195299196988 ], [ -659420.346183134359308, 2129520.998052256181836 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44986, "origarea": 477472.65951497306, "origlen": 0, "partialDec": 0.40999738592553259, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -658389.357993305195123, 2126666.714983499608934 ], [ -658361.791610523476265, 2126664.686247928068042 ], [ -658334.442908015451394, 2126660.667545913718641 ], [ -658307.45125024439767, 2126654.67935599992052 ], [ -658280.954182202811353, 2126646.752192817628384 ], [ -658255.086728487163782, 2126636.926451556384563 ], [ -658229.980705206049606, 2126625.252202128525823 ], [ -658205.764048243523575, 2126611.788934027310461 ], [ -658182.560161303495988, 2126596.605253192596138 ], [ -658160.487287048832513, 2126579.778532353229821 ], [ -658139.65790453995578, 2126561.394516810309142 ], [ -658120.178156056674197, 2126541.546887464355677 ], [ -658102.14730620617047, 2126520.336783428676426 ], [ -658085.657236095168628, 2126497.872286662925035 ], [ -658070.791975114378147, 2126474.267871207091957 ], [ -658065.0, 2126463.434212466701865 ], [ -658065.0, 2126325.0 ], [ -658788.16966440633405, 2126325.0 ], [ -658785.367028334876522, 2126344.308892672415823 ], [ -658779.398289790959097, 2126371.702402450144291 ], [ -658771.519198779133148, 2126398.598925666883588 ], [ -658761.769903731532395, 2126424.861403628252447 ], [ -658750.200083329807967, 2126450.356008558068424 ], [ -658736.868693378637545, 2126474.952825609128922 ], [ -658721.843666416825727, 2126498.526514826342463 ], [ -658705.201565562398173, 2126520.956949916202575 ], [ -658687.027194395894185, 2126542.129830337595195 ], [ -658667.413164822966792, 2126561.937263780273497 ], [ -658646.459425167995505, 2126580.278315940406173 ], [ -658624.272750857053325, 2126597.059524904936552 ], [ -658600.966200321912766, 2126612.195377354510128 ], [ -658576.658538872026838, 2126625.60874437680468 ], [ -658551.473633483168669, 2126637.231274466961622 ], [ -658525.539821589482017, 2126647.003741845488548 ], [ -658498.989257082226686, 2126654.876348257996142 ], [ -658471.957236864138395, 2126660.808976714499295 ], [ -658444.581511377356946, 2126664.771395970601588 ], [ -658417.001582632656209, 2126666.743414520751685 ], [ -658389.357993305195123, 2126666.714983499608934 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2118645.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2118645.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2118645.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2122485.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2122485.0.geojson new file mode 100644 index 00000000..f644cfcc --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2122485.0.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 49389, "origarea": 183249.13547319669, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -663283.073593405890279, 2119779.762437678407878 ], [ -663253.822182945325039, 2120444.366674417164177 ], [ -663252.602443580864929, 2120458.681742427870631 ], [ -663248.521530967671424, 2120458.6886699013412 ], [ -663222.924045348539948, 2120456.738284546416253 ], [ -663197.553661965415813, 2120452.805372496601194 ], [ -663172.559265924151987, 2120446.913013793993741 ], [ -663148.087535830563866, 2120439.09578731097281 ], [ -663124.282082987134345, 2120429.399567834567279 ], [ -663101.282608592882752, 2120417.881256833206862 ], [ -663079.224083889159374, 2120404.608448529615998 ], [ -663058.235958066419698, 2120389.659033250529319 ], [ -663038.44139857776463, 2120373.120740294456482 ], [ -663019.956568334018812, 2120355.090623137541115 ], [ -663002.889943994348869, 2120335.674489835742861 ], [ -662987.341679371893406, 2120314.986282147467136 ], [ -662973.403017695178278, 2120293.147406825330108 ], [ -662961.155756158172153, 2120270.286023183260113 ], [ -662950.671765910810791, 2120246.536290997639298 ], [ -662942.012570308870636, 2120222.037583190016448 ], [ -662935.228983889217488, 2120196.933667931240052 ], [ -662930.360814187210053, 2120171.371864984277636 ], [ -662927.436628174269572, 2120145.502181126270443 ], [ -662926.473584638326429, 2120119.476429886650294 ], [ -662927.477333525661379, 2120093.447340620681643 ], [ -662930.441982820746489, 2120067.567662256769836 ], [ -662935.350133164785802, 2120041.989266885910183 ], [ -662942.172979998518713, 2120016.862258536275476 ], [ -662950.870482631260529, 2119992.334092317149043 ], [ -662961.391599258990027, 2119968.548709051683545 ], [ -662973.674586524371989, 2119945.645690641365945 ], [ -662987.647361883427948, 2119923.759440896566957 ], [ -663003.227926639490761, 2119903.018396837171167 ], [ -663020.324847167124972, 2119883.544274964835495 ], [ -663038.837791492813267, 2119865.451356981415302 ], [ -663058.658118097577244, 2119848.845819177106023 ], [ -663079.669513472821563, 2119833.825109289959073 ], [ -663101.74867468723096, 2119820.477374695241451 ], [ -663124.766032977961004, 2119808.880945119075477 ], [ -663148.58651409659069, 2119799.103872939012945 ], [ -663173.070330958231352, 2119791.203533890191466 ], [ -663198.073803949402645, 2119785.22629027068615 ], [ -663223.450204065418802, 2119781.207218958530575 ], [ -663249.050613947096281, 2119779.169905507471412 ], [ -663274.724801751435734, 2119779.126305778045207 ], [ -663283.073593405890279, 2119779.762437678407878 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 49390, "origarea": 357258.99490978097, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -663346.62979113869369, 2119279.060939743649215 ], [ -663344.000321470550261, 2119308.702645138371736 ], [ -663339.404817673144862, 2119338.101558745838702 ], [ -663332.864246130455285, 2119367.123539878055453 ], [ -663324.40844818088226, 2119395.636167690623552 ], [ -663314.076003984780982, 2119423.509345383383334 ], [ -663301.914056529174559, 2119450.615893825422972 ], [ -663287.978096553124487, 2119476.832131807692349 ], [ -663272.331709379563108, 2119502.038440420757979 ], [ -663255.046284817159176, 2119526.119808808900416 ], [ -663236.200691437814385, 2119548.966359012294561 ], [ -663215.880916743539274, 2119570.473847224842757 ], [ -663194.179674824583344, 2119590.544139507692307 ], [ -663171.195983343175612, 2119609.085659546777606 ], [ -663147.034711733227596, 2119626.01380647206679 ], [ -663121.806102711590938, 2119641.251340882387012 ], [ -663095.625269248150289, 2119654.728737305384129 ], [ -663068.611669332254678, 2119666.384501378517598 ], [ -663040.888560887891799, 2119676.165450478903949 ], [ -663012.582439361605793, 2119684.026956347748637 ], [ -662983.82246052834671, 2119689.933148756623268 ], [ -662954.739851160906255, 2119693.857079146429896 ], [ -662925.467310235835612, 2119695.780843579676002 ], [ -662896.138403434073552, 2119695.695664482191205 ], [ -662866.8869536772836, 2119693.601930618751794 ], [ -662837.846430486883037, 2119689.509195384103805 ], [ -662809.149340966134332, 2119683.436133151873946 ], [ -662780.926625158521347, 2119675.410454124212265 ], [ -662753.307058575563133, 2119665.468777829781175 ], [ -662726.41666458058171, 2119653.656466093380004 ], [ -662700.378139353590086, 2119640.02741600619629 ], [ -662675.310292023699731, 2119624.64381402824074 ], [ -662651.327502547646873, 2119607.575852252542973 ], [ -662633.458797906874679, 2119592.932897321414202 ], [ -662649.665591727825813, 2119259.050650794059038 ], [ -662861.041964669013396, 2119240.197332561481744 ], [ -662936.911776579800062, 2119207.398018904961646 ], [ -663015.696438976679929, 2119099.011640863027424 ], [ -663165.474128923728131, 2118896.517805930227041 ], [ -663186.449792176950723, 2118912.547428961377591 ], [ -663208.601418016827665, 2118931.993573479354382 ], [ -663229.404818454175256, 2118952.915762934368104 ], [ -663248.765072659938596, 2118975.218534371349961 ], [ -663266.593844299437478, 2118998.800125535111874 ], [ -663282.80978459387552, 2119023.552939210087061 ], [ -663297.338903489755467, 2119049.364034105092287 ], [ -663310.114907275652513, 2119076.115640216972679 ], [ -663321.079501070897095, 2119103.685696195345372 ], [ -663330.18265483470168, 2119131.948406239971519 ], [ -663337.382831657887436, 2119160.774814092088491 ], [ -663342.647177310776897, 2119190.033391451463103 ], [ -663345.951670174137689, 2119219.590638105757535 ], [ -663347.28123087878339, 2119249.311691022012383 ], [ -663346.62979113869369, 2119279.060939743649215 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 43442, "origarea": 113587.70720488419, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662585.537716711056419, 2120972.474544008728117 ], [ -662672.193167518475093, 2121003.358062254730612 ], [ -662683.819169561844319, 2121016.083868674002588 ], [ -662730.204866083106026, 2121070.055986914318055 ], [ -662753.759822092601098, 2121166.205419245176017 ], [ -662722.394043221371248, 2121272.60513280890882 ], [ -662653.243543352698907, 2121337.645981988403946 ], [ -662552.476378657971509, 2121358.484082465525717 ], [ -662480.353142374427989, 2121343.507765955291688 ], [ -662406.980393495410681, 2121282.385037379804999 ], [ -662373.522658198489808, 2121207.38051318237558 ], [ -662377.310087871621363, 2121109.174894538708031 ], [ -662419.592080263071693, 2121033.914584087673575 ], [ -662494.318861099774949, 2120981.374514529015869 ], [ -662585.537716711056419, 2120972.474544008728117 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2126325.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2126325.0.geojson new file mode 100644 index 00000000..06985716 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2126325.0.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 43137, "origarea": 361647.34465168085, "origlen": 0, "partialDec": 0.071160515251336834, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662137.953397833625786, 2126325.0 ], [ -662233.919334881473333, 2126261.930289819836617 ], [ -662358.255713250837289, 2126236.236304953694344 ], [ -662483.128394665312953, 2126258.443210243247449 ], [ -662590.461963808513246, 2126325.0 ], [ -662137.953397833625786, 2126325.0 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 44985, "origarea": 380546.08557477366, "origlen": 0, "partialDec": 0.15565470545258336, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -661905.0, 2124643.37455078586936 ], [ -661924.644355155993253, 2124658.308898446150124 ], [ -661944.308738291496411, 2124675.769930709153414 ], [ -661962.611357989604585, 2124694.692774272989482 ], [ -661979.447378740878776, 2124714.969040958676487 ], [ -661994.720365330926143, 2124736.482590271625668 ], [ -662008.342835211078636, 2124759.110194658860564 ], [ -662020.236759598483332, 2124782.722245336975902 ], [ -662030.33401042688638, 2124807.183494668453932 ], [ -662038.576750600477681, 2124832.353830857668072 ], [ -662044.917765291407704, 2124858.089080506004393 ], [ -662049.320732422638685, 2124884.241834385786206 ], [ -662051.760430733207613, 2124910.662291845306754 ], [ -662052.222884287592024, 2124937.199118802323937 ], [ -662050.705442559788935, 2124963.700314601883292 ], [ -662047.216795651824214, 2124990.014082662761211 ], [ -662041.776924558798783, 2125015.989699948579073 ], [ -662034.416986754979007, 2125041.478380300104618 ], [ -662025.179137763916515, 2125066.33412669133395 ], [ -662014.11628973193001, 2125090.414567464962602 ], [ -662001.291808383306488, 2125113.581771849654615 ], [ -661986.779150097514503, 2125135.703039995860308 ], [ -661970.661441179923713, 2125156.651663101743907 ], [ -661953.031001744908281, 2125176.307649174239486 ], [ -661933.988816928351298, 2125194.55841034417972 ], [ -661913.643958465545438, 2125211.299407756421715 ], [ -661905.0, 2125217.375729425344616 ], [ -661905.0, 2124643.37455078586936 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2130165.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2130165.0.geojson new file mode 100644 index 00000000..d73fca2e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-665745.0_2130165.0.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "id": 43137, "origarea": 361647.34465168085, "origlen": 0, "partialDec": 0.92883948474866762, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662702.666498517501168, 2126587.452882915735245 ], [ -662695.501050548395142, 2126651.66860979096964 ], [ -662646.853386092116125, 2126770.599066926166415 ], [ -662558.725325931911357, 2126863.336458752863109 ], [ -662443.336543769808486, 2126917.021936025004834 ], [ -662316.686776358401403, 2126924.211544466204941 ], [ -662196.337272424250841, 2126883.908394473604858 ], [ -662098.97568173869513, 2126801.700882449746132 ], [ -662038.102083388133906, 2126688.987801236566156 ], [ -662022.157051441608928, 2126561.39779051579535 ], [ -662053.351335313753225, 2126436.622288749087602 ], [ -662127.359409167896956, 2126331.962468302343041 ], [ -662137.953397833625786, 2126325.0 ], [ -662590.461963808513246, 2126325.0 ], [ -662591.222891321522184, 2126325.47184578794986 ], [ -662667.551083773723803, 2126428.028137710876763 ], [ -662690.278055311879143, 2126488.32739687897265 ], [ -662702.666498517501168, 2126587.452882915735245 ] ] ] } }, +{ "type": "Feature", "properties": { "id": 45023, "origarea": 393571.68178765418, "origlen": 0, "partialDec": 0.95329518442156402, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -662180.079371348954737, 2129426.582739786244929 ], [ -662153.667974605807103, 2129424.594752465374768 ], [ -662127.481689385371283, 2129420.610031333751976 ], [ -662101.666961695766076, 2129414.650860731489956 ], [ -662076.368159586563706, 2129406.750566921196878 ], [ -662051.72676575742662, 2129396.953331731725484 ], [ -662027.88058628689032, 2129385.313945461530238 ], [ -662004.962979941163212, 2129371.897500451188534 ], [ -661983.102112341905013, 2129356.779027095064521 ], [ -661962.420239194761962, 2129340.043074189685285 ], [ -661943.033022560644895, 2129321.783236138522625 ], [ -661925.048884019372053, 2129302.101629505399615 ], [ -661908.56839831720572, 2129281.108321944717318 ], [ -661905.0, 2129275.789142734836787 ], [ -661905.0, 2128860.775435580406338 ], [ -661909.279501089360565, 2128854.421389662660658 ], [ -661925.82992043858394, 2128833.403318539727479 ], [ -661943.879602658911608, 2128813.694695955142379 ], [ -661963.327606209204532, 2128795.4057403691113 ], [ -661984.065169813809916, 2128778.638730920385569 ], [ -662005.976320713292807, 2128763.487435414455831 ], [ -662028.938523216173053, 2128750.036585953552276 ], [ -662052.823363971081562, 2128738.361405096016824 ], [ -662077.49727008480113, 2128728.527185152284801 ], [ -662102.822256104671396, 2128720.588923064991832 ], [ -662128.656695660669357, 2128714.591012839693576 ], [ -662154.856113473535515, 2128710.566997264511883 ], [ -662181.273993284790777, 2128708.539380367845297 ], [ -662207.762597204186022, 2128708.519501485396177 ], [ -662234.173791883862577, 2128710.507471913471818 ], [ -662260.359876902075484, 2128714.492174254730344 ], [ -662286.174410723964684, 2128720.451324599329382 ], [ -662311.473029629676603, 2128728.351597126107663 ], [ -662336.114255011081696, 2128738.148810496088117 ], [ -662359.960284553235397, 2128749.788174892310053 ], [ -662382.877762841060758, 2128763.204598455224186 ], [ -662404.738527109497227, 2128778.323051281739026 ], [ -662425.420323965139687, 2128795.058985020965338 ], [ -662444.807493047672324, 2128813.318805709481239 ], [ -662462.791613842360675, 2128833.000397164840251 ], [ -662479.272111999685876, 2128853.993692078627646 ], [ -662494.156821784097701, 2128876.181287525687367 ], [ -662507.362501504831016, 2128899.439101570751518 ], [ -662518.815299049252644, 2128923.637067125178874 ], [ -662528.451164900208823, 2128948.639859389513731 ], [ -662529.036220424692146, 2128950.573794241063297 ], [ -662504.430104913422838, 2129239.355072253849357 ], [ -662493.522054941044189, 2129258.470812628511339 ], [ -662478.563559090718627, 2129280.680811395402998 ], [ -662462.013216034742072, 2129301.698901677038521 ], [ -662443.963581364718266, 2129321.40754162427038 ], [ -662424.515595561126247, 2129339.696512387134135 ], [ -662403.778019501478411, 2129356.463534492067993 ], [ -662381.866826227051206, 2129371.61483984393999 ], [ -662358.904552371706814, 2129385.065696123987436 ], [ -662335.019612883334048, 2129396.740880620200187 ], [ -662310.345582859939896, 2129406.575100938789546 ], [ -662285.020450522075407, 2129414.51336013013497 ], [ -662259.185845508240163, 2129420.511264255270362 ], [ -662232.986246794578619, 2129424.535270656459033 ], [ -662206.568174679763615, 2129426.56287552928552 ], [ -662180.079371348954737, 2129426.582739786244929 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2118645.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2118645.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2118645.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2122485.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2122485.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2122485.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2126325.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2126325.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2126325.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2130165.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2130165.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-669585.0_2130165.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2118645.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2118645.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2118645.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2122485.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2122485.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2122485.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2126325.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2126325.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2126325.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2130165.0.geojson b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2130165.0.geojson new file mode 100644 index 00000000..1b48f12f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_custom_proj_expected/geoms_-673425.0_2130165.0.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724734.geojson new file mode 100644 index 00000000..da9ac4c9 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724734.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134696, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 160.8812060779413, "origlen": 0, "partialDec": 0.33276744108985457, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733631.017092985799536, 3724696.563397473655641 ], [ 733637.916874720831402, 3724689.0 ], [ 733626.758141876780428, 3724689.0 ], [ 733624.904465531464666, 3724691.031963157467544 ], [ 733631.017092985799536, 3724696.563397473655641 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724824.geojson new file mode 100644 index 00000000..4cb854c0 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724824.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117300, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 56.69138110426168, "origlen": 0, "partialDec": 0.82298268439083644, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733637.712568847462535, 3724812.644189648330212 ], [ 733637.935811243369244, 3724818.720185527112335 ], [ 733637.983977672527544, 3724821.69559862697497 ], [ 733645.835462932707742, 3724821.387535042595118 ], [ 733646.0, 3724821.378123895265162 ], [ 733646.0, 3724817.318846063688397 ], [ 733644.187826864537783, 3724817.38542799977586 ], [ 733642.700126820825972, 3724817.105018282774836 ], [ 733640.485648880014196, 3724816.174316430930048 ], [ 733639.697913700132631, 3724812.692572094965726 ], [ 733637.712568847462535, 3724812.644189648330212 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724869.geojson new file mode 100644 index 00000000..497d5474 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724869.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 0.22027170560859002, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724863.492486663162708 ], [ 733636.398522177478299, 3724862.375186257530004 ], [ 733636.446408499730751, 3724866.504775203764439 ], [ 733636.346274557523429, 3724869.0 ], [ 733646.0, 3724869.0 ], [ 733646.0, 3724863.492486663162708 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102938, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 241.49355060420999, "origlen": 0, "partialDec": 0.88389656023284158, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733645.532881900900975, 3724858.180826778523624 ], [ 733646.0, 3724854.806816860102117 ], [ 733646.0, 3724834.694837557617575 ], [ 733643.618271068204194, 3724833.519004478584975 ], [ 733637.692060588742606, 3724833.674225233960897 ], [ 733636.89257507189177, 3724836.007498409133404 ], [ 733637.419333599274978, 3724844.865368209313601 ], [ 733637.096330924076028, 3724858.119497096166015 ], [ 733645.532881900900975, 3724858.180826778523624 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724914.geojson new file mode 100644 index 00000000..be089ed1 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724914.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102932, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 250.90410248692208, "origlen": 0, "partialDec": 0.8735442193360704, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733643.912090259022079, 3724914.0 ], [ 733643.061781426658854, 3724892.15789200225845 ], [ 733632.952742423396558, 3724892.544111663941294 ], [ 733633.78803826845251, 3724914.0 ], [ 733643.912090259022079, 3724914.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 0.70369949427404388, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733644.666782932821661, 3724887.62466867826879 ], [ 733646.0, 3724884.255406009964645 ], [ 733646.0, 3724869.0 ], [ 733636.346274557523429, 3724869.0 ], [ 733636.068220987217501, 3724875.928781074937433 ], [ 733635.770354353357106, 3724888.151417502202094 ], [ 733644.666782932821661, 3724887.62466867826879 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724959.geojson new file mode 100644 index 00000000..989c4297 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3724959.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102932, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 250.90410248692208, "origlen": 0, "partialDec": 0.12645578066316202, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733633.917563493945636, 3724917.327059258706868 ], [ 733644.026576642645523, 3724916.940842032898217 ], [ 733643.912090259022079, 3724914.0 ], [ 733633.78803826845251, 3724914.0 ], [ 733633.917563493945636, 3724917.327059258706868 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102940, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 247.85698155347222, "origlen": 0, "partialDec": 0.46588867952968854, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724923.920079745352268 ], [ 733642.8083045554813, 3724925.412150491029024 ], [ 733636.785855265101418, 3724926.852379398420453 ], [ 733638.080213227891363, 3724931.256500165909529 ], [ 733642.490936725749634, 3724931.197525009978563 ], [ 733643.028865780681372, 3724937.691814653109759 ], [ 733641.684800133458339, 3724942.564334524329752 ], [ 733641.276853826944716, 3724944.829464028123766 ], [ 733643.267351940623485, 3724949.61678282963112 ], [ 733646.0, 3724949.535827656742185 ], [ 733646.0, 3724923.920079745352268 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 7.6890011379658681e-06, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724957.285058092791587 ], [ 733645.967119043460116, 3724957.295747282914817 ], [ 733646.0, 3724957.395778697449714 ], [ 733646.0, 3724957.285058092791587 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725004.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725004.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725049.geojson new file mode 100644 index 00000000..d7783644 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725049.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135943, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 293.80215091143663, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733614.692407695925795, 3725025.917704849969596 ], [ 733618.232930953730829, 3725025.759830723050982 ], [ 733618.361258932505734, 3725028.493043820839375 ], [ 733623.40737501042895, 3725028.260883178096265 ], [ 733623.226638415828347, 3725024.25013302732259 ], [ 733629.22064378275536, 3725023.974487836007029 ], [ 733628.499683096189983, 3725008.231178386602551 ], [ 733624.467038923874497, 3725005.347326850984246 ], [ 733613.770959213492461, 3725005.830230219755322 ], [ 733614.692407695925795, 3725025.917704849969596 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725094.geojson new file mode 100644 index 00000000..25196dfc --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725094.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 0.075357171616733137, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3725089.934429133776575 ], [ 733638.289124431437813, 3725094.0 ], [ 733646.0, 3725094.0 ], [ 733646.0, 3725089.934429133776575 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725139.geojson new file mode 100644 index 00000000..1e78fc70 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733601_3725139.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102923, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 371.9610521071786, "origlen": 0, "partialDec": 0.40890572019922689, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733601.0, 3725137.577448081690818 ], [ 733604.893619947950356, 3725135.093213116750121 ], [ 733604.174474695930257, 3725128.416942054405808 ], [ 733603.743797679431736, 3725124.377905528992414 ], [ 733610.68987981416285, 3725120.074717226438224 ], [ 733610.059207651182078, 3725110.148913879878819 ], [ 733601.0, 3725111.934446161147207 ], [ 733601.0, 3725137.577448081690818 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 0.59749945752301825, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733644.636568528716452, 3725102.534991428721696 ], [ 733646.0, 3725101.754577273502946 ], [ 733646.0, 3725094.0 ], [ 733638.289124431437813, 3725094.0 ], [ 733627.684122170670889, 3725099.591503564734012 ], [ 733630.799799439031631, 3725105.438350759446621 ], [ 733640.795360824326053, 3725100.177396830171347 ], [ 733644.636568528716452, 3725102.534991428721696 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724734.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724734.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724824.geojson new file mode 100644 index 00000000..58db95a9 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724824.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117300, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 56.69138110426168, "origlen": 0, "partialDec": 0.17701731560750186, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724821.378123895265162 ], [ 733648.828701670165174, 3724821.216328571550548 ], [ 733648.656602984643541, 3724817.993740823119879 ], [ 733646.185421441216022, 3724817.312033404130489 ], [ 733646.0, 3724817.318846063688397 ], [ 733646.0, 3724821.378123895265162 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724869.geojson new file mode 100644 index 00000000..b9d1bad1 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724869.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 0.029826769182349354, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733647.285931088379584, 3724869.0 ], [ 733647.617427871096879, 3724863.680702774319798 ], [ 733646.0, 3724863.492486663162708 ], [ 733646.0, 3724869.0 ], [ 733647.285931088379584, 3724869.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102938, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 241.49355060420999, "origlen": 0, "partialDec": 0.11610343976548934, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724854.806816860102117 ], [ 733646.794668328016996, 3724849.066900496836752 ], [ 733648.42761501041241, 3724842.625518082175404 ], [ 733647.709180656238459, 3724835.538641034159809 ], [ 733646.0, 3724834.694837557617575 ], [ 733646.0, 3724854.806816860102117 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724914.geojson new file mode 100644 index 00000000..4c2999ad --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724914.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102939, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.99532490416749, "origlen": 0, "partialDec": 0.046202030937300832, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3724884.255406009964645 ], [ 733646.397882855031639, 3724883.249889441765845 ], [ 733647.285931088379584, 3724869.0 ], [ 733646.0, 3724869.0 ], [ 733646.0, 3724884.255406009964645 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724959.geojson new file mode 100644 index 00000000..24e4d42e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3724959.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102940, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 247.85698155347222, "origlen": 0, "partialDec": 0.53411132047277343, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733653.132692800136283, 3724949.324520394671708 ], [ 733648.855268120998517, 3724922.585283832624555 ], [ 733646.0, 3724923.920079745352268 ], [ 733646.0, 3724949.535827656742185 ], [ 733653.132692800136283, 3724949.324520394671708 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 0.17080870764493319, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733658.305873904144391, 3724959.0 ], [ 733656.60582423210144, 3724953.837236555293202 ], [ 733646.0, 3724957.285058092791587 ], [ 733646.0, 3724957.395778697449714 ], [ 733646.527317655156367, 3724959.0 ], [ 733658.305873904144391, 3724959.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725004.geojson new file mode 100644 index 00000000..7693d3e5 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725004.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135783, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 236.74072382805585, "origlen": 0, "partialDec": 0.82918360335723085, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733653.939914708724245, 3724982.094132591970265 ], [ 733664.747478068107739, 3724978.562062832526863 ], [ 733658.305873904144391, 3724959.0 ], [ 733646.527317655156367, 3724959.0 ], [ 733649.078211254440248, 3724966.760403643827885 ], [ 733654.418122929870151, 3724970.852504897862673 ], [ 733657.441431801649742, 3724975.920252304058522 ], [ 733657.77753429254517, 3724979.268915843218565 ], [ 733653.939914708724245, 3724982.094132591970265 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102924, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.59457935772951, "origlen": 0, "partialDec": 0.67253016461917026, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733678.195862984284759, 3725004.0 ], [ 733682.16668538027443, 3725001.970434435643256 ], [ 733678.337399649550207, 3724994.552469162270427 ], [ 733663.362111695343629, 3725002.26673267967999 ], [ 733663.028553360374644, 3725004.0 ], [ 733678.195862984284759, 3725004.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725049.geojson new file mode 100644 index 00000000..73f1bfd2 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725049.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102924, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 151.59457935772951, "origlen": 0, "partialDec": 0.3274698353796876, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733665.773527625598945, 3725010.34930036123842 ], [ 733678.195862984284759, 3725004.0 ], [ 733663.028553360374644, 3725004.0 ], [ 733662.883814595406875, 3725004.752105239313096 ], [ 733665.773527625598945, 3725010.34930036123842 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725094.geojson new file mode 100644 index 00000000..4cfff6dc --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725094.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 0.12153410671769142, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733652.800486668245867, 3725094.0 ], [ 733649.850147562567145, 3725088.956138697918504 ], [ 733646.575522312079556, 3725089.630984119605273 ], [ 733646.0, 3725089.934429133776575 ], [ 733646.0, 3725094.0 ], [ 733652.800486668245867, 3725094.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102920, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 226.41975901786444, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733667.977394764660858, 3725090.163731965236366 ], [ 733676.199185358826071, 3725084.937261826358736 ], [ 733677.677698997664265, 3725082.16552930790931 ], [ 733680.178836030769162, 3725080.473026779480278 ], [ 733682.073956778040156, 3725075.081243939697742 ], [ 733686.535091507947072, 3725072.570883402135223 ], [ 733684.217532261973247, 3725066.732374109327793 ], [ 733679.064045730396174, 3725068.704256920609623 ], [ 733676.880473150406033, 3725071.458802872337401 ], [ 733671.348778790328652, 3725074.475772328674793 ], [ 733667.915225020376965, 3725078.243055400904268 ], [ 733663.415438750991598, 3725082.339483730494976 ], [ 733667.977394764660858, 3725090.163731965236366 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725139.geojson new file mode 100644 index 00000000..e65ef72e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733646_3725139.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 135941, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.00350114029035, "origlen": 0, "partialDec": 0.2056092641335639, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733646.0, 3725101.754577273502946 ], [ 733654.492900374345481, 3725096.893328425474465 ], [ 733652.800486668245867, 3725094.0 ], [ 733646.0, 3725094.0 ], [ 733646.0, 3725101.754577273502946 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724734.geojson new file mode 100644 index 00000000..75b152a5 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724734.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134694, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 98.143961268411516, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733707.923785710008815, 3724706.18411343311891 ], [ 733716.751506693311967, 3724697.820616477634758 ], [ 733709.214741862844676, 3724689.402245204430073 ], [ 733706.274902248056605, 3724694.235860752407461 ], [ 733708.154135095421225, 3724696.734307382255793 ], [ 733707.539887185208499, 3724700.22627462958917 ], [ 733702.747399608721025, 3724701.85182610200718 ], [ 733707.923785710008815, 3724706.18411343311891 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724824.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724824.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724869.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724869.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724959.geojson new file mode 100644 index 00000000..e8c3dce5 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3724959.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102925, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 235.62354171707437, "origlen": 0, "partialDec": 0.19134224537340172, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733723.705273776431568, 3724959.0 ], [ 733721.837239123298787, 3724956.803474905434996 ], [ 733713.769853547797538, 3724952.64484176505357 ], [ 733711.143068718956783, 3724957.585964888799936 ], [ 733713.499046904733405, 3724959.0 ], [ 733723.705273776431568, 3724959.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725004.geojson new file mode 100644 index 00000000..57600998 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725004.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102925, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 235.62354171707437, "origlen": 0, "partialDec": 0.80865775462712375, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733716.606242132140324, 3724973.75564177101478 ], [ 733725.094902680953965, 3724960.633992579299957 ], [ 733723.705273776431568, 3724959.0 ], [ 733713.499046904733405, 3724959.0 ], [ 733714.034097261959687, 3724959.32113200193271 ], [ 733712.084855253109708, 3724961.981503693852574 ], [ 733706.699992214213125, 3724959.353198084048927 ], [ 733702.652436110540293, 3724965.458262465894222 ], [ 733716.606242132140324, 3724973.75564177101478 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725049.geojson new file mode 100644 index 00000000..62b2dc60 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725049.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 0.43161612025689933, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733736.0, 3725046.363777188584208 ], [ 733735.121561629115604, 3725043.957477799151093 ], [ 733732.919629639131017, 3725040.230379342567176 ], [ 733731.032719954149798, 3725038.43090241169557 ], [ 733727.129203135380521, 3725039.390032827854156 ], [ 733715.550556391477585, 3725045.233793378807604 ], [ 733711.973885777289979, 3725047.255200332496315 ], [ 733712.888976000715047, 3725049.0 ], [ 733736.0, 3725049.0 ], [ 733736.0, 3725046.363777188584208 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725094.geojson new file mode 100644 index 00000000..4cb415d4 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725094.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 117299, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 17.925629600560526, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733714.078736086841673, 3725075.905853402335197 ], [ 733714.823168934090063, 3725080.019134919159114 ], [ 733719.046768112573773, 3725079.256469215732068 ], [ 733718.293059892137535, 3725075.14296110207215 ], [ 733714.078736086841673, 3725075.905853402335197 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 0.54836180845584703, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733719.886159604880959, 3725062.341508098412305 ], [ 733722.739838666282594, 3725060.657610790804029 ], [ 733723.763353180838749, 3725062.080902691464871 ], [ 733727.644603378139436, 3725061.653928931802511 ], [ 733727.184225898468867, 3725058.834930552169681 ], [ 733736.0, 3725054.352273083757609 ], [ 733736.0, 3725049.0 ], [ 733712.888976000715047, 3725049.0 ], [ 733719.886159604880959, 3725062.341508098412305 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725139.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733691_3725139.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724734.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724734.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724824.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724824.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724869.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724869.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724959.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3724959.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725004.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725004.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725049.geojson new file mode 100644 index 00000000..594f229c --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725049.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 0.0031608728154630481, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733736.803118143696338, 3725049.0 ], [ 733736.609508816385642, 3725048.033399943262339 ], [ 733736.0, 3725046.363777188584208 ], [ 733736.0, 3725049.0 ], [ 733736.803118143696338, 3725049.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725094.geojson new file mode 100644 index 00000000..485e6f92 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725094.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 102919, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 376.96788638895055, "origlen": 0, "partialDec": 0.016861198470121515, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733736.0, 3725054.352273083757609 ], [ 733737.701844487339258, 3725053.486916756257415 ], [ 733736.803118143696338, 3725049.0 ], [ 733736.0, 3725049.0 ], [ 733736.0, 3725054.352273083757609 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725139.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733736_3725139.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724734.geojson new file mode 100644 index 00000000..798f51e6 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724734.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93019, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 223.11504089232326, "origlen": 0, "partialDec": 0.71792570273177214, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733808.825040770112537, 3724703.694553496781737 ], [ 733812.694460124941543, 3724699.57170610036701 ], [ 733813.810271165333688, 3724692.263191219884902 ], [ 733809.834030322846957, 3724689.0 ], [ 733793.844497522106394, 3724689.0 ], [ 733808.825040770112537, 3724703.694553496781737 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93018, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 189.51651421072748, "origlen": 0, "partialDec": 0.024760218872170288, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733824.194954353850335, 3724717.886300034821033 ], [ 733826.0, 3724718.606595266610384 ], [ 733826.0, 3724713.40731359552592 ], [ 733824.194954353850335, 3724717.886300034821033 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724824.geojson new file mode 100644 index 00000000..50e2003f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724824.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93146, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 85.499318876340681, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733797.19361286086496, 3724803.425375143066049 ], [ 733809.65190442930907, 3724803.396256368607283 ], [ 733810.314092590706423, 3724796.798043267335743 ], [ 733797.529922557529062, 3724796.486277254763991 ], [ 733797.19361286086496, 3724803.425375143066049 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724869.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724869.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724959.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3724959.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725004.geojson new file mode 100644 index 00000000..0b9db42a --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725004.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86007, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 288.75968082249199, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733804.608246845542453, 3724992.315301816910505 ], [ 733814.888789982767776, 3724991.744788535870612 ], [ 733813.477104315534234, 3724966.273909805342555 ], [ 733801.90457292238716, 3724966.912798856850713 ], [ 733803.019650391303003, 3724987.038337231613696 ], [ 733804.311609080410562, 3724986.969964746385813 ], [ 733804.608246845542453, 3724992.315301816910505 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86008, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 231.57048190271922, "origlen": 0, "partialDec": 0.078160449292484446, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733815.287342350929976, 3725004.0 ], [ 733815.140452889609151, 3725001.217458509840071 ], [ 733808.102239650906995, 3725001.678382671438158 ], [ 733808.159776426036842, 3725004.0 ], [ 733815.287342350929976, 3725004.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725049.geojson new file mode 100644 index 00000000..458fe867 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725049.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86008, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 231.57048190271922, "origlen": 0, "partialDec": 0.92183955070685608, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733817.060308103798889, 3725026.023760433774441 ], [ 733816.30381879361812, 3725016.316818532999605 ], [ 733814.319134745746851, 3725016.623545987997204 ], [ 733813.591841582325287, 3725009.525329754687846 ], [ 733815.572198782116175, 3725009.396063346415758 ], [ 733815.287342350929976, 3725004.0 ], [ 733808.159776426036842, 3725004.0 ], [ 733808.237916652811691, 3725007.152969629038125 ], [ 733806.608725623344071, 3725011.152880643028766 ], [ 733803.99797402555123, 3725014.673837821930647 ], [ 733804.473099919268861, 3725017.648571964353323 ], [ 733806.2293102737749, 3725019.478170173708349 ], [ 733806.057821782305837, 3725022.703484487254173 ], [ 733804.33324929792434, 3725023.38278932031244 ], [ 733804.607062857598066, 3725026.61896403087303 ], [ 733817.060308103798889, 3725026.023760433774441 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86009, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 259.07434426271021, "origlen": 0, "partialDec": 0.71855108760884145, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733817.00581841240637, 3725049.0 ], [ 733816.334671155898832, 3725030.65609390148893 ], [ 733806.096146776108071, 3725031.027864479925483 ], [ 733806.753711150959134, 3725049.0 ], [ 733817.00581841240637, 3725049.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725094.geojson new file mode 100644 index 00000000..c628b883 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725094.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86009, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 259.07434426271021, "origlen": 0, "partialDec": 0.28144891239269454, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733807.020737620303407, 3725056.298199352808297 ], [ 733817.2592352849897, 3725055.926431227475405 ], [ 733817.00581841240637, 3725049.0 ], [ 733806.753711150959134, 3725049.0 ], [ 733807.020737620303407, 3725056.298199352808297 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86013, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 168.5733291901596, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733819.335845608613454, 3725081.535611427389085 ], [ 733818.589080677134916, 3725066.102373758796602 ], [ 733815.155530380317941, 3725061.113343438133597 ], [ 733811.214473494095728, 3725061.327962883748114 ], [ 733810.384644210571423, 3725062.239949475973845 ], [ 733810.523116171592847, 3725064.174366268794984 ], [ 733811.440289434045553, 3725067.293057959526777 ], [ 733810.916349143604748, 3725070.887105803471059 ], [ 733809.174980869051069, 3725073.397157665342093 ], [ 733809.349399076891132, 3725081.469605537131429 ], [ 733819.335845608613454, 3725081.535611427389085 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 0.091442241875016386, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733824.826035436824895, 3725094.0 ], [ 733824.310515563818626, 3725090.701761959586293 ], [ 733814.219372081570327, 3725092.264592101797462 ], [ 733814.490603572456166, 3725094.0 ], [ 733824.826035436824895, 3725094.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725139.geojson new file mode 100644 index 00000000..a349aad4 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733781_3725139.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 0.75634556221898208, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733826.0, 3725101.510892950929701 ], [ 733824.826035436824895, 3725094.0 ], [ 733814.490603572456166, 3725094.0 ], [ 733818.105646131793037, 3725117.12995953951031 ], [ 733826.0, 3725115.904065827373415 ], [ 733826.0, 3725101.510892950929701 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86014, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 269.2410676574849, "origlen": 0, "partialDec": 0.015222220907925837, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733826.0, 3725124.38049762416631 ], [ 733824.230409230804071, 3725125.192175134085119 ], [ 733826.0, 3725129.012582149822265 ], [ 733826.0, 3725124.38049762416631 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724734.geojson new file mode 100644 index 00000000..cdcd40e7 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724734.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 93018, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 189.51651421072748, "origlen": 0, "partialDec": 0.9752397811307828, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733826.0, 3724718.606595266610384 ], [ 733841.671712412848137, 3724724.860320621170104 ], [ 733845.427343191811815, 3724715.507587827276438 ], [ 733827.959576514316723, 3724708.544878867920488 ], [ 733826.0, 3724713.40731359552592 ], [ 733826.0, 3724718.606595266610384 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724824.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724824.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724869.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724869.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724959.geojson new file mode 100644 index 00000000..89db0316 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3724959.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86006, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 300.47300947479948, "origlen": 0, "partialDec": 0.98819774517170944, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733848.800145414541475, 3724959.0 ], [ 733848.919941028230824, 3724958.981422422919422 ], [ 733847.980877452413552, 3724952.954536063130945 ], [ 733864.939495211467147, 3724950.327385626267642 ], [ 733863.365486120572314, 3724940.267557054758072 ], [ 733847.021852035541087, 3724942.798733331263065 ], [ 733846.740707991877571, 3724941.005108900833875 ], [ 733839.323592993430793, 3724942.155940910801291 ], [ 733841.955895113293082, 3724959.0 ], [ 733848.800145414541475, 3724959.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725004.geojson new file mode 100644 index 00000000..44f29499 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725004.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86006, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 300.47300947479948, "origlen": 0, "partialDec": 0.011802254828676802, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733842.117838621605188, 3724960.036273914854974 ], [ 733848.800145414541475, 3724959.0 ], [ 733841.955895113293082, 3724959.0 ], [ 733842.117838621605188, 3724960.036273914854974 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 0.58115918743729356, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733871.0, 3724981.793367408216 ], [ 733859.904205449391156, 3724982.077810946386307 ], [ 733860.067030058125965, 3724988.341016149614006 ], [ 733863.800775169045664, 3724988.254532156512141 ], [ 733864.206809974275529, 3725004.0 ], [ 733871.0, 3725004.0 ], [ 733871.0, 3724981.793367408216 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725049.geojson new file mode 100644 index 00000000..29a97b57 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725049.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134680, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 101.16730006427393, "origlen": 0, "partialDec": 0.20008370299942965, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733869.672710774117149, 3725049.0 ], [ 733867.962342513957992, 3725047.874265642371029 ], [ 733866.364284909097478, 3725046.792074438650161 ], [ 733863.243467739550397, 3725044.585138092748821 ], [ 733863.183629220700823, 3725042.852400838397443 ], [ 733862.012820417294279, 3725042.901524438522756 ], [ 733862.195312751107849, 3725047.97773150773719 ], [ 733863.279007132863626, 3725049.0 ], [ 733869.672710774117149, 3725049.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86011, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.33402454110015, "origlen": 0, "partialDec": 0.81644336831442044, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733862.708252763142809, 3725039.888753946404904 ], [ 733871.0, 3725039.569324703887105 ], [ 733871.0, 3725014.132537124678493 ], [ 733862.252876899787225, 3725014.796318253036588 ], [ 733861.065796526381746, 3725019.317511857487261 ], [ 733860.456022532889619, 3725022.2435885919258 ], [ 733864.300192805239931, 3725024.47926747566089 ], [ 733865.408540649805218, 3725028.512659384403378 ], [ 733862.343076531891711, 3725032.788266675546765 ], [ 733862.708252763142809, 3725039.888753946404904 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 0.090266316781838496, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733864.315949483308941, 3725008.232279121875763 ], [ 733871.0, 3725008.06125667039305 ], [ 733871.0, 3725004.0 ], [ 733864.206809974275529, 3725004.0 ], [ 733864.315949483308941, 3725008.232279121875763 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725094.geojson new file mode 100644 index 00000000..d1bdd2bb --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725094.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134680, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 101.16730006427393, "origlen": 0, "partialDec": 0.79991629700049249, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733869.037673469400033, 3725063.914816930424422 ], [ 733868.948807091452181, 3725060.328016446903348 ], [ 733869.699439586838707, 3725057.716118558309972 ], [ 733869.12773916113656, 3725056.037481418810785 ], [ 733870.693983497098088, 3725049.672183774411678 ], [ 733869.672710774117149, 3725049.0 ], [ 733863.279007132863626, 3725049.0 ], [ 733863.91026226838585, 3725049.595474375877529 ], [ 733866.030543527216651, 3725053.242929659318179 ], [ 733865.282733293948695, 3725054.978159906808287 ], [ 733863.307564386399463, 3725056.417098031844944 ], [ 733863.502039127284661, 3725064.046122215222567 ], [ 733869.037673469400033, 3725063.914816930424422 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725139.geojson new file mode 100644 index 00000000..f2bf7065 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733826_3725139.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86012, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 284.46838311737099, "origlen": 0, "partialDec": 0.15221219590616589, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733830.582235559704714, 3725115.192502366378903 ], [ 733828.823811884387396, 3725103.940700557082891 ], [ 733826.438338553067297, 3725104.315333605743945 ], [ 733826.0, 3725101.510892950929701 ], [ 733826.0, 3725115.904065827373415 ], [ 733830.582235559704714, 3725115.192502366378903 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86014, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 269.2410676574849, "origlen": 0, "partialDec": 0.66304888315714261, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733843.10121820657514, 3725139.0 ], [ 733840.877073246985674, 3725134.199127878062427 ], [ 733839.731750019127503, 3725134.726086068432778 ], [ 733833.373904321575537, 3725120.998228457290679 ], [ 733826.0, 3725124.38049762416631 ], [ 733826.0, 3725129.012582149822265 ], [ 733830.626115061459132, 3725139.0 ], [ 733843.10121820657514, 3725139.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724734.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724734.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724824.geojson new file mode 100644 index 00000000..dafeb67b --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724824.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 0.01344511640873805, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733916.0, 3724819.008678769227117 ], [ 733914.877926233806647, 3724824.0 ], [ 733916.0, 3724824.0 ], [ 733916.0, 3724819.008678769227117 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724869.geojson new file mode 100644 index 00000000..cdee9782 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724869.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 0.29052810106459787, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733914.877926233806647, 3724824.0 ], [ 733913.923271550564095, 3724828.246590849943459 ], [ 733907.995921526453458, 3724826.925592739600688 ], [ 733906.674282207386568, 3724832.775251807179302 ], [ 733916.0, 3724834.853403809480369 ], [ 733916.0, 3724824.0 ], [ 733914.877926233806647, 3724824.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724959.geojson new file mode 100644 index 00000000..30042a7f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3724959.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86005, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 263.65732906731142, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733873.500091013731435, 3724936.93015677202493 ], [ 733873.77284804196097, 3724947.058124471455812 ], [ 733881.724138652090915, 3724946.841479253023863 ], [ 733881.803844684036449, 3724949.662298649549484 ], [ 733887.590841862838715, 3724949.503837334923446 ], [ 733887.518520563142374, 3724946.760883453302085 ], [ 733898.116790315601975, 3724946.486752765718848 ], [ 733897.845682443701662, 3724936.292234980501235 ], [ 733873.500091013731435, 3724936.93015677202493 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134689, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 28.431725003743587, "origlen": 0, "partialDec": 0.11793113430313892, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733898.702365371049382, 3724959.0 ], [ 733899.437801164225675, 3724957.783386053517461 ], [ 733896.328320293803699, 3724957.77410851046443 ], [ 733896.323598282644525, 3724959.0 ], [ 733898.702365371049382, 3724959.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725004.geojson new file mode 100644 index 00000000..28711397 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725004.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 0.29487274175170175, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733873.539359263842925, 3725004.0 ], [ 733873.247122836532071, 3724992.580114854499698 ], [ 733877.046077472623438, 3724992.48412902886048 ], [ 733876.762816092930734, 3724981.645636139903218 ], [ 733871.0, 3724981.793367408216 ], [ 733871.0, 3725004.0 ], [ 733873.539359263842925, 3725004.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134689, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 28.431725003743587, "origlen": 0, "partialDec": 0.88206886569721232, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733898.297767688753083, 3724968.365196610335261 ], [ 733897.317986879032105, 3724965.910844214726239 ], [ 733898.783516068593599, 3724963.294194012414664 ], [ 733898.249386645853519, 3724961.216946474276483 ], [ 733898.518854100489989, 3724959.303578331600875 ], [ 733898.702365371049382, 3724959.0 ], [ 733896.323598282644525, 3724959.0 ], [ 733896.316138221649453, 3724960.936722508631647 ], [ 733895.295106926350854, 3724960.934007437899709 ], [ 733895.271784850629047, 3724968.35795633494854 ], [ 733898.297767688753083, 3724968.365196610335261 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725049.geojson new file mode 100644 index 00000000..214b537a --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725049.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86011, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 255.33402454110015, "origlen": 0, "partialDec": 0.18355663168738637, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733871.0, 3725039.569324703887105 ], [ 733873.467645566677675, 3725039.474261729512364 ], [ 733872.213034232496284, 3725014.040485232602805 ], [ 733871.0, 3725014.132537124678493 ], [ 733871.0, 3725039.569324703887105 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86010, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 309.6189312678942, "origlen": 0, "partialDec": 0.033701754027512569, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733871.0, 3725008.06125667039305 ], [ 733873.641557777300477, 3725007.993668059818447 ], [ 733873.539359263842925, 3725004.0 ], [ 733871.0, 3725004.0 ], [ 733871.0, 3725008.06125667039305 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725094.geojson new file mode 100644 index 00000000..479358af --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725094.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86015, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 251.33309650398371, "origlen": 0, "partialDec": 0.86354391451294721, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733892.678339317324571, 3725094.0 ], [ 733892.969486426794901, 3725093.91931293066591 ], [ 733895.139970269752666, 3725093.606038105674088 ], [ 733877.763453568681143, 3725074.182422619313002 ], [ 733875.624623022042215, 3725075.861519815400243 ], [ 733873.833603138453327, 3725081.167029351461679 ], [ 733874.96624890586827, 3725088.008793785702437 ], [ 733880.208773081074469, 3725094.0 ], [ 733892.678339317324571, 3725094.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725139.geojson new file mode 100644 index 00000000..b40efe91 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733871_3725139.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86015, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 251.33309650398371, "origlen": 0, "partialDec": 0.13645608548626761, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733884.809803572017699, 3725099.258101164363325 ], [ 733887.820667246589437, 3725100.263786776456982 ], [ 733887.97904269839637, 3725096.81619278434664 ], [ 733889.399402834707871, 3725094.908708232920617 ], [ 733892.678339317324571, 3725094.0 ], [ 733880.208773081074469, 3725094.0 ], [ 733884.809803572017699, 3725099.258101164363325 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724734.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724734.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724779.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724779.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724824.geojson new file mode 100644 index 00000000..ad1bfa6a --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724824.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 0.26662346769845036, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733925.038212854298763, 3724824.0 ], [ 733926.087521769106388, 3724819.343201762996614 ], [ 733916.41197619389277, 3724817.176084883511066 ], [ 733916.0, 3724819.008678769227117 ], [ 733916.0, 3724824.0 ], [ 733925.038212854298763, 3724824.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724869.geojson new file mode 100644 index 00000000..30aaa0c5 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724869.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85995, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 208.27750543142528, "origlen": 0, "partialDec": 0.42940331482458827, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733922.277427969733253, 3724836.252271949779242 ], [ 733925.038212854298763, 3724824.0 ], [ 733916.0, 3724824.0 ], [ 733916.0, 3724834.853403809480369 ], [ 733922.277427969733253, 3724836.252271949779242 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85996, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 147.78075500471959, "origlen": 0, "partialDec": 0.91029046907941491, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733960.773279143380933, 3724839.677568545099348 ], [ 733961.0, 3724837.512521461583674 ], [ 733961.0, 3724829.087063139304519 ], [ 733960.194102098466828, 3724829.175883492454886 ], [ 733957.964056476601399, 3724830.786153580993414 ], [ 733952.526077670161612, 3724831.474698279984295 ], [ 733949.761529997806065, 3724829.121061434503645 ], [ 733946.739561799215153, 3724828.947437366005033 ], [ 733946.682754538487643, 3724839.644461845047772 ], [ 733960.773279143380933, 3724839.677568545099348 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724959.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3724959.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725004.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725004.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725049.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725049.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725094.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725094.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725139.geojson new file mode 100644 index 00000000..2f6d9d6f --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733916_3725139.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 134690, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 75.233357615263444, "origlen": 0, "partialDec": 0.5426079647770381, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733933.053643652936444, 3725139.0 ], [ 733933.981720102136023, 3725136.448536148294806 ], [ 733932.724451179732569, 3725136.617617703508586 ], [ 733929.566822279826738, 3725137.062165400478989 ], [ 733928.701306592440233, 3725138.295111690182239 ], [ 733927.683588467072695, 3725137.016209401190281 ], [ 733927.95271526533179, 3725134.736601110547781 ], [ 733928.741625818191096, 3725132.458578986581415 ], [ 733926.893847053404897, 3725130.959658264648169 ], [ 733925.461097773630172, 3725129.570747075602412 ], [ 733925.616007869830355, 3725131.971682267263532 ], [ 733924.303450533887371, 3725134.02606556750834 ], [ 733924.030880918144248, 3725136.827193052973598 ], [ 733923.15020942944102, 3725138.681254582013935 ], [ 733923.05790709448047, 3725139.0 ], [ 733933.053643652936444, 3725139.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724734.geojson new file mode 100644 index 00000000..8c93eb06 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724734.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 0.50582372917952123, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.0, 3724733.314095525071025 ], [ 734005.036672754795291, 3724732.308777935802937 ], [ 734004.700672700069845, 3724728.960097201168537 ], [ 733985.17671379854437, 3724727.518070545978844 ], [ 733980.793273733812384, 3724729.49750511161983 ], [ 733981.172765030758455, 3724734.0 ], [ 734006.0, 3724734.0 ], [ 734006.0, 3724733.314095525071025 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724779.geojson new file mode 100644 index 00000000..58a1b216 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724779.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 0.49174194516610154, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733998.172171951970086, 3724738.955371181480587 ], [ 734005.087085773120634, 3724738.990968229714781 ], [ 734006.0, 3724735.776267854962498 ], [ 734006.0, 3724734.0 ], [ 733981.172765030758455, 3724734.0 ], [ 733981.333166613709182, 3724735.903093201573938 ], [ 733988.48206047678832, 3724740.80528543330729 ], [ 733992.534389728447422, 3724740.981871583964676 ], [ 733996.584479778190143, 3724740.86985838599503 ], [ 733998.172171951970086, 3724738.955371181480587 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724824.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724824.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724869.geojson new file mode 100644 index 00000000..b1f9321a --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724869.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 85996, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 147.78075500471959, "origlen": 0, "partialDec": 0.089709530921962946, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733961.0, 3724837.512521461583674 ], [ 733961.184794776490889, 3724835.747843090444803 ], [ 733963.223460652050562, 3724833.61130030779168 ], [ 733963.607368887402117, 3724830.812890837434679 ], [ 733962.502454621368088, 3724828.921473243273795 ], [ 733961.0, 3724829.087063139304519 ], [ 733961.0, 3724837.512521461583674 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724959.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3724959.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725004.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725004.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725049.geojson new file mode 100644 index 00000000..46900b41 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725049.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86607, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 109.09963485201257, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733984.247434898978099, 3725029.170712447725236 ], [ 733984.469720786786638, 3725037.177755209617317 ], [ 733988.266932822414674, 3725039.434536231681705 ], [ 733990.833932525943965, 3725040.751259885728359 ], [ 733991.039521950762719, 3725035.751105143688619 ], [ 733997.483796045300551, 3725035.664246738888323 ], [ 733997.398187489830889, 3725028.903508787043393 ], [ 733984.247434898978099, 3725029.170712447725236 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725094.geojson new file mode 100644 index 00000000..d18e6357 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725094.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86606, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 298.78711714810817, "origlen": 0, "partialDec": 0.86838371522454494, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.0, 3725065.888844077009708 ], [ 734002.152090000803582, 3725065.742649260908365 ], [ 734002.463489620480686, 3725057.548868575133383 ], [ 733989.952479137689807, 3725057.088115018326789 ], [ 733989.289290938875638, 3725074.750953383278102 ], [ 734006.0, 3725075.374068238772452 ], [ 734006.0, 3725065.888844077009708 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 0.22456785130355603, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.0, 3725086.290827450808138 ], [ 733999.194156195735559, 3725086.14615699602291 ], [ 733999.022649019025266, 3725094.0 ], [ 734006.0, 3725094.0 ], [ 734006.0, 3725086.290827450808138 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725139.geojson new file mode 100644 index 00000000..94159dcb --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_733961_3725139.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86604, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 243.05210583905563, "origlen": 0, "partialDec": 0.64773908923557733, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733998.347550055012107, 3725137.941802813205868 ], [ 734006.0, 3725138.167202132754028 ], [ 734006.0, 3725114.898354505188763 ], [ 734003.339624392450787, 3725114.824559542350471 ], [ 733998.844389547826722, 3725120.630035975016654 ], [ 733998.347550055012107, 3725137.941802813205868 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 0.4327024436850192, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 733999.022649019025266, 3725094.0 ], [ 733998.721341388998553, 3725107.797806410584599 ], [ 734002.757127131801099, 3725107.885221907868981 ], [ 734004.217231130925938, 3725109.674339018762112 ], [ 734006.0, 3725109.713915592059493 ], [ 734006.0, 3725094.0 ], [ 733999.022649019025266, 3725094.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724734.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724734.geojson new file mode 100644 index 00000000..00dc9b12 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724734.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 0.000791905883559901, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.504426542669535, 3724734.0 ], [ 734006.539364616852254, 3724733.876970435027033 ], [ 734006.0, 3724733.314095525071025 ], [ 734006.0, 3724734.0 ], [ 734006.504426542669535, 3724734.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92641, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 127.89418152890968, "origlen": 0, "partialDec": 0.45229163370845532, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734031.973043444682844, 3724734.0 ], [ 734033.127172784181312, 3724732.295262097381055 ], [ 734018.804850079119205, 3724729.581800156738609 ], [ 734016.222719809040427, 3724732.304357184097171 ], [ 734011.883401251398027, 3724732.475890559609979 ], [ 734011.748591097886674, 3724734.0 ], [ 734031.973043444682844, 3724734.0 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92640, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 140.04971670918377, "origlen": 0, "partialDec": 0.19622806628268313, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734044.256298960419372, 3724728.538379620760679 ], [ 734044.193610417540185, 3724734.0 ], [ 734051.0, 3724734.0 ], [ 734051.0, 3724729.625226940959692 ], [ 734048.398788033635356, 3724730.725917739327997 ], [ 734045.49420094571542, 3724729.545217602048069 ], [ 734044.256298960419372, 3724728.538379620760679 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724779.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724779.geojson new file mode 100644 index 00000000..6ffaf9fd --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724779.geojson @@ -0,0 +1,9 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92642, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 272.76725138138551, "origlen": 0, "partialDec": 0.0016424197706948571, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.0, 3724735.776267854962498 ], [ 734006.504426542669535, 3724734.0 ], [ 734006.0, 3724734.0 ], [ 734006.0, 3724735.776267854962498 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92641, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 127.89418152890968, "origlen": 0, "partialDec": 0.54770836629094588, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734011.306465386645868, 3724738.998495681677014 ], [ 734020.362710424815305, 3724739.918715209700167 ], [ 734021.868912066216581, 3724735.638380894903094 ], [ 734030.806304456898943, 3724735.723363324068487 ], [ 734031.973043444682844, 3724734.0 ], [ 734011.748591097886674, 3724734.0 ], [ 734011.306465386645868, 3724738.998495681677014 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 92640, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 140.04971670918377, "origlen": 0, "partialDec": 0.19012403421520796, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734044.193610417540185, 3724734.0 ], [ 734044.149200432701036, 3724737.8691356764175 ], [ 734051.0, 3724737.92929164506495 ], [ 734051.0, 3724734.0 ], [ 734044.193610417540185, 3724734.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724824.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724824.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724824.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724869.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724869.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724869.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724914.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724914.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724914.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724959.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724959.geojson new file mode 100644 index 00000000..b20c417c --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3724959.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86004, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 245.87085671707746, "origlen": 0, "partialDec": 0.20134007160948594, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734039.951275395345874, 3724957.361701209563762 ], [ 734020.141815275885165, 3724955.846008125692606 ], [ 734019.899857406038791, 3724959.0 ], [ 734041.972572033060715, 3724959.0 ], [ 734039.951275395345874, 3724957.361701209563762 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725004.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725004.geojson new file mode 100644 index 00000000..7e8f51b3 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725004.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86004, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 245.87085671707746, "origlen": 0, "partialDec": 0.79865992839011157, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734019.899857406038791, 3724959.0 ], [ 734019.51552347233519, 3724964.009905667975545 ], [ 734026.204987838980742, 3724964.528337045572698 ], [ 734026.000307232956402, 3724967.20904346043244 ], [ 734028.35346275055781, 3724967.399663022719324 ], [ 734028.161849318654276, 3724969.925317186396569 ], [ 734033.76750397705473, 3724970.350708946119994 ], [ 734033.998593332478777, 3724967.348806756082922 ], [ 734043.10642178892158, 3724968.048365659546107 ], [ 734043.692446619272232, 3724960.393990571144968 ], [ 734041.972572033060715, 3724959.0 ], [ 734019.899857406038791, 3724959.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725049.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725049.geojson new file mode 100644 index 00000000..c7751493 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725049.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:32616"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725094.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725094.geojson new file mode 100644 index 00000000..dd27eb2d --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725094.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86606, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 298.78711714810817, "origlen": 0, "partialDec": 0.13161628477513404, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734010.322496132925153, 3725066.053069988265634 ], [ 734006.0, 3725065.888844077009708 ], [ 734006.0, 3725075.374068238772452 ], [ 734009.97067632782273, 3725075.522128228098154 ], [ 734010.322496132925153, 3725066.053069988265634 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 0.11780849332891749, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734009.584178030490875, 3725094.0 ], [ 734009.752270760363899, 3725086.37058871705085 ], [ 734006.0, 3725086.290827450808138 ], [ 734006.0, 3725094.0 ], [ 734009.584178030490875, 3725094.0 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725139.geojson b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725139.geojson new file mode 100644 index 00000000..d4531a24 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_expected/geoms_734006_3725139.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32616" } }, +"features": [ +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86604, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 243.05210583905563, "origlen": 0, "partialDec": 0.35226091076888344, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734006.0, 3725138.167202132754028 ], [ 734009.348738314933144, 3725138.265837647020817 ], [ 734010.00927426526323, 3725115.009565961547196 ], [ 734006.0, 3725114.898354505188763 ], [ 734006.0, 3725138.167202132754028 ] ] ] } }, +{ "type": "Feature", "properties": { "access": "", "addr_house": "", "addr_hou_1": "", "addr_inter": "", "admin_leve": "", "aerialway": "", "aeroway": "", "amenity": "", "area": "", "barrier": "", "bicycle": "", "boundary": "", "brand": "", "bridge": "", "building": "yes", "constructi": "", "covered": "", "culvert": "", "cutting": "", "denominati": "", "disused": "", "embankment": "", "foot": "", "generator_": "", "harbour": "", "highway": "", "historic": "", "horse": "", "intermitte": "", "junction": "", "landuse": "", "layer": "", "leisure": "", "lock": "", "man_made": "", "military": "", "motorcar": "", "name": "Occlusion", "natural": "", "office": "", "oneway": "", "operator": "", "osm_id": 86605, "place": "", "population": "", "power": "", "power_sour": "", "public_tra": "", "railway": "", "ref": "", "religion": "", "route": "", "service": "", "shop": "", "sport": "", "surface": "", "tags": "\"security:classification\"=>\"UNCLASSIFIED\",\"source\"=>\"Unknown\"", "toll": "", "tourism": "", "tower_type": "", "tunnel": "", "water": "", "waterway": "", "wetland": "", "width": "", "wood": "", "z_order": -999999, "tracktype": "", "way_area": -999999.0, "origarea": 238.82858297724087, "origlen": 0, "partialDec": 0.22492121168800522, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 734009.23638294194825, 3725109.785761667881161 ], [ 734009.584178030490875, 3725094.0 ], [ 734006.0, 3725094.0 ], [ 734006.0, 3725109.713915592059493 ], [ 734009.23638294194825, 3725109.785761667881161 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.003_41.774.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.003_41.774.tif new file mode 100644 index 00000000..58d64807 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.003_41.774.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.691.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.691.tif new file mode 100644 index 00000000..f6083891 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.691.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.732.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.732.tif new file mode 100644 index 00000000..1de2442a Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.044_41.732.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.085_41.774.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.085_41.774.tif new file mode 100644 index 00000000..dac29923 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.085_41.774.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.127_41.774.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.127_41.774.tif new file mode 100644 index 00000000..e3940cf6 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/empty_geoms_-101.127_41.774.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.044_41.774.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.044_41.774.tif new file mode 100644 index 00000000..fd85dc37 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.044_41.774.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.691.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.691.tif new file mode 100644 index 00000000..4363ec64 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.691.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.732.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.732.tif new file mode 100644 index 00000000..28e95d8c Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.085_41.732.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.691.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.691.tif new file mode 100644 index 00000000..97606a70 Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.691.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.732.tif b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.732.tif new file mode 100644 index 00000000..20c0ce4d Binary files /dev/null and b/docker/solaris/solaris/data/vectortile_test_filled_expected/geoms_-101.127_41.732.tif differ diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.003_41.774.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.003_41.774.geojson new file mode 100644 index 00000000..b1286b1e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.003_41.774.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.691.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.691.geojson new file mode 100644 index 00000000..b1286b1e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.691.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.732.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.732.geojson new file mode 100644 index 00000000..b1286b1e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.732.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.774.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.774.geojson new file mode 100644 index 00000000..17d4c000 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.044_41.774.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 759, "AREA": 5628503.107, "PERIMETER": 8410.962, "ACRES": 129.213, "HECTARES": 52.291, "origarea": 5.6641060016761724e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.02986150942003, 41.753320763387563 ], [ -101.029618736775561, 41.753318431460016 ], [ -101.029376415257985, 41.753307111572269 ], [ -101.029135137968922, 41.753286831430636 ], [ -101.0288954954529, 41.753257640672238 ], [ -101.028658074251169, 41.753219610743585 ], [ -101.028423455465443, 41.753172834725504 ], [ -101.02819221333489, 41.753117427105281 ], [ -101.027964913830047, 41.753053523496206 ], [ -101.027742113266996, 41.752981280305683 ], [ -101.027524356945193, 41.752900874352044 ], [ -101.027312177812405, 41.752812502431638 ], [ -101.027106095159809, 41.752716380837029 ], [ -101.026906613350775, 41.752612744827267 ], [ -101.026714220586101, 41.752501848051921 ], [ -101.026529387708976, 41.752383961929972 ], [ -101.026352567052498, 41.75225937498525 ], [ -101.02618419133259, 41.752128392140079 ], [ -101.026024672589017, 41.751991333968618 ], [ -101.025874401177035, 41.75184853591206 ], [ -101.025733744812371, 41.751700347457387 ], [ -101.025603047671453, 41.751547131281775 ], [ -101.025482629549629, 41.751389262364597 ], [ -101.025372785078858, 41.751227127069669 ], [ -101.025273783007265, 41.75106112219926 ], [ -101.025185865542056, 41.750891654022801 ], [ -101.025109247757442, 41.750719137282502 ], [ -101.025044117069058, 41.750543994178003 ], [ -101.024990632776081, 41.750366653333082 ], [ -101.024948925672291, 41.75018754874651 ], [ -101.024919097726837, 41.750007118729819 ], [ -101.024901221835705, 41.749825804834586 ], [ -101.024895341644239, 41.749644050771778 ], [ -101.024901471441297, 41.749462301325806 ], [ -101.024919596125301, 41.749281001266191 ], [ -101.024949671242197, 41.749100594258984 ], [ -101.024991623095175, 41.748921521781199 ], [ -101.025045348926, 41.748744222040585 ], [ -101.025110717167394, 41.748569128903192 ], [ -101.025187567765926, 41.74839667083198 ], [ -101.025275712574498, 41.748227269838416 ], [ -101.025374935813559, 41.74806134044988 ], [ -101.025484994599992, 41.747899288695649 ], [ -101.025605619541921, 41.747741511113439 ], [ -101.025736515398776, 41.747588393779317 ], [ -101.025877361804163, 41.747440311363249 ], [ -101.026027814050309, 41.747297626212536 ], [ -101.026187503931908, 41.747160687465453 ], [ -101.026356040647471, 41.747029830197135 ], [ -101.026533011755774, 41.746905374600097 ], [ -101.026717984185197, 41.746787625200838 ], [ -101.0269105052935, 41.746676870115124 ], [ -101.027110103975289, 41.746573380343264 ], [ -101.027316291814742, 41.746477409107207 ], [ -101.027528564280402, 41.746389191231316 ], [ -101.027746401959419, 41.746308942567843 ], [ -101.027969271828184, 41.746236859469292 ], [ -101.028196628556032, 41.746173118307951 ], [ -101.028427915839188, 41.746117875044682 ], [ -101.028662567761288, 41.746071264847444 ], [ -101.028900010177509, 41.746033401760762 ], [ -101.029139662118766, 41.746004378426797 ], [ -101.029380937212494, 41.745984265858766 ], [ -101.029623245116696, 41.745973113267269 ], [ -101.029865992963565, 41.745970947940087 ], [ -101.03010858680949, 41.745977775175191 ], [ -101.030350433087435, 41.745993578267985 ], [ -101.030590940058559, 41.746018318552146 ], [ -101.030829519259285, 41.746051935494044 ], [ -101.031065586940471, 41.746094346840955 ], [ -101.031298565494993, 41.746145448822062 ], [ -101.031527884870385, 41.746205116402379 ], [ -101.03175298396296, 41.746273203588366 ], [ -101.031973311990214, 41.746349543785186 ], [ -101.032188329837993, 41.746433950203922 ], [ -101.032397511379074, 41.746526216318536 ], [ -101.032600344760155, 41.746626116370997 ], [ -101.032796333654019, 41.746733405923251 ], [ -101.032984998473651, 41.746847822455287 ], [ -101.033165877545628, 41.746969086007084 ], [ -101.03333852823971, 41.747096899863443 ], [ -101.033502528051883, 41.747230951279619 ], [ -101.033657475638279, 41.74737091224646 ], [ -101.033802991797515, 41.7475164402926 ], [ -101.033938720398694, 41.747667179322242 ], [ -101.034064329253326, 41.74782276048623 ], [ -101.034179510928539, 41.747982803084348 ], [ -101.034283983499918, 41.748146915496669 ], [ -101.034377491241955, 41.748314696141506 ], [ -101.034459805254457, 41.748485734457994 ], [ -101.03453072402344, 41.748659611910433 ], [ -101.03459007391497, 41.748835903012377 ], [ -101.034637709600943, 41.749014176367631 ], [ -101.034673514415573, 41.749193995725712 ], [ -101.034697400641832, 41.749374921049352 ], [ -101.034709309727035, 41.749556509591351 ], [ -101.034709212427046, 41.749738316977663 ], [ -101.034697108878916, 41.749919898295182 ], [ -101.034673028601532, 41.750100809180225 ], [ -101.034637030424278, 41.750280606906259 ], [ -101.034589202344094, 41.750458851467179 ], [ -101.034529661311083, 41.750635106654258 ], [ -101.034458552943249, 41.750808941123822 ], [ -101.034376051170938, 41.750979929452981 ], [ -101.034282357812089, 41.751147653180794 ], [ -101.034177702079077, 41.751311701832734 ], [ -101.034062340018508, 41.751471673925217 ], [ -101.033936553885269, 41.751627177948656 ], [ -101.033800651452282, 41.75177783332564 ], [ -101.03365496525781, 41.751923271342662 ], [ -101.033499851792129, 41.752063136052797 ], [ -101.033335690625293, 41.752197085147046 ], [ -101.033162883478568, 41.752324790792429 ], [ -101.032981853241267, 41.752445940434605 ], [ -101.032793042936092, 41.752560237563124 ], [ -101.032596914634624, 41.752667402437325 ], [ -101.032393948326543, 41.752767172771364 ], [ -101.032184640744617, 41.752859304376344 ], [ -101.031969504148734, 41.75294357175828 ], [ -101.031749065071807, 41.753019768670157 ], [ -101.031523863030799, 41.753087708617038 ], [ -101.031294449205674, 41.753147225312667 ], [ -101.031061385089899, 41.753198173086709 ], [ -101.03082524111565, 41.75324042724138 ], [ -101.030586595256949, 41.753273884356865 ], [ -101.0303460316144, 41.753298462544471 ], [ -101.030104138984981, 41.753314101647341 ], [ -101.02986150942003, 41.753320763387563 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 760, "AREA": 5453408.859, "PERIMETER": 8279.116, "ACRES": 125.193, "HECTARES": 50.664, "origarea": 5.4877660828143003e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.016621281545525, 41.751606983733346 ], [ -101.016380425397784, 41.751604607113471 ], [ -101.016140026919231, 41.751593242642009 ], [ -101.015900683877803, 41.751572918577466 ], [ -101.015662991415752, 41.75154368545693 ], [ -101.015427540569007, 41.751505615970402 ], [ -101.015194916796801, 41.751458804779809 ], [ -101.014965698525245, 41.751403368283711 ], [ -101.01474045570842, 41.751339444327542 ], [ -101.014519748410535, 41.75126719186084 ], [ -101.014304125412821, 41.751186790541702 ], [ -101.014094122848434, 41.751098440290036 ], [ -101.013890262868998, 41.751002360789911 ], [ -101.0136930523459, 41.750898790943502 ], [ -101.013502981609747, 41.750787988276457 ], [ -101.013320523230945, 41.750670228297523 ], [ -101.013146130844518, 41.750545803813012 ], [ -101.012980238022308, 41.750415024198745 ], [ -101.012823257194853, 41.750278214630342 ], [ -101.012675578626101, 41.750135715274467 ], [ -101.012537569443381, 41.749987880442745 ], [ -101.012409572724806, 41.749835077710564 ], [ -101.012291906646752, 41.749677687002823 ], [ -101.012184863693221, 41.749516099649121 ], [ -101.012088709929188, 41.749350717410493 ], [ -101.012003684339746, 41.74918195148031 ], [ -101.011929998236624, 41.749010221461646 ], [ -101.011867834733607, 41.748835954323887 ], [ -101.011817348292027, 41.748659583341031 ], [ -101.011778664337683, 41.748481547014286 ], [ -101.011751878949894, 41.748302287981687 ], [ -101.011737058623567, 41.74812225191765 ], [ -101.011734240104872, 41.747941886424719 ], [ -101.011743430300854, 41.747761639920789 ], [ -101.011764606263284, 41.747581960524229 ], [ -101.011797715246658, 41.7474032949399 ], [ -101.011842674840366, 41.747226087348501 ], [ -101.011899373174515, 41.747050778302516 ], [ -101.011967669198938, 41.746877803630937 ], [ -101.012047393034862, 41.746707593355985 ], [ -101.012138346397975, 41.746540570624127 ], [ -101.012240303092213, 41.746377150654375 ], [ -101.012353009572848, 41.746217739706168 ], [ -101.012476185577484, 41.746062734069717 ], [ -101.012609524823347, 41.745912519080996 ], [ -101.012752695769251, 41.745767468164075 ], [ -101.012905342440291, 41.745627941903059 ], [ -101.013067085313054, 41.745494287146016 ], [ -101.013237522259445, 41.745366836142971 ], [ -101.013416229546536, 41.74524590572026 ], [ -101.01360276288996, 41.745131796493204 ], [ -101.013796658558448, 41.745024792119175 ], [ -101.01399743452653, 41.744925158592601 ], [ -101.014204591672595, 41.744833143584096 ], [ -101.014417615019383, 41.744748975825068 ], [ -101.014635975013903, 41.744672864539375 ], [ -101.014859128843355, 41.744604998923343 ], [ -101.015086521784028, 41.744545547675898 ], [ -101.015317588579691, 41.744494658579171 ], [ -101.015551754846186, 41.744452458131498 ], [ -101.015788438498546, 41.744419051233052 ], [ -101.016027051197284, 41.744394520925191 ], [ -101.016266999810185, 41.744378928184155 ], [ -101.016507687885905, 41.744372311769617 ], [ -101.016748517135966, 41.744374688128303 ], [ -101.016988888921134, 41.744386051353217 ], [ -101.017228205738675, 41.744406373198188 ], [ -101.017465872707007, 41.744435603148197 ], [ -101.01770129904358, 41.744473668544806 ], [ -101.017933899532736, 41.744520474766688 ], [ -101.018163095979759, 41.744575905464657 ], [ -101.018388318647467, 41.744639822850928 ], [ -101.018609007671927, 41.74471206804126 ], [ -101.018824614453507, 41.74479246144984 ], [ -101.019034603020188, 41.744880803235475 ], [ -101.019238451359499, 41.744976873798095 ], [ -101.0194356527157, 41.745080434324422 ], [ -101.019625716849319, 41.74519122738144 ], [ -101.01980817125559, 41.745308977555943 ], [ -101.019982562338896, 41.74543339213897 ], [ -101.020148456540397, 41.745564161853139 ], [ -101.020305441415843, 41.745700961621274 ], [ -101.020453126661053, 41.74584345137405 ], [ -101.020591145082349, 41.745991276895332 ], [ -101.020719153509802, 41.746144070702229 ], [ -101.020836833650634, 41.746301452958541 ], [ -101.020943892881121, 41.746463032418667 ], [ -101.021040064974514, 41.746628407400024 ], [ -101.021125110763535, 41.746797166781406 ], [ -101.021198818735726, 41.746968891024792 ], [ -101.021261005559936, 41.747143153218161 ], [ -101.021311516543079, 41.747319520136685 ], [ -101.021350226015429, 41.747497553319583 ], [ -101.021377037644072, 41.747676810159994 ], [ -101.021391884673307, 41.747856845005337 ], [ -101.021394730091544, 41.748037210265188 ], [ -101.021385566724462, 41.748217457523957 ], [ -101.021364417253608, 41.74839713865574 ], [ -101.021331334161204, 41.748575806938483 ], [ -101.021286399600555, 41.748753018164599 ], [ -101.021229725192782, 41.74892833174556 ], [ -101.021161451750217, 41.749101311807237 ], [ -101.021081748927216, 41.749271528273901 ], [ -101.020990814799291, 41.749438557937509 ], [ -101.020888875371313, 41.749601985510182 ], [ -101.020776184016384, 41.74976140465688 ], [ -101.020653020846609, 41.749916419005928 ], [ -101.020519692017217, 41.75006664313478 ], [ -101.020376528965841, 41.750211703528542 ], [ -101.020223887588926, 41.75035123950888 ], [ -101.02006214735728, 41.750484904131163 ], [ -101.019891710372747, 41.750612365047324 ], [ -101.01971300036864, 41.750733305332439 ], [ -101.019526461656312, 41.750847424273239 ], [ -101.019332558020395, 41.750954438115841 ], [ -101.019131771565554, 41.751054080771638 ], [ -101.018924601517639, 41.751146104479297 ], [ -101.018711562982091, 41.75123028042097 ], [ -101.018493185662876, 41.751306399291529 ], [ -101.018270012544974, 41.751374271819181 ], [ -101.018042598543815, 41.751433729236368 ], [ -101.017811509124883, 41.751484623699596 ], [ -101.017577318897239, 41.751526828657212 ], [ -101.017340610183936, 41.751560239164242 ], [ -101.017101971573553, 41.751584772143467 ], [ -101.016861996455845, 41.751600366592022 ], [ -101.016621281545525, 41.751606983733346 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.691.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.691.geojson new file mode 100644 index 00000000..5ee99a78 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.691.geojson @@ -0,0 +1,7 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 727, "AREA": 5539022.672, "PERIMETER": 8343.414, "ACRES": 127.158, "HECTARES": 51.459, "origarea": 5.5689275881359236e-05, "origlen": 0, "partialDec": 0.82056769297408683, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.06911307861499, 41.6909693625486 ], [ -101.069042650681794, 41.690882808136045 ], [ -101.068958931292116, 41.690771294146259 ], [ -101.068880484897363, 41.690657646549361 ], [ -101.068807408122595, 41.690542005369387 ], [ -101.068739790975613, 41.69042451308588 ], [ -101.06867771673609, 41.690305314458278 ], [ -101.068621261853082, 41.69018455634766 ], [ -101.068570495851063, 41.690062387535598 ], [ -101.068525481244308, 41.689938958540971 ], [ -101.068486273460039, 41.689814421434356 ], [ -101.068452920770355, 41.689688929650806 ], [ -101.068425464232845, 41.689562637800577 ], [ -101.068403937640213, 41.689435701478864 ], [ -101.068388367478761, 41.689308277073842 ], [ -101.068378772895969, 41.689180521574237 ], [ -101.068375165677082, 41.689052592375667 ], [ -101.068377550230736, 41.688924647086871 ], [ -101.068385923583733, 41.688796843335489 ], [ -101.068400275384803, 41.688669338573895 ], [ -101.068420587917643, 41.688542289885135 ], [ -101.068446836122845, 41.688415853789529 ], [ -101.068478987628936, 41.688290186051809 ], [ -101.068517002792476, 41.68816544148919 ], [ -101.068560834746989, 41.688041773780682 ], [ -101.068610429460904, 41.687919335277876 ], [ -101.068665725804323, 41.687798276817155 ], [ -101.068726655624332, 41.68767874753393 ], [ -101.068793143829282, 41.687560894679017 ], [ -101.068865108481276, 41.687444863437193 ], [ -101.068942460897361, 41.687330796748384 ], [ -101.069025105758797, 41.687218835131674 ], [ -101.069112941228639, 41.687109116512147 ], [ -101.069205859077357, 41.687001776051098 ], [ -101.069303744816096, 41.68689694597947 ], [ -101.069406477837944, 41.686794755435187 ], [ -101.069513931566533, 41.68669533030392 ], [ -101.069625973611991, 41.686598793064128 ], [ -101.069742465934127, 41.686505262636317 ], [ -101.069863265012515, 41.686414854236453 ], [ -101.069988222023298, 41.686327679234218 ], [ -101.070117183022575, 41.686243845015703 ], [ -101.070249989136002, 41.686163454851297 ], [ -101.070386476754507, 41.686086607768587 ], [ -101.070526477735882, 41.68601339843017 ], [ -101.070669819611808, 41.68594391701739 ], [ -101.070816325800294, 41.685878249119028 ], [ -101.070965815823186, 41.685816475626126 ], [ -101.071118105528413, 41.685758672632261 ], [ -101.071273007316734, 41.685704911339911 ], [ -101.07143033037282, 41.685655257972755 ], [ -101.071589880900163, 41.685609773694118 ], [ -101.071751462359757, 41.685568514531738 ], [ -101.071914875712082, 41.68553153130874 ], [ -101.072079919662201, 41.685498869581011 ], [ -101.072246390907537, 41.68547056958117 ], [ -101.072414084388313, 41.685446666169092 ], [ -101.072582793539894, 41.685427188788786 ], [ -101.07275231054723, 41.685412161432431 ], [ -101.072922426600655, 41.685401602610568 ], [ -101.073092932152903, 41.685395525329469 ], [ -101.07326361717729, 41.685393937075077 ], [ -101.073434271426052, 41.685396839803779 ], [ -101.0736046846894, 41.685404229940048 ], [ -101.073774647054222, 41.685416098380735 ], [ -101.073943949162484, 41.685432430506424 ], [ -101.07411238246911, 41.685453206199327 ], [ -101.074279739498635, 41.685478399868067 ], [ -101.074445814100685, 41.685507980479251 ], [ -101.074610401703779, 41.685541911595543 ], [ -101.074773299567269, 41.685580151420709 ], [ -101.074934307030816, 41.685622652850896 ], [ -101.075093225761634, 41.68566936353276 ], [ -101.075249859998593, 41.685720225927831 ], [ -101.075404016793314, 41.685775177383483 ], [ -101.075555506247682, 41.685834150209857 ], [ -101.075704141747821, 41.685897071763492 ], [ -101.075849740193775, 41.685963864536483 ], [ -101.075992122225074, 41.68603444625213 ], [ -101.076131112441601, 41.686108729966108 ], [ -101.076266539619624, 41.686186624173644 ], [ -101.076398236922657, 41.686268032922051 ], [ -101.076526042106977, 41.686352855928973 ], [ -101.076649797721473, 41.686440988705897 ], [ -101.07676935130165, 41.686532322686702 ], [ -101.076884555557356, 41.686626745361444 ], [ -101.076995268554313, 41.686724140414839 ], [ -101.07710135388902, 41.686824387869578 ], [ -101.077202680856743, 41.686927364234052 ], [ -101.077299124612637, 41.687032942654291 ], [ -101.077390566325661, 41.687140993070422 ], [ -101.077476893324885, 41.687251382376694 ], [ -101.07755799923855, 41.687363974585374 ], [ -101.077633784125069, 41.687478630994356 ], [ -101.077704154596361, 41.687595210357891 ], [ -101.077769023932859, 41.687713569060556 ], [ -101.077828312190675, 41.687833561294198 ], [ -101.07788194630001, 41.687955039237458 ], [ -101.077929860155422, 41.6880778532379 ], [ -101.077971994697378, 41.688201851996276 ], [ -101.078008297985164, 41.688326882752968 ], [ -101.078038725261052, 41.688452791476102 ], [ -101.078063239005559, 41.688579423051294 ], [ -101.078081808983839, 41.688706621472754 ], [ -101.078094412283178, 41.688834230035432 ], [ -101.078101033341227, 41.688962091528126 ], [ -101.078101663965555, 41.689090048427047 ], [ -101.078096303343742, 41.689217943090007 ], [ -101.078084958044656, 41.689345617950543 ], [ -101.078067642010467, 41.689472915712081 ], [ -101.078044376539765, 41.689599679541693 ], [ -101.078015190261283, 41.68972575326341 ], [ -101.077980119099024, 41.689850981550599 ], [ -101.077939206228024, 41.689975210117275 ], [ -101.077892502021314, 41.690098285908356 ], [ -101.077840063988134, 41.690220057288123 ], [ -101.07778195670312, 41.690340374227162 ], [ -101.077718251726893, 41.690459088487152 ], [ -101.077649027518106, 41.690576053803596 ], [ -101.077574369336887, 41.690691126066127 ], [ -101.077494369139828, 41.690804163495827 ], [ -101.077409125466929, 41.690915026820363 ], [ -101.077363884923301, 41.6909693625486 ], [ -101.06911307861499, 41.6909693625486 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.732.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.732.geojson new file mode 100644 index 00000000..ce506193 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.732.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 727, "AREA": 5539022.672, "PERIMETER": 8343.414, "ACRES": 127.158, "HECTARES": 51.459, "origarea": 5.5689275881359236e-05, "origlen": 0, "partialDec": 0.1794323070259046, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.07329900677108, 41.692684692434021 ], [ -101.073128291648388, 41.692684035022793 ], [ -101.072957712488247, 41.692678887227785 ], [ -101.072787479478762, 41.692669255392126 ], [ -101.072617802381302, 41.692655151384095 ], [ -101.072448890271886, 41.692636592582694 ], [ -101.072280951283432, 41.69261360185601 ], [ -101.072114192349176, 41.692586207533154 ], [ -101.071948818947575, 41.692554443369303 ], [ -101.071785034849043, 41.692518348504017 ], [ -101.071623041864626, 41.692477967413105 ], [ -101.071463039597361, 41.692433349853751 ], [ -101.071305225196141, 41.692384550803112 ], [ -101.071149793112752, 41.69233163039064 ], [ -101.070996934862123, 41.692274653823908 ], [ -101.070846838786338, 41.69221369130824 ], [ -101.070699689822433, 41.692148817960096 ], [ -101.070555669274512, 41.692080113714638 ], [ -101.07041495459022, 41.692007663227024 ], [ -101.070277719142126, 41.691931555768157 ], [ -101.07014413201405, 41.691851885114559 ], [ -101.070014357792715, 41.69176874943291 ], [ -101.069888556364845, 41.691682251158944 ], [ -101.069766882720302, 41.691592496871138 ], [ -101.069649486761008, 41.691499597159456 ], [ -101.069536513116319, 41.691403666489002 ], [ -101.069428100964842, 41.691304823058928 ], [ -101.069324383862991, 41.691203188656701 ], [ -101.069225489580447, 41.69109888850813 ], [ -101.069131539942887, 41.690992051122848 ], [ -101.06911307861499, 41.6909693625486 ], [ -101.077363884923301, 41.6909693625486 ], [ -101.07731874332022, 41.691023579445243 ], [ -101.0772233340345, 41.691129687622386 ], [ -101.077123015140231, 41.691233220614876 ], [ -101.077017910218885, 41.691334050858067 ], [ -101.076908148750633, 41.691432054116888 ], [ -101.076793865954969, 41.691527109638855 ], [ -101.076675202624088, 41.691619100302972 ], [ -101.076552304949459, 41.691707912764109 ], [ -101.07642532434177, 41.691793437592594 ], [ -101.076294417244284, 41.691875569409227 ], [ -101.076159744940227, 41.691954207015115 ], [ -101.076021473353904, 41.692029253516381 ], [ -101.075879772846349, 41.692100616443625 ], [ -101.075734818005358, 41.692168207865947 ], [ -101.075586787430282, 41.692231944499234 ], [ -101.07543586351197, 41.692291747808994 ], [ -101.075282232207996, 41.69234754410698 ], [ -101.075126082813398, 41.692399264642113 ], [ -101.074967607727388, 41.692446845685254 ], [ -101.074807002216204, 41.69249022860776 ], [ -101.074644464172437, 41.692529359953703 ], [ -101.074480193871011, 41.692564191505788 ], [ -101.074314393722418, 41.692594680344918 ], [ -101.074147268023125, 41.692620788902836 ], [ -101.073979022703753, 41.69264248500874 ], [ -101.073809865075177, 41.69265974192875 ], [ -101.073640003573033, 41.692672538398888 ], [ -101.073469647500644, 41.692680858651386 ], [ -101.07329900677108, 41.692684692434021 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 730, "AREA": 5555421.276, "PERIMETER": 8355.751, "ACRES": 127.535, "HECTARES": 51.612, "origarea": 5.5871437204791753e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.053446719493977, 41.713052995259162 ], [ -101.053276648430568, 41.713052323701795 ], [ -101.053106712269866, 41.713047204970984 ], [ -101.052937118087783, 41.713037645304148 ], [ -101.05276807254333, 41.713023656350167 ], [ -101.052599781626611, 41.713005255155295 ], [ -101.052432450407778, 41.7129824641422 ], [ -101.052266282786888, 41.712955311082794 ], [ -101.052101481245444, 41.712923829064273 ], [ -101.051938246599462, 41.712888056448811 ], [ -101.051776777754739, 41.71284803682677 ], [ -101.051617271464266, 41.7128038189636 ], [ -101.051459922088483, 41.71275545674041 ], [ -101.051304921358295, 41.712703009088123 ], [ -101.051152458141374, 41.712646539915866 ], [ -101.051002718211905, 41.71258611803286 ], [ -101.05085588402423, 41.712521817064648 ], [ -101.05071213449034, 41.712453715363317 ], [ -101.050571644761916, 41.712381895911967 ], [ -101.050434586016848, 41.712306446223572 ], [ -101.05030112525057, 41.71222745823426 ], [ -101.050171425072548, 41.712145028191294 ], [ -101.050045643508199, 41.712059256535738 ], [ -101.049923933806255, 41.711970247780016 ], [ -101.049806444252113, 41.711878110380482 ], [ -101.049693317987092, 41.711782956605269 ], [ -101.049584692834102, 41.711684902397408 ], [ -101.049480701129696, 41.711584067233588 ], [ -101.049381469562931, 41.711480573978378 ], [ -101.049287119020988, 41.711374548734625 ], [ -101.049197764442027, 41.711266120689679 ], [ -101.049113514675142, 41.711155421957983 ], [ -101.049034472347898, 41.711042587419954 ], [ -101.048960733741282, 41.710927754557744 ], [ -101.04889238867257, 41.710811063287636 ], [ -101.048829520386022, 41.710692655789472 ], [ -101.048772205451499, 41.710572676333378 ], [ -101.04872051367137, 41.710451271104063 ], [ -101.048674507995571, 41.710328588022584 ], [ -101.04863424444504, 41.710204776566073 ], [ -101.048599772043616, 41.710079987585701 ], [ -101.048571132758468, 41.709954373122727 ], [ -101.048548361449164, 41.709828086223347 ], [ -101.048531485825265, 41.709701280752057 ], [ -101.048520526412773, 41.709574111204368 ], [ -101.048515496529376, 41.709446732518487 ], [ -101.048516402268206, 41.709319299886445 ], [ -101.048523242490802, 41.709191968565143 ], [ -101.048536008828478, 41.70906489368717 ], [ -101.048554685692821, 41.708938230071709 ], [ -101.048579250294793, 41.708812132036087 ], [ -101.04860967267264, 41.708686753207651 ], [ -101.048645915728642, 41.708562246336605 ], [ -101.048687935274359, 41.708438763110017 ], [ -101.048735680084718, 41.708316453967022 ], [ -101.048789091960558, 41.70819546791553 ], [ -101.048848105799621, 41.708075952350647 ], [ -101.048912649676069, 41.707958052875306 ], [ -101.048982644928188, 41.70784191312277 ], [ -101.049058006254427, 41.707727674581783 ], [ -101.049138641817351, 41.707615476424124 ], [ -101.049224453355663, 41.70750545533518 ], [ -101.049315336304076, 41.707397745347443 ], [ -101.049411179920696, 41.707292477677299 ], [ -101.049511867422069, 41.707189780565137 ], [ -101.049617276125517, 41.707089779119272 ], [ -101.049727277598663, 41.706992595163449 ], [ -101.049841737815967, 41.706898347088668 ], [ -101.049960517321978, 41.70680714970878 ], [ -101.050083471401322, 41.706719114120872 ], [ -101.050210450254994, 41.706634347569825 ], [ -101.050341299182833, 41.706552953317797 ], [ -101.050475858772032, 41.706475030518469 ], [ -101.050613965091301, 41.70640067409623 ], [ -101.050755449890502, 41.706329974630734 ], [ -101.05090014080568, 41.706263018246439 ], [ -101.051047861568918, 41.706199886507825 ], [ -101.051198432223032, 41.706140656320088 ], [ -101.051351669340789, 41.706085399835487 ], [ -101.051507386248161, 41.706034184365429 ], [ -101.051665393251795, 41.705987072298548 ], [ -101.051825497869956, 41.705944121024764 ], [ -101.051987505066862, 41.705905382865438 ], [ -101.052151217490234, 41.705870905009519 ], [ -101.052316435711575, 41.705840729456284 ], [ -101.052482958469028, 41.705814892963978 ], [ -101.052650582912378, 41.705793427005354 ], [ -101.052819104850073, 41.705776357729007 ], [ -101.052988318997862, 41.705763705927843 ], [ -101.053158019228661, 41.705755487013555 ], [ -101.05332799882359, 41.705751710997959 ], [ -101.053498050723675, 41.70575238248081 ], [ -101.053667967781834, 41.705757500644125 ], [ -101.053837543015206, 41.705767059253262 ], [ -101.054006569857108, 41.705781046664441 ], [ -101.054174842408528, 41.705799445838977 ], [ -101.054342155688801, 41.705822234364014 ], [ -101.054508305885278, 41.705849384479784 ], [ -101.054673090601341, 41.705880863113414 ], [ -101.054836309102924, 41.705916631919237 ], [ -101.054997762562934, 41.705956647325436 ], [ -101.055157254303268, 41.706000860587096 ], [ -101.055314590034428, 41.706049217845575 ], [ -101.055469578091945, 41.706101660194058 ], [ -101.055622029669905, 41.706158123749354 ], [ -101.055771759050813, 41.706218539729512 ], [ -101.055918583831755, 41.706282834537802 ], [ -101.056062325146527, 41.706350929852121 ], [ -101.056202807883508, 41.706422742720513 ], [ -101.056339860898859, 41.706498185662056 ], [ -101.056473317225098, 41.706577166773492 ], [ -101.056603014274359, 41.706659589841074 ], [ -101.056728794036474, 41.706745354457709 ], [ -101.056850503271463, 41.706834356145293 ], [ -101.056967993696233, 41.706926486481876 ], [ -101.057081122165229, 41.707021633233772 ], [ -101.057189750844799, 41.707119680492113 ], [ -101.057293747381209, 41.707220508814132 ], [ -101.057392985061853, 41.707323995368526 ], [ -101.057487342969765, 41.707430014085141 ], [ -101.057576706130902, 41.707538435808402 ], [ -101.05766096565435, 41.707649128454705 ], [ -101.057740018865033, 41.707761957173219 ], [ -101.057813769428932, 41.707876784510169 ], [ -101.057882127470492, 41.707993470576206 ], [ -101.057945009682285, 41.708111873216879 ], [ -101.058002339426579, 41.708231848185605 ], [ -101.058054046828872, 41.708353249319664 ], [ -101.058100068863126, 41.708475928717903 ], [ -101.058140349428669, 41.708599736921094 ], [ -101.058174839418811, 41.708724523093949 ], [ -101.058203496780649, 41.708850135208905 ], [ -101.058226286566594, 41.708976420231238 ], [ -101.058243180977044, 41.709103224305501 ], [ -101.058254159394451, 41.709230392943027 ], [ -101.0582592084086, 41.709357771210072 ], [ -101.058258321833065, 41.709485203916635 ], [ -101.058251500713041, 41.709612535805419 ], [ -101.058238753324062, 41.709739611741128 ], [ -101.058220095162284, 41.709866276899369 ], [ -101.05819554892561, 41.709992376955341 ], [ -101.05816514448631, 41.710117758271828 ], [ -101.058128918854734, 41.710242268086525 ], [ -101.0580869161344, 41.710365754697982 ], [ -101.05803918746841, 41.710488067650608 ], [ -101.057985790977298, 41.710609057917949 ], [ -101.057926791688374, 41.710728578084343 ], [ -101.057862261456563, 41.710846482524403 ], [ -101.057792278877116, 41.710962627580699 ], [ -101.057716929189823, 41.711076871738712 ], [ -101.05763630417546, 41.711189075799204 ], [ -101.057550502043881, 41.71129910304807 ], [ -101.057459627314586, 41.711406819422812 ], [ -101.057363790689408, 41.711512093675921 ], [ -101.057263108917752, 41.711614797534935 ], [ -101.057157704654358, 41.711714805858676 ], [ -101.057047706309973, 41.711811996789862 ], [ -101.056933247894918, 41.711906251903571 ], [ -101.056814468855848, 41.71199745635159 ], [ -101.056691513905861, 41.712085499002448 ], [ -101.056564532848213, 41.712170272576834 ], [ -101.056433680393795, 41.712251673778347 ], [ -101.056299115972593, 41.712329603419505 ], [ -101.056161003539458, 41.712403966542553 ], [ -101.056019511374288, 41.712474672535194 ], [ -101.055874811876933, 41.71254163524118 ], [ -101.055727081357119, 41.712604773065188 ], [ -101.055576499819566, 41.712664009072419 ], [ -101.055423250744553, 41.712719271082278 ], [ -101.055267520864305, 41.712770491756402 ], [ -101.05510949993544, 41.712817608680808 ], [ -101.054949380507495, 41.712860564441932 ], [ -101.05478735768844, 41.712899306696528 ], [ -101.054623628906626, 41.712933788235695 ], [ -101.054458393670245, 41.712963967042256 ], [ -101.054291853324088, 41.712989806342044 ], [ -101.054124210804034, 41.713011274648693 ], [ -101.053955670389698, 41.71302834580213 ], [ -101.053786437455372, 41.713040999000299 ], [ -101.053616718219644, 41.713049218824736 ], [ -101.053446719493977, 41.713052995259162 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.774.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.774.geojson new file mode 100644 index 00000000..b1286b1e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.085_41.774.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.691.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.691.geojson new file mode 100644 index 00000000..eb094b12 --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.691.geojson @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 729, "AREA": 5250033.07, "PERIMETER": 8122.856, "ACRES": 120.524, "HECTARES": 48.775, "origarea": 5.2762995726160596e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.093373895150492, 41.666682720015125 ], [ -101.093205882274361, 41.666682077016581 ], [ -101.093038005700407, 41.666676962986571 ], [ -101.092870476986292, 41.666667384369802 ], [ -101.09270350725113, 41.666653353237152 ], [ -101.092537306909236, 41.666634887270611 ], [ -101.092372085404875, 41.666612009740838 ], [ -101.092208050948187, 41.666584749477927 ], [ -101.092045410252695, 41.666553140834992 ], [ -101.091884368274648, 41.666517223644874 ], [ -101.09172512795466, 41.666477043169976 ], [ -101.091567889961865, 41.666432650045088 ], [ -101.091412852440897, 41.666384100213648 ], [ -101.091260210762144, 41.666331454857136 ], [ -101.091110157275438, 41.666274780318041 ], [ -101.090962881067554, 41.666214148016103 ], [ -101.090818567723915, 41.666149634358312 ], [ -101.090677399094616, 41.666081320642647 ], [ -101.090539553065241, 41.666009292955522 ], [ -101.090405203332665, 41.665933642063194 ], [ -101.090274519186067, 41.665854463297528 ], [ -101.090147665293642, 41.665771856435626 ], [ -101.090024801495105, 41.665685925574117 ], [ -101.08990608260018, 41.665596778998001 ], [ -101.089791658193633, 41.665504529044028 ], [ -101.089681672446716, 41.665409291959136 ], [ -101.08957626393557, 41.665311187753971 ], [ -101.0894755654666, 41.665210340051516 ], [ -101.089379703909245, 41.665106875931379 ], [ -101.089288800036115, 41.665000925769426 ], [ -101.089202968370941, 41.664892623073726 ], [ -101.089122317044229, 41.664782104315954 ], [ -101.089046947657266, 41.664669508759673 ], [ -101.088976955154038, 41.664554978284578 ], [ -101.088912427701842, 41.664438657207839 ], [ -101.088853446580217, 41.664320692102102 ], [ -101.088800086078663, 41.664201231610839 ], [ -101.088752413403242, 41.664080426260995 ], [ -101.088710488591957, 41.663958428273261 ], [ -101.088674364439299, 41.663835391370256 ], [ -101.088644086429852, 41.663711470582832 ], [ -101.088619692681149, 41.66358682205464 ], [ -101.088601213895842, 41.663461602845494 ], [ -101.08858867332313, 41.663335970733328 ], [ -101.088582086729673, 41.663210084015397 ], [ -101.088581462379864, 41.663084101308947 ], [ -101.088586801025656, 41.662958181351193 ], [ -101.088598095905709, 41.662832482799381 ], [ -101.088615332754159, 41.662707164030877 ], [ -101.088638489818706, 41.662582382943675 ], [ -101.088667537888213, 41.662458296757336 ], [ -101.088702440329698, 41.662335061815057 ], [ -101.088743153134644, 41.662212833386548 ], [ -101.088789624974595, 41.662091765472589 ], [ -101.088841797266014, 41.661972010610896 ], [ -101.088899604244205, 41.66185371968394 ], [ -101.088962973046364, 41.661737041729033 ], [ -101.089031823803481, 41.661622123750398 ], [ -101.089106069741121, 41.661509110534105 ], [ -101.089185617288848, 41.661398144465714 ], [ -101.089270366198321, 41.661289365350754 ], [ -101.089360209669522, 41.661182910238814 ], [ -101.089455034485582, 41.661078913250833 ], [ -101.089554721155466, 41.660977505410145 ], [ -101.089659144064541, 41.66087881447752 ], [ -101.089768171632926, 41.660782964790158 ], [ -101.089881666481347, 41.660690077105194 ], [ -101.089999485604238, 41.66060026844751 ], [ -101.090121480549925, 41.660513651962411 ], [ -101.090247497607749, 41.660430336773118 ], [ -101.090377378001619, 41.660350427843298 ], [ -101.090510958090164, 41.660274025844963 ], [ -101.090648069572794, 41.660201227031557 ], [ -101.090788539701748, 41.660132123116888 ], [ -101.090932191499718, 41.660066801159495 ], [ -101.0910788439827, 41.660005343453122 ], [ -101.09122831238804, 41.659947827422997 ], [ -101.09138040840709, 41.659894325528406 ], [ -101.091534940422378, 41.659844905171404 ], [ -101.091691713748943, 41.659799628611971 ], [ -101.091850530879469, 41.659758552889542 ], [ -101.092011191733121, 41.65972172975129 ], [ -101.092173493907367, 41.659689205586844 ], [ -101.092337232933005, 41.659661021370006 ], [ -101.092502202531477, 41.659637212607024 ], [ -101.092668194874804, 41.659617809291987 ], [ -101.092835000847117, 41.659602835868974 ], [ -101.093002410308102, 41.659592311201358 ], [ -101.093170212357492, 41.65958624854801 ], [ -101.093338195600737, 41.659584655546553 ], [ -101.093506148415102, 41.659587534203858 ], [ -101.093673859216196, 41.659594880893401 ], [ -101.093841116724363, 41.659606686359922 ], [ -101.09400771023067, 41.659622935731043 ], [ -101.094173429862323, 41.659643608535958 ], [ -101.094338066846859, 41.659668678731286 ], [ -101.094501413775092, 41.659698114733786 ], [ -101.094663264862248, 41.65973187946021 ], [ -101.094823416207049, 41.659769930373876 ], [ -101.094981666048611, 41.659812219538381 ], [ -101.095137815020379, 41.65985869367784 ], [ -101.095291666401266, 41.659909294244066 ], [ -101.095443026363398, 41.65996395749022 ], [ -101.095591704216176, 41.660022614551096 ], [ -101.095737512646437, 41.660085191529888 ], [ -101.095880267954414, 41.660151609591196 ], [ -101.096019790285055, 41.660221785060365 ], [ -101.096155903854637, 41.660295629528768 ], [ -101.096288437172063, 41.660373049965187 ], [ -101.096417223255017, 41.660453948832995 ], [ -101.096542099840192, 41.660538224212928 ], [ -101.096662909587849, 41.660625769931535 ], [ -101.096779500279879, 41.660716475694777 ], [ -101.096891725011773, 41.660810227226982 ], [ -101.096999442377594, 41.660906906414802 ], [ -101.097102516648164, 41.661006391455913 ], [ -101.097200817942237, 41.661108557012447 ], [ -101.097294222390062, 41.661213274368883 ], [ -101.097382612289579, 41.661320411594104 ], [ -101.097465876254731, 41.661429833707587 ], [ -101.097543909355991, 41.661541402849501 ], [ -101.097616613252555, 41.661654978454202 ], [ -101.097683896316411, 41.661770417427455 ], [ -101.09774567374788, 41.661887574326528 ], [ -101.097801867682563, 41.662006301543535 ], [ -101.097852407289636, 41.662126449491289 ], [ -101.097897228861143, 41.662247866791738 ], [ -101.097936275892522, 41.662370400466777 ], [ -101.097969499153834, 41.662493896130776 ], [ -101.097996856752047, 41.662618198185264 ], [ -101.098018314183903, 41.662743150014833 ], [ -101.0980338443796, 41.662868594184495 ], [ -101.098043427737124, 41.662994372638025 ], [ -101.098047052146953, 41.663120326897143 ], [ -101.098044713007667, 41.663246298261043 ], [ -101.098036413231824, 41.6633721280066 ], [ -101.098022163242518, 41.663497657588117 ], [ -101.098001980960348, 41.663622728837197 ], [ -101.097975891781076, 41.663747184162119 ], [ -101.097943928543785, 41.663870866746244 ], [ -101.097906131489623, 41.663993620745806 ], [ -101.097862548211367, 41.664115291486212 ], [ -101.097813233593456, 41.66423572565688 ], [ -101.097758249743151, 41.66435477150462 ], [ -101.097697665912349, 41.664472279024722 ], [ -101.097631558410413, 41.664588100150098 ], [ -101.097560010508246, 41.66470208893778 ], [ -101.097483112333478, 41.664814101753024 ], [ -101.097400960756929, 41.664923997450209 ], [ -101.097313659270768, 41.665031637550683 ], [ -101.097221317858114, 41.665136886417464 ], [ -101.097124052854639, 41.665239611426102 ], [ -101.09702198680192, 41.665339683131826 ], [ -101.096915248293243, 41.665436975432755 ], [ -101.096803971811482, 41.665531365728803 ], [ -101.096688297559808, 41.665622735076298 ], [ -101.096568371285002, 41.66571096833782 ], [ -101.096444344093754, 41.665795954327329 ], [ -101.096316372262436, 41.665877585950454 ], [ -101.096184617040009, 41.665955760339386 ], [ -101.096049244444913, 41.666030378982519 ], [ -101.095910425055834, 41.666101347848794 ], [ -101.095768333796698, 41.666168577506077 ], [ -101.095623149716275, 41.666231983233956 ], [ -101.09547505576235, 41.666291485130643 ], [ -101.095324238551328, 41.666347008213542 ], [ -101.095170888132841, 41.666398482513863 ], [ -101.095015197750271, 41.666445843164873 ], [ -101.094857363597058, 41.666489030483589 ], [ -101.094697584569502, 41.666527990046141 ], [ -101.094536062015905, 41.6665626727562 ], [ -101.094372999482772, 41.666593034907073 ], [ -101.094208602458238, 41.666619038236703 ], [ -101.094043078112932, 41.666640649975939 ], [ -101.093876635038811, 41.66665784288984 ], [ -101.093709482986185, 41.666670595312013 ], [ -101.093541832599229, 41.666678891171912 ], [ -101.093373895150492, 41.666682720015125 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 728, "AREA": 5163530.754, "PERIMETER": 8055.665, "ACRES": 118.538, "HECTARES": 47.971, "origarea": 5.1914453844142963e-05, "origlen": 0, "partialDec": 0.75652517021531696, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.08684557989308, 41.6909693625486 ], [ -101.086801421108277, 41.690895860973342 ], [ -101.086738088821946, 41.690779522720376 ], [ -101.086680336932773, 41.690661564219006 ], [ -101.086628239010238, 41.690542135797756 ], [ -101.086581861417201, 41.690421389657686 ], [ -101.086541263225456, 41.690299479678309 ], [ -101.086506496140515, 41.690176561221499 ], [ -101.08647760443597, 41.6900527909334 ], [ -101.086454624897215, 41.68992832654493 ], [ -101.086437586774707, 41.689803326670564 ], [ -101.086426511746893, 41.689677950606338 ], [ -101.086421413892751, 41.689552358126782 ], [ -101.08642229967397, 41.689426709281257 ], [ -101.086429167927008, 41.68930116419012 ], [ -101.086442009864612, 41.689175882840487 ], [ -101.086460809087313, 41.689051024882573 ], [ -101.086485541604446, 41.688926749426038 ], [ -101.086516175864872, 41.688803214837449 ], [ -101.08655267279741, 41.688680578538282 ], [ -101.086594985860742, 41.688558996804524 ], [ -101.086643061102933, 41.688438624567418 ], [ -101.086696837230306, 41.688319615216081 ], [ -101.086756245685677, 41.688202120402188 ], [ -101.086821210735934, 41.688086289846488 ], [ -101.086891649568599, 41.687972271148418 ], [ -101.086967472397546, 41.687860209597709 ], [ -101.087048582577495, 41.687750247989499 ], [ -101.087134876727319, 41.687642526442353 ], [ -101.08722624486181, 41.687537182219735 ], [ -101.087322570532024, 41.687434349555197 ], [ -101.08742373097364, 41.687334159481303 ], [ -101.087529597263526, 41.687236739662815 ], [ -101.087640034484096, 41.687142214233944 ], [ -101.087754901895238, 41.687050703640352 ], [ -101.08787405311368, 41.686962324485577 ], [ -101.087997336299622, 41.686877189382663 ], [ -101.088124594350134, 41.686795406810617 ], [ -101.088255665099396, 41.686717080976273 ], [ -101.088390381525329, 41.686642311681524 ], [ -101.088528571962371, 41.68657119419624 ], [ -101.088670060320226, 41.686503819136888 ], [ -101.088814666308181, 41.686440272351213 ], [ -101.088962205664757, 41.686380634808714 ], [ -101.089112490392495, 41.68632498249773 ], [ -101.089265328997385, 41.68627338632848 ], [ -101.089420526732781, 41.686225912042893 ], [ -101.089577885847547, 41.686182620130744 ], [ -101.089737205837793, 41.686143565752715 ], [ -101.089898283702368, 41.686108798670148 ], [ -101.090060914201374, 41.686078363181572 ], [ -101.09022489011754, 41.686052298066386 ], [ -101.090390002520166, 41.686030636535399 ], [ -101.090556041031192, 41.686013406188628 ], [ -101.090722794093111, 41.686000628980047 ], [ -101.090890049238467, 41.685992321189666 ], [ -101.091057593360361, 41.685988493402803 ], [ -101.091225212983872, 41.685989150496631 ], [ -101.09139269453793, 41.685994291633918 ], [ -101.091559824627296, 41.686003910264098 ], [ -101.091726390304359, 41.686017994131639 ], [ -101.091892179340249, 41.686036525291676 ], [ -101.092056980495215, 41.686059480132762 ], [ -101.092220583787565, 41.686086829407053 ], [ -101.092382780761156, 41.686118538267472 ], [ -101.092543364750838, 41.686154566312183 ], [ -101.092702131145657, 41.686194867635948 ], [ -101.092858877649533, 41.686239390888581 ], [ -101.093013404538766, 41.686288079340507 ], [ -101.093165514916606, 41.686340870954801 ], [ -101.093315014963849, 41.686397698466344 ], [ -101.093461714185892, 41.686458489467398 ], [ -101.093605425655255, 41.686523166499974 ], [ -101.093745966249756, 41.686591647154238 ], [ -101.093883156885838, 41.686663844173751 ], [ -101.094016822746568, 41.686739665566407 ], [ -101.094146793504507, 41.686819014721635 ], [ -101.094272903538624, 41.686901790533575 ], [ -101.094394992145354, 41.686987887529661 ], [ -101.09451290374335, 41.68707719600512 ], [ -101.094626488071796, 41.687169602162712 ], [ -101.094735600381782, 41.687264988257496 ], [ -101.094840101620903, 41.687363232747067 ], [ -101.09493985861036, 41.687464210446208 ], [ -101.095034744214843, 41.687567792686401 ], [ -101.095124637504512, 41.687673847479701 ], [ -101.095209423909182, 41.68778223968701 ], [ -101.095288995364456, 41.687892831190005 ], [ -101.095363250449395, 41.688005481067307 ], [ -101.095432094516056, 41.688120045773935 ], [ -101.095495439810009, 41.688236379324053 ], [ -101.095553205582419, 41.688354333477179 ], [ -101.095605318193037, 41.688473757926893 ], [ -101.095651711204155, 41.688594500492407 ], [ -101.095692325465436, 41.688716407312405 ], [ -101.095727109189468, 41.68883932304113 ], [ -101.095756018017852, 41.688963091046304 ], [ -101.095779015077966, 41.68908755360868 ], [ -101.095796071030037, 41.689212552123067 ], [ -101.095807164104784, 41.689337927300379 ], [ -101.09581228013127, 41.689463519370577 ], [ -101.095811412555179, 41.689589168286389 ], [ -101.095804562447327, 41.689714713927145 ], [ -101.095791738502456, 41.689839996302872 ], [ -101.095772957028359, 41.68996485575817 ], [ -101.095748241925278, 41.690089133175704 ], [ -101.095717624655606, 41.690212670178958 ], [ -101.095681144203937, 41.69033530933411 ], [ -101.095638847027615, 41.690456894350646 ], [ -101.09559078699759, 41.690577270280606 ], [ -101.095537025330088, 41.690696283716008 ], [ -101.095477630508526, 41.690813782984449 ], [ -101.09541267819661, 41.690929618342359 ], [ -101.095388130103927, 41.6909693625486 ], [ -101.08684557989308, 41.6909693625486 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.732.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.732.geojson new file mode 100644 index 00000000..3d12cbbf --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.732.geojson @@ -0,0 +1,10 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "ID": 725, "AREA": 5476760.938, "PERIMETER": 8296.389, "ACRES": 125.729, "HECTARES": 50.881, "origarea": 5.5082270675977609e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.111815161434677, 41.715330608692398 ], [ -101.111645348776378, 41.715330011671831 ], [ -101.111475668706532, 41.715324949556383 ], [ -101.111306330305197, 41.715315428583601 ], [ -101.111137542231177, 41.715301460485222 ], [ -101.110969512464891, 41.715283062472679 ], [ -101.110802448051842, 41.715260257215945 ], [ -101.110636554847488, 41.715233072815579 ], [ -101.110472037263392, 41.715201542768014 ], [ -101.110309098015222, 41.715165705924363 ], [ -101.110147937872952, 41.715125606442449 ], [ -101.109988755413227, 41.715081293732354 ], [ -101.109831746774702, 41.715032822395649 ], [ -101.109677105416225, 41.714980252157872 ], [ -101.109525021878326, 41.714923647795082 ], [ -101.10937568354845, 41.714863079053906 ], [ -101.109229274429907, 41.714798620565595 ], [ -101.10908597491516, 41.714730351754014 ], [ -101.108945961563435, 41.714658356737743 ], [ -101.10880940688314, 41.714582724226396 ], [ -101.108676479119325, 41.714503547411276 ], [ -101.108547342046307, 41.71442092385044 ], [ -101.108422154765819, 41.714334955348527 ], [ -101.108301071511121, 41.714245747831271 ], [ -101.108184241456769, 41.714153411214831 ], [ -101.108071808535001, 41.714058059270407 ], [ -101.107963911258338, 41.713959809484038 ], [ -101.107860682548946, 41.713858782911707 ], [ -101.107762249574989, 41.71375510403017 ], [ -101.107668733593897, 41.713648900583593 ], [ -101.107580249803121, 41.713540303425994 ], [ -101.107496907198254, 41.713429446360102 ], [ -101.107418808438794, 41.713316465972412 ], [ -101.107346049721826, 41.713201501464795 ], [ -101.10727872066353, 41.713084694483008 ], [ -101.107216904188917, 41.712966188942154 ], [ -101.107160676429814, 41.712846130849293 ], [ -101.107110106631154, 41.712724668123528 ], [ -101.107065257065827, 41.712601950413763 ], [ -101.107026182958094, 41.712478128914221 ], [ -101.106992932415665, 41.712353356178205 ], [ -101.106965546370688, 41.712227785930153 ], [ -101.106944058529336, 41.712101572876158 ], [ -101.106928495330578, 41.711974872513345 ], [ -101.106918875913692, 41.711847840938361 ], [ -101.106915212094862, 41.711720634654966 ], [ -101.106917508352851, 41.711593410381283 ], [ -101.106925761823604, 41.711466324856708 ], [ -101.10693996230394, 41.711339534648729 ], [ -101.10696009226433, 41.711213195960148 ], [ -101.106986126870638, 41.711087464436595 ], [ -101.107018034014899, 41.710962494974829 ], [ -101.107055774355047, 41.710838441531855 ], [ -101.107099301363547, 41.710715456935318 ], [ -101.107148561384818, 41.710593692695284 ], [ -101.107203493701547, 41.710473298817526 ], [ -101.107264030609656, 41.710354423618845 ], [ -101.107330097501773, 41.710237213544289 ], [ -101.107401612959293, 41.710121812986877 ], [ -101.107478488852891, 41.71000836410964 ], [ -101.107560630451104, 41.709897006670623 ], [ -101.107647936537191, 41.709787877850744 ], [ -101.107740299533901, 41.709681112084745 ], [ -101.107837605636163, 41.709576840895771 ], [ -101.107939734951231, 41.709475192733279 ], [ -101.108046561646574, 41.709376292814902 ], [ -101.108157954104868, 41.709280262972193 ], [ -101.108273775086246, 41.709187221500699 ], [ -101.108393881897371, 41.709097283014117 ], [ -101.108518126567191, 41.709010558303333 ], [ -101.108646356029368, 41.708927154199849 ], [ -101.108778412310727, 41.708847173444362 ], [ -101.108914132725872, 41.708770714560117 ], [ -101.109053350077659, 41.708697871731651 ], [ -101.109195892863013, 41.708628734688858 ], [ -101.109341585484259, 41.708563388596453 ], [ -101.109490248465363, 41.708501913949078 ], [ -101.109641698672917, 41.708444386472266 ], [ -101.109795749541689, 41.708390877029188 ], [ -101.109952211304403, 41.70834145153335 ], [ -101.110110891225432, 41.708296170867435 ], [ -101.110271593837993, 41.708255090808414 ], [ -101.110434121185023, 41.708218261958834 ], [ -101.110598273062763, 41.708185729684502 ], [ -101.110763847267393, 41.708157534058593 ], [ -101.110930639843858, 41.708133709812429 ], [ -101.111098445337163, 41.708114286292584 ], [ -101.111267057045112, 41.708099287424837 ], [ -101.111436267273021, 41.708088731684711 ], [ -101.111605867589262, 41.708082632074685 ], [ -101.111775649081949, 41.708080996108187 ], [ -101.111945402616143, 41.708083825800429 ], [ -101.112114919091354, 41.708091117665759 ], [ -101.112283989698909, 41.708102862722193 ], [ -101.112452406179102, 41.708119046502198 ], [ -101.112619961077556, 41.708139649070709 ], [ -101.112786448000676, 41.708164645049592 ], [ -101.112951661869701, 41.70819400364892 ], [ -101.113115399173267, 41.708227688704831 ], [ -101.113277458217965, 41.708265658724109 ], [ -101.113437639376613, 41.708307866935243 ], [ -101.113595745334152, 41.708354261346031 ], [ -101.113751581330533, 41.70840478480752 ], [ -101.113904955400542, 41.708459375084509 ], [ -101.1140556786102, 41.708517964932064 ], [ -101.114203565289429, 41.708580482178313 ], [ -101.114348433260673, 41.708646849813434 ], [ -101.11449010406325, 41.708716986084276 ], [ -101.114628403173171, 41.70879080459521 ], [ -101.114763160218004, 41.708868214414437 ], [ -101.114894209186829, 41.708949120185849 ], [ -101.115021388634631, 41.709033422246669 ], [ -101.115144541881222, 41.709121016750004 ], [ -101.115263517204156, 41.709211795792719 ], [ -101.115378168025785, 41.709305647548412 ], [ -101.115488353093724, 41.70940245640503 ], [ -101.115593936654932, 41.70950210310734 ], [ -101.115694788623003, 41.709604464903656 ], [ -101.115790784738422, 41.709709415697063 ], [ -101.115881806721802, 41.709816826200765 ], [ -101.115967742419514, 41.709926564097266 ], [ -101.116048485942073, 41.7100384942012 ], [ -101.116123937794612, 41.710152478626107 ], [ -101.116194004999556, 41.710268376953955 ], [ -101.116258601211285, 41.71038604640831 ], [ -101.116317646822651, 41.710505342030032 ], [ -101.116371069063106, 41.710626116855934 ], [ -101.11641880208856, 41.710748222099681 ], [ -101.116460787062579, 41.710871507335192 ], [ -101.116496972229058, 41.710995820681809 ], [ -101.116527312976174, 41.711121008991476 ], [ -101.11655177189138, 41.71124691803729 ], [ -101.11657031880776, 41.711373392703607 ], [ -101.116582930841361, 41.711500277177016 ], [ -101.116589592419501, 41.711627415138381 ], [ -101.116590295300142, 41.711754649955296 ], [ -101.116585038582258, 41.711881824875135 ], [ -101.116573828707047, 41.712008783218288 ], [ -101.116556679450269, 41.71213536857082 ], [ -101.116533611905325, 41.712261424977655 ], [ -101.116504654457515, 41.712386797134364 ], [ -101.116469842749254, 41.712511330578664 ], [ -101.116429219636217, 41.712634871880752 ], [ -101.116382835134829, 41.712757268832299 ], [ -101.116330746360745, 41.712878370634002 ], [ -101.116273017458553, 41.712998028081429 ], [ -101.116209719523042, 41.713116093748908 ], [ -101.116140930511605, 41.713232422171096 ], [ -101.116066735148436, 41.713346870022356 ], [ -101.115987224820145, 41.713459296293259 ], [ -101.115902497463424, 41.713569562464443 ], [ -101.115812657444323, 41.713677532677295 ], [ -101.115717815429917, 41.713783073901403 ], [ -101.115618088251892, 41.713886056098438 ], [ -101.115513598762789, 41.713986352382555 ], [ -101.11540447568467, 41.714083839176588 ], [ -101.115290853450546, 41.714178396364495 ], [ -101.115172872038812, 41.714269907439416 ], [ -101.115050676800806, 41.71435825964717 ], [ -101.11492441828176, 41.7144433441253 ], [ -101.114794252035281, 41.714525056037239 ], [ -101.114660338431733, 41.714603294701583 ], [ -101.114522842460616, 41.714677963716056 ], [ -101.11438193352727, 41.71474897107651 ], [ -101.114237785244043, 41.714816229290165 ], [ -101.114090575216508, 41.714879655483628 ], [ -101.113940484824326, 41.714939171504938 ], [ -101.113787698997953, 41.714994704019894 ], [ -101.113632405990529, 41.715046184602535 ], [ -101.113474797145955, 41.715093549819443 ], [ -101.113315066662963, 41.715136741307923 ], [ -101.113153411355782, 41.71517570584804 ], [ -101.112990030411495, 41.715210395428066 ], [ -101.11282512514461, 41.715240767303833 ], [ -101.112658898748705, 41.715266784051316 ], [ -101.112491556046137, 41.715288413612804 ], [ -101.112323303235414, 41.715305629336449 ], [ -101.112154347637031, 41.715318410009111 ], [ -101.111984897437893, 41.715326739882457 ], [ -101.111815161434677, 41.715330608692398 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 724, "AREA": 6975423.905, "PERIMETER": 9362.892, "ACRES": 160.134, "HECTARES": 64.804, "origarea": 7.0168311314344295e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.105563544642948, 41.728275581313468 ], [ -101.105382960788944, 41.728275074220257 ], [ -101.105202498070824, 41.728270094395427 ], [ -101.105022353859226, 41.728260647285339 ], [ -101.104842725176226, 41.728246743222186 ], [ -101.104663808479785, 41.728228397412693 ], [ -101.104485799448696, 41.728205629921433 ], [ -101.104308892768501, 41.728178465648881 ], [ -101.104133281918436, 41.72814693430422 ], [ -101.103959158959753, 41.728111070372762 ], [ -101.103786714325466, 41.72807091307822 ], [ -101.103616136612061, 41.72802650633983 ], [ -101.103447612373103, 41.727977898724284 ], [ -101.103281325915148, 41.727925143392511 ], [ -101.103117459095969, 41.727868298041606 ], [ -101.102956191125756, 41.727807424841664 ], [ -101.102797698370921, 41.727742590367683 ], [ -101.102642154161146, 41.727673865526825 ], [ -101.102489728599892, 41.727601325480784 ], [ -101.102340588378112, 41.727525049563468 ], [ -101.102194896592096, 41.727445121194364 ], [ -101.102052812564949, 41.727361627787076 ], [ -101.101914491672375, 41.72727466065384 ], [ -101.10178008517272, 41.727184314905514 ], [ -101.101649740041566, 41.727090689347555 ], [ -101.101523598810971, 41.726993886371901 ], [ -101.101401799413608, 41.72689401184499 ], [ -101.101284475031946, 41.72679117499186 ], [ -101.10117175395267, 41.726685488276757 ], [ -101.101063759426353, 41.726577067279969 ], [ -101.100960609532791, 41.726466030571515 ], [ -101.100862417051857, 41.726352499581296 ], [ -101.100769289340334, 41.726236598466365 ], [ -101.100681328214478, 41.726118453975133 ], [ -101.100598629838856, 41.725998195308549 ], [ -101.100521284621195, 41.725875953978949 ], [ -101.100449377113691, 41.72575186366609 ], [ -101.100382985920604, 41.725626060071001 ], [ -101.100322183612462, 41.725498680767473 ], [ -101.100267036646741, 41.725369865051682 ], [ -101.100217605295441, 41.725239753789722 ], [ -101.100173943579236, 41.725108489263597 ], [ -101.100136099208513, 41.724976215015616 ], [ -101.100104113531444, 41.724843075691396 ], [ -101.100078021488841, 41.724709216881621 ], [ -101.100057851576153, 41.724574784962911 ], [ -101.100043625812404, 41.724439926937606 ], [ -101.10003535971633, 41.724304790273187 ], [ -101.10003306228954, 41.724169522740858 ], [ -101.100036736006871, 41.724034272254002 ], [ -101.100046376813779, 41.723899186706561 ], [ -101.100061974131037, 41.723764413811033 ], [ -101.100083510866341, 41.723630100937285 ], [ -101.100110963433266, 41.723496394951219 ], [ -101.100144301777163, 41.723363442054179 ], [ -101.100183489408209, 41.72323138762323 ], [ -101.100228483441441, 41.723100376052102 ], [ -101.100279234643779, 41.722970550593374 ], [ -101.100335687488055, 41.722842053201845 ], [ -101.100397780213811, 41.722715024379333 ], [ -101.100465444895022, 41.722589603021106 ], [ -101.100538607514494, 41.722465926263936 ], [ -101.100617188044822, 41.722344129336413 ], [ -101.100701100536128, 41.722224345410815 ], [ -101.100790253210121, 41.722106705457811 ], [ -101.100884548560472, 41.721991338103066 ], [ -101.10098388345962, 41.721878369486845 ], [ -101.101088149271547, 41.721767923126016 ], [ -101.101197231970673, 41.721660119779024 ], [ -101.101311012266549, 41.72155507731398 ], [ -101.101429365734404, 41.721452910579792 ], [ -101.101552162951222, 41.721353731280622 ], [ -101.101679269637202, 41.721257647853804 ], [ -101.101810546802739, 41.721164765351439 ], [ -101.101945850900336, 41.721075185325354 ], [ -101.10208503398151, 41.720989005716312 ], [ -101.102227943858679, 41.720906320746941 ], [ -101.102374424271432, 41.720827220818684 ], [ -101.102524315057408, 41.720751792413054 ], [ -101.102677452327384, 41.720680117997162 ], [ -101.102833668644394, 41.72061227593354 ], [ -101.102992793206781, 41.720548340394501 ], [ -101.10315465203486, 41.720488381281157 ], [ -101.103319068161056, 41.720432464146946 ], [ -101.103485861823401, 41.720380650126074 ], [ -101.103654850661854, 41.720332995866649 ], [ -101.10382584991774, 41.720289553468788 ], [ -101.103998672635541, 41.720250370427756 ], [ -101.104173129867277, 41.720215489581967 ], [ -101.104349030878936, 41.720184949066265 ], [ -101.104526183358942, 41.720158782270182 ], [ -101.104704393628225, 41.72013701780147 ], [ -101.104883466851959, 41.720119679454939 ], [ -101.105063207252329, 41.720106786186236 ], [ -101.105243418322559, 41.720098352091412 ], [ -101.105423903041611, 41.720094386391253 ], [ -101.105604464089396, 41.720094893421411 ], [ -101.10578490406246, 41.72009987262755 ], [ -101.105965025689613, 41.720109318566031 ], [ -101.106144632047545, 41.720123220909734 ], [ -101.106323526775924, 41.720141564459503 ], [ -101.10650151429202, 41.720164329160603 ], [ -101.106678400004341, 41.720191490124776 ], [ -101.106853990525366, 41.720223017657318 ], [ -101.107028093882747, 41.720258877289588 ], [ -101.107200519729147, 41.720299029816644 ], [ -101.107371079550248, 41.720343431340126 ], [ -101.107539586870715, 41.720392033316124 ], [ -101.107705857458015, 41.720444782608389 ], [ -101.107869709523712, 41.720501621546227 ], [ -101.108030963922175, 41.7205624879876 ], [ -101.108189444346337, 41.72062731538702 ], [ -101.108344977520389, 41.720696032868304 ], [ -101.108497393389186, 41.720768565301952 ], [ -101.10864652530411, 41.720844833387268 ], [ -101.108792210205223, 41.720924753739091 ], [ -101.108934288799503, 41.721008238978818 ], [ -101.109072605735037, 41.72109519782996 ], [ -101.109207009770742, 41.72118553521787 ], [ -101.109337353941839, 41.721279152373668 ], [ -101.109463495720476, 41.721375946942096 ], [ -101.109585297171549, 41.721475813093548 ], [ -101.109702625103623, 41.721578641639539 ], [ -101.109815351214493, 41.721684320152235 ], [ -101.109923352231576, 41.721792733087149 ], [ -101.110026510046723, 41.721903761909573 ], [ -101.110124711845373, 41.722017285223998 ], [ -101.110217850230043, 41.722133178906986 ], [ -101.110305823337768, 41.722251316242641 ], [ -101.110388534951625, 41.722371568061291 ], [ -101.11046589460598, 41.72249380288055 ], [ -101.110537817685483, 41.72261788704909 ], [ -101.110604225517832, 41.722743684892812 ], [ -101.11066504545974, 41.722871058863028 ], [ -101.110720210976567, 41.722999869686916 ], [ -101.110769661715238, 41.723129976519743 ], [ -101.1108133435703, 41.723261237098882 ], [ -101.110851208743284, 41.723393507899267 ], [ -101.11088321579507, 41.723526644290395 ], [ -101.110909329691353, 41.723660500694386 ], [ -101.110929521841186, 41.723794930745164 ], [ -101.110943770128245, 41.72392978744849 ], [ -101.110952058935325, 41.724064923342745 ], [ -101.110954379161527, 41.724200190660035 ], [ -101.110950728232297, 41.724335441487924 ], [ -101.110941110102516, 41.724470527931039 ], [ -101.110925535252321, 41.72460530227287 ], [ -101.11090402067569, 41.724739617137288 ], [ -101.110876589862187, 41.724873325649696 ], [ -101.110843272771334, 41.725006281597665 ], [ -101.110804105800042, 41.725138339590806 ], [ -101.11075913174291, 41.725269355219801 ], [ -101.11070839974569, 41.725399185214336 ], [ -101.110651965251535, 41.725527687599801 ], [ -101.110589889940684, 41.7256547218525 ], [ -101.110522241663006, 41.725780149053463 ], [ -101.110449094364, 41.725903832040295 ], [ -101.110370528004012, 41.726025635557271 ], [ -101.110286628471016, 41.726145426403207 ], [ -101.110197487486602, 41.72626307357725 ], [ -101.110103202506011, 41.726378448422075 ], [ -101.110003876611444, 41.726491424764689 ], [ -101.109899618399552, 41.726601879054513 ], [ -101.109790541862679, 41.72670969049836 ], [ -101.109676766264329, 41.726814741192712 ], [ -101.109558416008724, 41.726916916252705 ], [ -101.10943562050484, 41.727016103937778 ], [ -101.109308514025003, 41.727112195773849 ], [ -101.109177235557866, 41.727205086672164 ], [ -101.10904192865668, 41.727294675044106 ], [ -101.108902741282151, 41.727380862912426 ], [ -101.108759825640647, 41.7274635560184 ], [ -101.108613338017776, 41.727542663924993 ], [ -101.108463438607444, 41.727618100115791 ], [ -101.108310291336565, 41.727689782089676 ], [ -101.108154063685845, 41.727757631451041 ], [ -101.107994926506507, 41.727821573995669 ], [ -101.107833053833403, 41.727881539791802 ], [ -101.107668622694646, 41.727937463256779 ], [ -101.10750181291786, 41.727989283228759 ], [ -101.107332806933528, 41.728036943033572 ], [ -101.107161789575315, 41.728080390546836 ], [ -101.10698894787788, 41.72811957825094 ], [ -101.106814470872237, 41.728154463287069 ], [ -101.106638549378815, 41.728185007502084 ], [ -101.106461375798773, 41.728211177490259 ], [ -101.106283143903468, 41.728232944629845 ], [ -101.106104048622271, 41.728250285114449 ], [ -101.105924285829431, 41.728263179978946 ], [ -101.105744052129666, 41.728271615120434 ], [ -101.105563544642948, 41.728275581313468 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 726, "AREA": 5621916.146, "PERIMETER": 8405.609, "ACRES": 129.061, "HECTARES": 52.23, "origarea": 5.6539462652480083e-05, "origlen": 0, "partialDec": 1.0, "truncated": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.092358560362669, 41.712236645375988 ], [ -101.092187476272855, 41.712236027505718 ], [ -101.092016525201814, 41.712230935878544 ], [ -101.091845915462471, 41.71222137669892 ], [ -101.091675854951504, 41.712207361615128 ], [ -101.091506550896057, 41.712188907705233 ], [ -101.091338209600977, 41.712166037456207 ], [ -101.091171036197437, 41.712138778736531 ], [ -101.09100523439264, 41.712107164762187 ], [ -101.090841006221723, 41.712071234056168 ], [ -101.090678551801261, 41.712031030401548 ], [ -101.090518069085391, 41.711986602788066 ], [ -101.090359753624497, 41.711938005352415 ], [ -101.090203798326854, 41.711885297312257 ], [ -101.090050393223422, 41.71182854289404 ], [ -101.089899725236208, 41.711767811254639 ], [ -101.089751977950485, 41.711703176397116 ], [ -101.089607331390965, 41.711634717080472 ], [ -101.089465961802446, 41.711562516723617 ], [ -101.089328041434939, 41.711486663303752 ], [ -101.089193738333805, 41.711407249249085 ], [ -101.089063216134861, 41.711324371326086 ], [ -101.088936633865188, 41.711238130521629 ], [ -101.088814145749097, 41.711148631919848 ], [ -101.088695901020387, 41.711055984574088 ], [ -101.088582043740487, 41.710960301373937 ], [ -101.088472712622874, 41.710861698907621 ], [ -101.088368040864225, 41.710760297319908 ], [ -101.088268155981993, 41.710656220165646 ], [ -101.088173179659279, 41.710549594259213 ], [ -101.088083227596513, 41.710440549519916 ], [ -101.087998409370556, 41.710329218813698 ], [ -101.087918828301383, 41.710215737791096 ], [ -101.087844581326138, 41.710100244722028 ], [ -101.087775758881222, 41.709982880327225 ], [ -101.08771244479226, 41.709863787606778 ], [ -101.087654716171983, 41.709743111665787 ], [ -101.087602643326434, 41.709620999537606 ], [ -101.087556289669521, 41.709497600004646 ], [ -101.087515711645779, 41.709373063417026 ], [ -101.087480958661871, 41.709247541509406 ], [ -101.087452073026412, 41.709121187216155 ], [ -101.08742908989872, 41.708994154484792 ], [ -101.087412037246025, 41.708866598088676 ], [ -101.087400935809669, 41.708738673438191 ], [ -101.087395799079857, 41.708610536391518 ], [ -101.087396633279582, 41.708482343064702 ], [ -101.087403437357068, 41.70835424964141 ], [ -101.087416202987271, 41.708226412182761 ], [ -101.087434914582204, 41.708098986437001 ], [ -101.087459549310097, 41.70797212764996 ], [ -101.087490077123377, 41.707845990375809 ], [ -101.087526460795445, 41.707720728288791 ], [ -101.087568655966152, 41.70759649399605 ], [ -101.087616611196069, 41.707473438851657 ], [ -101.087670268029314, 41.707351712772336 ], [ -101.087729561064805, 41.707231464054779 ], [ -101.087794418036225, 41.707112839195005 ], [ -101.087864759900086, 41.706995982709998 ], [ -101.087940500932234, 41.706881036961569 ], [ -101.088021548832344, 41.706768141983048 ], [ -101.088107804836511, 41.706657435308621 ], [ -101.088199163837672, 41.706549051805972 ], [ -101.088295514513732, 41.706443123511917 ], [ -101.088396739463306, 41.70633977947157 ], [ -101.088502715348753, 41.706239145581264 ], [ -101.088613313046537, 41.706141344435196 ], [ -101.088728397804601, 41.706046495176075 ], [ -101.088847829406532, 41.705954713350145 ], [ -101.088971462342442, 41.705866110766308 ], [ -101.089099145986211, 41.705780795360148 ], [ -101.089230724779028, 41.705698871062339 ], [ -101.089366038418902, 41.705620437672188 ], [ -101.089504922055909, 41.705545590736023 ], [ -101.089647206492998, 41.705474421430935 ], [ -101.089792718392104, 41.705407016453698 ], [ -101.089941280485206, 41.70534345791517 ], [ -101.090092711790305, 41.705283823240414 ], [ -101.090246827831777, 41.705228185074311 ], [ -101.090403440865018, 41.705176611193181 ], [ -101.090562360105139, 41.705129164422253 ], [ -101.090723391959258, 41.705085902559084 ], [ -101.090886340262216, 41.705046878303285 ], [ -101.091051006515443, 41.70501213919237 ], [ -101.091217190128731, 41.704981727543725 ], [ -101.091384688664334, 41.704955680403259 ], [ -101.09155329808361, 41.704934029500201 ], [ -101.091722812995371, 41.704916801208455 ], [ -101.091893026905936, 41.704904016514604 ], [ -101.092063732470578, 41.704895690992224 ], [ -101.092234721745982, 41.704891834782991 ], [ -101.09240578644345, 41.704892452584275 ], [ -101.0925767181824, 41.704897543643568 ], [ -101.092747308744137, 41.704907101759176 ], [ -101.092917350325365, 41.704921115288009 ], [ -101.093086635791209, 41.704939567159542 ], [ -101.093254958927346, 41.704962434896757 ], [ -101.093422114691123, 41.704989690643394 ], [ -101.093587899461212, 41.705021301197988 ], [ -101.093752111285539, 41.705057228054173 ], [ -101.093914550127124, 41.705097427447669 ], [ -101.094075018107745, 41.705141850409454 ], [ -101.094233319748781, 41.705190442825504 ], [ -101.094389262209319, 41.705243145502642 ], [ -101.094542655520925, 41.705299894240532 ], [ -101.094693312819047, 41.705360619909953 ], [ -101.094841050570466, 41.705425248536955 ], [ -101.094985688796925, 41.705493701392903 ], [ -101.095127051294256, 41.705565895090309 ], [ -101.09526496584698, 41.70564174168446 ], [ -101.095399264438072, 41.705721148780405 ], [ -101.095529783453628, 41.705804019645576 ], [ -101.095656363882171, 41.705890253327446 ], [ -101.0957788515083, 41.70597974477662 ], [ -101.095897097100604, 41.706072384974561 ], [ -101.09601095659346, 41.706168061066549 ], [ -101.096120291262594, 41.706266656498968 ], [ -101.096224967894088, 41.706368051161334 ], [ -101.096324858946659, 41.706472121532485 ], [ -101.096419842707192, 41.706578740831169 ], [ -101.096509803439034, 41.706687779170217 ], [ -101.096594631523033, 41.706799103714886 ], [ -101.096674223591279, 41.706912578844658 ], [ -101.09674848265297, 41.707028066318294 ], [ -101.0968173182128, 41.70714542544237 ], [ -101.096880646381322, 41.707264513242443 ], [ -101.096938389977169, 41.707385184637403 ], [ -101.096990478621336, 41.707507292616043 ], [ -101.097036848822981, 41.707630688416138 ], [ -101.097077444056907, 41.707755221705803 ], [ -101.097112214832663, 41.707880740766385 ], [ -101.097141118754919, 41.708007092677434 ], [ -101.097164120575258, 41.708134123503022 ], [ -101.097181192235411, 41.70826167847909 ], [ -101.097192312901441, 41.708389602202139 ], [ -101.097197468989407, 41.708517738818522 ], [ -101.097196654182056, 41.708645932214303 ], [ -101.097189869436662, 41.708774026205475 ], [ -101.097177122984078, 41.708901864728148 ], [ -101.097158430318871, 41.709029292028895 ], [ -101.09713381418058, 41.709156152854284 ], [ -101.097103304526229, 41.709282292640246 ], [ -101.097066938493967, 41.709407557700274 ], [ -101.097024760357939, 41.709531795412786 ], [ -101.096976821474598, 41.709654854407006 ], [ -101.096923180220244, 41.70977658474748 ], [ -101.096863901920017, 41.70989683811672 ], [ -101.09679905876844, 41.710015467996087 ], [ -101.096728729741685, 41.710132329844114 ], [ -101.096653000501362, 41.710247281272878 ], [ -101.096571963290359, 41.710360182221379 ], [ -101.096485716820496, 41.710470895126313 ], [ -101.096394366152367, 41.710579285089665 ], [ -101.096298022567481, 41.710685220043167 ], [ -101.096196803432704, 41.710788570909223 ], [ -101.096090832057328, 41.710889211758257 ], [ -101.095980237542918, 41.710987019962168 ], [ -101.095865154625997, 41.711081876343876 ], [ -101.095745723514014, 41.711173665322434 ], [ -101.09562208971451, 41.711262275054047 ], [ -101.095494403857785, 41.71134759756837 ], [ -101.095362821513433, 41.711429528900069 ], [ -101.095227503000743, 41.711507969215589 ], [ -101.095088613193383, 41.711582822934844 ], [ -101.09494632131846, 41.711653998847709 ], [ -101.094800800750292, 41.711721410225188 ], [ -101.09465222879912, 41.711784974925266 ], [ -101.094500786494976, 41.711844615492851 ], [ -101.094346658367073, 41.711900259254385 ], [ -101.09419003221889, 41.711951838406229 ], [ -101.094031098899208, 41.711999290097545 ], [ -101.093870052069533, 41.712042556506759 ], [ -101.093707087967942, 41.71208158491207 ], [ -101.093542405169998, 41.712116327755865 ], [ -101.093376204346526, 41.71214674270248 ], [ -101.093208688019075, 41.712172792689977 ], [ -101.093040060312987, 41.712194445975236 ], [ -101.09287052670858, 41.712211676172728 ], [ -101.092700293790571, 41.712224462286642 ], [ -101.09252956899627, 41.712232788736458 ], [ -101.092358560362669, 41.712236645375988 ] ] ] } }, +{ "type": "Feature", "properties": { "ID": 728, "AREA": 5163530.754, "PERIMETER": 8055.665, "ACRES": 118.538, "HECTARES": 47.971, "origarea": 5.1914453844142963e-05, "origlen": 0, "partialDec": 0.24347482978467894, "truncated": 1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -101.09117611353696, 41.693027572351383 ], [ -101.091008475708321, 41.693026915187247 ], [ -101.09084097600909, 41.693021773500362 ], [ -101.090673827926977, 41.693012153844087 ], [ -101.090507244501296, 41.69299806847917 ], [ -101.090341438051354, 41.692979535358141 ], [ -101.090176619905648, 41.692956578102397 ], [ -101.09001300013243, 41.692929225972108 ], [ -101.089850787271828, 41.692897513828903 ], [ -101.089690188069966, 41.692861482091345 ], [ -101.08953140721529, 41.692821176683502 ], [ -101.089374647077605, 41.692776648976348 ], [ -101.089220107450032, 41.692727955722169 ], [ -101.089067985294292, 41.692675158982375 ], [ -101.08891847448956, 41.692618326048212 ], [ -101.088771765585278, 41.692557529354964 ], [ -101.088628045558252, 41.692492846389754 ], [ -101.088487497574263, 41.692424359592536 ], [ -101.08835030075457, 41.692352156251133 ], [ -101.088216629947567, 41.692276328389845 ], [ -101.088086655505933, 41.692196972652141 ], [ -101.087960543069443, 41.69211419017752 ], [ -101.087838453353925, 41.692028086472455 ], [ -101.087720541946325, 41.691938771275879 ], [ -101.08760695910658, 41.69184635841934 ], [ -101.087497849575939, 41.691750965681869 ], [ -101.087393352392681, 41.69165271463973 ], [ -101.087293600714915, 41.691551730511556 ], [ -101.087198721650893, 41.691448141998599 ], [ -101.087108836097144, 41.691342081120787 ], [ -101.08702405858439, 41.691233683048196 ], [ -101.086944497131768, 41.691123085929064 ], [ -101.086870253109183, 41.691010430713369 ], [ -101.08684557989308, 41.6909693625486 ], [ -101.095388130103927, 41.6909693625486 ], [ -101.095342251141858, 41.691043642165944 ], [ -101.0952664390704, 41.691155709139295 ], [ -101.095185338572676, 41.69126567643967 ], [ -101.09509905298053, 41.691373403919485 ], [ -101.095007692235498, 41.691478754285022 ], [ -101.094911372748911, 41.691581593271465 ], [ -101.094810217253567, 41.69168178981402 ], [ -101.094704354647391, 41.691779216214968 ], [ -101.094593919829194, 41.691873748306499 ], [ -101.094479053526854, 41.69196526560912 ], [ -101.094359902117972, 41.692053651484976 ], [ -101.094236617443343, 41.692138793286837 ], [ -101.094109356613487, 41.692220582501562 ], [ -101.093978281808361, 41.692298914888482 ], [ -101.093843560070752, 41.692373690612264 ], [ -101.09370536309325, 41.692444814370319 ], [ -101.093563866999474, 41.692512195514162 ], [ -101.093419252119617, 41.692575748165147 ], [ -101.093271702760376, 41.692635391323755 ], [ -101.093121406970269, 41.692691048973138 ], [ -101.092968556299638, 41.692742650175802 ], [ -101.092813345556607, 41.692790129164223 ], [ -101.092655972558603, 41.692833425424638 ], [ -101.092496637880288, 41.692872483774181 ], [ -101.092335544597617, 41.692907254431297 ], [ -101.092172898029077, 41.692937693079259 ], [ -101.092008905473833, 41.692963760922552 ], [ -101.091843775947368, 41.692985424736435 ], [ -101.091677719914998, 41.693002656909293 ], [ -101.091510949023501, 41.693015435477861 ], [ -101.091343675831197, 41.69302374415517 ], [ -101.09117611353696, 41.693027572351383 ] ] ] } } +] +} diff --git a/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.774.geojson b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.774.geojson new file mode 100644 index 00000000..b1286b1e --- /dev/null +++ b/docker/solaris/solaris/data/vectortile_test_nonfilled_expected/geoms_-101.127_41.774.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG:4326"}}, "features": []} \ No newline at end of file diff --git a/docker/solaris/solaris/data/w_multipolygon.csv b/docker/solaris/solaris/data/w_multipolygon.csv new file mode 100644 index 00000000..8e8f3e95 --- /dev/null +++ b/docker/solaris/solaris/data/w_multipolygon.csv @@ -0,0 +1,11 @@ +,access,addr_house,addr_hou_1,addr_inter,admin_leve,aerialway,aeroway,amenity,area,barrier,bicycle,boundary,brand,bridge,building,constructi,covered,culvert,cutting,denominati,disused,embankment,foot,generator_,harbour,highway,historic,horse,intermitte,junction,landuse,layer,leisure,lock,man_made,military,motorcar,name,natural,office,oneway,operator,osm_id,place,population,power,power_sour,public_tra,railway,ref,religion,route,service,shop,sport,surface,tags,toll,tourism,tower_type,tunnel,water,waterway,wetland,width,wood,z_order,tracktype,way_area,origarea,origlen,partialDec,truncated,geometry +660,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8086,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,137.5869833444336,0,1.0,0,"POLYGON ((742959.5157261142 3739469.858595584, 742964.2412947685 3739469.934550551, 742964.2835617226 3739467.18306186, 742968.7404872194 3739467.252177057, 742968.7827553968 3739464.50068832, 742970.1818431471 3739464.5252224, 742970.3714329029 3739451.621851874, 742963.6070103869 3739451.527265817, 742963.4608848467 3739461.268512606, 742959.6434285438 3739461.204586885, 742959.5157261142 3739469.858595584))" +661,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8229,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,232.75674536334253,0,1.0,0,"POLYGON ((743020.5285401859 3739472.034202539, 743027.7523358399 3739473.017358276, 743027.9560968133 3739471.568572332, 743030.4316793848 3739471.909114651, 743031.0890603127 3739467.208761358, 743031.6330556386 3739464.048288816, 743033.5432770674 3739464.385528093, 743035.4323498101 3739453.545475867, 743022.4840836683 3739451.306581199, 743020.9414738066 3739460.1909273, 743022.0743392245 3739460.841333462, 743020.5285401859 3739472.034202539))" +662,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,8228,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,115.83593626400437,0,1.0,0,"POLYGON ((743003.1399996518 3739467.617796136, 743015.3542409713 3739467.740245839, 743015.3977939934 3739462.391609387, 743017.5941185456 3739462.414260812, 743017.6373293742 3739457.442983567, 743011.2706792641 3739457.380694342, 743011.2414975561 3739460.709668207, 743005.2546582168 3739460.657057802, 743005.2795396517 3739457.860729276, 743003.231432164 3739457.841856197, 743003.1399996518 3739467.617796136))" +663,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,7812,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,1117.803722432596,0,1.0,0,"POLYGON ((742737.216587933 3739529.428720969, 742737.2726519796 3739527.953976202, 742741.2542792484 3739528.121883995, 742741.9995199341 3739510.127116858, 742744.0181279983 3739510.211778471, 742744.1330298374 3739507.517637256, 742742.058839861 3739507.431561362, 742742.5669390478 3739495.113465066, 742744.3449797556 3739495.180906138, 742745.0815230057 3739477.163720175, 742722.4685948562 3739476.244281095, 742720.3770908483 3739527.479686408, 742728.5166346187 3739527.808876015, 742728.4659341584 3739529.072875594, 742737.216587933 3739529.428720969))" +664,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120819,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,868.6465770175508,0,1.0,0,"POLYGON ((742779.0589662758 3739517.785098479, 742785.7264630584 3739518.043576586, 742785.7797116076 3739516.679750225, 742787.9002177101 3739516.767014552, 742788.018007018 3739513.595687717, 742789.4963797061 3739512.689894631, 742794.7850222825 3739512.85779129, 742794.7384953512 3739514.321680113, 742800.8142568704 3739514.520712196, 742800.8579616819 3739513.167741883, 742804.8404431387 3739513.302400339, 742806.719179487 3739511.962838294, 742809.5193322457 3739513.754459812, 742810.8216116317 3739515.39696687, 742810.8425385487 3739518.216655498, 742815.6076734723 3739518.193654652, 742817.1555002977 3739514.559269692, 742818.4727785089 3739513.427399684, 742820.7698578427 3739513.496967576, 742821.0155526869 3739505.30103076, 742818.8296341306 3739505.234292389, 742818.9082965751 3739502.872199133, 742817.049548522 3739503.79050431, 742814.6007570606 3739498.755805385, 742814.9873573856 3739494.492515358, 742817.0303021728 3739493.257025897, 742819.6744798073 3739492.436405651, 742805.5144255088 3739491.920608293, 742805.3067322085 3739497.531435517, 742789.7855063399 3739496.958818469, 742787.9062348148 3739496.134058065, 742787.21331656 3739494.95102441, 742788.1948983755 3739493.533130954, 742788.6153025262 3739492.311837214, 742788.6233625122 3739490.902464679, 742775.3039837473 3739490.86316813, 742776.4324709803 3739492.778722805, 742776.4256696098 3739495.231436581, 742779.1062013684 3739497.352975133, 742778.8313719494 3739499.41040163, 742777.2480186591 3739502.255856039, 742776.2833758653 3739503.008239368, 742773.9964049477 3739502.905642527, 742773.7390761343 3739508.282126087, 742779.373780194 3739510.878410715, 742779.8477320484 3739514.109192995, 742779.1370028395 3739515.811456004, 742779.0589662758 3739517.785098479))" +665,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,7813,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,446.8580438238858,0,0.4599659863913662,1,"POLYGON ((742693.3122179032 3739539, 742694.5911374168 3739534.692852943, 742701.8596583691 3739536.831200783, 742703.3902353786 3739531.686894269, 742687.859493556 3739527.118546354, 742687.4643547137 3739528.440377403, 742683.2411743457 3739527.200840465, 742681.172764875 3739530.167156974, 742679.1856038158 3739536.498545914, 742677.5853192207 3739536.002776217, 742676.6595880231 3739539, 742693.3122179032 3739539))" +666,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,120818,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,921.0009706238087,0,0.13212004440894673,1,"MULTIPOLYGON (((742820.9164519846 3739539, 742819.2061650158 3739537.419993884, 742819.3015388706 3739534.037214165, 742810.5031569091 3739533.735573504, 742810.4563450023 3739535.210554277, 742808.8447725036 3739535.158436355, 742808.7962669341 3739536.699968245, 742807.3359817594 3739538.716122539, 742806.6252731845 3739539, 742820.9164519846 3739539)), ((742784.7538719983 3739539, 742786.5276360018 3739535.73363368, 742785.0281770587 3739534.918539529, 742785.0537910719 3739532.455205162, 742775.2400383659 3739532.349736623, 742775.1763434813 3739538.130713525, 742775.8829740167 3739539, 742784.7538719983 3739539)))" +667,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,122421,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,628.0617074431335,0,0.6854550148892067,1,"POLYGON ((743051 3739240.429075356, 743019.7339072112 3739241.220183459, 743020.0951837152 3739255.058807612, 743051 3739254.279686034, 743051 3739240.429075356))" +668,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,122449,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,164.34510745720252,0,0.3268898818394317,1,"POLYGON ((743046.4084919207 3739315.608520228, 743051 3739315.560417519, 743051 3739304.00325697, 743046.2950427994 3739304.051510714, 743046.4084919207 3739315.608520228))" +669,,,,,,,,,,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,84910,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,67.0691122589702,0,1.0,0,"POLYGON ((741360.3983888365 3743875.358136298, 741367.4885894872 3743871.975018986, 741362.9199707231 3743862.502813682, 741357.5458472944 3743865.074794827, 741360.0725632336 3743870.299843532, 741360.3983888365 3743875.358136298))" diff --git a/docker/solaris/solaris/data/yolo_gdf_result.csv b/docker/solaris/solaris/data/yolo_gdf_result.csv new file mode 100644 index 00000000..f681bd0c --- /dev/null +++ b/docker/solaris/solaris/data/yolo_gdf_result.csv @@ -0,0 +1,44 @@ +,access,addr_house,addr_hou_1,addr_inter,admin_leve,aerialway,aeroway,amenity,area,barrier,bicycle,boundary,brand,bridge,building,constructi,covered,culvert,cutting,denominati,disused,embankment,foot,generator_,harbour,highway,historic,horse,intermitte,junction,landuse,layer,leisure,lock,man_made,military,motorcar,name,natural,office,oneway,operator,osm_id,place,population,power,power_sour,public_tra,railway,ref,religion,route,service,shop,sport,surface,tags,toll,tourism,tower_type,tunnel,water,waterway,wetland,width,wood,z_order,tracktype,way_area,origarea,origlen,partialDec,truncated,geometry,image_fname,intersection,minx,miny,maxx,maxy,xmid,ymid,w0,h0,x,y,w,h +4,,,,,,,,,608.3880075917921,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,102923,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,371.9610521071786,0,0.4089057201992269,1,"POLYGON ((0 2.845103836618364, 7.787239895900711 7.813573766499758, 6.348949391860515 21.16611589118838, 5.487595358863473 29.24418894201517, 19.3797596283257 37.85056554712355, 18.11841530236416 57.70217224024236, 0 54.13110767770559, 0 2.845103836618364))",sample_geotiff.tif,1.0,0.0,2.845103836618364,19.3797596283257,57.70217224024236,9.68987981416285,30.273638038430363,19.3797596283257,54.857068403624,0.010766533126847612,0.033637375598255956,0.021533066253695225,0.06095229822624889 +2,,,,,,,,,1175.2086036457465,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,135943,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,293.8021509114366,0,1.0,0,"POLYGON ((27.38481539185159 226.1645903000608, 34.46586190746166 226.480338553898, 34.72251786501147 221.0139123583212, 44.8147500208579 221.4782336438075, 44.45327683165669 229.4997339453548, 56.44128756551072 230.0510243279859, 54.99936619237997 261.5376432267949, 46.93407784774899 267.3053462980315, 25.54191842698492 266.3395395604894, 27.38481539185159 226.1645903000608))",sample_geotiff.tif,1.0,25.54191842698492,221.01391235832125,56.44128756551072,267.3053462980315,40.99160299624782,244.15962932817638,30.8993691385258,46.29143393971026,0.04554622555138647,0.2712884770313071,0.034332632376139774,0.05143492659967806 +39,,,,,,,,,214.14410906402435,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,134696,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,160.8812060779413,0,0.33276744108985457,1,"POLYGON ((60.03418597159907 884.8732050526887, 73.8337494416628 900, 51.51628375356086 900, 47.80893106292933 895.9360736850649, 60.03418597159907 884.8732050526887))",sample_geotiff.tif,1.0,47.80893106292933,884.8732050526887,73.8337494416628,900.0,60.82134025229607,892.4366025263444,26.02481837873347,15.126794947311282,0.06757926694699563,0.9915962250292715,0.028916464865259412,0.01680754994145698 +0,,,,,,,,,1003.6164099476883,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,102932,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,250.90410248692208,0,1.0,0,"POLYGON ((65.83512698789127 443.3458814825863, 86.05315328529105 444.1183159342036, 84.12356285331771 493.6842159954831, 63.90548484679312 492.9117766721174, 65.83512698789127 443.3458814825863))",sample_geotiff.tif,0.9999999999999999,63.905484846793115,443.34588148258626,86.05315328529105,493.6842159954831,74.97931906604208,468.5150487390347,22.14766843849793,50.338334512896836,0.08331035451782454,0.5205722763767052,0.024608520487219922,0.05593148279210759 +3,,,,,,,,,832.0140045611614,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,135941,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,208.00350114029035,0,1.0,0,"POLYGON ((87.2731370574329 72.93001714255661, 106.985800748691 84.21334314905107, 97.70029512513429 100.087722604163, 91.15104462415911 98.73803176078945, 53.36824434134178 78.81699287053198, 59.59959887806326 67.12329848110676, 79.59072164865211 77.64520633965731, 87.2731370574329 72.93001714255661))",sample_geotiff.tif,0.9999999999999999,53.36824434134178,67.12329848110676,106.98580074869096,100.08772260416299,80.17702254501637,83.60551054263487,53.617556407349184,32.96442412305623,0.08908558060557374,0.09289501171403874,0.059575062674832424,0.036627137914506926 +42,,,,,,,,,1055.98129961667,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,102939,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,263.9953249041675,0,1.0,0,"POLYGON ((87.33356586564332 502.7506626434624, 90.79576571006328 511.5002211164683, 93.23485574219376 550.6385944513604, 70.7970443549566 553.24962748494, 70.8928169994615 544.9904495924711, 70.136441974435 526.1424378501251, 69.54070870671421 501.6971649955958, 87.33356586564332 502.7506626434624))",sample_geotiff.tif,1.0,69.54070870671421,501.6971649955958,93.23485574219376,553.24962748494,81.38778222445399,527.4733962402679,23.694147035479546,51.55246248934418,0.0904308691382822,0.5860815513780755,0.02632683003942172,0.05728051387704909 +41,,,,,,,,,965.97420241684,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,102938,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,241.49355060421,0,1.0,0,"POLYGON ((89.06576380180195 561.6383464429528, 91.58933665603399 579.8661990063265, 94.85523002082482 592.7489638356492, 93.41836131247692 606.9227179316804, 85.23654213640839 610.96199104283, 73.38412117748521 610.6515495320782, 71.78515014378354 605.9850031817332, 72.83866719854996 588.2692635813728, 72.19266184815206 561.761005807668, 89.06576380180195 561.6383464429528))",sample_geotiff.tif,1.0,71.78515014378354,561.6383464429528,94.85523002082482,610.96199104283,83.32019008230418,586.3001687428914,23.07007987704128,49.3236445998773,0.09257798898033798,0.651444631936546,0.02563342208560142,0.05480404955541922 +40,,,,,,,,,226.76552441704672,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,117300,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,56.69138110426168,0,1.0,0,"POLYGON ((73.42513769492507 652.7116207033396, 73.87162248673849 640.5596289457753, 73.96795534505509 634.6088027460501, 89.67092586541548 635.2249299148098, 95.65740334033035 635.5673428568989, 95.31320596928708 642.0125183537602, 90.37084288243204 643.375933191739, 86.37565372907557 643.2291440004483, 83.40025364165194 643.7899634344503, 78.97129776002839 645.6513671381399, 77.39582740026526 652.6148558100685, 73.42513769492507 652.7116207033396))",sample_geotiff.tif,0.9999999999999999,73.42513769492507,634.6088027460501,95.65740334033035,652.7116207033396,84.54127051762771,643.6602117246948,22.232265645405278,18.102817957289517,0.09393474501958635,0.7151780130274387,0.02470251738378364,0.02011424217476613 +1,,,,,,,,,991.4279262138889,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,102940,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,247.85698155347222,0,1.0,0,"POLYGON ((104.2653856002726 379.3509592106566, 95.71053624199703 432.8294323347509, 83.6166091109626 427.175699017942, 71.57171053020284 424.2952412031591, 74.16042645578273 415.4869996681809, 82.98187345149927 415.6049499800429, 84.05773156136274 402.6163706937805, 81.36960026691668 392.8713309513405, 80.55370765388943 388.3410719437525, 84.53470388124697 378.7664343407378, 104.2653856002726 379.3509592106566))",sample_geotiff.tif,1.0000000000000002,71.57171053020284,378.76643434073776,104.26538560027257,432.8294323347509,87.9185480652377,405.7979333377443,32.69367507006973,54.06299799401313,0.09768727562804189,0.4508865925974937,0.03632630563341081,0.0600699977711257 +19,,,,,,,,,946.9628953122234,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,135783,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,236.74072382805585,0,1.0,0,"POLYGON ((105.8798294174485 313.8117348160595, 127.4949561362155 320.8758743349463, 111.2116484642029 370.3255268894136, 89.93423808692023 363.4085054341704, 96.1564225088805 344.4791927123442, 106.8362458597403 336.2949902042747, 112.8828636032995 326.159495391883, 113.5550685850903 319.4621683135629, 105.8798294174485 313.8117348160595))",sample_geotiff.tif,0.9999999999999999,89.93423808692023,313.81173481605947,127.49495613621548,370.3255268894136,108.71459711156785,342.06863085273653,37.56071804929525,56.513792073354125,0.12079399679063095,0.3800762565030406,0.041734131165883606,0.06279310230372681 +22,,,,,,,,,606.3783174309181,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,102924,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,151.59457935772951,0,1.0,0,"POLYGON ((129.5470552511979 257.3013992775232, 162.3333707605489 274.0591311287135, 154.6747992991004 288.8950616754591, 124.7242233906873 273.46653464064, 123.7676291908138 268.4957895213738, 129.5470552511979 257.3013992775232))",sample_geotiff.tif,1.0,123.76762919081375,257.30139927752316,162.33337076054886,288.89506167545915,143.0504999756813,273.09823047649115,38.56574156973511,31.593662397935987,0.15894499997297923,0.3034424783072124,0.042850823966372346,0.03510406933103998 +32,,,,,,,,,905.6790360714577,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,102920,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,226.41975901786444,0,1.0,0,"POLYGON ((133.9547895293217 97.67253606952727, 150.3983707176521 108.1254763472825, 153.3553979953285 113.6689413841814, 158.3576720615383 117.0539464410394, 162.1479135560803 127.8375121206045, 171.0701830158941 132.8582331957296, 166.4350645239465 144.5352517813444, 156.1280914607923 140.5914861587808, 151.7609463008121 135.0823942553252, 140.6975575806573 129.0484553426504, 133.8304500407539 121.5138891981915, 124.8308775019832 113.32103253901, 133.9547895293217 97.67253606952727))",sample_geotiff.tif,1.0000000000000002,124.8308775019832,97.67253606952727,171.07018301589414,144.5352517813444,147.95053025893867,121.10389392543584,46.23930551391095,46.862715711817145,0.1643894780654874,0.13455988213937314,0.051377006126567724,0.052069684124241275 +6,,,,,,,,,392.57584507364606,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,134694,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,98.14396126841152,0,1.0,0,"POLYGON ((213.8475714200176 865.6317731337622, 231.5030133866239 882.3587670447305, 216.4294837256894 899.1955095911399, 210.5498044961132 889.5282784951851, 214.3082701908424 884.5313852354884, 213.079774370417 877.5474507408217, 203.4947992174421 874.2963477959856, 213.8475714200176 865.6317731337622))",sample_geotiff.tif,1.0,203.49479921744205,865.6317731337622,231.50301338662393,899.1955095911399,217.498906302033,882.413641362451,28.008214169181883,33.56373645737767,0.24166545144670332,0.9804596015138345,0.03112023796575765,0.037293040508197414 +18,,,,,,,,,942.4941668682975,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,102925,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,235.62354171707437,0,1.0,0,"POLYGON ((231.2124842642806 330.4887164579704, 248.1898053619079 356.7320148414001, 241.6744782465976 364.39305018913, 225.5397070955951 372.7103164698929, 220.2861374379136 362.8280702224001, 226.0681945239194 359.3577359961346, 222.1697105062194 354.0369926122949, 211.3999844284263 359.2936038319021, 203.3048722210806 347.0834750682116, 231.2124842642806 330.4887164579704))",sample_geotiff.tif,1.0,203.3048722210806,330.48871645797044,248.18980536190793,372.71031646989286,225.74733879149426,351.59951646393165,44.88493314082734,42.22160001192242,0.2508303764349936,0.3906661294043685,0.049872147934252604,0.04691288890213602 +31,,,,,,,,,71.7025184022421,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,117299,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,17.925629600560526,0,1.0,0,"POLYGON ((226.1574721736833 126.1882931953296, 227.6463378681801 117.9617301616818, 236.0935362251475 119.4870615685359, 234.5861197842751 127.7140777958557, 226.1574721736833 126.1882931953296))",sample_geotiff.tif,1.0,226.15747217368335,117.96173016168177,236.09353622514755,127.7140777958557,231.12550419941545,122.83790397876874,9.9360640514642,9.75234763417393,0.25680611577712825,0.1364865599764097,0.011040071168293556,0.01083594181574881 +27,,,,,,,,,1507.8715455558022,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,102919,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,376.96788638895055,0,1.0,0,"POLYGON ((237.7723192097619 153.3169838031754, 243.4796773325652 156.6847784183919, 245.5267063616775 153.8381946170703, 253.2892067562789 154.692142136395, 252.3684517969377 160.3301388956606, 273.4036889746785 171.0261664874852, 271.2190176327713 181.9332001134753, 268.2431232582312 190.0850444016978, 263.839259278262 197.5392413148656, 260.0654399082996 201.1381951766089, 252.258406270761 199.2199343442917, 229.1011127829552 187.5324132423848, 221.94777155458 183.4895993350074, 237.7723192097619 153.3169838031754))",sample_geotiff.tif,1.0,221.94777155457996,153.3169838031754,273.4036889746785,201.13819517660886,247.67573026462924,177.22758948989213,51.45591742009856,47.82121137343347,0.27519525584958804,0.19691954387765792,0.057173241577887286,0.05313467930381497 +11,,,,,,,,,341.9972755053627,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,93146,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,85.49931887634068,0,1.0,0,"POLYGON ((392.3872257217299 671.1492497138679, 417.3038088586181 671.2074872627854, 418.6281851814128 684.4039134653285, 393.0598451150581 685.027445490472, 392.3872257217299 671.1492497138679))",sample_geotiff.tif,1.0,392.3872257217299,671.1492497138679,418.62818518141285,685.027445490472,405.5077054515714,678.08834760217,26.240959459682927,13.878195776604116,0.45056411716841266,0.7534314973357444,0.029156621621869917,0.015420217529560128 +5,,,,,,,,,640.7200900905971,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,93019,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,223.11504089232326,0,0.7179257027317721,1,"POLYGON ((415.6500815402251 870.6108930064365, 423.3889202498831 878.856587799266, 425.6205423306674 893.4736175602302, 417.6680606456939 900, 385.6889950442128 900, 415.6500815402251 870.6108930064365))",sample_geotiff.tif,1.0,385.6889950442128,870.6108930064365,425.6205423306674,900.0,405.6547686874401,885.3054465032183,39.93154728645459,29.389106993563473,0.45072752076382233,0.9836727183369092,0.04436838587383843,0.032654563326181635 +20,,,,,,,,,1155.038723289968,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86007,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,288.759680822492,0,1.0,0,"POLYGON ((407.2164936910849 293.369396366179, 427.7775799655356 294.5104229282588, 424.9542086310685 345.4521803893149, 401.8091458447743 344.1744022862986, 404.039300782606 303.9233255367726, 406.6232181608211 304.0600705072284, 407.2164936910849 293.369396366179))",sample_geotiff.tif,1.0,401.8091458447743,293.369396366179,427.77757996553555,345.4521803893149,414.79336290515494,319.41078837774694,25.96843412076123,52.0827840231359,0.46088151433906105,0.35490087597527437,0.0288538156897347,0.057869760025706554 +23,,,,,,,,,926.2819276108769,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86008,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,231.57048190271922,0,1.0,0,"POLYGON ((432.1206162075978 225.9524791324511, 430.6076375872362 245.3663629340008, 426.6382694914937 244.7529080240056, 425.1836831646506 258.9493404906243, 429.1443975642323 259.2078733071685, 428.2809057792183 275.5650829803199, 414.204479301814 274.6432346571237, 414.4758333056234 263.6940607419237, 411.2174512466881 255.6942387139425, 405.9959480511025 248.6523243561387, 406.9461998385377 242.7028560712934, 410.4586205475498 239.0436596525833, 410.1156435646117 232.5930310254917, 406.6664985958487 231.2344213593751, 407.2141257151961 224.7620719382539, 432.1206162075978 225.9524791324511))",sample_geotiff.tif,0.9999999999999999,405.99594805110246,224.76207193825394,432.1206162075978,275.56508298031986,419.0582821293501,250.1635774592869,26.124668156495318,50.80301104206592,0.4656203134770557,0.2779595305103188,0.029027409062772576,0.05644779004673991 +26,,,,,,,,,1036.2973770508409,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86009,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,259.0743442627102,0,1.0,0,"POLYGON ((412.0414752406068 165.4036012943834, 432.5184705699794 166.1471375450492, 430.6693423117977 216.6878121970221, 410.1922935522161 215.944271040149, 412.0414752406068 165.4036012943834))",sample_geotiff.tif,1.0000000000000002,410.19229355221614,165.4036012943834,432.5184705699794,216.68781219702214,421.35538206109777,191.04570674570277,22.326177017763257,51.28421090263873,0.46817264673455306,0.2122730074952253,0.024806863353070287,0.05698245655848748 +30,,,,,,,,,674.2933167606384,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86013,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,168.5733291901596,0,1.0,0,"POLYGON ((436.6716912172269 114.9287771452218, 435.1781613542698 145.7952524824068, 428.3110607606359 155.7733131237328, 420.4289469881915 155.3440742325038, 418.7692884211428 153.5201010480523, 419.0462323431857 149.65126746241, 420.8805788680911 143.4138840809464, 419.8326982872095 136.2257883930579, 416.3499617381021 131.2056846693158, 416.6987981537823 115.0607889257371, 436.6716912172269 114.9287771452218))",sample_geotiff.tif,1.0,416.34996173810214,114.92877714522183,436.6716912172269,155.7733131237328,426.5108264776645,135.35104513447732,20.32172947912477,40.844535978510976,0.47390091830851616,0.15039005014941925,0.022579699421249745,0.045382817753901086 +35,,,,,,,,,1137.873532469484,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86012,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,284.468383117371,0,1.0,0,"POLYGON ((459.1644711194094 47.61499526724219, 455.6476237687748 70.11859888583422, 450.8766771061346 69.36933278851211, 446.6210311276373 96.59647608082741, 426.4387441631407 93.47081579640508, 434.2112922635861 43.74008092097938, 459.1644711194094 47.61499526724219))",sample_geotiff.tif,1.0,426.43874416314065,43.74008092097938,459.1644711194094,96.59647608082741,442.80160764127504,70.1682785009034,32.72572695626877,52.856395159848034,0.4920017862680834,0.07796475388989266,0.036361918840298636,0.058729327955386705 +37,,,,,,,,,730.4737448745893,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86014,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,269.2410676574849,0,0.6782711040611584,1,"POLYGON ((484.2024364131503 0, 479.7541464939713 9.601744243875146, 477.463500038255 8.547827863134444, 464.7478086431511 36.00354308541864, 446.4608184616081 27.61564973182976, 459.2522301229183 0, 484.2024364131503 0))",sample_geotiff.tif,1.0000000000000002,446.46081846160814,0.0,484.2024364131503,36.00354308541864,465.3316274373792,18.00177154270932,37.74161795154214,36.00354308541864,0.517035141597088,0.020001968380788136,0.041935131057269044,0.04000393676157627 +7,,,,,,,,,758.0660568429099,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,93018,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,189.51651421072748,0,1.0,0,"POLYGON ((446.3899087077007 842.2273999303579, 481.3434248256963 828.2793587576598, 488.8546863836236 846.9848243454471, 453.9191530286334 860.910242264159, 446.3899087077007 842.2273999303579))",sample_geotiff.tif,1.0,446.38990870770067,828.2793587576598,488.85468638362363,860.910242264159,467.62229754566215,844.5948005109094,42.46477767592296,32.63088350649923,0.5195803306062913,0.9384386672343438,0.047183086306581065,0.03625653722944359 +15,,,,,,,,,1201.892037899198,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86006,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,300.4730094747995,0,1.0,0,"POLYGON ((482.2356772432104 357.9274521702901, 495.8398820564616 360.0371551541612, 493.9617549048271 372.0909278737381, 527.8789904229343 377.3452287474647, 524.7309722411446 397.4648858904839, 492.0437040710822 392.4025333374739, 491.4814159837551 395.9897821983323, 476.6471859868616 393.6881181783974, 482.2356772432104 357.9274521702901))",sample_geotiff.tif,0.9999999999999998,476.6471859868616,357.92745217029005,527.8789904229343,397.46488589048386,502.26308820489794,377.69616903038695,51.23180443607271,39.5374337201938,0.5580700980054422,0.41966241003376326,0.0569242271511919,0.043930481911326445 +28,,,,,,,,,404.6692002570957,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,134680,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,101.16730006427393,0,1.0,0,"POLYGON ((536.0753469388001 150.1703661391512, 535.8976141829044 157.3439671061933, 537.3988791736774 162.5677628833801, 536.2554783222731 165.9250371623784, 539.3879669941962 178.6556324511766, 533.924685027916 182.2514687152579, 530.728569818195 184.4158511226997, 524.4869354791008 188.8297238145024, 524.3672584414016 192.2951983232051, 522.0256408345886 192.1969511229545, 522.3906255022157 182.0445369845256, 525.8205245367717 178.8090512482449, 530.0610870544333 171.5141406813636, 528.5654665878974 168.0436801863834, 524.6151287727989 165.1658039363101, 525.0040782545693 149.9077555695549, 536.0753469388001 150.1703661391512))",sample_geotiff.tif,1.0000000000000002,522.0256408345886,149.90775556955487,539.3879669941962,192.2951983232051,530.7068039143924,171.10147694638,17.36232615960762,42.38744275365025,0.5896742265715471,0.19011275216264442,0.01929147351067513,0.047097158615166945 +24,,,,,,,,,1021.3360981644006,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86011,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,255.33402454110015,0,1.0,0,"POLYGON ((523.4165055262856 198.2224921071902, 544.9352911333553 199.0514765409753, 542.4260684649926 249.9190295347944, 522.5057537995744 248.4073634939268, 520.1315930527635 239.3649762850255, 518.9120450657792 233.5128228161484, 526.6003856104799 229.0414650486782, 528.8170812996104 220.9746812311932, 522.6861530637834 212.4234666489065, 523.4165055262856 198.2224921071902))",sample_geotiff.tif,0.9999999999999998,518.9120450657792,198.2224921071902,544.9352911333553,249.9190295347944,531.9236680995673,224.0707608209923,26.02324606757611,51.6965374276042,0.5910262978884081,0.24896751202332476,0.028914717852862346,0.05744059714178244 +21,,,,,,,,,1238.4757250715768,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86010,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,309.6189312678942,0,1.0,0,"POLYGON ((526.6318989666179 261.5354417562485, 545.283115554601 262.0126638803631, 544.4942456730641 292.8397702910006, 552.0921549452469 293.031741942279, 551.5256321858615 314.7087277201936, 517.8084108987823 313.8443781072274, 518.1340601162519 301.317967700772, 525.6015503380913 301.4909356869757, 526.6318989666179 261.5354417562485))",sample_geotiff.tif,1.0,517.8084108987823,261.5354417562485,552.0921549452469,314.70872772019356,534.9502829220146,288.122084738221,34.28374404646456,53.17328596394509,0.5943892032466829,0.32013564970913444,0.03809304894051618,0.05908142884882788 +33,,,,,,,,,1005.3323860159348,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86015,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,251.3330965039837,0,1.0,0,"POLYGON ((567.6196071440354 79.48379767127335, 573.6413344931789 77.47242644708604, 573.9580853967927 84.36761443130672, 576.7988056694157 88.18258353415877, 583.9389728535898 90.16137413866818, 588.2799405395053 90.78792378865182, 553.5269071373623 129.635154761374, 549.2492460440844 126.2769603691995, 545.6672062769067 115.6659412970766, 547.9324978117365 101.9824124285951, 567.6196071440354 79.48379767127335))",sample_geotiff.tif,1.0000000000000002,545.6672062769067,77.47242644708604,588.2799405395053,129.635154761374,566.973573408206,103.55379060423002,42.61273426259868,52.16272831428796,0.6299706371202288,0.11505976733803334,0.047347482513998534,0.057958587015875515 +14,,,,,,,,,1054.6293162692457,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86005,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,263.6573290673114,0,1.0,0,"POLYGON ((545.0001820274629 404.1396864559501, 545.5456960839219 383.8837510570884, 561.4482773041818 384.3170414939523, 561.6076893680729 378.675402700901, 573.1816837256774 378.9923253301531, 573.0370411262847 384.4782330933958, 594.2335806312039 385.0264944685623, 593.6913648874033 405.4155300389975, 545.0001820274629 404.1396864559501))",sample_geotiff.tif,1.0,545.0001820274629,378.67540270090103,594.233580631204,405.41553003899753,569.6168813293334,392.0454663699493,49.23339860374108,26.7401273380965,0.6329076459214815,0.4356060737443881,0.05470377622637898,0.029711252597885 +16,,,,,,,,,113.72690001497435,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,134689,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,28.431725003743587,0,1.0,0,"POLYGON ((594.5955353775062 341.2696067793295, 592.6359737580642 346.1783115705475, 595.5670321371872 351.4116119751707, 594.498773291707 355.566107051447, 595.03770820098 359.3928433367983, 596.8756023284514 362.4332278929651, 590.6566405876074 362.4517829790711, 590.6322764432989 356.1265549827367, 588.5902138527017 356.1319851242006, 588.5435697012581 341.2840873301029, 594.5955353775062 341.2696067793295))",sample_geotiff.tif,1.0,588.5435697012581,341.2696067793295,596.8756023284514,362.45178297907114,592.7095860148547,351.8606948792003,8.332032627193257,21.18217619974166,0.6585662066831719,0.3909563276435559,0.00925781403021473,0.02353575133304629 +12,,,,,,,,,833.1100217257011,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,85995,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,208.27750543142528,0,1.0,0,"POLYGON ((642.5548559394665 605.4954561004415, 650.1750435382128 639.3135964740068, 630.8239523877855 643.6478302329779, 625.8465431011282 621.5068183001131, 613.9918430529069 624.1488145207986, 611.3485644147731 612.4494963856414, 642.5548559394665 605.4954561004415))",sample_geotiff.tif,0.9999999999999999,611.3485644147731,605.4954561004415,650.1750435382128,643.6478302329779,630.761803976493,624.5716431667097,38.82647912343964,38.15237413253635,0.7008464488627699,0.6939684924074552,0.04314053235937738,0.04239152681392928 +38,,,,,,,,,163.2888762358447,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,134690,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,75.23335761526344,0,0.5426079647770381,1,"POLYGON ((664.1072873058729 0, 665.963440204272 5.102927703410387, 663.4489023594651 4.764764592982829, 657.1336445596535 3.875669199042022, 655.4026131848805 1.409776619635522, 653.3671769341454 3.967581197619438, 653.9054305306636 8.526797778904438, 655.4832516363822 13.08284202683717, 651.7876941068098 16.08068347070366, 648.9221955472603 18.85850584879518, 649.2320157396607 14.05663546547294, 646.6069010677747 9.94786886498332, 646.0617618362885 4.345613894052804, 644.300418858882 0.6374908359721303, 644.1158141889609 0, 664.1072873058729 0))",sample_geotiff.tif,1.0000000000000002,644.1158141889609,0.0,665.963440204272,18.858505848795176,655.0396271966165,9.429252924397588,21.847626015311107,18.858505848795176,0.7278218079962405,0.010476947693775097,0.02427514001701234,0.020953895387550193 +13,,,,,,,,,591.1230200188784,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,85996,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,147.7807550047196,0,1.0,0,"POLYGON ((719.5465582867619 598.6448629098013, 720.3695895529818 606.5043138191104, 724.4469213041011 610.7773993844166, 725.2147377748042 616.3742183251306, 723.0049092427362 620.1570535134524, 718.3882041969337 619.6482330150902, 713.9281129532028 616.4276928380132, 703.0521553403232 615.0506034400314, 697.5230599956121 619.7578771309927, 691.4791235984303 620.1051252679899, 691.3655090769753 598.7110763099045, 719.5465582867619 598.6448629098013))",sample_geotiff.tif,1.0000000000000002,691.3655090769753,598.6448629098013,725.2147377748042,620.1570535134524,708.2901234258898,609.4009582116269,33.84922869782895,21.512190603651106,0.7869890260287664,0.6771121757906965,0.03761025410869883,0.023902434004056786 +25,,,,,,,,,436.3985394080503,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86607,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,109.09963485201257,0,1.0,0,"POLYGON ((766.4948697979562 219.6585751045495, 766.9394415735733 203.6444895807654, 774.5338656448293 199.1309275366366, 779.6678650518879 196.4974802285433, 780.0790439015254 206.4977897126228, 792.9675920906011 206.6715065222234, 792.7963749796618 220.1929824259132, 766.4948697979562 219.6585751045495))",sample_geotiff.tif,1.0,766.4948697979562,196.49748022854328,792.9675920906011,220.19298242591321,779.7312309442786,208.34523132722825,26.472722292644903,23.695502197369933,0.8663680343825318,0.23149470147469806,0.02941413588071656,0.02632833577485548 +9,,,,,,,,,1091.069005525542,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,92642,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,272.7672513813855,0,1.0,0,"POLYGON ((794.3443439039402 800.0892576370388, 808.1741715462413 800.0180635405704, 811.0787292337045 810.2460591299459, 808.0733455095906 813.3824441283941, 807.4013454001397 820.0798055976629, 768.3534275970887 822.9638589080423, 759.5865474676248 819.0049897767603, 760.6663332274184 806.1938135968521, 774.9641209535766 796.3894291333854, 783.0687794568948 796.0362568320706, 791.1689595563803 796.2602832280099, 794.3443439039402 800.0892576370388))",sample_geotiff.tif,1.0,759.5865474676248,796.0362568320706,811.0787292337045,822.9638589080423,785.3326383506646,809.5000578700565,51.49218176607974,26.927602075971663,0.8725918203896273,0.8994445087445072,0.05721353529564415,0.02991955786219074 +29,,,,,,,,,1195.1484685924327,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86606,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,298.78711714810817,0,1.0,0,"POLYGON ((818.6449922658503 145.8938600234687, 802.3041800016072 146.5147014781833, 802.9269792409614 162.9022628497332, 777.9049582753796 163.8237699633464, 776.5785818777513 128.4980932334438, 817.9413526556455 126.9557435438037, 818.6449922658503 145.8938600234687))",sample_geotiff.tif,1.0,776.5785818777513,126.95574354380369,818.6449922658503,163.82376996334642,797.6117870718008,145.38975675357506,42.06641038809903,36.86802641954273,0.8862353189686676,0.16154417417063896,0.0467404559867767,0.040964473799491925 +36,,,,,,,,,972.2084233562225,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86604,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,243.05210583905563,0,1.0,0,"POLYGON ((794.6951001100242 2.116394373588264, 816.6974766298663 1.468324705958366, 818.0185485305265 47.98086807690561, 804.6792487849016 48.35088091529906, 795.6887790956534 36.73992804996669, 794.6951001100242 2.116394373588264))",sample_geotiff.tif,1.0,794.6951001100242,1.4683247059583664,818.0185485305265,48.35088091529906,806.3568243202753,24.909602810628712,23.323448420502245,46.88255620934069,0.8959520270225282,0.027677336456254125,0.02591494268944694,0.052091729121489655 +34,,,,,,,,,955.3143319089635,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,86605,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,238.82858297724087,0,1.0,0,"POLYGON ((816.4727658838965 58.42847666423768, 817.5045415207278 105.2588225658983, 796.3883123914711 105.7076860079542, 795.4426827779971 62.4043871788308, 803.5142542636022 62.22955618426204, 806.4344622618519 58.65132196247578, 816.4727658838965 58.42847666423768))",sample_geotiff.tif,1.0,795.4426827779971,58.42847666423768,817.5045415207278,105.70768600795418,806.4736121493625,82.06808133609593,22.061858742730692,47.2792093437165,0.8960817912770694,0.09118675704010659,0.02451317638081188,0.05253245482635167 +10,,,,,,,,,511.57672611563873,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,92641,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,127.89418152890968,0,1.0,0,"POLYGON ((820.6129307732917 800.003008636646, 838.7254208496306 798.1625695805997, 841.7378241324332 806.7232382101938, 859.6126089137979 806.553273351863, 864.2543455683626 813.4094758052379, 835.6097001582384 818.8363996865228, 830.4454396180809 813.3912856318057, 821.7668025027961 813.04821888078, 820.6129307732917 800.003008636646))",sample_geotiff.tif,0.9999999999999999,820.6129307732917,798.1625695805997,864.2543455683626,818.8363996865228,842.4336381708272,808.4994846335612,43.64141479507089,20.673830105923116,0.9360373757453635,0.898332760703957,0.0484904608834121,0.022970922339914573 +17,,,,,,,,,983.4834268683098,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,,,,,,86004,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,245.87085671707746,0,1.0,0,"POLYGON ((877.9025507906917 363.2765975808725, 838.2836305517703 366.3079837486148, 837.0310469446704 349.9801886640489, 850.4099756779615 348.9433259088546, 850.0006144659128 343.5819130791351, 854.7069255011156 343.2006739545614, 854.3236986373086 338.1493656272069, 865.5350079541095 337.29858210776, 865.9971866649576 343.3023864878342, 884.2128435778432 341.9032686809078, 885.3848932385445 357.2120188577101, 877.9025507906917 363.2765975808725))",sample_geotiff.tif,1.0,837.0310469446704,337.29858210776,885.3848932385445,366.3079837486148,861.2079700916074,351.8032829281874,48.353846293874085,29.009401640854776,0.9568977445462304,0.3908925365868749,0.053726495882082316,0.03223266848983864 +8,,,,,,,,,216.4340088998764,,,,,,yes,,,,,,,,,,,,,,,,,,,,,,,Occlusion,,,,,92640,,,,,,,,,,,,,,"""security:classification""=>""UNCLASSIFIED"",""source""=>""Unknown""",,,,,,,,,,-999999,,-999999.0,140.04971670918377,0,0.38635210049961444,1,"POLYGON ((886.5125979208387 820.9232407584786, 886.2984008654021 802.261728647165, 900 802.1414167098701, 900 818.7495461180806, 894.7975760672707 816.548164521344, 888.9884018914308 818.9095647959039, 886.5125979208387 820.9232407584786))",sample_geotiff.tif,1.0,886.2984008654021,802.1414167098701,900.0,820.9232407584786,893.149200432701,811.5323287341744,13.701599134597927,18.78182404860854,0.9923880004807789,0.9017025874824159,0.015223999038442142,0.020868693387342825 diff --git a/docker/solaris/solaris/data/zoo/model_reference.yml b/docker/solaris/solaris/data/zoo/model_reference.yml new file mode 100644 index 00000000..9fc97cf2 --- /dev/null +++ b/docker/solaris/solaris/data/zoo/model_reference.yml @@ -0,0 +1,10 @@ +# AN ITEM MUST BE ADDED TO BOTH model_paths AND model_urls FOR EACH MODEL +# THAT SOLARIS CAN USE BY DEFAULT + +model_paths: + placeholder1: path1 + placeholder2: path2 + +model_urls: + placeholder1: https://path/to/model1.pt + placeholder2: https://path/to/model2.h5 diff --git a/docker/solaris/solaris/eval/__init__.py b/docker/solaris/solaris/eval/__init__.py new file mode 100644 index 00000000..38d40f17 --- /dev/null +++ b/docker/solaris/solaris/eval/__init__.py @@ -0,0 +1 @@ +from . import base, iou, scot, challenges, pixel, vector diff --git a/docker/solaris/solaris/eval/base.py b/docker/solaris/solaris/eval/base.py new file mode 100644 index 00000000..35f05ff3 --- /dev/null +++ b/docker/solaris/solaris/eval/base.py @@ -0,0 +1,591 @@ +import shapely.wkt +import geopandas as gpd +import pandas as pd +from tqdm.auto import tqdm +import os +from . import iou +from fiona.errors import DriverError +from fiona._err import CPLE_OpenFailedError + + +class Evaluator(): + """Object to test IoU for predictions and ground truth polygons. + + Attributes + ---------- + ground_truth_fname : str + The filename for the ground truth CSV or JSON. + ground_truth_GDF : :class:`geopandas.GeoDataFrame` + A :class:`geopandas.GeoDataFrame` containing the ground truth vector + labels. + ground_truth_GDF_Edit : :class:`geopandas.GeoDataFrame` + A copy of ``ground_truth_GDF`` which will be manipulated during + processing. + proposal_GDF : :class:`geopandas.GeoDataFrame` + The proposal :class:`geopandas.GeoDataFrame`, added using + ``load_proposal()``. + + Arguments + --------- + ground_truth_vector_file : str + Path to .geojson file for ground truth. + + """ + + def __init__(self, ground_truth_vector_file): + # Load Ground Truth : Ground Truth should be in geojson or shape file + try: + if ground_truth_vector_file.lower().endswith('json'): + self.load_truth(ground_truth_vector_file) + elif ground_truth_vector_file.lower().endswith('csv'): + self.load_truth(ground_truth_vector_file, truthCSV=True) + self.ground_truth_fname = ground_truth_vector_file + except AttributeError: # handles passing gdf instead of path to file + self.ground_truth_GDF = ground_truth_vector_file + self.ground_truth_fname = 'GeoDataFrame variable' + self.ground_truth_sindex = self.ground_truth_GDF.sindex # get sindex + # create deep copy of ground truth file for calculations + self.ground_truth_GDF_Edit = self.ground_truth_GDF.copy(deep=True) + self.proposal_GDF = gpd.GeoDataFrame([]) # initialize proposal GDF + + def __repr__(self): + return 'Evaluator {}'.format(os.path.split( + self.ground_truth_fname)[-1]) + + def get_iou_by_building(self): + """Returns a copy of the ground truth table, which includes a + per-building IoU score column after eval_iou_spacenet_csv() has run. + """ + + output_ground_truth_GDF = self.ground_truth_GDF.copy(deep=True) + return output_ground_truth_GDF + + def eval_iou_spacenet_csv(self, miniou=0.5, iou_field_prefix="iou_score", + imageIDField="ImageId", debug=False, min_area=0): + """Evaluate IoU between the ground truth and proposals in CSVs. + + Arguments + --------- + miniou : float , optional + Minimum intersection over union score to qualify as a successful + object detection event. Defaults to ``0.5``. + iou_field_prefix : str , optional + The name of the IoU score column in ``self.proposal_GDF``. Defaults + to ``"iou_score"`` . + imageIDField : str , optional + The name of the column corresponding to the image IDs in the + ground truth data. Defaults to ``"ImageId"``. + debug : bool , optional + Argument for verbose execution during debugging. Defaults to + ``False`` (silent execution). + min_area : float or int , optional + Minimum area of a ground truth polygon to be considered during + evaluation. Often set to ``20`` in SpaceNet competitions. Defaults + to ``0`` (consider all ground truth polygons). + + Returns + ------- + scoring_dict_list : list + list of score output dicts for each image in the ground + truth and evaluated image datasets. The dicts contain + the following keys: :: + + ('imageID', 'iou_field', 'TruePos', 'FalsePos', 'FalseNeg', + 'Precision', 'Recall', 'F1Score') + + """ + # Get List of all ImageID in both ground truth and proposals + imageIDList = [] + imageIDList.extend(list(self.ground_truth_GDF[imageIDField].unique())) + if not self.proposal_GDF.empty: + imageIDList.extend(list(self.proposal_GDF[imageIDField].unique())) + imageIDList = list(set(imageIDList)) + iou_field = iou_field_prefix + scoring_dict_list = [] + self.ground_truth_GDF[iou_field] = 0. + iou_index = self.ground_truth_GDF.columns.get_loc(iou_field) + id_cols = 2 + ground_truth_ids = self.ground_truth_GDF.iloc[:, :id_cols] + + for imageID in tqdm(imageIDList): + self.ground_truth_GDF_Edit = self.ground_truth_GDF[ + self.ground_truth_GDF[imageIDField] == imageID + ].copy(deep=True) + self.ground_truth_GDF_Edit = self.ground_truth_GDF_Edit[ + self.ground_truth_GDF_Edit.area >= min_area + ] + proposal_GDF_copy = self.proposal_GDF[self.proposal_GDF[ + imageIDField] == imageID].copy(deep=True) + proposal_GDF_copy = proposal_GDF_copy[proposal_GDF_copy.area + > min_area] + if debug: + print(iou_field) + for _, pred_row in proposal_GDF_copy.iterrows(): + if debug: + print(pred_row.name) + if pred_row.geometry.area > 0: + pred_poly = pred_row.geometry + iou_GDF = iou.calculate_iou(pred_poly, + self.ground_truth_GDF_Edit) + # Get max iou + if not iou_GDF.empty: + max_index = iou_GDF['iou_score'].idxmax(axis=0, + skipna=True) + max_iou_row = iou_GDF.loc[max_index] + # Update entry in full ground truth table + previous_iou = self.ground_truth_GDF.iloc[ + max_index, iou_index] + new_iou = max_iou_row[iou_field] + if new_iou > previous_iou: + self.ground_truth_GDF.iloc[max_index, iou_index] \ + = new_iou + if max_iou_row['iou_score'] > miniou: + self.proposal_GDF.loc[pred_row.name, iou_field] \ + = max_iou_row['iou_score'] + self.ground_truth_GDF_Edit \ + = self.ground_truth_GDF_Edit.drop( + max_iou_row.name, axis=0) + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + if debug: + print(self.proposal_GDF.loc[pred_row.name]) + + if self.proposal_GDF.empty: + TruePos = 0 + FalsePos = 0 + else: + proposal_GDF_copy = self.proposal_GDF[ + self.proposal_GDF[imageIDField] == imageID].copy(deep=True) + proposal_GDF_copy = proposal_GDF_copy[ + proposal_GDF_copy.area > min_area] + if not proposal_GDF_copy.empty: + if iou_field in proposal_GDF_copy.columns: + TruePos = proposal_GDF_copy[ + proposal_GDF_copy[iou_field] >= miniou].shape[0] + FalsePos = proposal_GDF_copy[ + proposal_GDF_copy[iou_field] < miniou].shape[0] + else: + print("iou field {} missing".format(iou_field)) + TruePos = 0 + FalsePos = 0 + else: + print("Empty Proposal Id") + TruePos = 0 + FalsePos = 0 + + # false negatives is the number of objects remaining in ground + # truth after pulling out matched objects + FalseNeg = self.ground_truth_GDF_Edit[ + self.ground_truth_GDF_Edit.area > 0].shape[0] + if float(TruePos+FalsePos) > 0: + Precision = TruePos / float(TruePos + FalsePos) + else: + Precision = 0 + if float(TruePos + FalseNeg) > 0: + Recall = TruePos / float(TruePos + FalseNeg) + else: + Recall = 0 + if Recall * Precision > 0: + F1Score = 2*Precision*Recall/(Precision+Recall) + else: + F1Score = 0 + + score_calc = {'imageID': imageID, + 'iou_field': iou_field, + 'TruePos': TruePos, + 'FalsePos': FalsePos, + 'FalseNeg': FalseNeg, + 'Precision': Precision, + 'Recall': Recall, + 'F1Score': F1Score + } + scoring_dict_list.append(score_calc) + + return scoring_dict_list + + def eval_iou(self, miniou=0.5, iou_field_prefix='iou_score', + ground_truth_class_field='', calculate_class_scores=True, + class_list=['all']): + """Evaluate IoU between the ground truth and proposals. + + Arguments + --------- + miniou : float, optional + Minimum intersection over union score to qualify as a successful + object detection event. Defaults to ``0.5``. + iou_field_prefix : str, optional + The name of the IoU score column in ``self.proposal_GDF``. Defaults + to ``"iou_score"``. + ground_truth_class_field : str, optional + The column in ``self.ground_truth_GDF`` that indicates the class of + each polygon. Required if using ``calculate_class_scores``. + calculate_class_scores : bool, optional + Should class-by-class scores be calculated? Defaults to ``True``. + class_list : list, optional + List of classes to be scored. Defaults to ``['all']`` (score all + classes). + + Returns + ------- + scoring_dict_list : list + list of score output dicts for each image in the ground + truth and evaluated image datasets. The dicts contain + the following keys: :: + + ('class_id', 'iou_field', 'TruePos', 'FalsePos', 'FalseNeg', + 'Precision', 'Recall', 'F1Score') + + """ + + scoring_dict_list = [] + + if calculate_class_scores: + if not ground_truth_class_field: + raise ValueError('Must provide ground_truth_class_field ' + 'if using calculate_class_scores.') + if class_list == ['all']: + class_list = list( + self.ground_truth_GDF[ground_truth_class_field].unique()) + if not self.proposal_GDF.empty: + class_list.extend( + list(self.proposal_GDF['__max_conf_class'].unique())) + class_list = list(set(class_list)) + + for class_id in class_list: + iou_field = "{}_{}".format(iou_field_prefix, class_id) + if class_id is not 'all': # this is probably unnecessary now + self.ground_truth_GDF_Edit = self.ground_truth_GDF[ + self.ground_truth_GDF[ + ground_truth_class_field] == class_id].copy(deep=True) + else: + self.ground_truth_GDF_Edit = self.ground_truth_GDF.copy( + deep=True) + + for _, pred_row in tqdm(self.proposal_GDF.iterrows()): + if pred_row['__max_conf_class'] == class_id \ + or class_id == 'all': + pred_poly = pred_row.geometry + iou_GDF = iou.calculate_iou(pred_poly, + self.ground_truth_GDF_Edit) + # Get max iou + if not iou_GDF.empty: + max_iou_row = iou_GDF.loc[iou_GDF['iou_score'].idxmax( + axis=0, skipna=True)] + if max_iou_row['iou_score'] > miniou: + self.proposal_GDF.loc[pred_row.name, iou_field] \ + = max_iou_row['iou_score'] + self.ground_truth_GDF_Edit \ + = self.ground_truth_GDF_Edit.drop( + max_iou_row.name, axis=0) + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + + if self.proposal_GDF.empty: + TruePos = 0 + FalsePos = 0 + else: + try: + TruePos = self.proposal_GDF[ + self.proposal_GDF[iou_field] >= miniou].shape[0] + FalsePos = self.proposal_GDF[ + self.proposal_GDF[iou_field] < miniou].shape[0] + except KeyError: # handle missing iou_field + print("iou field {} missing") + TruePos = 0 + FalsePos = 0 + + # number of remaining rows in ground_truth_gdf_edit after removing + # matches is number of false negatives + FalseNeg = self.ground_truth_GDF_Edit.shape[0] + if float(TruePos+FalsePos) > 0: + Precision = TruePos / float(TruePos + FalsePos) + else: + Precision = 0 + if float(TruePos + FalseNeg) > 0: + Recall = TruePos / float(TruePos + FalseNeg) + else: + Recall = 0 + if Recall*Precision > 0: + F1Score = 2*Precision*Recall/(Precision+Recall) + else: + F1Score = 0 + + score_calc = {'class_id': class_id, + 'iou_field': iou_field, + 'TruePos': TruePos, + 'FalsePos': FalsePos, + 'FalseNeg': FalseNeg, + 'Precision': Precision, + 'Recall': Recall, + 'F1Score': F1Score + } + scoring_dict_list.append(score_calc) + + return scoring_dict_list + + def eval_iou_return_GDFs(self, miniou=0.5, iou_field_prefix='iou_score', + ground_truth_class_field='', calculate_class_scores=True, + class_list=['all']): + """Evaluate IoU between the ground truth and proposals. + Arguments + --------- + miniou : float, optional + Minimum intersection over union score to qualify as a successful + object detection event. Defaults to ``0.5``. + iou_field_prefix : str, optional + The name of the IoU score column in ``self.proposal_GDF``. Defaults + to ``"iou_score"``. + ground_truth_class_field : str, optional + The column in ``self.ground_truth_GDF`` that indicates the class of + each polygon. Required if using ``calculate_class_scores``. + calculate_class_scores : bool, optional + Should class-by-class scores be calculated? Defaults to ``True``. + class_list : list, optional + List of classes to be scored. Defaults to ``['all']`` (score all + classes). + Returns + ------- + scoring_dict_list : list + list of score output dicts for each image in the ground + truth and evaluated image datasets. The dicts contain + the following keys: :: + ('class_id', 'iou_field', 'TruePos', 'FalsePos', 'FalseNeg', + 'Precision', 'Recall', 'F1Score') + True_Pos_gdf : gdf + A geodataframe containing only true positive predictions + False_Neg_gdf : gdf + A geodataframe containing only false negative predictions + False_Pos_gdf : gdf + A geodataframe containing only false positive predictions + """ + + scoring_dict_list = [] + + if calculate_class_scores: + if not ground_truth_class_field: + raise ValueError('Must provide ground_truth_class_field if using calculate_class_scores.') + if class_list == ['all']: + class_list = list( + self.ground_truth_GDF[ground_truth_class_field].unique()) + if not self.proposal_GDF.empty: + class_list.extend( + list(self.proposal_GDF['__max_conf_class'].unique())) + class_list = list(set(class_list)) + + for class_id in class_list: + iou_field = "{}_{}".format(iou_field_prefix, class_id) + if class_id is not 'all': # this is probably unnecessary now + self.ground_truth_GDF_Edit = self.ground_truth_GDF[ + self.ground_truth_GDF[ + ground_truth_class_field] == class_id].copy(deep=True) + else: + self.ground_truth_GDF_Edit = self.ground_truth_GDF.copy( + deep=True) + + for _, pred_row in tqdm(self.proposal_GDF.iterrows()): + if pred_row['__max_conf_class'] == class_id or class_id == 'all': + pred_poly = pred_row.geometry + iou_GDF = iou.calculate_iou(pred_poly, + self.ground_truth_GDF_Edit) + # Get max iou + if not iou_GDF.empty: + max_iou_row = iou_GDF.loc[iou_GDF['iou_score'].idxmax( + axis=0, skipna=True)] + if max_iou_row['iou_score'] > miniou: + self.proposal_GDF.loc[pred_row.name, iou_field] = max_iou_row['iou_score'] + self.ground_truth_GDF_Edit = self.ground_truth_GDF_Edit.drop(max_iou_row.name, axis=0) + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + else: + self.proposal_GDF.loc[pred_row.name, iou_field] = 0 + + if self.proposal_GDF.empty: + TruePos = 0 + FalsePos = 0 + else: + try: + True_Pos_gdf = self.proposal_GDF[ + self.proposal_GDF[iou_field] >= miniou] + TruePos = True_Pos_gdf.shape[0] + if TruePos == 0: + True_Pos_gdf = None + False_Pos_gdf = self.proposal_GDF[ + self.proposal_GDF[iou_field] < miniou] + FalsePos = False_Pos_gdf.shape[0] + if FalsePos == 0: + False_Pos_gdf = None + except KeyError: # handle missing iou_field + print("iou field {} missing") + TruePos = 0 + FalsePos = 0 + False_Pos_gdf = None + True_Pos_gdf = None + + # number of remaining rows in ground_truth_gdf_edit after removing + # matches is number of false negatives + False_Neg_gdf = self.ground_truth_GDF_Edit + FalseNeg = False_Neg_gdf.shape[0] + if FalseNeg == 0: + False_Neg_gdf = None + if float(TruePos + FalsePos) > 0: + Precision = TruePos / float(TruePos + FalsePos) + else: + Precision = 0 + if float(TruePos + FalseNeg) > 0: + Recall = TruePos / float(TruePos + FalseNeg) + else: + Recall = 0 + if Recall * Precision > 0: + F1Score = 2 * Precision * Recall / (Precision + Recall) + else: + F1Score = 0 + + score_calc = {'class_id': class_id, + 'iou_field': iou_field, + 'TruePos': TruePos, + 'FalsePos': FalsePos, + 'FalseNeg': FalseNeg, + 'Precision': Precision, + 'Recall': Recall, + 'F1Score': F1Score + } + scoring_dict_list.append(score_calc) + + return scoring_dict_list, True_Pos_gdf, False_Neg_gdf, False_Pos_gdf + + def load_proposal(self, proposal_vector_file, conf_field_list=['conf'], + proposalCSV=False, pred_row_geo_value='PolygonWKT_Pix', + conf_field_mapping=None): + """Load in a proposal geojson or CSV. + + Arguments + --------- + proposal_vector_file : str + Path to the file containing proposal vector objects. This can be + a .geojson or a .csv. + conf_field_list : list, optional + List of columns corresponding to confidence value(s) in the + proposal vector file. Defaults to ``['conf']``. + proposalCSV : bool, optional + Is the proposal file a CSV? Defaults to no (``False``), in which + case it's assumed to be a .geojson. + pred_row_geo_value : str, optional + The name of the geometry-containing column in the proposal vector + file. Defaults to ``'PolygonWKT_Pix'``. Note: this method assumes + the geometry is in WKT format. + conf_field_mapping : dict, optional + ``'__max_conf_class'`` column value:class ID mapping dict for + multiclass use. Only required in multiclass cases. + + Returns + ------- + ``0`` upon successful completion. + + Notes + ----- + Loads in a .geojson or .csv-formatted file of proposal polygons for + comparison to the ground truth and stores it as part of the + ``Evaluator`` instance. This method assumes the geometry contained in + the proposal file is in WKT format. + + """ + + # Load Proposal if proposal_vector_file is a path to a file + if os.path.isfile(proposal_vector_file): + # if it's a CSV format, first read into a pd df and then convert + # to gpd gdf by loading in geometries using shapely + if proposalCSV: + pred_data = pd.read_csv(proposal_vector_file) + self.proposal_GDF = gpd.GeoDataFrame( + pred_data, geometry=[ + shapely.wkt.loads(pred_row[pred_row_geo_value]) + for idx, pred_row in pred_data.iterrows() + ] + ) + else: # if it's a .geojson + try: + self.proposal_GDF = gpd.read_file( + proposal_vector_file).dropna() + except (CPLE_OpenFailedError, DriverError): + self.proposal_GDF = gpd.GeoDataFrame(geometry=[]) + + if conf_field_list: + self.proposal_GDF['__total_conf'] = self.proposal_GDF[ + conf_field_list].max(axis=1) + self.proposal_GDF['__max_conf_class'] = self.proposal_GDF[ + conf_field_list].idxmax(axis=1) + else: + # set arbitrary (meaningless) values otherwise + self.proposal_GDF['__total_conf'] = 1.0 + self.proposal_GDF['__max_conf_class'] = 1 + + if conf_field_mapping is not None: + self.proposal_GDF['__max_conf_class'] = [ + conf_field_mapping[item] for item in + self.proposal_GDF['__max_conf_class'].values] + self.proposal_GDF = self.proposal_GDF.sort_values( + by='__total_conf', ascending=False) + else: + self.proposal_GDF = gpd.GeoDataFrame(geometry=[]) + + def load_truth(self, ground_truth_vector_file, truthCSV=False, + truth_geo_value='PolygonWKT_Pix'): + """Load in the ground truth geometry data. + + Arguments + --------- + ground_truth_vector_file : str + Path to the ground truth vector file. Must be either .geojson or + .csv format. + truthCSV : bool, optional + Is the ground truth a CSV? Defaults to ``False``, in which case + it's assumed to be a .geojson. + truth_geo_value : str, optional + Column of the ground truth vector file that corresponds to + geometry. + + Returns + ------- + Nothing. + + Notes + ----- + Loads the ground truth vector data into the ``Evaluator`` instance. + + """ + if truthCSV: + truth_data = pd.read_csv(ground_truth_vector_file) + self.ground_truth_GDF = gpd.GeoDataFrame( + truth_data, geometry=[ + shapely.wkt.loads(truth_row[truth_geo_value]) + for idx, truth_row in truth_data.iterrows()]) + else: + try: + self.ground_truth_GDF = gpd.read_file(ground_truth_vector_file) + except (CPLE_OpenFailedError, DriverError): # empty geojson + self.ground_truth_GDF = gpd.GeoDataFrame({'sindex': [], + 'condition': [], + 'geometry': []}) + # force calculation of spatialindex + self.ground_truth_sindex = self.ground_truth_GDF.sindex + # create deep copy of ground truth file for calculations + self.ground_truth_GDF_Edit = self.ground_truth_GDF.copy(deep=True) + + def eval(self, type='iou'): + pass + + +def eval_base(ground_truth_vector_file, csvFile=False, + truth_geo_value='PolygonWKT_Pix'): + """Deprecated API to Evaluator. + + .. deprecated:: 0.3 + Use :class:`Evaluator` instead.""" + + return Evaluator(ground_truth_vector_file) diff --git a/docker/solaris/solaris/eval/challenges.py b/docker/solaris/solaris/eval/challenges.py new file mode 100644 index 00000000..52cdbdd5 --- /dev/null +++ b/docker/solaris/solaris/eval/challenges.py @@ -0,0 +1,284 @@ +import pandas as pd +import geopandas as gpd +from .base import Evaluator +from .scot import scot_multi_aoi +import re + + +def spacenet_buildings_2(prop_csv, truth_csv, miniou=0.5, min_area=20, challenge='spacenet_2'): + """Evaluate a SpaceNet building footprint competition proposal csv. + + Uses :class:`Evaluator` to evaluate SpaceNet challenge proposals. + + Arguments + --------- + prop_csv : str + Path to the proposal polygon CSV file. + truth_csv : str + Path to the ground truth polygon CSV file. + miniou : float, optional + Minimum IoU score between a region proposal and ground truth to define + as a successful identification. Defaults to 0.5. + min_area : float or int, optional + Minimum area of ground truth regions to include in scoring calculation. + Defaults to ``20``. + challenge: str, optional + The challenge id for evaluation. + One of + ``['spacenet_2', 'spacenet_3', 'spacenet_off_nadir', 'spacenet_6']``. + The name of the challenge that `chip_name` came from. Defaults to + ``'spacenet_2'``. + + Returns + ------- + + results_DF, results_DF_Full + + results_DF : :py:class:`pd.DataFrame` + Summary :py:class:`pd.DataFrame` of score outputs grouped by nadir + angle bin, along with the overall score. + + results_DF_Full : :py:class:`pd.DataFrame` + :py:class:`pd.DataFrame` of scores by individual image chip across + the ground truth and proposal datasets. + + """ + + evaluator = Evaluator(ground_truth_vector_file=truth_csv) + evaluator.load_proposal(prop_csv, + conf_field_list=['Confidence'], + proposalCSV=True + ) + results = evaluator.eval_iou_spacenet_csv(miniou=miniou, + iou_field_prefix="iou_score", + imageIDField="ImageId", + min_area=min_area + ) + results_DF_Full = pd.DataFrame(results) + + results_DF_Full['AOI'] = [get_chip_id(imageID, challenge=challenge) + for imageID in results_DF_Full['imageID'].values] + + results_DF = results_DF_Full.groupby(['AOI']).sum() + + # Recalculate Values after Summation of AOIs + for indexVal in results_DF.index: + rowValue = results_DF[results_DF.index == indexVal] + # Precision = TruePos / float(TruePos + FalsePos) + if float(rowValue['TruePos'] + rowValue['FalsePos']) > 0: + Precision = float( + rowValue['TruePos'] / float( + rowValue['TruePos'] + rowValue['FalsePos'])) + else: + Precision = 0 + # Recall = TruePos / float(TruePos + FalseNeg) + if float(rowValue['TruePos'] + rowValue['FalseNeg']) > 0: + Recall = float(rowValue['TruePos'] / float( + rowValue['TruePos'] + rowValue['FalseNeg'])) + else: + Recall = 0 + if Recall * Precision > 0: + F1Score = 2 * Precision * Recall / (Precision + Recall) + else: + F1Score = 0 + results_DF.loc[results_DF.index == indexVal, 'Precision'] = Precision + results_DF.loc[results_DF.index == indexVal, 'Recall'] = Recall + results_DF.loc[results_DF.index == indexVal, 'F1Score'] = F1Score + + return results_DF, results_DF_Full + + +def off_nadir_buildings(prop_csv, truth_csv, image_columns={}, miniou=0.5, + min_area=20, verbose=False): + """Evaluate an off-nadir competition proposal csv. + + Uses :class:`Evaluator` to evaluate off-nadir challenge proposals. See + ``image_columns`` in the source code for how collects are broken into + Nadir, Off-Nadir, and Very-Off-Nadir bins. + + Arguments + --------- + prop_csv : str + Path to the proposal polygon CSV file. + truth_csv : str + Path to the ground truth polygon CSV file. + image_columns : dict, optional + dict of ``(collect: nadir bin)`` pairs used to separate collects into + sets. Nadir bin values must be one of + ``["Nadir", "Off-Nadir", "Very-Off-Nadir"]`` . See source code for + collect name options. + miniou : float, optional + Minimum IoU score between a region proposal and ground truth to define + as a successful identification. Defaults to 0.5. + min_area : float or int, optional + Minimum area of ground truth regions to include in scoring calculation. + Defaults to ``20``. + + Returns + ------- + + results_DF, results_DF_Full + + results_DF : :py:class:`pd.DataFrame` + Summary :py:class:`pd.DataFrame` of score outputs grouped by nadir + angle bin, along with the overall score. + + results_DF_Full : :py:class:`pd.DataFrame` + :py:class:`pd.DataFrame` of scores by individual image chip across + the ground truth and proposal datasets. + + """ + + evaluator = Evaluator(ground_truth_vector_file=truth_csv) + evaluator.load_proposal(prop_csv, + conf_field_list=['Confidence'], + proposalCSV=True + ) + results = evaluator.eval_iou_spacenet_csv(miniou=miniou, + iou_field_prefix="iou_score", + imageIDField="ImageId", + min_area=min_area + ) + results_DF_Full = pd.DataFrame(results) + + if not image_columns: + image_columns = { + 'Atlanta_nadir7_catid_1030010003D22F00': "Nadir", + 'Atlanta_nadir8_catid_10300100023BC100': "Nadir", + 'Atlanta_nadir10_catid_1030010003993E00': "Nadir", + 'Atlanta_nadir10_catid_1030010003CAF100': "Nadir", + 'Atlanta_nadir13_catid_1030010002B7D800': "Nadir", + 'Atlanta_nadir14_catid_10300100039AB000': "Nadir", + 'Atlanta_nadir16_catid_1030010002649200': "Nadir", + 'Atlanta_nadir19_catid_1030010003C92000': "Nadir", + 'Atlanta_nadir21_catid_1030010003127500': "Nadir", + 'Atlanta_nadir23_catid_103001000352C200': "Nadir", + 'Atlanta_nadir25_catid_103001000307D800': "Nadir", + 'Atlanta_nadir27_catid_1030010003472200': "Off-Nadir", + 'Atlanta_nadir29_catid_1030010003315300': "Off-Nadir", + 'Atlanta_nadir30_catid_10300100036D5200': "Off-Nadir", + 'Atlanta_nadir32_catid_103001000392F600': "Off-Nadir", + 'Atlanta_nadir34_catid_1030010003697400': "Off-Nadir", + 'Atlanta_nadir36_catid_1030010003895500': "Off-Nadir", + 'Atlanta_nadir39_catid_1030010003832800': "Off-Nadir", + 'Atlanta_nadir42_catid_10300100035D1B00': "Very-Off-Nadir", + 'Atlanta_nadir44_catid_1030010003CCD700': "Very-Off-Nadir", + 'Atlanta_nadir46_catid_1030010003713C00': "Very-Off-Nadir", + 'Atlanta_nadir47_catid_10300100033C5200': "Very-Off-Nadir", + 'Atlanta_nadir49_catid_1030010003492700': "Very-Off-Nadir", + 'Atlanta_nadir50_catid_10300100039E6200': "Very-Off-Nadir", + 'Atlanta_nadir52_catid_1030010003BDDC00': "Very-Off-Nadir", + 'Atlanta_nadir53_catid_1030010003193D00': "Very-Off-Nadir", + 'Atlanta_nadir53_catid_1030010003CD4300': "Very-Off-Nadir", + } + + results_DF_Full['nadir-category'] = [ + image_columns[get_chip_id(imageID, challenge='spacenet_off_nadir')] + for imageID in results_DF_Full['imageID'].values] + + results_DF = results_DF_Full.groupby(['nadir-category']).sum() + + # Recalculate Values after Summation of AOIs + for indexVal in results_DF.index: + if verbose: + print(indexVal) + rowValue = results_DF[results_DF.index == indexVal] + # Precision = TruePos / float(TruePos + FalsePos) + if float(rowValue['TruePos'] + rowValue['FalsePos']) > 0: + Precision = float( + rowValue['TruePos'] / float(rowValue['TruePos'] + + rowValue['FalsePos']) + ) + else: + Precision = 0 + # Recall = TruePos / float(TruePos + FalseNeg) + if float(rowValue['TruePos'] + rowValue['FalseNeg']) > 0: + Recall = float(rowValue['TruePos'] / float(rowValue['TruePos'] + + rowValue['FalseNeg'])) + else: + Recall = 0 + if Recall * Precision > 0: + F1Score = 2 * Precision * Recall / (Precision + Recall) + else: + F1Score = 0 + results_DF.loc[results_DF.index == indexVal, 'Precision'] = Precision + results_DF.loc[results_DF.index == indexVal, 'Recall'] = Recall + results_DF.loc[results_DF.index == indexVal, 'F1Score'] = F1Score + + return results_DF, results_DF_Full + + +def multi_temporal_buildings(prop_csv, truth_csv, miniou=0.25, min_area=4., + beta=2., stats=False, verbose=False): + """ + Evaluate submissions to SpaceNet 7: Multi-Temporal Urban Development + Input CSV files should have "filename", "id", and "geometry" columns. + """ + + # Load dataframes + grnd_df = gpd.read_file(truth_csv, GEOM_POSSIBLE_NAMES="geometry", KEEP_GEOM_COLUMNS="NO") + prop_df = gpd.read_file(prop_csv, GEOM_POSSIBLE_NAMES="geometry", KEEP_GEOM_COLUMNS="NO") + if verbose: + print("init len grnd_df:", len(grnd_df)) + print("init len prop_df:", len(prop_df)) + + # Filter out small buildings from ground truth + if min_area is not None: + grnd_df['area'] = grnd_df.area + grnd_df = grnd_df[grnd_df['area'] >= min_area] + grnd_df = grnd_df.drop(columns=['area']) + if verbose: + print("filtered len grnd_df:", len(grnd_df)) + + # Extract place (aoi) and time (timestep) from the "filename" column + grnd_df['aoi'] = grnd_df['filename'].str.slice(30, 58) + prop_df['aoi'] = prop_df['filename'].str.slice(30, 58) + grnd_df['timestep'] = grnd_df['filename'].str.slice(15, 22) + prop_df['timestep'] = prop_df['filename'].str.slice(15, 22) + aois = sorted(list(grnd_df.aoi.drop_duplicates())) + if verbose: + print("Number of AOIS:", len(aois)) + + # Compute the score for this proposal + score, all_stats = scot_multi_aoi(grnd_df, prop_df, + threshold=miniou, base_reward=100., + beta=beta, + stats=True, verbose=verbose) + if verbose: + print('The submission "%s" receives a score of %f' + % (prop_csv, score)) + if stats: + return (score, all_stats) + else: + return score + + +def get_chip_id(chip_name, challenge="spacenet_2"): + """Get the unique identifier for a chip location from SpaceNet images. + + Arguments + --------- + chip_name: str + The name of the chip to extract the identifier from. + challenge: str, optional + One of + ``['spacenet_2', 'spacenet_3', 'spacenet_off_nadir', 'spacenet_6']``. + The name of the challenge that `chip_name` came from. Defaults to + ``'spacenet_2'``. + + Returns + ------- + chip_id : str + The unique identifier for the chip location. + """ + # AS NEW CHALLENGES ARE ADDED, ADD THE CHIP MATCHING FUNCTIONALITY WITHIN + # THIS FUNCTION. + if challenge in ['spacenet_2', 'spacenet_3']: + chip_id = '_'.join(chip_name.split('_')[:-1]) + elif challenge == 'spacenet_off_nadir': + chip_id = re.findall('Atlanta_nadir[0-9]{1,2}_catid_[0-9A-Z]{16}', + chip_name)[0] + elif challenge == 'spacenet_6': + chip_id = '_'.join(chip_name.split('_')[-4:]).split(".")[0] + + return chip_id diff --git a/docker/solaris/solaris/eval/iou.py b/docker/solaris/solaris/eval/iou.py new file mode 100644 index 00000000..c53cecb3 --- /dev/null +++ b/docker/solaris/solaris/eval/iou.py @@ -0,0 +1,80 @@ +import geopandas as gpd + + +def calculate_iou(pred_poly, test_data_GDF): + """Get the best intersection over union for a predicted polygon. + + Arguments + --------- + pred_poly : :py:class:`shapely.Polygon` + Prediction polygon to test. + test_data_GDF : :py:class:`geopandas.GeoDataFrame` + GeoDataFrame of ground truth polygons to test ``pred_poly`` against. + + Returns + ------- + iou_GDF : :py:class:`geopandas.GeoDataFrame` + A subset of ``test_data_GDF`` that overlaps ``pred_poly`` with an added + column ``iou_score`` which indicates the intersection over union value. + + """ + + # Fix bowties and self-intersections + if not pred_poly.is_valid: + pred_poly = pred_poly.buffer(0.0) + + precise_matches = test_data_GDF[test_data_GDF.intersects(pred_poly)] + + iou_row_list = [] + for _, row in precise_matches.iterrows(): + # Load ground truth polygon and check exact iou + test_poly = row.geometry + # Ignore invalid polygons for now + if pred_poly.is_valid and test_poly.is_valid: + intersection = pred_poly.intersection(test_poly).area + union = pred_poly.union(test_poly).area + # Calculate iou + iou_score = intersection / float(union) + else: + iou_score = 0 + row['iou_score'] = iou_score + iou_row_list.append(row) + + iou_GDF = gpd.GeoDataFrame(iou_row_list) + return iou_GDF + + +def process_iou(pred_poly, test_data_GDF, remove_matching_element=True): + """Get the maximum intersection over union score for a predicted polygon. + + Arguments + --------- + pred_poly : :py:class:`shapely.geometry.Polygon` + Prediction polygon to test. + test_data_GDF : :py:class:`geopandas.GeoDataFrame` + GeoDataFrame of ground truth polygons to test ``pred_poly`` against. + remove_matching_element : bool, optional + Should the maximum IoU row be dropped from ``test_data_GDF``? Defaults + to ``True``. + + Returns + ------- + *This function doesn't currently return anything.* + + """ + + iou_GDF = calculate_iou(pred_poly, test_data_GDF) + + max_iou_row = iou_GDF.loc[iou_GDF['iou_score'].idxmax(axis=0, skipna=True)] + + if remove_matching_element: + test_data_GDF.drop(max_iou_row.name, axis=0, inplace=True) + + # Prediction poly had no overlap with anything + # if not iou_list: + # return max_iou_row, 0, test_data_DF + # else: + # max_iou_idx, max_iou = max(iou_list, key=lambda x: x[1]) + # # Remove ground truth polygon from tree + # test_tree.delete(max_iou_idx, Polygon(test_data[max_iou_idx]['geometry']['coordinates'][0]).bounds) + # return max_iou_row['iou_score'], iou_GDF, test_data_DF diff --git a/docker/solaris/solaris/eval/pixel.py b/docker/solaris/solaris/eval/pixel.py new file mode 100644 index 00000000..34f0d0a9 --- /dev/null +++ b/docker/solaris/solaris/eval/pixel.py @@ -0,0 +1,344 @@ +import time +import cv2 +import numpy as np +import matplotlib +import matplotlib.pyplot as plt + + +def iou(truth_mask, prop_mask, prop_threshold=0.5, verbose=False): + """Compute pixel-wise intersection over union. + + Multiplies truth_mask by 2, and subtract. Make sure arrays are clipped + so that overlapping regions don't cause problems + + Arguments + --------- + truth_mask : :class:`numpy.ndarray` + 2-D binary array of ground truth pixels. + prop_mask : :class:`numpy.ndarray` + 2-D array of proposals. + prop_threshold : float, optional + The threshold for proposal values to be defined as positive (``1``) or + negative (``0``) predictions. Values >= `prop_threshold` will be set to + ``1``, values < `prop_threshold` will be set to ``0``. + verbose : bool, optional + Switch to print relevant values. + + Returns + ------- + iou : float + Intersection over union of ground truth and proposal + """ + if truth_mask.shape != prop_mask.shape: + raise ValueError("The shape of `truth_mask` and `prop_mask` must " + "be the same.") + truth_mask_clip = np.clip(truth_mask, 0, 1).astype(float) + prop_mask_clip = (np.clip(prop_mask, 0, 1) >= prop_threshold).astype(float) + # subtract array + sub_mask = 2*prop_mask_clip - truth_mask_clip + add_mask = prop_mask_clip + truth_mask_clip + + # true pos = 1, false_pos = 2, true_neg = 0, false_neg = -1 + tp_count = np.sum(sub_mask == 1) + union = np.sum(add_mask > 0) + intersection = tp_count + + iou = 1. * intersection / union + + if verbose: + print("intersection:", intersection) + print("union:", union) + print("iou:", iou) + + return iou + + +def f1(truth_mask, prop_mask, prop_threshold=0.5, show_plot=False, im_file='', + show_colorbar=False, plot_file='', dpi=200, verbose=False): + """Compute pixel-wise precision, recall, and f1 score. + + Find true pos, false pos, true neg, false neg as well as f1 score. + Multiply truth_mask by 2, and subtract. Make sure arrays are clipped + so that overlapping regions don't cause problems. + + Arguments + --------- + truth_mask : :class:`numpy.ndarray` + 2-D binary array of ground truth pixels. + prop_mask : :class:`numpy.ndarray` + 2-D array of proposals. + prop_threshold : float, optional + The threshold for proposal values to be defined as positive (``1``) or + negative (``0``) predictions. Values >= `prop_threshold` will be set to + ``1``, values < `prop_threshold` will be set to ``0``. + show_plot : bool, optional + Switch to plot the outputs. Defaults to ``False``. + im_file : str, optional + Image file corresponding to the masks. Ignored if + ``show_plot == False``. Defaults to ``''``. + show_colorbar : bool, optional + Switch to show colorbar. Ignored if ``show_plot == False``. + Defaults to ``False``. + plot_file : str, optional + Output file if plotting. Ignored if ``show_plot == False``. + Defaults to ``''``. + dpi : int, optional + Dots per inch for plotting. Ignored if ``show_plot == False``. + Defaults to ``200``. + verbose : bool, optional + Switch to print relevant values. + + Returns + ------- + f1 : float + Pixel-wise F1 score. + precision : float + Pixel-wise precision. + recall : float + Pixel-wise recall. + """ + + truth_mask_clip = np.clip(truth_mask, 0, 1).astype(float) + prop_mask_clip = (np.clip(prop_mask, 0, 1) >= prop_threshold).astype(float) + # subtract array + sub_mask = 2*prop_mask_clip - truth_mask_clip + # sub_mask2 = prop_mask_clip - truth_mask_clip + + # true pos = 1, false_pos = 2, true_neg = 0, false_neg = -1 + n_pos = len(np.where(truth_mask_clip == 1)[0]) + tp_count = len(np.where(sub_mask == 1)[0]) + fp_count = len(np.where(sub_mask == 2)[0]) + tn_count = len(np.where(sub_mask == 0)[0]) + fn_count = len(np.where(sub_mask == -1)[0]) + + if (n_pos > 0) and (tp_count > 0): + precision = float(tp_count) / float(tp_count + fp_count) + recall = float(tp_count) / float(tp_count + fn_count) + f1 = 2 * precision * recall / (precision + recall) + else: + precision, recall, f1 = 0, 0, 0 + + if verbose: + print("mask.shape:\t", truth_mask.shape) + print("num pixels:\t", truth_mask.size) + print("false_neg:\t", fn_count) + print("false_pos:\t", fp_count) + print("true_neg:\t", tn_count) + print("true_pos:\t", tp_count) + print("precision:\t", precision) + print("recall:\t\t", recall) + print("F1 score:\t", f1) + + # TODO: split this out into a separate function + if show_plot: + + fontsize = 6 + t0 = time.time() + title = "Precision: " + str(np.round(precision, 3)) \ + + " Recall: " + str(np.round(recall, 3)) \ + + " F1: " + str(np.round(f1, 3)) + + if show_colorbar: + fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True, + sharey=True, + figsize=(6, 6)) + else: + fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, + sharey=True, + figsize=(9.5, 3)) + # fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3, sharex=True, + # sharey=True, figsize=(13,4)) + plt.suptitle(title, fontsize=fontsize) + + # ground truth + if len(im_file) > 0: + # raw image + ax1.imshow(cv2.imread(im_file, 1)) + # ground truth + # set zeros to nan + palette = plt.cm.gray + palette.set_over('orange', 1.0) + palette.set_over('orange', 1.0) + + z = truth_mask.astype(float) + z[z == 0] = np.nan + ax1.imshow(z, cmap=palette, alpha=0.5, + norm=matplotlib.colors.Normalize( + vmin=0.5, vmax=0.9, clip=False)) + ax1.set_title('truth_mask_clip + image', fontsize=fontsize) + else: + ax1.imshow(truth_mask_clip) + ax1.set_title('truth_mask_clip', fontsize=fontsize) + ax1.axis('off') + + # proposal mask + ax2.imshow(prop_mask_clip) + ax2.axis('off') + ax2.set_title('prop_mask_clip', fontsize=fontsize) + + # mask + if show_colorbar: + z = ax3.pcolor(sub_mask) + fig.colorbar(z) + ax4.axis('off') + + else: + ax3.imshow(sub_mask) + # z = ax3.pcolor(sub_mask2) + ax3.axis('off') + ax3.set_title('subtract_mask', fontsize=fontsize) + + # plt.tight_layout() + # fig.tight_layout(rect=[0, 0.03, 1, 0.95]) + plt.subplots_adjust(top=0.8) + + if len(plot_file) > 0: + plt.savefig(plot_file, dpi=dpi) + print("Time to create and save F1 plots:", time.time() - t0, "seconds") + + plt.show() + + return f1, precision, recall + + +def _get_neighborhood_limits(row, col, h, w, rho=3): + '''Get neighbors of point p with pixel coords row, col''' + + rowmin = max(0, row-rho) + rowmax = min(h, row + rho) + colmin = max(0, col-rho) + colmax = min(w, col + rho) + + return rowmin, rowmax, colmin, colmax + + +def relaxed_f1(truth_mask, prop_mask, radius=3, verbose=False): + """ + Compute relaxed f1 score + + Notes + ----- + Also find relaxed precision, recall, f1. + http://www.cs.toronto.edu/~fritz/absps/road_detection.pdf + "completenetess represents the fraction of true road pixels that are + within ρ pixels of a predicted road pixel, while correctness measures + the fraction of predicted road pixels that are within ρ pixels of a + true road pixel." + https://arxiv.org/pdf/1711.10684.pdf + The relaxed precision is defined as the fraction of number of pixels + predicted as road + within a range of ρ pixels from pixels labeled as road. The + relaxed recall is the fraction of number of pixels labeled as + road that are within a range of ρ pixels from pixels predicted + as road. + http://ceur-ws.org/Vol-156/paper5.pdf + + Arguments + --------- + truth_mask : np array + 2-D array of ground truth. + prop_mask : np array + 2-D array of proposals. + radius : int + Radius in pixels to use for relaxed f1. + verbose : bool + Switch to print relevant values + + Returns + ------- + output : tuple + Tuple containing [relaxed_f1, relaxed_precision, relaxed_recall] + + Examples + -------- + + >>> truth_mask = np.zeros(shape=(10, 10)) + >>> prop_mask = np.zeros(shape=(10, 10)) + + >>> truth_mask[5, :] = 1 + >>> prop_mask[5, :] = 1 + >>> prop_mask[:, 2] = 0 + >>> prop_mask[:, 3] = 1 + >>> prop_mask[6:8, :] = 0 + >>> prop_mask + array([[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [1., 1., 0., 1., 1., 1., 1., 1., 1., 1.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]]) + >>>truth_mask + array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) + >>> relaxed_f1(truth_mask, prop_mask, radius=3) + (0.8571428571428571, 0.75, 1.0) + """ + + truth_mask_clip = np.clip(truth_mask, 0, 1).astype(float) + prop_mask_clip = np.clip(prop_mask, 0, 1).astype(float) + + # true pos = 1, false_pos = 2, true_neg = 0, false_neg = -1 + n_truth = len(np.where(truth_mask_clip == 1)[0]) + n_prop = len(np.where(prop_mask_clip == 1)[0]) + + # iterate through truth pixels + precision_count = 0 + recall_count = 0 + h, w = truth_mask.shape + for row in range(h): + for col in range(w): + truth_val = truth_mask_clip[row][col] + prop_val = prop_mask_clip[row][col] + # get window limits + rowmin, rowmax, colmin, colmax = _get_neighborhood_limits( + row, col, h, w, rho=radius) + # get windows + truth_win = truth_mask_clip[rowmin:rowmax, colmin:colmax] + prop_win = prop_mask_clip[rowmin:rowmax, colmin:colmax] + + # add precision_count if proposal is within the radius of a gt node + if prop_val == 1: + if np.max(truth_win) > 0: + precision_count += 1 + + if truth_val == 1: + if np.max(prop_win) > 0: + recall_count += 1 + + # get fractions + if n_truth == 0: + relaxed_recall = 0 + else: + relaxed_recall = 1. * recall_count / n_truth + if n_prop == 0: + relaxed_precision = 0 + else: + relaxed_precision = 1. * precision_count / n_prop + + if (relaxed_recall > 0) and (relaxed_precision > 0): + relaxed_f1 = 2 * relaxed_precision * relaxed_recall \ + / (relaxed_precision + relaxed_recall) + else: + relaxed_f1 = 0 + + if verbose: + print("mask.shape:\t", truth_mask.shape) + print("num pixels:\t", truth_mask.size) + print("precision:\t", relaxed_precision) + print("recall:\t\t", relaxed_recall) + print("rF1 score:\t", f1) + + output = (relaxed_f1, relaxed_precision, relaxed_recall) + return output diff --git a/docker/solaris/solaris/eval/scot.py b/docker/solaris/solaris/eval/scot.py new file mode 100644 index 00000000..f95e26ef --- /dev/null +++ b/docker/solaris/solaris/eval/scot.py @@ -0,0 +1,232 @@ +import geopandas as gpd +import scipy.optimize +import scipy.sparse + +def match_footprints(grnd_df, prop_df, + threshold=0.25, base_reward=100.): + """ + Optimal matching of ground truth footprints with proposal footprints + (for a single timestep). + Input dataframes should have "id" & "geometry" columns. + """ + + # Supplement IDs with indices (which run from zero + # to one less than the number of unique IDs) + grnd_id_set = set(grnd_df['id']) + prop_id_set = set(prop_df['id']) + grnd_id_to_index = {id: index for index, id in + enumerate(sorted(list(grnd_id_set)))} + prop_id_to_index = {id: index for index, id in + enumerate(sorted(list(prop_id_set)))} + grnd_index_to_id = {index: id for id, index in grnd_id_to_index.items()} + prop_index_to_id = {index: id for id, index in prop_id_to_index.items()} + grnd_df['index'] = grnd_df.id.apply(lambda id: grnd_id_to_index[id]) + prop_df['index'] = prop_df.id.apply(lambda id: prop_id_to_index[id]) + + # Calculate IOU for all intersections, and the corresponding reward + grnd_df['grnd_area'] = grnd_df.area + prop_df['prop_area'] = prop_df.area + if not (grnd_df.empty or prop_df.empty): + intersect = gpd.overlay(grnd_df, prop_df) + else: + intersect = None + if intersect is None or len(intersect) == 0: + return [], [], len(grnd_df), len(prop_df), 0, len(prop_df), len(grnd_df), 0., grnd_id_set, prop_id_set + intersect['inter_area'] = intersect.area + intersect['iou'] = intersect['inter_area'] / (intersect['grnd_area'] + + intersect['prop_area'] - intersect['inter_area']) + intersect['reward'] = intersect.apply(lambda row: (row.iou > threshold) + * (base_reward + row.iou), axis=1) + + # Convert IOUs and rewards to 2D arrays (by way of sparse matrices) + iou_matrix = scipy.sparse.coo_matrix((intersect.iou, (intersect.index_1, intersect.index_2)), + shape=(len(grnd_df), len(prop_df))) + iou_arr = iou_matrix.toarray() + reward_matrix = scipy.sparse.coo_matrix((intersect.reward, (intersect.index_1, intersect.index_2)), + shape=(len(grnd_df), len(prop_df))) + reward_arr = reward_matrix.toarray() + + # Solve unbalanced linear assignment problem + grnd_match, prop_match = scipy.optimize.linear_sum_assignment(reward_arr, maximize=True) + iou_match = iou_arr[grnd_match, prop_match] + + # Remove matches that don't actually contribute to the total score + grnd_match_pruned = grnd_match[iou_match>threshold] + prop_match_pruned = prop_match[iou_match>threshold] + iou_match_pruned = iou_match[iou_match>threshold] + + # Look up IDs for each match, and calculate descriptive statistics + grnd_match_ids = [grnd_index_to_id[index] for index in grnd_match_pruned] + prop_match_ids = [prop_index_to_id[index] for index in prop_match_pruned] + num_grnd = len(grnd_df) + num_prop = len(prop_df) + tp = len(iou_match_pruned) + fp = num_prop - tp + fn = num_grnd - tp + if 2*tp + fp + fn > 0: + f1 = (2*tp) / (2*tp + fp + fn) + else: + f1 = 0 + + return grnd_match_ids, prop_match_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set + + +def scot_one_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2., + stats=False, verbose=False): + """ + SpaceNet Change and Object Tracking (SCOT) metric, for one AOI. + Input dataframes should have "timestep", "id", & "geometry" columns. + """ + + # Get list of timesteps from ground truth and proposal dataframes + grnd_timestep_set = set(grnd_df.timestep.drop_duplicates()) + prop_timestep_set = set(grnd_df.timestep.drop_duplicates()) + timesteps = sorted(list(grnd_timestep_set.union(prop_timestep_set))) + + # Loop through timesteps + if verbose: + print('Matching footprints') + tp_net = 0 + fp_net = 0 + fn_net = 0 + num_grnd_net = 0 + num_prop_net = 0 + all_grnd_ids = [] + all_prop_ids = [] + change_tp_net = 0 + change_fp_net = 0 + change_fn_net = 0 + change_grnd_ids = set() + change_prop_ids = set() + for i, timestep in enumerate(timesteps): + + # Get just the data for this timestep + grnd_df_one_timestep = grnd_df.loc[grnd_df.timestep == timestep].copy() + prop_df_one_timestep = prop_df.loc[prop_df.timestep == timestep].copy() + + # Find footprint matches for this timestep + grnd_ids, prop_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set = match_footprints( + grnd_df_one_timestep, prop_df_one_timestep, + threshold=threshold, base_reward=base_reward) + + # Collect aggregate statistics for tracking, and retain all match IDs + tp_net += tp + fp_net += fp + fn_net += fn + num_grnd_net += num_grnd + num_prop_net += num_prop + all_grnd_ids = grnd_ids + all_grnd_ids # newest first + all_prop_ids = prop_ids + all_prop_ids # newest first + if verbose: + print(' %2i: F1 = %.4f' % (i + 1, f1)) + + # Collect aggregate statistics for change detection + if i > 0: + # Find change detection TPs, FPs, and FNs among matched footprints + new_grnd = [grnd_id not in change_grnd_ids for grnd_id in grnd_ids] + new_prop = [prop_id not in change_prop_ids for prop_id in prop_ids] + change_tp_list = [g and p for g, p in zip(new_grnd, new_prop)] + change_fp_list = [p and not g for g, p in zip(new_grnd, new_prop)] + change_fn_list = [g and not p for g, p in zip(new_grnd, new_prop)] + change_tp_net += sum(change_tp_list) + change_fp_net += sum(change_fp_list) + change_fn_net += sum(change_fn_list) + # Find change detection FPs and FNs among unmatched footprints + unmatched_fp = prop_id_set.difference(prop_ids).difference(change_prop_ids) + unmatched_fn = grnd_id_set.difference(grnd_ids).difference(change_grnd_ids) + change_fp_net += len(unmatched_fp) + change_fn_net += len(unmatched_fn) + change_grnd_ids = change_grnd_ids.union(grnd_id_set) + change_prop_ids = change_prop_ids.union(prop_id_set) + + # Identify which matches are mismatches + # (i.e., inconsistent with previous timesteps) + if verbose: + print('Identifying mismatches') + mm_net = 0 + for i in range(len(all_grnd_ids)): + grnd_id = all_grnd_ids[i] + prop_id = all_prop_ids[i] + previous_grnd_ids = all_grnd_ids[i+1:] + previous_prop_ids = all_prop_ids[i+1:] + grnd_mismatch = grnd_id in previous_grnd_ids and previous_prop_ids[previous_grnd_ids.index(grnd_id)] != prop_id + prop_mismatch = prop_id in previous_prop_ids and previous_grnd_ids[previous_prop_ids.index(prop_id)] != grnd_id + mismatch = grnd_mismatch or prop_mismatch + if mismatch: + mm_net += 1 + + # Compute and return score according to the metric + track_tp_net = tp_net - mm_net + track_fp_net = fp_net + mm_net + track_fn_net = fn_net + mm_net + if track_tp_net + (track_fp_net + track_fn_net)/2. > 0: + track_score = (track_tp_net) / (track_tp_net + + (track_fp_net + track_fn_net)/2.) + else: + track_score = 0 + if change_tp_net + (change_fp_net + change_fn_net)/2. > 0: + change_score = (change_tp_net) / (change_tp_net + + (change_fp_net + change_fn_net)/2.) + else: + change_score = 0 + if beta * beta * change_score + track_score > 0: + combo_score = (1 + beta * beta) * (change_score * track_score) / (beta * beta * change_score + track_score) + else: + combo_score = 0 + if verbose: + print('Tracking:') + print(' Mismatches: %i' % mm_net) + print(' True Pos: %i' % track_tp_net) + print(' False Pos: %i' % track_fp_net) + print(' False Neg: %i' % track_fn_net) + print(' Track Score: %.4f' % track_score) + print('Change Detection:') + print(' True Pos: %i' % change_tp_net) + print(' False Pos: %i' % change_fp_net) + print(' False Neg: %i' % change_fn_net) + print(' Change Score: %.4f' % change_score) + print('Combined Score: %.4f' % combo_score) + if stats: + return combo_score, [mm_net, track_tp_net, track_fp_net, track_fn_net, + track_score, change_tp_net, change_fp_net, + change_fn_net, change_score, combo_score] + else: + return combo_score + + +def scot_multi_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2., + stats=True, verbose=False): + """ + SpaceNet Change and Object Tracking (SCOT) metric, + for a SpaceNet 7 submission with multiple AOIs. + Input dataframes should have "aoi", "timestep", "id", & "geometry" columns. + """ + + # Get list of AOIs from ground truth dataframe + aois = sorted(list(grnd_df.aoi.drop_duplicates())) + + # Evaluate SCOT metric for each AOI + cumulative_score = 0. + all_stats = {} + for i, aoi in enumerate(aois): + if verbose: + print() + print('%i / %i: AOI %s' % (i + 1, len(aois), aoi)) + grnd_df_one_aoi = grnd_df.loc[grnd_df.aoi == aoi].copy() + prop_df_one_aoi = prop_df.loc[prop_df.aoi == aoi].copy() + score_one_aoi, stats_one_aoi = scot_one_aoi( + grnd_df_one_aoi, prop_df_one_aoi, + threshold=threshold, + base_reward=base_reward, + beta=beta, stats=True, verbose=verbose) + cumulative_score += score_one_aoi + all_stats[aoi] = stats_one_aoi + + # Return combined SCOT metric score + score = cumulative_score / len(aois) + if verbose: + print('Overall score: %f' % score) + if stats: + return score, all_stats + else: + return score diff --git a/docker/solaris/solaris/eval/vector.py b/docker/solaris/solaris/eval/vector.py new file mode 100644 index 00000000..427bbecb --- /dev/null +++ b/docker/solaris/solaris/eval/vector.py @@ -0,0 +1,513 @@ +import os +import glob +from tqdm import tqdm +import numpy as np +import geopandas as gpd +from .iou import calculate_iou + + +def average_score_by_class(ious, threshold=0.5): + """ for a list of object ious by class, test if they are a counted as a + positive or a negative. + Arguments + --------- + ious : list of lists + A list containing individual lists of ious for eachobject class. + threshold : float + A value between 0.0 and 1.0 that determines the threshold for a true positve. + Returns + --------- + average_by_class : list + A list containing the ratio of true positives for each class + """ + binary_scoring_lists = [] + for x in ious: + items = [] + for i in x: + if i >= threshold: + items.append(1) + else: + items.append(0) + binary_scoring_lists.append(items) + average_by_class = [] + for l in binary_scoring_lists: + average_by_class.append(np.nanmean(l)) + return average_by_class + + +def get_all_objects(proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib="class", gt_cat_attrib='make', + file_format="geojson"): + """ Using the proposal and ground truth polygons, calculate the total. + Filenames of predictions and ground-truth must be identical. + unique classes present in each + Arguments + --------- + proposal_polygons_dir : str + The path that contains any model proposal polygons + gt_polygons_dir : str + The path that contains the ground truth polygons + prediction_cat_attrib : str + The column or attribute within the predictions that specifies + unique classes + gt_cat_attrib : str + The column or attribute within the ground truth that + specifies unique classes + file_format : str + The extension or file format for predictions + Returns + --------- + prop_objs : list + All unique objects that exist in the proposals + gt_obj : list + All unique objects that exist in the ground truth + all_objs : list + A union of the prop_objs and gt_objs lists + """ + objs = [] + os.chdir(proposal_polygons_dir) + search = "*" + file_format + proposal_geojsons = glob.glob(search) + for geojson in tqdm(proposal_geojsons): + ground_truth_poly = os.path.join(gt_polygons_dir, geojson) + if os.path.exists(ground_truth_poly): + ground_truth_gdf = gpd.read_file(ground_truth_poly) + proposal_gdf = gpd.read_file(geojson) + for index, row in (proposal_gdf.iterrows()): + objs.append(row[prediction_cat_attrib]) + prop_objs = list(set(objs)) + os.chdir(gt_polygons_dir) + search = "*" + file_format + objs = [] + gt_geojsons = glob.glob(search) + for geojson in tqdm(gt_geojsons): + proposal_poly = os.path.join(proposal_polygons_dir, geojson) + if os.path.exists(proposal_poly): + proposal_gdf = gpd.read_file(proposal_poly) + ground_truth_gdf = gpd.read_file(geojson) + for index, row in (ground_truth_gdf.iterrows()): + objs.append(row[gt_cat_attrib]) + gt_objs = list(set(objs)) + all_objs = gt_objs + prop_objs + all_objs = list(set(all_objs)) + return prop_objs, gt_objs, all_objs + + +def precision_calc(proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib="class", gt_cat_attrib='make', confidence_attrib=None, + object_subset=[], threshold=0.5, file_format="geojson"): + """ Using the proposal and ground truth polygons, calculate precision metrics. + Filenames of predictions and ground-truth must be identical. Will only + calculate metric for classes that exist in the ground truth. + Arguments + --------- + proposal_polygons_dir : str + The path that contains any model proposal polygons + gt_polygons_dir : str + The path that contains the ground truth polygons + prediction_cat_attrib : str + The column or attribute within the predictions that specifies + unique classes + gt_cat_attrib : str + The column or attribute within the ground truth that + specifies unique classes + confidence_attrib : str + The column or attribute within the proposal polygons that + specifies model confidence for each prediction + object_subset : list + A list or subset of the unique objects that are contained within the + ground truth polygons. If empty, this will be + auto-created using all classes that appear ground truth polygons. + threshold : float + A value between 0.0 and 1.0 that determines the IOU threshold for a + true positve. + file_format : str + The extension or file format for predictions + Returns + --------- + iou_holder : list of lists + An iou score for each object per class (precision specific) + precision_by_class : list + A list containing the precision score for each class + mPrecision : float + The mean precision score of precision_by_class + confidences : list of lists + All confidences for each object for each class + """ + ious = [] + os.chdir(proposal_polygons_dir) + search = "*" + file_format + proposal_geojsons = glob.glob(search) + iou_holder = [] + confidences = [] + if len(object_subset) == 0: + prop_objs, object_subset, all_objs = get_all_objects( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, file_format=file_format) + for i in range(len(object_subset)): + iou_holder.append([]) + confidences.append([]) + + for geojson in tqdm(proposal_geojsons): + ground_truth_poly = os.path.join(gt_polygons_dir, geojson) + if os.path.exists(ground_truth_poly): + ground_truth_gdf = gpd.read_file(ground_truth_poly) + proposal_gdf = gpd.read_file(geojson) + i = 0 + for obj in object_subset: + conf_holder = [] + proposal_gdf2 = proposal_gdf[proposal_gdf[prediction_cat_attrib] == obj] + for index, row in (proposal_gdf2.iterrows()): + if confidence_attrib is not None: + conf_holder.append(row[confidence_attrib]) + iou_GDF = calculate_iou(row.geometry, ground_truth_gdf) + if 'iou_score' in iou_GDF.columns: + iou = iou_GDF.iou_score.max() + max_iou_row = iou_GDF.loc[iou_GDF['iou_score'].idxmax(axis=0, skipna=True)] + id_1 = row[prediction_cat_attrib] + id_2 = ground_truth_gdf.loc[max_iou_row.name][gt_cat_attrib] + if id_1 == id_2: + ious.append(iou) + ground_truth_gdf.drop(max_iou_row.name, axis=0, inplace=True) + else: + iou = 0 + ious.append(iou) + else: + iou = 0 + ious.append(iou) + for item in ious: + iou_holder[i].append(item) + if confidence_attrib is not None: + for conf in conf_holder: + confidences[i].append(conf) + ious = [] + i += 1 + else: + print("Warning- No ground truth for:", geojson) + proposal_gdf = gpd.read_file(geojson) + i = 0 + + for obj in object_subset: + proposal_gdf2 = proposal_gdf[proposal_gdf[gt_cat_attrib] == obj] + for z in range(len(proposal_gdf2)): + ious.append(0) + for item in ious: + iou_holder[i].append(item) + if confidence_attrib is not None: + for conf in conf_holder: + confidences[i].append(conf) + i += 1 + ious = [] + precision_by_class = average_score_by_class(iou_holder, threshold=threshold) + precision_by_class = list(np.nan_to_num(precision_by_class)) + mPrecision = np.nanmean(precision_by_class) + print("mPrecision:", mPrecision) + + return iou_holder, precision_by_class, mPrecision, confidences + + +def recall_calc(proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib="class", gt_cat_attrib='make', + object_subset=[], threshold=0.5, file_format="geojson"): + """ Using the proposal and ground truth polygons, calculate recall metrics. + Filenames of predictions and ground-truth must be identical. Will only + calculate metric for classes that exist in the ground truth. + Arguments + --------- + proposal_polygons_dir : str + The path that contains any model proposal polygons + gt_polygons_dir : str + The path that contains the ground truth polygons + prediction_cat_attrib : str + The column or attribute within the predictions that specifies + unique classes + gt_cat_attrib : str + The column or attribute within the ground truth that + specifies unique classes + object_subset : list + A list or subset of the unique objects that are contained within the + ground truth polygons. If empty, this will be + auto-created using all classes that appear ground truth polygons. + threshold : float + A value between 0.0 and 1.0 that determines the IOU threshold for a + true positve. + file_format : str + The extension or file format for predictions + Returns + --------- + iou_holder : list of lists + An iou score for each object per class (recall specific) + recall_by_class : list + A list containing the recall score for each class + mRecall : float + The mean recall score of recall_by_class + """ + ious = [] + os.chdir(gt_polygons_dir) + search = "*" + file_format + gt_geojsons = glob.glob(search) + iou_holder = [] + if len(object_subset) == 0: + prop_objs, object_subset, all_objs = get_all_objects( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, file_format=file_format) + for i in range(len(object_subset)): + iou_holder.append([]) + for geojson in tqdm(gt_geojsons): + proposal_poly = os.path.join(proposal_polygons_dir, geojson) + if os.path.exists(proposal_poly): + proposal_gdf = gpd.read_file(proposal_poly) + ground_truth_gdf = gpd.read_file(geojson) + i = 0 + for obj in object_subset: + ground_truth_gdf2 = ground_truth_gdf[ground_truth_gdf[gt_cat_attrib] == obj] + for index, row in (ground_truth_gdf2.iterrows()): + iou_GDF = calculate_iou(row.geometry, proposal_gdf) + if 'iou_score' in iou_GDF.columns: + iou = iou_GDF.iou_score.max() + max_iou_row = iou_GDF.loc[iou_GDF['iou_score'].idxmax(axis=0, skipna=True)] + id_1 = row[gt_cat_attrib] + id_2 = proposal_gdf.loc[max_iou_row.name][prediction_cat_attrib] + if id_1 == id_2: + ious.append(iou) + proposal_gdf.drop(max_iou_row.name, axis=0, inplace=True) + else: + iou = 0 + ious.append(iou) + else: + iou = 0 + ious.append(iou) + for item in ious: + iou_holder[i].append(item) + i += 1 + ious = [] + else: + ground_truth_gdf = gpd.read_file(geojson) + i = 0 + for obj in object_subset: + ground_truth_gdf2 = ground_truth_gdf[ground_truth_gdf[gt_cat_attrib] == obj] + for z in range(len(ground_truth_gdf2)): + ious.append(0) + for item in ious: + iou_holder[i].append(item) + i += 1 + ious = [] + + recall_by_class = average_score_by_class(iou_holder, threshold=threshold) + recall_by_class = list(np.nan_to_num(recall_by_class)) + mRecall = np.nanmean(recall_by_class) + print("mRecall:", mRecall) + return iou_holder, recall_by_class, mRecall + + +def mF1(proposal_polygons_dir, gt_polygons_dir, prediction_cat_attrib="class", + gt_cat_attrib='make', object_subset=[], threshold=0.5, confidence_attrib=None, + file_format="geojson", all_outputs=False): + """ Using the proposal and ground truth polygons, calculate F1 and mF1 + metrics. Filenames of predictions and ground-truth must be identical. Will + only calculate metric for classes that exist in the ground truth. + Arguments + --------- + proposal_polygons_dir : str + The path that contains any model proposal polygons + gt_polygons_dir : str + The path that contains the ground truth polygons + prediction_cat_attrib : str + The column or attribute within the predictions that specifies + unique classes + gt_cat_attrib : str + The column or attribute within the ground truth that + specifies unique classes + object_subset : list + A list or subset of the unique objects that are contained within the + proposal and ground truth polygons. If empty, this will be + auto-created using all classes that appear in the proposal and + ground truth polygons. + threshold : float + A value between 0.0 and 1.0 that determines the IOU threshold for a + true positve. + confidence_attrib : str + The column or attribute within the proposal polygons that + specifies model confidence for each prediction + file_format : str + The extension or file format for predictions + all_outputs : bool + `True` or `False`. If `True` returns an expanded output. + Returns + --------- + if all_outputs is `True`: + mF1 : float + The mean F1 score of f1s_by_class + f1s_by_class : list + A list containing the f1 score for each class + precision_iou_by_obj : list of lists + An iou score for each object per class (precision specific) + precision_by_class : list + A list containing the precision score for each class + mPrecision : float + The mean precision score of precision_by_class + recall_iou_by_obj : list of lists + An iou score for each object per class (recall specific) + recall_by_class : list + A list containing the recall score for each class + mRecall : float + The mean recall score of recall_by_class + object_subset : list + All unique objects that exist in the ground truth polygons + confidences : list of lists + All confidences for each object for each class + if all_outputs is `False`: + mF1_score : float + The mean F1 score of f1s_by_class (only calculated for ground + ground truth classes) + f1s_by_class : list + A list containing the f1 score for each class + """ + if len(object_subset) == 0: + print("getting unique objects...") + prop_objs, object_subset, all_objs = get_all_objects( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, file_format=file_format) + print("calculating recall...") + recall_iou_by_obj, recall_by_class, mRecall = recall_calc( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, object_subset=object_subset, + threshold=threshold, file_format=file_format) + print("calculating precision...") + precision_iou_by_obj, precision_by_class, mPrecision, confidences = precision_calc( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, object_subset=object_subset, + threshold=threshold, confidence_attrib=confidence_attrib, file_format=file_format) + print("calculating F1 scores...") + f1s_by_class = [] + for recall, precision in zip(recall_by_class, precision_by_class): + f1 = 2 * precision * recall / (precision + recall) + f1 = np.nan_to_num(f1) + f1s_by_class.append(f1) + mF1_score = np.nanmean(f1s_by_class) + print("mF1:", mF1_score) + if all_outputs is True: + return mF1_score, f1s_by_class, precision_iou_by_obj, precision_by_class, mPrecision, recall_iou_by_obj, recall_by_class, mRecall, object_subset, confidences + else: + return mF1_score, f1s_by_class + + +def mAP_score(proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib="class", gt_cat_attrib='make', + object_subset=[], threshold=0.5, confidence_attrib="confidence", + file_format="geojson"): + """ Using the proposal and ground truth polygons calculate the Mean Average + Precision (mAP) and mF1 metrics. Filenames of predictions and ground-truth + must be identical. Will only calculate metric for classes that exist in + the ground truth. + + Arguments + --------- + proposal_polygons_dir : str + The path that contains any model proposal polygons + gt_polygons_dir : str + The path that contains the ground truth polygons + prediction_cat_attrib : str + The column or attribute within the predictions that specifies + unique classes + gt_cat_attrib : str + The column or attribute within the ground truth that + specifies unique classes + object_subset : list + A list or subset of the unique objects that are contained within the + proposal and ground truth polygons. If empty, this will be + auto-created using all classes that appear in the proposal and + ground truth polygons. + threshold : float + A value between 0.0 and 1.0 that determines the IOU threshold for a + true positve. + confidence_attrib : str + The column or attribute within the proposal polygons that + specifies model confidence for each prediction + file_format : str + The extension or file format for predictions + Returns + --------- + mAP : float + The mean average precision score of APs_by_class + APs_by_class : list + A list containing the AP score for each class + mF1 : float + The mean F1 score of f1s_by_class + f1s_by_class : list + A list containing the f1 score for each class + precision_iou_by_obj : list of lists + An iou score for each object per class (precision specific) + precision_by_class : list + A list containing the precision score for each class + mPrecision : float + The mean precision score of precision_by_class + recall_iou_by_obj : list of lists + An iou score for each object per class (recall specific) + recall_by_class : list + A list containing the recall score for each class + mRecall : float + The mean recall score of recall_by_class + object_subset : list + All unique objects that exist in the ground truth polygons + confidences : list of lists + All confidences for each object for each class + """ + + mF1_score, f1s_by_class, precision_iou_by_obj, precision_by_class, mPrecision, recall_iou_by_obj, recall_by_class, mRecall, object_subset, confidences = mF1( + proposal_polygons_dir, gt_polygons_dir, + prediction_cat_attrib=prediction_cat_attrib, + gt_cat_attrib=gt_cat_attrib, object_subset=object_subset, + threshold=threshold, confidence_attrib=confidence_attrib, + file_format=file_format, all_outputs=True) + + recall_thresholds = np.arange(0, 1.01, 0.01).tolist() + APs_by_class = [] + for p_obj_list, c_obj_list, r_obj_list in zip(precision_iou_by_obj, confidences, recall_iou_by_obj): + num_objs = len(r_obj_list) + p_obj_list_sorted = [x for _, x in sorted(zip(c_obj_list, p_obj_list))] + p_obj_list_sorted.reverse() + TPs = [] + FPs = [] + for p in p_obj_list_sorted: + if p >= threshold: + TPs.append(1) + FPs.append(0) + else: + TPs.append(0) + FPs.append(1) + Acc_TPs = [] + Acc_FPs = [] + t_sum = 0 + f_sum = 0 + for t, f in zip(TPs, FPs): + t_sum += t + f_sum += f + Acc_TPs.append(t_sum) + Acc_FPs.append(f_sum) + precisions = [] + recalls = [] + + for aTP, aFP in zip(Acc_TPs, Acc_FPs): + precision = (aTP / (aTP + aFP)) + precisions.append(precision) + recall = (aTP / num_objs) + recalls.append(recall) + interp = [] + for t in recall_thresholds: + precisions2 = [p for r, p in zip(recalls, precisions) if r >= t] + if len(precisions2) > 0: + interp.append(np.max(precisions2)) + else: + interp.append(0) + + AP = np.average(interp) + APs_by_class.append(AP) + mAP = np.average(APs_by_class) + print("mAP:", mAP, "@IOU:", threshold) + return mAP, APs_by_class, mF1_score, f1s_by_class, precision_iou_by_obj, precision_by_class, mPrecision, recall_iou_by_obj, recall_by_class, mRecall, object_subset, confidences diff --git a/docker/solaris/solaris/nets/__init__.py b/docker/solaris/solaris/nets/__init__.py new file mode 100644 index 00000000..63f614e7 --- /dev/null +++ b/docker/solaris/solaris/nets/__init__.py @@ -0,0 +1,11 @@ +import os + +weights_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), + 'weights') + +from . import callbacks, datagen, infer, losses, metrics, model_io +from . import optimizers, train, transform, zoo + + +if not os.path.isdir(weights_dir): + os.mkdir(weights_dir) diff --git a/docker/solaris/solaris/nets/_keras_losses.py b/docker/solaris/solaris/nets/_keras_losses.py new file mode 100644 index 00000000..229f4e3b --- /dev/null +++ b/docker/solaris/solaris/nets/_keras_losses.py @@ -0,0 +1,222 @@ +from tensorflow.keras import losses +from tensorflow.keras import backend as K +from .metrics import dice_coef_binary +import tensorflow as tf + + +def k_dice_loss(y_true, y_pred): + return 1 - dice_coef_binary(y_true, y_pred) + + +def k_jaccard_loss(y_true, y_pred): + """Jaccard distance for semantic segmentation. + + Modified from the `keras-contrib` package. + + """ + eps = 1e-12 # for stability + y_pred = K.clip(y_pred, eps, 1-eps) + intersection = K.sum(K.abs(y_true*y_pred), axis=-1) + sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1) + jac = intersection/(sum_ - intersection) + return 1 - jac + + +def k_focal_loss(gamma=2, alpha=0.75): + # from github.com/atomwh/focalloss_keras + + def focal_loss_fixed(y_true, y_pred): # with tensorflow + + eps = 1e-12 # improve the stability of the focal loss + y_pred = K.clip(y_pred, eps, 1.-eps) + pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred)) + pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred)) + return -K.sum( + alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum( + (1-alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0)) + + return focal_loss_fixed + + +def k_lovasz_hinge(per_image=False): + """Wrapper for the Lovasz Hinge Loss Function, for use in Keras. + + This is a mess. I'm sorry. + """ + + def lovasz_hinge_flat(y_true, y_pred): + # modified from Maxim Berman's GitHub repo tf implementation for Lovasz + eps = 1e-12 # for stability + y_pred = K.clip(y_pred, eps, 1-eps) + logits = K.log(y_pred/(1-y_pred)) + logits = tf.reshape(logits, (-1,)) + y_true = tf.reshape(y_true, (-1,)) + y_true = tf.cast(y_true, logits.dtype) + signs = 2. * y_true - 1. + errors = 1. - logits * tf.stop_gradient(signs) + errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], + name="descending_sort") + gt_sorted = tf.gather(y_true, perm) + grad = tf_lovasz_grad(gt_sorted) + loss = tf.tensordot(tf.nn.relu(errors_sorted), + tf.stop_gradient(grad), + 1, name="loss_non_void") + return loss + + def lovasz_hinge_per_image(y_true, y_pred): + # modified from Maxim Berman's GitHub repo tf implementation for Lovasz + losses = tf.map_fn(_treat_image, (y_true, y_pred), dtype=tf.float32) + loss = tf.reduce_mean(losses) + return loss + + def _treat_image(ytrue_ypred): + y_true, y_pred = ytrue_ypred + y_true, y_pred = tf.expand_dims(y_true, 0), tf.expand_dims(y_pred, 0) + return lovasz_hinge_flat(y_true, y_pred) + + if per_image: + return lovasz_hinge_per_image + else: + return lovasz_hinge_flat + + +def tf_lovasz_grad(gt_sorted): + """ + Code from Maxim Berman's GitHub repo for Lovasz. + + Computes gradient of the Lovasz extension w.r.t sorted errors + See Alg. 1 in paper + """ + gts = tf.reduce_sum(gt_sorted) + intersection = gts - tf.cumsum(gt_sorted) + union = gts + tf.cumsum(1. - gt_sorted) + jaccard = 1. - intersection / union + jaccard = tf.concat((jaccard[0:1], jaccard[1:] - jaccard[:-1]), 0) + return jaccard + + +# matching dicts to get the right loss function based on the config file +keras_losses = { + 'binary_crossentropy': losses.binary_crossentropy, + 'bce': losses.binary_crossentropy, + 'categorical_crossentropy': losses.categorical_crossentropy, + 'cce': losses.categorical_crossentropy, + 'cosine': losses.cosine, + 'hinge': losses.hinge, + 'kullback_leibler_divergence': losses.kullback_leibler_divergence, + 'kld': losses.kullback_leibler_divergence, + 'mean_absolute_error': losses.mean_absolute_error, + 'mae': losses.mean_absolute_error, + 'mean_squared_logarithmic_error': losses.mean_squared_logarithmic_error, + 'msle': losses.mean_squared_logarithmic_error, + 'mean_squared_error': losses.mean_squared_error, + 'mse': losses.mean_squared_error, + 'sparse_categorical_crossentropy': losses.sparse_categorical_crossentropy, + 'squared_hinge': losses.squared_hinge, + 'jaccard': k_jaccard_loss, + 'dice': k_dice_loss +} + + +def k_weighted_bce(y_true, y_pred, weight): + """Weighted binary cross-entropy for Keras. + + Arguments: + ---------- + y_true : ``tf.Tensor`` + passed silently by Keras during model training. + y_pred : ``tf.Tensor`` + passed silently by Keras during model training. + weight : :class:`float` or :class:`int` + Weight to assign to mask foreground pixels. Use values + >1 to over-weight foreground or 0 1: + class_two = class_two*(weight-1) + final_mask = weight_mask + class_two # foreground pixels weighted + return K.binary_crossentropy(y_pred, y_true) * final_mask + + +def k_layered_weighted_bce(y_true, y_pred, weights): + """Binary cross-entropy function with different weights for mask channels. + + Arguments: + ---------- + y_true (tensor): passed silently by Keras during model training. + y_pred (tensor): passed silently by Keras during model training. + weights (list-like): Weights to assign to mask foreground pixels for each + channel in the 3rd axis of the mask. + + Returns: + -------- + The binary cross-entropy loss function output multiplied by a weighting + mask. + + Usage: + ------ + See implementation instructions for `weighted_bce`. + + This loss function is intended to allow different weighting of different + segmentation outputs - for example, if a model outputs a 3D image mask, + where the first channel corresponds to foreground objects and the second + channel corresponds to object edges. `weights` must be a list of length + equal to the depth of the output mask. The output mask's "z-axis" + corresponding to the mask channel must be the third axis in the output + array. + + """ + weight_mask = K.ones_like(y_true) + submask_list = [] + for i in range(len(weights)): + class_two = K.equal(y_true[:, :, :, i], weight_mask[:, :, :, i]) + class_two = K.cast(class_two, 'float32') + if weights[i] < 1: + class_two = class_two*(1-weights[i]) + layer_mask = weight_mask[:, :, :, i] - class_two + elif weights[i] > 1: + class_two = class_two*(weights[i]-1) + layer_mask = weight_mask[:, :, :, i] + class_two + else: + layer_mask = weight_mask[:, :, :, i] + submask_list.append(layer_mask) + final_mask = K.stack(submask_list, axis=-1) + return K.binary_crossentropy(y_pred, y_true) * final_mask diff --git a/docker/solaris/solaris/nets/_torch_losses.py b/docker/solaris/solaris/nets/_torch_losses.py new file mode 100644 index 00000000..fa9a0a9d --- /dev/null +++ b/docker/solaris/solaris/nets/_torch_losses.py @@ -0,0 +1,379 @@ +import torch +from torch.autograd import Variable +import torch.nn.functional as F +import numpy as np +from torch import nn +try: + from itertools import ifilterfalse +except ImportError: # py3k + from itertools import filterfalse as ifilterfalse + + +class TorchDiceLoss(nn.Module): + def __init__(self, weight=None, size_average=True, + per_image=False, logits=False): + super().__init__() + self.size_average = size_average + self.register_buffer('weight', weight) + self.per_image = per_image + self.logits = logits + + def forward(self, input, target): + if self.logits: + input = torch.sigmoid(input) + return soft_dice_loss(input, target, per_image=self.per_image) + + +class TorchFocalLoss(nn.Module): + """Implementation of Focal Loss[1]_ modified from Catalyst [2]_ . + + Arguments + --------- + gamma : :class:`int` or :class:`float` + Focusing parameter. See [1]_ . + alpha : :class:`int` or :class:`float` + Normalization factor. See [1]_ . + + References + ---------- + .. [1] https://arxiv.org/pdf/1708.02002.pdf + .. [2] https://catalyst-team.github.io/catalyst/ + """ + + def __init__(self, gamma=2, reduce=True, logits=False): + super().__init__() + self.gamma = gamma + self.reduce = reduce + self.logits = logits + + # TODO refactor + def forward(self, outputs, targets): + """Calculate the loss function between `outputs` and `targets`. + + Arguments + --------- + outputs : :class:`torch.Tensor` + The output tensor from a model. + targets : :class:`torch.Tensor` + The training target. + + Returns + ------- + loss : :class:`torch.Variable` + The loss value. + """ + + if self.logits: + BCE_loss = F.binary_cross_entropy_with_logits(outputs, targets, + reduction='none') + else: + BCE_loss = F.binary_cross_entropy(outputs, targets, + reduction='none') + pt = torch.exp(-BCE_loss) + F_loss = (1-pt)**self.gamma * BCE_loss + if self.reduce: + return torch.mean(F_loss) + else: + return F_loss + + # def forward(self, outputs, targets): + # """Calculate the loss function between `outputs` and `targets`. + # + # Arguments + # --------- + # outputs : :class:`torch.Tensor` + # The output tensor from a model. + # targets : :class:`torch.Tensor` + # The training target. + # + # Returns + # ------- + # loss : :class:`torch.Variable` + # The loss value. + # """ + # if targets.size() != outputs.size(): + # raise ValueError( + # f"Targets and inputs must be same size. " + # f"Got ({targets.size()}) and ({outputs.size()})" + # ) + # + # max_val = (-outputs).clamp(min=0) + # log_ = ((-max_val).exp() + (-outputs - max_val).exp()).log() + # loss = outputs - outputs * targets + max_val + log_ + # + # invprobs = F.logsigmoid(-outputs * (targets * 2.0 - 1.0)) + # loss = self.alpha*(invprobs * self.gamma).exp() * loss + # + # return loss.sum(dim=-1).mean() + + +def torch_lovasz_hinge(logits, labels, per_image=False, ignore=None): + """Lovasz Hinge Loss. Implementation edited from Maxim Berman's GitHub. + + References + ---------- + https://github.com/bermanmaxim/LovaszSoftmax/ + https://arxiv.org/abs/1705.08790 + + Arguments + --------- + logits: :class:`torch.Variable` + logits at each pixel (between -inf and +inf) + labels: :class:`torch.Tensor` + binary ground truth masks (0 or 1) + per_image: bool, optional + compute the loss per image instead of per batch. Defaults to ``False``. + ignore: optional void class id. + + Returns + ------- + loss : :class:`torch.Variable` + Lovasz loss value for the input logits and labels. Compatible with + ``loss.backward()`` as its a :class:`torch.Variable` . + """ + # TODO: Restructure into a class like TorchFocalLoss for compatibility + if per_image: + loss = mean( + lovasz_hinge_flat(*flatten_binary_scores(log.unsqueeze(0), + lab.unsqueeze(0), + ignore)) + for log, lab in zip(logits, labels)) + else: + loss = lovasz_hinge_flat(*flatten_binary_scores(logits, + labels, + ignore)) + return loss + + +def lovasz_hinge_flat(logits, labels): + """Binary Lovasz hinge loss. + + Arguments + --------- + logits: :class:`torch.Variable` + Logits at each prediction (between -inf and +inf) + labels: :class:`torch.Tensor` + binary ground truth labels (0 or 1) + + Returns + ------- + loss : :class:`torch.Variable` + Lovasz loss value for the input logits and labels. + """ + if len(labels) == 0: + # only void pixels, the gradients should be 0 + return logits.sum() * 0. + signs = 2. * labels.float() - 1. + errors = (1. - logits * Variable(signs)) + errors_sorted, perm = torch.sort(errors, dim=0, descending=True) + perm = perm.data + gt_sorted = labels[perm] + grad = lovasz_grad(gt_sorted) + loss = torch.dot(F.relu(errors_sorted), Variable(grad)) + return loss + + +def flatten_binary_scores(scores, labels, ignore=None): + """ + Flattens predictions in the batch (binary case) + Remove labels equal to 'ignore' + """ + scores = scores.view(-1) + labels = labels.view(-1) + if ignore is None: + return scores, labels + valid = (labels != ignore) + vscores = scores[valid] + vlabels = labels[valid] + return vscores, vlabels + + +class TorchJaccardLoss(torch.nn.modules.Module): + # modified from XD_XD's implementation + def __init__(self): + super(TorchJaccardLoss, self).__init__() + + def forward(self, outputs, targets): + eps = 1e-15 + + jaccard_target = (targets == 1).float() + jaccard_output = torch.sigmoid(outputs) + intersection = (jaccard_output * jaccard_target).sum() + union = jaccard_output.sum() + jaccard_target.sum() + jaccard_score = ((intersection + eps) / (union - intersection + eps)) + self._stash_jaccard = jaccard_score + loss = 1. - jaccard_score + + return loss + + +class TorchStableBCELoss(torch.nn.modules.Module): + def __init__(self): + super(TorchStableBCELoss, self).__init__() + + def forward(self, input, target): + neg_abs = - input.abs() + loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log() + return loss.mean() + + +def binary_xloss(logits, labels, ignore=None): + """ + Binary Cross entropy loss + logits: [B, H, W] Variable, logits at each pixel (between -inf and +inf) + labels: [B, H, W] Tensor, binary ground truth masks (0 or 1) + ignore: void class id + """ + logits, labels = flatten_binary_scores(logits, labels, ignore) + loss = TorchStableBCELoss()(logits, Variable(labels.float())) + return loss + + +def lovasz_grad(gt_sorted): + """ + Computes gradient of the Lovasz extension w.r.t sorted errors + See Alg. 1 in paper + """ + p = len(gt_sorted) + gts = gt_sorted.sum() + intersection = gts - gt_sorted.float().cumsum(0) + union = gts + (1 - gt_sorted).float().cumsum(0) + jaccard = 1. - intersection / union + if p > 1: # cover 1 - pixel case + jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] + return jaccard + + +def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True): + """ + IoU for foreground class + binary: 1 foreground, 0 background + """ + if not per_image: + preds, labels = (preds,), (labels,) + ious = [] + for pred, label in zip(preds, labels): + intersection = ((label == 1) & (pred == 1)).sum() + union = ((label == 1) | ((pred == 1) & (label != ignore))).sum() + if not union: + iou = EMPTY + else: + iou = float(intersection) / float(union) + ious.append(iou) + iou = mean(ious) # mean accross images if per_image + return 100 * iou + + +def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False): + """ + Array of IoU for each (non ignored) class + """ + if not per_image: + preds, labels = (preds,), (labels,) + ious = [] + for pred, label in zip(preds, labels): + iou = [] + for i in range(C): + if i != ignore: + intersection = ((label == i) & (pred == i)).sum() + union = ((label == i) | ((pred == i) & (label != ignore))).sum() + if not union: + iou.append(EMPTY) + else: + iou.append(float(intersection) / float(union)) + ious.append(iou) + ious = [mean(iou) for iou in zip(*ious)] # mean across images if per_image + return 100 * np.array(ious) + + +# helper functions +def isnan(x): + return x != x + + +def mean(l, ignore_nan=False, empty=0): + """ + nanmean compatible with generators. + """ + l = iter(l) + if ignore_nan: + l = ifilterfalse(isnan, l) + try: + n = 1 + acc = next(l) + except StopIteration: + if empty == 'raise': + raise ValueError('Empty mean') + return empty + for n, v in enumerate(l, 2): + acc += v + if n == 1: + return acc + return acc / n + + +def dice_round(preds, trues): + preds = preds.float() + return soft_dice_loss(preds, trues) + + +def soft_dice_loss(outputs, targets, per_image=False): + batch_size = outputs.size()[0] + eps = 1e-5 + if not per_image: + batch_size = 1 + dice_target = targets.contiguous().view(batch_size, -1).float() + dice_output = outputs.contiguous().view(batch_size, -1) + intersection = torch.sum(dice_output * dice_target, dim=1) + union = torch.sum(dice_output, dim=1) + torch.sum(dice_target, dim=1) + eps + loss = (1 - (2 * intersection + eps) / union).mean() + + return loss + + +torch_losses = { + 'l1loss': nn.L1Loss, + 'l1': nn.L1Loss, + 'mae': nn.L1Loss, + 'mean_absolute_error': nn.L1Loss, + 'smoothl1loss': nn.SmoothL1Loss, + 'smoothl1': nn.SmoothL1Loss, + 'mean_squared_error': nn.MSELoss, + 'mse': nn.MSELoss, + 'mseloss': nn.MSELoss, + 'categorical_crossentropy': nn.CrossEntropyLoss, + 'cce': nn.CrossEntropyLoss, + 'crossentropyloss': nn.CrossEntropyLoss, + 'negative_log_likelihood': nn.NLLLoss, + 'nll': nn.NLLLoss, + 'nllloss': nn.NLLLoss, + 'poisson_negative_log_likelihood': nn.PoissonNLLLoss, + 'poisson_nll': nn.PoissonNLLLoss, + 'poissonnll': nn.PoissonNLLLoss, + 'kullback_leibler_divergence': nn.KLDivLoss, + 'kld': nn.KLDivLoss, + 'kldivloss': nn.KLDivLoss, + 'binary_crossentropy': nn.BCELoss, + 'bce': nn.BCELoss, + 'bceloss': nn.BCELoss, + 'bcewithlogits': nn.BCEWithLogitsLoss, + 'bcewithlogitsloss': nn.BCEWithLogitsLoss, + 'hinge': nn.HingeEmbeddingLoss, + 'hingeembeddingloss': nn.HingeEmbeddingLoss, + 'multiclass_hinge': nn.MultiMarginLoss, + 'multimarginloss': nn.MultiMarginLoss, + 'softmarginloss': nn.SoftMarginLoss, + 'softmargin': nn.SoftMarginLoss, + 'multiclass_softmargin': nn.MultiLabelSoftMarginLoss, + 'multilabelsoftmarginloss': nn.MultiLabelSoftMarginLoss, + 'cosine': nn.CosineEmbeddingLoss, + 'cosineloss': nn.CosineEmbeddingLoss, + 'cosineembeddingloss': nn.CosineEmbeddingLoss, + 'lovaszhinge': torch_lovasz_hinge, + 'focalloss': TorchFocalLoss, + 'focal': TorchFocalLoss, + 'jaccard': TorchJaccardLoss, + 'jaccardloss': TorchJaccardLoss, + 'dice': TorchDiceLoss, + 'diceloss': TorchDiceLoss +} diff --git a/docker/solaris/solaris/nets/callbacks.py b/docker/solaris/solaris/nets/callbacks.py new file mode 100644 index 00000000..d1833e3a --- /dev/null +++ b/docker/solaris/solaris/nets/callbacks.py @@ -0,0 +1,258 @@ +import numpy as np +from tensorflow import keras +from tensorflow.keras.callbacks import Callback +from .torch_callbacks import torch_callback_dict +import torch + + +def get_callbacks(framework, config): + """Load callbacks based on a config file for a specific framework. + + Usage + ----- + Note that this function is primarily intended for use with Keras. PyTorch + does not use the same object-oriented training approach as Keras, and + therefore doesn't generally have the same checkpointing objects to pass to + model compilers - instead these are defined in model training code. See + solaris.nets.train for examples of this. The only torch callback + instantiated here is a learning rate scheduler. + + Arguments + --------- + framework : str + Deep learning framework used for the model. Options are + ``['keras', 'torch']`` . + config : dict + Configuration dict generated from the YAML config file. + + Returns + ------- + callbacks : list + A `list` of callbacks to pass to the compiler (Keras) or to wrap the + optimizer (torch learning rate scheduling) for model training. + """ + + callbacks = [] + + if framework == 'keras': + for callback, params in config['training']['callbacks'].items(): + if callback == 'lr_schedule': + callbacks.append(get_lr_schedule(framework, config)) + else: + callbacks.append(keras_callbacks[callback](**params)) + elif framework == 'torch': + for callback, params in config['training']['callbacks'].items(): + if callback == 'lr_schedule': + callbacks.append(get_lr_schedule(framework, config)) + else: + callbacks.append(torch_callback_dict[callback](**params)) + + return callbacks + + +class KerasTerminateOnMetricNaN(Callback): + """Callback to stop training if a metric has value NaN or infinity. + + Notes + ----- + Instantiate as you would any other keras callback. For example, to end + training if a validation metric called `f1_score` reaches value NaN:: + + m = Model(inputs, outputs) + m.compile() + m.fit(X, y, callbacks=[TerminateOnMetricNaN('val_f1_score')]) + + + Attributes + ---------- + metric : str, optional + Name of the metric being monitored. + checkpoint : str, optional + One of ``['epoch', 'batch']``: Should the metric be checked at the end + of every epoch (default) or every batch? + + Methods + ------- + on_epoch_end : operations to complete at the end of each epoch. + on_batch_end : operations to complete at the end of each batch. + """ + + def __init__(self, metric=None, checkpoint='epoch'): + """ + + Parameters + ---------- + metric (str): The name of the metric to be tested for NaN value. + checkpoint (['epoch', 'batch']): Should the metric be checked at the end of + every epoch (default) or every batch? + + """ + super(KerasTerminateOnMetricNaN, self).__init__() + self.metric = metric + self.ckpt = checkpoint + + def on_epoch_end(self, epoch, logs=None): + if self.ckpt == 'epoch': + logs = logs or {} + metric_score = logs.get(self.metric) + if self.metric is not None: + if np.isnan(metric_score) or np.isinf(metric_score): + print('Epoch {}: Invalid score for metric {}, terminating' + ' training'.format(epoch, self.metric)) + self.model.stop_training = True + + def on_batch_end(self, batch, logs=None): + if self.ckpt == 'batch': + logs = logs or {} + metric_score = logs.get(self.metric) + print('metric score: {}'.format(metric_score)) + if np.isnan(metric_score) or np.isinf(metric_score): + print('Batch {}: Invalid score for metric' + '{}, terminating training'.format(batch, self.metric)) + self.model.stop_training = True + + +keras_callbacks = { + 'terminate_on_nan': keras.callbacks.TerminateOnNaN, + 'terminate_on_metric_nan': KerasTerminateOnMetricNaN, + 'model_checkpoint': keras.callbacks.ModelCheckpoint, + 'early_stopping': keras.callbacks.EarlyStopping, + 'reduce_lr_on_plateau': keras.callbacks.ReduceLROnPlateau, + 'csv_logger': keras.callbacks.CSVLogger + } + + +def get_lr_schedule(framework, config): + """Get a LR scheduling function for model training. + + Arguments + --------- + framework : str + Deep learning framework used for the model. Options are + ``['keras', 'torch']`` . + config : dict + Configuration dict generated from the YAML config file. + + Returns + ------- + lr_scheduler : :class:`tensorflow.keras.callbacks.LearningRateScheduler` or + ``torch.optim.lr_schedule`` scheduler class + A scheduler to provide during training. For Keras, this takes the form + of a callback passed to the optimizer; for PyTorch, it's a class object + that wraps the optimizer. Because the torch version must wrap the + optimizer, it's not instantiated here - the class is returned instead. + + """ + + schedule_type = config['training'][ + 'callbacks']['lr_schedule']['schedule_type'] + initial_lr = config['training']['lr'] + update_frequency = config['training']['callbacks']['lr_schedule'].get( + 'update_frequency', 1) + factor = config['training']['callbacks']['lr_schedule'].get( + 'factor', 0) + schedule_dict = config['training']['callbacks']['lr_schedule'].get( + 'schedule_dict', None) + if framework == 'keras': + lr_sched_func = keras_lr_schedule(schedule_type, initial_lr, + update_frequency, factor, + schedule_dict) + lr_scheduler = keras.callbacks.LearningRateScheduler(lr_sched_func) + elif framework == 'torch': + # just get the class itself to use; don't instantiate until the + # optimizer has been created. + if config['training'][ + 'callbacks']['lr_schedule']['schedule_type'] == 'linear': + lr_scheduler = torch.optim.lr_scheduler.StepLR + elif config['training'][ + 'callbacks']['lr_schedule']['schedule_type'] == 'exponential': + lr_scheduler = torch.optim.lr_scheduler.ExponentialLR + elif config['training'][ + 'callbacks']['lr_schedule']['schedule_type'] == 'arbitrary': + lr_scheduler = torch.optim.lr_scheduler.MultiStepLR + + return lr_scheduler + + +def keras_lr_schedule(schedule_type, initial_lr=0.001, update_frequency=1, + factor=0, schedule_dict=None): + """Create a learning rate schedule for Keras from a schedule dict. + + Arguments + --------- + schedule_type : str + Type of learning rate schedule to use. Options are: + ``['arbitrary', 'exponential', 'linear']`` . + initial_lr : float, optional + The initial learning rate to use. Defaults to ``0.001`` . + update_frequency : int, optional + How frequently should learning rate be reduced (or increased)? Defaults + to ``1`` (every epoch). Has no effect if ``schedule_type='arbitrary'``. + factor : float, optional + The magnitude by which learning rate should be changed at each update. + Use a positive number to increase learning rate and a negative number + to decrease learning rate. See Usage for more details. + schedule_dict : dict, optional + A dictionary with ``{epoch: learning rate}`` pairs. The learning rate + defined in each pair will be used beginning at the specified epoch and + continuing until the next highest epoch number is reached during + training. + + Returns + ------- + lr_schedule : func + a function that takes epoch number integers as an argument and returns + a learning rate. + + Usage + ----- + ``schedule_type='arbitrary'`` usage is documented in the arguments above. + For ``schedule_type='exponential'``, the following equation is applied to + determine the learning rate at each epoch: + + .. math:: + + lr = initial_lr*e^{factor\times(floor(epoch/update_frequency))} + + For ``schedule_type='linear'``, the following equation is applied: + + .. math:: + + lr = initial_lr\times(1+factor\times(floor(epoch/update_frequency))) + + """ + if schedule_type == 'arbitrary': + if schedule_dict is None: + raise ValueError('If using an arbitrary schedule, an epoch: lr ' + 'dict must be provided.') + lookup_dict = {} + epoch_vals = np.array(list(schedule_dict.keys())) + for e in range(0, epoch_vals.max() + 1): + if e < epoch_vals.min(): + lookup_dict[e] = schedule_dict[epoch_vals.min()] + else: + # get all the epochs from the dict <= e + lower_epochs = epoch_vals[epoch_vals <= e] + # get the LR for the highest epoch number <= e + lookup_dict[e] = schedule_dict[lower_epochs.max()] + + def lr_schedule(epoch): + if epoch < epoch_vals.min(): + return initial_lr + elif epoch > epoch_vals.max(): + return lookup_dict[epoch_vals.max()] + else: + return lookup_dict[epoch] + + elif schedule_type == 'exponential': + def lr_schedule(epoch): + if not np.floor(epoch/update_frequency): + return initial_lr + else: + return initial_lr*factor/np.floor(epoch/update_frequency) + + elif schedule_type == 'linear': + def lr_schedule(epoch): + return initial_lr*(1+factor*np.floor(epoch/update_frequency)) + + return lr_schedule diff --git a/docker/solaris/solaris/nets/configs/config_skeleton.yml b/docker/solaris/solaris/nets/configs/config_skeleton.yml new file mode 100644 index 00000000..0b86a9e4 --- /dev/null +++ b/docker/solaris/solaris/nets/configs/config_skeleton.yml @@ -0,0 +1,127 @@ +################################################################################ +################# SOLARIS MODEL CONFIGURATION SKELETON ######################### +################################################################################ + +# This skeleton lays out the required instructions for running a model using +# solaris. See the full documentation at [INCLUDE DOC LINK HERE] for details on +# options, required arguments, and sample usage. + +model_name: # include the name of the model to be used here. See the docs + # for options. +model_path: # leave this blank unless you're using a custom model not + # native to solaris. solaris will automatically find your + # model. +train: true # set to false for inference only +infer: true # set to false for training only + +pretrained: true # use pretrained weights associated with the model? +nn_framework: # if not using a model included with the package, use this + # argument to specify if it uses keras, pytorch, tf, etc. +batch_size: # size of each batch fed into nn. + +data_specs: + width: # width of the input images taken in by the neural net. + height: # height of the input images taken in by the neural net. + dtype: # dtype of the inputs ingested by the neural net. + rescale: false # should image pixel values be rescaled before pre-processing? + # If so, the image will be rescaled to the pixel range defined + # by rescale_min and rescale_max below. + rescale_minima: auto # the minimum values to use in rescaling (if + # rescale=true). If 'auto', the minimum pixel intensity + # in each channel of the image will be subtracted. If + # a single value is provided, that value will be set to + # zero for each channel in the input image. + # if a list of values are provided, then minima in the + # separate channels (in that order) will be set to that + # value PRIOR to any augmentation. + rescale_maxima: auto # same as rescale_minima, but for the maximum value for + # each channel in the image. + channels: # number of channels in the input imagery. + label_type: mask # one of ['mask', 'bbox'] (CURRENTLY ONLY MASK IMPLEMENTED) + is_categorical: false # are the labels binary (default) or categorical? + num_classes: 1 # number of classes to train on, should match the number + # of channels in training mask + mask_channels: 1 # number of channels in the training mask + val_holdout_frac: # if empty, assumes that separate data ref files define the + # training and validation dataset. If a float between 0 and + # 1, indicates the fraction of training data that's held + # out for validation (and validation_data_csv will be + # ignored) + data_workers: # number of cpu threads to use for loading and preprocessing + # input images. +# other_inputs: # this can provide a list of additional inputs to pass to the + # neural net for training. These inputs should be specified in + # extra columns of the csv files (denoted below), either as + # filepaths to additional data to load or as values to include. + # NOTE: This is not currently implemented. + +training_data_csv: # path to the reference csv that defines training data. + # see the documentation for the specifications of this file. +validation_data_csv: # path to the validation ref csv. See the docs. If + # val_holdout_frac is specified (under data_specs), then + # this argument will be ignored. +inference_data_csv: # path to the reference csv that defines inference data. + # see the documentation for the specs of this file. + +training_augmentation: # augmentations for use with training data + augmentations: + # include augmentations here. See the documentation for options and + # required arguments. + p: 1.0 # probability of applying the entire training augmentation pipeline. + shuffle: true # should the image order be shuffled in each epoch. +validation_augmentation: # augmentations for use with validation data + augmentations: + # include augmentations here + p: # probability of applying the full validation augmentation pipeline. +inference_augmentation: # this is optional. If not provided, + # validation_augmentation will be used instead. + +training: + epochs: # number of epochs. A list can also be provided here indicating + # distinct sets of epochs at different learning rates, etc; if so, + # a list of equal length must be provided in the parameter that varies + # with the values for each set of epochs. + steps_per_epoch: # optional argument defining # steps/epoch. If not provided, + # each epoch will include the number of steps needed to go + # through the entire training dataset. + optimizer: # optimizer function name. see docs for options. + lr: # learning rate. + opt_args: # dictionary of values (e.g. alpha, gamma, momentum) specific to + # the optimizer. + loss: # loss function(s). See docs for options. This should be a list of loss + # names with sublists of loss function hyperparameters (if applicable). + # See the docs for more details and usage examples. + loss_weights: # (optional) weights to use for each loss function if using + # loss: composite. This must be a set of key:value pairs where + # defining the weight for each sub-loss within the composite. + # If using a composite and a value isn't provided here, all + # losses will be weighted equally. + metrics: # metrics to monitor on the training and validation sets. + training: # training set metrics. + validation: # validation set metrics. + checkpoint_frequency: # how frequently should checkpoints be saved? + # this can be an int, in which case every n epochs + # a checkpoint will be made, or a list, in which case + # checkpoints will be saved on those specific epochs. + # if not provided, only the final model is saved. + callbacks: # a list of callbacks to use. + model_dest_path: # path to save the trained model output and checkpoint(s) + # to. Should be a filename ending in .h5, .hdf5 for keras + # or .pth, .pt for torch. Epoch numbers will be appended + # for checkpoints. + verbose: true # verbose text output during training + +inference: + window_step_size_x: # size of each step for the sliding window for inference. + # set to the same size as the input image size for zero + # overlap; to average predictions across multiple images, + # use a smaller step size. + window_step_size_y: # size of each step for the sliding window for inference. + # set to the same size as the input image size for zero + # overlap; to average predictions across multiple images, + # use a smaller step size. + stitching_method: # the method to use to stitch together tiles used during + # inference. defaults to average if not provided. see + # the documentation for sol.raster.image.stitch_images() + # for more. + output_dir: inference_out # the path to save inference outputs to. diff --git a/docker/solaris/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml b/docker/solaris/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml new file mode 100644 index 00000000..3fa2ef63 --- /dev/null +++ b/docker/solaris/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml @@ -0,0 +1,140 @@ +model_name: selimsef_spacenet4_densenet121unet + +model_path: +train: true +infer: false + +pretrained: true +nn_framework: torch +batch_size: 32 + +data_specs: + width: 384 + height: 384 + dtype: + image_type: zscore + rescale: false + rescale_minima: auto + rescale_maxima: auto + additional_inputs: + channels: 4 + label_type: mask + is_categorical: false + mask_channels: 3 + val_holdout_frac: 0.2 + data_workers: + +training_data_csv: '/path/to/training_df.csv' +validation_data_csv: +inference_data_csv: '/path/to/test_df.csv' + +training_augmentation: + augmentations: + RandomScale: + scale_limit: + - 0.5 + - 1.5 + interpolation: nearest + Rotate: + limit: + - 5 + - 6 + border_mode: constant + p: 0.3 + RandomCrop: + height: 416 + width: 416 + always_apply: true + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + shuffle: true + +validation_augmentation: + augmentations: + CenterCrop: + height: 384 + width: 384 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + +inference_augmentation: + augmentations: + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +training: + epochs: 70 + steps_per_epoch: + optimizer: AdamW + lr: 2e-4 + opt_args: + weight_decay: 0.0001 + loss: + focal: + dice: + loss_weights: + focal: 1 + dice: 1 + metrics: + training: + validation: + checkpoint_frequency: 10 + callbacks: + lr_schedule: + schedule_type: 'arbitrary' + schedule_dict: + milestones: + - 1 + - 5 + - 15 + - 30 + - 50 + - 60 + gamma: 0.5 + model_checkpoint: + filepath: 'selimsef_densenet121_best.pth' + monitor: val_loss + model_dest_path: 'selimsef_densenet121.pth' + verbose: true + +inference: + window_step_size_x: + window_step_size_y: + output_dir: 'inference_out' diff --git a/docker/solaris/solaris/nets/configs/selimsef_densenet161unet_spacenet4.yml b/docker/solaris/solaris/nets/configs/selimsef_densenet161unet_spacenet4.yml new file mode 100644 index 00000000..e2bc8de4 --- /dev/null +++ b/docker/solaris/solaris/nets/configs/selimsef_densenet161unet_spacenet4.yml @@ -0,0 +1,140 @@ +model_name: selimsef_spacenet4_densenet161unet + +model_path: +train: true +infer: false + +pretrained: true +nn_framework: torch +batch_size: 20 + +data_specs: + width: 384 + height: 384 + dtype: + image_type: zscore + rescale: false + rescale_minima: auto + rescale_maxima: auto + additional_inputs: + channels: 4 + label_type: mask + is_categorical: false + mask_channels: 3 + val_holdout_frac: 0.2 + data_workers: + +training_data_csv: '/path/to/training_df.csv' +validation_data_csv: +inference_data_csv: '/path/to/test_df.csv' + +training_augmentation: + augmentations: + RandomScale: + scale_limit: + - 0.5 + - 1.5 + interpolation: nearest + Rotate: + limit: + - 5 + - 6 + border_mode: constant + p: 0.3 + RandomCrop: + height: 416 + width: 416 + always_apply: true + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + shuffle: true + +validation_augmentation: + augmentations: + CenterCrop: + height: 384 + width: 384 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + +inference_augmentation: + augmentations: + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +training: + epochs: 60 + steps_per_epoch: + optimizer: AdamW + lr: 2e-4 + opt_args: + weight_decay: 0.0001 + loss: + focal: + dice: + loss_weights: + focal: 1 + dice: 1 + metrics: + training: + validation: + checkpoint_frequency: 10 + callbacks: + lr_schedule: + schedule_type: 'arbitrary' + schedule_dict: + milestones: + - 1 + - 5 + - 15 + - 30 + - 45 + - 55 + gamma: 0.5 + model_checkpoint: + filepath: 'selimsef_densenet161_best.pth' + monitor: val_loss + model_dest_path: 'selimsef_densenet161.pth' + verbose: true + +inference: + window_step_size_x: + window_step_size_y: + output_dir: 'inference_out' diff --git a/docker/solaris/solaris/nets/configs/selimsef_resnet34unet_spacenet4.yml b/docker/solaris/solaris/nets/configs/selimsef_resnet34unet_spacenet4.yml new file mode 100644 index 00000000..f6b482db --- /dev/null +++ b/docker/solaris/solaris/nets/configs/selimsef_resnet34unet_spacenet4.yml @@ -0,0 +1,140 @@ +model_name: selimsef_spacenet4_resnet34unet + +model_path: +train: false +infer: true + +pretrained: true +nn_framework: torch +batch_size: 42 + +data_specs: + width: 384 + height: 384 + dtype: + image_type: zscore + rescale: false + rescale_minima: auto + rescale_maxima: auto + additional_inputs: + channels: 4 + label_type: mask + is_categorical: false + mask_channels: 3 + val_holdout_frac: 0.2 + data_workers: 12 + +training_data_csv: '/path/to/training_df.csv' +validation_data_csv: +inference_data_csv: '/path/to/test_df.csv' + +training_augmentation: + augmentations: + RandomScale: + scale_limit: + - 0.5 + - 1.5 + interpolation: nearest + Rotate: + limit: + - 5 + - 6 + border_mode: constant + p: 0.3 + RandomCrop: + height: 416 + width: 416 + always_apply: true + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + shuffle: true + +validation_augmentation: + augmentations: + CenterCrop: + height: 384 + width: 384 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + +inference_augmentation: + augmentations: + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +training: + epochs: 70 + steps_per_epoch: + optimizer: AdamW + lr: 2e-4 + opt_args: + weight_decay: 0.0001 + loss: + focal: + dice: + loss_weights: + focal: 1 + dice: 1 + metrics: + training: + validation: + checkpoint_frequency: 10 + callbacks: + lr_schedule: + schedule_type: 'arbitrary' + schedule_dict: + milestones: + - 1 + - 5 + - 15 + - 30 + - 50 + - 60 + gamma: 0.5 + model_checkpoint: + filepath: 'selimsef_resnet34_best.pth' + monitor: val_loss + model_dest_path: 'selimsef_resnet34.pth' + verbose: true + +inference: + window_step_size_x: + window_step_size_y: + output_dir: 'inference_out/' diff --git a/docker/solaris/solaris/nets/configs/selimsef_scse50unet_spacenet4.yml b/docker/solaris/solaris/nets/configs/selimsef_scse50unet_spacenet4.yml new file mode 100644 index 00000000..6f70c763 --- /dev/null +++ b/docker/solaris/solaris/nets/configs/selimsef_scse50unet_spacenet4.yml @@ -0,0 +1,198 @@ +################################################################################ +################# SOLARIS MODEL CONFIGURATION SKELETON ######################### +################################################################################ + +# This skeleton lays out the required instructions for running a model using +# solaris. See the full documentation at [INCLUDE DOC LINK HERE] for details on +# options, required arguments, and sample usage. + +model_name: selimsef_scse50unet_spacenet4 + +model_path: # leave this blank unless you're using a custom model not + # native to solaris. solaris will automatically find your + # model. +train: true # set to false for inference only +infer: false # set to false for training only + +pretrained: false # use pretrained weights associated with the model? +nn_framework: torch +batch_size: 8 + +data_specs: + width: 384 + height: 384 + image_type: zscore # format of images read into the neural net. options + # are 'normalized', 'zscore', '8bit', '16bit'. + rescale: false # should image pixel values be rescaled before pre-processing? + # If so, the image will be rescaled to the pixel range defined + # by rescale_min and rescale_max below. + rescale_minima: auto # the minimum values to use in rescaling (if + # rescale=true). If 'auto', the minimum pixel intensity + # in each channel of the image will be subtracted. If + # a single value is provided, that value will be set to + # zero for each channel in the input image. + # if a list of values are provided, then minima in the + # separate channels (in that order) will be set to that + # value PRIOR to any augmentation. + rescale_maxima: auto # same as rescale_minima, but for the maximum value for + # each channel in the image. + additional_inputs: # a list of additional columns in the training CSV (and + - angle # validation CSV if applicable) That will be passed to + # the model. Those values will not be augmented. + # This list MUST be in the same order as the additional + # input values are expected by the model. + channels: 4 # number of channels in the input imagery. + label_type: mask # one of ['mask', 'bbox'] + is_categorical: false # are the labels binary (default) or categorical? + mask_channels: 3 # number of channels in the training mask + val_holdout_frac: 0.2 # if empty, assumes that separate data ref files define the + # training and validation dataset. If a float between 0 and + # 1, indicates the fraction of training data that's held + # out for validation (and validation_data_csv will be + # ignored) + data_workers: # number of cpu threads to use for loading and preprocessing + # input images. +# other_inputs: # this can provide a list of additional inputs to pass to the + # neural net for training. These inputs should be specified in + # extra columns of the csv files (denoted below), either as + # filepaths to additional data to load or as values to include. + # NOTE: This is not currently implemented. + +training_data_csv: '/path/to/training_df.csv' +validation_data_csv: +inference_data_csv: '/path/to/test_df.csv' # TODO # path to the reference csv that defines inference data. + # see the documentation for the specs of this file. + +training_augmentation: # augmentations for use with training data + augmentations: + RandomScale: + scale_limit: + - 0.5 + - 1.5 + interpolation: nearest + RandomCrop: + height: 384 + width: 384 + p: 1.0 + Rotate: + limit: + - 5 + - 6 + border_mode: constant + p: 0.3 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + # include augmentations here. See the documentation for options and + # required arguments. + p: 1.0 # probability of applying the entire training augmentation pipeline. + shuffle: true # should the image order be shuffled in each epoch. + +validation_augmentation: # augmentations for use with validation data + augmentations: + CenterCrop: + height: 384 + width: 384 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + +inference_augmentation: + augmentations: + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + - 0.02082 + std: + - 0.004986 + - 0.004964 + - 0.004950 + - 0.004878 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +training: + epochs: 52 # number of epochs. A list can also be provided here indicating + # distinct sets of epochs at different learning rates, etc; if so, + # a list of equal length must be provided in the parameter that varies + # with the values for each set of epochs. + steps_per_epoch: # optional argument defining # steps/epoch. If not provided, + # each epoch will include the number of steps needed to go + # through the entire training dataset. + optimizer: AdamW # optimizer function name. see docs for options. + lr: 2e-4 # learning rate. + opt_args: # dictionary of values (e.g. alpha, gamma, momentum) specific to + weight_decay: 0.0001 + loss: + focal: # loss function(s). See docs for options. This should be a list of loss + dice: # names with sublists of loss function hyperparameters (if applicable). + # See the docs for more details and usage examples. + loss_weights: + focal: 1 # (optional) weights to use for each loss function if using + dice: 1 # loss: composite. This must be a set of key:value pairs where + # defining the weight for each sub-loss within the composite. + # If using a composite and a value isn't provided here, all + # losses will be weighted equally. + metrics: # metrics to monitor on the training and validation sets. + training: # training set metrics. + validation: # validation set metrics. + checkpoint_frequency: 10 # how frequently should checkpoints be saved? + # this can be an int, in which case every n epochs + # a checkpoint will be made, or a list, in which case + # checkpoints will be saved on those specific epochs. + # if not provided, only the final model is saved. + callbacks: + lr_schedule: + schedule_type: 'arbitrary' + schedule_dict: + milestones: + - 1 + - 5 + - 15 + - 30 + - 40 + - 50 + gamma: 0.5 + model_checkpoint: + filepath: 'selimsef_best.pth' + monitor: val_loss + model_dest_path: 'selimsef.pth' # path to save the trained model output and checkpoint(s) + # to. Should be a filename ending in .h5, .hdf5 for keras + # or .pth, .pt for torch. Epoch numbers will be appended + # for checkpoints. + verbose: true # verbose text output during training + +inference: + window_step_size_x: # size of each step for the sliding window for inference. + # set to the same size as the input image size for zero + # overlap; to average predictions across multiple images, + # use a smaller step size. + window_step_size_y: # size of each step for the sliding window for inference. + # set to the same size as the input image size for zero + # overlap; to average predictions across multiple images, + # use a smaller step size. + output_dir: 'inference_out/' diff --git a/docker/solaris/solaris/nets/configs/xdxd_spacenet4.yml b/docker/solaris/solaris/nets/configs/xdxd_spacenet4.yml new file mode 100644 index 00000000..f235518d --- /dev/null +++ b/docker/solaris/solaris/nets/configs/xdxd_spacenet4.yml @@ -0,0 +1,121 @@ +model_name: xdxd_spacenet4 + +model_path: +train: false +infer: true + +pretrained: true +nn_framework: torch +batch_size: 12 + +data_specs: + width: 512 + height: 512 + dtype: + image_type: zscore + rescale: false + rescale_minima: auto + rescale_maxima: auto + channels: 4 + label_type: mask + is_categorical: false + mask_channels: 1 + val_holdout_frac: 0.2 + data_workers: + +training_data_csv: '/path/to/training_df.csv' +validation_data_csv: +inference_data_csv: '/path/to/test_df.csv' + +training_augmentation: + augmentations: + DropChannel: + idx: 3 + axis: 2 + HorizontalFlip: + p: 0.5 + RandomRotate90: + p: 0.5 + RandomCrop: + height: 512 + width: 512 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + std: + - 0.004986 + - 0.004964 + - 0.004950 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 + shuffle: true +validation_augmentation: + augmentations: + DropChannel: + idx: 3 + axis: 2 + CenterCrop: + height: 512 + width: 512 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + std: + - 0.004986 + - 0.004964 + - 0.004950 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +inference_augmentation: + augmentations: + DropChannel: + idx: 3 + axis: 2 + p: 1.0 + Normalize: + mean: + - 0.006479 + - 0.009328 + - 0.01123 + std: + - 0.004986 + - 0.004964 + - 0.004950 + max_pixel_value: 65535.0 + p: 1.0 + p: 1.0 +training: + epochs: 60 + steps_per_epoch: + optimizer: Adam + lr: 1e-4 + opt_args: + loss: + bcewithlogits: + jaccard: + loss_weights: + bcewithlogits: 10 + jaccard: 2.5 + metrics: + training: + validation: + checkpoint_frequency: 10 + callbacks: + model_checkpoint: + filepath: 'xdxd_best.pth' + monitor: val_loss + model_dest_path: 'xdxd.pth' + verbose: true + +inference: + window_step_size_x: + window_step_size_y: + output_dir: 'inference_out/' diff --git a/docker/solaris/solaris/nets/datagen.py b/docker/solaris/solaris/nets/datagen.py new file mode 100644 index 00000000..b9efd3d7 --- /dev/null +++ b/docker/solaris/solaris/nets/datagen.py @@ -0,0 +1,497 @@ +from tensorflow import keras +import numpy as np +import rasterio +from torch.utils.data import Dataset, DataLoader +from .transform import _check_augs, process_aug_dict +from ..utils.core import _check_df_load +from ..utils.geo import split_geom +from ..utils.io import imread, _check_channel_order + + +def make_data_generator(framework, config, df, stage='train'): + """Create an appropriate data generator based on the framework used. + + A wrapper for the high-end ``solaris`` API to create data generators. + Using the ``config`` dictionary, this function creates an instance of + either :class:`KerasSegmentationSequence` or :class:`TorchDataset` + (depending on the framework used for the pipeline). If using Torch, this + instance is then wrapped in a :class:`torch.utils.data.DataLoader` and + returned; if Keras, the sequence object is directly returned. + + Arguments + --------- + framework : str + One of ['keras', 'pytorch', 'simrdwn', 'tf', 'tf_obj_api'], the deep + learning framework used for the model to be used. + config : dict + The config dictionary for the entire pipeline. + df : :class:`pandas.DataFrame` or :class:`str` + A :class:`pandas.DataFrame` containing two columns: ``'image'``, with + the path to images for training, and ``'label'``, with the path to the + label file corresponding to each image. + stage : str, optional + Either ``'train'`` or ``'validate'``, indicates whether the object + created is being used for training or validation. This determines which + augmentations from the config file are applied within the returned + object. + + Returns + ------- + data_gen : :class:`KerasSegmentationSequence` or :class:`torch.utils.data.DataLoader` + An object to pass data into the :class:`solaris.nets.train.Trainer` + instance during model training. + + See Also + -------- + :class:`KerasSegmentationSequence` + :class:`TorchDataset` + :class:`InferenceTiler` + """ + + if framework.lower() not in ['keras', 'pytorch', 'torch']: + raise ValueError('{} is not an accepted value for `framework`'.format( + framework)) + + # make sure the df is loaded + df = _check_df_load(df) + + if stage == 'train': + augs = config['training_augmentation'] + shuffle = config['training_augmentation']['shuffle'] + elif stage == 'validate': + augs = config['validation_augmentation'] + shuffle = False + try: + num_classes = config['data_specs']['num_classes'] + except KeyError: + num_classes = 1 + + if framework.lower() == 'keras': + data_gen = KerasSegmentationSequence( + df, + height=config['data_specs']['height'], + width=config['data_specs']['width'], + input_channels=config['data_specs']['channels'], + output_channels=config['data_specs']['mask_channels'], + augs=augs, + batch_size=config['batch_size'], + label_type=config['data_specs']['label_type'], + is_categorical=config['data_specs']['is_categorical'], + num_classes=num_classes, + shuffle=shuffle) + + elif framework in ['torch', 'pytorch']: + dataset = TorchDataset( + df, + augs=augs, + batch_size=config['batch_size'], + label_type=config['data_specs']['label_type'], + is_categorical=config['data_specs']['is_categorical'], + num_classes=num_classes, + dtype=config['data_specs']['dtype']) + # set up workers for DataLoader for pytorch + data_workers = config['data_specs'].get('data_workers') + if data_workers == 1 or data_workers is None: + data_workers = 0 # for DataLoader to run in main process + data_gen = DataLoader( + dataset, + batch_size=config['batch_size'], + shuffle=config['training_augmentation']['shuffle'], + num_workers=data_workers) + + return data_gen + + +class KerasSegmentationSequence(keras.utils.Sequence): + """An object to stream images from files into a Keras model in solaris. + + + Attributes + ---------- + df : :class:`pandas.DataFrame` + The :class:`pandas.DataFrame` specifying where inputs are stored. + height : int + The height of generated images. + width : int + The width of generated images. + input_channels : int + The number of channels in generated inputs. + output_channels : int + The number of channels in target masks created. + aug : :class:`albumentations.core.composition.Compose` + An albumentations Compose object to pass imagery through before + passing it into the neural net. If an augmentation config subdict + was provided during initialization, this is created by parsing the + dict with :func:`solaris.nets.transform.process_aug_dict`. + batch_size : int + The batch size generated. + n_batches : int + The number of batches per epoch. Inferred based on the number of + input files in `df` and `batch_size`. + label_type : str + Type of labels. Currently always ``"mask"``. + is_categorical : bool + Indicates whether masks output are boolean or categorical labels. + num_classes: int + Indicates the number of classes in the dataset + shuffle : bool + Indicates whether or not input order is shuffled for each epoch. + """ + + def __init__(self, df, height, width, input_channels, output_channels, + augs, batch_size, label_type='mask', is_categorical=False, + num_classes=1, shuffle=True): + """Create an instance of KerasSegmentationSequence. + + Arguments + --------- + df : :class:`pandas.DataFrame` + A pandas DataFrame specifying images and label files to read into + the model. See `the reference file creation tutorial`_ for more. + height : int + The height of model inputs in pixels. + width : int + The width of model inputs in pixels. + input_channels : int + The number of channels in model input imagery. + output_channels : int + The number of channels in the model output. + augs : :class:`dict` or :class:`albumentations.core.composition.Compose` + Either the config subdict specifying augmentations to apply, or + a pre-created :class:`albumentations.core.composition.Compose` object + containing all of the augmentations to apply. + batch_size : int + The number of samples in a training batch. + label_type : str, optional + The type of labels to be used. At present, only ``"mask"`` is + supported. + is_categorical : bool, optional + Is the data categorical or boolean (default)? + num_classes: int + Indicates the number of classes in the dataset + shuffle : bool, optional + Should image order be shuffled in each epoch? + + + .. _the reference file creation tutorial: https://solaris.readthedocs.io/en/latest/tutorials/notebooks/creating_im_reference_csvs.html + """ + + # TODO: IMPLEMENT GETTING INPUT FILE LISTS HERE! + self.df = df + self.height = height + self.width = width + self.input_channels = input_channels + self.output_channels = output_channels + self.aug = _check_augs(augs) # checks if they're loaded; loads if not + self.batch_size = batch_size + self.n_batches = int(np.floor(len(self.df)/self.batch_size)) + self.label_type = label_type + self.is_categorical = is_categorical + self.num_classes = num_classes + self.shuffle = shuffle + self.on_epoch_end() + + def on_epoch_end(self): + """Update indices after each epoch.""" + # reorder images + self.image_indexes = np.arange(len(self.df)) + if self.shuffle: + np.random.shuffle(self.image_indexes) + + def _data_generation(self, image_idxs): + # initialize the output array + X = np.empty((self.batch_size, + self.height, + self.width, + self.input_channels)) + if self.label_type == 'mask': + y = np.empty((self.batch_size, + self.height, + self.width, + self.output_channels)) + else: + pass # TODO: IMPLEMENT BBOX LABEL SETUP HERE! + for i in range(self.batch_size): + im = imread(self.df['image'].iloc[image_idxs[i]]) + im = _check_channel_order(im, 'keras') + if self.label_type == 'mask': + label = imread(self.df['label'].iloc[image_idxs[i]]) + if not self.is_categorical: + label[label != 0] = 1 + aug_result = self.aug(image=im, mask=label) + # if image shape is 2D, convert to 3D + if len(aug_result['image'].shape) == 2: + aug_result['image'] = aug_result['image'][:, :, np.newaxis] + X[i, :, :, :] = aug_result['image'] + if len(aug_result['mask'].shape) == 2: + aug_result['mask'] = aug_result['mask'][:, :, np.newaxis] + y[i, :, :, :] = aug_result['mask'] + else: + raise NotImplementedError( + 'Usage of non-mask labels is not implemented yet.') + + return X, y + + def __len__(self): + """Denotes the number of batches per epoch. + + This is a required method for Keras Sequence objects. + """ + return self.n_batches + + def __getitem__(self, index): + """Generate one batch of data.""" + # Generate indexes of the batch + im_inds = self.image_indexes[index*self.batch_size: + (index+1)*self.batch_size] + + # Generate data + X, y = self._data_generation(image_idxs=im_inds) + return X, y + + +class TorchDataset(Dataset): + """A PyTorch dataset object for solaris. + + Note that this object is wrapped in a :class:`torch.utils.data.DataLoader` + before being passed to the :class:solaris.nets.train.Trainer` instance. + + Attributes + ---------- + df : :class:`pandas.DataFrame` + The :class:`pandas.DataFrame` specifying where inputs are stored. + aug : :class:`albumentations.core.composition.Compose` + An albumentations Compose object to pass imagery through before + passing it into the neural net. If an augmentation config subdict + was provided during initialization, this is created by parsing the + dict with :func:`solaris.nets.transform.process_aug_dict`. + batch_size : int + The batch size generated. + n_batches : int + The number of batches per epoch. Inferred based on the number of + input files in `df` and `batch_size`. + dtype : :class:`numpy.dtype` + The numpy dtype that image inputs should be when passed to the model. + is_categorical : bool + Indicates whether masks output are boolean or categorical labels. + num_classes: int + Indicates the number of classes in the dataset + dtype : class:`numpy.dtype` + The data type images should be converted to before being passed to + neural nets. + """ + + def __init__(self, df, augs, batch_size, label_type='mask', + is_categorical=False, num_classes=1, dtype=None): + """ + Create an instance of TorchDataset for use in model training. + + Arguments + --------- + df : :class:`pandas.DataFrame` + A pandas DataFrame specifying images and label files to read into + the model. See `the reference file creation tutorial`_ for more. + augs : :class:`dict` or :class:`albumentations.core.composition.Compose` + Either the config subdict specifying augmentations to apply, or + a pre-created :class:`albumentations.core.composition.Compose` + object containing all of the augmentations to apply. + batch_size : int + The number of samples in a training batch. + label_type : str, optional + The type of labels to be used. At present, only ``"mask"`` is + supported. + is_categorical : bool, optional + Is the data categorical or boolean (default)? + num_classes: int + Indicates the number of classes in the dataset + dtype : str, optional + The dtype that image arrays should be converted to before being + passed to the neural net. If not provided, defaults to + ``"float32"``. Must be one of the `numpy dtype options`_. + + .. _numpy dtype options: https://docs.scipy.org/doc/numpy/user/basics.types.html + """ + super().__init__() + + self.df = df + self.batch_size = batch_size + self.n_batches = int(np.floor(len(self.df)/self.batch_size)) + self.aug = _check_augs(augs) + self.is_categorical = is_categorical + self.num_classes = num_classes + + if dtype is None: + self.dtype = np.float32 # default + # if it's a string, get the appropriate object + elif isinstance(dtype, str): + try: + self.dtype = getattr(np, dtype) + except AttributeError: + raise ValueError( + 'The data type {} is not supported'.format(dtype)) + # lastly, check if it's already defined in the right format for use + elif issubclass(dtype, np.number) or isinstance(dtype, np.dtype): + self.dtype = dtype + + def __len__(self): + return len(self.df) + + def __getitem__(self, idx): + """Get one image, mask pair""" + # Generate indexes of the batch + image = imread(self.df['image'].iloc[idx]) + mask = imread(self.df['label'].iloc[idx]) + if not self.is_categorical: + mask[mask != 0] = 1 + if len(mask.shape) == 2: + mask = mask[:, :, np.newaxis] + if len(image.shape) == 2: + image = image[:, :, np.newaxis] + + sample = {'image': image, 'mask': mask} + + if self.aug: + sample = self.aug(**sample) + # add in additional inputs (if applicable) + # additional_inputs = self.config['data_specs'].get('additional_inputs', + # None) + # if additional_inputs is not None: + # for input in additional_inputs: + # sample[input] = self.df[input].iloc[idx] + + sample['image'] = _check_channel_order(sample['image'], + 'torch').astype(self.dtype) + sample['mask'] = _check_channel_order(sample['mask'], + 'torch').astype(np.float32) + return sample + + +class InferenceTiler(object): + """An object to tile fragments of images for inference. + + This object allows you to pass images of arbitrary size into Solaris for + inference, similar to the pre-existing CosmiQ Works tool, BASISS_. The + object will step across an input image creating tiles of size + ``[height, width]``, taking steps of size ``[y_step, x_step]`` as it goes. + When it reaches an edge, it will take tiles from ``-height`` or ``-width`` + to the edge. Clearly, these can overlap with one another; the intention + is that overlaps will be resolved using + :func:`solaris.raster.image.stitch_images` when re-creating the output. + + .. _BASISS: https://github.com/cosmiq/basiss + + Attributes + ---------- + framework : str + The deep learning framework used. Can be one of ``"torch"``, + ``"pytorch"``, or ``"keras"``. + width : int + The width of images to load into the neural net. + height : int + The height of images to load into the neural net. + x_step : int, optional + The step size taken in the x direction when sampling for new images. + y_step : int, optional + The step size taken in the y direction when sampling for new images. + aug : :class:`albumentations.core.composition.Compose` + Augmentations to apply before passing to a neural net. Generally used + for pre-processing. + + See Also + -------- + :func:`solaris.raster.image.stitch_images` + :func:`make_data_generator` + """ + + def __init__(self, framework, width, height, x_step=None, y_step=None, + augmentations=None): + """Create the tiler instance. + + Arguments + --------- + framework : str + The deep learning framework used. Can be one of ``"torch"``, + ``"pytorch"``, or ``"keras"``. + width : int + The width of images to load into the neural net. + height : int + The height of images to load into the neural net. + x_step : int, optional + The step size taken in the x direction when sampling for new + images. If not provided, defaults to `width`. + y_step : int, optional + The step size taken in the y direction when sampling for new images. + If not provided, defaults to `height`. + aug : :class:`albumentations.core.composition.Compose` + Augmentations to apply before passing to a neural net. Generally used + for pre-processing. + """ + self.framework = framework + self.width = width + self.height = height + if x_step is None: + self.x_step = self.width + else: + self.x_step = x_step + if y_step is None: + self.y_step = self.height + else: + self.y_step = y_step + self.aug = _check_augs(augmentations) + + def __call__(self, im): + """Create an inference array along with an indexing reference list. + + Arguments + --------- + im : :class:`str` or :class:`numpy.array` + An image to perform inference on. + + Returns + ------- + output_arr, top_left_corner_idxs + output_arr : ``[N, Y, X, C]`` :class:`numpy.array` + A :class:`numpy.array` for use in model inferencing. Each + item along the first axis corresponds to a single sample for + the model. + top_left_corner_idxs : :class:`list` of :class:`tuple` s of :class:`int` s + A :class:`list` of ``(top, left)`` tuples corresponding to the + top left corner indices of each sample along the first axis of + ``inference_arr`` . These values can be used to stitch the + inferencing result back together. + """ + # read in the image if it's a path + if isinstance(im, str): + im = imread(im) + # determine how many samples will be generated with the sliding window + src_im_height = im.shape[0] + src_im_width = im.shape[1] + y_steps = int(1+np.ceil((src_im_height-self.height)/self.y_step)) + x_steps = int(1+np.ceil((src_im_width-self.width)/self.x_step)) + if len(im.shape) == 2: # if there's no channel axis + im = im[:, :, np.newaxis] # create one - will be needed for model + top_left_corner_idxs = [] + output_arr = [] + for y in range(y_steps): + if self.y_step*y + self.height > im.shape[0]: + y_min = im.shape[0] - self.height + else: + y_min = self.y_step*y + + for x in range(x_steps): + if self.x_step*x + self.width > im.shape[1]: + x_min = im.shape[1] - self.width + else: + x_min = self.x_step*x + + subarr = im[y_min:y_min + self.height, + x_min:x_min + self.width, + :] + if self.aug is not None: + subarr = self.aug(image=subarr)['image'] + output_arr.append(subarr) + top_left_corner_idxs.append((y_min, x_min)) + output_arr = np.stack(output_arr).astype(np.float32) + if self.framework in ['torch', 'pytorch']: + output_arr = np.moveaxis(output_arr, 3, 1) + return output_arr, top_left_corner_idxs, (src_im_height, src_im_width) diff --git a/docker/solaris/solaris/nets/infer.py b/docker/solaris/solaris/nets/infer.py new file mode 100644 index 00000000..2a0b784e --- /dev/null +++ b/docker/solaris/solaris/nets/infer.py @@ -0,0 +1,136 @@ +import os +import torch +import gdal +import numpy as np +from warnings import warn +from .model_io import get_model +from .transform import process_aug_dict +from .datagen import InferenceTiler +from ..raster.image import stitch_images, create_multiband_geotiff +from ..utils.core import get_data_paths + + +class Inferer(object): + """Object for training `solaris` models using PyTorch or Keras.""" + + def __init__(self, config, custom_model_dict=None): + self.config = config + self.batch_size = self.config['batch_size'] + self.framework = self.config['nn_framework'] + self.model_name = self.config['model_name'] + # check if the model was trained as part of the same pipeline; if so, + # use the output from that. If not, use the pre-trained model directly. + if self.config['train']: + warn('Because the configuration specifies both training and ' + 'inference, solaris is switching the model weights path ' + 'to the training output path.') + self.model_path = self.config['training']['model_dest_path'] + if custom_model_dict is not None: + custom_model_dict['weight_path'] = self.config[ + 'training']['model_dest_path'] + else: + self.model_path = self.config.get('model_path', None) + self.model = get_model(self.model_name, self.framework, + self.model_path, pretrained=True, + custom_model_dict=custom_model_dict) + self.window_step_x = self.config['inference'].get('window_step_size_x', + None) + self.window_step_y = self.config['inference'].get('window_step_size_y', + None) + if self.window_step_x is None: + self.window_step_x = self.config['data_specs']['width'] + if self.window_step_y is None: + self.window_step_y = self.config['data_specs']['height'] + self.stitching_method = self.config['inference'].get( + 'stitching_method', 'average') + self.output_dir = self.config['inference']['output_dir'] + if not os.path.isdir(self.output_dir): + os.makedirs(self.output_dir) + + def __call__(self, infer_df=None): + """Run inference. + Arguments + --------- + infer_df : :class:`pandas.DataFrame` or `str` + A :class:`pandas.DataFrame` with a column, ``'image'``, specifying + paths to images for inference. Alternatively, `infer_df` can be a + path to a CSV file containing the same information. Defaults to + ``None``, in which case the file path specified in the Inferer's + configuration dict is used. + """ + + if infer_df is None: + infer_df = get_infer_df(self.config) + + inf_tiler = InferenceTiler( + self.framework, + width=self.config['data_specs']['width'], + height=self.config['data_specs']['height'], + x_step=self.window_step_x, + y_step=self.window_step_y, + augmentations=process_aug_dict( + self.config['inference_augmentation'])) + for idx, im_path in enumerate(infer_df['image']): + temp_im = gdal.Open(im_path) + proj = temp_im.GetProjection() + gt = temp_im.GetGeoTransform() + inf_input, idx_refs, ( + src_im_height, src_im_width) = inf_tiler(im_path) + + if self.framework == 'keras': + subarr_preds = self.model.predict(inf_input, + batch_size=self.batch_size) + + elif self.framework in ['torch', 'pytorch']: + with torch.no_grad(): + self.model.eval() + if torch.cuda.is_available(): + device = torch.device('cuda') + self.model = self.model.cuda() + else: + device = torch.device('cpu') + inf_input = torch.from_numpy(inf_input).float().to(device) + # add additional input data, if applicable + if self.config['data_specs'].get('additional_inputs', + None) is not None: + inf_input = [inf_input] + for i in self.config['data_specs']['additional_inputs']: + inf_input.append( + infer_df[i].iloc[idx].to(device)) + + subarr_preds = self.model(inf_input) + subarr_preds = subarr_preds.cpu().data.numpy() + stitched_result = stitch_images(subarr_preds, + idx_refs=idx_refs, + out_width=src_im_width, + out_height=src_im_height, + method=self.stitching_method) + stitched_result = np.swapaxes(stitched_result, 1, 0) + stitched_result = np.swapaxes(stitched_result, 2, 0) + create_multiband_geotiff(stitched_result, + os.path.join(self.output_dir, + os.path.split(im_path)[1]), + proj=proj, geo=gt, nodata=np.nan, + out_format=gdal.GDT_Float32) + + +def get_infer_df(config): + """Get the inference df based on the contents of ``config`` . + This function uses the logic described in the documentation for the config + file to determine where to find images to be used for inference. + See the docs and the comments in solaris/data/config_skeleton.yml for + details. + Arguments + --------- + config : dict + The loaded configuration dict for model training and/or inference. + Returns + ------- + infer_df : :class:`dict` + :class:`dict` containing at least one column: ``'image'`` . The values + in this column correspond to the path to filenames to perform inference + on. + """ + + infer_df = get_data_paths(config['inference_data_csv'], infer=True) + return infer_df diff --git a/docker/solaris/solaris/nets/losses.py b/docker/solaris/solaris/nets/losses.py new file mode 100644 index 00000000..64543ac8 --- /dev/null +++ b/docker/solaris/solaris/nets/losses.py @@ -0,0 +1,114 @@ +import numpy as np +from tensorflow.keras import backend as K +from ._keras_losses import keras_losses, k_focal_loss +from ._torch_losses import torch_losses +from torch import nn + + +def get_loss(framework, loss, loss_weights=None, custom_losses=None): + """Load a loss function based on a config file for the specified framework. + + Arguments + --------- + framework : string + Which neural network framework to use. + loss : dict + Dictionary of loss functions to use. Each key is a loss function name, + and each entry is a (possibly-empty) dictionary of hyperparameter-value + pairs. + loss_weights : dict, optional + Optional dictionary of weights for loss functions. Each key is a loss + function name (same as in the ``loss`` argument), and the corresponding + entry is its weight. + custom_losses : dict, optional + Optional dictionary of Pytorch classes or Keras functions of any + user-defined loss functions. Each key is a loss function name, and the + corresponding entry is the Python object implementing that loss. + """ + # lots of exception handling here. TODO: Refactor. + if not isinstance(loss, dict): + raise TypeError('The loss description is formatted improperly.' + ' See the docs for details.') + if len(loss) > 1: + + # get the weights for each loss within the composite + if loss_weights is None: + # weight all losses equally + weights = {k: 1 for k in loss.keys()} + else: + weights = loss_weights + + # check if sublosses dict and weights dict have the same keys + if list(loss.keys()).sort() != list(weights.keys()).sort(): + raise ValueError( + 'The losses and weights must have the same name keys.') + + if framework == 'keras': + return keras_composite_loss(loss, weights, custom_losses) + elif framework in ['pytorch', 'torch']: + return TorchCompositeLoss(loss, weights, custom_losses) + + else: # parse individual loss functions + loss_name, loss_dict = list(loss.items())[0] + return get_single_loss(framework, loss_name, loss_dict, custom_losses) + + +def get_single_loss(framework, loss_name, params_dict, custom_losses=None): + if framework == 'keras': + if loss_name.lower() == 'focal': + return k_focal_loss(**params_dict) + else: + # keras_losses in the next line is a matching dict + # TODO: the next block doesn't handle non-focal loss functions that + # have hyperparameters associated with them. It would be great to + # refactor this to handle that possibility. + if custom_losses is not None and loss_name in custom_losses: + return custom_losses.get(loss_name) + else: + return keras_losses.get(loss_name.lower()) + elif framework in ['torch', 'pytorch']: + if params_dict is None: + if custom_losses is not None and loss_name in custom_losses: + return custom_losses.get(loss_name)() + else: + return torch_losses.get(loss_name.lower())() + else: + if custom_losses is not None and loss_name in custom_losses: + return custom_losses.get(loss_name)(**params_dict) + else: + return torch_losses.get(loss_name.lower())(**params_dict) + + +def keras_composite_loss(loss_dict, weight_dict, custom_losses=None): + """Wrapper to other loss functions to create keras-compatible composite.""" + + def composite(y_true, y_pred): + loss = K.sum(K.flatten(K.stack([weight_dict[loss_name]*get_single_loss( + 'keras', loss_name, loss_params, custom_losses)(y_true, y_pred) + for loss_name, loss_params in loss_dict.items()], axis=-1))) + return loss + + return composite + + +class TorchCompositeLoss(nn.Module): + """Composite loss function.""" + + def __init__(self, loss_dict, weight_dict=None, custom_losses=None): + """Create a composite loss function from a set of pytorch losses.""" + super().__init__() + self.weights = weight_dict + self.losses = {loss_name: get_single_loss('pytorch', + loss_name, + loss_params, + custom_losses) + for loss_name, loss_params in loss_dict.items()} + self.values = {} # values from the individual loss functions + + def forward(self, outputs, targets): + loss = 0 + for func_name, weight in self.weights.items(): + self.values[func_name] = self.losses[func_name](outputs, targets) + loss += weight*self.values[func_name] + + return loss diff --git a/docker/solaris/solaris/nets/metrics.py b/docker/solaris/solaris/nets/metrics.py new file mode 100644 index 00000000..1f9dfa20 --- /dev/null +++ b/docker/solaris/solaris/nets/metrics.py @@ -0,0 +1,117 @@ +from tensorflow.keras import backend as K +from tensorflow import keras + + +def get_metrics(framework, config): + """Load model training metrics from a config file for a specific framework. + """ + training_metrics = [] + validation_metrics = [] + + # TODO: enable passing kwargs to these metrics. This will require + # writing a wrapper function that'll receive the inputs from the model + # and pass them along with the kwarg to the metric function. + if config['training']['metrics'].get('training', []) is None: + training_metrics = [] + else: + for m in config['training']['metrics'].get('training', []): + training_metrics.append(metric_dict[m]) + if config['training']['metrics'].get('validation', []) is None: + validation_metrics = [] + else: + for m in config['training']['metrics'].get('validation', []): + validation_metrics.append(metric_dict[m]) + + return {'train': training_metrics, 'val': validation_metrics} + + +def dice_coef_binary(y_true, y_pred, smooth=1e-7): + ''' + Dice coefficient for 2 categories. Ignores background pixel label 0 + Pass to model as metric during compile statement + ''' + y_true_f = K.flatten(K.one_hot(K.cast(y_true, 'int32'), + num_classes=2)[..., 1:]) + y_pred_f = K.flatten(y_pred[..., 1:]) + intersect = K.sum(y_true_f * y_pred_f, axis=-1) + denom = K.sum(y_true_f + y_pred_f, axis=-1) + return K.mean((2. * intersect / (denom + smooth))) + + +def precision(y_true, y_pred): + """Precision for foreground pixels. + + Calculates pixelwise precision TP/(TP + FP). + + """ + # count true positives + truth = K.round(K.clip(y_true, K.epsilon(), 1)) + pred_pos = K.round(K.clip(y_pred, K.epsilon(), 1)) + true_pos = K.sum(K.cast(K.all(K.stack([truth, pred_pos], axis=2), axis=2), + dtype='float64')) + pred_pos_ct = K.sum(pred_pos) + K.epsilon() + precision = true_pos/pred_pos_ct + + return precision + + +def recall(y_true, y_pred): + """Precision for foreground pixels. + + Calculates pixelwise recall TP/(TP + FN). + + """ + # count true positives + truth = K.round(K.clip(y_true, K.epsilon(), 1)) + pred_pos = K.round(K.clip(y_pred, K.epsilon(), 1)) + true_pos = K.sum(K.cast(K.all(K.stack([truth, pred_pos], axis=2), axis=2), + dtype='float64')) + truth_ct = K.sum(K.round(K.clip(y_true, K.epsilon(), 1))) + if truth_ct == 0: + return 0 + recall = true_pos/truth_ct + + return recall + + +def f1_score(y_true, y_pred): + """F1 score for foreground pixels ONLY. + + Calculates pixelwise F1 score for the foreground pixels (mask value == 1). + Returns NaN if the model does not identify any foreground pixels in the + image. + + """ + + prec = precision(y_true, y_pred) + rec = recall(y_true, y_pred) + # Calculate f1_score + f1_score = 2 * (prec * rec) / (prec + rec) + + return f1_score + + +# the keras metrics functions _should_ also work if provided with a +# (y_true, y_pred) pair from pytorch, so I'll use those for both. +metric_dict = { + 'accuracy': keras.metrics.binary_accuracy, + 'binary_accuracy': keras.metrics.binary_accuracy, + 'precision': precision, + 'recall': recall, + 'f1_score': f1_score, + 'categorical_accuracy': keras.metrics.categorical_accuracy, + 'cosine': keras.metrics.cosine_proximity, + 'cosine_proximity': keras.metrics.cosine_proximity, + 'hinge': keras.metrics.hinge, + 'squared_hinge': keras.metrics.squared_hinge, + 'kld': keras.metrics.kullback_leibler_divergence, + 'kullback_leibler_divergence': keras.metrics.kullback_leibler_divergence, + 'mae': keras.metrics.mean_absolute_error, + 'mean_absolute_error': keras.metrics.mean_absolute_error, + 'mse': keras.metrics.mean_squared_error, + 'mean_squared_error': keras.metrics.mean_squared_error, + 'msle': keras.metrics.mean_squared_logarithmic_error, + 'mean_squared_logarithmic_error': keras.metrics.mean_squared_logarithmic_error, + 'sparse_categorical_accuracy': keras.metrics.sparse_categorical_accuracy, + 'top_k_categorical_accuracy': keras.metrics.top_k_categorical_accuracy +} diff --git a/docker/solaris/solaris/nets/model_io.py b/docker/solaris/solaris/nets/model_io.py new file mode 100644 index 00000000..ca7ce574 --- /dev/null +++ b/docker/solaris/solaris/nets/model_io.py @@ -0,0 +1,139 @@ +import os +from tensorflow import keras +import torch +from warnings import warn +import requests +import numpy as np +from tqdm.auto import tqdm +from ..nets import weights_dir +from .zoo import model_dict + + +def get_model(model_name, framework, model_path=None, pretrained=False, + custom_model_dict=None, num_classes=1): + """Load a model from a file based on its name.""" + if custom_model_dict is not None: + md = custom_model_dict + else: + md = model_dict.get(model_name, None) + if md is None: # if the model's not provided by solaris + raise ValueError(f"{model_name} can't be found in solaris and no " + "custom_model_dict was provided. Check your " + "model_name in the config file and/or provide a " + "custom_model_dict argument to Trainer(). ") + if model_path is None or custom_model_dict is not None: + model_path = md.get('weight_path') + if num_classes == 1: + model = md.get('arch')(pretrained=pretrained) + else: + model = md.get('arch')(num_classes=num_classes, pretrained=pretrained) + + if model is not None and pretrained: + try: + model = _load_model_weights(model, model_path, framework) + except (OSError, FileNotFoundError): + warn(f'The model weights file {model_path} was not found.' + ' Attempting to download from the SpaceNet repository.') + weight_path = _download_weights(md) + model = _load_model_weights(model, weight_path, framework) + + return model + + +def _load_model_weights(model, path, framework): + """Backend for loading the model.""" + + if framework.lower() == 'keras': + try: + model.load_weights(path) + except OSError: + # first, check to see if the weights are in the default sol dir + default_path = os.path.join(weights_dir, os.path.split(path)[1]) + try: + model.load_weights(default_path) + except OSError: + # if they can't be found anywhere, raise the error. + raise FileNotFoundError("{} doesn't exist.".format(path)) + + elif framework.lower() in ['torch', 'pytorch']: + # pytorch already throws the right error on failed load, so no need + # to fix exception + if torch.cuda.is_available(): + try: + loaded = torch.load(path) + except FileNotFoundError: + # first, check to see if the weights are in the default sol dir + default_path = os.path.join(weights_dir, + os.path.split(path)[1]) + loaded = torch.load(path) + else: + try: + loaded = torch.load(path, map_location='cpu') + except FileNotFoundError: + default_path = os.path.join(weights_dir, + os.path.split(path)[1]) + loaded = torch.load(path, map_location='cpu') + + if isinstance(loaded, torch.nn.Module): # if it's a full model already + model.load_state_dict(loaded.state_dict()) + else: + model.load_state_dict(loaded) + + return model + + +def reset_weights(model, framework): + """Re-initialize model weights for training. + + Arguments + --------- + model : :class:`tensorflow.keras.Model` or :class:`torch.nn.Module` + A pre-trained, compiled model with weights saved. + framework : str + The deep learning framework used. Currently valid options are + ``['torch', 'keras']`` . + + Returns + ------- + reinit_model : model object + The model with weights re-initialized. Note this model object will also + lack an optimizer, loss function, etc., which will need to be added. + """ + + if framework == 'keras': + model_json = model.to_json() + reinit_model = keras.models.model_from_json(model_json) + elif framework == 'torch': + reinit_model = model.apply(_reset_torch_weights) + + return reinit_model + + +def _reset_torch_weights(torch_layer): + if isinstance(torch_layer, torch.nn.Conv2d) or \ + isinstance(torch_layer, torch.nn.Linear): + torch_layer.reset_parameters() + + +def _download_weights(model_dict): + """Download pretrained weights for a model.""" + weight_url = model_dict.get('weight_url', None) + weight_dest_path = model_dict.get('weight_path', os.path.join( + weights_dir, weight_url.split('/')[-1])) + if weight_url is None: + raise KeyError("Can't find the weights file.") + else: + r = requests.get(weight_url, stream=True) + if r.status_code != 200: + raise ValueError('The file could not be downloaded. Check the URL' + ' and network connections.') + total_size = int(r.headers.get('content-length', 0)) + block_size = 1024 + with open(weight_dest_path, 'wb') as f: + for chunk in tqdm(r.iter_content(block_size), + total=np.ceil(total_size//block_size), + unit='KB', unit_scale=False): + if chunk: + f.write(chunk) + + return weight_dest_path diff --git a/docker/solaris/solaris/nets/optimizers.py b/docker/solaris/solaris/nets/optimizers.py new file mode 100644 index 00000000..ec7921d9 --- /dev/null +++ b/docker/solaris/solaris/nets/optimizers.py @@ -0,0 +1,163 @@ +"""Wrappers for training optimizers.""" +import math +import torch +from tensorflow import keras + + +def get_optimizer(framework, config): + """Get the optimizer specified in config for model training. + + Arguments + --------- + framework : str + Name of the deep learning framework used. Current options are + ``['torch', 'keras']``. + config : dict + The config dict generated from the YAML config file. + + Returns + ------- + An optimizer object for the specified deep learning framework. + """ + + if config['training']['optimizer'] is None: + raise ValueError('An optimizer must be specified in the config ' + 'file.') + + if framework in ['torch', 'pytorch']: + return torch_optimizers.get(config['training']['optimizer'].lower()) + elif framework == 'keras': + return keras_optimizers.get(config['training']['optimizer'].lower()) + + +class TorchAdamW(torch.optim.Optimizer): + """AdamW algorithm as implemented in `Torch_AdamW`_. + + The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. + The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. + Arguments: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay coefficient (default: 1e-2) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + (default: False) + .. _Torch_AdamW: https://github.com/pytorch/pytorch/pull/3740 + .. _Adam\: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _Decoupled Weight Decay Regularization: + https://arxiv.org/abs/1711.05101 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, + weight_decay=1e-2, amsgrad=False): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad) + super(TorchAdamW, self).__init__(params, defaults) + + def __setstate__(self, state): + super(TorchAdamW, self).__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + def step(self, closure=None): + """Performs a single optimization step. + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + + # Perform stepweight decay + p.data.mul_(1 - group['lr'] * group['weight_decay']) + + # Perform optimization step + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError('Adam does not support sparse' + 'gradients, please consider SparseAdam' + ' instead') + amsgrad = group['amsgrad'] + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p.data) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p.data) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p.data) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + if amsgrad: + max_exp_avg_sq = state['max_exp_avg_sq'] + beta1, beta2 = group['betas'] + + state['step'] += 1 + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(1 - beta1, grad) + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + if amsgrad: + # Maintains the maximum of all 2nd moment running avg. till now + torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) + # Use the max. for normalizing running avg. of gradient + denom = max_exp_avg_sq.sqrt().add_(group['eps']) + else: + denom = exp_avg_sq.sqrt().add_(group['eps']) + + bias_correction1 = 1 - beta1 ** state['step'] + bias_correction2 = 1 - beta2 ** state['step'] + step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 + + p.data.addcdiv_(-step_size, exp_avg, denom) + + return loss + + +torch_optimizers = { + 'adadelta': torch.optim.Adadelta, + 'adam': torch.optim.Adam, + 'adamw': TorchAdamW, + 'sparseadam': torch.optim.SparseAdam, + 'adamax': torch.optim.Adamax, + 'asgd': torch.optim.ASGD, + 'rmsprop': torch.optim.RMSprop, + 'sgd': torch.optim.SGD, +} + +keras_optimizers = { + 'adadelta': keras.optimizers.Adadelta, + 'adagrad': keras.optimizers.Adagrad, + 'adam': keras.optimizers.Adam, + 'adamax': keras.optimizers.Adamax, + 'nadam': keras.optimizers.Nadam, + 'rmsprop': keras.optimizers.RMSprop, + 'sgd': keras.optimizers.SGD +} diff --git a/docker/solaris/solaris/nets/torch_callbacks.py b/docker/solaris/solaris/nets/torch_callbacks.py new file mode 100644 index 00000000..d643eae0 --- /dev/null +++ b/docker/solaris/solaris/nets/torch_callbacks.py @@ -0,0 +1,251 @@ +"""PyTorch Callbacks.""" + +import os +import numpy as np +from .metrics import metric_dict +import torch + + +class TorchEarlyStopping(object): + """Tracks if model training should stop based on rate of improvement. + + Arguments + --------- + patience : int, optional + The number of epochs to wait before stopping the model if the metric + didn't improve. Defaults to 5. + threshold : float, optional + The minimum metric improvement required to count as "improvement". + Defaults to ``0.0`` (any improvement satisfies the requirement). + verbose : bool, optional + Verbose text output. Defaults to off (``False``). _NOTE_ : This + currently does nothing. + """ + + def __init__(self, patience=5, threshold=0.0, verbose=False): + self.patience = patience + self.threshold = threshold + self.counter = 0 + self.best = None + self.stop = False + + def __call__(self, metric_score): + + if self.best is None: + self.best = metric_score + self.counter = 0 + else: + if self.best - self.threshold < metric_score: + self.counter += 1 + else: + self.best = metric_score + self.counter = 0 + + if self.counter >= self.patience: + self.stop = True + + +class TorchTerminateOnNaN(object): + """Sets a stop condition if the model loss achieves an NaN or inf value. + + Arguments + --------- + patience : int, optional + The number of epochs that must display an NaN loss value before + stopping. Defaults to ``1``. + verbose : bool, optional + Verbose text output. Defaults to off (``False``). _NOTE_ : This + currently does nothing. + """ + + def __init__(self, patience=1, verbose=False): + self.patience = patience + self.counter = 0 + self.stop = False + + def __call__(self, loss): + if np.isnan(loss) or np.isinf(loss): + self.counter += 1 + if self.counter >= self.patience: + self.stop = True + else: + self.counter = 0 + + +class TorchTerminateOnMetricNaN(object): + """Sets a stop condition if a training metric achieves an NaN or inf value. + + Arguments + --------- + stopping_metric : str + The name of the metric to stop on. The name must match a key in + :const:`solaris.nets.metrics.metric_dict` . + patience : int, optional + The number of epochs that must display an NaN loss value before + stopping. Defaults to ``1``. + verbose : bool, optional + Verbose text output. Defaults to off (``False``). _NOTE_ : This + currently does nothing. + """ + + def __init__(self, stopping_metric, patience=1, verbose=False): + self.metric = metric_dict[stopping_metric] + self.patience = patience + self.counter = 0 + self.stop = False + + def __call__(self, y_true, y_pred): + if np.isinf(self.metric(y_true, y_pred)) or \ + np.isnan(self.metric(y_true, y_pred)): + self.counter += 1 + if self.counter >= self.patience: + self.stop = True + else: + self.counter = 0 + + +class TorchModelCheckpoint(object): + """Save the model at specific points using Keras checkpointing args. + + Arguments + --------- + filepath : str, optional + Path to save the model file to. The end of the path (before the + file extension) will have ``'_[epoch]'`` added to it to ID specific + checkpoints. + monitor : str, optional + The loss value to monitor. Options are + ``['loss', 'val_loss', 'periodic']`` or a metric from the keys in + :const:`solaris.nets.metrics.metric_dict` . Defaults to ``'loss'`` . If + ``'periodic'``, it saves every n epochs (see `period` below). + verbose : bool, optional + Verbose text output. Defaults to ``False`` . + save_best_only : bool, optional + Save only the model with the best value? Defaults to no (``False`` ). + mode : str, optional + One of ``['auto', 'min', 'max']``. Is a better value higher or lower? + Defaults to ``'auto'`` in which case it tries to infer it (if + ``monitor='loss'`` or ``monitor='val_loss'`` , it assumes ``'min'`` , + if it's a metric it assumes ``'max'`` .) If ``'min'``, it assumes lower + values are better; if ``'max'`` , it assumes higher values are better. + period : int, optional + If using ``monitor='periodic'`` , this saves models every `period` + epochs. Otherwise, it sets the minimum number of epochs between + checkpoints. + """ + + def __init__(self, filepath='', monitor='loss', verbose=False, + save_best_only=False, mode='auto', period=1, + weights_only=True): + + self.filepath = filepath + self.monitor = monitor + if self.monitor not in ['loss', 'val_loss', 'periodic']: + self.monitor = metric_dict[self.monitor] + self.verbose = verbose + self.save_best_only = save_best_only + self.period = period + self.weights_only = weights_only + self.mode = mode + if self.mode == 'auto': + if self.monitor in ['loss', 'val_loss']: + self.mode = 'min' + else: + self.mode = 'max' + + self.epoch = 0 + self.last_epoch = 0 + self.last_saved_value = None + + def __call__(self, model, loss_value=None, y_true=None, y_pred=None): + """Run a round of model checkpointing for an epoch. + + Arguments + --------- + model : model object + The model to be saved during checkpoints. Must be a PyTorch model. + loss_value : numeric, optional + The numeric output of the loss function. Only required if using + ``monitor='loss'`` or ``monitor='val_loss'`` . + y_true : :class:`np.array` , optional + The labels for the validation data. Only required if using + a metric as the monitored value. + y_pred : :class:`np.array` , optional + The predicted values from the model. Only required if using + a metric as the monitored value. + """ + + self.epoch += 1 + if self.monitor == 'periodic': # update based on period + if self.last_epoch + self.period <= self.epoch: + # self.last_saved_value = loss_value if loss_value else 0 + self.save(model, self.weights_only) + self.last_epoch = self.epoch + + + elif self.monitor in ['loss', 'val_loss']: + if self.last_saved_value is None: + self.last_saved_value = loss_value + if self.last_epoch + self.period <= self.epoch: + self.save(model, self.weights_only) + self.last_epoch = self.epoch + if self.last_epoch + self.period <= self.epoch: + if self.check_is_best_value(loss_value): + self.last_saved_value = loss_value + self.save(model, self.weights_only) + self.last_epoch = self.epoch + + else: + if self.last_saved_value is None: + self.last_saved_value = self.monitor(y_true, y_pred) + if self.last_epoch + self.period <= self.epoch: + self.save(model, self.weights_only) + self.last_epoch = self.epoch + if self.last_epoch + self.period <= self.epoch: + metric_value = self.monitor(y_true, y_pred) + if self.check_is_best_value(metric_value): + self.last_saved_value = metric_value + self.save(model, self.weights_only) + self.last_epoch = self.epoch + + def check_is_best_value(self, value): + """Check if `value` is better than the best stored value.""" + if self.mode == 'min' and self.last_saved_value > value: + return True + elif self.mode == 'max' and self.last_saved_value < value: + return True + else: + return False + + def save(self, model, weights_only): + """Save the model. + + Arguments + --------- + model : :class:`torch.nn.Module` + A PyTorch model instance to save. + weights_only : bool, optional + Should the entire model be saved, or only its weights (also known + as the state_dict)? Defaults to ``False`` (saves entire model). The + entire model must be saved to resume training without re-defining + the model architecture, optimizer, and loss function. + """ + save_name = os.path.splitext(self.filepath)[0] + '_epoch{}_{}'.format( + self.epoch, np.round(self.last_saved_value, 3)) + save_name = save_name + os.path.splitext(self.filepath)[1] + if isinstance(model, torch.nn.DataParallel): + to_save = model.module + else: + to_save = model + if weights_only: + torch.save(to_save.state_dict(), save_name) + else: + torch.save(to_save, save_name) + + +torch_callback_dict = { + "early_stopping": TorchEarlyStopping, + "model_checkpoint": TorchModelCheckpoint, + "terminate_on_nan": TorchTerminateOnNaN, + "terminate_on_metric_nan": TorchTerminateOnMetricNaN +} diff --git a/docker/solaris/solaris/nets/train.py b/docker/solaris/solaris/nets/train.py new file mode 100644 index 00000000..b6fb36cb --- /dev/null +++ b/docker/solaris/solaris/nets/train.py @@ -0,0 +1,285 @@ +"""Training code for `solaris` models.""" + +import numpy as np +import pandas as pd +from .model_io import get_model, reset_weights +from .datagen import make_data_generator +from .losses import get_loss +from .optimizers import get_optimizer +from .callbacks import get_callbacks +from .torch_callbacks import TorchEarlyStopping, TorchTerminateOnNaN +from .torch_callbacks import TorchModelCheckpoint +from .metrics import get_metrics +import torch +from torch.optim.lr_scheduler import _LRScheduler +import tensorflow as tf + +class Trainer(object): + """Object for training `solaris` models using PyTorch or Keras. """ + + def __init__(self, config, custom_model_dict=None, custom_losses=None): + self.config = config + self.pretrained = self.config['pretrained'] + self.batch_size = self.config['batch_size'] + self.framework = self.config['nn_framework'] + self.model_name = self.config['model_name'] + self.model_path = self.config.get('model_path', None) + try: + self.num_classes = self.config['data_specs']['num_classes'] + except KeyError: + self.num_classes = 1 + self.model = get_model(self.model_name, self.framework, + self.model_path, self.pretrained, + custom_model_dict, self.num_classes) + + self.train_df, self.val_df = get_train_val_dfs(self.config) + self.train_datagen = make_data_generator(self.framework, self.config, + self.train_df, stage='train') + self.val_datagen = make_data_generator(self.framework, self.config, + self.val_df, stage='validate') + self.epochs = self.config['training']['epochs'] + self.optimizer = get_optimizer(self.framework, self.config) + self.lr = self.config['training']['lr'] + self.custom_losses = custom_losses + self.loss = get_loss(self.framework, + self.config['training'].get('loss'), + self.config['training'].get('loss_weights'), + self.custom_losses) + self.checkpoint_frequency = self.config['training'].get('checkpoint_' + + 'frequency') + self.callbacks = get_callbacks(self.framework, self.config) + self.metrics = get_metrics(self.framework, self.config) + self.verbose = self.config['training']['verbose'] + if self.framework in ['torch', 'pytorch']: + self.gpu_available = torch.cuda.is_available() + if self.gpu_available: + self.gpu_count = torch.cuda.device_count() + else: + self.gpu_count = 0 + elif self.framework == 'keras': + self.gpu_available = tf.test.is_gpu_available() + + self.is_initialized = False + self.stop = False + + self.initialize_model() + + def initialize_model(self): + """Load in and create all model training elements.""" + if not self.pretrained: + self.model = reset_weights(self.model, self.framework) + + if self.framework == 'keras': + self.model = self.model.compile(optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics['train']) + + elif self.framework == 'torch': + if self.gpu_available: + self.model = self.model.cuda() + if self.gpu_count > 1: + self.model = torch.nn.DataParallel(self.model) + # create optimizer + if self.config['training']['opt_args'] is not None: + self.optimizer = self.optimizer( + self.model.parameters(), lr=self.lr, + **self.config['training']['opt_args'] + ) + else: + self.optimizer = self.optimizer( + self.model.parameters(), lr=self.lr + ) + # wrap in lr_scheduler if one was created + for cb in self.callbacks: + if isinstance(cb, _LRScheduler): + self.optimizer = cb( + self.optimizer, + **self.config['training']['callbacks'][ + 'lr_schedule'].get(['schedule_dict'], {}) + ) + # drop the LRScheduler callback from the list + self.callbacks = [i for i in self.callbacks if i != cb] + + self.is_initialized = True + + def train(self): + """Run training on the model.""" + if not self.is_initialized: + self.initialize_model() + + if self.framework == 'keras': + self.model.fit_generator(self.train_datagen, + validation_data=self.val_datagen, + epochs=self.epochs, + callbacks=self.callbacks) + + elif self.framework == 'torch': +# tf_sess = tf.Session() + for epoch in range(self.epochs): + if self.verbose: + print('Beginning training epoch {}'.format(epoch)) + # TRAINING + self.model.train() + for batch_idx, batch in enumerate(self.train_datagen): + if torch.cuda.is_available(): + if self.config['data_specs'].get('additional_inputs', + None) is not None: + data = [] + for i in ['image'] + self.config[ + 'data_specs']['additional_inputs']: + data.append(torch.Tensor(batch[i]).cuda()) + else: + data = batch['image'].cuda() + target = batch['mask'].cuda().float() + else: + if self.config['data_specs'].get('additional_inputs', + None) is not None: + data = [] + for i in ['image'] + self.config[ + 'data_specs']['additional_inputs']: + data.append(torch.Tensor(batch[i])) + else: + data = batch['image'] + target = batch['mask'].float() + self.optimizer.zero_grad() + output = self.model(data) + loss = self.loss(output, target) + loss.backward() + self.optimizer.step() + + if self.verbose and batch_idx % 10 == 0: + + print(' loss at batch {}: {}'.format( + batch_idx, loss), flush=True) + + # VALIDATION + with torch.no_grad(): + self.model.eval() + torch.cuda.empty_cache() + val_loss = [] + for batch_idx, batch in enumerate(self.val_datagen): + if torch.cuda.is_available(): + if self.config['data_specs'].get( + 'additional_inputs', None) is not None: + data = [] + for i in ['image'] + self.config[ + 'data_specs']['additional_inputs']: + data.append(torch.Tensor(batch[i]).cuda()) + else: + data = batch['image'].cuda() + target = batch['mask'].cuda().float() + else: + if self.config['data_specs'].get( + 'additional_inputs', None) is not None: + data = [] + for i in ['image'] + self.config[ + 'data_specs']['additional_inputs']: + data.append(torch.Tensor(batch[i])) + else: + data = batch['image'] + target = batch['mask'].float() + val_output = self.model(data) + val_loss.append(self.loss(val_output, target)) + val_loss = torch.mean(torch.stack(val_loss)) + if self.verbose: + print() + print(' Validation loss at epoch {}: {}'.format( + epoch, val_loss)) + print() + + check_continue = self._run_torch_callbacks( + loss.detach().cpu().numpy(), + val_loss.detach().cpu().numpy()) + if not check_continue: + break + + self.save_model() + + def _run_torch_callbacks(self, loss, val_loss): + for cb in self.callbacks: + if isinstance(cb, TorchEarlyStopping): + cb(val_loss) + if cb.stop: + if self.verbose: + print('Early stopping triggered - ' + 'ending training') + return False + + elif isinstance(cb, TorchTerminateOnNaN): + cb(val_loss) + if cb.stop: + if self.verbose: + print('Early stopping triggered - ' + 'ending training') + return False + + elif isinstance(cb, TorchModelCheckpoint): + # set minimum num of epochs btwn checkpoints (not periodic) + # or + # frequency of model saving (periodic) + # cb.period = self.checkpoint_frequency + + if cb.monitor == 'loss': + cb(self.model, loss_value=loss) + elif cb.monitor == 'val_loss': + cb(self.model, loss_value=val_loss) + elif cb.monitor == 'periodic': + # no loss_value specification needed; defaults to `loss` + # cb(self.model, loss_value=loss) + cb(self.model) + + return True + + def save_model(self): + """Save the final model output.""" + if self.framework == 'keras': + self.model.save(self.config['training']['model_dest_path']) + elif self.framework == 'torch': + if isinstance(self.model, torch.nn.DataParallel): + torch.save(self.model.module.state_dict(), + self.config['training']['model_dest_path']) + else: + torch.save(self.model.state_dict(), + self.config['training']['model_dest_path']) + + +def get_train_val_dfs(config): + """Get the training and validation dfs based on the contents of ``config``. + + This function uses the logic described in the documentation for the config + files to determine where to find training and validation dataset files. + See the docs and the comments in solaris/data/config_skeleton.yml for + details. + + Arguments + --------- + config : dict + The loaded configuration dict for model training and/or inference. + + Returns + ------- + train_df, val_df : :class:`tuple` of :class:`dict` s + :class:`dict` s containing two columns: ``'image'`` and ``'label'``. + Each column corresponds to paths to find matching image and label files + for training. + """ + + train_df = pd.read_csv(config['training_data_csv']) + + if config['data_specs']['val_holdout_frac'] is None: + if config['validation_data_csv'] is None: + raise ValueError( + "If val_holdout_frac isn't specified in config," + " validation_data_csv must be.") + val_df = pd.read_csv(config['validation_data_csv']) + + else: + val_frac = config['data_specs']['val_holdout_frac'] + val_subset = np.random.choice(train_df.index, + int(len(train_df)*val_frac), + replace=False) + val_df = train_df.loc[val_subset] + # remove the validation samples from the training df + train_df = train_df.drop(index=val_subset) + + return train_df, val_df diff --git a/docker/solaris/solaris/nets/transform.py b/docker/solaris/solaris/nets/transform.py new file mode 100755 index 00000000..9436c72c --- /dev/null +++ b/docker/solaris/solaris/nets/transform.py @@ -0,0 +1,507 @@ +#!/usr/bin/env python +""" +Image transformation, augmentation, etc. for use in models. +----------------------------------------------------------- + +Where possible, the codebase uses albumentations implementations for transforms +because they checked various different implementations and use the fastest one. +However, in some cases albumentations uses a cv2 backend, +which is incompatible with unusual channel counts in imagery, and therefore +other implementations are used for those functions here. + +Note: Some augmentations are unavailable in this library. + + +Functionality used directly from albumentations: +- Crop +- VerticalFlip +- HorizontalFlip +- Flip +- Transpose +- Resize +- CenterCrop +- RandomCrop +- RandomSizedCrop +- OpticalDistortion +- GridDistortion +- ElasticTransform +- Normalize +- HueSaturationValue # NOTE: CAN ONLY HANDLE RGB 3-CHANNEL! +- RGBShift # NOTE: CAN ONLY HANDLE RGB 3-CHANNEL! +- RandomRotate90 +- RandomBrightnessContrast +- Blur +- MotionBlur +- MedianBlur +- GaussNoise +- CLAHE +- RandomGamma +- ToFloat +- NoOp +- PadIfNeeded + +Implemented here: +- Rotate +- RandomScale +- Cutout +""" + +import numpy as np +from PIL.Image import BICUBIC, BILINEAR, HAMMING, NEAREST, LANCZOS +from PIL import Image +from scipy import ndimage as ndi + +from albumentations.augmentations import functional as F +from albumentations.augmentations.functional import preserve_channel_dim +from albumentations.core.transforms_interface import DualTransform, to_tuple, \ + ImageOnlyTransform, NoOp +from albumentations.augmentations.transforms import Crop, VerticalFlip, \ + HorizontalFlip, Flip, Transpose, Resize, CenterCrop, RandomCrop, Cutout, \ + RandomSizedCrop, OpticalDistortion, GridDistortion, ElasticTransform, \ + Normalize, HueSaturationValue, RGBShift, RandomBrightnessContrast, \ + Blur, MotionBlur, MedianBlur, GaussNoise, CLAHE, RandomGamma, ToFloat, \ + RandomRotate90, PadIfNeeded +from albumentations.core.composition import Compose, OneOf, OneOrOther + + +__all__ = ['Crop', 'VerticalFlip', 'HorizontalFlip', 'Flip', 'Transpose', + 'Resize', 'CenterCrop', 'RandomCrop', 'RandomSizedCrop', + 'OpticalDistortion', 'GridDistortion', 'ElasticTransform', + 'Normalize', 'HueSaturationValue', 'RGBShift', + 'RandomBrightnessContrast', 'Blur', 'MotionBlur', 'MedianBlur', + 'GaussNoise', 'CLAHE', 'RandomGamma', 'ToFloat', 'Rotate', 'RandomRotate90', + 'PadIfNeeded', 'RandomScale', 'Cutout', 'Compose', 'OneOf', 'OneOrOther', 'NoOp', + 'RandomRotate90', 'SwapChannels', 'process_aug_dict', 'get_augs', 'build_pipeline'] + + +class DropChannel(ImageOnlyTransform): + """Drop a channel from an input image. + + Arguments + --------- + idx : int + The channel index to drop. + axis : int, optional (default: 1) + The axis to drop the channel from. Defaults to ``1`` (torch channel + axis). Set to ``3`` for TF models where the channel is the last axis + of an image. + always_apply : bool, optional (default: False) + Apply this transformation to every image? Defaults to no (``False``). + p : float [0, 1], optional (default: 1.0) + Probability that the augmentation is performed to each image. Defaults + to ``1.0``. + """ + + def __init__(self, idx, axis=1, always_apply=False, p=1.0): + super().__init__(always_apply, p) + + self.idx = idx + self.axis = axis + + def apply(self, im_arr, **params): + return np.delete(im_arr, self.idx, self.axis) + + +class SwapChannels(ImageOnlyTransform): + """Swap channels in an input image. + + Arguments + --------- + first_idx : int + The first channel in the pair to swap. + second_idx : int + The second channel in the pair to swap. + axis : int, optional (default: 1) + The axis to drop the channel from. Defaults to ``0`` (torch channel + axis). Set to ``2`` for TF models where the channel is the last axis + of an image. + always_apply : bool, optional (default: False) + Apply this transformation to every image? Defaults to no (``False``). + p : float [0, 1], optional (default: 1.0) + Probability that the augmentation is performed to each image. Defaults + to ``1.0``. + """ + + def __init__(self, first_idx, second_idx, axis=0, + always_apply=False, p=1.0): + super().__init__(always_apply, p) + if axis not in [0, 2]: + raise ValueError("Solaris can only accommodate axis values of 0 " + "(Torch axis style) or 2 (TensorFlow/Keras axis " + "style) for SwapChannel.") + self.first_idx = first_idx + self.second_idx = second_idx + self.axis = axis + + def apply(self, im_arr, **params): + if self.axis == 0: + subarr = im_arr[self.first_idx, ...].copy() + im_arr[self.first_idx, ...] = im_arr[self.second_idx, ...] + im_arr[self.second_idx, ...] = subarr + elif self.axis == 2: + subarr = im_arr[..., self.first_idx].copy() + im_arr[..., self.first_idx] = im_arr[..., self.second_idx] + im_arr[..., self.second_idx] = subarr + + return im_arr + + +class Rotate(DualTransform): + """Array rotation using scipy.ndimage's implementation. + + Arguments + --------- + limit : ``[int, int]`` or ``int`` + Range from which a random angle is picked. If only a single `int` is + provided, an angle is picked from range(-angle, angle) + border_mode : str, optional + One of ``['reflect', 'nearest', 'constant', 'wrap']``. Defaults to + ``'reflect'``. See :func:`scipy.ndimage.interpolation.rotate` + ``mode`` argument. + cval : int or float, optional + constant value to fill borders with if ``border_mode=='constant'``. + Defaults to 0. + always_apply : bool, optional + Apply this transformation to every image? Defaults to no (``False``). + p : float [0, 1], optional + Probability that the augmentation is performed to each image. Defaults + to ``0.5``. + + """ + + def __init__(self, limit=90, border_mode='reflect', cval=0.0, + always_apply=False, p=0.5): + super(Rotate, self).__init__(always_apply, p) + + self.limit = to_tuple(limit) + self.border_mode = border_mode + self.cval = cval + + def apply(self, im_arr, angle=0, border_mode='reflect', cval=0, **params): + return ndi.interpolation.rotate(im_arr, angle=angle, + mode=self.border_mode, cval=self.cval) + + def get_params(self): + return {'angle': np.random.randint(self.limit[0], self.limit[1])} + + def apply_to_bbox(self, bbox, angle=0, **params): + return F.bbox_rotate(bbox, angle, **params) + + def apply_to_keypoint(self): + raise NotImplementedError + + +class RandomScale(DualTransform): + """Randomly resize the input array in X and Y. + + Arguments + --------- + scale_limit : ``(float, float)`` tuple or float + Limit to the amount of scaling to perform on the image. If provided + as a tuple, the limits are + ``[shape*scale_limit[0], shape*scale_limit[1]]``. If only a single + vaue is passed, this is converted to a tuple by converting to + ``(1-scale_limit, 1+scale_limit)``, i.e. ``scale_limit=0.2`` is + equivalent to ``scale_limit=(0.8, 1.2)``. + axis : str, optional + Which axis should be rescaled? Options are + ``['width', 'height', 'both'].`` + interpolation : str, optional + Interpolation method to use for resizing. One of + ``['bilinear', 'bicubic', 'lanczos', 'nearest', or 'hamming']``. + Defaults to ``'bicubic'``. See the Pillow_ documentation for more + information. + always_apply : bool, optional + Apply this transformation to every image? Defaults to no (``False``). + p : float [0, 1], optional + Probability that the augmentation is performed to each image. Defaults + to ``0.5``. + + .. _: https://pillow.readthedocs.io/en/4.1.x/handbook/concepts.html#filters-comparison-table + + """ + + def __init__(self, scale_limit, axis='both', interpolation='bicubic', + always_apply=False, p=0.5): + super(RandomScale, self).__init__(always_apply, p) + + self.scale_limit = to_tuple(scale_limit) + # post-processing to fix values if only a single number was passed + self.axis = axis + if self.scale_limit[0] == -self.scale_limit[1]: + self.scale_limit = tuple([self.scale_limit[0]+1, + self.scale_limit[1]+1]) + if interpolation == 'bicubic': + self.interpolation = BICUBIC + elif interpolation == 'bilinear': + self.interpolation = BILINEAR + elif interpolation == 'lanczos': + self.interpolation = LANCZOS + elif interpolation == 'nearest': + self.interpolation = NEAREST + elif interpolation == 'hamming': + self.interpolation = HAMMING + else: + raise ValueError( + 'The interpolation argument is not one of: ' + + '["bicubic", "bilinear", "hamming", "lanczos", "nearest"]') + + def get_params(self): + if self.axis == 'height': + x = 1 + y = np.random.uniform(self.scale_limit[0], self.scale_limit[1]) + elif self.axis == 'width': + x = np.random.uniform(self.scale_limit[0], self.scale_limit[1]) + y = 1 + elif self.axis == 'both': + x = np.random.uniform(self.scale_limit[0], self.scale_limit[1]) + y = np.random.uniform(self.scale_limit[0], self.scale_limit[1]) + return {'scale_x': x, 'scale_y': y} + + def apply(self, img, scale_x=1, scale_y=1, **params): + return scale(img, scale_x, scale_y, self.interpolation) + + def apply_to_bbox(self, bbox, **params): + # per Albumentations, bbox coords are scale-invariant + return bbox + + def apply_to_keypoint(self, keypoint): + raise NotImplementedError + + +# NOTE ON THE ShiftScaleRotate CLASS BELOW: +# Aside from cv2, there is currently no good implementation that enables +# handling the border in any way other than filling. I'm not 100% sure this +# is going to work for >3-channel images but we're going to roll the dice for +# now. + +# class ShiftScaleRotate(DualTransform): +# """Shift/scale/rotate using Pillow. +# +# Arguments +# --------- +# scale_limits : `int` or `tuple`, optional +# Limits of re-scaling amount, in fraction of starting size. If an `int` +# is passed, the image size will be rescaled randomly in the range +# ``(1-scale_limits, 1+scale_limits)``. If a 2-tuple is provided, both +# x and y scaling will have values drawn from +# ``np.random.uniform(scale_limits[0], scale_limits[1])``. If a 4-tuple +# is provided, x scaling will be selected from +# ``np.random.uniform(scale_limits[0], scale_limits[1])``, and y scaling +# will be selected from +# ``np.random.uniform(scale_limits[2], scale_limits[3]).`` To maintain +# aspect ratio, use `lock_aspect=True` (has no effect if a 4-tuple is +# provided). If not passed, image is not scaled. +# lock_aspect : bool, optional +# Should aspect ratio be locked to the input ratio? Defaults to no +# (``True``). If ``True`` and a 4-tuple is passed for `scale_limits`, +# `lock_aspect` is ignored. +# rotation_limits : `int` or 2-`tuple`, optional +# Limit of degrees of rotation of the image. If an integer, then the +# image will be rotated an angle randomly selected from +# ``range(-rotation_limits, rotation_limits)`` degrees. +# If a tuple of form ``(min_rotation, max_rotation)``, the image will be +# rotated an angle randomly selected from +# ``range(min_rotation, max_rotation)``. Defaults to 0 (no rotation). +# translation_limits: int or 2-tuple, optional +# Magnitude of x and y translations to perform in pixels. If a single int +# is provided, both x and y translation will be selected from the random +# uniform range of ``([-limit, +limit])``. If a 2-tuple +# ``(x_limit, y_limit)`` is provided, the limits are set separately based +# on the provided tuple. +# interpolation : str, optional +# Interpolation method to use for resizing. One of +# ``['bilinear', 'bicubic', or 'nearest']``. +# Defaults to ``'bicubic'``. See the Pillow_ documentation for more +# information. +# p : float [0, 1], optional +# Probability that the augmentation is performed to each image. Defaults +# to ``0.5``. +# +# .. _: https://pillow.readthedocs.io/en/4.1.x/handbook/concepts.html#filters-comparison-table +# """ +# def __init__(self, size=None, scale_limits=0, lock_aspect=True, +# rotation_limits=0, translation_limits=0, +# interpolation="bicubic", p=0.5, always_apply=False): +# super(ShiftScaleRotate, self).__init__(always_apply, p) +# self.lock_aspect = lock_aspect +# if type(scale_limits) == float: +# self.scale_limits = (1-scale_limits, 1+scale_limits, +# 1-scale_limits, 1+scale_limits) +# elif type(scale_limits) == tuple: +# if len(scale_limits) == 2: +# self.scale_limits = (scale_limits[0], scale_limits[1], +# scale_limits[0], scale_limits[1]) +# elif len(scale_limits == 4): +# self.scale_limits = scale_limits +# self.lock_aspect = False # force if a 4-tuple was provided +# else: +# raise ValueError( +# "len(scale_limits) must be len 2 or 4 if it's a tuple.") +# else: +# raise TypeError('scale_limits must be a float or tuple.') +# self.rotation_limits = to_tuple(rotation_limits) +# self.translation_limits = to_tuple(translation_limits) +# if interpolation == 'bicubic': +# self.interpolation = BICUBIC +# elif interpolation == 'bilinear': +# self.interpolation = BILINEAR +# elif interpolation == 'nearest': +# self.interpolation = NEAREST +# else: +# raise ValueError( +# 'The interpolation argument is not one of: ' + +# '["bicubic", "bilinear", "nearest"]') +# +# def apply(self, img, angle=0, scale=0, dx=0, dy=0, +# interpolation=self.interpolation, **params): +# +# +# +# def get_params(self): # parameters to use in apply() +# param_dict = { +# 'angle': np.random.uniform(self.rotation_limits[0], +# self.rotation_limits[1]), +# 'xscale': np.random.uniform(1+self.scale_limits[0], +# 1+self.scale_limits[1]), +# 'yscale': np.random.uniform(1+self.scale_limits[2], +# 1+self.scale_limits[3]), +# 'dx': np.random.randint(self.translation_limits[0], +# self.translation_limits[1]), +# 'dy': np.random.randint(self.translation_limits[0], +# self.translation_limits[1])} +# if self.lock_aspect: # reset param_dict['yscale'] to same as x +# param_dict['yscale'] = param_dict['xscale'] +# return param_dict + + +@preserve_channel_dim +def scale(im, scale_x, scale_y, interpolation): + """Scale an image using Pillow.""" + im_shape = im.shape + y_size = int(scale_y*im_shape[0]) + x_size = int(scale_x*im_shape[1]) + return np.array(Image.fromarray(im.astype('uint8')).resize((x_size, y_size), + interpolation)) + + +def build_pipeline(config): + """Create train and val augmentation pipelines from a config object. + + Arguments + --------- + config : dict + A configuration dictionary created by parsing a .yaml config file. + See documentation to the project. + + Returns + ------- + Two ``albumentations.core.composition.Compose`` instances with the entire + augmentation pipeline assembled: one for training and one for validation/ + inference. + """ + + train_aug_dict = config['training_augmentation'] + val_aug_dict = config['validation_augmentation'] + train_aug_pipeline = process_aug_dict(train_aug_dict) + val_aug_pipeline = process_aug_dict(val_aug_dict) + + return train_aug_pipeline, val_aug_pipeline + + +def _check_augs(augs): + """Check if augmentations are loaded in already or not.""" + if isinstance(augs, dict): + return process_aug_dict(augs) + elif isinstance(augs, Compose): + return augs + + +def process_aug_dict(pipeline_dict, meta_augs_list=['oneof', 'oneorother']): + """Create a Compose object from an augmentation config dict. + + Notes + ----- + See the documentation for instructions on formatting the config .yaml to + enable utilization by get_augs. + + Arguments + --------- + aug_dict : dict + The ``'training_augmentation'`` or ``'validation_augmentation'`` + sub-dict from the ``config`` object. + meta_augs_list : dict, optional + The list of augmentation names that correspond to "meta-augmentations" + in all lowercase (e.g. ``oneof``, ``oneorother``). This will be used to + find augmentation dictionary items that need further parsing. + + Returns + ------- + ``Compose`` instance + The composed augmentation pipeline. + """ + if pipeline_dict is None: + return None + p = pipeline_dict.get('p', 1.0) # probability of applying augs in pipeline + xforms = pipeline_dict['augmentations'] + composer_list = get_augs(xforms, meta_augs_list) + return Compose(composer_list, p=p) + + +def get_augs(aug_dict, meta_augs_list=['oneof', 'oneorother']): + """Get the set of augmentations contained in a dict. + + aug_dict : dict + The ``'augmentations'`` sub-dict of a ``'training_augmentation'`` or + ``'validation_augmentation'`` item in the ``'config'`` object. + sub-dict from the ``config`` object. + meta_augs_list : dict, optional + The list of augmentation names that correspond to "meta-augmentations" + in all lowercase (e.g. ``oneof``, ``oneorother``). This will be used to + find augmentation dictionary items that need further parsing. + + Returns + ------- + list + `list` of augmentations to pass to a ``Compose`` object. + """ + aug_list = [] + if aug_dict is not None: + for aug, params in aug_dict.items(): + if aug.lower() in meta_augs_list: + # recurse into sub-dict + aug_list.append(aug_matcher[aug](get_augs(aug_dict[aug]))) + else: + aug_list.append(_get_aug(aug, params)) + return aug_list + + +def _get_aug(aug, params): + """Get augmentations (recursively if needed) from items in the aug_dict.""" + aug_obj = aug_matcher[aug.lower()] + if params is None: + return aug_obj() + elif isinstance(params, dict): + return aug_obj(**params) + else: + raise ValueError( + '{} is not a valid aug param (must be dict of args)'.format(params) + ) + + +aug_matcher = { + 'crop': Crop, 'centercrop': CenterCrop, 'randomcrop': RandomCrop, + 'randomsizedcrop': RandomSizedCrop, 'verticalflip': VerticalFlip, + 'horizontalflip': HorizontalFlip, 'flip': Flip, 'transpose': Transpose, + 'resize': Resize, 'centercrop': CenterCrop, 'randomcrop': RandomCrop, + 'randomsizedcrop': RandomSizedCrop, 'opticaldistortion': OpticalDistortion, + 'griddistortion': GridDistortion, 'elastictransform': ElasticTransform, + 'normalize': Normalize, 'huesaturationvalue': HueSaturationValue, + 'rgbshift': RGBShift, 'randombrightnesscontrast': RandomBrightnessContrast, + 'blur': Blur, 'motionblur': MotionBlur, 'medianblur': MedianBlur, + 'gaussnoise': GaussNoise, 'clahe': CLAHE, 'randomgamma': RandomGamma, + 'tofloat': ToFloat, 'rotate': Rotate, 'randomscale': RandomScale, + 'cutout': Cutout, 'oneof': OneOf, 'oneorother': OneOrOther, 'noop': NoOp, + 'randomrotate90': RandomRotate90, 'dropchannel': DropChannel, + 'swapchannels': SwapChannels, 'padifneeded': PadIfNeeded +} diff --git a/docker/solaris/solaris/nets/zoo/__init__.py b/docker/solaris/solaris/nets/zoo/__init__.py new file mode 100644 index 00000000..60491f91 --- /dev/null +++ b/docker/solaris/solaris/nets/zoo/__init__.py @@ -0,0 +1,53 @@ +import os +from .. import weights_dir +from .xdxd_sn4 import XDXD_SpaceNet4_UNetVGG16 +from .selim_sef_sn4 import SelimSef_SpaceNet4_ResNet34UNet +from .selim_sef_sn4 import SelimSef_SpaceNet4_DenseNet121UNet +from .selim_sef_sn4 import SelimSef_SpaceNet4_DenseNet161UNet +from .multiclass_segmentation import MultiClass_Resnet34 +from .multiclass_segmentation import MultiClass_UNet_VGG11 +from .multiclass_segmentation import MultiClass_UNet_VGG16 +from .multiclass_segmentation import MultiClass_LinkNet34 + +model_dict = { + 'xdxd_spacenet4': { + 'weight_path': os.path.join(weights_dir, 'xdxd_spacenet4_solaris_weights.pth'), + 'weight_url': 'https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/xdxd_spacenet4_solaris_weights.pth', + 'arch': XDXD_SpaceNet4_UNetVGG16 + }, + 'selimsef_spacenet4_resnet34unet': { + 'weight_path': os.path.join(weights_dir, 'selimsef_spacenet4_resnet34unet_solaris_weights.pth'), + 'weight_url': 'https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_resnet34unet_solaris_weights.pth', + 'arch': SelimSef_SpaceNet4_ResNet34UNet + }, + 'selimsef_spacenet4_densenet121unet': { + 'weight_path': os.path.join(weights_dir, 'selimsef_spacenet4_densenet121unet_solaris_weights.pth'), + 'weight_url': 'https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_densenet121unet_solaris_weights.pth', + 'arch': SelimSef_SpaceNet4_DenseNet121UNet + }, + 'selimsef_spacenet4_densenet161unet': { + 'weight_path': os.path.join(weights_dir, 'selimsef_spacenet4_densenet161unet_solaris_weights.pth'), + 'weight_url': 'https://s3.amazonaws.com/spacenet-dataset/spacenet-model-weights/spacenet-4/selimsef_spacenet4_densenet161unet_solaris_weights.pth', + 'arch': SelimSef_SpaceNet4_DenseNet161UNet + }, + 'multiclass_resnet34': { + 'weight_path': None, + 'weight_url': None, + 'arch': MultiClass_Resnet34 + }, + 'multiclass_unet_vgg11': { + 'weight_path': None, + 'weight_url': None, + 'arch': MultiClass_UNet_VGG11 + }, + 'multiclass_unet_vgg16': { + 'weight_path': None, + 'weight_url': None, + 'arch': MultiClass_UNet_VGG16 + }, + 'multiclass_linknet34': { + 'weight_path': None, + 'weight_url': None, + 'arch': MultiClass_LinkNet34 + } +} diff --git a/docker/solaris/solaris/nets/zoo/multiclass_segmentation.py b/docker/solaris/solaris/nets/zoo/multiclass_segmentation.py new file mode 100644 index 00000000..3d3caa70 --- /dev/null +++ b/docker/solaris/solaris/nets/zoo/multiclass_segmentation.py @@ -0,0 +1,282 @@ +import torch +from torch import nn +from torchvision.models import vgg11, vgg16, resnet34 + +""" Code heavily adapted from ternaus robot-surgery-segmentation +https://github.com/ternaus/robot-surgery-segmentation """ + + +class MultiClass_Resnet34(nn.Module): + def __init__(self, num_classes=1, num_filters=32, pretrained=True, is_deconv=False): + super().__init__() + self.num_classes = num_classes + self.pool = nn.MaxPool2d(2, 2) + self.encoder = resnet34(pretrained=pretrained) + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Sequential(self.encoder.conv1, + self.encoder.bn1, + self.encoder.relu, + self.pool) + self.conv2 = self.encoder.layer1 + self.conv3 = self.encoder.layer2 + self.conv4 = self.encoder.layer3 + self.conv5 = self.encoder.layer4 + self.center = MultiClass_DecoderBlock(512, num_filters * 8 * 2, num_filters * 8, is_deconv) + self.dec5 = MultiClass_DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv) + self.dec4 = MultiClass_DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv) + self.dec3 = MultiClass_DecoderBlock(128 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv) + self.dec2 = MultiClass_DecoderBlock(64 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2, is_deconv) + self.dec1 = MultiClass_DecoderBlock(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv) + self.dec0 = MultiClass_ConvRelu(num_filters, num_filters) + self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1) + + def forward(self, x): + conv1 = self.conv1(x) + conv2 = self.conv2(conv1) + conv3 = self.conv3(conv2) + conv4 = self.conv4(conv3) + conv5 = self.conv5(conv4) + + center = self.center(self.pool(conv5)) + + dec5 = self.dec5(torch.cat([center, conv5], 1)) + dec4 = self.dec4(torch.cat([dec5, conv4], 1)) + dec3 = self.dec3(torch.cat([dec4, conv3], 1)) + dec2 = self.dec2(torch.cat([dec3, conv2], 1)) + dec1 = self.dec1(dec2) + dec0 = self.dec0(dec1) + x_out = self.final(dec0) + + return x_out + + +class MultiClass_UNet_VGG16(nn.Module): + def __init__(self, num_classes=1, num_filters=32, pretrained=True): + super().__init__() + self.num_classes = num_classes + self.encoder = vgg16(pretrained=pretrained).features + self.pool = nn.MaxPool2d(2, 2) + + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Sequential(self.encoder[0], self.relu, self.encoder[2], self.relu) + + self.conv2 = nn.Sequential(self.encoder[5], self.relu, self.encoder[7], self.relu) + + self.conv3 = nn.Sequential(self.encoder[10], self.relu, self.encoder[12], self.relu, + self.encoder[14], self.relu) + + self.conv4 = nn.Sequential(self.encoder[17], self.relu, self.encoder[19], self.relu, + self.encoder[21], self.relu) + + self.conv5 = nn.Sequential(self.encoder[24], self.relu, self.encoder[26], self.relu, + self.encoder[28], self.relu) + + self.center = MultiClass_DecoderBlock(512, num_filters * 8 * 2, + num_filters * 8) + + self.dec5 = MultiClass_DecoderBlock( + 512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8) + self.dec4 = MultiClass_DecoderBlock( + 512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8) + self.dec3 = MultiClass_DecoderBlock( + 256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2) + self.dec2 = MultiClass_DecoderBlock( + 128 + num_filters * 2, num_filters * 2 * 2, num_filters) + self.dec1 = MultiClass_ConvRelu(64 + num_filters, num_filters) + self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1) + + def forward(self, x): + conv1 = self.conv1(x) + conv2 = self.conv2(self.pool(conv1)) + conv3 = self.conv3(self.pool(conv2)) + conv4 = self.conv4(self.pool(conv3)) + conv5 = self.conv5(self.pool(conv4)) + center = self.center(self.pool(conv5)) + dec5 = self.dec5(torch.cat([center, conv5], 1)) + dec4 = self.dec4(torch.cat([dec5, conv4], 1)) + dec3 = self.dec3(torch.cat([dec4, conv3], 1)) + dec2 = self.dec2(torch.cat([dec3, conv2], 1)) + dec1 = self.dec1(torch.cat([dec2, conv1], 1)) + x_out = self.final(dec1) + return x_out + + +class MultiClass_UNet_VGG11(nn.Module): + def __init__(self, num_classes=1, num_filters=32, pretrained=True): + super().__init__() + self.num_classes = num_classes + self.pool = nn.MaxPool2d(2, 2) + self.encoder = vgg11(pretrained=pretrained).features + + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Sequential(self.encoder[0], self.relu) + self.conv2 = nn.Sequential(self.encoder[3], self.relu) + + self.conv3 = nn.Sequential( + self.encoder[6], + self.relu, + self.encoder[8], + self.relu, + ) + self.conv4 = nn.Sequential( + self.encoder[11], + self.relu, + self.encoder[13], + self.relu, + ) + + self.conv5 = nn.Sequential( + self.encoder[16], + self.relu, + self.encoder[18], + self.relu, + ) + + self.center = MultiClass_DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=True) + self.dec5 = MultiClass_DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=True) + self.dec4 = MultiClass_DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 4, is_deconv=True) + self.dec3 = MultiClass_DecoderBlock(256 + num_filters * 4, num_filters * 4 * 2, num_filters * 2, is_deconv=True) + self.dec2 = MultiClass_DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters, is_deconv=True) + self.dec1 = MultiClass_ConvRelu(64 + num_filters, num_filters) + + self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1) + + def forward(self, x): + conv1 = self.conv1(x) + conv2 = self.conv2(self.pool(conv1)) + conv3 = self.conv3(self.pool(conv2)) + conv4 = self.conv4(self.pool(conv3)) + conv5 = self.conv5(self.pool(conv4)) + center = self.center(self.pool(conv5)) + dec5 = self.dec5(torch.cat([center, conv5], 1)) + dec4 = self.dec4(torch.cat([dec5, conv4], 1)) + dec3 = self.dec3(torch.cat([dec4, conv3], 1)) + dec2 = self.dec2(torch.cat([dec3, conv2], 1)) + dec1 = self.dec1(torch.cat([dec2, conv1], 1)) + x_out = self.final(dec1) + return x_out + + +class MultiClass_LinkNet34(nn.Module): + def __init__(self, num_classes=1, num_channels=3, pretrained=True): + super().__init__() + assert num_channels == 3 + self.num_classes = num_classes + filters = [64, 128, 256, 512] + resnet = resnet34(pretrained=pretrained) + + self.firstconv = resnet.conv1 + self.firstbn = resnet.bn1 + self.firstrelu = resnet.relu + self.firstmaxpool = resnet.maxpool + self.encoder1 = resnet.layer1 + self.encoder2 = resnet.layer2 + self.encoder3 = resnet.layer3 + self.encoder4 = resnet.layer4 + + # Decoder + self.decoder4 = DecoderBlockLinkNet(filters[3], filters[2]) + self.decoder3 = DecoderBlockLinkNet(filters[2], filters[1]) + self.decoder2 = DecoderBlockLinkNet(filters[1], filters[0]) + self.decoder1 = DecoderBlockLinkNet(filters[0], filters[0]) + + # Final Classifier + self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2) + self.finalrelu1 = nn.ReLU(inplace=True) + self.finalconv2 = nn.Conv2d(32, 32, 3) + self.finalrelu2 = nn.ReLU(inplace=True) + self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1) + + # noinspection PyCallingNonCallable + def forward(self, x): + # Encoder + x = self.firstconv(x) + x = self.firstbn(x) + x = self.firstrelu(x) + x = self.firstmaxpool(x) + e1 = self.encoder1(x) + e2 = self.encoder2(e1) + e3 = self.encoder3(e2) + e4 = self.encoder4(e3) + + # Decoder with Skip Connections + d4 = self.decoder4(e4) + e3 + d3 = self.decoder3(d4) + e2 + d2 = self.decoder2(d3) + e1 + d1 = self.decoder1(d2) + + # Final Classification + f1 = self.finaldeconv1(d1) + f2 = self.finalrelu1(f1) + f3 = self.finalconv2(f2) + f4 = self.finalrelu2(f3) + f5 = self.finalconv3(f4) + x_out = f5 + return x_out + + +class MultiClass_ConvRelu(nn.Module): + def __init__(self, in_, out): + super().__init__() + self.conv = nn.Conv2d(in_, out, 3, padding=1) + self.activation = nn.ReLU(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.activation(x) + return x + + +class MultiClass_DecoderBlock(nn.Module): + def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True): + super().__init__() + self.in_channels = in_channels + + if is_deconv: + self.block = nn.Sequential( + MultiClass_ConvRelu(in_channels, middle_channels), + nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2, + padding=1), + nn.ReLU(inplace=True) + ) + else: + self.block = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear'), + MultiClass_ConvRelu(in_channels, middle_channels), + MultiClass_ConvRelu(middle_channels, out_channels), + ) + + def forward(self, x): + return self.block(x) + + +class DecoderBlockLinkNet(nn.Module): + def __init__(self, in_channels, n_filters): + super().__init__() + + self.relu = nn.ReLU(inplace=True) + + # B, C, H, W -> B, C/4, H, W + self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) + self.norm1 = nn.BatchNorm2d(in_channels // 4) + + # B, C/4, H, W -> B, C/4, 2 * H, 2 * W + self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, kernel_size=4, + stride=2, padding=1, output_padding=0) + self.norm2 = nn.BatchNorm2d(in_channels // 4) + + # B, C/4, H, W -> B, C, H, W + self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1) + self.norm3 = nn.BatchNorm2d(n_filters) + + def forward(self, x): + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.deconv2(x) + x = self.norm2(x) + x = self.relu(x) + x = self.conv3(x) + x = self.norm3(x) + x = self.relu(x) + return x diff --git a/docker/solaris/solaris/nets/zoo/selim_sef_sn4.py b/docker/solaris/solaris/nets/zoo/selim_sef_sn4.py new file mode 100644 index 00000000..748132ba --- /dev/null +++ b/docker/solaris/solaris/nets/zoo/selim_sef_sn4.py @@ -0,0 +1,535 @@ +import torch +from torch import nn +import torch.nn.functional as F +from functools import partial +from collections import OrderedDict +import math + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ConvBottleneck(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.seq = nn.Sequential( + nn.Conv2d(in_channels, out_channels, 3, padding=1), + nn.ReLU(inplace=True) + ) + + def forward(self, dec, enc): + x = torch.cat([dec, enc], dim=1) + return self.seq(x) + + +class UnetDecoderBlock(nn.Module): + def __init__(self, in_channels, middle_channels, out_channels): + super().__init__() + self.layer = nn.Sequential( + nn.Upsample(scale_factor=2), + nn.Conv2d(in_channels, out_channels, 3, padding=1), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.layer(x) + + +class ResNet(nn.Module): + def __init__(self, block, layers, in_channels=3): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, + padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + return x + + +def resnet34(**kwargs): + """Constructs a ResNet-34 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + return model + + +def densenet121(pretrained=True, **kwargs): + r"""Densenet-121 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), + **kwargs) + return model + + +def densenet161(pretrained=True, **kwargs): + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), + **kwargs) + + return model + + +encoder_params = { + 'resnet34': { + 'filters': [64, 64, 128, 256, 512], + 'decoder_filters': [64, 128, 256, 512], + 'last_upsample': 64, + 'init_op': partial(resnet34, in_channels=4) + }, + 'densenet161': + {'filters': [96, 384, 768, 2112, 2208], + 'decoder_filters': [64, 128, 256, 256], + 'last_upsample': 64, + 'url': None, + 'init_op': densenet161 + }, + 'densenet121': + {'filters': [64, 256, 512, 1024, 1024], + 'decoder_filters': [64, 128, 256, 256], + 'last_upsample': 64, + 'url': None, + 'init_op': densenet121 + } + } + + +class AbstractModel(nn.Module): + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): + m.weight.data = nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + @property + def first_layer_params_name(self): + return 'conv1' + + +class EncoderDecoder(AbstractModel): + def __init__(self, num_classes, num_channels=3, encoder_name='resnet34'): + if not hasattr(self, 'first_layer_stride_two'): + self.first_layer_stride_two = False + if not hasattr(self, 'decoder_block'): + self.decoder_block = UnetDecoderBlock + if not hasattr(self, 'bottleneck_type'): + self.bottleneck_type = ConvBottleneck + + self.filters = encoder_params[encoder_name]['filters'] + self.decoder_filters = encoder_params[encoder_name].get( + 'decoder_filters', self.filters[:-1]) + self.last_upsample_filters = encoder_params[encoder_name].get( + 'last_upsample', self.decoder_filters[0]//2) + + super().__init__() + + self.num_channels = num_channels + self.num_classes = num_classes + + self.bottlenecks = nn.ModuleList([ + self.bottleneck_type(self.filters[-i - 2] + f, f) for i, f in + enumerate(reversed(self.decoder_filters[:]))]) + + self.decoder_stages = nn.ModuleList([ + self.get_decoder(idx) for idx in + range(0, len(self.decoder_filters))]) + + if self.first_layer_stride_two: + self.last_upsample = self.decoder_block(self.decoder_filters[0], + self.last_upsample_filters, + self.last_upsample_filters) + + self.final = self.make_final_classifier( + self.last_upsample_filters if self.first_layer_stride_two else self.decoder_filters[0], num_classes) + + self._initialize_weights() + + encoder = encoder_params[encoder_name]['init_op']() + self.encoder_stages = nn.ModuleList([self.get_encoder(encoder, idx) + for idx in + range(len(self.filters))]) + + # noinspection PyCallingNonCallable + def forward(self, x): + enc_results = [] + for stage in self.encoder_stages: + x = stage(x) + enc_results.append(torch.cat(x, dim=1) if isinstance(x, tuple) else x.clone()) + last_dec_out = enc_results[-1] + x = last_dec_out + for idx, bottleneck in enumerate(self.bottlenecks): + rev_idx = - (idx + 1) + x = self.decoder_stages[rev_idx](x) + x = bottleneck(x, enc_results[rev_idx - 1]) + + if self.first_layer_stride_two: + x = self.last_upsample(x) + + f = self.final(x) + + return f + + def get_decoder(self, layer): + in_channels = self.filters[layer + 1] if layer + 1 == len( + self.decoder_filters + ) else self.decoder_filters[layer + 1] + return self.decoder_block(in_channels, + self.decoder_filters[layer], + self.decoder_filters[max(layer, 0)]) + + def make_final_classifier(self, in_filters, num_classes): + return nn.Sequential( + nn.Conv2d(in_filters, num_classes, 1, padding=0) + ) + + def get_encoder(self, encoder, layer): + raise NotImplementedError + + @property + def first_layer_params(self): + return _get_layers_params([self.encoder_stages[0]]) + + @property + def layers_except_first_params(self): + layers = get_slice(self.encoder_stages, 1, -1) + [self.bottlenecks, + self.decoder_stages, + self.final] + return _get_layers_params(layers) + + +class _DenseLayer(nn.Sequential): + def __init__(self, num_input_features, growth_rate, bn_size, drop_rate): + super(_DenseLayer, self).__init__() + self.add_module('norm1', nn.BatchNorm2d(num_input_features)), + self.add_module('relu1', nn.ReLU(inplace=True)), + self.add_module('conv1', nn.Conv2d(num_input_features, + bn_size * growth_rate, + kernel_size=1, stride=1, + bias=False)), + self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)), + self.add_module('relu2', nn.ReLU(inplace=True)), + self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, + kernel_size=3, stride=1, padding=1, + bias=False)), + self.drop_rate = drop_rate + + def forward(self, x): + new_features = super(_DenseLayer, self).forward(x) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, + training=self.training) + return torch.cat([x, new_features], 1) + + +class _DenseBlock(nn.Sequential): + def __init__(self, num_layers, num_input_features, bn_size, + growth_rate, drop_rate): + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer(num_input_features + i * growth_rate, + growth_rate, bn_size, drop_rate) + self.add_module('denselayer%d' % (i + 1), layer) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features, num_output_features): + super(_Transition, self).__init__() + self.add_module('norm', nn.BatchNorm2d(num_input_features)) + self.add_module('relu', nn.ReLU(inplace=True)) + self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, + kernel_size=1, stride=1, bias=False)) + self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_ + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + """ + + def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), + num_init_features=64, bn_size=4, drop_rate=0, + num_classes=1000): + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential(OrderedDict([ + ('conv0', nn.Conv2d(4, num_init_features, kernel_size=7, stride=2, + padding=3, bias=False)), + ('norm0', nn.BatchNorm2d(num_init_features)), + ('relu0', nn.ReLU(inplace=True)), + ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ])) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock(num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate) + self.features.add_module('denseblock%d' % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, + num_output_features=num_features // 2) + self.features.add_module('transition%d' % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module('norm5', nn.BatchNorm2d(num_features)) + + # Linear layer + self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal(m.weight.data) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.bias.data.zero_() + + def forward(self, x): + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), + -1) + out = self.classifier(out) + return out + + +class SelimSef_SpaceNet4_ResNet34UNet(EncoderDecoder): + def __init__(self): + self.first_layer_stride_two = True + super().__init__(3, 4, 'resnet34') + + def get_encoder(self, encoder, layer): + if layer == 0: + return nn.Sequential( + encoder.conv1, + encoder.bn1, + encoder.relu) + elif layer == 1: + return nn.Sequential( + encoder.maxpool, + encoder.layer1) + elif layer == 2: + return encoder.layer2 + elif layer == 3: + return encoder.layer3 + elif layer == 4: + return encoder.layer4 + + +class SelimSef_SpaceNet4_DenseNet121Unet(EncoderDecoder): + def __init__(self): + self.first_layer_stride_two = True + super().__init__(3, 3, 'densenet121') + + def get_encoder(self, encoder, layer): + if layer == 0: + return nn.Sequential( + encoder.features.conv0, # conv + encoder.features.norm0, # bn + encoder.features.relu0 # relu + ) + elif layer == 1: + return nn.Sequential(encoder.features.pool0, + encoder.features.denseblock1) + elif layer == 2: + return nn.Sequential(encoder.features.transition1, + encoder.features.denseblock2) + elif layer == 3: + return nn.Sequential(encoder.features.transition2, + encoder.features.denseblock3) + elif layer == 4: + return nn.Sequential(encoder.features.transition3, + encoder.features.denseblock4, + encoder.features.norm5, + nn.ReLU()) + + +class SelimSef_SpaceNet4_DenseNet161Unet(EncoderDecoder): + def __init__(self): + self.first_layer_stride_two = True + super().__init__(3, 3, 'densenet161') + + def get_encoder(self, encoder, layer): + if layer == 0: + return nn.Sequential( + encoder.features.conv0, # conv + encoder.features.norm0, # bn + encoder.features.relu0 # relu + ) + elif layer == 1: + return nn.Sequential(encoder.features.pool0, + encoder.features.denseblock1) + elif layer == 2: + return nn.Sequential(encoder.features.transition1, + encoder.features.denseblock2) + elif layer == 3: + return nn.Sequential(encoder.features.transition2, + encoder.features.denseblock3) + elif layer == 4: + return nn.Sequential(encoder.features.transition3, + encoder.features.denseblock4, + encoder.features.norm5, + nn.ReLU()) + + +def _get_layers_params(layers): + return sum((list(l.parameters()) for l in layers), []) + + +def get_slice(features, start, end): + if end == -1: + end = len(features) + return [features[i] for i in range(start, end)] + + +SelimSef_SpaceNet4_DenseNet161UNet = SelimSef_SpaceNet4_DenseNet161Unet +SelimSef_SpaceNet4_DenseNet121UNet = SelimSef_SpaceNet4_DenseNet121Unet diff --git a/docker/solaris/solaris/nets/zoo/xdxd_sn4.py b/docker/solaris/solaris/nets/zoo/xdxd_sn4.py new file mode 100644 index 00000000..96310b5f --- /dev/null +++ b/docker/solaris/solaris/nets/zoo/xdxd_sn4.py @@ -0,0 +1,84 @@ +import os +import torch +from torch import nn +from torchvision.models import vgg16 + + +class XDXD_SpaceNet4_UNetVGG16(nn.Module): + def __init__(self, num_filters=32, pretrained=False): + super().__init__() + self.encoder = vgg16(pretrained=pretrained).features + self.pool = nn.MaxPool2d(2, 2) + + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Sequential( + self.encoder[0], self.relu, self.encoder[2], self.relu) + self.conv2 = nn.Sequential( + self.encoder[5], self.relu, self.encoder[7], self.relu) + self.conv3 = nn.Sequential( + self.encoder[10], self.relu, self.encoder[12], self.relu, + self.encoder[14], self.relu) + self.conv4 = nn.Sequential( + self.encoder[17], self.relu, self.encoder[19], self.relu, + self.encoder[21], self.relu) + self.conv5 = nn.Sequential( + self.encoder[24], self.relu, self.encoder[26], self.relu, + self.encoder[28], self.relu) + + self.center = XDXD_SN4_DecoderBlock(512, num_filters * 8 * 2, + num_filters * 8) + self.dec5 = XDXD_SN4_DecoderBlock( + 512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8) + self.dec4 = XDXD_SN4_DecoderBlock( + 512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8) + self.dec3 = XDXD_SN4_DecoderBlock( + 256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2) + self.dec2 = XDXD_SN4_DecoderBlock( + 128 + num_filters * 2, num_filters * 2 * 2, num_filters) + self.dec1 = XDXD_SN4_ConvRelu(64 + num_filters, num_filters) + self.final = nn.Conv2d(num_filters, 1, kernel_size=1) + + def forward(self, x): + conv1 = self.conv1(x) + conv2 = self.conv2(self.pool(conv1)) + conv3 = self.conv3(self.pool(conv2)) + conv4 = self.conv4(self.pool(conv3)) + conv5 = self.conv5(self.pool(conv4)) + center = self.center(self.pool(conv5)) + dec5 = self.dec5(torch.cat([center, conv5], 1)) + dec4 = self.dec4(torch.cat([dec5, conv4], 1)) + dec3 = self.dec3(torch.cat([dec4, conv3], 1)) + dec2 = self.dec2(torch.cat([dec3, conv2], 1)) + dec1 = self.dec1(torch.cat([dec2, conv1], 1)) + x_out = self.final(dec1) + return x_out + + +class XDXD_SN4_ConvRelu(nn.Module): + def __init__(self, in_, out): + super().__init__() + self.conv = nn.Conv2d(in_, out, 3, padding=1) + self.activation = nn.ReLU(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.activation(x) + return x + + +class XDXD_SN4_DecoderBlock(nn.Module): + def __init__(self, in_channels, middle_channels, out_channels): + super(XDXD_SN4_DecoderBlock, self).__init__() + self.in_channels = in_channels + self.block = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear'), + XDXD_SN4_ConvRelu(in_channels, middle_channels), + XDXD_SN4_ConvRelu(middle_channels, out_channels), + ) + + def forward(self, x): + return self.block(x) + +# below dictionary lists models compatible with solaris. alternatively, your +# own model can be used by using the path to the model as the value for +# model_name in the config file. diff --git a/docker/solaris/solaris/preproc/__init__.py b/docker/solaris/solaris/preproc/__init__.py new file mode 100644 index 00000000..94f25369 --- /dev/null +++ b/docker/solaris/solaris/preproc/__init__.py @@ -0,0 +1 @@ +from . import pipesegment, image, sar, optical, label diff --git a/docker/solaris/solaris/preproc/image.py b/docker/solaris/solaris/preproc/image.py new file mode 100644 index 00000000..c662a246 --- /dev/null +++ b/docker/solaris/solaris/preproc/image.py @@ -0,0 +1,500 @@ +import math +import matplotlib.pyplot as plt +import numpy as np +import os +from osgeo import gdal, gdal_array +import pandas as pd +import uuid +import warnings + +from .pipesegment import PipeSegment, LoadSegment, MergeSegment + + +class Image: + def __init__(self, data, name='image', metadata={}): + self.name = name + self.metadata = metadata + self.set_data(data) + def set_data(self, data): + if isinstance(data, np.ndarray) and data.ndim == 2: + data = np.expand_dims(data, axis=0) + self.data = data + def __str__(self): + if self.data.ndim < 3: + raise Exception('! Image data has too few dimensions.') + metastring = str(self.metadata) + if len(metastring)>400: + metastring = metastring[:360] + '...' + return '%s: %d bands, %dx%d, %s, %s' % (self.name, + *np.shape(self.data), + str(self.data.dtype), + metastring) + + +class Identity(PipeSegment): + """ + This class is an alias for the PipeSegment base class to emphasize + its role as the identity element. + """ + pass + + +class LoadImageFromDisk(LoadSegment): + """ + Load an image from the file system using GDAL, so it can be fed + into subsequent PipeSegments. + """ + def __init__(self, pathstring, name=None, verbose=False): + super().__init__() + self.pathstring = pathstring + self.name = name + self.verbose = verbose + def load(self): + return self.load_from_disk(self.pathstring, self.name, self.verbose) + def load_from_disk(self, pathstring, name=None, verbose=False): + # Use GDAL to open image file + dataset = gdal.Open(pathstring) + if dataset is None: + raise Exception('! Image file ' + pathstring + ' not found.') + data = dataset.ReadAsArray() + if data.ndim == 2: + data = np.expand_dims(data, axis=0) + metadata = { + 'geotransform': dataset.GetGeoTransform(), + 'projection_ref': dataset.GetProjectionRef(), + 'gcps': dataset.GetGCPs(), + 'gcp_projection': dataset.GetGCPProjection(), + 'meta': dataset.GetMetadata() + } + metadata['band_meta'] = [dataset.GetRasterBand(band).GetMetadata() + for band in range(1, dataset.RasterCount+1)] + if name is None: + name = os.path.splitext(os.path.split(pathstring)[1])[0] + dataset = None + # Create an Image-class object, and return it + imageobj = Image(data, name, metadata) + if verbose: + print(imageobj) + return imageobj + + +class LoadImageFromMemory(LoadSegment): + """ + Points to an 'Image'-class image so it can be fed + into subsequent PipeSegments. + """ + def __init__(self, imageobj, name=None, verbose=False): + super().__init__() + self.imageobj = imageobj + self.name = name + self.verbose = verbose + def load(self): + return self.load_from_memory(self.imageobj, self.name, self.verbose) + def load_from_memory(self, imageobj, name=None, verbose=False): + if type(imageobj) is not Image: + raise Exception('! Invalid input type in LoadImageFromMemory.') + if name is not None: + imageobj.name = name + if verbose: + print(imageobj) + return(imageobj) + + +class LoadImage(LoadImageFromDisk, LoadImageFromMemory): + """ + Makes an image available to subsequent PipeSegments, whether the image + is in the filesystem (in which case 'imageinput' is the path) or an + Image-class variable (in which case 'imageinput' is the variable name). + """ + def __init__(self, imageinput, name=None, verbose=False): + PipeSegment.__init__(self) + self.imageinput = imageinput + self.name = name + self.verbose = verbose + def load(self): + if type(self.imageinput) is Image: + return self.load_from_memory(self.imageinput, self.name, self.verbose) + elif type(self.imageinput) in (str, np.str_): + return self.load_from_disk(self.imageinput, self.name, self.verbose) + else: + raise Exception('! Invalid input type in LoadImage.') + + +class SaveImage(PipeSegment): + """ + Save an image to disk using GDAL. + """ + def __init__(self, pathstring, driver='GTiff', return_image=True, + save_projection=True, save_metadata=True, no_data_value=None): + super().__init__() + self.pathstring = pathstring + self.driver = driver + self.return_image = return_image + self.save_projection = save_projection + self.save_metadata = save_metadata + self.no_data_value = no_data_value + def transform(self, pin): + # Save image to disk + driver = gdal.GetDriverByName(self.driver) + datatype = gdal_array.NumericTypeCodeToGDALTypeCode(pin.data.dtype) + if datatype is None: + if pin.data.dtype in (bool, np.dtype('bool')): + datatype = gdal.GDT_Byte + else: + warnings.warn('! SaveImage did not find data type match; saving as float.') + datatype = gdal.GDT_Float32 + dataset = driver.Create(self.pathstring, pin.data.shape[2], pin.data.shape[1], pin.data.shape[0], datatype) + for band in range(pin.data.shape[0]): + bandptr = dataset.GetRasterBand(band+1) + bandptr.WriteArray(pin.data[band, :, :]) + if isinstance(self.no_data_value, str) \ + and self.no_data_value.lower() == 'nan': + bandptr.SetNoDataValue(math.nan) + elif self.no_data_value is not None: + bandptr.SetNoDataValue(self.no_data_value) + bandptr.FlushCache() + if self.save_projection: + #First determine which projection system, if any, is used + proj_lens = [0, 0] + proj_keys = ['projection_ref', 'gcp_projection'] + for i, proj_key in enumerate(proj_keys): + if proj_key in pin.metadata.keys(): + proj_lens[i] = len(pin.metadata[proj_key]) + if proj_lens[0] > 0 and proj_lens[0] >= proj_lens[1]: + dataset.SetGeoTransform(pin.metadata['geotransform']) + dataset.SetProjection(pin.metadata['projection_ref']) + elif proj_lens[1] > 0 and proj_lens[1] >= proj_lens[0]: + dataset.SetGCPs(pin.metadata['gcps'], + pin.metadata['gcp_projection']) + if self.save_metadata and 'meta' in pin.metadata.keys(): + dataset.SetMetadata(pin.metadata['meta']) + dataset.FlushCache() + # Optionally return image + if self.driver.lower() == 'mem': + return dataset + elif self.return_image: + return pin + else: + return None + + +class ShowImage(PipeSegment): + """ + Display an image using matplotlib. + """ + def __init__(self, show_text=False, show_image=True, cmap='gray', + vmin=None, vmax=None, bands=None, caption=None, + width=None, height=None): + super().__init__() + self.show_text = show_text + self.show_image = show_image + self.cmap = cmap + self.vmin = vmin + self.vmax = vmax + self.bands = bands + self.caption = caption + self.width = width + self.height = height + def transform(self, pin): + if self.caption is not None: + print(self.caption) + if self.show_text: + print(pin) + if self.show_image: + # Select data, and format it for matplotlib + if self.bands is None: + image_formatted = pin.data + else: + image_formatted = pin.data[self.bands] + pyplot_formatted = np.squeeze(np.moveaxis(image_formatted, 0, -1)) + if np.ndim(pyplot_formatted)==3 and self.vmin is not None and self.vmax is not None: + pyplot_formatted = np.clip((pyplot_formatted - self.vmin) / (self.vmax - self.vmin), 0., 1.) + # Select image size + if self.height is None and self.width is None: + rc = {} + elif self.height is None and self.width is not None: + rc = {'figure.figsize': [self.width, self.width]} + elif self.height is not None and self.width is None: + rc = {'figure.figsize': [self.height, self.height]} + else: + rc = {'figure.figsize': [self.width, self.height]} + # Show image + with plt.rc_context(rc): + plt.imshow(pyplot_formatted, cmap=self.cmap, + vmin=self.vmin, vmax=self.vmax) + plt.show() + return pin + + +class ImageStats(PipeSegment): + """ + Calculate descriptive statististics about an image + """ + def __init__(self, print_desc=True, print_props=True, return_image=True, return_props=False, median=True, caption=None): + super().__init__() + self.print_desc = print_desc + self.print_props = print_props + self.return_image = return_image + self.return_props = return_props + self.median = median + self.caption = caption + def transform(self, pin): + if self.caption is not None: + print(self.caption) + if self.print_desc: + print(pin) + print() + props = pd.DataFrame({ + 'min': np.nanmin(pin.data, (1,2)), + 'max': np.nanmax(pin.data, (1,2)), + 'mean': np.nanmean(pin.data, (1,2)), + 'std': np.nanstd(pin.data, (1,2)), + 'pos': np.count_nonzero(np.nan_to_num(pin.data, nan=-1.)>0, (1,2)), + 'zero': np.count_nonzero(pin.data==0, (1,2)), + 'neg': np.count_nonzero(np.nan_to_num(pin.data, nan=1.)<0, (1,2)), + 'nan': np.count_nonzero(np.isnan(pin.data), (1,2)), + }) + if self.median: + props.insert(3, 'median', np.nanmedian(pin.data, (1,2))) + if self.print_props: + print(props) + print() + if self.return_image and self.return_props: + return (pin, props) + elif self.return_image: + return pin + elif self.return_props: + return props + else: + return None + + +class MergeToStack(PipeSegment): + """ + Given an iterable of equal-sized images, combine + all of their bands into a single image. + """ + def __init__(self, master=0): + super().__init__() + self.master = master + def transform(self, pin): + # Make list of all the input bands + datalist = [imageobj.data for imageobj in pin] + # Create output image, using name and metadata from designated source + pout = Image(None, pin[self.master].name, pin[self.master].metadata) + pout.data = np.concatenate(datalist, axis=0) + return pout + + +class MergeToSum(PipeSegment): + """ + Combine an iterable of images by summing the corresponding bands. + Assumes that images are of equal size and have equal numbers of bands. + """ + def __init__(self, master=0): + super().__init__() + self.master = master + def transform(self, pin): + total = pin[self.master].data.copy() + for i in range(len(pin)): + if not i == self.master: + total += pin[i].data + return Image(total, pin[self.master].name, pin[self.master].metadata) + + +class MergeToProduct(PipeSegment): + """ + Combine an iterable of images by multiplying the corresponding bands. + Assumes that images are of equal size and have equal numbers of bands. + """ + def __init__(self, master=0): + super().__init__() + self.master = master + def transform(self, pin): + product = pin[self.master].data.copy() + for i in range(len(pin)): + if not i == self.master: + product *= pin[i].data + return Image(product, pin[self.master].name, pin[self.master].metadata) + + +class SelectItem(PipeSegment): + """ + Given an iterable, return one of its items. This is useful when passing + a list of items into, or out of, a custom class. + """ + def __init__(self, index=0): + super().__init__() + self.index = index + def transform(self, pin): + return pin[self.index] + + +class SelectBands(PipeSegment): + """ + Reorganize the bands in an image. This class can be used to + select, delete, duplicate, or reorder bands. + """ + def __init__(self, bands=[0]): + super().__init__() + if not hasattr(bands, '__iter__'): + bands = [bands] + self.bands = bands + def transform(self, pin): + return Image(pin.data[self.bands, :, :], pin.name, pin.metadata) + + +class Bounds(PipeSegment): + """ + Output the boundary coordinates [xmin, ymin, xmax, ymax] of an image. + Note: Requires the image to have an affine geotransform, not GCPs. + Note: Only works for a north-up image without rotation or shearing + """ + def transform(self, pin): + gt = pin.metadata['geotransform'] + numrows = pin.data.shape[1] + numcols = pin.data.shape[2] + bounds = [gt[0], gt[3] + gt[5]*numrows, gt[0] + gt[1]*numcols, gt[3]] + return bounds + + +class Scale(PipeSegment): + """ + Scale data by a multiplicative factor. + """ + def __init__(self, factor=1.): + super().__init__() + self.factor = factor + def transform(self, pin): + return Image(self.factor * pin.data, pin.name, pin.metadata) + + +class Crop(PipeSegment): + """ + Crop image based on either pixel coordinates or georeferenced coordinates. + 'bounds' is a list specifying the edges: [left, bottom, right, top] + """ + def __init__(self, bounds, mode='pixel'): + super().__init__() + self.bounds = bounds + self.mode = mode + def transform(self, pin): + row_min = self.bounds[3] + row_max = self.bounds[1] + col_min = self.bounds[0] + col_max = self.bounds[2] + if self.mode in ['pixel', 'p', 0]: + srcWin = [col_min, row_min, + col_max - col_min + 1, row_max - row_min + 1] + projWin = None + elif self.mode in ['geo', 'g', 1]: + srcWin = None + projWin = [col_min, row_min, col_max, row_max] + else: + raise Exception('! Invalid mode in Crop') + drivername = 'GTiff' + srcpath = '/vsimem/crop_input_' + str(uuid.uuid4()) + '.tif' + dstpath = '/vsimem/crop_output_' + str(uuid.uuid4()) + '.tif' + (pin * SaveImage(srcpath, driver=drivername))() + gdal.Translate(dstpath, srcpath, srcWin=srcWin, projWin=projWin) + pout = LoadImage(dstpath)() + pout.name = pin.name + if pin.data.dtype in (bool, np.dtype('bool')): + pout.data = pout.data.astype('bool') + driver = gdal.GetDriverByName(drivername) + driver.Delete(srcpath) + driver.Delete(dstpath) + return pout + + +class CropVariable(Crop): + """ + Like 'Crop', but window coordinates are accepted from another + PipeSegment at runtime instead of via initialization arguments. + """ + def __init__(self, mode='pixel'): + PipeSegment.__init__(self) + self.mode = mode + def transform(self, pin): + imagetocrop = pin[0] + self.bounds = pin[1] + return super().transform(imagetocrop) + + +class Resize(PipeSegment): + """ + Resize an image to the requested number of pixels + """ + def __init__(self, rows, cols): + super().__init__() + self.rows = rows + self.cols = cols + def transform(self, pin): + return self.resize(pin, self.rows, self.cols) + def resize(self, pin, rows, cols): + drivername = 'GTiff' + srcpath = '/vsimem/resize_input_' + str(uuid.uuid4()) + '.tif' + dstpath = '/vsimem/resize_output_' + str(uuid.uuid4()) + '.tif' + (pin * SaveImage(srcpath, driver=drivername))() + gdal.Translate(dstpath, srcpath, width=cols, height=rows) + pout = LoadImage(dstpath)() + pout.name = pin.name + if pin.data.dtype in (bool, np.dtype('bool')): + pout.data = pout.data.astype('bool') + driver = gdal.GetDriverByName(drivername) + driver.Delete(srcpath) + driver.Delete(dstpath) + return pout + + +class GetMask(PipeSegment): + """ + Extract a Boolean mask from an image band. NaN is assumed to be the + mask value, unless otherwise specified. + """ + def __init__(self, band=0, flag='nan'): + super().__init__() + self.band = band + self.flag = flag + def transform(self, pin): + if self.flag == 'nan': + data = np.expand_dims(np.invert(np.isnan(pin.data[self.band])), axis=0) + else: + data = np.expand_dims(pin.data[self.band]==self.flag, axis=0) + return Image(data, pin.name, pin.metadata) + + +class SetMask(PipeSegment): + """ + Given an image and a mask, apply the mask to the image. + More specifically, set the image's pixel value to NaN + (or other specified value) for every pixel where the + mask value is False. + """ + def __init__(self, flag=math.nan, band=None, reverse_order=False): + super().__init__() + self.flag = flag + self.band = band + self.reverse_order = reverse_order + def transform(self, pin): + if not self.reverse_order: + img = pin[0] + mask = pin[1] + else: + img = pin[1] + mask = pin[0] + mark = np.invert(np.squeeze(mask.data)) + data = np.copy(img.data) + if self.band is None: + data[:, mark] = self.flag + else: + data[self.band, mark] = self.flag + return Image(data, img.name, img.metadata) + + +class InvertMask(PipeSegment): + """ + Sets all True values in a mask to False and vice versa. + """ + def transform(self, pin): + return Image(np.invert(pin.data), pin.name, pin.metadata) diff --git a/docker/solaris/solaris/preproc/label.py b/docker/solaris/solaris/preproc/label.py new file mode 100644 index 00000000..3bed393e --- /dev/null +++ b/docker/solaris/solaris/preproc/label.py @@ -0,0 +1,208 @@ +import geopandas as gpd +import pandas as pd +import shapely.geometry +import shapely.wkt + +from .pipesegment import PipeSegment, LoadSegment, MergeSegment +from ..vector.polygon import convert_poly_coords + + +class LoadString(LoadSegment): + """ + Load a string from a file. + """ + def __init__(self, pathstring): + super().__init__() + self.pathstring = pathstring + def load(self): + infile = open(self.pathstring, 'r') + content = infile.read() + infile.close() + return content + + +class SaveString(PipeSegment): + """ + Write a string to a file. + """ + def __init__(self, pathstring, append=False): + super().__init__() + self.pathstring = pathstring + self.append = append + def transform(self, pin): + mode = 'a' if self.append else 'w' + outfile = open(self.pathstring, mode) + outfile.write(str(pin)) + outfile.close() + return pin + + +class ShowString(PipeSegment): + """ + Print a string to the screen. + """ + def transform(self, pin): + print(pin) + return pin + + +class LoadDataFrame(LoadSegment): + """ + Load a GeoPandas GeoDataFrame from a file. + """ + def __init__(self, pathstring, geom_col='geometry', projection=None): + super().__init__() + self.pathstring = pathstring + self.geom_col = geom_col + self.projection = projection + def load(self): + if self.pathstring.lower()[-4:] == '.csv': + df = pd.read_csv(self.pathstring) + geometry = df.apply(lambda row: + shapely.wkt.loads(row[self.geom_col]), axis=1) + df.drop(columns=[self.geom_col]) + gdf = gpd.GeoDataFrame(df, geometry=geometry) + if self.projection is not None: + gdf.crs = 'epsg:' + str(self.projection) + return gdf + else: + return gpd.read_file(self.pathstring) + + +class SaveDataFrame(PipeSegment): + """ + Save a GeoPandas GeoDataFrame to disk. + """ + def __init__(self, pathstring, driver='GeoJSON'): + super().__init__() + self.pathstring = pathstring + self.driver = driver + def transform(self, pin): + pin.to_file(self.pathstring, driver=self.driver) + return pin + + +class ShowDataFrame(PipeSegment): + """ + Print a GeoPandas GeoDataFrame to the screen. + """ + def transform(self, pin): + print(pin) + return pin + + +class ReprojectDataFrame(PipeSegment): + """ + Reproject a GeoPandas GeoDataFrame. + """ + def __init__(self, projection=3857): + super().__init__() + self.projection = projection + def transform(self, pin): + return pin.to_crs('epsg:' + str(self.projection)) + + +class ExplodeDataFrame(PipeSegment): + """ + Given a GeoPandas GeoDataFrame, break multi-part geometries + into multiple lines. + """ + def transform(self, pin): + return pin.explode().reset_index() + + +class IntersectDataFrames(PipeSegment): + """ + Given an iterable of GeoPandas GeoDataFrames, returns their intersection + """ + def __init__(self, master=0): + super().__init__() + self.master = master + def transform(self, pin): + result = pin[self.master] + for i, gdf in enumerate(pin): + if not i==self.master: + result = gpd.overlay(result, gdf) + result.crs = pin[self.master].crs + return result + + +#class DataFrameToMask(PipeSegment): +# """ +# Given a GeoPandas GeoDataFrame and an Image-class image, +# convert the DataFrame to the corresponding Boolean mask +# """ +# pass +# +# +#class MaskToDataFrame(PipeSegment): +# """ +# Given a boolean mask, convert it to a GeoPandas GeoDataFrame of polygons. +# """ +# pass + + +class DataFramePixelCoords(PipeSegment): + """ + Given a GeoPandas GeoDataFrame, converts between georeferenced + coordinates and pixel coordinates. Assumes image has affine geotransform. + """ + def __init__(self, inverse=False, reverse_order=False, *args, **kwargs): + super().__init__() + self.inverse = inverse + self.reverse_order = reverse_order + self.args = args + self.kwargs = kwargs + def transform(self, pin): + if not self.reverse_order: + gdf = pin[0] + img = pin[1] + else: + gdf = pin[1] + img = pin[0] + affine = img.metadata['geotransform'] + gdf = gdf.copy() + newgeoms = gdf.apply(lambda row: convert_poly_coords( + row.geometry, affine_obj=affine, inverse=self.inverse, + *self.args, **self.kwargs + ), axis=1) + gdf.geometry = newgeoms + return gdf + + +class DataFrameToString(PipeSegment): + """ + Given a GeoPandas GeoDataFrame, convert it into a GeoJSON string. + Caveat emptor: This follows the GeoJSON 2016 standard, which does + not include any coordinate reference system information. + """ + def __init__(self, crs=True, **kwargs): + super().__init__() + self.crs = crs + self.kwargs = kwargs + def transform(self, pin): + geojson = pin.to_json(**(self.kwargs)) + if self.crs: + geojson = '{"type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::' \ + + str(pin.crs.to_epsg()) \ + + '" } }, ' \ + + geojson[30:] + return geojson + + +class BoundsToDataFrame(PipeSegment): + """ + Given a set of tile bounds [left, lower, right, upper], + convert it to a GeoPandas GeoDataFrame. Note: User must + specify projection, since a simple set of bounds doesn't + include that. + """ + def __init__(self, projection=None): + super().__init__() + self.projection = projection + def transform(self, pin): + gdf = gpd.GeoDataFrame() + if self.projection is not None: + gdf.crs = 'epsg:' + str(self.projection) + gdf.loc[0, 'geometry'] = shapely.geometry.box(*pin) + return gdf diff --git a/docker/solaris/solaris/preproc/optical.py b/docker/solaris/solaris/preproc/optical.py new file mode 100644 index 00000000..d5e24216 --- /dev/null +++ b/docker/solaris/solaris/preproc/optical.py @@ -0,0 +1,92 @@ +import colorsys +import numpy as np + +from .pipesegment import PipeSegment, LoadSegment, MergeSegment +from .image import Image +from . import image + + +class RGBToHSL(PipeSegment): + """ + Convert an RGB image into an HSL (hue/saturation/lightness) image + using colorsys. + """ + def __init__(self, rband=0, gband=1, bband=2, rgbmax=255.): + super().__init__() + self.rband = rband + self.gband = gband + self.bband = bband + self.rgbmax = rgbmax + def transform(self, pin): + m = self.rgbmax + convertarray = np.vectorize(colorsys.rgb_to_hls) + outbands = convertarray(np.clip(pin.data[self.rband] / m, 0, 1), + np.clip(pin.data[self.gband] / m, 0, 1), + np.clip(pin.data[self.bband] / m, 0, 1)) + pout = Image(None, pin.name, pin.metadata) + pout.data = np.stack((outbands[0], outbands[2], outbands[1]), axis=0) + return pout + + +class HSLToRGB(PipeSegment): + """ + Convert an HSL (hue/saturation/lightness) image into an RGB image + using colorsys. + """ + def __init__(self, hband=0, sband=1, lband=2, rgbmax=255.): + super().__init__() + self.hband = hband + self.sband = sband + self.lband = lband + self.rgbmax = rgbmax + def transform(self, pin): + convertarray = np.vectorize(colorsys.hls_to_rgb) + outbands = convertarray(np.clip(pin.data[self.hband], 0, 1), + np.clip(pin.data[self.lband], 0, 1), + np.clip(pin.data[self.sband], 0, 1)) + pout = Image(None, pin.name, pin.metadata) + pout.data = self.rgbmax * np.stack(outbands, axis=0) + return pout + + +class RGBToHSV(PipeSegment): + """ + Convert an RGB image into an HSV (hue/saturation/value) image + using colorsys. + """ + def __init__(self, rband=0, gband=1, bband=2, rgbmax=255.): + super().__init__() + self.rband = rband + self.gband = gband + self.bband = bband + self.rgbmax = rgbmax + def transform(self, pin): + m = self.rgbmax + convertarray = np.vectorize(colorsys.rgb_to_hsv) + outbands = convertarray(np.clip(pin.data[self.rband] / m, 0, 1), + np.clip(pin.data[self.gband] / m, 0, 1), + np.clip(pin.data[self.bband] / m, 0, 1)) + pout = Image(None, pin.name, pin.metadata) + pout.data = np.stack(outbands, axis=0) + return pout + + +class HSVToRGB(PipeSegment): + """ + Convert an HSV (hue/saturation/value) image into an RGB image + using colorsys. + """ + def __init__(self, hband=0, sband=1, vband=2, rgbmax=255.): + super().__init__() + self.hband = hband + self.sband = sband + self.vband = vband + self.rgbmax = rgbmax + def transform(self, pin): + convertarray = np.vectorize(colorsys.hsv_to_rgb) + outbands = convertarray(np.clip(pin.data[self.hband], 0, 1), + np.clip(pin.data[self.sband], 0, 1), + np.clip(pin.data[self.vband], 0, 1)) + pout = Image(None, pin.name, pin.metadata) + pout.data = self.rgbmax * np.stack(outbands, axis=0) + return pout diff --git a/docker/solaris/solaris/preproc/pipesegment.py b/docker/solaris/solaris/preproc/pipesegment.py new file mode 100644 index 00000000..a6e8ed3b --- /dev/null +++ b/docker/solaris/solaris/preproc/pipesegment.py @@ -0,0 +1,346 @@ +import multiprocessing +def _parallel_compute_function(x): + return (x[0])(*(x[1]),**(x[2]))(x[3],x[4]) + + +class PipeSegment: + def __init__(self): + self.feeder = None + self.procout = None + self.procstart = False + self.procfinish = False + self._cited = 0 + self._used = 0 + self._saveall = 0 + self._verbose = 0 + def __call__(self, saveall=0, verbose=0): + self._saveall = saveall + self._verbose = verbose + if self.procstart and not self.procfinish: + raise Exception('(!) Circular dependency in workflow.') + if not self.procfinish: + self.procstart = True + self.procout = self.process() + self.procfinish = True + return self.procout + def process(self): + pin = self.feeder(self._saveall, self._verbose) + self.feeder._used += 1 + if self._saveall == 0 and self.feeder._used == self.feeder._cited: + self.feeder.reset(recursive=False) + if self._verbose > 0: + self.printout(self._verbose, pin) + return self.transform(pin) + def transform(self, pin): + return pin + def reset(self, recursive=True): + self.procout = None + self.procstart = False + self.procfinish = False + if recursive: + self.feeder.reset(recursive=True) + def printout(self, verbose, *args): + if verbose >= 1: + print(type(self)) + if verbose >= 2: + print(vars(self)) + if verbose >= 3: + for x in args: + print(x) + if verbose >= 2: + print() + def selfstring(self, offset=0): + return ' '*2*offset + type(self).__name__ + '\n' + def __str__(self, offset=0): + return self.selfstring(offset) + self.feeder.__str__(offset+1) + def attach_check(self, ps): + if not self.attach(ps): + raise Exception('(!) ' + type(self).__name__ + + ' has no free input at which to attach ' + + type(ps).__name__ + '.') + def attach(self, ps): + if self.feeder is None: + self.feeder = ps + self.feeder._cited += 1 + return True + else: + return self.feeder.attach(ps) or ps is self + def __mul__(self, other): + other.attach_check(self) + return other + def __or__(self, other): + other.attach_check(self) + return other + def __add__(self, other): + return MergeSegment(self, other) + def __rmul__(self, other): + return LoadSegment(other) * self + def __ror__(self, other): + return LoadSegment(other) * self + @classmethod + def parallel(cls, input_args=None, input_kwargs=None, processes=None, + saveall=0, verbose=0): + if input_args is not None and input_kwargs is None: + input_kwargs = [{}] * len(input_args) + elif input_kwargs is not None and input_args is None: + input_args = [[]] * len(input_kwargs) + elif input_args is None and input_kwargs is None: + input_args = [[]] + input_kwargs = [{}] + all_inputs = list(zip([cls]*len(input_args), input_args, input_kwargs, + [saveall]*len(input_args), + [verbose]*len(input_args))) + #with multiprocessing.get_context('spawn').Pool(processes) as pool: + with multiprocessing.Pool(processes) as pool: + return pool.map(_parallel_compute_function, all_inputs) + + +class LoadSegment(PipeSegment): + def __init__(self, source=None): + super().__init__() + self.source = source + def process(self): + if self._verbose > 0: + self.printout(self._verbose) + return self.load() + def load(self): + return self.source + def reset(self, recursive=True): + self.procout = None + self.procstart = False + self.procfinish = False + def __str__(self, offset=0): + return self.selfstring(offset) + def attach(self, ps): + return ps is self + + +class MergeSegment(PipeSegment): + def __init__(self, feeder1, feeder2): + super().__init__() + self.feeder1 = feeder1 + self.feeder1._cited += 1 + self.feeder2 = feeder2 + self.feeder2._cited += 1 + def process(self): + p1 = self.feeder1(self._saveall, self._verbose) + p2 = self.feeder2(self._saveall, self._verbose) + self.feeder1._used += 1 + if self._saveall == 0 and self.feeder1._used == self.feeder1._cited: + self.feeder1.reset(recursive=False) + self.feeder2._used += 1 + if self._saveall == 0 and self.feeder2._used == self.feeder2._cited: + self.feeder2.reset(recursive=False) + if self._verbose > 0: + self.printout(self._verbose, p1, p2) + if not isinstance(p1, tuple): + p1 = (p1,) + if not isinstance(p2, tuple): + p2 = (p2,) + return p1 + p2 + def reset(self, recursive=True): + self.procout = None + self.procstart = False + self.procfinish = False + if recursive: + self.feeder1.reset(recursive=True) + self.feeder2.reset(recursive=True) + def __str__(self, offset=0): + return self.selfstring(offset) \ + + self.feeder1.__str__(offset+1) \ + + self.feeder2.__str__(offset+1) + def attach(self, ps): + if self.feeder1 is None: + self.feeder1 = ps + self.feeder._cited += 1 + flag1 = True + else: + flag1 = self.feeder1.attach(ps) + if self.feeder2 is None: + self.feeder2 = ps + self.feeder._cited += 1 + flag2 = True + else: + flag2 = self.feeder2.attach(ps) + return flag1 or flag2 or ps is self + + +class SelectItem(PipeSegment): + """ + Given an iterable, return one of its items. This can be used to select + a single output from a class that returns a tuple of outputs. + """ + def __init__(self, index=0): + super().__init__() + self.index = index + def transform(self, pin): + return pin[self.index] + + +class Identity(PipeSegment): + """ + This class is an alias for the PipeSegment base class, to + emphasize its property of passing data through, unchanged. + Formally, this is the identity element for the '*' operation. + """ + pass + + +class ReturnEmpty(PipeSegment): + """ + Regardless of input, returns an empty tuple. + This can be useful in Map and Conditional classes. + Formally, this is the identity element for the '+' operation. + """ + def transform(self, pin): + return () + + +class Conditional(PipeSegment): + """ + This is the pipesegment version of an if statement. + The piped input is fed into an object of the 'condition_class' class. + If 'True' is returned, then the input is fed through an 'if_class' object. + Otherwise, the input is fed through an 'else_class' object. + """ + def __init__(self, condition_class, + if_class=Identity, else_class=ReturnEmpty, + condition_args=[], if_args=[], else_args=[], + condition_kwargs={}, if_kwargs={}, else_kwargs={}): + super().__init__() + self.condition_class = condition_class + self.if_class = if_class + self.else_class = else_class + self.condition_args = condition_args + self.if_args = if_args + self.else_args = else_args + self.condition_kwargs = condition_kwargs + self.if_kwargs = if_kwargs + self.else_kwargs = else_kwargs + if issubclass(self.condition_class, LoadSegment) \ + and issubclass(self.if_class, LoadSegment) \ + and issubclass(self.else_class, LoadSegment): + self.feeder = LoadSegment(()) + def transform(self, pin): + condition_obj = self.condition_class(*self.condition_args, + **self.condition_kwargs) + if not isinstance(condition_obj, LoadSegment): + condition_obj = LoadSegment(pin) * condition_obj + if condition_obj(self._saveall, self._verbose): + inner_obj = self.if_class(*self.if_args, **self.if_kwargs) + else: + inner_obj = self.else_class(*self.else_args, **self.else_kwargs) + if not isinstance(inner_obj, LoadSegment): + inner_obj = LoadSegment(pin) * inner_obj + return inner_obj(self._saveall, self._verbose) + + +class Map(PipeSegment): + """ + This is the pipesegment version of a for-loop. + Given an iterable of inputs, applies the PipeSegment-derived class + specified by 'inner_class' to each one, then returns all the results + as a tuple. + """ + def __init__(self, inner_class, *args, **kwargs): + super().__init__() + self.inner_class = inner_class + self.args = args + self.kwargs = kwargs + def transform(self, pin): + pout = () + for entry in pin: + outp = (LoadSegment(entry) * self.inner_class(*self.args, + **self.kwargs))(self._saveall, self._verbose) + if not isinstance(outp, tuple): + outp = (outp,) + pout = pout + outp + return pout + + +class While(PipeSegment): + """ + This is the pipesegment version of a while-loop. + Applies the the PipeSegment-derived class specified by 'inner_class' + to the piped input over and over again, until sending the piped input + through an object of class 'condition_class' returns false. + """ + def __init__(self, condition_class, inner_class, + condition_args=[], inner_args=[], + condition_kwargs={}, inner_kwargs={}): + super().__init__() + self.condition_class = condition_class + self.inner_class = inner_class + self.condition_args = condition_args + self.inner_args = inner_args + self.condition_kwargs = condition_kwargs + self.inner_kwargs = inner_kwargs + def transform(self, pin): + condition_obj = self.condition_class(*self.condition_args, + **self.condition_kwargs) + while (LoadSegment(pin) * condition_obj)(self._saveall, self._verbose): + inner_obj = self.inner_class(*self.inner_args, + **self.inner_kwargs) + pin = (LoadSegment(pin) * inner_obj)(self._saveall, self._verbose) + condition_obj = self.condition_class(*self.condition_args, + **self.condition_kwargs) + return pin + + +class PipeArgs(PipeSegment): + """ + Wrapper for any PipeSegment subclass which enables it to accept + initialization arguments from piped input. + """ + def __init__(self, inner_class, *args, **kwargs): + super().__init__() + self.inner_class = inner_class + self.args = args + self.kwargs = kwargs + def transform(self, pin): + if issubclass(self.inner_class, LoadSegment): + isloadsegment = True + argstart = 0 + else: + isloadsegment = False + argstart = 1 + inner_pin = pin[0] + # Gather all initialization arguments + args = self.args + kwargs = self.kwargs.copy() + pargs = (pin if isinstance(pin, tuple) else (pin,))[argstart:] + for p in pargs: + if isinstance(p, dict): + kwargs.update(p) + else: + args = args + (p,) + #Initialize and call object + obj = self.inner_class(*args, **kwargs) + if isloadsegment: + return obj(self._saveall, self._verbose) + else: + return (LoadSegment(inner_pin) * obj)(self._saveall, self._verbose) + + +class FunctionPipe(PipeSegment): + """ + Turns a user-supplied function into a PipeSegment + """ + def __init__(self, function): + super().__init__() + self.function = function + def transform(self, pin): + return self.function(pin) + + +def PipeFunction(inner_class=PipeSegment, pin=(), *args, **kwargs): + """ + Turns a PipeSegment into a standalone function. + inner_class is the PipeSegment class, pin is the input to pipe into it, + and *args and **kwargs are sent to the PipeSegment's constructor. + """ + psobject = inner_class(*args, **kwargs) + if issubclass(self.inner_class, LoadSegment): + return psobject() + else: + return (pin * psobject)() diff --git a/docker/solaris/solaris/preproc/sar.py b/docker/solaris/solaris/preproc/sar.py new file mode 100644 index 00000000..9d201730 --- /dev/null +++ b/docker/solaris/solaris/preproc/sar.py @@ -0,0 +1,656 @@ +from osgeo import gdal, osr +import json +import math +import numpy as np +import os +import scipy.signal +import uuid +import warnings +import xml.etree.ElementTree as ET + +from .pipesegment import PipeSegment, LoadSegment, MergeSegment +from .image import Image +from . import image + + +class BandMath(PipeSegment): + """ + Modify the array holding an image's pixel values, + using a user-supplied function. + """ + def __init__(self, function, master=0): + super().__init__() + self.function = function + self.master = master + def transform(self, pin): + if isinstance(pin, tuple): + pin = (pin * image.MergeToStack(self.master))() + data = self.function(pin.data) + if data.ndim == 2: + data = np.expand_dims(data, axis=0) + return Image(data, pin.name, pin.metadata) + + +class Amplitude(PipeSegment): + """ + Convert complex image to amplitude, by taking the magnitude of each pixel + """ + def transform(self, pin): + return Image(np.absolute(pin.data), pin.name, pin.metadata) + + +class Intensity(PipeSegment): + """ + Convert amplitude (or complex values) to intensity, by squaring each pixel + """ + def transform(self, pin): + pout = Image(None, pin.name, pin.metadata) + if not np.iscomplexobj(pin.data): + pout.data = np.square(pin.data) + else: + pout.data = np.square(np.absolute(pin.data)) + return pout + + +class InPhase(PipeSegment): + """ + Get in-phase (real) component of complex-valued data + """ + def transform(self, pin): + return Image(np.real(pin.data), pin.name, pin.metadata) + + +class Quadrature(PipeSegment): + """ + Get quadrature (imaginary) component of complex-valued data + """ + def transform(self, pin): + return Image(np.imag(pin.data), pin.name, pin.metadata) + + +class Phase(PipeSegment): + """ + Return the phase of the input image + """ + def transform(self, pin): + return Image(np.angle(pin.data), pin.name, pin.metadata) + + +class Conjugate(PipeSegment): + """ + Return complex conjugate of the input image + """ + def transform(self, pin): + return Image(np.conj(pin.data), pin.name, pin.metadata) + + +class MultiplyConjugate(PipeSegment): + """ + Given an iterable of two images, multiply the first + by the complex conjugate of the second. + """ + def __init__(self, master=0): + super().__init__() + self.master = master + def transform(self, pin): + return Image( + pin[0].data * np.conj(pin[1].data), + pin[self.master].name, + pin[self.master].metadata + ) + + +class Decibels(PipeSegment): + """ + Express quantity in decibels + The 'flag' argument indicates how to handle nonpositive inputs: + 'min' outputs the log of the image's smallest positive value, + 'nan' outputs NaN, and any other value is used as the flag value itself. + """ + def __init__(self, flag='min'): + super().__init__() + self.flag = flag + def transform(self, pin): + pout = Image(None, pin.name, pin.metadata) + if isinstance(self.flag, str) and self.flag.lower() == 'min': + flagval = 10. * np.log10((pin.data)[pin.data>0].min()) + elif isinstance(self.flag, str) and self.flag.lower() == 'nan': + flagval = math.nan + else: + flagval = self.flag / 10. + pout.data = 10. * np.log10( + pin.data, + out=np.full(np.shape(pin.data), flagval).astype(pin.data.dtype), + where=pin.data>0 + ) + return pout + + +class Multilook(PipeSegment): + """ + Multilook filter to reduce speckle in SAR magnitude imagery + Note: Set kernel_size to a tuple to vary it by direction. + """ + def __init__(self, kernel_size=5, method='avg'): + super().__init__() + self.kernel_size = kernel_size + self.method = method + def transform(self, pin): + if self.method == 'avg': + filter = scipy.ndimage.filters.uniform_filter + elif self.method == 'med': + filter = scipy.ndimage.filters.median_filter + elif self.method == 'max': + filter = scipy.ndimage.filters.maximum_filter + else: + raise Exception('! Invalid method in Multilook.') + pout = Image(np.zeros(pin.data.shape, dtype=pin.data.dtype), + pin.name, pin.metadata) + for i in range(pin.data.shape[0]): + pout.data[i, :, :] = filter( + pin.data[i, :, :], + size=self.kernel_size, + mode='reflect') + return pout + + +class MultilookComplex(Multilook): + """ + Like 'Multilook', but supports complex input + """ + def transform(self, pin): + mkwargs = {'kernel_size':self.kernel_size, 'method':self.method} + pout = (pin + * (InPhase() * Multilook(**mkwargs) * image.Scale(1.+0.j) + + Quadrature() * Multilook(**mkwargs) * image.Scale(1.j)) + * image.MergeToSum() + )() + return pout + + +class Orthorectify(PipeSegment): + """ + Orthorectify an image using its ground control points (GCPs) with GDAL + """ + def __init__(self, projection=3857, algorithm='lanczos', + row_res=1., col_res=1.): + super().__init__() + self.projection = projection + self.algorithm = algorithm + self.row_res = row_res + self.col_res = col_res + def transform(self, pin): + drivername = 'GTiff' + srcpath = '/vsimem/orthorectify_input_' + str(uuid.uuid4()) + '.tif' + dstpath = '/vsimem/orthorectify_output_' + str(uuid.uuid4()) + '.tif' + (pin * image.SaveImage(srcpath, driver=drivername))() + gdal.Warp(dstpath, srcpath, + dstSRS='epsg:' + str(self.projection), + resampleAlg=self.algorithm, + xRes=self.row_res, yRes=self.col_res, + dstNodata=math.nan) + pout = image.LoadImage(dstpath)() + pout.name = pin.name + if pin.data.dtype in (bool, np.dtype('bool')): + pout.data = pout.data.astype('bool') + driver = gdal.GetDriverByName(drivername) + driver.Delete(srcpath) + driver.Delete(dstpath) + return pout + + +class DecompositionPauli(PipeSegment): + """ + Compute the Pauli decomposition of quad-pol SAR data. + Note: Convention is alpha-->blue, beta-->red, gamma-->green + """ + def __init__(self, hh_band=0, vv_band=1, xx_band=2): + super().__init__() + self.hh_band = hh_band + self.vv_band = vv_band + self.xx_band = xx_band + def transform(self, pin): + hh = pin.data[self.hh_band] + vv = pin.data[self.vv_band] + xx = pin.data[self.xx_band] + alpha2 = 0.5 * np.square(np.absolute(hh + vv)) + beta2 = 0.5 * np.square(np.absolute(hh - vv)) + gamma2 = 2 * np.square(np.absolute(xx)) + alpha2 = np.expand_dims(alpha2, axis=0) + beta2 = np.expand_dims(beta2, axis=0) + gamma2 = np.expand_dims(gamma2, axis=0) + pout = Image(np.concatenate((alpha2, beta2, gamma2), axis=0), + pin.name, + pin.metadata) + return pout + + +class DecompositionFreemanDurden(PipeSegment): + """ + Compute the three-component polarimetric decomposition of quad-pol SAR data + proposed by Freeman and Durden. + Note: Convention is Ps-->blue, Pd-->red, Pv-->green + """ + def __init__(self, hh_band=0, vv_band=1, xx_band=2, kernel_size=5): + super().__init__() + self.hh_band = hh_band + self.vv_band = vv_band + self.xx_band = xx_band + self.kernel_size = kernel_size + def transform(self, pin): + # Scattering matrix terms + hh = pin * image.SelectBands(self.hh_band) + vv = pin * image.SelectBands(self.vv_band) + xx = pin * image.SelectBands(self.xx_band) + # Covariance matrix terms + C11 = hh * Intensity() + C22 = vv * Intensity() + C33 = xx * Intensity() + C12 = (hh + vv) * MultiplyConjugate() + mkwargs = {'kernel_size':self.kernel_size, 'method':'avg'} + C11 = C11 * Multilook(**mkwargs) + C22 = C22 * Multilook(**mkwargs) + C33 = C33 * Multilook(**mkwargs) + C12 = C12 * MultilookComplex(**mkwargs) + # Volume amplitude, and volume-subtracted matrix terms + fv = C33 * image.Scale(1.5) + c11 = (C11 + fv) * BandMath(lambda x: x[0] - x[1]) + c22 = (C22 + fv) * BandMath(lambda x: x[0] - x[1]) + c12 = (C12 + fv) * BandMath(lambda x: x[0] - x[1] / 3.) + c12 = (c11 + c22 + c12) * BandMath(lambda x: np.where(x[0]*x[1] + < np.square(np.abs(x[2])), np.sqrt(x[0]*x[1]) \ + * x[2]/np.abs(x[2]), x[2]))# + # Surface and dihedral amplitudes + surfacedominates = c12 * BandMath(lambda x: np.real(x) >= 0) + term1 = (c11 + c22 + c12 * InPhase() + c12 * Quadrature() + + surfacedominates) * BandMath(lambda x: + (x[0]*x[1] - (x[2])**2 - (x[3])**2) / + (x[0] + x[1] + 2*x[2]*np.where(x[4], 1, -1))) + term1 = term1 * Amplitude()# + term2 = (c22 + term1) * BandMath(lambda x: x[0] - x[1]) + term2 = term2 * Amplitude()# + term3 = (term1 + term2 + c12 * InPhase() + c12 * Quadrature() + + surfacedominates) * BandMath(lambda x: + (x[2] + np.where(x[4], 1, -1) * x[0] + x[3] * 1.j) / x[1]) + fs = ((term2 + surfacedominates) * image.SetMask(flag=0) + (term1 + + surfacedominates * image.InvertMask()) * image.SetMask(flag=0)) \ + * image.MergeToSum() + fd = ((term1 + surfacedominates) * image.SetMask(flag=0) + (term2 + + surfacedominates * image.InvertMask()) * image.SetMask(flag=0)) \ + * image.MergeToSum() + alpha = (surfacedominates * image.Scale(-1.) + (term3 + + surfacedominates * image.InvertMask()) * image.SetMask(flag=0)) \ + * BandMath(lambda x: x[0] + x[1]) + beta = ((term3 + surfacedominates) * image.SetMask(flag=0) \ + + surfacedominates * image.InvertMask() * image.Scale(1.)) \ + * BandMath(lambda x: x[0] + x[1]) + # Power + Ps = (fs + beta * Intensity()) * BandMath(lambda x: x[0] * (1. + x[1])) + Pd = (fd + alpha *Intensity()) * BandMath(lambda x: x[0] * (1. + x[1])) + Pv = fv + Pmask = (c11 + c22) * BandMath(lambda x: np.logical_and( + x[0]==0, x[1]==0)) * image.InvertMask() + Ps = (Ps + Pmask) * image.SetMask(flag=0)# + Pd = (Pd + Pmask) * image.SetMask(flag=0)# + Pstack = (Ps + Pd + Pv) * image.MergeToStack() + return Pstack() + + +class DecompositionHAlpha(PipeSegment): + """ + Compute H-Alpha (Entropy-alpha) dual-polarization decomposition + """ + def __init__(self, band0=0, band1=1, kernel_size=5): + super().__init__() + self.band0 = band0 + self.band1 = band1 + self.kernel_size = kernel_size + def transform(self, pin): + mkwargs = {'kernel_size':self.kernel_size, 'method':'avg'} + image0 = pin * image.SelectBands(self.band0) + image1 = pin * image.SelectBands(self.band1) + # Coherence matrix terms + c00 = image0 * Intensity() * Multilook(**mkwargs) + c11 = image1 * Intensity() * Multilook(**mkwargs) + c01 = (image0 + image1) * MultiplyConjugate() \ + * MultilookComplex(**mkwargs) + c01sq = c01 * Intensity() + # Calculate eigenvalues and some eigenvector terms (assumes c01 != 0) + # tr=trace; det=determinant; l1,l2=eigenvalues; v..=eigenvector terms + tr = (c00 + c11) * BandMath(lambda x: x[0] + x[1]) + det = (c00 + c11 + c01sq) * BandMath(lambda x: x[0]*x[1] - x[2]) + l1 = (tr + det) * BandMath(lambda x: + 0.5*x[0] + np.sqrt(0.25*x[0]**2-x[1])) + l2 = (tr + det) * BandMath(lambda x: + 0.5*x[0] - np.sqrt(0.25*x[0]**2-x[1])) + absv11 = (c00 + c01 + l1) * BandMath(lambda x: np.abs(x[1]) / np.sqrt(np.abs(x[1])**2 + np.abs(x[2] - x[0])**2)) + absv12 = (c00 + c01 + l2) * BandMath(lambda x: np.abs(x[1]) / np.sqrt(np.abs(x[1])**2 + np.abs(x[2] - x[0])**2)) + # Calculate entropy (H) and alpha + P1 = (l1 + l2) * BandMath(lambda x: x[0] / (x[0] + x[1])) + P2 = (l1 + l2) * BandMath(lambda x: x[1] / (x[0] + x[1])) + H = (P1 + P2) * BandMath(lambda x: -x[0] * np.log(x[0]) + - x[1] * np.log(x[1])) + alpha = (P1 + P2 + absv11 + absv12) * BandMath(lambda x: x[0] * np.arccos(x[2]) + x[1] * np.arccos(x[3])) + outputs = (H + alpha) * image.MergeToStack() + return outputs() + + +class CapellaScaleFactor(PipeSegment): + """ + Calibrate Capella single-look complex data (or amplitude thereof) + using the scale factor in the metadata + """ + def transform(self, pin): + tiffjson = json.loads(pin.metadata['meta']['TIFFTAG_IMAGEDESCRIPTION']) + scale_factor = tiffjson['collect']['image']['scale_factor'] + return Image(scale_factor * pin.data, pin.name, pin.metadata) + + +class CapellaGridToGCPs(PipeSegment): + """ + Generate ground control points (GCPs) from a Capella grid file + and save them in a corresponding image's metadata. Input is a tuple + with the image in the 0 position and the grid in the 1 position. + Output is the image with modified metadata. Spacing between points + is in pixels. + """ + def __init__(self, reverse_order=False, row_range=None, col_range=None, + spacing=150, row_spacing=None, col_spacing=None): + super().__init__() + self.reverse_order = reverse_order + self.row_range = row_range + self.col_range = col_range + self.spacing = spacing + self.row_spacing = row_spacing + self.col_spacing = col_spacing + def transform(self, pin): + if not self.reverse_order: + img = pin[0] + grid = pin[1] + else: + img = pin[1] + grid = pin[0] + pout = Image(img.data, img.name, img.metadata.copy()) + if self.row_range is None: + rlo = 0 + rhi = img.data.shape[1] - 1 + else: + rlo = self.row_range[0] + rhi = self.row_range[1] + if self.col_range is None: + clo = 0 + chi = img.data.shape[2] - 1 + else: + clo = self.col_range[0] + chi = self.col_range[1] + rspace = self.spacing + cspace = self.spacing + if self.row_spacing is not None: + rspace = self.row_spacing + if self.col_spacing is not None: + cspace = self.col_spacing + gcps = [] + for ri in range(rlo, rhi + 1, rspace): + for ci in range(clo, chi + 1, cspace): + gcps.append(gdal.GCP( + grid.data[1, ri, ci], #longitude + grid.data[0, ri, ci], #latitude + grid.data[2, ri, ci], #altitude + ci, ri #pixel=column=x, line=row=y + )) + if len(gcps) > 10922: + warnings.warn('! Many GCPs generated in CapellaGridToGCPs.') + pout.metadata['gcps'] = gcps + return pout + + +class CapellaGridToPolygon(PipeSegment): + """ + Given a Capella grid file, return a GeoJSON string indicating its boundary. + 'step' is number of pixels between each recorded point. + """ + def __init__(self, step=100, flags=False): + super().__init__() + self.step = step + self.flags = flags + def transform(self, pin): + # Get indices of selected points along the edges of the grid file + nrows = pin.data.shape[1] + ncols = pin.data.shape[2] + step = self.step + allri = [] + allci = [] + cornerri = [] + cornerci = [] + for edge in range(4): + if edge == 0: + ri = list(range(0, nrows - 1, step)) + ci = [0] * len(ri) + elif edge == 1: + ci = list(range(0, ncols - 1, step)) + ri = [nrows - 1] * len(ci) + elif edge == 2: + ri = list(range(nrows - 1, 0, -step)) + ci = [ncols - 1] * len(ri) + elif edge == 3: + ci = list(range(ncols - 1, 0, -step)) + ri = [0] * len(ci) + allri.extend(ri) + allci.extend(ci) + cornerri.append(ri[0]) + cornerci.append(ci[0]) + allri.append(allri[0]) + allci.append(allci[0]) + # Get latitude/longitude values, and ensure they're counterclockwise + lats = [pin.data[0, ri, ci] for ri, ci in zip(allri, allci)] + lons = [pin.data[1, ri, ci] for ri, ci in zip(allri, allci)] + cornerlats = [pin.data[0, ri, ci] for ri,ci in zip(cornerri, cornerci)] + cornerlons = [pin.data[1, ri, ci] for ri,ci in zip(cornerri, cornerci)] + vi = (cornerlons[1] - cornerlons[0], cornerlats[1] - cornerlats[0]) + vf = (cornerlons[0] - cornerlons[3], cornerlats[0] - cornerlats[3]) + counterclockwise = vf[0] * vi[1] - vf[1] * vi[0] > 0 + if not counterclockwise: + lats.reverse() + lons.reverse() + northlooking = cornerlats[3] > cornerlats[0] + eastlooking = cornerlons[3] > cornerlons[0] + flags = (counterclockwise, northlooking, eastlooking) + # Write latitudes & longitudes of the selected points to a JSON string + jsonstring = '{\n' \ + '"type": "FeatureCollection",\n' \ + '"name": "region_' + pin.name + '",\n' \ + '"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4326" } },\n' \ + '"features": [\n' \ + '{ "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [ ' + for i, (lat, lon) in enumerate(zip(lats, lons)): + if i>0: + jsonstring += ', ' + jsonstring += '[ ' + str(lon) + ', ' + str(lat) + ', 0.0 ]' + jsonstring += '] ] } }\n]\n}' + if self.flags: + return (jsonstring,) + flags + else: + return jsonstring + + +class CapellaGridCommonWindow(PipeSegment): + """ + Given an iterable of Capella grid files with equal orientations and pixel + sizes but translational offsets, find the overlapping region and return + its array indices for each grid file. Optionally, also return the subpixel + offset of each grid file needed for exact alignment. + """ + def __init__(self, master=0, subpixel=True): + super().__init__() + self.master = master + self.subpixel = subpixel + def transform(self, pin): + # Find the pixel in each grid that's closest to center of master grid. + # 'x' and 'y' are the latitude and longitude bands of the grid files, + # and (refx, refy) is the (lat, lon) of that center. + m = self.master + l = len(pin) + order = [m] + list(range(m)) + list(range(m+1, l)) + localrefs = [[]] * len(pin) + fineoffsets = [[]] * len(pin) + extents = [[]] * len(pin) + windows = [[]] * len(pin) + for step, index in enumerate(order): + x = pin[index].data[0] + y = pin[index].data[1] + if step==0: + localrefs[index] = (int(0.5 * x.shape[0]), + int(0.5 * x.shape[1])) + fineoffsets[index] = (0., 0.) + # Get latitude and longitude of the reference point + refx = x[localrefs[index]] + refy = y[localrefs[index]] + else: + # Find pixel closest to reference point + localrefs[index] = self.courseoffset(x, y, refx, refy) + # Find subpixel offset of reference point + fineoffsets[index] = self.fineoffset(x, y, refx, refy, + localrefs[index][0], + localrefs[index][1]) + # Find how far from the reference pixel each grid extends + # Convention is [left, bottom, right, top] + extents[index] = [ + localrefs[index][1], + x.shape[0] - localrefs[index][0] - 1, + x.shape[1] - localrefs[index][1] - 1, + localrefs[index][0] + ] + if step==0: + minextents = extents[index].copy() + else: + for i in range(4): + if extents[index][i] < minextents[i]: + minextents[i] = extents[index][i] + # Calculate col_min, row_max, col_max, row_min of overlapping window + for step, index in enumerate(order): + windows[index] = [ + localrefs[index][1] - minextents[0], + localrefs[index][0] + minextents[1], + localrefs[index][1] + minextents[2], + localrefs[index][0] - minextents[3] + ] + # Optionally return subpixel offsets + if self.subpixel: + finearray = np.array(fineoffsets) + windows.append(finearray) + return windows + + def haversine(self, lat1, lon1, lat2, lon2, rad=False, radius=6.371E6): + """ + Haversine formula for distance between two points given their + latitude and longitude, assuming a spherical earth. + """ + if not rad: + lat1 = np.radians(lat1) + lon1 = np.radians(lon1) + lat2 = np.radians(lat2) + lon2 = np.radians(lon2) + dlat = lat2 - lat1 + dlon = lon2 - lon1 + a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2 + return 2 * radius * np.arcsin(np.sqrt(a)) + + def courseoffset(self, latgrid, longrid, lattarget, lontarget): + """ + Given a latitude/longitude pair, find the closest point in + a grid of almost-regularly-spaced latitude/longitude pairs. + """ + bound0 = np.shape(latgrid)[0] - 1 + bound1 = np.shape(latgrid)[1] - 1 + pos0 = int(bound0 / 2) + pos1 = int(bound1 / 2) + def score(pos0, pos1): + return self.haversine(latgrid[pos0, pos1], longrid[pos0, pos1], lattarget, lontarget) + while True: + scorenow = score(pos0, pos1) + if pos0>0 and score(pos0-1, pos1)0 and score(pos0, pos1-1)`_. + nodata : `int` + The value for nodata in the outputs. Will be set to zero in outputs if + ``None``. + alpha : `int` + The band index corresponding to an alpha channel (if one exists). + ``None`` if there is no alpha channel. + tile_bounds : list + A :class:`list` containing ``[left, bottom, right, top]`` bounds + sublists for each tile created. + resampling : str + The resampling method for any resizing. Possible values are + ``['bilinear', 'cubic', 'nearest', 'lanczos', 'average']`` (or any + other option from :class:`rasterio.warp.Resampling`). + aoi_boundary : :class:`shapely.geometry.Polygon` + A :class:`shapely.geometry.Polygon` defining the bounds of the AOI that + tiles will be created for. If a tile will extend beyond the boundary, + the "extra" pixels will have the value `nodata`. Can be provided at + initialization of the :class:`Tiler` instance or when the input is + loaded. + """ + + def __init__(self, dest_dir=None, dest_crs=None, project_to_meters=False, + channel_idxs=None, src_tile_size=(900, 900), use_src_metric_size=False, + dest_tile_size=None, dest_metric_size=False, + aoi_boundary=None, nodata=None, alpha=None, + force_load_cog=False, resampling=None, tile_bounds=None, + verbose=False): + # set up attributes + if verbose: + print("Initializing Tiler...") + self.dest_dir = dest_dir + if not os.path.exists(self.dest_dir): + os.makedirs(self.dest_dir) + if dest_crs is not None: + self.dest_crs = _check_crs(dest_crs) + else: + self.dest_crs = None + self.src_tile_size = src_tile_size + self.use_src_metric_size = use_src_metric_size + if dest_tile_size is None: + self.dest_tile_size = src_tile_size + else: + self.dest_tile_size = dest_tile_size + self.resampling = resampling + self.force_load_cog = force_load_cog + self.nodata = nodata + self.alpha = alpha + self.aoi_boundary = aoi_boundary + self.tile_bounds = tile_bounds + self.project_to_meters = project_to_meters + self.tile_paths = [] # retains the paths of the last call to .tile() +# self.cog_output = cog_output + self.verbose = verbose + if self.verbose: + print('Tiler initialized.') + print('dest_dir: {}'.format(self.dest_dir)) + if dest_crs is not None: + print('dest_crs: {}'.format(self.dest_crs)) + else: + print('dest_crs will be inferred from source data.') + print('src_tile_size: {}'.format(self.src_tile_size)) + print('tile size units metric: {}'.format(self.use_src_metric_size)) + if self.resampling is not None: + print('Resampling is set to {}'.format(self.resampling)) + else: + print('Resampling is set to None') + + def tile(self, src, dest_dir=None, channel_idxs=None, nodata=None, + alpha=None, restrict_to_aoi=False, + dest_fname_base=None, nodata_threshold = None): + """An object to tile geospatial image strips into smaller pieces. + + Arguments + --------- + src : :class:`rasterio.io.DatasetReader` or str + The source dataset to tile. + nodata_threshold : float, optional + Nodata percentages greater than this threshold will not be saved as tiles. + restrict_to_aoi : bool, optional + Requires aoi_boundary. Sets all pixel values outside the aoi_boundary to the nodata value of the src image. + """ + src = _check_rasterio_im_load(src) + restricted_im_path = os.path.join(self.dest_dir, "aoi_restricted_"+ os.path.basename(src.name)) + self.src_name = src.name # preserves original src name in case restrict is used + if restrict_to_aoi is True: + if self.aoi_boundary is None: + raise ValueError("aoi_boundary must be specified when RasterTiler is called.") + mask_geometry = self.aoi_boundary.intersection(box(*src.bounds)) # prevents enlarging raster to size of aoi_boundary + index_lst = list(np.arange(1,src.meta['count']+1)) + # no need to use transform t since we don't crop. cropping messes up transform of tiled outputs + arr, t = rasterio_mask(src, [mask_geometry], all_touched=False, invert=False, nodata=src.meta['nodata'], + filled=True, crop=False, pad=False, pad_width=0.5, indexes=list(index_lst)) + with rasterio.open(restricted_im_path, 'w', **src.profile) as dest: + dest.write(arr) + dest.close() + src.close() + src = _check_rasterio_im_load(restricted_im_path) #if restrict_to_aoi, we overwrite the src to be the masked raster + + tile_gen = self.tile_generator(src, dest_dir, channel_idxs, nodata, + alpha, self.aoi_boundary, restrict_to_aoi) + + if self.verbose: + print('Beginning tiling...') + self.tile_paths = [] + if nodata_threshold is not None: + if nodata_threshold > 1: + raise ValueError("nodata_threshold should be expressed as a float less than 1.") + print("nodata value threshold supplied, filtering based on this percentage.") + new_tile_bounds = [] + for tile_data, mask, profile, tb in tqdm(tile_gen): + nodata_count = np.logical_or.reduce((tile_data == profile['nodata']), axis=0).sum() + nodata_perc = nodata_count / (tile_data.shape[1] * tile_data.shape[2]) + if nodata_perc < nodata_threshold: + dest_path = self.save_tile( + tile_data, mask, profile, dest_fname_base) + self.tile_paths.append(dest_path) + new_tile_bounds.append(tb) + else: + print("{} of nodata is over the nodata_threshold, tile not saved.".format(nodata_perc)) + self.tile_bounds = new_tile_bounds # only keep the tile bounds that make it past the nodata threshold + else: + for tile_data, mask, profile, tb in tqdm(tile_gen): + dest_path = self.save_tile( + tile_data, mask, profile, dest_fname_base) + self.tile_paths.append(dest_path) + if self.verbose: + print('Tiling complete. Cleaning up...') + self.src.close() + if os.path.exists(os.path.join(self.dest_dir, 'tmp.tif')): + os.remove(os.path.join(self.dest_dir, 'tmp.tif')) + if os.path.exists(restricted_im_path): + os.remove(restricted_im_path) + if self.verbose: + print("Done. CRS returned for vector tiling.") + return _check_crs(profile['crs']) # returns the crs to be used for vector tiling + + def tile_generator(self, src, dest_dir=None, channel_idxs=None, + nodata=None, alpha=None, aoi_boundary=None, + restrict_to_aoi=False): + """Create the tiled output imagery from input tiles. + + Uses the arguments provided at initialization to generate output tiles. + First, tile locations are generated based on `Tiler.tile_size` and + `Tiler.size_in_meters` given the bounds of the input image. + + Arguments + --------- + src : `str` or :class:`Rasterio.DatasetReader` + The source data to tile from. If this is a "classic" + (non-cloud-optimized) GeoTIFF, the whole image will be loaded in; + if it's cloud-optimized, only the required portions will be loaded + during tiling unless ``force_load_cog=True`` was specified upon + initialization. + dest_dir : str, optional + The path to the destination directory to output images to. If the + path doesn't exist, it will be created. This argument is required + if it wasn't provided during initialization. + channel_idxs : list, optional + The list of channel indices to be included in the output array. + If not provided, all channels will be included. *Note:* per + ``rasterio`` convention, indexing starts at ``1``, not ``0``. + nodata : int, optional + The value in `src` that specifies nodata. If this value is not + provided, solaris will attempt to infer the nodata value from the + `src` metadata. + alpha : int, optional + The band to specify as alpha. If not provided, solaris will attempt + to infer if an alpha band is present from the `src` metadata. + aoi_boundary : `list`-like or :class:`shapely.geometry.Polygon`, optional + AOI bounds can be provided either as a + ``[left, bottom, right, top]`` :class:`list`-like or as a + :class:`shapely.geometry.Polygon`. + restrict_to_aoi : bool, optional + Should output tiles be restricted to the limits of the AOI? If + ``True``, any tile that partially extends beyond the limits of the + AOI will not be returned. This is the inverse of the ``boundless`` + argument for :class:`rasterio.io.DatasetReader` 's ``.read()`` + method. + + Yields + ------ + tile_data, mask, tile_bounds + tile_data : :class:`numpy.ndarray` + A list of lists of each tile's bounds in the order they were + created, to be used in tiling vector data. These data are also + stored as an attribute of the :class:`Tiler` instance named + `tile_bounds`. + + """ + # parse arguments + if self.verbose: + print("Checking input data...") + # if isinstance(src, str): + # self.is_cog = cog_validate(src) + # else: + # self.is_cog = cog_validate(src.name) + # if self.verbose: + # print('COG: {}'.format(self.is_cog)) + self.src = _check_rasterio_im_load(src) + if channel_idxs is None: # if not provided, include them all + channel_idxs = list(range(1, self.src.count + 1)) + print(channel_idxs) + self.src_crs = _check_crs(self.src.crs, return_rasterio=True) # necessary to use rasterio crs for reproject + if self.verbose: + print('Source CRS: EPSG:{}'.format(self.src_crs.to_epsg())) + if self.dest_crs is None: + self.dest_crs = self.src_crs + if self.verbose: + print('Destination CRS: EPSG:{}'.format(self.dest_crs.to_epsg())) + self.src_path = self.src.name + self.proj_unit = raster_get_projection_unit(self.src) # for rounding + if self.verbose: + print("Inputs OK.") + if self.use_src_metric_size: + if self.verbose: + print("Checking if inputs are in metric units...") + if self.project_to_meters: + if self.verbose: + print("Input CRS is not metric. " + "Reprojecting the input to UTM.") + self.src = reproject(self.src, + resampling_method=self.resampling, + dest_path=os.path.join(self.dest_dir, + 'tmp.tif')) + if self.verbose: + print('Done reprojecting.') + if nodata is None and self.nodata is None: + self.nodata = self.src.nodata + elif nodata is not None: + self.nodata = nodata + + # get index of alpha channel + if alpha is None and self.alpha is None: + mf_list = [rasterio.enums.MaskFlags.alpha in i for i in + self.src.mask_flag_enums] # list with True at idx of alpha c + try: + self.alpha = np.where(mf_list)[0] + 1 + except IndexError: # if there isn't a True + self.alpha = None + else: + self.alpha = alpha + + if getattr(self, 'tile_bounds', None) is None: + self.get_tile_bounds() + + for tb in self.tile_bounds: + # removing the following line until COG functionality implemented + if True: # not self.is_cog or self.force_load_cog: + window = rasterio.windows.from_bounds( + *tb, transform=self.src.transform, + width=self.src_tile_size[1], + height=self.src_tile_size[0]) + print('reading data from window') + print(self.nodata) + if self.src.count != 1: + src_data = self.src.read( + window=window, + indexes=channel_idxs, + boundless=True, + fill_value=self.nodata) + else: + src_data = self.src.read( + window=window, + boundless=True, + fill_value=self.nodata) + + dst_transform, width, height = calculate_default_transform( + self.src.crs, self.dest_crs, + self.src.width, self.src.height, *tb, + dst_height=self.dest_tile_size[0], + dst_width=self.dest_tile_size[1]) + + if self.dest_crs != self.src_crs and self.resampling_method is not None: + tile_data = np.zeros(shape=(src_data.shape[0], height, width), dtype=src_data.dtype) + rasterio.warp.reproject( + source=src_data, + destination=tile_data, + src_transform=self.src.window_transform(window), + src_crs=self.src.crs, + dst_transform=dst_transform, + dst_crs=self.dest_crs, + dst_nodata=self.nodata, + resampling=getattr(Resampling, self.resampling)) + + elif self.dest_crs != self.src_crs and self.resampling_method is None: + print("Warning: You've set resampling to None but your " + "destination projection differs from the source " + "projection. Using bilinear resampling by default.") + tile_data = np.zeros(shape=(src_data.shape[0], height, width), + dtype=src_data.dtype) + tile_data = np.zeros(shape=(src_data.shape[0], height, width), dtype=src_data.dtype) + rasterio.warp.reproject( + source=src_data, + destination=tile_data, + src_transform=self.src.window_transform(window), + src_crs=self.src.crs, + dst_transform=dst_transform, + dst_crs=self.dest_crs, + dst_nodata=self.nodata, + resampling=getattr(Resampling, "bilinear")) + + else: # for the case where there is no resampling and no dest_crs specified, no need to reproject or resample + + tile_data = src_data + + + if self.nodata: + mask = np.all(tile_data != nodata, + axis=0).astype(np.uint8) * 255 + elif self.alpha: + mask = self.src.read(self.alpha, window=window) + else: + mask = None # placeholder + + # else: + # tile_data, mask, window, aff_xform = read_cog_tile( + # src=self.src, + # bounds=tb, + # tile_size=self.dest_tile_size, + # indexes=channel_idxs, + # nodata=self.nodata, + # resampling_method=self.resampling + # ) + profile = self.src.profile + + ## bugfix CJ 20220726 + ## added 'nodata' to the list of profile items to update, + ## so that it matches the 'fill value' used to pad the tiles. + profile.update(width=self.dest_tile_size[1], + height=self.dest_tile_size[0], + crs=self.dest_crs, + transform=dst_transform, + nodata=self.nodata) + if len(tile_data.shape) == 2: # if there's no channel band + profile.update(count=1) + else: + profile.update(count=tile_data.shape[0]) + + yield tile_data, mask, profile, tb + + def save_tile(self, tile_data, mask, profile, dest_fname_base=None): + """Save a tile created by ``Tiler.tile_generator()``.""" + if dest_fname_base is None: + dest_fname_root = os.path.splitext( + os.path.split(self.src_path)[1])[0] + else: + dest_fname_root = dest_fname_base + if self.proj_unit not in ['meter', 'metre']: + dest_fname = '{}_{}_{}.tif'.format( + dest_fname_root, + np.round(profile['transform'][2], 6), + np.round(profile['transform'][5], 6)) + else: + dest_fname = '{}_{}_{}.tif'.format( + dest_fname_root, + int(profile['transform'][2]), + int(profile['transform'][5])) + # if self.cog_output: + # dest_path = os.path.join(self.dest_dir, 'tmp.tif') + # else: + dest_path = os.path.join(self.dest_dir, dest_fname) + + with rasterio.open(dest_path, 'w', + **profile) as dest: + if profile['count'] == 1: + dest.write(tile_data[0, :, :], 1) + else: + for band in range(1, profile['count'] + 1): + # base-1 vs. base-0 indexing...bleh + dest.write(tile_data[band-1, :, :], band) + if self.alpha: + # write the mask if there's an alpha band + dest.write(mask, profile['count'] + 1) + + dest.close() + + return dest_path + + # if self.cog_output: + # self._create_cog(os.path.join(self.dest_dir, 'tmp.tif'), + # os.path.join(self.dest_dir, dest_fname)) + # os.remove(os.path.join(self.dest_dir, 'tmp.tif')) + + def fill_all_nodata(self, nodata_fill): + """ + Fills all tile nodata values with a fill value. + + The standard workflow is to run this function only after generating label masks and using the original output + from the raster tiler to filter out label pixels that overlap nodata pixels in a tile. For example, + solaris.vector.mask.instance_mask will filter out nodata pixels from a label mask if a reference_im is provided, + and after this step nodata pixels may be filled by calling this method. + + nodata_fill : int, float, or str, optional + Default is to not fill any nodata values. Otherwise, pixels outside of the aoi_boundary and pixels inside + the aoi_boundary with the nodata value will be filled. "mean" will fill pixels with the channel-wise mean. + Providing an int or float will fill pixels in all channels with the provided value. + + Returns: list + The fill values, in case the mean of the src image should be used for normalization later. + """ + src = _check_rasterio_im_load(self.src_name) + if nodata_fill == "mean": + arr = src.read() + arr_nan = np.where(arr!=src.nodata, arr, np.nan) + fill_values = np.nanmean(arr_nan, axis=tuple(range(1, arr_nan.ndim))) + print('Fill values set to {}'.format(fill_values)) + elif isinstance(nodata_fill, (float, int)): + fill_values = src.meta['count'] * [nodata_fill] + print('Fill values set to {}'.format(fill_values)) + else: + raise TypeError('nodata_fill must be "mean", int, or float. {} was supplied.'.format(nodata_fill)) + src.close() + for tile_path in self.tile_paths: + tile_src = rasterio.open(tile_path, "r+") + tile_data = tile_src.read() + for i in np.arange(tile_data.shape[0]): + tile_data[i,...][tile_data[i,...] == tile_src.nodata] = fill_values[i] # set fill value for each band + if tile_src.meta['count'] == 1: + tile_src.write(tile_data[0, :, :], 1) + else: + for band in range(1, tile_src.meta['count'] + 1): + # base-1 vs. base-0 indexing...bleh + tile_src.write(tile_data[band-1, :, :], band) + tile_src.close() + return fill_values + + def _create_cog(self, src_path, dest_path): + """Overwrite non-cloud-optimized GeoTIFF with a COG.""" + cog_translate(src_path=src_path, dst_path=dest_path, + dst_kwargs={'crs': self.dest_crs}, + resampling=self.resampling, + latitude_adjustment=False) + + def get_tile_bounds(self): + """Get tile bounds for each tile to be created in the input CRS.""" + if not self.aoi_boundary: + if not self.src: + raise ValueError('aoi_boundary and/or a source file must be ' + 'provided.') + else: + # set to the bounds of the image + # split_geom can take a list + self.aoi_boundary = list(self.src.bounds) + + self.tile_bounds = split_geom(geometry=self.aoi_boundary, tile_size=self.src_tile_size, resolution=( + self.src.transform[0], -self.src.transform[4]), use_projection_units=self.use_src_metric_size, src_img=self.src) + + def load_src_vrt(self): + """Load a source dataset's VRT into the destination CRS.""" + vrt_params = dict(crs=self.dest_crs, + resampling=getattr(Resampling, self.resampling), + src_nodata=self.nodata, dst_nodata=self.nodata) + return WarpedVRT(self.src, **vrt_params) diff --git a/docker/solaris/solaris/tile/vector_tile.py b/docker/solaris/solaris/tile/vector_tile.py new file mode 100644 index 00000000..c3a781ff --- /dev/null +++ b/docker/solaris/solaris/tile/vector_tile.py @@ -0,0 +1,335 @@ +import os +import numpy as np +from shapely.geometry import box, Polygon +import geopandas as gpd +from ..utils.core import _check_gdf_load, _check_crs +from ..utils.tile import save_empty_geojson +from ..utils.geo import get_projection_unit, split_multi_geometries +from ..utils.geo import reproject_geometry +from tqdm.auto import tqdm + + +class VectorTiler(object): + """An object to tile geospatial vector data into smaller pieces. + + Arguments + --------- + + + Attributes + ---------- + """ + + def __init__(self, dest_dir=None, dest_crs=None, output_format='GeoJSON', + verbose=False, super_verbose=False): + if verbose or super_verbose: + print('Preparing the tiler...') + self.dest_dir = dest_dir + if not os.path.isdir(self.dest_dir): + os.makedirs(self.dest_dir) + if dest_crs is not None: + self.dest_crs = _check_crs(dest_crs) + self.output_format = output_format + self.verbose = verbose + self.super_verbose = super_verbose + self.tile_paths = [] # retains the paths of the last call to .tile() + if self.verbose or self.super_verbose: + print('Initialization done.') + + def tile(self, src, tile_bounds, tile_bounds_crs=None, geom_type='Polygon', + split_multi_geoms=True, min_partial_perc=0.0, + dest_fname_base='geoms', obj_id_col=None, + output_ext='.geojson'): + """Tile `src` into vector data tiles bounded by `tile_bounds`. + + Arguments + --------- + src : `str` or :class:`geopandas.GeoDataFrame` + The source vector data to tile. Must either be a path to a GeoJSON + or a :class:`geopandas.GeoDataFrame`. + tile_bounds : list + A :class:`list` made up of ``[left, top, right, bottom] `` sublists + (this can be extracted from + :class:`solaris.tile.raster_tile.RasterTiler` after tiling imagery) + tile_bounds_crs : int, optional + The EPSG code or rasterio.crs.CRS object for the CRS that the tile + bounds are in. RasterTiler.tile returns the CRS of the raster tiles + and can be used here. If not provided, it's assumed that the CRS is the + same as in `src`. This argument must be provided if the bound + coordinates and `src` are not in the same CRS, otherwise tiling will + not occur correctly. + geom_type : str, optional (default: "Polygon") + The type of geometries contained within `src`. Defaults to + ``"Polygon"``, can also be ``"LineString"``. + split_multi_geoms : bool, optional (default: True) + Should multi-polygons or multi-linestrings generated by clipping + a geometry into discontinuous pieces be separated? Defaults to yes + (``True``). + min_partial_perc : float, optional (default: 0.0) + The minimum percentage of a :class:`shapely.geometry.Polygon` 's + area or :class:`shapely.geometry.LineString` 's length that must + be retained within a tile's bounds to be included in the output. + Defaults to ``0.0``, meaning that the contained portion of a + clipped geometry will be included, no matter how small. + dest_fname_base : str, optional (default: 'geoms') + The base filename to use when creating outputs. The lower left + corner coordinates of the tile's bounding box will be appended + when saving. + obj_id_col : str, optional (default: None) + If ``split_multi_geoms=True``, the name of a column that specifies + a unique identifier for each geometry (e.g. the ``"BuildingId"`` + column in many SpaceNet datasets.) See + :func:`solaris.utils.geo.split_multi_geometries` for more. + output_ext : str, optional, (default: geojson) + Extension of output files, can be 'geojson' or 'json'. + """ + + if isinstance(src, gpd.GeoDataFrame) and src.crs is None: + raise ValueError("If the src input is a geopandas.GeoDataFrame, it must have a crs attribute.") + + tile_gen = self.tile_generator(src, tile_bounds, tile_bounds_crs, + geom_type, split_multi_geoms, + min_partial_perc, + obj_id_col=obj_id_col) + self.tile_paths = [] + for tile_gdf, tb in tqdm(tile_gen): + if self.proj_unit not in ['meter', 'metre']: + dest_path = os.path.join( + self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, + np.round(tb[0], 6), + np.round(tb[3], 6), + output_ext)) + else: + dest_path = os.path.join( + self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, + int(tb[0]), + int(tb[3]), + output_ext)) + self.tile_paths.append(dest_path) + if len(tile_gdf) > 0: + tile_gdf.to_file(dest_path, driver='GeoJSON') + else: + save_empty_geojson(dest_path, self.dest_crs) + + def tile_generator(self, src, tile_bounds, tile_bounds_crs=None, + geom_type='Polygon', split_multi_geoms=True, + min_partial_perc=0.0, obj_id_col=None): + """Generate `src` vector data tiles bounded by `tile_bounds`. + + Arguments + --------- + src : `str` or :class:`geopandas.GeoDataFrame` + The source vector data to tile. Must either be a path to a GeoJSON + or a :class:`geopandas.GeoDataFrame`. + tile_bounds : list + A :class:`list` made up of ``[left, top, right, bottom] `` sublists + (this can be extracted from + :class:`solaris.tile.raster_tile.RasterTiler` after tiling imagery) + tile_bounds_crs : int, optional + The EPSG code for the CRS that the tile bounds are in. If not + provided, it's assumed that the CRS is the same as in `src`. This + argument must be provided if the bound coordinates and `src` are + not in the same CRS, otherwise tiling will not occur correctly. + geom_type : str, optional (default: "Polygon") + The type of geometries contained within `src`. Defaults to + ``"Polygon"``, can also be ``"LineString"``. + split_multi_geoms : bool, optional (default: True) + Should multi-polygons or multi-linestrings generated by clipping + a geometry into discontinuous pieces be separated? Defaults to yes + (``True``). + min_partial_perc : float, optional (default: 0.0) + The minimum percentage of a :class:`shapely.geometry.Polygon` 's + area or :class:`shapely.geometry.LineString` 's length that must + be retained within a tile's bounds to be included in the output. + Defaults to ``0.0``, meaning that the contained portion of a + clipped geometry will be included, no matter how small. + obj_id_col : str, optional (default: None) + If ``split_multi_geoms=True``, the name of a column that specifies + a unique identifier for each geometry (e.g. the ``"BuildingId"`` + column in many SpaceNet datasets.) See + :func:`solaris.utils.geo.split_multi_geometries` for more. + + Yields + ------ + tile_gdf : :class:`geopandas.GeoDataFrame` + A tile geodataframe. + tb : list + A list with ``[left, top, right, bottom] `` coordinates for the + boundaries contained by `tile_gdf`. + """ + self.src = _check_gdf_load(src) + if self.verbose: + print("Num tiles:", len(tile_bounds)) + + self.src_crs = _check_crs(self.src.crs) + # check if the tile bounds and vector are in the same crs + if tile_bounds_crs is not None: + tile_bounds_crs = _check_crs(tile_bounds_crs) + else: + tile_bounds_crs = self.src_crs + if self.src_crs != tile_bounds_crs: + reproject_bounds = True # used to transform tb for clip_gdf() + else: + reproject_bounds = False + + + ## CJ bugfix 2022.04.05: self.proj_unit appears to be used only for determining + ## how many decimal places to use in the file name (if metric, 0, if lat/long, 6). + ## I ran into a problem when the image was UTM and the labels were lat/long. + ## The labels get converted to UTM during tiling, but the names were being saved with 6 digits. + ## so not only were they too long, they didn't match the raster filenames. + ## Therefore I'm setting self.proj_unit to that of the raster/reprojection CRS. + self.proj_unit = get_projection_unit(tile_bounds_crs) + # self.proj_unit = get_projection_unit(self.src_crs) + + + print(f'VectorTiler projection unit: {self.proj_unit}') + if getattr(self, 'dest_crs', None) is None: + self.dest_crs = self.src_crs + for i, tb in enumerate(tile_bounds): + if self.super_verbose: + print("\n", i, "/", len(tile_bounds)) + if reproject_bounds: + tile_gdf = clip_gdf(self.src, + reproject_geometry(box(*tb), + tile_bounds_crs, + self.src_crs), + min_partial_perc, + geom_type, verbose=self.super_verbose) + else: + tile_gdf = clip_gdf(self.src, tb, min_partial_perc, geom_type, + verbose=self.super_verbose) + if self.src_crs != self.dest_crs: + tile_gdf = tile_gdf.to_crs(crs=self.dest_crs.to_wkt()) + if split_multi_geoms: + split_multi_geometries(tile_gdf, obj_id_col=obj_id_col) + yield tile_gdf, tb + + +def search_gdf_polygon(gdf, tile_polygon): + """Find polygons in a GeoDataFrame that overlap with `tile_polygon` . + + Arguments + --------- + gdf : :py:class:`geopandas.GeoDataFrame` + A :py:class:`geopandas.GeoDataFrame` of polygons to search. + tile_polygon : :py:class:`shapely.geometry.Polygon` + A :py:class:`shapely.geometry.Polygon` denoting a tile's bounds. + + Returns + ------- + precise_matches : :py:class:`geopandas.GeoDataFrame` + The subset of `gdf` that overlaps with `tile_polygon` . If + there are no overlaps, this will return an empty + :py:class:`geopandas.GeoDataFrame`. + + """ + sindex = gdf.sindex + possible_matches_index = list(sindex.intersection(tile_polygon.bounds)) + possible_matches = gdf.iloc[possible_matches_index] + precise_matches = possible_matches[ + possible_matches.intersects(tile_polygon) + ] + if precise_matches.empty: + precise_matches = gpd.GeoDataFrame(geometry=[]) + return precise_matches + + +def clip_gdf(gdf, tile_bounds, min_partial_perc=0.0, geom_type="Polygon", + use_sindex=True, verbose=False): + """Clip GDF to a provided polygon. + + Clips objects within `gdf` to the region defined by + `poly_to_cut`. Also adds several columns to the output:: + + `origarea` + The original area of the polygons (only used if `geom_type` == + ``"Polygon"``). + `origlen` + The original length of the objects (only used if `geom_type` == + ``"LineString"``). + `partialDec` + The fraction of the object that remains after clipping + (fraction of area for Polygons, fraction of length for + LineStrings.) Can filter based on this by using `min_partial_perc`. + `truncated` + Boolean indicator of whether or not an object was clipped. + + Arguments + --------- + gdf : :py:class:`geopandas.GeoDataFrame` + A :py:class:`geopandas.GeoDataFrame` of polygons to clip. + tile_bounds : `list` or :class:`shapely.geometry.Polygon` + The geometry to clip objects in `gdf` to. This can either be a + ``[left, top, right, bottom] `` bounds list or a + :class:`shapely.geometry.Polygon` object defining the area to keep. + min_partial_perc : float, optional + The minimum fraction of an object in `gdf` that must be + preserved. Defaults to 0.0 (include any object if any part remains + following clipping). + geom_type : str, optional + Type of objects in `gdf`. Can be one of + ``["Polygon", "LineString"]`` . Defaults to ``"Polygon"`` . + use_sindex : bool, optional + Use the `gdf` sindex be used for searching. Improves efficiency + but requires `libspatialindex `__ . + verbose : bool, optional + Switch to print relevant values. + + Returns + ------- + cut_gdf : :py:class:`geopandas.GeoDataFrame` + `gdf` with all contained objects clipped to `poly_to_cut` . + See notes above for details on additional clipping columns added. + + """ + if isinstance(tile_bounds, tuple): + tb = box(*tile_bounds) + elif isinstance(tile_bounds, list): + tb = box(*tile_bounds) + elif isinstance(tile_bounds, Polygon): + tb = tile_bounds + if use_sindex and (geom_type == "Polygon"): + gdf = search_gdf_polygon(gdf, tb) + + # if geom_type == "LineString": + if 'origarea' in gdf.columns: + pass + else: + if "geom_type" == "LineString": + gdf['origarea'] = 0 + else: + gdf['origarea'] = gdf.area + + if 'origlen' in gdf.columns: + pass + else: + if "geom_type" == "LineString": + gdf['origlen'] = gdf.length + else: + gdf['origlen'] = 0 + # TODO must implement different case for lines and for spatialIndex + # (Assume RTree is already performed) + + cut_gdf = gdf.copy() + cut_gdf.geometry = gdf.intersection(tb) + + if geom_type == 'Polygon': + cut_gdf['partialDec'] = cut_gdf.area / cut_gdf['origarea'] + cut_gdf = cut_gdf.loc[cut_gdf['partialDec'] > min_partial_perc, :] + cut_gdf['truncated'] = (cut_gdf['partialDec'] != 1.0).astype(int) + else: + # assume linestrings + # remove null + cut_gdf = cut_gdf[cut_gdf['geometry'].notnull()] + cut_gdf['partialDec'] = 1 + cut_gdf['truncated'] = 0 + # cut_gdf = cut_gdf[cut_gdf.geom_type != "GeometryCollection"] + if len(cut_gdf) > 0 and verbose: + print("clip_gdf() - gdf.iloc[0]:", gdf.iloc[0]) + print("clip_gdf() - tb:", tb) + print("clip_gdf() - gdf_cut:", cut_gdf) + + # TODO: IMPLEMENT TRUNCATION MEASUREMENT FOR LINESTRINGS + + return cut_gdf diff --git a/docker/solaris/solaris/utils/__init__.py b/docker/solaris/solaris/utils/__init__.py new file mode 100644 index 00000000..cb117b60 --- /dev/null +++ b/docker/solaris/solaris/utils/__init__.py @@ -0,0 +1 @@ +from . import cli, config, core, geo, io, tile, data diff --git a/docker/solaris/solaris/utils/cli.py b/docker/solaris/solaris/utils/cli.py new file mode 100644 index 00000000..f5e6e838 --- /dev/null +++ b/docker/solaris/solaris/utils/cli.py @@ -0,0 +1,2 @@ +def _func_wrapper(func_to_call, arg_dict): + return func_to_call(**arg_dict) diff --git a/docker/solaris/solaris/utils/config.py b/docker/solaris/solaris/utils/config.py new file mode 100644 index 00000000..0f92e7aa --- /dev/null +++ b/docker/solaris/solaris/utils/config.py @@ -0,0 +1,35 @@ +import yaml + +def parse(path): + """Parse a config file for running a model. + + Arguments + --------- + path : str + Path to the YAML-formatted config file to parse. + + Returns + ------- + config : dict + A `dict` containing the information from the config file at `path`. + + """ + with open(path, 'r') as f: + config = yaml.safe_load(f) + f.close() + if not config['train'] and not config['infer']: + raise ValueError('"train", "infer", or both must be true.') + if config['train'] and config['training_data_csv'] is None: + raise ValueError('"training_data_csv" must be provided if training.') + if config['infer'] and config['inference_data_csv'] is None: + raise ValueError('"inference_data_csv" must be provided if "infer".') + if config['training']['lr'] is not None: + config['training']['lr'] = float(config['training']['lr']) + + # TODO: IMPLEMENT UPDATING VALUES BASED ON EMPTY ELEMENTS HERE! + + if config['validation_augmentation'] is not None \ + and config['inference_augmentation'] is None: + config['inference_augmentation'] = config['validation_augmentation'] + + return config diff --git a/docker/solaris/solaris/utils/core.py b/docker/solaris/solaris/utils/core.py new file mode 100644 index 00000000..871d766f --- /dev/null +++ b/docker/solaris/solaris/utils/core.py @@ -0,0 +1,152 @@ +import os +import numpy as np +from shapely.wkt import loads +from shapely.geometry import Point +from shapely.geometry.base import BaseGeometry +import pandas as pd +import geopandas as gpd +import pyproj +import rasterio +from distutils.version import LooseVersion +import skimage +from fiona._err import CPLE_OpenFailedError +from fiona.errors import DriverError +from warnings import warn + + +def _check_rasterio_im_load(im): + """Check if `im` is already loaded in; if not, load it in.""" + if isinstance(im, str): + return rasterio.open(im) + elif isinstance(im, rasterio.DatasetReader): + return im + else: + raise ValueError( + "{} is not an accepted image format for rasterio.".format(im)) + + +def _check_skimage_im_load(im): + """Check if `im` is already loaded in; if not, load it in.""" + if isinstance(im, str): + return skimage.io.imread(im) + elif isinstance(im, np.ndarray): + return im + else: + raise ValueError( + "{} is not an accepted image format for scikit-image.".format(im)) + + +def _check_df_load(df): + """Check if `df` is already loaded in, if not, load from file.""" + if isinstance(df, str): + if df.lower().endswith('json'): + return _check_gdf_load(df) + else: + return pd.read_csv(df) + elif isinstance(df, pd.DataFrame): + return df + else: + raise ValueError(f"{df} is not an accepted DataFrame format.") + + +def _check_gdf_load(gdf): + """Check if `gdf` is already loaded in, if not, load from geojson.""" + if isinstance(gdf, str): + # as of geopandas 0.6.2, using the OGR CSV driver requires some add'nal + # kwargs to create a valid geodataframe with a geometry column. see + # https://github.com/geopandas/geopandas/issues/1234 + if gdf.lower().endswith('csv'): + return gpd.read_file(gdf, GEOM_POSSIBLE_NAMES="geometry", + KEEP_GEOM_COLUMNS="NO") + try: + return gpd.read_file(gdf) + except (DriverError, CPLE_OpenFailedError): + warn(f"GeoDataFrame couldn't be loaded: either {gdf} isn't a valid" + " path or it isn't a valid vector file. Returning an empty" + " GeoDataFrame.") + return gpd.GeoDataFrame() + elif isinstance(gdf, gpd.GeoDataFrame): + return gdf + else: + raise ValueError(f"{gdf} is not an accepted GeoDataFrame format.") + + +def _check_geom(geom): + """Check if a geometry is loaded in. + + Returns the geometry if it's a shapely geometry object. If it's a wkt + string or a list of coordinates, convert to a shapely geometry. + """ + if isinstance(geom, BaseGeometry): + return geom + elif isinstance(geom, str): # assume it's a wkt + return loads(geom) + elif isinstance(geom, list) and len(geom) == 2: # coordinates + return Point(geom) + + +def _check_crs(input_crs, return_rasterio=False): + """Convert CRS to the ``pyproj.CRS`` object passed by ``solaris``.""" + if not isinstance(input_crs, pyproj.CRS) and input_crs is not None: + out_crs = pyproj.CRS(input_crs) + else: + out_crs = input_crs + + if return_rasterio: + if LooseVersion(rasterio.__gdal_version__) >= LooseVersion("3.0.0"): + out_crs = rasterio.crs.CRS.from_wkt(out_crs.to_wkt()) + else: + out_crs = rasterio.crs.CRS.from_wkt(out_crs.to_wkt("WKT1_GDAL")) + + return out_crs + + +def get_data_paths(path, infer=False): + """Get a pandas dataframe of images and labels from a csv. + + This file is designed to parse image:label reference CSVs (or just image) + for inferencde) as defined in the documentation. Briefly, these should be + CSVs containing two columns: + + ``'image'``: the path to images. + ``'label'``: the path to the label file that corresponds to the image. + + Arguments + --------- + path : str + Path to a .CSV-formatted reference file defining the location of + training, validation, or inference data. See docs for details. + infer : bool, optional + If ``infer=True`` , the ``'label'`` column will not be returned (as it + is unnecessary for inference), even if it is present. + + Returns + ------- + df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` containing the relevant `image` and `label` + information from the CSV at `path` (unless ``infer=True`` , in which + case only the `image` column is returned.) + + """ + df = pd.read_csv(path) + if infer: + return df[['image']] # no labels in those files + else: + return df[['image', 'label']] # remove anything extraneous + + +def get_files_recursively(path, traverse_subdirs=False, extension='.tif'): + """Get files from subdirs of `path`, joining them to the dir.""" + if traverse_subdirs: + walker = os.walk(path) + path_list = [] + for step in walker: + if not step[2]: # if there are no files in the current dir + continue + path_list += [os.path.join(step[0], fname) + for fname in step[2] if + fname.lower().endswith(extension)] + return path_list + else: + return [os.path.join(path, f) for f in os.listdir(path) + if f.endswith(extension)] diff --git a/docker/solaris/solaris/utils/data.py b/docker/solaris/solaris/utils/data.py new file mode 100644 index 00000000..b6681cad --- /dev/null +++ b/docker/solaris/solaris/utils/data.py @@ -0,0 +1,151 @@ +import os +import pandas as pd +from .log import _get_logging_level +from .core import get_files_recursively +import logging + + +def make_dataset_csv(im_dir, im_ext='tif', label_dir=None, label_ext='json', + output_path='dataset.csv', stage='train', match_re=None, + recursive=False, ignore_mismatch=None, verbose=0): + """Automatically generate dataset CSVs for training. + + This function creates basic CSVs for training and inference automatically. + See `the documentation tutorials `_ + for details on the specification. A regular expression string can be + provided to extract substrings for matching images to labels; if not + provided, it's assumed that the filename for the image and label files is + identical once extensions are stripped. By default, this function will + raise an exception if there are multiple label files that match to a given + image file, or if no label file matches an image file; see the + `ignore_mismatch` argument for alternatives. + + Arguments + --------- + im_dir : str + The path to the directory containing images to be used by your model. + Images in sub-directories can be included by setting + ``recursive=True``. + im_ext : str, optional + The file extension used by your images. Defaults to ``"tif"``. Not case + sensitive. + label_dir : str, optional + The path to the directory containing images to be used by your model. + Images in sub-directories can be included by setting + ``recursive=True``. This argument is required if `stage` is ``"train"`` + (default) or ``"val"``, but has no effect if `stage` is ``"infer"``. + output_path : str, optional + The path to save the generated CSV to. Defaults to ``"dataset.csv"``. + stage : str, optional + The stage that the csv is generated for. Can be ``"train"`` (default), + ``"val"``, or ``"infer"``. If set to ``"train"`` or ``"val"``, + `label_dir` must be provided or an error will occur. + match_re : str, optional + A regular expression pattern to extract substrings from image and + label filenames for matching. If not provided and labels must be + matched to images, it's assumed that image and label filenames are + identical after stripping directory and extension. Has no effect if + ``stage="infer"``. The pattern must contain at least one capture group + for compatibility with :func:`pandas.Series.str.extract`. + recursive : bool, optional + Should sub-directories in `im_dir` and `label_dir` be traversed to + find images and label files? Defaults to no (``False``). + ignore_mismatch : str, optional + Dictates how mismatches between image files and label files should be + handled. By default, having != 1 label file per image file will raise + a ``ValueError``. If ``ignore_mismatch="skip"``, any image files with + != 1 matching label will be skipped. + verbose : int, optional + Verbose text output. By default, none is provided; if ``True`` or + ``1``, information-level outputs are provided; if ``2``, extremely + verbose text is output. + + Returns + ------- + output_df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` with one column titled ``"image"`` and + a second titled ``"label"`` (if ``stage != "infer"``). The function + also saves a CSV at `output_path`. + """ + logger = logging.getLogger(__name__) + logger.setLevel(_get_logging_level(int(verbose))) + logger.debug('Checking arguments.') + + if stage != 'infer' and label_dir is None: + raise ValueError("label_dir must be provided if stage is not infer.") + logger.info('Matching images to labels.') + logger.debug('Getting image file paths.') + im_fnames = get_files_recursively(im_dir, traverse_subdirs=recursive, + extension=im_ext) + logger.debug(f"Got {len(im_fnames)} image file paths.") + temp_im_df = pd.DataFrame({'image_path': im_fnames}) + + if stage != 'infer': + logger.debug('Preparing training or validation set.') + logger.debug('Getting label file paths.') + label_fnames = get_files_recursively(label_dir, + traverse_subdirs=recursive, + extension=label_ext) + logger.debug(f"Got {len(label_fnames)} label file paths.") + if len(im_fnames) != len(label_fnames): + logger.warn('The number of images and label files is not equal.') + + logger.debug("Matching image files to label files.") + logger.debug("Extracting image filename substrings for matching.") + temp_label_df = pd.DataFrame({'label_path': label_fnames}) + temp_im_df['image_fname'] = temp_im_df['image_path'].apply( + lambda x: os.path.split(x)[1]) + temp_label_df['label_fname'] = temp_label_df['label_path'].apply( + lambda x: os.path.split(x)[1]) + if match_re: + logger.debug('match_re is True, extracting regex matches') + im_match_strs = temp_im_df['image_fname'].str.extract(match_re) + label_match_strs = temp_label_df['label_fname'].str.extract( + match_re) + if len(im_match_strs.columns) > 1 or \ + len(label_match_strs.columns) > 1: + raise ValueError('Multiple regex matches occurred within ' + 'individual filenames.') + else: + temp_im_df['match_str'] = im_match_strs + temp_label_df['match_str'] = label_match_strs + else: + logger.debug('match_re is False, will match by fname without ext') + temp_im_df['match_str'] = temp_im_df['image_fname'].apply( + lambda x: os.path.splitext(x)[0]) + temp_label_df['match_str'] = temp_label_df['label_fname'].apply( + lambda x: os.path.splitext(x)[0]) + + logger.debug('Aligning label and image dataframes by' + ' match_str.') + temp_join_df = pd.merge(temp_im_df, temp_label_df, on='match_str', + how='inner') + logger.debug(f'Length of joined dataframe: {len(temp_join_df)}') + if len(temp_join_df) < len(temp_im_df) and \ + ignore_mismatch is None: + raise ValueError('There is not a perfect 1:1 match of images ' + 'to label files. To allow this behavior, see ' + 'the make_dataset_csv() ignore_mismatch ' + 'argument.') + elif len(temp_join_df) > len(temp_im_df) and ignore_mismatch is None: + raise ValueError('There are multiple label files matching at ' + 'least one image file.') + elif len(temp_join_df) > len(temp_im_df) and ignore_mismatch == 'skip': + logger.info('ignore_mismatch="skip", so dropping any images with ' + f'duplicates. Original images: {len(temp_im_df)}') + dup_rows = temp_join_df.duplicated(subset='match_str', keep=False) + temp_join_df = temp_join_df.loc[~dup_rows, :] + logger.info('Remaining images after dropping duplicates: ' + f'{len(temp_join_df)}') + logger.debug('Dropping extra columns from output dataframe.') + output_df = temp_join_df[['image_path', 'label_path']].rename( + columns={'image_path': 'image', 'label_path': 'label'}) + + elif stage == 'infer': + logger.debug('Preparing inference dataset dataframe.') + output_df = temp_im_df.rename(columns={'image_path': 'image'}) + + logger.debug(f'Saving output dataframe to {output_path} .') + output_df.to_csv(output_path, index=False) + + return output_df diff --git a/docker/solaris/solaris/utils/geo.py b/docker/solaris/solaris/utils/geo.py new file mode 100644 index 00000000..31937968 --- /dev/null +++ b/docker/solaris/solaris/utils/geo.py @@ -0,0 +1,836 @@ +import os +from .core import _check_df_load, _check_gdf_load, _check_rasterio_im_load +from .core import _check_geom, _check_crs +import numpy as np +import pandas as pd +import geopandas as gpd +from affine import Affine +import rasterio +from rasterio.warp import calculate_default_transform, Resampling +from rasterio.warp import transform_bounds +from shapely.affinity import affine_transform +from shapely.wkt import loads +from shapely.geometry import Point, Polygon, LineString +from shapely.geometry import MultiLineString, MultiPolygon, mapping, box, shape +from shapely.geometry.collection import GeometryCollection +from shapely.ops import cascaded_union +from osgeo import gdal, osr +import json +from warnings import warn +import sys + + +def reproject(input_object, input_crs=None, + target_crs=None, target_object=None, dest_path=None, + resampling_method='cubic'): + """Reproject a dataset (df, gdf, or image) to a new coordinate system. + + This function takes a georegistered image or a dataset of vector geometries + and converts them to a new coordinate reference system. If no target CRS + is provided, the data will be converted to the appropriate UTM zone by + default. To convert a pixel-coordinate dataset to geographic coordinates or + vice versa, use :func:`solaris.vector.polygon.georegister_px_df` or + :func:`solaris.vector.polygon.geojson_to_px_gdf` instead. + + Arguments + --------- + input_object : `str` or :class:`rasterio.DatasetReader` or :class:`gdal.Dataset` or :class:`geopandas.GeoDataFrame` + An object to transform to a new CRS. If a string, it must be a path + to a georegistered image or vector dataset (e.g. a .GeoJSON). If the + object itself does not contain georeferencing information, the + coordinate reference system can be provided with `input_crs`. + input_crs : int, optional + The EPSG code integer for the input data's CRS. If provided and a CRS + is also associated with `input_object`, this argument's value has + precedence. + target_crs : int, optional + The EPSG code for the output projection. If values are not provided + for this argument or `target_object`, the input data will be + re-projected into the appropriate UTM zone. If both `target_crs` and + `target_object` are provided, `target_crs` takes precedence (and a + warning is raised). + target_object : str, optional + An object in the desired destination CRS. If neither this argument nor + `target_crs` is provided, the input will be projected into the + appropriate UTM zone. `target_crs` takes precedence if both it and + `target_object` are provided. + dest_path : str, optional + The path to save the output to (if desired). This argument is only + required if the input is a :class:`gdal.Dataset`; otherwise, it is + optional. + resampling_method : str, optional + The resampling method to use during reprojection of raster data. **Only + has an effect if the input is a :class:`rasterio.DatasetReader` !** + Possible values are + ``['cubic' (default), 'bilinear', 'nearest', 'average']``. + + Returns + ------- + output : :class:`rasterio.DatasetReader` or :class:`gdal.Dataset` or :class:`geopandas.GeoDataFrame` + An output in the same format as `input_object`, but reprojected + into the destination CRS. + """ + input_data, input_type = _parse_geo_data(input_object) + if input_crs is None: + input_crs = _check_crs(get_crs(input_data)) + else: + input_crs = _check_crs(input_crs) + if target_object is not None: + target_data, _ = _parse_geo_data(target_object) + else: + target_data = None + # get CRS from target_object if it's not provided + if target_crs is None and target_data is not None: + target_crs = get_crs(target_data) + + if target_crs is not None: + target_crs = _check_crs(target_crs) + output = _reproject(input_data, input_type, input_crs, target_crs, + dest_path, resampling_method) + else: + output = reproject_to_utm(input_data, input_type, input_crs, + dest_path, resampling_method) + return output + + +def _reproject(input_data, input_type, input_crs, target_crs, dest_path, + resampling_method='bicubic'): + + input_crs = _check_crs(input_crs) + target_crs = _check_crs(target_crs) + if input_type == 'vector': + output = input_data.to_crs(target_crs) + if dest_path is not None: + output.to_file(dest_path, driver='GeoJSON') + + elif input_type == 'raster': + + if isinstance(input_data, rasterio.DatasetReader): + transform, width, height = calculate_default_transform( + input_crs.to_wkt("WKT1_GDAL"), target_crs.to_wkt("WKT1_GDAL"), + input_data.width, input_data.height, *input_data.bounds + ) + kwargs = input_data.meta.copy() + kwargs.update({'crs': target_crs.to_wkt("WKT1_GDAL"), + 'transform': transform, + 'width': width, + 'height': height}) + + if dest_path is not None: + with rasterio.open(dest_path, 'w', **kwargs) as dst: + for band_idx in range(1, input_data.count + 1): + rasterio.warp.reproject( + source=rasterio.band(input_data, band_idx), + destination=rasterio.band(dst, band_idx), + src_transform=input_data.transform, + src_crs=input_data.crs, + dst_transform=transform, + dst_crs=target_crs.to_wkt("WKT1_GDAL"), + resampling=getattr(Resampling, resampling_method) + ) + output = rasterio.open(dest_path) + input_data.close() + + else: + output = np.zeros(shape=(height, width, input_data.count)) + for band_idx in range(1, input_data.count + 1): + rasterio.warp.reproject( + source=rasterio.band(input_data, band_idx), + destination=output[:, :, band_idx-1], + src_transform=input_data.transform, + src_crs=input_data.crs, + dst_transform=transform, + dst_crs=target_crs, + resampling=getattr(Resampling, resampling_method) + ) + + elif isinstance(input_data, gdal.Dataset): + if dest_path is not None: + gdal.Warp(dest_path, input_data, + dstSRS='EPSG:' + str(target_crs.to_epsg())) + output = gdal.Open(dest_path) + else: + raise ValueError('An output path must be provided for ' + 'reprojecting GDAL datasets.') + return output + + +def reproject_to_utm(input_data, input_type, input_crs=None, dest_path=None, + resampling_method='bicubic'): + """Convert an input to a UTM CRS (after determining the correct UTM zone). + + """ + if input_crs is None: + input_crs = get_crs(input_data) + if input_crs is None: + raise ValueError('An input CRS must be provided by input_data or' + ' input_crs.') + input_crs = _check_crs(input_crs) + + bounds = get_bounds(input_data, crs=_check_crs(4326)) # need in wkt84 for UTM zone + midpoint = [(bounds[1] + bounds[3])/2., (bounds[0] + bounds[2])/2.] + utm_epsg = latlon_to_utm_epsg(*midpoint) + + output = _reproject(input_data, input_type=input_type, input_crs=input_crs, + target_crs=utm_epsg, dest_path=dest_path, + resampling_method=resampling_method) + # cleanup + if os.path.isfile('tmp'): + os.remove('tmp') + + return output + + +def get_bounds(geo_obj, crs=None): + """Get the ``[left, bottom, right, top]`` bounds in any CRS. + + Arguments + --------- + geo_obj : a georeferenced raster or vector dataset. + crs : int, optional + The EPSG code (or other CRS format supported by rasterio.warp) + for the CRS the bounds should be returned in. If not provided, + the bounds will be returned in the same crs as `geo_obj`. + + Returns + ------- + bounds : list + ``[left, bottom, right, top]`` bounds in the input crs (if `crs` is + ``None``) or in `crs` if it was provided. + """ + input_data, input_type = _parse_geo_data(geo_obj) + if input_type == 'vector': + bounds = list(input_data.geometry.total_bounds) + elif input_type == 'raster': + if isinstance(input_data, rasterio.DatasetReader): + bounds = list(input_data.bounds) + elif isinstance(input_data, gdal.Dataset): + input_gt = input_data.GetGeoTransform() + min_x = input_gt[0] + max_x = min_x + input_gt[1]*input_data.RasterXSize + max_y = input_gt[3] + min_y = max_y + input_gt[5]*input_data.RasterYSize + + bounds = [min_x, min_y, max_x, max_y] + + if crs is not None: + crs = _check_crs(crs) + src_crs = get_crs(input_data) + # transform bounds to desired CRS + bounds = transform_bounds(src_crs.to_wkt("WKT1_GDAL"), + crs.to_wkt("WKT1_GDAL"), *bounds) + + return bounds + + +def get_crs(obj): + """Get a coordinate reference system from any georegistered object.""" + if isinstance(obj, gpd.GeoDataFrame): + return _check_crs(obj.crs) + elif isinstance(obj, rasterio.DatasetReader): + return _check_crs(obj.crs) + elif isinstance(obj, gdal.Dataset): + # rawr + return _check_crs(int(osr.SpatialReference(wkt=obj.GetProjection()).GetAttrValue( + 'AUTHORITY', 1))) + else: + raise TypeError("solaris doesn't know how to extract a crs from an " + "object of type {}".format(type(obj))) + + +def _parse_geo_data(input): + if isinstance(input, str): + if input.lower().endswith('json') or input.lower().endswith('csv'): + input_type = 'vector' + input_data = _check_df_load(input) + elif input.lower().endswith('tif') or input.lower().endswith('tiff'): + input_type = 'raster' + input_data = _check_rasterio_im_load(input) + else: + input_data = input + if isinstance(input_data, pd.DataFrame): + input_type = 'vector' + elif isinstance( + input_data, rasterio.DatasetReader + ) or isinstance( + input_data, gdal.Dataset + ): + input_type = 'raster' + else: + raise ValueError('The input format {} is not compatible with ' + 'solaris.'.format(type(input))) + return input_data, input_type + + +def reproject_geometry(input_geom, input_crs=None, target_crs=None, + affine_obj=None): + """Reproject a geometry or coordinate into a new CRS. + + Arguments + --------- + input_geom : `str`, `list`, or `Shapely `_ geometry + A geometry object to re-project. This can be a 2-member ``list``, in + which case `input_geom` is assumed to coorespond to ``[x, y]`` + coordinates in `input_crs`. It can also be a Shapely geometry object or + a wkt string. + input_crs : int, optional + The coordinate reference system for `input_geom`'s coordinates, as an + EPSG :class:`int`. Required unless `affine_transform` is provided. + target_crs : int, optional + The target coordinate reference system to re-project the geometry into. + If not provided, the appropriate UTM zone will be selected by default, + unless `affine_transform` is provided (and therefore CRSs are ignored.) + affine_transform : :class:`affine.Affine`, optional + An :class:`affine.Affine` object (or a ``[a, b, c, d, e, f]`` list to + convert to that format) to use for transformation. Has no effect unless + `input_crs` **and** `target_crs` are not provided. + + Returns + ------- + output_geom : Shapely geometry + A shapely geometry object: + - in `target_crs`, if one was provided; + - in the appropriate UTM zone, if `input_crs` was provided and + `target_crs` was not; + - with `affine_transform` applied to it if neither `input_crs` nor + `target_crs` were provided. + """ + input_geom = _check_geom(input_geom) + + if input_crs is not None: + input_crs = _check_crs(input_crs) + if target_crs is None: + geom = reproject_geometry(input_geom, input_crs, + target_crs=_check_crs(4326)) + target_crs = latlon_to_utm_epsg(geom.centroid.y, geom.centroid.x) + target_crs = _check_crs(target_crs) + gdf = gpd.GeoDataFrame(geometry=[input_geom], crs=input_crs.to_wkt()) + # create a new instance of the same geometry class as above with the + # new coordinates + output_geom = gdf.to_crs(target_crs.to_wkt()).iloc[0]['geometry'] + + else: + if affine_obj is None: + raise ValueError('If an input CRS is not provided, ' + 'affine_transform is required to complete the ' + 'transformation.') + elif isinstance(affine_obj, Affine): + affine_obj = affine_to_list(affine_obj) + + output_geom = affine_transform(input_geom, affine_obj) + + return output_geom + + +def gdf_get_projection_unit(vector_file): + """Get the projection unit for a vector_file or gdf. + + Arguments + --------- + vector_file : :py:class:`geopandas.GeoDataFrame` or geojson/shapefile + A vector file or gdf with georeferencing + + Notes + ----- + If vector file is already in UTM coords, the projection WKT is complex: + https://www.spatialreference.org/ref/epsg/wgs-84-utm-zone-11n/html/ + In this case, return the second instance of 'UNIT'. + + Returns + ------- + unit : String + The unit i.e. meter, metre, or degree, of the projection + """ + c = _check_gdf_load(vector_file).crs + return get_projection_unit(c) + + +def raster_get_projection_unit(image): + """Get the projection unit for an image. + + Arguments + --------- + image : raster image, GeoTIFF or other format + A raster file with georeferencing + + Notes + ----- + If raster is already in UTM coords, the projection WKT is complex: + https://www.spatialreference.org/ref/epsg/wgs-84-utm-zone-11n/html/ + In this case, return the second instance of 'UNIT'. + + Returns + ------- + unit : String + The unit i.e. meters or degrees, of the projection + """ + c = _check_rasterio_im_load(image).crs + return get_projection_unit(c) + + +def get_projection_unit(crs): + """Get the units of a specific SRS. + + Arguments + --------- + crs : :class:`pyproj.crs.CRS`, :class:`rasterio.crs.CRS`, `str`, or `int` + The coordinate reference system to retrieve a unit for. + + Returns + ------- + unit : str + The string-formatted unit. + """ + crs = _check_crs(crs) + unit = crs.axis_info[0].unit_name + + return unit + + + +def list_to_affine(xform_mat): + """Create an Affine from a list or array-formatted [a, b, d, e, xoff, yoff] + + Arguments + --------- + xform_mat : `list` or :class:`numpy.array` + A `list` of values to convert to an affine object. + + Returns + ------- + aff : :class:`affine.Affine` + An affine transformation object. + """ + # first make sure it's not in gdal order + if len(xform_mat) > 6: + xform_mat = xform_mat[0:6] + if rasterio.transform.tastes_like_gdal(xform_mat): + return Affine.from_gdal(*xform_mat) + else: + return Affine(*xform_mat) + + +def affine_to_list(affine_obj): + """Convert a :class:`affine.Affine` instance to a list for Shapely.""" + return [affine_obj.a, affine_obj.b, + affine_obj.d, affine_obj.e, + affine_obj.xoff, affine_obj.yoff] + + +def geometries_internal_intersection(polygons): + """Get the intersection geometries between all geometries in a set. + + Arguments + --------- + polygons : `list`-like + A `list`-like containing geometries. These will be placed in a + :class:`geopandas.GeoSeries` object to take advantage of `rtree` + spatial indexing. + + Returns + ------- + intersect_list + A `list` of geometric intersections between polygons in `polygons`, in + the same CRS as the input. + """ + # convert `polygons` to geoseries and get spatialindex + # TODO: Implement test to see if `polygon` items are actual polygons or + # WKT strings + if isinstance(polygons, gpd.GeoSeries): + gs = polygons + else: + gs = gpd.GeoSeries(polygons).reset_index(drop=True) + sindex = gs.sindex + gs_bboxes = gs.apply(lambda x: x.bounds) + + # find indices of polygons that overlap in gs + intersect_lists = gs_bboxes.apply(lambda x: list(sindex.intersection(x))) + intersect_lists = intersect_lists.dropna() + # drop all objects that only have self-intersects + # first, filter down to the ones that have _some_ intersection with others + intersect_lists = intersect_lists[ + intersect_lists.apply(lambda x: len(x) > 1)] + if len(intersect_lists) == 0: # if there are no real intersections + return GeometryCollection() # same result as failed union below + # the below is a royal pain to follow. what it does is create a dataframe + # with two columns: 'gs_idx' and 'intersectors'. 'gs_idx' corresponds to + # a polygon's original index in gs, and 'intersectors' gives a list of + # gs indices for polygons that intersect with its bbox. + intersect_lists.name = 'intersectors' + intersect_lists.index.name = 'gs_idx' + intersect_lists = intersect_lists.reset_index() + # first, we get rid of self-intersection indices in 'intersectors': + intersect_lists['intersectors'] = intersect_lists.apply( + lambda x: [i for i in x['intersectors'] if i != x['gs_idx']], + axis=1) + # for each row, we next create a union of the polygons in 'intersectors', + # and find the intersection of that with the polygon at gs[gs_idx]. this + # (Multi)Polygon output corresponds to all of the intersections for the + # polygon at gs[gs_idx]. we add that to a list of intersections stored in + # output_polys. + output_polys = [] + _ = intersect_lists.apply(lambda x: output_polys.append( + gs[x['gs_idx']].intersection(cascaded_union(gs[x['intersectors']])) + ), axis=1) + # we then generate the union of all of these intersections and return it. + return cascaded_union(output_polys) + + +def split_multi_geometries(gdf, obj_id_col=None, group_col=None, + geom_col='geometry'): + """Split apart MultiPolygon or MultiLineString geometries. + + Arguments + --------- + gdf : :class:`geopandas.GeoDataFrame` or `str` + A :class:`geopandas.GeoDataFrame` or path to a geojson containing + geometries. + obj_id_col : str, optional + If one exists, the name of the column that uniquely identifies each + geometry (e.g. the ``"BuildingId"`` column in many SpaceNet datasets). + This will be tracked so multiple objects don't get produced with + the same ID. Note that object ID column will be renumbered on output. + If passed, `group_col` must also be provided. + group_col : str, optional + A column to identify groups for sequential numbering (for example, + ``'ImageId'`` for sequential number of ``'BuildingId'``). Must be + provided if `obj_id_col` is passed. + geom_col : str, optional + The name of the column in `gdf` that corresponds to geometry. Defaults + to ``'geometry'``. + + Returns + ------- + :class:`geopandas.GeoDataFrame` + A `geopandas.GeoDataFrame` that's identical to the input, except with + the multipolygons split into separate rows, and the object ID column + renumbered (if one exists). + + """ + if obj_id_col and not group_col: + raise ValueError('group_col must be provided if obj_id_col is used.') + gdf2 = _check_gdf_load(gdf) + # drop duplicate columns (happens if loading a csv with geopandas) + gdf2 = gdf2.loc[:, ~gdf2.columns.duplicated()] + if len(gdf2) == 0: + return gdf2 + # check if the values in gdf2[geometry] are polygons; if strings, do loads + if isinstance(gdf2[geom_col].iloc[0], str): + gdf2[geom_col] = gdf2[geom_col].apply(loads) + split_geoms_gdf = pd.concat( + gdf2.apply(_split_multigeom_row, axis=1, geom_col=geom_col).tolist()) + gdf2 = gdf2.drop(index=split_geoms_gdf.index.unique()) # remove multipolygons + gdf2 = gpd.GeoDataFrame(pd.concat([gdf2, split_geoms_gdf], + ignore_index=True), crs=gdf2.crs) + + if obj_id_col: + gdf2[obj_id_col] = gdf2.groupby(group_col).cumcount()+1 + + return gdf2 + + +def get_subgraph(G, node_subset): + """ + Create a subgraph from G. Code almost directly copied from osmnx. + + Arguments + --------- + G : :class:`networkx.MultiDiGraph` + A graph to be subsetted + node_subset : `list`-like + The subset of nodes to induce a subgraph of `G` + + Returns + ------- + G2 : :class:`networkx`.MultiDiGraph + The subgraph of G that includes node_subset + """ + + node_subset = set(node_subset) + + # copy nodes into new graph + G2 = G.fresh_copy() + G2.add_nodes_from((n, G.nodes[n]) for n in node_subset) + + # copy edges to new graph, including parallel edges + if G2.is_multigraph: + G2.add_edges_from( + (n, nbr, key, d) + for n, nbrs in G.adj.items() if n in node_subset + for nbr, keydict in nbrs.items() if nbr in node_subset + for key, d in keydict.items()) + else: + G2.add_edges_from( + (n, nbr, d) + for n, nbrs in G.adj.items() if n in node_subset + for nbr, d in nbrs.items() if nbr in node_subset) + + # update graph attribute dict, and return graph + G2.graph.update(G.graph) + return G2 + + +def _split_multigeom_row(gdf_row, geom_col): + new_rows = [] + if isinstance(gdf_row[geom_col], MultiPolygon) \ + or isinstance(gdf_row[geom_col], MultiLineString): + new_polys = _split_multigeom(gdf_row[geom_col]) + for poly in new_polys: + row_w_poly = gdf_row.copy() + row_w_poly[geom_col] = poly + new_rows.append(row_w_poly) + return pd.DataFrame(new_rows) + + +def _split_multigeom(multigeom): + return list(multigeom) + + +def _reduce_geom_precision(geom, precision=2): + geojson = mapping(geom) + geojson['coordinates'] = np.round(np.array(geojson['coordinates']), + precision) + return shape(geojson) + + +def latlon_to_utm_epsg(latitude, longitude, return_proj4=False): + """Get the WGS84 UTM EPSG code based on a latitude and longitude value. + + Arguments + --------- + latitude : numeric + The latitude value for the coordinate. + longitude : numeric + The longitude value for the coordinate. + return_proj4 : bool, optional + Should the proj4 string be returned as well as the EPSG code? Defaults + to no (``False``)` + + Returns + ------- + epsg : int + The integer corresponding to the EPSG code for the relevant UTM zone + in WGS 84. + proj4 : str + The proj4 string for the CRS. Only returned if ``return_proj4=True``. + """ + zone_number, zone_letter = _latlon_to_utm_zone(latitude, longitude) + + if return_proj4: + if zone_letter == 'N': + direction_indicator = '+north' + elif zone_letter == 'S': + direction_indicator = '+south' + proj = "+proj=utm +zone={} {}".format(zone_number, + direction_indicator) + proj += " +ellps=WGS84 +datum=WGS84 +units=m +no_defs" + + if zone_letter == 'N': + epsg = 32600 + zone_number + elif zone_letter == 'S': + epsg = 32700 + zone_number + + return (epsg, proj) if return_proj4 else epsg + + +def _latlon_to_utm_zone(latitude, longitude, ns_only=True): + """Convert latitude and longitude to a UTM zone ID. + + This function modified from + `the python utm library `_. + + Arguments + --------- + latitude : numeric or :class:`numpy.ndarray` + The latitude value of a coordinate. + longitude : numeric or :class:`numpy.ndarray` + The longitude value of a coordinate. + ns_only : bool, optional + Should the full list of possible zone numbers be used or just the N/S + options? Defaults to N/S only (``True``). + + Returns + ------- + zone_number : int + The numeric portion of the UTM zone ID. + zone_letter : str + The string portion of the UTM zone ID. Note that by default this + function uses only the N/S designation rather than the full range of + possible letters. + """ + + # If the input is a numpy array, just use the first element + # User responsibility to make sure that all points are in one zone + if isinstance(latitude, np.ndarray): + latitude = latitude.flat[0] + if isinstance(longitude, np.ndarray): + longitude = longitude.flat[0] + + utm_val = None + + if 56 <= latitude < 64 and 3 <= longitude < 12: + utm_val = 32 + + elif 72 <= latitude <= 84 and longitude >= 0: + if longitude < 9: + utm_val = 31 + elif longitude < 21: + utm_val = 33 + elif longitude < 33: + utm_val = 35 + elif longitude < 42: + utm_val = 37 + + if latitude < 0: + zone_letter = "S" + else: + zone_letter = "N" + + if not -80 <= latitude <= 84: + warn('Warning: UTM projections not recommended for ' + 'latitude {}'.format(latitude)) + if utm_val is None: + utm_val = int((longitude + 180) / 6) + 1 + + return utm_val, zone_letter + + +def _get_coords(geom): + """Get coordinates from various shapely geometry types.""" + if isinstance(geom, Point) or isinstance(geom, LineString): + return geom.coords.xy + elif isinstance(geom, Polygon): + return geom.exterior.coords.xy + + +def bbox_corners_to_coco(bbox): + """Convert bbox from ``[minx, miny, maxx, maxy]`` to coco format. + + COCO formats bounding boxes as ``[minx, miny, width, height]``. + + Arguments + --------- + bbox : :class:`list`-like of numerics + A 4-element list of the form ``[minx, miny, maxx, maxy]``. + + Returns + ------- + coco_bbox : list + ``[minx, miny, width, height]`` shape. + """ + + return [bbox[0], bbox[1], bbox[2]-bbox[0], bbox[3]-bbox[1]] + + +def polygon_to_coco(polygon): + """Convert a geometry to COCO polygon format.""" + if isinstance(polygon, Polygon): + coords = polygon.exterior.coords.xy + elif isinstance(polygon, str): # assume it's WKT + coords = loads(polygon).exterior.coords.xy + elif isinstance(polygon, MultiPolygon): + raise ValueError("You have MultiPolygon types in your label df. Remove, explode, or fix these to be Polygon geometry types.") + else: + raise ValueError('polygon must be a shapely geometry or WKT.') + # zip together x,y pairs + coords = list(zip(coords[0], coords[1])) + coords = [item for coordinate in coords for item in coordinate] + + return coords + + +def split_geom(geometry, tile_size, resolution=None, + use_projection_units=False, src_img=None): + """Splits a vector into approximately equal sized tiles. + + Adapted from @lossyrob's Gist__ + + .. Gist: https://gist.github.com/lossyrob/7b620e6d2193cb55fbd0bffacf27f7f2 + + The more complex the geometry, the slower this will run, but geometrys with + around 10000 coordinates run in a few seconds time. You can simplify + geometries with shapely.geometry.Polygon.simplify if necessary. + + Arguments + --------- + geometry : str, optional + A shapely.geometry.Polygon, path to a single feature geojson, + or list-like bounding box shaped like [left, bottom, right, top]. + The geometry must be in the projection coordinates corresponding to + the resolution units. + tile_size : `tuple` of `int`s + The size of the input tiles in ``(y, x)`` coordinates. By default, + this is in pixel units; this can be changed to metric units using the + `use_metric_size` argument. + use_projection_units : bool, optional + Is `tile_size` in pixel units (default) or distance units? To set to distance units + use ``use_projection_units=True``. If False, resolution must be supplied. + resolution: `tuple` of `float`s, optional + (x resolution, y resolution). Used by default if use_metric_size is False. + Can be acquired from rasterio dataset object's metadata. + src_img: `str` or `raster`, optional + A rasterio raster object or path to a geotiff. The bounds of this raster and the geometry will be + intersected and the result of the intersection will be tiled. Useful in cases where the extent of + collected labels and source imagery partially overlap. The src_img must have the same projection units + as the geometry. + + Returns + ------- + tile_bounds : list (containing sublists like [left, bottom, right, top]) + + """ + if isinstance(geometry, str): + gj = json.loads(open(geometry).read()) + + features = gj['features'] + if not len(features) == 1: + print('Feature collection must only contain one feature') + sys.exit(1) + + geometry = shape(features[0]['geometry']) + + elif isinstance(geometry, list) or isinstance(geometry, np.ndarray): + assert len(geometry) == 4 + geometry = box(*geometry) + + if use_projection_units is False: + if resolution is None: + print("Resolution must be specified if use_projection_units is" + " False. Access it from src raster meta.") + return + # convert pixel units to CRS units to use during image tiling. + # NOTE: This will be imperfect for large AOIs where there isn't + # a constant relationship between the src CRS units and src pixel + # units. + if isinstance(resolution, (float, int)): + resolution = (resolution, resolution) + tmp_tile_size = [tile_size[0]*resolution[0], + tile_size[1]*resolution[1]] + else: + tmp_tile_size = tile_size + + if src_img is not None: + src_img = _check_rasterio_im_load(src_img) + geometry = geometry.intersection(box(*src_img.bounds)) + bounds = geometry.bounds + else: + bounds = geometry.bounds + + xmin = bounds[0] + xmax = bounds[2] + ymin = bounds[1] + ymax = bounds[3] + x_extent = xmax - xmin + y_extent = ymax - ymin + x_steps = np.ceil(x_extent/tmp_tile_size[1]) + y_steps = np.ceil(y_extent/tmp_tile_size[0]) + x_mins = np.arange(xmin, xmin + tmp_tile_size[1]*x_steps, + tmp_tile_size[1]) + y_mins = np.arange(ymin, ymin + tmp_tile_size[0]*y_steps, + tmp_tile_size[0]) + tile_bounds = [ + (i, j, i+tmp_tile_size[1], j+tmp_tile_size[0]) + for i in x_mins for j in y_mins if not geometry.intersection( + box(*(i, j, i+tmp_tile_size[1], j+tmp_tile_size[0]))).is_empty + ] + return tile_bounds diff --git a/docker/solaris/solaris/utils/io.py b/docker/solaris/solaris/utils/io.py new file mode 100644 index 00000000..35f6f63f --- /dev/null +++ b/docker/solaris/solaris/utils/io.py @@ -0,0 +1,305 @@ +"""Utility functions for data io.""" +import numpy as np +import skimage.io + + +def imread(path, make_8bit=False, rescale=False, + rescale_min='auto', rescale_max='auto'): + """Read in an image file and rescale pixel values (if applicable). + + Note + ---- + Because overhead imagery is often either 16-bit or multispectral (i.e. >3 + channels or bands that don't directly translate into the RGB scheme of + photographs), this package using scikit-image_ ``io`` algorithms. Though + slightly slower, these algorithms are compatible with any bit depth or + channel count. + + .. _scikit-image: https://scikit-image.org + + Arguments + --------- + path : str + Path to the image file to load. + make_8bit : bool, optional + Should the image be converted to an 8-bit format? Defaults to False. + rescale : bool, optional + Should pixel intensities be rescaled? Defaults to no (False). + rescale_min : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The minimum pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_min`, the minimum pixel intensity in + each channel of the image will be subtracted such that the minimum + value becomes zero. If a single number is provided, that number will be + subtracted from each channel. If a list of values is provided that is + the same length as the number of channels, then those values will be + subtracted from the corresponding channels. + rescale_max : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The max pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_max`, each channel will be rescaled such + that the maximum value in the channel is set to the bit range's max. + If a single number is provided, that number will be set as the upper + limit for all channels. If a list of values is provided that is the + same length as the number of channels, then those values will be + set to the maximum value in the corresponding channels. + + Returns + ------- + im : :func:`numpy.array` + A NumPy array of shape ``[Y, X, C]`` containing the imagery, with dtype + ``uint8``. + + """ + im_arr = skimage.io.imread(path) + # check dtype for preprocessing + if im_arr.dtype == np.uint8: + dtype = 'uint8' + elif im_arr.dtype == np.uint16: + dtype = 'uint16' + elif im_arr.dtype in [np.float16, np.float32, np.float64]: + if np.amax(im_arr) <= 1 and np.amin(im_arr) >= 0: + dtype = 'zero-one normalized' # range = 0-1 + elif np.amax(im_arr) > 0 and np.amin(im_arr) < 0: + dtype = 'z-scored' + elif np.amax(im_arr) <= 255: + dtype = '255 float' + elif np.amax(im_arr) <= 65535: + dtype = '65535 float' + else: + raise TypeError('The loaded image array is an unexpected dtype.') + else: + raise TypeError('The loaded image array is an unexpected dtype.') + if make_8bit: + im_arr = preprocess_im_arr(im_arr, dtype, rescale=rescale, + rescale_min=rescale_min, + rescale_max=rescale_max) + return im_arr + + +def preprocess_im_arr(im_arr, im_format, rescale=False, + rescale_min='auto', rescale_max='auto'): + """Convert image to standard shape and dtype for use in the pipeline. + + Notes + ----- + This repo will require the following of images: + + - Their shape is of form [X, Y, C] + - Input images are dtype ``uint8`` + + This function will take an image array `im_arr` and reshape it accordingly. + + Arguments + --------- + im_arr : :func:`numpy.array` + A numpy array representation of an image. `im_arr` should have either + two or three dimensions. + im_format : str + One of ``'uint8'``, ``'uint16'``, ``'z-scored'``, + ``'zero-one normalized'``, ``'255 float'``, or ``'65535 float'``. + String indicating the dtype of the input, which will dictate the + preprocessing applied. + rescale : bool, optional + Should pixel intensities be rescaled? Defaults to no (False). + rescale_min : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The minimum pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_min`, the minimum pixel intensity in + each channel of the image will be subtracted such that the minimum + value becomes zero. If a single number is provided, that number will be + subtracted from each channel. If a list of values is provided that is + the same length as the number of channels, then those values will be + subtracted from the corresponding channels. + rescale_max : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The max pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_max`, each channel will be rescaled such + that the maximum value in the channel is set to the bit range's max. + If a single number is provided, that number will be set as the upper + limit for all channels. If a list of values is provided that is the + same length as the number of channels, then those values will be + set to the maximum value in the corresponding channels. + + Returns + ------- + A :func:`numpy.array` with shape ``[X, Y, C]`` and dtype ``uint8``. + + """ + # get [Y, X, C] axis order set up + if im_arr.ndim not in [2, 3]: + raise ValueError('This package can only read two-dimensional' + 'image data with an optional channel dimension.') + if im_arr.ndim == 2: + im_arr = im_arr[:, :, np.newaxis] + if im_arr.shape[0] < im_arr.shape[2]: # if the channel axis comes first + im_arr = np.moveaxis(im_arr, 0, -1) # move 0th axis tolast position + + # rescale images (if applicable) + if rescale: + im_arr = rescale_arr(im_arr, im_format, rescale_min, rescale_max) + + if im_format == 'uint8': + return im_arr.astype('uint8') # just to be sure + elif im_format == 'uint16': + im_arr = (im_arr.astype('float64')*255./65535.).astype('uint8') + elif im_format == 'z-scored': + im_arr = ((im_arr+1)*177.5).astype('uint8') + elif im_format == 'zero-one normalized': + im_arr = (im_arr*255).astype('uint8') + elif im_format == '255 float': + im_arr = im_arr.astype('uint8') + elif im_format == '65535 float': + # why are you using this format? + im_arr = (im_arr*255/65535).astype('uint8') + return im_arr + + +def scale_for_model(image, output_type=None): + """Scale an image to a model's required parameters. + + Arguments + --------- + image : :class:`np.array` + The image array to be transformed to a desired output format. + output_type : str, optional + The data format of the output to pass into the model. There are five + possible values: + + * ``'normalized'`` : values rescaled to 0-1. + * ``'zscored'`` : image converted to zero mean and unit stdev. + * ``'8bit'`` : image converted to 8-bit format. + * ``'16bit'`` : image converted to 16-bit format. + + If no value is provided, no re-scaling is performed (input array is + returned directly). + """ + + if output_type is None: + return image + elif output_type == 'normalized': + out_im = image/image.max() + return out_im + elif output_type == 'zscored': + return (image - np.mean(image))/np.std(image) + elif output_type == '8bit': + if image.max() > 255: + # assume it's 16-bit, rescale to 8-bit scale to min/max + out_im = 255.*image/65535 + return out_im.astype('uint8') + elif image.max() <= 1: + out_im = 255.*image + return out_im.astype('uint8') + else: + return image.astype('uint8') + elif output_type == '16bit': + if (image.max() < 255) and (image.max() > 1): + # scale to min/max + out_im = 65535.*image/255 + return out_im.astype('uint16') + elif image.max() <= 1: + out_im = 65535.*image + return out_im.astype('uint16') + else: + return image.astype('uint16') + else: + raise ValueError('output_type must be one of' + ' "normalized", "zscored", "8bit", "16bit"') + + +def rescale_arr(im_arr, im_format, rescale_min='auto', rescale_max='auto'): + """Rescale array values in a 3D image array with channel order [Y, X, C]. + + Arguments + --------- + im_arr : :class:`numpy.array` + A numpy array representation of an image. `im_arr` should have either + two or three dimensions. + im_format : str + One of ``'uint8'``, ``'uint16'``, ``'z-scored'``, + ``'zero-one normalized'``, ``'255 float'``, or ``'65535 float'``. + String indicating the dtype of the input, which will dictate the + preprocessing applied. + rescale_min : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The minimum pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_min`, the minimum pixel intensity in + each channel of the image will be subtracted such that the minimum + value becomes zero. If a single number is provided, that number will be + subtracted from each channel. If a list of values is provided that is + the same length as the number of channels, then those values will be + subtracted from the corresponding channels. + rescale_max : ``'auto'`` or :class:`int` or :class:`float` or :class:`list` + The max pixel value(s) for rescaling. If ``rescale=True`` but no + value is provided for `rescale_max`, each channel will be rescaled such + that the maximum value in the channel is set to the bit range's max. + If a single number is provided, that number will be set as the upper + limit for all channels. If a list of values is provided that is the + same length as the number of channels, then those values will be + set to the maximum value in the corresponding channels. + + Returns + ------- + normalized_arr : :class:`numpy.array` + """ + + if isinstance(rescale_min, list): + if len(rescale_min) != im_arr.shape[2]: # if list len != channels + raise ValueError('The channel rescaling parameters must be ' + 'either a single value or a list of length ' + 'n_channels.') + else: + rescale_min = np.array(rescale_min) + elif isinstance(rescale_min, int) or isinstance(rescale_min, float): + rescale_min = np.array([rescale_min]*im_arr.shape[2]) + elif rescale_min == 'auto': + rescale_min = np.amin(im_arr, axis=(0, 1)) + + if isinstance(rescale_max, list): + if len(rescale_max) != im_arr.shape[2]: # if list len != channels + raise ValueError('The channel rescaling parameters must be ' + 'either a single value or a list of length ' + 'n_channels.') + else: + rescale_max = np.array(rescale_max) + elif isinstance(rescale_max, int) or isinstance(rescale_max, float): + rescale_max = np.array([rescale_max]*im_arr.shape[2]) + elif rescale_max == 'auto': + rescale_max = np.amax(im_arr, axis=(0, 1)) + + scale_factor = None + if im_format in ['uint8', '255 float']: + scale_factor = 255 + elif im_format in ['uint16', '65535 float']: + scale_factor = 65535 + elif im_format == 'zero-one normalized': + scale_factor = 1 + + # set all values above the scale max to the scale max, and all values + # below the scale min to the scale min + for channel in range(im_arr.shape[2]): + subarr = im_arr[:, :, channel] + subarr[subarr < rescale_min[channel]] = rescale_min[channel] + subarr[subarr > rescale_max[channel]] = rescale_max[channel] + im_arr[:, :, channel] = subarr + + if scale_factor is not None: + im_arr = (im_arr-rescale_min)*( + scale_factor/(rescale_max-rescale_min)) + + return im_arr + + +def _check_channel_order(im_arr, framework): + im_shape = im_arr.shape + if len(im_shape) == 3: # doesn't matter for 1-channel images + if im_shape[0] > im_shape[2] and framework in ['torch', 'pytorch']: + # in [Y, X, C], needs to be in [C, Y, X] + im_arr = np.moveaxis(im_arr, 2, 0) + elif im_shape[2] > im_shape[0] and framework == 'keras': + # in [C, Y, X], needs to be in [Y, X, C] + im_arr = np.moveaxis(im_arr, 0, 2) + elif len(im_shape) == 4: # for a whole minibatch + if im_shape[1] > im_shape[3] and framework in ['torch', 'pytorch']: + # in [Y, X, C], needs to be in [C, Y, X] + im_arr = np.moveaxis(im_arr, 3, 1) + elif im_shape[3] > im_shape[1] and framework == 'keras': + # in [C, Y, X], needs to be in [Y, X, C] + im_arr = np.moveaxis(im_arr, 1, 3) + + return im_arr diff --git a/docker/solaris/solaris/utils/log.py b/docker/solaris/solaris/utils/log.py new file mode 100644 index 00000000..3918934d --- /dev/null +++ b/docker/solaris/solaris/utils/log.py @@ -0,0 +1,22 @@ +import logging + + +def _get_logging_level(level_int): + """Convert a logging level integer into a log level.""" + if isinstance(level_int, bool): + level_int = int(level_int) + if level_int < 0: + return logging.CRITICAL + 1 # silence all possible outputs + elif level_int == 0: + return logging.WARNING + elif level_int == 1: + return logging.INFO + elif level_int == 2: + return logging.DEBUG + elif level_int in [10, 20, 30, 40, 50]: # if user provides the logger int + return level_int + elif isinstance(level_int, int): # if it's an int but not one of the above + return level_int + else: + raise ValueError(f"logging level set to {level_int}, " + "but it must be an integer <= 2.") diff --git a/docker/solaris/solaris/utils/raster.py b/docker/solaris/solaris/utils/raster.py new file mode 100644 index 00000000..d188a417 --- /dev/null +++ b/docker/solaris/solaris/utils/raster.py @@ -0,0 +1,62 @@ +import numpy as np +import tensorflow as tf + + +# Modified to use only tf +def reorder_axes(arr, target='tensorflow'): + """Check order of axes in an array or tensor and convert to desired format. + + Arguments + --------- + arr : :class:`numpy.array` or :class:`torch.Tensor` or :class:`tensorflow.Tensor` + target : str, optional + Desired axis order type. Possible values: + - ``'tensorflow'`` (default): ``[N, Y, X, C]`` or ``[Y, X, C]`` + - ``'torch'`` : ``[N, C, Y, X]`` or ``[C, Y, X]`` + + Returns + ------- + out_arr : an object of the same class as `arr` with axes in the desired + order. + """ + + if isinstance(arr, torch.Tensor): + raise RuntimeError("Pytorch not supported") + if isinstance(arr, np.ndarray): + axes = list(arr.shape) + elif isinstance(arr, tf.Tensor): + axes = arr.get_shape().as_list() + + if isinstance(arr, torch.Tensor): + raise RuntimeError("Pytorch not supported") + # if len(axes) == 3: + # if target == 'tensorflow' and axes[0] < axes[1]: + # arr = arr.permute(1, 2, 0) + # elif target == 'torch' and axes[2] < axes[1]: + # arr = arr.permute(2, 0, 1) + # elif len(axes) == 4: + # if target == 'tensorflow' and axes[1] < axes[2]: + # arr = arr.permute(0, 2, 3, 1) + # elif target == 'torch' and axes[3] < axes[2]: + # arr = arr.permute(0, 3, 1, 2) + + elif isinstance(arr, np.ndarray): + if len(axes) == 3: + if target == 'tensorflow' and axes[0] < axes[1]: + arr = np.moveaxis(arr, 0, -1) + elif target == 'torch' and axes[2] < axes[1]: + arr = np.moveaxis(arr, 2, 0) + elif len(axes) == 4: + if target == 'tensorflow' and axes[1] < axes[2]: + arr = np.moveaxis(arr, 1, -1) + elif target == 'torch' and axes[3] < axes[2]: + arr = np.moveaxis(arr, 3, 1) + + elif isinstance(arr, tf.Tensor): + # permutation is obnoxious in tensorflow; convert to numpy, permute, + # convert back. + np_version = arr.eval() + np_version = reorder_axes(np_version, target=target) + arr = tf.convert_to_tensor(np_version) + + return arr diff --git a/docker/solaris/solaris/utils/tdigest.py b/docker/solaris/solaris/utils/tdigest.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/solaris/utils/tile.py b/docker/solaris/solaris/utils/tile.py new file mode 100644 index 00000000..2fc9b0a0 --- /dev/null +++ b/docker/solaris/solaris/utils/tile.py @@ -0,0 +1,158 @@ +from .core import _check_crs +import geopandas as gpd +import json +from affine import Affine +from rasterio.windows import Window +from rasterio.vrt import WarpedVRT +from rasterio.enums import Resampling +# temporarily removing the below until I can get COG functionality implemented +# from rio_tiler.utils import get_vrt_transform, has_alpha_band +# from rio_tiler.utils import _requested_tile_aligned_with_internal_tile + + +def save_empty_geojson(path, crs): + crs = _check_crs(crs) + empty_geojson_dict = { + "type": "FeatureCollection", + "crs": + { + "type": "name", + "properties": + { + "name": "urn:ogc:def:crs:EPSG:{}".format(crs.to_epsg()) + } + }, + "features": + [] + } + + with open(path, 'w') as f: + json.dump(empty_geojson_dict, f) + f.close() + + +# def read_cog_tile(src, +# bounds, +# tile_size, +# indexes=None, +# nodata=None, +# resampling_method="bilinear", +# tile_edge_padding=2): +# """ +# Read cloud-optimized geotiff tile. +# +# Notes +# ----- +# Modified from `rio-tiler `_. +# License included below per terms of use. +# +# BSD 3-Clause License +# (c) 2017 Mapbox +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# * Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +# Arguments +# --------- +# src : rasterio.io.DatasetReader +# rasterio.io.DatasetReader object +# bounds : list +# Tile bounds (left, bottom, right, top) +# tile_size : list +# Output image size +# indexes : list of ints or a single int, optional, (defaults: None) +# If `indexes` is a list, the result is a 3D array, but is +# a 2D array if it is a band index number. +# nodata: int or float, optional (defaults: None) +# resampling_method : str, optional (default: "bilinear") +# Resampling algorithm +# tile_edge_padding : int, optional (default: 2) +# Padding to apply to each edge of the tile when retrieving data +# to assist in reducing resampling artefacts along edges. +# +# Returns +# ------- +# out : array, int +# returns pixel value. +# """ +# if isinstance(indexes, int): +# indexes = [indexes] +# elif isinstance(indexes, tuple): +# indexes = list(indexes) +# +# vrt_params = dict( +# add_alpha=True, crs='epsg:' + str(src.crs.to_epsg()), +# resampling=Resampling[resampling_method] +# ) +# +# vrt_transform, vrt_width, vrt_height = get_vrt_transform( +# src, bounds, bounds_crs='epsg:' + str(src.crs.to_epsg())) +# out_window = Window(col_off=0, row_off=0, +# width=vrt_width, height=vrt_height) +# +# if tile_edge_padding > 0 and not \ +# _requested_tile_aligned_with_internal_tile(src, bounds, tile_size): +# vrt_transform = vrt_transform * Affine.translation( +# -tile_edge_padding, -tile_edge_padding +# ) +# orig__vrt_height = vrt_height +# orig_vrt_width = vrt_width +# vrt_height = vrt_height + 2 * tile_edge_padding +# vrt_width = vrt_width + 2 * tile_edge_padding +# out_window = Window( +# col_off=tile_edge_padding, +# row_off=tile_edge_padding, +# width=orig_vrt_width, +# height=orig__vrt_height, +# ) +# +# vrt_params.update(dict(transform=vrt_transform, +# width=vrt_width, +# height=vrt_height)) +# +# indexes = indexes if indexes is not None else src.indexes +# out_shape = (len(indexes), tile_size[1], tile_size[0]) +# +# nodata = nodata if nodata is not None else src.nodata +# if nodata is not None: +# vrt_params.update(dict(nodata=nodata, +# add_alpha=False, +# src_nodata=nodata)) +# +# if has_alpha_band(src): +# vrt_params.update(dict(add_alpha=False)) +# +# with WarpedVRT(src, **vrt_params) as vrt: +# data = vrt.read( +# out_shape=out_shape, +# indexes=indexes, +# window=out_window, +# resampling=Resampling[resampling_method], +# ) +# mask = vrt.dataset_mask(out_shape=(tile_size[1], tile_size[0]), +# window=out_window) +# +# return data, mask, out_window, vrt_transform diff --git a/docker/solaris/solaris/vector/__init__.py b/docker/solaris/solaris/vector/__init__.py new file mode 100644 index 00000000..4323d396 --- /dev/null +++ b/docker/solaris/solaris/vector/__init__.py @@ -0,0 +1 @@ +from . import graph, mask, polygon diff --git a/docker/solaris/solaris/vector/graph.py b/docker/solaris/solaris/vector/graph.py new file mode 100644 index 00000000..051f1a26 --- /dev/null +++ b/docker/solaris/solaris/vector/graph.py @@ -0,0 +1,584 @@ +import os +import numpy as np +import geopandas as gpd +from ..utils.geo import get_subgraph +import shapely +from shapely.geometry import Point, LineString +import networkx as nx +import rasterio as rio +import fiona +import pickle +from multiprocessing import Pool + + +class Node(object): + """An object to hold node attributes. + + Attributes + ---------- + idx : int + The numerical index of the node. Used as a unique identifier + when the nodes are added to the graph. + x : `int` or `float` + Numeric x location of the node, in either a geographic CRS or in pixel + coordinates. + y : `int` or `float` + Numeric y location of the node, in either a geographic CRS or in pixel + coordinates. + + """ + + def __init__(self, idx, x, y): + self.idx = idx + self.x = x + self.y = y + + def __repr__(self): + return 'Node {} at ({}, {})'.format(self.idx, self.x, self.y) + + +class Edge(object): + """An object to hold edge attributes. + + Attributes + ---------- + nodes : 2-`tuple` of :class:`Node` s + :class:`Node` instances connected by the edge. + weight : int or float + The weight of the edge. + + """ + + def __init__(self, nodes, edge_weight=None): + self.nodes = nodes + self.weight = edge_weight + + def __repr__(self): + return 'Edge between {} and {} with weight {}'.format(self.nodes[0], + self.nodes[1], + self.weight) + + def set_edge_weight(self, normalize_factor=None, inverse=False): + """Get the edge weight based on Euclidean distance between nodes. + + Note + ---- + This method does not account for spherical deformation (i.e. does not + use the Haversine equation). It is a simple linear distance. + + Arguments + --------- + normalize_factor : `int` or `float`, optional + a number to multiply (or divide, if + ``inverse=True``) the Euclidean distance by. Defaults to ``None`` + (no normalization) + inverse : bool, optional + if ``True``, the Euclidean distance weight will be divided by + ``normalize_factor`` instead of multiplied by it. + """ + weight = np.linalg.norm( + np.array((self.nodes[0].x, self.nodes[0].y)) - + np.array((self.nodes[1].x, self.nodes[1].y))) + + if normalize_factor is not None: + if inverse: + weight = weight/normalize_factor + else: + weight = weight*normalize_factor + self.weight = weight + + def get_node_idxs(self): + """Return the Node.idx for the nodes in the edge.""" + return (self.nodes[0].idx, self.nodes[1].idx) + + +class Path(object): + """An object to hold :class:`Edge` s with common properties. + + Attributes + ---------- + edges : `list` of :class:`Edge` s + A `list` of :class:`Edge` s + properties : dict + A dictionary of property: value pairs that provide relevant metadata + about edges along the path (e.g. road type, speed limit, etc.) + + """ + + def __init__(self, edges=None, properties=None): + self.edges = edges + if properties is None: + properties = {} + self.properties = properties + + def __repr__(self): + return 'Path including {}'.format([e for e in self.edges]) + + def add_edge(self, edge): + """Add an edge to the path.""" + self.edges.append(edge) + + def set_edge_weights(self, data_key=None, inverse=False, overwrite=True): + """Calculate edge weights for all edges in the Path.""" + for edge in self.edges: + if not overwrite and edge.weight is not None: + continue + if data_key is not None: + edge.set_edge_weight( + normalize_factor=self.properties[data_key], + inverse=inverse) + else: + edge.set_edge_weight() + + def add_data(self, property, value): + """Add a property: value pair to the Path.properties attribute.""" + self.properties[property] = value + + def __iter__(self): + """Iterate through edges in the path.""" + yield from self.edges + + +def geojson_to_graph(geojson, graph_name=None, retain_all=True, + valid_road_types=None, road_type_field='type', edge_idx=0, + first_node_idx=0, weight_norm_field=None, inverse=False, + workers=1, verbose=False, output_path=None): + """Convert a geojson of path strings to a network graph. + + Arguments + --------- + geojson : str + Path to a geojson file (or any other OGR-compatible vector file) to + load network edges and nodes from. + graph_name : str, optional + Name of the graph. If not provided, graph will be named ``'unnamed'`` . + retain_all : bool, optional + If ``True`` , the entire graph will be returned even if some parts are + not connected. Defaults to ``True``. + valid_road_types : :class:`list` of :class:`int` s, optional + The road types to permit in the graph. If not provided, it's assumed + that all road types are permitted. The possible values are integers + ``1``-``7``, which map as follows:: + + 1: Motorway + 2: Primary + 3: Secondary + 4: Tertiary + 5: Residential + 6: Unclassified + 7: Cart track + + road_type_field : str, optional + The name of the property in the vector data that delineates road type. + Defaults to ``'type'`` . + edge_idx : int, optional + The first index to use for an edge. This can be set to a higher value + so that a graph's edge indices don't overlap with existing values in + another graph. + first_node_idx : int, optional + The first index to use for a node. This can be set to a higher value + so that a graph's node indices don't overlap with existing values in + another graph. + weight_norm_field : str, optional + The name of a field in `geojson` to pass to argument ``data_key`` in + :func:`Path.set_edge_weights`. Defaults to ``None``, in which case + no weighting is performed (weights calculated solely using Euclidean + distance.) + workers : int, optional + Number of parallel processes to run for parallelization. Defaults to 1. + Should not be greater than the number of CPUs available. + verbose : bool, optional + Verbose print output. Defaults to ``False`` . + output_path : str, optional + Path to a pickle file to save the output graph to. Nothing will be + saved to disk if not provided. + + Returns + ------- + G : :class:`networkx.MultiDiGraph` + A :class:`networkx.MultiDiGraph` containing all of the nodes and edges + from the geojson (or only the largest connected component if + `retain_all` = ``False``). Edge lengths are weighted based on + geographic distance. + + """ + with fiona.open(geojson, 'r') as f: + crs = f.crs + f.close() + # due to an annoying feature of loading these graphs, the numeric road + # type identifiers are presented as string versions. we therefore reformat + # the valid_road_types list as strings. + if valid_road_types is not None: + valid_road_types = [str(i) for i in valid_road_types] + + # create the graph as a MultiGraph and set the original CRS to EPSG 4326 + + # extract nodes and paths + nodes, paths = get_nodes_paths(geojson, + valid_road_types=valid_road_types, + first_node_idx=first_node_idx, + road_type_field=road_type_field, + workers=workers, verbose=verbose) + # nodes is a dict of node_idx: node_params (e.g. location, metadata) + # pairs. + # paths is a dict of path dicts. the path key is the path_idx. + # each path dict has a list of node_idxs as well as properties metadata. + + # initialize the graph object + G = nx.MultiDiGraph(name=graph_name, crs=crs) + if not nodes: # if there are no nodes in the graph + return G + if verbose: + print("nodes:", nodes) + print("paths:", paths) + # add each osm node to the graph + for node in nodes: + G.add_node(node.idx, **{'x': node.x, 'y': node.y}) + # add each path to the graph + for path in paths: + # calculate edge length using euclidean distance and a weighting term + path.set_edge_weights(data_key=weight_norm_field, inverse=inverse) + edges = [(*[node.idx for node in edge.nodes], + edge.weight) for edge in path] + if verbose: + print(edges) + G.add_weighted_edges_from(edges) + if not retain_all: + # keep only largest connected component of graph unless retain_all + # code modified from osmnx.core.get_largest_component & induce_subgraph + largest_cc = max(nx.weakly_connected_components(G), key=len) + G = get_subgraph(G, largest_cc) + + if output_path: + with open(output_path, 'wb') as f: + pickle.dump(G, f) + f.close() + + return G + + +def get_nodes_paths(vector_file, first_node_idx=0, node_gdf=gpd.GeoDataFrame(), + valid_road_types=None, road_type_field='type', workers=1, + verbose=False): + """ + Extract nodes and paths from a vector file. + + Arguments + --------- + vector_file : str + Path to an OGR-compatible vector file containing line segments (e.g., + JSON response from from the Overpass API, or a SpaceNet GeoJSON). + first_path_idx : int, optional + The first index to use for a path. This can be set to a higher value + so that a graph's path indices don't overlap with existing values in + another graph. + first_node_idx : int, optional + The first index to use for a node. This can be set to a higher value + so that a graph's node indices don't overlap with existing values in + another graph. + node_gdf : :class:`geopandas.GeoDataFrame` , optional + A :class:`geopandas.GeoDataFrame` containing nodes to add to the graph. + New nodes will be added to this object incrementally during the + function call. + valid_road_types : :class:`list` of :class:`int` s, optional + The road types to permit in the graph. If not provided, it's assumed + that all road types are permitted. The possible values are integers + ``1``-``7``, which map as follows:: + + 1: Motorway + 2: Primary + 3: Secondary + 4: Tertiary + 5: Residential + 6: Unclassified + 7: Cart track + + road_type_field : str, optional + The name of the attribute containing road type information in + `vector_file`. Defaults to ``'type'``. + workers : int, optional + Number of worker processes to use for parallelization. Defaults to 1. + Should not exceed the number of CPUs available. + verbose : bool, optional + Verbose print output. Defaults to ``False``. + + Returns + ------- + nodes, paths : `tuple` of `dict` s + nodes : list + A `list` of :class:`Node` s to be added to the graph. + paths : list + A list of :class:`Path` s containing the :class:`Edge` s and + :class:`Node` s to be added to the graph. + + """ + if valid_road_types is None: + valid_road_types = ['1', '2', '3', '4', '5', '6', '7'] + + with fiona.open(vector_file, 'r') as source: + + with Pool(processes=workers) as pool: + node_list = pool.map(_get_all_nodes, source, + chunksize=10) + pool.close() + source.close() + + # convert to geoseries and drop duplicates (have to flatten first) + node_series = gpd.GeoSeries([i for sublist in node_list for i in sublist]) + # NOTE: It is ESSENTIAL to use keep='last' in the line below; otherwise, it + # misses a duplicate if it includes the first element of the series. + node_series = node_series.drop_duplicates(keep='last') + node_series = node_series.reset_index(drop=True) + node_series.name = 'geometry' + node_series.index.name = 'node_idx' + node_gdf = gpd.GeoDataFrame(node_series.reset_index()) + node_gdf['node'] = node_gdf.apply( + lambda p: Node(p['node_idx'], p['geometry'].x, p['geometry'].y), + axis=1) + + # create another parallelized operation to iterate through edges + # _init_worker passes the node_series to every process in the pool + with fiona.open(vector_file, 'r') as source: + with Pool( + processes=workers, initializer=_init_worker, + initargs=(node_gdf, valid_road_types, road_type_field) + ) as pool: + zipped_edges_properties = pool.map(parallel_linestring_to_path, + source, chunksize=10) + pool.close() + source.close() + + nodes = node_gdf['node'].tolist() + paths = [] + # it would've been better to do this within the multiprocessing pool but + # it's REALLY hard to share objects in memory across processes without + # copies being made (and therefore nodes being duplicated) + for edges, properties in zipped_edges_properties: + path = Path( + edges=[Edge((nodes[edge[0]], nodes[edge[1]])) for edge in edges], + properties=properties + ) + paths.append(path) + return nodes, paths + + +def parallel_linestring_to_path(feature): + """Read in a feature line from a fiona-opened shapefile and get the edges. + + Arguments + --------- + feature : dict + An item from a :class:`fiona.open` iterable with the key ``'geometry'`` + containing :class:`shapely.geometry.line.LineString` s or + :class:`shapely.geometry.line.MultiLineString` s. + + Returns + ------- + A list of :class:`Path` s containing all edges in the LineString or + MultiLineString. + + Notes + ----- + This function depends on ``node_series`` and ``valid_road_types``, which + are passed by an initializer as items in ``var_dict``. + + """ + + properties = feature['properties'] + # TODO: create more adjustable filter + if var_dict['road_type_field'] in properties: + road_type = properties[var_dict['road_type_field']] + elif 'highway' in properties: + road_type = properties['highway'] + elif 'road_type' in properties: + road_type = properties['road_type'] + else: + road_type = 'None' + + geom = feature['geometry'] + if geom['type'] == 'LineString' or \ + geom['type'] == 'MultiLineString': + if road_type not in var_dict['valid_road_types'] or \ + 'LINESTRING EMPTY' in properties.values(): + return + + if geom['type'] == 'LineString': + linestring = shapely.geometry.shape(geom) + edges = linestring_to_edges(linestring, var_dict['node_gdf']) + + elif geom['type'] == 'MultiLineString': + # do the same thing as above, but do it for each piece + edges = [] + for linestring in shapely.geometry.shape(geom): + edge_set, node_idx, node_gdf = linestring_to_edges( + linestring, var_dict['node_gdf']) + edges.extend(edge_set) + + return edges, properties + + +def linestring_to_edges(linestring, node_gdf): + """Collect nodes in a linestring and add them to an edge. + + Arguments + --------- + linestring : :class:`shapely.geometry.LineString` + A :class:`shapely.geometry.LineString` object to extract nodes and + edges from. + node_series : :class:`geopandas.GeoSeries` + A :class:`geopandas.GeoSeries` containing a + :class:`shapely.geometry.point.Point` for every node to be added to the + graph. + + Returns + ------- + edges : list + A list of :class:`Edge` s from ``linestring``. + + """ + edges = [] + nodes = [] + + for point in linestring.coords: + point_shp = shapely.geometry.shape(Point(point)) + nodes.append( + node_gdf.node_idx[node_gdf.distance(point_shp) == 0.0].values[0] + ) + if len(nodes) > 1: + edges.append(nodes[-2:]) + + return edges + + +def graph_to_geojson(G, output_path, encoding='utf-8', overwrite=False, + verbose=False): + """ + Save graph to two geojsons: one containing nodes, the other edges. + Arguments + --------- + G : :class:`networkx.MultiDiGraph` + A graph object to save to geojson files. + output_path : str + Path to save the geojsons to. ``'_nodes.geojson'`` and + ``'_edges.geojson'`` will be appended to ``output_path`` (after + stripping the extension). + encoding : str, optional + The character encoding for the saved files. + overwrite : bool, optional + Should files at ``output_path`` be overwritten? Defaults to no + (``False``). + verbose : bool, optional + Switch to print relevant values. Defaults to no (``False``). + + Notes + ----- + This function is based on ``osmnx.save_load.save_graph_shapefile``, with + tweaks to make it work with our graph objects. It will save two geojsons: + a file containing all of the nodes and a file containing all of the edges. + When writing to geojson, must convert the coordinate reference system + (crs) to string if it's a dict, otherwise no crs will be appended to the + geojson. + + Returns + ------- + None + """ + + # convert directed graph G to an undirected graph for saving as a shapefile + G_to_save = G.copy().to_undirected() + # create GeoDataFrame containing all of the nodes + nodes, data = zip(*G_to_save.nodes(data=True)) + gdf_nodes = gpd.GeoDataFrame(list(data), index=nodes) + + # get coordinate reference system + g_crs = G_to_save.graph['crs'] + if type(g_crs) == dict: + # convert from dict + g_crs = rio.crs.CRS.from_dict(g_crs) + gdf_nodes.crs = g_crs + if verbose: + print("crs:", g_crs) + + gdf_nodes['geometry'] = gdf_nodes.apply( + lambda row: Point(row['x'], row['y']), axis=1 + ) + gdf_nodes = gdf_nodes.drop(['x', 'y'], axis=1) + # gdf_nodes['node_idx'] = gdf_nodes['node_idx'].astype(np.int32) + + # # make everything but geometry column a string + # for col in [c for c in gdf_nodes.columns if not c == 'geometry']: + # gdf_nodes[col] = gdf_nodes[col].fillna('').map(make_str) + + # create GeoDataFrame containing all of the edges + edges = [] + for u, v, key, data in G_to_save.edges(keys=True, data=True): + edge = {'key': key} + for attr_key in data: + edge[attr_key] = data[attr_key] + if 'geometry' not in data: + point_u = Point((G_to_save.nodes[u]['x'], G_to_save.nodes[u]['y'])) + point_v = Point((G_to_save.nodes[v]['x'], G_to_save.nodes[v]['y'])) + edge['geometry'] = LineString([point_u, point_v]) + edges.append(edge) + + gdf_edges = gpd.GeoDataFrame(edges) + gdf_edges.crs = g_crs + + for col in [c for c in gdf_nodes.columns if c != 'geometry']: + gdf_nodes[col] = gdf_nodes[col].fillna('').apply(str) + for col in [c for c in gdf_edges.columns if c != 'geometry']: + gdf_edges[col] = gdf_edges[col].fillna('').apply(str) + + # make directory structure + if not os.path.exists(os.path.split(output_path)[0]): + os.makedirs(os.path.split(output_path)[0]) + + edges_path = os.path.splitext(output_path)[0] + '_edges.geojson' + nodes_path = os.path.splitext(output_path)[0] + '_nodes.geojson' + if overwrite: + if os.path.exists(edges_path): + os.remove(edges_path) + if os.path.exists(nodes_path): + os.remove(nodes_path) + + gdf_edges.to_file(edges_path, encoding=encoding, driver='GeoJSON') + gdf_nodes.to_file(nodes_path, encoding=encoding, driver='GeoJSON') + + +def _get_all_nodes(feature): + """Create a list of node geometries from a geojson of (multi)linestrings. + + Note + ---- + This function is intended to be used with pool.imap_unordered for + parallelization. + + Returns + ------- + A list of :class:`shapely.geometry.Point` instances. DUPLICATES CAN EXIST. + """ + points = [] + geom = feature['geometry'] + if geom['type'] == 'LineString': + linestring = shapely.geometry.shape(geom) + points.extend(_get_linestring_points(linestring)) + elif geom['type'] == 'MultiLineString': + for linestring in shapely.geometry.shape(geom): + points.extend(_get_linestring_points(linestring)) + + return points + + +def _get_linestring_points(linestring): + points = [] + for point in linestring.coords: + points.append(shapely.geometry.shape(Point(point))) + return points + + +def _init_worker(node_gdf, valid_road_types, road_type_field): + the_dict = {'node_gdf': node_gdf, + 'valid_road_types': valid_road_types, + 'road_type_field': road_type_field} + global var_dict + var_dict = the_dict diff --git a/docker/solaris/solaris/vector/mask.py b/docker/solaris/solaris/vector/mask.py new file mode 100644 index 00000000..b7f3f609 --- /dev/null +++ b/docker/solaris/solaris/vector/mask.py @@ -0,0 +1,1032 @@ +from ..utils.core import _check_df_load, _check_geom, _check_crs +from ..utils.core import _check_skimage_im_load, _check_rasterio_im_load +from ..utils.geo import gdf_get_projection_unit, reproject +from ..utils.geo import geometries_internal_intersection +from ..utils.tile import save_empty_geojson +from .polygon import georegister_px_df, geojson_to_px_gdf, affine_transform_gdf +import numpy as np +from shapely.geometry import shape +from shapely.geometry import Polygon +import geopandas as gpd +import pandas as pd +import rasterio +from rasterio import features +from affine import Affine +from skimage.morphology import square, erosion, dilation +import os +from tqdm.auto import tqdm + +def df_to_px_mask(df, channels=['footprint'], out_file=None, reference_im=None, + geom_col='geometry', do_transform=None, affine_obj=None, + shape=(900, 900), out_type='int', burn_value=255, **kwargs): + """Convert a dataframe of geometries to a pixel mask. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If the + geometries in `df` are not in pixel coordinates, then `affine` or + `reference_im` must be passed to provide the transformation to convert. + channels : list, optional + The mask channels to generate. There are three values that this can + contain: + + - ``"footprint"``: Create a full footprint mask, with 0s at pixels + that don't fall within geometries and `burn_value` at pixels that + do. + - ``"boundary"``: Create a mask with geometries outlined. Use + `boundary_width` to set how thick the boundary will be drawn. + - ``"contact"``: Create a mask with regions between >= 2 closely + juxtaposed geometries labeled. Use `contact_spacing` to set the + maximum spacing between polygons to be labeled. + + Each channel correspond to its own `shape` plane in the output. + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + do_transform : bool, optional + Should the values in `df` be transformed from geospatial coordinates + to pixel coordinates? Defaults to ``None``, in which case the function + attempts to infer whether or not a transformation is required based on + the presence or absence of a CRS in `df`. If ``True``, either + `reference_im` or `affine_obj` must be provided as a source for the + the required affine transformation matrix. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert from geo coordinates to pixel + space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + shape : tuple, optional + An ``(x_size, y_size)`` tuple defining the pixel extent of the output + mask. Ignored if `reference_im` is provided. + burn_value : `int` or `float` + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. + kwargs + Additional arguments to pass to `boundary_mask` or `contact_mask`. See + those functions for requirements. + + Returns + ------- + mask : :class:`numpy.array` + A pixel mask with 0s for non-object pixels and `burn_value` at object + pixels. `mask` dtype will coincide with `burn_value`. Shape will be + ``(shape[0], shape[1], len(channels))``, with channels ordered per the + provided `channels` `list`. + + """ + if isinstance(channels, str): # e.g. if "contact", not ["contact"] + channels = [channels] + + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + + mask_dict = {} + if 'footprint' in channels: + mask_dict['footprint'] = footprint_mask( + df=df, reference_im=reference_im, geom_col=geom_col, + do_transform=do_transform, affine_obj=affine_obj, shape=shape, + out_type=out_type, burn_value=burn_value + ) + if 'boundary' in channels: + mask_dict['boundary'] = boundary_mask( + footprint_msk=mask_dict.get('footprint', None), + reference_im=reference_im, geom_col=geom_col, + boundary_width=kwargs.get('boundary_width', 3), + boundary_type=kwargs.get('boundary_type', 'inner'), + burn_value=burn_value, df=df, affine_obj=affine_obj, + shape=shape, out_type=out_type + ) + if 'contact' in channels: + mask_dict['contact'] = contact_mask( + df=df, reference_im=reference_im, geom_col=geom_col, + affine_obj=affine_obj, shape=shape, out_type=out_type, + contact_spacing=kwargs.get('contact_spacing', 10), + burn_value=burn_value, + meters=kwargs.get('meters', False) + ) + + output_arr = np.stack([mask_dict[c] for c in channels], axis=-1) + + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + if out_file: + meta = reference_im.meta.copy() + meta.update(count=output_arr.shape[-1]) + meta.update(dtype='uint8') + with rasterio.open(out_file, 'w', **meta) as dst: + # I hate band indexing. + for c in range(1, 1 + output_arr.shape[-1]): + dst.write(output_arr[:, :, c-1], indexes=c) + + return output_arr + + +def footprint_mask(df, out_file=None, reference_im=None, geom_col='geometry', + do_transform=None, affine_obj=None, shape=(900, 900), + out_type='int', burn_value=255, burn_field=None): + """Convert a dataframe of geometries to a pixel mask. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If the + geometries in `df` are not in pixel coordinates, then `affine` or + `reference_im` must be passed to provide the transformation to convert. + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + do_transform : bool, optional + Should the values in `df` be transformed from geospatial coordinates + to pixel coordinates? Defaults to ``None``, in which case the function + attempts to infer whether or not a transformation is required based on + the presence or absence of a CRS in `df`. If ``True``, either + `reference_im` or `affine_obj` must be provided as a source for the + the required affine transformation matrix. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert from geo coordinates to pixel + space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + shape : tuple, optional + An ``(x_size, y_size)`` tuple defining the pixel extent of the output + mask. Ignored if `reference_im` is provided. + out_type : 'float' or 'int' + burn_value : `int` or `float`, optional + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. Ignored if `burn_field` is provided. + burn_field : str, optional + Name of a column in `df` that provides values for `burn_value` for each + independent object. If provided, `burn_value` is ignored. + + Returns + ------- + mask : :class:`numpy.array` + A pixel mask with 0s for non-object pixels and `burn_value` at object + pixels. `mask` dtype will coincide with `burn_value`. + + """ + # start with required checks and pre-population of values + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + df = _check_df_load(df) + + if len(df) == 0 and not out_file: + return np.zeros(shape=shape, dtype='uint8') + + if do_transform is None: + # determine whether or not transform should be done + do_transform = _check_do_transform(df, reference_im, affine_obj) + + df[geom_col] = df[geom_col].apply(_check_geom) # load in geoms if wkt + if not do_transform: + affine_obj = Affine(1, 0, 0, 0, 1, 0) # identity transform + + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + shape = reference_im.shape + if do_transform: + affine_obj = reference_im.transform + + # extract geometries and pair them with burn values + if burn_field: + if out_type == 'int': + feature_list = list(zip(df[geom_col], + df[burn_field].astype('uint8'))) + else: + feature_list = list(zip(df[geom_col], + df[burn_field].astype('float32'))) + else: + feature_list = list(zip(df[geom_col], [burn_value]*len(df))) + + if len(df) > 0: + output_arr = features.rasterize(shapes=feature_list, out_shape=shape, + transform=affine_obj) + else: + output_arr = np.zeros(shape=shape, dtype='uint8') + if out_file: + meta = reference_im.meta.copy() + meta.update(count=1) + if out_type == 'int': + meta.update(dtype='uint8') + meta.update(nodata=0) + with rasterio.open(out_file, 'w', **meta) as dst: + dst.write(output_arr, indexes=1) + + return output_arr + + +def boundary_mask(footprint_msk=None, out_file=None, reference_im=None, + boundary_width=3, boundary_type='inner', burn_value=255, + **kwargs): + """Convert a dataframe of geometries to a pixel mask. + + Note + ---- + This function requires creation of a footprint mask before it can operate; + therefore, if there is no footprint mask already present, it will create + one. In that case, additional arguments for :func:`footprint_mask` (e.g. + ``df``) must be passed. + + By default, this function draws boundaries *within* the edges of objects. + To change this behavior, use the `boundary_type` argument. + + Arguments + --------- + footprint_msk : :class:`numpy.array`, optional + A filled in footprint mask created using :func:`footprint_mask`. If not + provided, one will be made by calling :func:`footprint_mask` before + creating the boundary mask, and the required arguments for that + function must be provided as kwargs. + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored + boundary_width : int, optional + The width of the boundary to be created **in pixels.** Defaults to 3. + boundary_type : ``"inner"`` or ``"outer"``, optional + Where to draw the boundaries: within the object (``"inner"``) or + outside of it (``"outer"``). Defaults to ``"inner"``. + burn_value : `int`, optional + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. Ignored if `burn_field` is provided. + **kwargs : optional + Additional arguments to pass to :func:`footprint_mask` if one needs to + be created. + + Returns + ------- + boundary_mask : :class:`numpy.array` + A pixel mask with 0s for non-object pixels and the same value as the + footprint mask `burn_value` for the boundaries of each object. + + """ + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + # need to have a footprint mask for this function, so make it if not given + if footprint_msk is None: + footprint_msk = footprint_mask(reference_im=reference_im, + burn_value=burn_value, **kwargs) + + # perform dilation or erosion of `footprint_mask` to get the boundary + strel = square(boundary_width) + if boundary_type == 'outer': + boundary_mask = dilation(footprint_msk, strel) + elif boundary_type == 'inner': + boundary_mask = erosion(footprint_msk, strel) + # use xor operator between border and footprint mask to get _just_ boundary + boundary_mask = boundary_mask ^ footprint_msk + # scale the `True` values to burn_value and return + boundary_mask = boundary_mask > 0 # need to binarize to get burn val right + output_arr = boundary_mask.astype('uint8')*burn_value + + if out_file: + meta = reference_im.meta.copy() + meta.update(count=1) + meta.update(dtype='uint8') + with rasterio.open(out_file, 'w', **meta) as dst: + dst.write(output_arr, indexes=1) + + return output_arr + + +def contact_mask(df, contact_spacing=10, meters=False, out_file=None, + reference_im=None, geom_col='geometry', + do_transform=None, affine_obj=None, shape=(900, 900), + out_type='int', burn_value=255): + """Create a pixel mask labeling closely juxtaposed objects. + + Notes + ----- + This function identifies pixels in an image that do not correspond to + objects, but fall within `contact_spacing` of >1 labeled object. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If the + geometries in `df` are not in pixel coordinates, then `affine` or + `reference_im` must be passed to provide the transformation to convert. + contact_spacing : `int` or `float`, optional + The desired maximum distance between adjacent polygons to be labeled + as contact. Will be in pixel units unless ``meters=True`` is provided. + meters : bool, optional + Should `width` be defined in units of meters? Defaults to no + (``False``). If ``True`` and `df` is not in a CRS with metric units, + the function will attempt to transform to the relevant CRS using + ``df.to_crs()`` (if `df` is a :class:`geopandas.GeoDataFrame`) or + using the data provided in `reference_im` (if not). + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + do_transform : bool, optional + Should the values in `df` be transformed from geospatial coordinates + to pixel coordinates? Defaults to ``None``, in which case the function + attempts to infer whether or not a transformation is required based on + the presence or absence of a CRS in `df`. If ``True``, either + `reference_im` or `affine_obj` must be provided as a source for the + the required affine transformation matrix. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert from geo coordinates to pixel + space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + shape : tuple, optional + An ``(x_size, y_size)`` tuple defining the pixel extent of the output + mask. Ignored if `reference_im` is provided. + out_type : 'float' or 'int' + burn_value : `int` or `float`, optional + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. + + Returns + ------- + output_arr : :class:`numpy.array` + A pixel mask with `burn_value` at contact points between polygons. + """ + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + df = _check_df_load(df) + + if len(df) == 0 and not out_file: + return np.zeros(shape=shape, dtype='uint8') + + if do_transform is None: + # determine whether or not transform should be done + do_transform = _check_do_transform(df, reference_im, affine_obj) + + df[geom_col] = df[geom_col].apply(_check_geom) # load in geoms if wkt + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + buffered_geoms = buffer_df_geoms(df, contact_spacing/2., meters=meters, + reference_im=reference_im, + geom_col=geom_col, affine_obj=affine_obj) + buffered_geoms = buffered_geoms[geom_col] + # create a single multipolygon that covers all of the intersections + if len(df) > 0: + intersect_poly = geometries_internal_intersection(buffered_geoms) + else: + intersect_poly = Polygon() + + # handle case where there's no intersection + if intersect_poly.is_empty: + output_arr = np.zeros(shape=shape, dtype='uint8') + + else: + # create a df containing the intersections to make footprints from + df_for_footprint = pd.DataFrame({'shape_name': ['overlap'], + 'geometry': [intersect_poly]}) + # catch bowties + df_for_footprint['geometry'] = df_for_footprint['geometry'].apply( + lambda x: x.buffer(0) + ) + # use `footprint_mask` to create the overlap mask + contact_msk = footprint_mask( + df_for_footprint, reference_im=reference_im, geom_col='geometry', + do_transform=do_transform, affine_obj=affine_obj, shape=shape, + out_type=out_type, burn_value=burn_value + ) + footprint_msk = footprint_mask( + df, reference_im=reference_im, geom_col=geom_col, + do_transform=do_transform, affine_obj=affine_obj, shape=shape, + out_type=out_type, burn_value=burn_value + ) + contact_msk[footprint_msk > 0] = 0 + contact_msk = contact_msk > 0 + output_arr = contact_msk.astype('uint8')*burn_value + + if out_file: + meta = reference_im.meta.copy() + meta.update(count=1) + if out_type == 'int': + meta.update(dtype='uint8') + with rasterio.open(out_file, 'w', **meta) as dst: + dst.write(output_arr, indexes=1) + + return output_arr + + +def road_mask(df, width=4, meters=False, out_file=None, reference_im=None, + geom_col='geometry', do_transform=None, affine_obj=None, + shape=(900, 900), out_type='int', burn_value=255, + burn_field=None, min_background_value=None, verbose=False): + """Convert a dataframe of geometries to a pixel mask. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If the + geometries in `df` are not in pixel coordinates, then `affine` or + `reference_im` must be passed to provide the transformation to convert. + width : `float` or `int`, optional + The total width to make a road (i.e. twice x if using + road.buffer(x)). In pixel units unless `meters` is ``True``. + meters : bool, optional + Should `width` be defined in units of meters? Defaults to no + (``False``). If ``True`` and `df` is not in a CRS with metric units, + the function will attempt to transform to the relevant CRS using + ``df.to_crs()`` (if `df` is a :class:`geopandas.GeoDataFrame`) or + using the data provided in `reference_im` (if not). + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + do_transform : bool, optional + Should the values in `df` be transformed from geospatial coordinates + to pixel coordinates? Defaults to ``None``, in which case the function + attempts to infer whether or not a transformation is required based on + the presence or absence of a CRS in `df`. If ``True``, either + `reference_im` or `affine_obj` must be provided as a source for the + the required affine transformation matrix. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert from geo coordinates to pixel + space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + shape : tuple, optional + An ``(x_size, y_size)`` tuple defining the pixel extent of the output + mask. Ignored if `reference_im` is provided. + out_type : 'float' or 'int' + burn_value : `int` or `float`, optional + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. Ignored if `burn_field` is provided. + burn_field : str, optional + Name of a column in `df` that provides values for `burn_value` for each + independent object. If provided, `burn_value` is ignored. + min_background_val : int + Minimum value for mask background. Optional, ignore if ``None``. + Defaults to ``None``. + verbose : str, optional + Switch to print relevant values. Defaults to ``False``. + + Returns + ------- + mask : :class:`numpy.array` + A pixel mask with 0s for non-object pixels and `burn_value` at object + pixels. `mask` dtype will coincide with `burn_value`. + """ + + # start with required checks and pre-population of values + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + df = _check_df_load(df) + if do_transform is None: + # determine whether or not transform should be done + do_transform = _check_do_transform(df, reference_im, affine_obj) + df[geom_col] = df[geom_col].apply(_check_geom) # ensure WKTs are loaded + + buffered_df = buffer_df_geoms(df, width/2., meters=meters, + reference_im=reference_im, geom_col=geom_col, + affine_obj=affine_obj) + + if not do_transform: + affine_obj = Affine(1, 0, 0, 0, 1, 0) # identity transform + + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + shape = reference_im.shape + if do_transform: + affine_obj = reference_im.transform + + # extract geometries and pair them with burn values + if burn_field: + if out_type == 'int': + feature_list = list(zip(buffered_df[geom_col], + buffered_df[burn_field].astype('uint8'))) + else: + feature_list = list(zip(buffered_df[geom_col], + buffered_df[burn_field].astype('uint8'))) + else: + feature_list = list(zip(buffered_df[geom_col], + [burn_value] * len(buffered_df))) + + output_arr = features.rasterize(shapes=feature_list, out_shape=shape, + transform=affine_obj) + if min_background_value: + output_arr = np.clip(output_arr, min_background_value, + np.max(output_arr)) + + if out_file: + meta = reference_im.meta.copy() + meta.update(count=1) + if out_type == 'int': + meta.update(dtype='uint8') + with rasterio.open(out_file, 'w', **meta) as dst: + dst.write(output_arr, indexes=1) + + return output_arr + + +def buffer_df_geoms(df, buffer, meters=False, reference_im=None, + geom_col='geometry', affine_obj=None): + """Buffer geometries within a pd.DataFrame or gpd.GeoDataFrame. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If `df` + lacks a ``crs`` attribute (isn't a :class:`geopandas.GeoDataFrame` ) + and ``meters=True``, then `reference_im` must be provided for + georeferencing. + buffer : `int` or `float` + The amount to buffer the geometries in `df`. In pixel units unless + ``meters=True``. This corresponds to width/2 in mask creation + functions. + meters : bool, optional + Should buffers be in pixel units (default) or metric units (if `meters` + is ``True``)? + reference_im : `str` or :class:`rasterio.DatasetReader`, optional + The path to a reference image covering the same geographic extent as + the area labeled in `df`. Provided for georeferencing of pixel + coordinate geometries in `df` or conversion of georeferenced geometries + to pixel coordinates as needed. Required if `meters` is ``True`` and + `df` lacks a ``crs`` attribute. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert geoms in `df` from a geographic + crs to pixel space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + + Returns + ------- + buffered_df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` in the original coordinate reference system + with objects buffered per `buffer`. + + See Also + -------- + road_mask : Function to create road network masks. + contact_mask : Function to create masks of contact points between polygons. + """ + if reference_im is not None: + reference_im = _check_rasterio_im_load(reference_im) + + if hasattr(df, 'crs'): + orig_crs = _check_crs(df.crs) + else: + orig_crs = None # will represent pixel crs + + # Check if dataframe is in the appropriate units and reproject if not + if not meters: + if hasattr(df, 'crs') and reference_im is not None: + # if the df is georeferenced and a reference_im is provided, + # use reference_im to transform df to px coordinates + df_for_buffer = geojson_to_px_gdf(df.copy(), reference_im) + elif hasattr(df, 'crs') and reference_im is None: + df_for_buffer = affine_transform_gdf(df.copy(), + affine_obj=affine_obj) + else: # if it's already in px coordinates + df_for_buffer = df.copy() + + else: + # check if the df is in a metric crs + if hasattr(df, 'crs'): + if crs_is_metric(df): + df_for_buffer = df.copy() + else: + df_for_buffer = reproject(df.copy()) # defaults to UTM + else: + # assume df is in px coords - use reference_im to georegister + if reference_im is not None: + df_for_buffer = georegister_px_df(df.copy(), + im_path=reference_im) + else: + raise ValueError('If using `meters=True`, either `df` must be ' + 'a geopandas GeoDataFrame or `reference_im` ' + 'must be provided for georegistration.') + + df_for_buffer[geom_col] = df_for_buffer[geom_col].apply( + lambda x: x.buffer(buffer)) + + # return to original crs + if _check_crs(getattr(df_for_buffer, 'crs', None)) != orig_crs: + if orig_crs is not None and \ + getattr(df_for_buffer, 'crs', None) is not None: + buffered_df = df_for_buffer.to_crs(orig_crs.to_wkt()) + elif orig_crs is None: # but df_for_buffer has one: meters=True case + buffered_df = geojson_to_px_gdf(df_for_buffer, reference_im) + else: # orig_crs exists, but df_for_buffer doesn't have one + buffered_df = georegister_px_df(df_for_buffer, + im_path=reference_im, + affine_obj=affine_obj, + crs=orig_crs) + else: + buffered_df = df_for_buffer + + return buffered_df + + +def preds_to_binary(pred_arr, channel_scaling=None, bg_threshold=0): + """Convert a set of predictions from a neural net to a binary mask. + + Arguments + --------- + pred_arr : :class:`numpy.ndarray` + A set of predictions generated by a neural net (generally in ``float`` + dtype). This can be a 2D array or a 3D array, in which case it will + be convered to a 2D mask output with optional channel scaling (see + the `channel_scaling` argument). If a filename is provided instead of + an array, the image will be loaded using scikit-image. + channel_scaling : `list`-like of `float`s, optional + If `pred_arr` is a 3D array, this argument defines how each channel + will be combined to generate a binary output. channel_scaling should + be a `list`-like of length equal to the number of channels in + `pred_arr`. The following operation will be performed to convert the + multi-channel prediction to a 2D output :: + + sum(pred_arr[channel]*channel_scaling[channel]) + + If not provided, no scaling will be performend and channels will be + summed. + + bg_threshold : `int` or `float`, optional + The cutoff to set to distinguish between background and foreground + pixels in the final binary mask. Binarization takes place *after* + channel scaling and summation (if applicable). Defaults to 0. + + Returns + ------- + mask_arr : :class:`numpy.ndarray` + A 2D boolean ``numpy`` array with ``True`` for foreground pixels and + ``False`` for background. + """ + pred_arr = _check_skimage_im_load(pred_arr).copy() + + if len(pred_arr.shape) == 3: + if pred_arr.shape[0] < pred_arr.shape[-1]: + pred_arr = np.moveaxis(pred_arr, 0, -1) + if channel_scaling is None: # if scale values weren't provided + channel_scaling = np.ones(shape=(pred_arr.shape[-1]), + dtype='float') + pred_arr = np.sum(pred_arr*np.array(channel_scaling), axis=-1) + + mask_arr = (pred_arr > bg_threshold).astype('uint8') + + return mask_arr*255 + + +def mask_to_poly_geojson(pred_arr, channel_scaling=None, reference_im=None, + output_path=None, output_type='geojson', min_area=40, + bg_threshold=0, do_transform=None, simplify=False, + tolerance=0.5, **kwargs): + """Get polygons from an image mask. + + Arguments + --------- + pred_arr : :class:`numpy.ndarray` + A 2D array of integers. Multi-channel masks are not supported, and must + be simplified before passing to this function. Can also pass an image + file path here. + channel_scaling : :class:`list`-like, optional + If `pred_arr` is a 3D array, this argument defines how each channel + will be combined to generate a binary output. channel_scaling should + be a `list`-like of length equal to the number of channels in + `pred_arr`. The following operation will be performed to convert the + multi-channel prediction to a 2D output :: + + sum(pred_arr[channel]*channel_scaling[channel]) + + If not provided, no scaling will be performend and channels will be + summed. + reference_im : str, optional + The path to a reference geotiff to use for georeferencing the polygons + in the mask. Required if saving to a GeoJSON (see the ``output_type`` + argument), otherwise only required if ``do_transform=True``. + output_path : str, optional + Path to save the output file to. If not provided, no file is saved. + output_type : ``'csv'`` or ``'geojson'``, optional + If ``output_path`` is provided, this argument defines what type of file + will be generated - a CSV (``output_type='csv'``) or a geojson + (``output_type='geojson'``). + min_area : int, optional + The minimum area of a polygon to retain. Filtering is done AFTER + any coordinate transformation, and therefore will be in destination + units. + bg_threshold : int, optional + The cutoff in ``mask_arr`` that denotes background (non-object). + Defaults to ``0``. + simplify : bool, optional + If ``True``, will use the Douglas-Peucker algorithm to simplify edges, + saving memory and processing time later. Defaults to ``False``. + tolerance : float, optional + The tolerance value to use for simplification with the Douglas-Peucker + algorithm. Defaults to ``0.5``. Only has an effect if + ``simplify=True``. + + Returns + ------- + gdf : :class:`geopandas.GeoDataFrame` + A GeoDataFrame of polygons. + + """ + + mask_arr = preds_to_binary(pred_arr, channel_scaling, bg_threshold) + + if do_transform and reference_im is None: + raise ValueError( + 'Coordinate transformation requires a reference image.') + + if do_transform: + with rasterio.open(reference_im) as ref: + transform = ref.transform + crs = ref.crs + ref.close() + else: + transform = Affine(1, 0, 0, 0, 1, 0) # identity transform + crs = rasterio.crs.CRS() + + mask = mask_arr > bg_threshold + mask = mask.astype('uint8') + + polygon_generator = features.shapes(mask_arr, + transform=transform, + mask=mask) + polygons = [] + values = [] # pixel values for the polygon in mask_arr + for polygon, value in polygon_generator: + p = shape(polygon).buffer(0.0) + if p.area >= min_area: + polygons.append(shape(polygon).buffer(0.0)) + values.append(value) + + polygon_gdf = gpd.GeoDataFrame({'geometry': polygons, 'value': values}, + crs=crs.to_wkt()) + if simplify: + polygon_gdf['geometry'] = polygon_gdf['geometry'].apply( + lambda x: x.simplify(tolerance=tolerance) + ) + # save output files + if output_path is not None: + if output_type.lower() == 'geojson': + if len(polygon_gdf) > 0: + polygon_gdf.to_file(output_path, driver='GeoJSON') + else: + save_empty_geojson(output_path, polygon_gdf.crs.to_epsg()) + elif output_type.lower() == 'csv': + polygon_gdf.to_csv(output_path, index=False) + + return polygon_gdf + + +def crs_is_metric(gdf): + """Check if a GeoDataFrame's CRS is in metric units.""" + units = str(gdf_get_projection_unit(gdf)).strip().lower() + if units in ['"meter"', '"metre"', "'meter'", "'meter'", + 'meter', 'metre']: + return True + else: + return False + + +def _check_do_transform(df, reference_im, affine_obj): + """Check whether or not a transformation should be performed.""" + try: + crs = getattr(df, 'crs') + except AttributeError: + return False # if it doesn't have a CRS attribute + + if not crs: + return False # return False for do_transform if crs is falsey + elif crs and (reference_im is not None or affine_obj is not None): + # if the input has a CRS and another obj was provided for xforming + return True + + +def instance_mask(df, out_file=None, reference_im=None, geom_col='geometry', + do_transform=None, affine_obj=None, shape=(900, 900), + out_type='int', burn_value=255, burn_field=None, nodata_value=0): + """Convert a dataframe of geometries to a pixel mask. + + Arguments + --------- + df : :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` + A :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` instance + with a column containing geometries (identified by `geom_col`). If the + geometries in `df` are not in pixel coordinates, then `affine` or + `reference_im` must be passed to provide the transformation to convert. + out_file : str, optional + Path to an image file to save the output to. Must be compatible with + :class:`rasterio.DatasetReader`. If provided, a `reference_im` must be + provided (for metadata purposes). + reference_im : :class:`rasterio.DatasetReader` or `str`, optional + An image to extract necessary coordinate information from: the + affine transformation matrix, the image extent, etc. If provided, + `affine_obj` and `shape` are ignored. + geom_col : str, optional + The column containing geometries in `df`. Defaults to ``"geometry"``. + do_transform : bool, optional + Should the values in `df` be transformed from geospatial coordinates + to pixel coordinates? Defaults to ``None``, in which case the function + attempts to infer whether or not a transformation is required based on + the presence or absence of a CRS in `df`. If ``True``, either + `reference_im` or `affine_obj` must be provided as a source for the + the required affine transformation matrix. + affine_obj : `list` or :class:`affine.Affine`, optional + Affine transformation to use to convert from geo coordinates to pixel + space. Only provide this argument if `df` is a + :class:`geopandas.GeoDataFrame` with coordinates in a georeferenced + coordinate space. Ignored if `reference_im` is provided. + shape : tuple, optional + An ``(x_size, y_size)`` tuple defining the pixel extent of the output + mask. Ignored if `reference_im` is provided. + out_type : 'float' or 'int' + burn_value : `int` or `float`, optional + The value to use for labeling objects in the mask. Defaults to 255 (the + max value for ``uint8`` arrays). The mask array will be set to the same + dtype as `burn_value`. Ignored if `burn_field` is provided. + burn_field : str, optional + Name of a column in `df` that provides values for `burn_value` for each + independent object. If provided, `burn_value` is ignored. + nodata_value : `int` or `float`, optional + The value to use for nodata pixels in the mask. Defaults to 0 (the + min value for ``uint8`` arrays). Used if reference_im nodata value is a float. + Ignored if reference_im nodata value is an int or if reference_im is not used. + Take care when visualizing these masks, the nodata value may cause labels to not + be visualized if nodata values are automatically masked by the software. + + Returns + ------- + mask : :class:`numpy.array` + A pixel mask with 0s for non-object pixels and `burn_value` at object + pixels. `mask` dtype will coincide with `burn_value`. + + """ + # TODO: Refactor to remove some duplicated code here and in other mask fxns + + if out_file and not reference_im: + raise ValueError( + 'If saving output to file, `reference_im` must be provided.') + df = _check_df_load(df) + + if len(df) == 0: # for saving an empty mask. + reference_im = _check_rasterio_im_load(reference_im) + shape = reference_im.shape + return np.zeros(shape=shape, dtype='uint8') + + if do_transform is None: + # determine whether or not transform should be done + do_transform = _check_do_transform(df, reference_im, affine_obj) + + df[geom_col] = df[geom_col].apply(_check_geom) # load in geoms if wkt + if not do_transform: + affine_obj = Affine(1, 0, 0, 0, 1, 0) # identity transform + + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + shape = reference_im.shape + if do_transform: + affine_obj = reference_im.transform + + # extract geometries and pair them with burn values + + if burn_field: + if out_type == 'int': + feature_list = list(zip(df[geom_col], + df[burn_field].astype('uint8'))) + else: + feature_list = list(zip(df[geom_col], + df[burn_field].astype('float32'))) + else: + feature_list = list(zip(df[geom_col], [burn_value]*len(df))) + + if out_type == 'int': + output_arr = np.empty(shape=(shape[0], shape[1], + len(feature_list)), dtype='uint8') + else: + output_arr = np.empty(shape=(shape[0], shape[1], + len(feature_list)), dtype='float32') + # initialize the output array + + for idx, feat in enumerate(feature_list): + output_arr[:, :, idx] = features.rasterize([feat], out_shape=shape, + transform=affine_obj) + + if reference_im: + reference_im = _check_rasterio_im_load(reference_im) + try: + bad_data_mask = (reference_im.read() == reference_im.nodata).any(axis=0) # take logical and along all dims so that all pixxels not -9999 across bands + except AttributeError as ae: # raise another, more verbose AttributeError + raise AttributeError("A nodata value is not defined for the source image. Make sure the reference_im has a nodata value defined.") from ae + if len(bad_data_mask.shape) > 2: + bad_data_mask = np.dstack([bad_data_mask]*output_arr.shape[2]) + output_arr = np.where(bad_data_mask, 0, output_arr) # mask is broadcasted to filter labels where there are non-nan image values + + if out_file: + meta = reference_im.meta.copy() + meta.update(count=output_arr.shape[-1]) + if out_type == 'int': + meta.update(dtype='uint8') + if isinstance(meta['nodata'], float): + meta.update(nodata=nodata_value) + with rasterio.open(out_file, 'w', **meta) as dst: + for c in range(1, 1 + output_arr.shape[-1]): + dst.write(output_arr[:, :, c-1], indexes=c) + dst.close() + + return output_arr + +def geojsons_to_masks_and_fill_nodata(rtiler, vtiler, label_tile_dir, fill_value=0): + """ + Converts tiled vectors to raster labels and fills nodata values in raster and vector tiles. + + This function must be run after a raster tiler and vector tiler have already been initialized + and the `.tile()` method for each has been called to generate raster and vector tiles. + Geojson labels are first converted to rasterized masks, then the labels are set to 0 + where the reference image, the corresponding image tile, has nodata values. Then, nodata + areas in the image tile are filled in place with the fill_value. Only works for rasterizing + all geometries as a single category with a burn value of 1. See test_tiler_fill_nodata in + tests/test_tile/test_tile.py for an example. + + Args + ------- + rtiler : RasterTiler + The RasterTiler that has had it's `.tile()` method called. + vtiler : VectorTiler + The VectorTiler that has had it's `.tile()` method called. + label_tile_dir : str + The folder path to save rasterized labels. This is created if it doesn't already exist. + fill_value : str, optional + The value to use to fill nodata values in images. Defaults to 0. + + Returns + ------- + rasterized_label_paths : list + A list of the paths to the rasterized instance masks. + """ + rasterized_label_paths = [] + print("starting label mask generation") + if not os.path.exists(label_tile_dir): + os.mkdir(label_tile_dir) + for img_tile, geojson_tile in tqdm(zip(sorted(rtiler.tile_paths), sorted(vtiler.tile_paths))): + fid = os.path.basename(geojson_tile).split(".geojson")[0] + rasterized_label_path = os.path.join(label_tile_dir, fid + ".tif") + rasterized_label_paths.append(rasterized_label_path) + gdf = gpd.read_file(geojson_tile) + # gdf.crs = rtiler.raster_bounds_crs # add this because gdfs can't be saved with wkt crs + arr = instance_mask(gdf, out_file=rasterized_label_path, reference_im=img_tile, + geom_col='geometry', do_transform=None, + out_type='int', burn_value=1, burn_field=None) # this saves the file, unless it is empty in which case we deal with it below. + if not arr.any(): # in case no instances in a tile we save it with "empty" at the front of the basename + with rasterio.open(img_tile) as reference_im: + meta = reference_im.meta.copy() + reference_im.close() + meta.update(count=1) + meta.update(dtype='uint8') + if isinstance(meta['nodata'], float): + meta.update(nodata=0) + rasterized_label_path = os.path.join(label_tile_dir, "empty_" + fid + ".tif") + with rasterio.open(rasterized_label_path, 'w', **meta) as dst: + dst.write(np.expand_dims(arr, axis=0)) + dst.close() + rtiler.fill_all_nodata(nodata_fill=fill_value) + return rasterized_label_paths diff --git a/docker/solaris/solaris/vector/polygon.py b/docker/solaris/solaris/vector/polygon.py new file mode 100644 index 00000000..b00858fe --- /dev/null +++ b/docker/solaris/solaris/vector/polygon.py @@ -0,0 +1,432 @@ +import os +import shapely +from affine import Affine +import rasterio +from rasterio.warp import transform_bounds +from rasterio.crs import CRS +from ..utils.geo import list_to_affine, _reduce_geom_precision +from ..utils.core import _check_gdf_load, _check_crs, _check_rasterio_im_load +from ..raster.image import get_geo_transform +from shapely.geometry import box, Polygon +import pandas as pd +import geopandas as gpd +from rtree.core import RTreeError +import shutil + + +def convert_poly_coords(geom, raster_src=None, affine_obj=None, inverse=False, + precision=None): + """Georegister geometry objects currently in pixel coords or vice versa. + + Arguments + --------- + geom : :class:`shapely.geometry.shape` or str + A :class:`shapely.geometry.shape`, or WKT string-formatted geometry + object currently in pixel coordinates. + raster_src : str, optional + Path to a raster image with georeferencing data to apply to `geom`. + Alternatively, an opened :class:`rasterio.Band` object or + :class:`osgeo.gdal.Dataset` object can be provided. Required if not + using `affine_obj`. + affine_obj: list or :class:`affine.Affine` + An affine transformation to apply to `geom` in the form of an + ``[a, b, d, e, xoff, yoff]`` list or an :class:`affine.Affine` object. + Required if not using `raster_src`. + inverse : bool, optional + If true, will perform the inverse affine transformation, going from + geospatial coordinates to pixel coordinates. + precision : int, optional + Decimal precision for the polygon output. If not provided, rounding + is skipped. + + Returns + ------- + out_geom + A geometry in the same format as the input with its coordinate system + transformed to match the destination object. + """ + + if not raster_src and not affine_obj: + raise ValueError("Either raster_src or affine_obj must be provided.") + + if raster_src is not None: + affine_xform = get_geo_transform(raster_src) + else: + if isinstance(affine_obj, Affine): + affine_xform = affine_obj + else: + # assume it's a list in either gdal or "standard" order + # (list_to_affine checks which it is) + if len(affine_obj) == 9: # if it's straight from rasterio + affine_obj = affine_obj[0:6] + affine_xform = list_to_affine(affine_obj) + + if inverse: # geo->px transform + affine_xform = ~affine_xform + + if isinstance(geom, str): + # get the polygon out of the wkt string + g = shapely.wkt.loads(geom) + elif isinstance(geom, shapely.geometry.base.BaseGeometry): + g = geom + else: + raise TypeError('The provided geometry is not an accepted format. ' + 'This function can only accept WKT strings and ' + 'shapely geometries.') + + xformed_g = shapely.affinity.affine_transform(g, [affine_xform.a, + affine_xform.b, + affine_xform.d, + affine_xform.e, + affine_xform.xoff, + affine_xform.yoff]) + if isinstance(geom, str): + # restore to wkt string format + xformed_g = shapely.wkt.dumps(xformed_g) + if precision is not None: + xformed_g = _reduce_geom_precision(xformed_g, precision=precision) + + return xformed_g + + +def affine_transform_gdf(gdf, affine_obj, inverse=False, geom_col="geometry", + precision=None): + """Perform an affine transformation on a GeoDataFrame. + + Arguments + --------- + gdf : :class:`geopandas.GeoDataFrame`, :class:`pandas.DataFrame`, or `str` + A GeoDataFrame, pandas DataFrame with a ``"geometry"`` column (or a + different column containing geometries, identified by `geom_col` - + note that this column will be renamed ``"geometry"`` for ease of use + with geopandas), or the path to a saved file in .geojson or .csv + format. + affine_obj : list or :class:`affine.Affine` + An affine transformation to apply to `geom` in the form of an + ``[a, b, d, e, xoff, yoff]`` list or an :class:`affine.Affine` object. + inverse : bool, optional + Use this argument to perform the inverse transformation. + geom_col : str, optional + The column in `gdf` corresponding to the geometry. Defaults to + ``'geometry'``. + precision : int, optional + Decimal precision to round the geometries to. If not provided, no + rounding is performed. + """ + if isinstance(gdf, str): # assume it's a geojson + if gdf.lower().endswith('json'): + gdf = gpd.read_file(gdf) + elif gdf.lower().endswith('csv'): + gdf = pd.read_csv(gdf) + else: + raise ValueError( + "The file format is incompatible with this function.") + if 'geometry' not in gdf.columns: + gdf = gdf.rename(columns={geom_col: 'geometry'}) + if not isinstance(gdf['geometry'][0], Polygon): + gdf['geometry'] = gdf['geometry'].apply(shapely.wkt.loads) + gdf["geometry"] = gdf["geometry"].apply(convert_poly_coords, + affine_obj=affine_obj, + inverse=inverse) + if precision is not None: + gdf['geometry'] = gdf['geometry'].apply( + _reduce_geom_precision, precision=precision) + + # the CRS is no longer valid - remove it + gdf.crs = None + + return gdf + + +def georegister_px_df(df, im_path=None, affine_obj=None, crs=None, + geom_col='geometry', precision=None, output_path=None): + """Convert a dataframe of geometries in pixel coordinates to a geo CRS. + + Arguments + --------- + df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` with polygons in a column named + ``"geometry"``. + im_path : str, optional + A filename or :class:`rasterio.DatasetReader` object containing an + image that has the same bounds as the pixel coordinates in `df`. If + not provided, `affine_obj` and `crs` must both be provided. + affine_obj : `list` or :class:`affine.Affine`, optional + An affine transformation to apply to `geom` in the form of an + ``[a, b, d, e, xoff, yoff]`` list or an :class:`affine.Affine` object. + Required if not using `raster_src`. + crs : valid CRS `str`, `int`, or :class:`rasterio.crs.CRS` instance + The coordinate reference system for the output GeoDataFrame as an EPSG + code integer. Required if not providing a raster image to extract the + information from. + geom_col : str, optional + The column containing geometry in `df`. If not provided, defaults to + ``"geometry"``. + precision : int, optional + The decimal precision for output geometries. If not provided, the + vertex locations won't be rounded. + output_path : str, optional + Path to save the resulting output to. If not provided, the object + won't be saved to disk. + + """ + if im_path is not None: + im = _check_rasterio_im_load(im_path) + affine_obj = im.transform + crs = im.crs + else: + if not affine_obj or not crs: + raise ValueError('If an image path is not provided, ' + 'affine_obj and crs must be.') + crs = _check_crs(crs) + tmp_df = affine_transform_gdf(df, affine_obj, geom_col=geom_col, + precision=precision) + result = gpd.GeoDataFrame(tmp_df, crs='epsg:' + str(crs.to_epsg())) + + if output_path is not None: + if output_path.lower().endswith('json'): + result.to_file(output_path, driver='GeoJSON') + else: + result.to_csv(output_path, index=False) + + return result + + +def geojson_to_px_gdf(geojson, im_path, geom_col='geometry', precision=None, + output_path=None, override_crs=False): + """Convert a geojson or set of geojsons from geo coords to px coords. + + Arguments + --------- + geojson : str + Path to a geojson. This function will also accept a + :class:`pandas.DataFrame` or :class:`geopandas.GeoDataFrame` with a + column named ``'geometry'`` in this argument. + im_path : str + Path to a georeferenced image (ie a GeoTIFF) that geolocates to the + same geography as the `geojson`(s). This function will also accept a + :class:`osgeo.gdal.Dataset` or :class:`rasterio.DatasetReader` with + georeferencing information in this argument. + geom_col : str, optional + The column containing geometry in `geojson`. If not provided, defaults + to ``"geometry"``. + precision : int, optional + The decimal precision for output geometries. If not provided, the + vertex locations won't be rounded. + output_path : str, optional + Path to save the resulting output to. If not provided, the object + won't be saved to disk. + override_crs: bool, optional + Useful if the geojsons generated by the vector tiler or otherwise were saved + out with a non EPSG code projection. True sets the gdf crs to that of the + image, the inputs should have the same underlying projection for this to work. + If False, and the gdf does not have an EPSG code, this function will fail. + Returns + ------- + output_df : :class:`pandas.DataFrame` + A :class:`pandas.DataFrame` with all geometries in `geojson` that + overlapped with the image at `im_path` converted to pixel coordinates. + Additional columns are included with the filename of the source + geojson (if available) and images for reference. + + """ + # get the bbox and affine transforms for the image + im = _check_rasterio_im_load(im_path) + if isinstance(im_path, rasterio.DatasetReader): + im_path = im_path.name + # make sure the geo vector data is loaded in as geodataframe(s) + gdf = _check_gdf_load(geojson) + + if len(gdf): # if there's at least one geometry + if override_crs: + gdf.crs = im.crs + overlap_gdf = get_overlapping_subset(gdf, im) + else: + overlap_gdf = gdf + + affine_obj = im.transform + transformed_gdf = affine_transform_gdf(overlap_gdf, affine_obj=affine_obj, + inverse=True, precision=precision, + geom_col=geom_col) + transformed_gdf['image_fname'] = os.path.split(im_path)[1] + + if output_path is not None: + if output_path.lower().endswith('json'): + transformed_gdf.to_file(output_path, driver='GeoJSON') + else: + transformed_gdf.to_csv(output_path, index=False) + return transformed_gdf + + +def get_overlapping_subset(gdf, im=None, bbox=None, bbox_crs=None): + """Extract a subset of geometries in a GeoDataFrame that overlap with `im`. + + Notes + ----- + This function uses RTree's spatialindex, which is much faster (but slightly + less accurate) than direct comparison of each object for overlap. + + Arguments + --------- + gdf : :class:`geopandas.GeoDataFrame` + A :class:`geopandas.GeoDataFrame` instance or a path to a geojson. + im : :class:`rasterio.DatasetReader` or `str`, optional + An image object loaded with `rasterio` or a path to a georeferenced + image (i.e. a GeoTIFF). + bbox : `list` or :class:`shapely.geometry.Polygon`, optional + A bounding box (either a :class:`shapely.geometry.Polygon` or a + ``[bottom, left, top, right]`` `list`) from an image. Has no effect + if `im` is provided (`bbox` is inferred from the image instead.) If + `bbox` is passed and `im` is not, a `bbox_crs` should be provided to + ensure correct geolocation - if it isn't, it will be assumed to have + the same crs as `gdf`. + bbox_crs : int, optional + The coordinate reference system that the bounding box is in as an EPSG + int. If not provided, it's assumed that the CRS is the same as `im` + (if provided) or `gdf` (if not). + + Returns + ------- + output_gdf : :class:`geopandas.GeoDataFrame` + A :class:`geopandas.GeoDataFrame` with all geometries in `gdf` that + overlapped with the image at `im`. + Coordinates are kept in the CRS of `gdf`. + + """ + if im is None and bbox is None: + raise ValueError('Either `im` or `bbox` must be provided.') + gdf = _check_gdf_load(gdf) + sindex = gdf.sindex + if im is not None: + im = _check_rasterio_im_load(im) + # currently, convert CRSs to WKT strings here to accommodate rasterio. + bbox = transform_bounds(im.crs, _check_crs(gdf.crs, return_rasterio=True), + *im.bounds) + bbox_crs = im.crs + # use transform_bounds in case the crs is different - no effect if not + if isinstance(bbox, Polygon): + bbox = bbox.bounds + if bbox_crs is None: + try: + bbox_crs = _check_crs(gdf.crs, return_rasterio=True) + except AttributeError: + raise ValueError('If `im` and `bbox_crs` are not provided, `gdf`' + 'must provide a coordinate reference system.') + else: + bbox_crs = _check_crs(bbox_crs, return_rasterio=True) + # currently, convert CRSs to WKT strings here to accommodate rasterio. + bbox = transform_bounds(bbox_crs, + _check_crs(gdf.crs, return_rasterio=True), + *bbox) + try: + intersectors = list(sindex.intersection(bbox)) + except RTreeError: + intersectors = [] + + return gdf.iloc[intersectors, :] + + +def gdf_to_yolo(geodataframe, image, output_dir, column='single_id', + im_size=(0, 0), min_overlap=0.66, remove_no_labels=1): + """Convert a geodataframe containing polygons to yolo/yolt format. + + Arguments + --------- + geodataframe : str + Path to a :class:`geopandas.GeoDataFrame` with a column named + ``'geometry'``. Can be created from a geojson with labels for unique + objects. Can be converted to this format with + ``geodataframe=gpd.read_file("./xView_30.geojson")``. + im_path : str + Path to a georeferenced image (ie a GeoTIFF or png created with GDAL) + that geolocates to the same geography as the `geojson`(s). If a + directory, the bounds of each GeoTIFF will be loaded in and all + overlapping geometries will be transformed. This function will also + accept a :class:`osgeo.gdal.Dataset` or :class:`rasterio.DatasetReader` + with georeferencing information in this argument. + output_dir : str + Path to an output directory where all of the yolo readable text files + will be placed. + column : str, optional + The column name that contians an unique integer id for each of object + class. + im_size : tuple, optional + A tuple specifying the x and y heighth of a an image. If specified as + ``(0,0)`` (the default,) then the size is determined automatically. + min_overlap : float, optional + A float value ranging from 0 to 1. This is a percantage. If a polygon + does not overlap the image by at least min_overlap, the polygon is + discarded. i.e. 0.66 = 66%. Default value of 0.66. + remove_no_labels : int, optional + An int value of 0 or 1. If 1, any image not containing any objects + will be moved to a directory in the same root path as your input image. + If 0, no images will be moved. Default value of 1. + + Returns + ------- + gdf : :class:`geopandas.GeoDataFrame`. + The txt file will be written to the output_dir, however the the output + gdf itself is returned. + """ + + if im_size == (0, 0): + imsize_extract = rasterio.open(image).read() + if len(imsize_extract.shape) == 3: + im_size = (imsize_extract.shape[1], imsize_extract.shape[2]) + else: + im_size = (imsize_extract.shape[0], imsize_extract.shape[1]) + [x0, y0, x1, y1] = [0, 0, im_size[0], im_size[1]] + out_coords = [[x0, y0], [x0, y1], [x1, y1], [x1, y0]] + points = [shapely.geometry.Point(coord) for coord in out_coords] + pix_poly = shapely.geometry.Polygon([[p.x, p.y] for p in points]) + dw = 1. / im_size[0] + dh = 1. / im_size[1] + header = [column, "x", "y", "w", "h"] + if os.path.isdir(output_dir) is False: + os.mkdir(output_dir) + output = os.path.join(output_dir, image.split('.png')[0] + ".txt") + gdf = geojson_to_px_gdf(geodataframe, image, precision=None) + gdf['area'] = gdf['geometry'].area + gdf['intersection'] = ( + gdf['geometry'].intersection(pix_poly).area / gdf['area']) + gdf = gdf[gdf['area'] != 0] + gdf = gdf[gdf['intersection'] >= min_overlap] + if not gdf.empty: + boxy = gdf['geometry'].bounds + boxy['xmid'] = (boxy['minx'] + boxy['maxx']) / 2.0 + boxy['ymid'] = (boxy['miny'] + boxy['maxy']) / 2.0 + boxy['w0'] = (boxy['maxx'] - boxy['minx']) + boxy['h0'] = (boxy['maxy'] - boxy['miny']) + boxy['x'] = boxy['xmid'] * dw + boxy['y'] = boxy['ymid'] * dh + boxy['w'] = boxy['w0'] * dw + boxy['h'] = boxy['h0'] * dh + if not boxy.empty: + gdf = gdf.join(boxy) + gdf.to_csv(path_or_buf=output, sep=' ', + columns=header, index=False, header=False) + + if remove_no_labels == 1: + remove_no_labels_dir = os.path.join( + os.path.dirname(os.path.abspath(image)), "No_Labels") + if os.path.isdir(remove_no_labels_dir) is False: + os.mkdir(remove_no_labels_dir) + if gdf.empty or boxy.empty: + shutil.move(image, remove_no_labels_dir) + + return gdf + +def remove_multipolygons(gdf): + """ + Filters out rows of a geodataframe containing MultiPolygons and GeometryCollections. + + This function is optionally used in geojson2coco. For instance segmentation, where + objects are composed of single polygons, multi part geometries need to be either removed or + inspected manually to be resolved as a single geometry. + """ + mask = (gdf.geom_type == "MultiPolygon") | (gdf.geom_type == "GeometryCollection") + if mask.any(): + return gdf.drop(gdf[mask].index).reset_index(drop=True) + else: + return gdf + diff --git a/docker/solaris/static/sol_logo.png b/docker/solaris/static/sol_logo.png new file mode 100644 index 00000000..abf94841 Binary files /dev/null and b/docker/solaris/static/sol_logo.png differ diff --git a/docker/solaris/tests/__init__.py b/docker/solaris/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/tests/test_cli/compare.py b/docker/solaris/tests/test_cli/compare.py new file mode 100644 index 00000000..f5a6396c --- /dev/null +++ b/docker/solaris/tests/test_cli/compare.py @@ -0,0 +1,36 @@ +import os +import numpy as np +import skimage +import pickle +import networkx as nx +import sys + +im_fnames = ['sample_fp_mask.tif', + 'sample_fbc_mask.tif', + 'sample_c_mask.tif', + 'sample_b_inner_mask.tif', + 'sample_b_outer10_mask.tif'] + + +def main(path): + os.chdir(path) # set to the directory containing this script + + # compare expected mask results and truth images + for im_fname in im_fnames: + truth_im = skimage.io.imread(os.path.join('expected', im_fname)) + result_im = skimage.io.imread(os.path.join('results', im_fname)) + assert np.array_equal(truth_im, result_im) + + # compare graphs + with open(os.path.join('expected', 'sample_graph.pkl'), 'rb') as f: + truth_graph = pickle.load(f) + f.close() + with open(os.path.join('results', 'sample_graph.pkl'), 'rb') as f: + result_graph = pickle.load(f) + f.close() + + assert nx.is_isomorphic(truth_graph, result_graph) + + +if __name__ == '__main__': + main(sys.argv[1]) diff --git a/docker/solaris/tests/test_cli/test_cli.py b/docker/solaris/tests/test_cli/test_cli.py new file mode 100644 index 00000000..e75ba214 --- /dev/null +++ b/docker/solaris/tests/test_cli/test_cli.py @@ -0,0 +1,86 @@ +import os +import numpy as np +import pickle +import subprocess +import skimage.io +from solaris.data import data_dir +import networkx as nx + + +class TestCLI(object): + """CLI tests using subprocess.""" + + def test_geotransform_footprints(self): + """test the geotransform_footprints CLI command.""" + # make sure the result directory is empty + dest_loc = os.path.join(data_dir, 'cli_test', 'result', + 'to_px_test.geojson') + if os.path.exists(dest_loc): + os.remove(dest_loc) + # run the CLI command + subprocess.run('geotransform_footprints -s ' + + os.path.join(data_dir, 'geotiff_labels.geojson') + + ' -r ' + + os.path.join(data_dir, 'sample_geotiff.tif') + + ' -o ' + + dest_loc + + ' -p -d 0', + shell=True) + # compare results + subprocess.run('diff ' + os.path.join(data_dir, 'cli_test', 'expected', + 'gj_to_px_result.geojson') + + ' ' + data_dir, + shell=True) + # clean up + os.remove(dest_loc) + + def test_make_graphs(self): + """test the make_graphs CLI command.""" + # prep paths and clear existing result + dest_loc = os.path.join(data_dir, 'cli_test', 'result', + 'sample_graph.pkl') + src_loc = os.path.join(data_dir, 'sample_roads.geojson') + truth_loc = os.path.join(data_dir, 'cli_test', 'expected', + 'sample_graph.pkl') + if os.path.exists(dest_loc): + os.remove(dest_loc) + # run the CLI command + subprocess.run('make_graphs -s ' + src_loc + ' -o ' + dest_loc, + shell=True) + with open(truth_loc, 'rb') as f: + truth_graph = pickle.load(f) + f.close() + with open(dest_loc, 'rb') as f: + result_graph = pickle.load(f) + f.close() + + assert nx.is_isomorphic(truth_graph, result_graph) + # clean up + os.remove(dest_loc) + + def test_make_masks(self): + """Test the make_masks CLI command.""" + # set up variables + args = (('sample_fp_mask.tif', ' -f '), + ('sample_b_inner_mask.tif', ' -e '), + ('sample_b_outer10_mask.tif', ' -e -et outer -ew 10 '), + ('sample_c_mask.tif', ' -c -cs 10 '), + ('sample_fbc_mask.tif', ' -f -e -c -et outer -ew 5 -cs 15 ')) + dest_dir = os.path.join(data_dir, 'cli_test/result') + expected_dir = os.path.join(data_dir, 'cli_test/expected') + src_vector_path = os.path.join(data_dir, 'sample.csv') + src_geotiff_path = os.path.join(data_dir, 'sample_geotiff.tif') + cmd_start = 'make_masks -s ' + src_vector_path + ' -r ' + src_geotiff_path + ' -g PolygonWKT_Pix -o ' + for im_fname, arg in args: + # clean up destination + if os.path.exists(os.path.join(dest_dir, im_fname)): + os.remove(os.path.join(dest_dir, im_fname)) + # run the CLI command + subprocess.run(cmd_start + os.path.join(dest_dir, im_fname) + arg, + shell=True) + truth_im = skimage.io.imread(os.path.join(expected_dir, im_fname)) + result_im = skimage.io.imread(os.path.join(dest_dir, im_fname)) + # compare the expected to the result + assert np.array_equal(truth_im, result_im) + # clean up after + os.remove(os.path.join(dest_dir, im_fname)) diff --git a/docker/solaris/tests/test_cli/test_cli.sh b/docker/solaris/tests/test_cli/test_cli.sh new file mode 100755 index 00000000..081da27e --- /dev/null +++ b/docker/solaris/tests/test_cli/test_cli.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +#set -ev # if any command in the script fails, the whole thing will fail + +# empty output space +rm -rf cw-geodata/tests/test_cli/results && mkdir cw-geodata/tests/test_cli/results + +# run tests +geotransform_footprints -s cw-geodata/cw_geodata/data/geotiff_labels.geojson -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/to_px_test.geojson -p -d 0 +make_graphs -s cw-geodata/cw_geodata/data/sample_roads.geojson -o cw-geodata/tests/test_cli/results/sample_graph.pkl +make_masks -s cw-geodata/cw_geodata/data/sample.csv -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/sample_fp_mask.tif -g PolygonWKT_Pix -f +make_masks -s cw-geodata/cw_geodata/data/sample.csv -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/sample_b_inner_mask.tif -g PolygonWKT_Pix -e +make_masks -s cw-geodata/cw_geodata/data/sample.csv -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/sample_b_outer10_mask.tif -g PolygonWKT_Pix -e -et outer -ew 10 +make_masks -s cw-geodata/cw_geodata/data/sample.csv -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/sample_c_mask.tif -g PolygonWKT_Pix -c -cs 10 +make_masks -s cw-geodata/cw_geodata/data/sample.csv -r cw-geodata/cw_geodata/data/sample_geotiff.tif -o cw-geodata/tests/test_cli/results/sample_fbc_mask.tif -g PolygonWKT_Pix -f -c -cs 15 -e -et outer -ew 5 + +diff cw-geodata/tests/test_cli/results/to_px_test.geojson cw-geodata/tests/test_cli/expected/gj_to_px_result.geojson +# run python-based testing of outputs from other CLI runs +python cw-geodata/tests/test_cli/compare.py cw-geodata/tests/test_cli diff --git a/docker/solaris/tests/test_data/test_coco.py b/docker/solaris/tests/test_data/test_coco.py new file mode 100644 index 00000000..7fb34992 --- /dev/null +++ b/docker/solaris/tests/test_data/test_coco.py @@ -0,0 +1,55 @@ +from solaris.data.coco import geojson2coco +from solaris.data import data_dir +import json +import os + + +class TestGeoJSON2COCO(object): + """Tests for the ``geojson2coco`` function.""" + + def test_multiclass_single_geojson(self): + sample_geojson = os.path.join(data_dir, 'geotiff_labels.geojson') + sample_image = os.path.join(data_dir, 'sample_geotiff.tif') + + coco_dict = geojson2coco(sample_image, sample_geojson, + category_attribute='truncated', + output_path=os.path.join(data_dir, + 'tmp_coco.json')) + with open(os.path.join(data_dir, 'coco_sample_2.json'), 'r') as f: + expected_dict = json.load(f) + with open(os.path.join(data_dir, 'tmp_coco.json'), 'r') as f: + saved_result = json.load(f) + ## Simplified test due to rounding errors- JSS + assert coco_dict['annotations'][0]['bbox'] == expected_dict['annotations'][0]['bbox'] + assert saved_result['annotations'][0]['bbox'] == expected_dict['annotations'][0]['bbox'] + os.remove(os.path.join(data_dir, 'tmp_coco.json')) + + def test_singleclass_multi_geojson(self): + sample_geojsons = [os.path.join(data_dir, 'vectortile_test_expected/geoms_733601_3724734.geojson'), + os.path.join(data_dir, 'vectortile_test_expected/geoms_733601_3724869.geojson')] + sample_images = [os.path.join(data_dir, 'rastertile_test_expected/sample_geotiff_733601_3724734.tif'), + os.path.join(data_dir, 'rastertile_test_expected/sample_geotiff_733601_3724869.tif')] + + coco_dict = geojson2coco(sample_images, + sample_geojsons, + matching_re=r'(\d+_\d+)', + license_dict={'CC-BY 4.0': 'https://creativecommons.org/licenses/by/4.0/'}, + verbose=0) + + with open(os.path.join(data_dir, 'coco_sample_1.json'), 'r') as f: + expected_dict = json.load(f) + ## Simplified test due to rounding errors- JSS + assert expected_dict['annotations'][0]['bbox'] == coco_dict['annotations'][0]['bbox'] + + def test_from_directories(self): + sample_geojsons = os.path.join(data_dir, 'vectortile_test_expected') + sample_images = os.path.join(data_dir, 'rastertile_test_expected') + coco_dict = geojson2coco(sample_images, + sample_geojsons, + matching_re=r'(\d+_\d+)', + verbose=0) + with open(os.path.join(data_dir, 'coco_sample_3.json'), 'r') as f: + expected_dict = json.load(f) + # this test had issues due to rounding errors, I therefore lowered the + # barrier to passing - NW + assert len(expected_dict['annotations']) == len(coco_dict['annotations']) diff --git a/docker/solaris/tests/test_eval/evaluator_test.py b/docker/solaris/tests/test_eval/evaluator_test.py new file mode 100644 index 00000000..30f4847d --- /dev/null +++ b/docker/solaris/tests/test_eval/evaluator_test.py @@ -0,0 +1,94 @@ +import os +from solaris.eval.base import Evaluator +import solaris +import geopandas as gpd +import pandas as pd + + +class TestEvaluator(object): + def test_init_from_file(self): + """Test instantiation of an Evaluator instance from a file.""" + base_instance = Evaluator(os.path.join(solaris.data.data_dir, + 'gt.geojson')) + gdf = solaris.data.gt_gdf() + assert base_instance.ground_truth_sindex.bounds == gdf.sindex.bounds + assert base_instance.proposal_GDF.equals(gpd.GeoDataFrame([])) + assert base_instance.ground_truth_GDF.equals( + base_instance.ground_truth_GDF_Edit) + + def test_init_from_gdf(self): + """Test instantiation of an Evaluator from a pre-loaded GeoDataFrame.""" + gdf = solaris.data.gt_gdf() + base_instance = Evaluator(gdf) + assert base_instance.ground_truth_sindex.bounds == gdf.sindex.bounds + assert base_instance.proposal_GDF.equals(gpd.GeoDataFrame([])) + assert base_instance.ground_truth_GDF.equals( + base_instance.ground_truth_GDF_Edit) + + def test_init_empty_geojson(self): + """Test instantiation of Evaluator with an empty geojson file.""" + base_instance = Evaluator(os.path.join(solaris.data.data_dir, + 'empty.geojson')) + expected_gdf = gpd.GeoDataFrame({'sindex': [], + 'condition': [], + 'geometry': []}) + assert base_instance.ground_truth_GDF.equals(expected_gdf) + + def test_score_proposals(self): + """Test reading in a proposal GDF from a geojson and scoring it.""" + eb = Evaluator(os.path.join(solaris.data.data_dir, 'gt.geojson')) + eb.load_proposal(os.path.join(solaris.data.data_dir, 'pred.geojson')) + pred_gdf = solaris.data.pred_gdf() + assert eb.proposal_GDF.iloc[:, 0:3].sort_index().equals(pred_gdf) + expected_score = [{'class_id': 'all', + 'iou_field': 'iou_score_all', + 'TruePos': 8, + 'FalsePos': 20, + 'FalseNeg': 20, + 'Precision': 0.2857142857142857, + 'Recall': 0.2857142857142857, + 'F1Score': 0.2857142857142857}] + scores = eb.eval_iou(calculate_class_scores=False) + assert scores == expected_score + + def test_score_proposals_return_gdfs(self): + eb = Evaluator(os.path.join(solaris.data.data_dir, 'gt.geojson')) + eb.load_proposal(os.path.join(solaris.data.data_dir, 'pred.geojson')) + expected_score = [{'class_id': 'all', + 'iou_field': 'iou_score_all', + 'TruePos': 8, + 'FalsePos': 20, + 'FalseNeg': 20, + 'Precision': 0.2857142857142857, + 'Recall': 0.2857142857142857, + 'F1Score': 0.2857142857142857}] + scores, tp_gdf, fn_gdf, fp_gdf = eb.eval_iou_return_GDFs( + calculate_class_scores=False) + assert scores == expected_score + assert len(tp_gdf) == expected_score[0]['TruePos'] + assert len(fp_gdf) == expected_score[0]['FalsePos'] + assert len(fn_gdf) == expected_score[0]['FalseNeg'] + + def test_iou_by_building(self): + """Test output of ground truth table with per-building IoU scores""" + data_folder = solaris.data.data_dir + path_truth = os.path.join(data_folder, 'SN2_sample_truth.csv') + path_pred = os.path.join(data_folder, 'SN2_sample_preds.csv') + path_ious = os.path.join(data_folder, 'SN2_sample_iou_by_building.csv') + path_temp = './temp.pd' + eb = Evaluator(path_truth) + eb.load_proposal(path_pred, conf_field_list=['Confidence'], + proposalCSV=True) + eb.eval_iou_spacenet_csv(miniou=0.5, imageIDField='ImageId', + min_area=20) + output = eb.get_iou_by_building() + result_actual = pd.DataFrame(output) + result_actual.sort_values(by=['ImageId', 'BuildingId'], inplace=True) + ious_actual = list(result_actual['iou_score']) + result_expected = pd.read_csv(path_ious, index_col=0) + result_expected.sort_values(by=['ImageId', 'BuildingId'], inplace=True) + ious_expected = list(result_expected['iou_score']) + maxdifference = max([abs(x-y) for x, y in zip(ious_actual, + ious_expected)]) + epsilon = 1E-9 + assert maxdifference < epsilon diff --git a/docker/solaris/tests/test_eval/iou_test.py b/docker/solaris/tests/test_eval/iou_test.py new file mode 100644 index 00000000..28e5abfe --- /dev/null +++ b/docker/solaris/tests/test_eval/iou_test.py @@ -0,0 +1,44 @@ +from solaris.eval.iou import calculate_iou, process_iou +from solaris import data +from shapely.geometry import Polygon + + +class TestEvalFuncs(object): + def test_overlap(self): + gt_gdf = data.gt_gdf() + pred_poly = Polygon(((736348.0, 3722762.5), + (736353.0, 3722762.0), + (736354.0, 3722759.0), + (736352.0, 3722755.5), + (736348.5, 3722755.5), + (736346.0, 3722757.5), + (736348.0, 3722762.5))) + overlap_pred_gdf = calculate_iou(pred_poly, gt_gdf) + assert overlap_pred_gdf.index[0] == 27 + assert overlap_pred_gdf.iou_score.iloc[0] == 0.073499798744833519 + + def test_process_iou(self): + gt_gdf = data.gt_gdf() + pred_poly = Polygon(((736414.0, 3722573.0), + (736417.5, 3722572.5), + (736420.0, 3722568.0), + (736421.0, 3722556.0), + (736418.5, 3722538.0), + (736424.0, 3722532.5), + (736424.0, 3722527.0), + (736422.5, 3722525.5), + (736412.0, 3722524.0), + (736410.5, 3722521.5), + (736407.0, 3722520.5), + (736383.5, 3722521.0), + (736376.5, 3722528.5), + (736378.0, 3722532.5), + (736402.0, 3722532.0), + (736410.0, 3722539.0), + (736411.0, 3722544.0), + (736408.5, 3722553.5), + (736409.0, 3722569.0), + (736414.0, 3722573.0))) + assert 21 in gt_gdf.index + process_iou(pred_poly, gt_gdf) + assert 21 not in gt_gdf.index diff --git a/docker/solaris/tests/test_eval/off_nadir_dataset_test.py b/docker/solaris/tests/test_eval/off_nadir_dataset_test.py new file mode 100644 index 00000000..676c0395 --- /dev/null +++ b/docker/solaris/tests/test_eval/off_nadir_dataset_test.py @@ -0,0 +1,51 @@ +import os +import solaris +from solaris.eval.challenges import off_nadir_buildings +import subprocess +import pandas as pd + + +class TestEvalOffNadir(object): + """Tests for the ``off_nadir`` function.""" + + def test_scoring(self): + """Test a round of scoring.""" + # load predictions + pred_results = pd.read_csv(os.path.join(solaris.data.data_dir, + 'test_results.csv')) + pred_results_full = pd.read_csv(os.path.join(solaris.data.data_dir, + 'test_results_full.csv')) + results_df, results_df_full = off_nadir_buildings( + os.path.join(solaris.data.data_dir, 'sample_preds.csv'), + os.path.join(solaris.data.data_dir, 'sample_truth.csv') + ) + assert pred_results.equals(results_df.reset_index()[pred_results.columns]) + assert pred_results_full.equals(results_df_full[pred_results_full.columns]) + + +class TestEvalCLI(object): + """Test the CLI ``spacenet_eval`` function.""" + + def test_cli(self): + """Test a round of scoring using the CLI.""" + pred_results = pd.read_csv(os.path.join( + solaris.data.data_dir, 'competition_test_results.csv')) + pred_results_full = pd.read_csv(os.path.join( + solaris.data.data_dir, 'competition_test_results_full.csv')) + proposal_csv = os.path.join(solaris.data.data_dir, + 'sample_preds_competition.csv') + truth_csv = os.path.join(solaris.data.data_dir, + 'sample_truth_competition.csv') + subprocess.call(['spacenet_eval', '--proposal_csv='+proposal_csv, + '--truth_csv='+truth_csv, + '--output_file=test_out']) + test_results = pd.read_csv('test_out.csv') + full_test_results = pd.read_csv('test_out_full.csv') + + assert pred_results.equals(test_results[pred_results.columns]) + pred_results_full_sorted = pred_results_full.sort_values(by='imageID').reset_index(drop=True) + full_test_results_sorted = full_test_results.sort_values(by='imageID').reset_index(drop=True) + assert pred_results_full_sorted.equals(full_test_results_sorted[pred_results_full_sorted.columns]) + + os.remove('test_out.csv') + os.remove('test_out_full.csv') diff --git a/docker/solaris/tests/test_eval/pixel_test.py b/docker/solaris/tests/test_eval/pixel_test.py new file mode 100644 index 00000000..520ce814 --- /dev/null +++ b/docker/solaris/tests/test_eval/pixel_test.py @@ -0,0 +1,108 @@ +"""Tests for ``solaris.eval.pixel_metrics`` functions.""" + +import numpy as np +from solaris.eval.pixel import iou, f1, relaxed_f1 + + +class TestIoU(object): + """Test ``sol.eval.pixel.iou()``.""" + + def test_iou_basic(self): + truth = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + prop = np.array([[0, 0, 0], + [1, 1, 0], + [1, 0, 1]]) + assert iou(truth, prop) == 0.5 + + def test_iou_pvals(self): + truth = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + prop = np.array([[0, 0.1, 0.4], + [0.8, 0.7, 0.5], + [1, 0, 1]]) + assert iou(truth, prop, prop_threshold=0.55) == 0.5 + + +class TestF1(object): + """Test ``sol.eval.pixel.f1()``.""" + + def test_f1_basic(self): + eps = 1e-7 + truth = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + prop = np.array([[0, 0, 0], + [1, 1, 0], + [1, 0, 1]]) + f1_score, precision, recall = f1(truth, prop) + assert (precision - 0.75) < eps + assert (recall - 0.6) < eps + assert (f1_score - 2./3) < eps + + def test_f1_pvals(self): + eps = 1e-7 + truth = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + prop = np.array([[0, 0.1, 0.4], + [0.8, 0.7, 0.5], + [1, 0, 1]]) + f1_score, precision, recall = f1(truth, prop, prop_threshold=0.55) + assert (precision - 0.75) < eps + assert (recall - 0.6) < eps + assert (f1_score - 2./3) < eps + + def test_f1_no_pos_preds(self): + truth = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + prop = np.zeros(shape=(3, 3)) + assert f1(truth, prop) == (0, 0, 0) + + def test_f1_no_pos_truth(self): + prop = np.array([[0, 0, 1], + [0, 1, 0], + [1, 1, 1]]) + truth = np.zeros(shape=(3, 3)) + assert f1(truth, prop) == (0, 0, 0) + + +class TestRelaxedF1(object): + """Test ``sol.eval.pixel.relaxed_f1()``.""" + + def test_relaxed_f1_basic(self): + eps = 1e-7 + truth_mask = np.array( + [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]] + ) + prop_mask = np.array( + [[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [1., 1., 0., 1., 1., 1., 1., 1., 1., 1.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], + [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]] + ) + + rel_f1, rel_prec, rel_rec = relaxed_f1(truth_mask, + prop_mask, + radius=3) + assert (rel_f1 - 0.8571428571428571) < eps + assert rel_prec - 0.75 < eps + assert rel_rec - 1.0 < eps diff --git a/docker/solaris/tests/test_eval/spacenet_buildings2_dataset_test.py b/docker/solaris/tests/test_eval/spacenet_buildings2_dataset_test.py new file mode 100644 index 00000000..b45c3bd9 --- /dev/null +++ b/docker/solaris/tests/test_eval/spacenet_buildings2_dataset_test.py @@ -0,0 +1,81 @@ +import os +from solaris.eval.challenges import spacenet_buildings_2 +import solaris +import subprocess +import pandas as pd + + +class TestEvalSpaceNetBuildings2(object): + """Tests for the ``spacenet_buildings_2`` function.""" + + def test_scoring(self): + """Test a round of scoring.""" + # load predictions + pred_results = pd.read_csv(os.path.join(solaris.data.data_dir, + 'SN2_test_results.csv')) + pred_results_full = pd.read_csv( + os.path.join(solaris.data.data_dir, + 'SN2_test_results_full.csv')) + results_df, results_df_full = spacenet_buildings_2( + os.path.join(solaris.data.data_dir, 'SN2_sample_preds.csv'), + os.path.join(solaris.data.data_dir, 'SN2_sample_truth.csv') + ) + + results_df_formatted = results_df.reset_index(drop=True) + pred_results_full_sorted = (pred_results_full + .sort_values('imageID') + .reset_index(drop=True)) + results_df_full_sorted = (results_df_full + .sort_values('imageID') + .reset_index(drop=True)) + assert almostequal(pred_results, results_df_formatted[pred_results.columns]) + assert almostequal(pred_results_full_sorted, results_df_full_sorted[pred_results_full_sorted.columns]) + + +class TestEvalCLISN2(object): + """Test the CLI ``spacenet_eval`` function, as applied to SpaceNet2.""" + + def test_cli(self): + """Test a round of scoring using the CLI.""" + pred_results = pd.read_csv(os.path.join( + solaris.data.data_dir, 'SN2_test_results.csv')) + pred_results_full = pd.read_csv(os.path.join( + solaris.data.data_dir, 'SN2_test_results_full.csv')) + proposal_csv = os.path.join(solaris.data.data_dir, + 'SN2_sample_preds.csv') + truth_csv = os.path.join(solaris.data.data_dir, + 'SN2_sample_truth.csv') + subprocess.call(['spacenet_eval', '--proposal_csv='+proposal_csv, + '--truth_csv='+truth_csv, + '--challenge=spacenet-buildings2', + '--output_file=test_out']) + test_results = pd.read_csv('test_out.csv') + full_test_results = pd.read_csv('test_out_full.csv') + + assert pred_results.equals(test_results[pred_results.columns]) + pred_results_full_sorted = pred_results_full.sort_values(by='imageID') \ + .reset_index(drop=True) + full_test_results_sorted = full_test_results.sort_values(by='imageID') \ + .reset_index(drop=True) + assert pred_results_full_sorted.equals(full_test_results_sorted[pred_results_full_sorted.columns]) + + os.remove('test_out.csv') + os.remove('test_out_full.csv') + + +def almostequal(df1, df2, epsilon=1E-9): + """ + Reports whether two dataframes are "almost" equal, allowing for a specified + rounding error. Non-identical elements that are not integers or floats + result in an error. + """ + if df1.shape != df2.shape: + return False + for i in range((df1.shape)[0]): + for j in range((df1.shape)[1]): + val1 = df1.iloc[i, j] + val2 = df2.iloc[i, j] + if val1 != val2: + if abs(val2-val1) > 0.5*epsilon*(abs(val1)+abs(val2)): + return False + return True diff --git a/docker/solaris/tests/test_eval/vector_test.py b/docker/solaris/tests/test_eval/vector_test.py new file mode 100644 index 00000000..b979b221 --- /dev/null +++ b/docker/solaris/tests/test_eval/vector_test.py @@ -0,0 +1,13 @@ +import os +from solaris.data import data_dir +from solaris.eval import vector + + +class TestVectorMetrics(object): + """Test the vector metrics.""" + + def test_vector_metrics(self): + proposal_polygons_dir = os.path.join(data_dir, "eval_vector/preds/") + gt_polygons_dir = os.path.join(data_dir, "eval_vector/gt/") + mAP, APs_by_class, mF1_score, f1s_by_class, precision_iou_by_obj, precision_by_class, mPrecision, recall_iou_by_obj, recall_by_class, mRecall, object_subset, confidences = vector.mAP_score(proposal_polygons_dir, gt_polygons_dir, prediction_cat_attrib="class", gt_cat_attrib='make') + assert mAP.round(2) == 0.85 diff --git a/docker/solaris/tests/test_imports.py b/docker/solaris/tests/test_imports.py new file mode 100644 index 00000000..a942425e --- /dev/null +++ b/docker/solaris/tests/test_imports.py @@ -0,0 +1,13 @@ +class TestImports(object): + + def test_imports(self): + import torch # workaround for TLS error + from solaris.utils import core, geo, config, tile, cli + from solaris import data + from solaris.vector import polygon, graph, mask + from solaris.tile import raster_tile, vector_tile + from solaris.raster import image + from solaris.nets import callbacks, datagen, infer, model_io, losses + from solaris.nets import train, transform, zoo + from solaris.eval import base, iou, challenges, pixel + import solaris diff --git a/docker/solaris/tests/test_nets/test_callbacks.py b/docker/solaris/tests/test_nets/test_callbacks.py new file mode 100644 index 00000000..0c431b4f --- /dev/null +++ b/docker/solaris/tests/test_nets/test_callbacks.py @@ -0,0 +1,189 @@ +from solaris.nets.callbacks import KerasTerminateOnMetricNaN, get_callbacks +from solaris.nets.callbacks import get_lr_schedule +from solaris.nets.torch_callbacks import TorchEarlyStopping, \ + TorchModelCheckpoint, TorchTerminateOnNaN, TorchTerminateOnMetricNaN + +import tensorflow as tf +import torch +import numpy as np + + +class TestGetCallbacksFunction(object): + """test sol.nets.callbacks.get_callbacks()""" + + def test_keras_get_callbacks(self): + framework = 'keras' + config = {'training': + {'lr': 0.001, + 'callbacks': + {'lr_schedule': + {'schedule_type': 'exponential', + 'factor': 1, + 'update_frequency': None + }, + 'terminate_on_nan': {}, + 'terminate_on_metric_nan': {}, + 'model_checkpoint': + {'filepath': 'sample_path.h5'}, + 'early_stopping': {}, + 'csv_logger': + {'filename': 'sample_path.csv'}, + 'reduce_lr_on_plateau': {} + } + } + } + result = get_callbacks(framework, config) + assert len(result) == 7 + has_lr_sched = False + has_term_nan = False + has_term_met_nan = False + has_mod_ckpt = False + has_early_stopping = False + has_csv_logger = False + has_red_lr_plat = False + for callback in result: + if isinstance(callback, tf.keras.callbacks.LearningRateScheduler): + has_lr_sched = True + elif isinstance(callback, tf.keras.callbacks.TerminateOnNaN): + has_term_nan = True + elif isinstance(callback, KerasTerminateOnMetricNaN): + has_term_met_nan = True + elif isinstance(callback, tf.keras.callbacks.ModelCheckpoint): + has_mod_ckpt = True + elif isinstance(callback, tf.keras.callbacks.EarlyStopping): + has_early_stopping = True + elif isinstance(callback, tf.keras.callbacks.CSVLogger): + has_csv_logger = True + elif isinstance(callback, tf.keras.callbacks.ReduceLROnPlateau): + has_red_lr_plat = True + assert has_lr_sched + assert has_term_nan + assert has_term_met_nan + assert has_mod_ckpt + assert has_early_stopping + assert has_csv_logger + assert has_red_lr_plat + + def test_get_torch_callbacks(self): + framework = 'torch' + config = {'training': + {'lr': 0.001, + 'callbacks': + {'lr_schedule': + {'schedule_type': 'exponential', + 'factor': 1, + 'update_frequency': None + }, + 'terminate_on_nan': {}, + 'terminate_on_metric_nan': { + 'stopping_metric': 'accuracy' + }, + 'model_checkpoint': { + 'filepath': 'sample_path.h5' + }, + 'early_stopping': {} + } + } + } + result = get_callbacks(framework, config) + assert len(result) == 5 + has_lr_sched = False + has_term_nan = False + has_term_met_nan = False + has_mod_ckpt = False + has_early_stopping = False + + for callback in result: + if callback == torch.optim.lr_scheduler.ExponentialLR: + has_lr_sched = True + elif isinstance(callback, TorchTerminateOnNaN): + has_term_nan = True + elif isinstance(callback, TorchTerminateOnMetricNaN): + has_term_met_nan = True + elif isinstance(callback, TorchModelCheckpoint): + has_mod_ckpt = True + elif isinstance(callback, TorchEarlyStopping): + has_early_stopping = True + + assert has_lr_sched + assert has_term_nan + assert has_term_met_nan + assert has_mod_ckpt + assert has_early_stopping + + +class TestLRSchedulers(object): + """Test LR scheduling from get_lr_scheduler().""" + + def test_keras_schedulers(self): + epsilon = 1e-9 + framework = 'keras' + config = {'training': + {'lr': 0.001, + 'callbacks': {} + } + } + schedule_dicts = [ + {'schedule_type': 'exponential', + 'factor': 0.5, + 'update_frequency': 1 + }, + {'schedule_type': 'arbitrary', + 'schedule_dict': { + 10: 0.0001, + 20: 0.00001 + } + }, + {'schedule_type': 'linear', + 'factor': -.01 + } + ] + + for schedule_dict in schedule_dicts: + config['training']['callbacks']['lr_schedule'] = schedule_dict + lr_scheduler = get_lr_schedule(framework, config) + # test lr schedule function outputs to make sure they're right + if schedule_dict['schedule_type'] == 'exponential': + assert np.abs(lr_scheduler.schedule(0) - 0.001) < epsilon + assert np.abs(lr_scheduler.schedule(1) - 0.0005) < epsilon + assert np.abs(lr_scheduler.schedule(2) - 0.00025) < epsilon + elif schedule_dict['schedule_type'] == 'linear': + assert np.abs(lr_scheduler.schedule(0) - 0.001) < epsilon + assert np.abs(lr_scheduler.schedule(1) - 0.00099) < epsilon + assert np.abs(lr_scheduler.schedule(10) - 0.0009) < epsilon + elif schedule_dict['schedule_type'] == 'arbitrary': + assert np.abs(lr_scheduler.schedule(0) - 0.001) < epsilon + assert np.abs(lr_scheduler.schedule(10) - 0.0001) < epsilon + assert np.abs(lr_scheduler.schedule(20) - 0.00001) < epsilon + + def test_torch_schedulers(self): + framework = 'torch' + config = {'training': + {'lr': 0.001, + 'callbacks': {} + } + } + schedule_dicts = [ + {'schedule_type': 'exponential', + 'factor': 0.5, + 'update_frequency': 1 + }, + {'schedule_type': 'arbitrary', + 'schedule_dict': { + 10: 0.0001, + 20: 0.00001 + } + }, + {'schedule_type': 'linear', + 'factor': -.01 + } + ] + for schedule_dict in schedule_dicts: + config['training']['callbacks']['lr_schedule'] = schedule_dict + lr_scheduler = get_lr_schedule(framework, config) + if schedule_dict['schedule_type'] == 'exponential': + assert lr_scheduler == torch.optim.lr_scheduler.ExponentialLR + elif schedule_dict['schedule_type'] == 'linear': + assert lr_scheduler == torch.optim.lr_scheduler.StepLR + elif schedule_dict['schedule_type'] == 'arbitrary': + assert lr_scheduler == torch.optim.lr_scheduler.MultiStepLR diff --git a/docker/solaris/tests/test_nets/test_datagen.py b/docker/solaris/tests/test_nets/test_datagen.py new file mode 100644 index 00000000..f17c4f47 --- /dev/null +++ b/docker/solaris/tests/test_nets/test_datagen.py @@ -0,0 +1,123 @@ +import os +from solaris.nets.datagen import make_data_generator, InferenceTiler +from solaris.data import data_dir +from solaris.utils.io import _check_channel_order +import pandas as pd +import numpy as np +import skimage.io + + +class TestDataGenerator(object): + """An object to test datagen creation from sol.nets.datagen.""" + + def test_keras_sequence(self): + """Test creating a keras sequence object for data generation.""" + + dataset_csv = os.path.join(data_dir, 'datagen_sample', 'sample_df.csv') + df = pd.read_csv(dataset_csv) + # add the path to the directory to the values in df + df = df.applymap(lambda x: os.path.join(data_dir, 'datagen_sample', x)) + + config = {'data_specs': + {'height': 30, + 'width': 30, + 'channels': 1, + 'dtype': None, + 'label_type': 'mask', + 'mask_channels': 1, + 'is_categorical': False + }, + 'batch_size': 1, + 'training_augmentation': + {'shuffle': True, # images all same in test, so no effect + 'augmentations': {} + } + } + + keras_seq = make_data_generator('keras', config, df, stage='train') + im, mask = keras_seq.__getitem__(0) + expected_im = skimage.io.imread(os.path.join(data_dir, + 'datagen_sample', + 'expected_im.tif')) + expected_mask = skimage.io.imread(os.path.join(data_dir, + 'datagen_sample', + 'sample_mask_1.tif')) + expected_mask[expected_mask != 0] = 1 # this should be binary + + assert np.array_equal(im, expected_im[np.newaxis, :, :, np.newaxis]) + assert np.array_equal(mask, + expected_mask[np.newaxis, :, :, np.newaxis]) + + def test_torch_dataset(self): + """Test creating a torch dataset object for data generation.""" + + dataset_csv = os.path.join(data_dir, 'datagen_sample', 'sample_df.csv') + df = pd.read_csv(dataset_csv) + # add the path to the directory to the values in df + df = df.applymap(lambda x: os.path.join(data_dir, 'datagen_sample', x)) + + config = {'data_specs': + {'height': 30, + 'width': 30, + 'channels': 1, + 'dtype': None, + 'label_type': 'mask', + 'mask_channels': 1, + 'is_categorical': False + }, + 'batch_size': 1, + 'training_augmentation': + {'shuffle': True, # images all same in test, so no effect + 'augmentations': {} + } + } + + torch_ds = make_data_generator('torch', config, df, stage='train') + sample = next(iter(torch_ds)) + + expected_im = skimage.io.imread(os.path.join(data_dir, + 'datagen_sample', + 'expected_im.tif')) + expected_mask = skimage.io.imread(os.path.join(data_dir, + 'datagen_sample', + 'sample_mask_1.tif')) + expected_mask[expected_mask != 0] = 1 # this should be binary + print(sample['mask'].shape) + assert np.array_equal(sample['image'].numpy(), + expected_im[np.newaxis, np.newaxis, :, :]) + assert np.array_equal(sample['mask'].numpy(), + expected_mask[np.newaxis, np.newaxis, :, :]) + + +class TestInferenceTiler(object): + """Test image tiling using sol.nets.datagen.InferenceTiler.""" + + def test_simple_geotiff_tile(self): + """Test tiling a geotiff without overlap.""" + inf_tiler = InferenceTiler('keras', 250, 250) + tiles, tile_inds, _ = inf_tiler(os.path.join(data_dir, + 'sample_geotiff.tif')) + + expected_tiles = np.load( + os.path.join(data_dir, 'inference_tiler_test_output.npy') + ) + + expected_tile_inds = [(0, 0), + (0, 250), + (0, 500), + (0, 650), + (250, 0), + (250, 250), + (250, 500), + (250, 650), + (500, 0), + (500, 250), + (500, 500), + (500, 650), + (650, 0), + (650, 250), + (650, 500), + (650, 650)] + + assert np.array_equal(expected_tiles, tiles) + assert expected_tile_inds == tile_inds diff --git a/docker/solaris/tests/test_nets/test_losses.py b/docker/solaris/tests/test_nets/test_losses.py new file mode 100644 index 00000000..4dde94da --- /dev/null +++ b/docker/solaris/tests/test_nets/test_losses.py @@ -0,0 +1,99 @@ +from solaris.nets.losses import get_loss +from solaris.nets._keras_losses import k_jaccard_loss, k_focal_loss +from solaris.nets._keras_losses import k_lovasz_hinge +from solaris.nets._torch_losses import TorchFocalLoss, torch_lovasz_hinge +from tensorflow import keras +import torch +import numpy as np +import tensorflow as tf + + +class TestGetLoss(object): + """Test solaris.nets.losses.get_loss().""" + + def test_keras_vanilla_loss(self): + loss_dict = {'bce' : {}} + lf = get_loss('keras', loss_dict) + assert lf == keras.losses.binary_crossentropy + + def test_keras_composite_loss_noweight(self): + epsilon = 1e-6 + loss_dict = {'bce' : {}, 'hinge' : {}} + lf = get_loss('keras', loss_dict) + y_true = tf.constant([0, 1, 1], dtype='float') + y_pred = tf.constant([.1, .9, .4], dtype='float') + sess = tf.Session() + with sess.as_default(): + assert np.abs( + lf(y_true, y_pred).eval() - 0.9423373) < epsilon + + def test_torch_vanilla_loss(self): + loss_dict = {'bce' : {}} + lf = get_loss('torch', loss_dict) + assert isinstance(lf, torch.nn.BCELoss) + + def test_torch_composite_loss(self): + epsilon = 1e-4 + loss_dict = {'bce' : {}, 'hinge' : {}} + lf = get_loss('torch', loss_dict) + y_true = torch.tensor([0, 1, 1], dtype=torch.float) + y_pred = torch.tensor([.1, .9, .4], dtype=torch.float) + assert np.abs( + lf.forward(y_pred, y_true) - 1.1423372030) < epsilon + + +class TestKerasCustomLosses(object): + + def test_keras_focal_loss(self): + epsilon = 1e-6 + y_true = tf.constant([0, 1, 1], dtype='float') + y_pred = tf.constant([.1, .9, .4], dtype='float') + sess = tf.Session() + with sess.as_default(): + foc_loss = k_focal_loss()(y_true, y_pred).eval() + assert np.abs(foc_loss - 0.24845211) < epsilon + + def test_keras_lovasz_hinge(self): + epsilon = 1e-6 + y_true = tf.constant([0, 1, 1], dtype='float') + y_pred = tf.constant([.1, .9, .4], dtype='float') + sess = tf.Session() + with sess.as_default(): + lov_loss = k_lovasz_hinge()(y_true, y_pred).eval() + assert np.abs(lov_loss - 0.70273256) < epsilon + + def test_keras_jaccard_loss(self): + epsilon = 1e-6 + y_true = tf.constant([0, 1, 1], dtype='float') + y_pred = tf.constant([.1, .9, .4], dtype='float') + sess = tf.Session() + with sess.as_default(): + jac_loss = k_jaccard_loss(y_true, y_pred).eval() + assert np.abs(jac_loss - 0.38095242) < epsilon + + +class TestTorchCustomLosses(object): + + def test_torch_focal_loss(self): + epsilon = 1e-6 + y_true = torch.tensor([0, 1, 1], dtype=torch.float) + y_pred = torch.tensor([.1, .9, .4], dtype=torch.float) + lf = TorchFocalLoss() + assert np.abs( + lf.forward(y_pred, y_true) - 0.1106572822) < epsilon + + def test_torch_focal_loss_same(self): + epsilon = 1e-5 + y_true = torch.tensor([0, 1, 1], dtype=torch.float) + y_pred = torch.tensor([0, 1, 1], dtype=torch.float) + lf = TorchFocalLoss() + assert np.abs( + lf.forward(y_pred, y_true) - 0.) < epsilon + + def test_torch_lovasz_hinge(self): + epsilon = 1e-6 + y_true = torch.tensor([0, 1, 1], dtype=torch.float) + y_pred = torch.tensor([.1, .9, .4], dtype=torch.float) + lf = torch_lovasz_hinge + assert np.abs( + lf(y_pred, y_true) - 0.6000000000) < epsilon diff --git a/docker/solaris/tests/test_nets/test_metrics.py b/docker/solaris/tests/test_nets/test_metrics.py new file mode 100644 index 00000000..9746f4e7 --- /dev/null +++ b/docker/solaris/tests/test_nets/test_metrics.py @@ -0,0 +1,83 @@ +import numpy as np +import tensorflow as tf +from tensorflow import keras +from solaris.nets.metrics import get_metrics, precision, recall, f1_score +from solaris.nets.metrics import metric_dict + + +class TestMetricLoads(object): + """Test that metrics load correctly and produce the expected value.""" + + def test_binary_metrics(self): + self.epsilon = 1e-6 + self.truth = tf.convert_to_tensor( + np.array( + [0., 1., 1., 1., 0., 1., 0., 1., 1.] + ).reshape((3, 3))) + self.pred = tf.convert_to_tensor( + np.array( + [0.1, 0.9, 1, 0.3, 0.8, 1., 0.7, 0.2, 0.8] + ).reshape((3, 3))) + self.metrics_and_exp_scores = { + 'accuracy': np.array([1., 0.33333334, 0.33333334]), + 'binary_accuracy': np.array([1., 0.33333334, 0.33333334]), + 'precision': 0.6666666555555557, + 'recall': 0.6666666666666666, + 'f1_score': 0.6666666611111112, + 'cosine': np.array([-0.99587059, -0.69888433, -0.65372045]), + 'cosine_proximity': np.array([-0.99587059, -0.69888433, + -0.65372045]), + 'hinge': np.array([0.36666667, 0.56666667, 0.66666667]), + 'squared_hinge': np.array([0.33666667, 0.49666667, 0.56]), + 'kld': np.array([0.10535913, 1.20397121, 1.83257989]), + 'kullback_leibler_divergence': np.array([0.10535913, 1.20397121, + 1.83257989]), + 'mae': np.array([0.06666667, 0.5, 0.56666667]), + 'mean_absolute_error': np.array([0.06666667, 0.5, 0.56666667]), + 'mse': np.array([0.00666667, 0.37666667, 0.39]), + 'mean_squared_error': np.array([0.00666667, 0.37666667, 0.39]), + 'msle': np.array([0.003905, 0.17702232, 0.18453663]), + 'mean_squared_logarithmic_error': np.array([0.003905, 0.17702232, + 0.18453663]) + } + sess = tf.Session() + with sess.as_default(): + for metric, expected_result in self.metrics_and_exp_scores.items(): + assert np.abs(np.sum( + metric_dict[metric]( + self.truth, self.pred).eval() - expected_result) + ) < self.epsilon + + +class TestGetMetrics(object): + """Test the get_metrics() function in solaris.nets.metrics.""" + + def test_get_metrics(self): + self.config = {'training': + {'metrics': + {'training': ['precision', 'recall', 'f1_score', + 'accuracy', 'categorical_accuracy', + 'cosine', 'hinge', 'squared_hinge', + 'kld', 'mae', 'mse', 'msle', + 'sparse_categorical_accuracy', + 'top_k_categorical_accuracy'], + 'validation': ['precision'] + } + } + } + self.expected_dict = { + 'train': [ + precision, recall, f1_score, keras.metrics.binary_accuracy, + keras.metrics.categorical_accuracy, + keras.metrics.cosine_proximity, + keras.metrics.hinge, keras.metrics.squared_hinge, + keras.metrics.kullback_leibler_divergence, + keras.metrics.mean_absolute_error, + keras.metrics.mean_squared_error, + keras.metrics.mean_squared_logarithmic_error, + keras.metrics.sparse_categorical_accuracy, + keras.metrics.top_k_categorical_accuracy + ], + 'val': [precision] + } + assert get_metrics('keras', self.config) == self.expected_dict diff --git a/docker/solaris/tests/test_nets/test_optimizers.py b/docker/solaris/tests/test_nets/test_optimizers.py new file mode 100644 index 00000000..61e4970c --- /dev/null +++ b/docker/solaris/tests/test_nets/test_optimizers.py @@ -0,0 +1,15 @@ +from solaris.nets.optimizers import get_optimizer +from tensorflow import keras +import torch + + +class TestGetOptimizer(object): + """Test the sol.nets.optimizers.get_optimizer() function.""" + + def test_get_optimizers(self): + config = {'training': + {'optimizer': 'sgd'}} + keras_sgd = get_optimizer('keras', config) + assert keras_sgd == keras.optimizers.SGD + torch_sgd = get_optimizer('torch', config) + assert torch_sgd == torch.optim.SGD diff --git a/docker/solaris/tests/test_nets/test_transform.py b/docker/solaris/tests/test_nets/test_transform.py new file mode 100644 index 00000000..48767201 --- /dev/null +++ b/docker/solaris/tests/test_nets/test_transform.py @@ -0,0 +1,86 @@ +import numpy as np +from solaris.nets.transform import Rotate, RandomScale, build_pipeline +from albumentations.core.composition import Compose, OneOf +from albumentations.augmentations.transforms import HorizontalFlip, Normalize + + +class TestRotate(object): + """Test the rotation transform.""" + + def test_rotate(self): + rot = Rotate() + arr = np.array([[3, 5, 3], + [5, 8, 10], + [1, 3, 8]]) + rot_arr = rot.apply(arr, angle=45) + + assert np.array_equal(rot_arr, + np.array([[5, 7, 5, 10], + [5, 6, 10, 7], + [5, 5, 4, 9], + [5, 2, 3, 3]])) + + +class TestRandomScale(object): + """Test the random scale transform.""" + + def test_random_scale(self): + rs = RandomScale(scale_limit=0.2) + assert rs.scale_limit == (0.8, 1.2) + arr = np.array([[3, 5, 3], + [5, 8, 10], + [1, 3, 8]]) + scaled_arr = rs.apply(arr, scale_x=2, scale_y=2) + assert np.array_equal(scaled_arr, np.array([[3, 3, 5, 5, 2, 2], + [3, 4, 5, 6, 4, 4], + [5, 6, 7, 8, 9, 9], + [4, 5, 6, 8, 10, 10], + [2, 2, 3, 5, 8, 9], + [1, 1, 2, 4, 7, 8]], + dtype='uint8')) + + +class TestBuildAugPipeline(object): + """Test sol.nets.transform.build_pipeline().""" + + def test_build_pipeline(self): + config = {'training_augmentation': + {'p': 0.75, + 'augmentations': + {'oneof': + {'normalize': {}, + 'rotate': + {'border_mode': 'reflect', + 'limit': 45} + }, + 'horizontalflip': {'p': 0.5} + } + }, + 'validation_augmentation': + {'augmentations': + {'horizontalflip': {'p': 0.5} + } + } + } + train_augs, val_augs = build_pipeline(config) + + assert isinstance(train_augs.transforms[0], OneOf) + assert isinstance(train_augs.transforms[1], HorizontalFlip) + assert train_augs.p == 0.75 + assert isinstance(train_augs.transforms[0].transforms[0], Normalize) + assert isinstance(train_augs.transforms[0].transforms[1], Rotate) + assert train_augs.transforms[0].p == 0.5 + assert train_augs.transforms[0].transforms[0].p == 1 + assert not train_augs.transforms[0].transforms[0].always_apply + assert train_augs.transforms[0].transforms[1].limit == (-45, 45) + assert train_augs.transforms[0].transforms[1].border_mode == 'reflect' + assert isinstance(val_augs, Compose) + assert isinstance(val_augs.transforms[0], HorizontalFlip) + + # test running the pipeline + arr = np.array([[3, 5, 3], + [5, 8, 10], + [1, 3, 8]]) + train_result = train_augs(image=arr) + # make sure this gave a 2D numpy array out in a dict with key 'image' + assert len(train_result['image'].shape) == 2 diff --git a/docker/solaris/tests/test_raster/__init__.py b/docker/solaris/tests/test_raster/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/tests/test_raster/test_image.py b/docker/solaris/tests/test_raster/test_image.py new file mode 100644 index 00000000..e49bb27f --- /dev/null +++ b/docker/solaris/tests/test_raster/test_image.py @@ -0,0 +1,78 @@ +import os +import numpy as np +import solaris as sol +from solaris.data import data_dir, sample_load_rasterio, sample_load_gdal +from solaris.raster.image import get_geo_transform, stitch_images +from affine import Affine +import skimage.io + + +class TestGetGeoTransform(object): + """Tests for sol.raster.image.get_geo_transform().""" + + def test_get_from_file(self): + affine_obj = get_geo_transform(os.path.join(data_dir, + 'sample_geotiff.tif')) + assert affine_obj == Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) + + def test_get_from_opened_raster(self): + src_obj = sample_load_rasterio() + affine_obj = get_geo_transform(src_obj) + assert affine_obj == Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) + src_obj.close() + + def test_get_from_gdal(self): + src_obj = sample_load_gdal() + affine_obj = get_geo_transform(src_obj) + assert affine_obj == Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) + + +class TestStitchImages(object): + """Tests for image stitching with sol.raster.image.stitch_images().""" + + def test_stitch_InferenceTiler_output(self): + inf_tiler = sol.nets.datagen.InferenceTiler('keras', + width=250, height=250) + tiles, tile_inds, _ = inf_tiler(os.path.join(data_dir, + 'sample_geotiff.tif')) + restored_im = stitch_images(tiles, idx_refs=tile_inds, + out_width=900, out_height=900) + expected_result = sol.utils.io.imread( + os.path.join(data_dir, 'sample_geotiff.tif')) + + assert np.array_equal(restored_im[:, :, 0], expected_result) + + def test_stitch_firstval(self): + inf_tiler = sol.nets.datagen.InferenceTiler('keras', + width=250, height=250) + tiles, tile_inds, _ = inf_tiler(os.path.join(data_dir, + 'sample_geotiff.tif')) + tiles[11, :, :, :] = tiles[11, :, :, :] + 10 # to have a diff to check + result = stitch_images(tiles, idx_refs=tile_inds, + out_width=900, out_height=900, method='first') + expected_result = np.load(os.path.join(data_dir, + 'stitching_first_output.npy')) + + assert np.array_equal(result, expected_result) + + def test_stitch_conf(self): + src_im = skimage.io.imread( + os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')) + src_im[src_im != 0] = 1 + src_im = src_im.astype('float64') + inf_tiler = sol.nets.datagen.InferenceTiler('keras', + width=250, height=250) + tiles, tile_inds, _ = inf_tiler(src_im) + rands = np.array([0.93794284, 0.88778908, 0.25066594, 0.76800494, + 0.43465608, 0.69903218, 0.13256956, 0.20246324, + 0.65134984, 0.98667763, 0.38168734, 0.85653983, + 0.34337332, 0.75118759, 0.01128917, 0.92725672]) + # scale the values for heterogeneity + tiles = tiles*rands[:, np.newaxis, np.newaxis, np.newaxis] + result = stitch_images(tiles, idx_refs=tile_inds, + out_width=900, out_height=900, + method='confidence') + expected_result = np.load(os.path.join(data_dir, + 'stitching_conf_output.npy')) + + assert np.array_equal(result, expected_result) diff --git a/docker/solaris/tests/test_tile/test_tile.py b/docker/solaris/tests/test_tile/test_tile.py new file mode 100644 index 00000000..318ef5e4 --- /dev/null +++ b/docker/solaris/tests/test_tile/test_tile.py @@ -0,0 +1,165 @@ +import os +import skimage.io +import numpy as np +from solaris.tile.raster_tile import RasterTiler +from solaris.tile.vector_tile import VectorTiler +from solaris.data import data_dir +from solaris.vector.mask import geojsons_to_masks_and_fill_nodata +import geopandas as gpd +from shapely.ops import cascaded_union + + +class TestTilers(object): + def test_tiler(self): + raster_tiler = RasterTiler(os.path.join(data_dir, + 'rastertile_test_result'), + src_tile_size=(90, 90)) + raster_tiler.tile(src=os.path.join(data_dir, 'sample_geotiff.tif')) + raster_tiling_result_files = os.listdir(os.path.join( + data_dir, 'rastertile_test_result')) + assert len(raster_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'rastertile_test_expected'))) + for f in raster_tiling_result_files: + result = skimage.io.imread(os.path.join(data_dir, + 'rastertile_test_result', + f)) + expected = skimage.io.imread( + os.path.join(data_dir, 'rastertile_test_expected', f)) + assert np.array_equal(result, expected) + os.remove(os.path.join(data_dir, 'rastertile_test_result', f)) + os.rmdir(os.path.join(data_dir, 'rastertile_test_result')) + vector_tiler = VectorTiler(os.path.join(data_dir, + 'vectortile_test_result')) + vector_tiler.tile(os.path.join(data_dir, 'geotiff_labels.geojson'), + raster_tiler.tile_bounds) + vector_tiling_result_files = os.listdir(os.path.join( + data_dir, 'vectortile_test_result')) + assert len(vector_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'vectortile_test_expected'))) + for f in vector_tiling_result_files: + result = gpd.read_file(os.path.join(data_dir, + 'vectortile_test_result', + f)) + expected = gpd.read_file(os.path.join(data_dir, + 'vectortile_test_expected', + f)) + if len(result) == 0: + assert len(expected) == 0 + else: + result = cascaded_union(result.geometry) + expected = cascaded_union(expected.geometry) + assert result.intersection(expected).area/result.area > 0.99999 + os.remove(os.path.join(data_dir, 'vectortile_test_result', f)) + os.rmdir(os.path.join(data_dir, 'vectortile_test_result')) + + def test_tiler_custom_proj(self): + raster_tiler = RasterTiler(os.path.join(data_dir, + 'rastertile_test_custom_proj_result'), + src_tile_size=(128, 128)) + raster_tiler.tile(src=os.path.join(data_dir, 'sample_geotiff_custom_proj.tif')) + raster_tiling_result_files = os.listdir(os.path.join( + data_dir, 'rastertile_test_custom_proj_result')) + assert len(raster_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'rastertile_test_custom_proj_expected'))) + for f in raster_tiling_result_files: + result = skimage.io.imread(os.path.join(data_dir, + 'rastertile_test_custom_proj_result', + f)) + expected = skimage.io.imread( + os.path.join(data_dir, 'rastertile_test_custom_proj_expected', f)) + assert np.array_equal(result, expected) + os.remove(os.path.join(data_dir, 'rastertile_test_custom_proj_result', f)) + os.rmdir(os.path.join(data_dir, 'rastertile_test_custom_proj_result')) + vector_tiler = VectorTiler(os.path.join(data_dir, + 'vectortile_test_custom_proj_result')) + vector_tiler.tile(os.path.join(data_dir, 'geotiff_custom_proj_labels.geojson'), + raster_tiler.tile_bounds) + vector_tiling_result_files = os.listdir(os.path.join( + data_dir, 'vectortile_test_custom_proj_result')) + assert len(vector_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'vectortile_test_custom_proj_expected'))) + for f in vector_tiling_result_files: + result = gpd.read_file(os.path.join(data_dir, + 'vectortile_test_custom_proj_result', + f)) + expected = gpd.read_file(os.path.join(data_dir, + 'vectortile_test_custom_proj_expected', + f)) + if len(result) == 0: + assert len(expected) == 0 + else: + result = cascaded_union(result.geometry) + expected = cascaded_union(expected.geometry) + assert result.intersection(expected).area/result.area > 0.99999 + os.remove(os.path.join(data_dir, 'vectortile_test_custom_proj_result', f)) + os.rmdir(os.path.join(data_dir, 'vectortile_test_custom_proj_result')) + + def test_tiler_fill_nodata(self): + # get non filled tiles + bounds_gdf= gpd.read_file(os.path.join(data_dir, "restrict_aoi_test.geojson")) + bounds_poly = bounds_gdf['geometry'].iloc[0] + raster_tiler = RasterTiler(os.path.join(data_dir, + 'rastertile_test_fill_nodata_result'), + src_tile_size=(128, 128), + nodata= -9999.0, + aoi_boundary=bounds_poly) + raster_tiler.tile(src=os.path.join(data_dir, 'nebraska_landsat5_with_nodata_wgs84.tif'), restrict_to_aoi=True) + + vector_tiler = VectorTiler(os.path.join(data_dir, + 'vectortile_test_nonfilled_result')) + + vector_tiler.tile(os.path.join(data_dir, 'nebraska_wgs84_with_nodata_labels.geojson'), + tile_bounds = raster_tiler.tile_bounds) + vector_tiling_result_files_nonfilled = os.listdir(os.path.join( + data_dir, 'vectortile_test_nonfilled_result')) + + # fills nodata in imagery and then fills same no data pixels in rasterized labels + geojsons_to_masks_and_fill_nodata(raster_tiler, vector_tiler, + os.path.join(data_dir, "vectortile_test_filled_result"), fill_value=0) + + # list the results + vector_tiling_result_files = os.listdir(os.path.join( + data_dir, 'vectortile_test_filled_result')) + assert len(vector_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'vectortile_test_filled_expected'))) + + raster_tiling_result_files = os.listdir(os.path.join( + data_dir, 'rastertile_test_fill_nodata_result')) + assert len(raster_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'rastertile_test_fill_nodata_expected'))) + + vector_tiling_result_files_nonfilled = os.listdir(os.path.join( + data_dir, 'vectortile_test_nonfilled_result')) + assert len(vector_tiling_result_files) == len(os.listdir(os.path.join( + data_dir, 'vectortile_test_nonfilled_expected'))) + + # check if the filling worked for both img and vector tiles + for f in raster_tiling_result_files: + result = skimage.io.imread(os.path.join(data_dir, + 'rastertile_test_fill_nodata_result', + f)) + expected = skimage.io.imread( + os.path.join(data_dir, 'rastertile_test_fill_nodata_expected', f)) + assert np.array_equal(result, expected) + + + for f in vector_tiling_result_files: + result = skimage.io.imread(os.path.join(data_dir, + 'vectortile_test_filled_result', + f)) + expected = skimage.io.imread(os.path.join(data_dir, + 'vectortile_test_filled_expected', + f)) + assert np.array_equal(result, expected) + + #cleanup + for f in vector_tiling_result_files_nonfilled: + os.remove(os.path.join(data_dir, 'vectortile_test_nonfilled_result', f)) + os.rmdir(os.path.join(data_dir, 'vectortile_test_nonfilled_result')) + for f in vector_tiling_result_files: + os.remove(os.path.join(data_dir, 'vectortile_test_filled_result', f)) + os.rmdir(os.path.join(data_dir, 'vectortile_test_filled_result')) + for f in raster_tiling_result_files: + os.remove(os.path.join(data_dir, 'rastertile_test_fill_nodata_result', f)) + os.rmdir(os.path.join(data_dir, 'rastertile_test_fill_nodata_result')) + diff --git a/docker/solaris/tests/test_utils/__init__.py b/docker/solaris/tests/test_utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/tests/test_utils/test_core.py b/docker/solaris/tests/test_utils/test_core.py new file mode 100644 index 00000000..43a84af2 --- /dev/null +++ b/docker/solaris/tests/test_utils/test_core.py @@ -0,0 +1,72 @@ +import os +import numpy as np +import pandas as pd +import geopandas as gpd +import rasterio +from solaris.data import data_dir +from solaris.utils.core import _check_df_load, _check_gdf_load +from solaris.utils.core import _check_rasterio_im_load, _check_crs +import pyproj + + +class TestLoadCheckers(object): + """Test objects for checking loading of various objects.""" + + def test_unloaded_geojson(self): + geojson_path = os.path.join(data_dir, 'sample.geojson') + truth_gdf = gpd.read_file(geojson_path) + test_gdf = _check_gdf_load(geojson_path) + + assert truth_gdf.equals(test_gdf) + + def test_loaded_geojson(self): + geojson_path = os.path.join(data_dir, 'sample.geojson') + truth_gdf = gpd.read_file(geojson_path) + test_gdf = _check_gdf_load(truth_gdf.copy()) + + assert truth_gdf.equals(test_gdf) + + def test_unloaded_df(self): + csv_path = os.path.join(data_dir, 'sample.csv') + truth_df = pd.read_csv(csv_path) + test_df = _check_df_load(csv_path) + + assert truth_df.equals(test_df) + + def test_loaded_df(self): + csv_path = os.path.join(data_dir, 'sample.csv') + truth_df = pd.read_csv(csv_path) + test_df = _check_df_load(truth_df.copy()) + + assert truth_df.equals(test_df) + + def test_unloaded_image(self): + im_path = os.path.join(data_dir, 'sample_geotiff.tif') + truth_im = rasterio.open(im_path) + test_im = _check_rasterio_im_load(im_path) + + assert truth_im.profile == test_im.profile + assert np.array_equal(truth_im.read(1), test_im.read(1)) + + truth_im.close() # need to close the rasterio datasetreader objects + test_im.close() + + def test_loaded_image(self): + im_path = os.path.join(data_dir, 'sample_geotiff.tif') + truth_im = rasterio.open(im_path) + test_im = _check_rasterio_im_load(truth_im) + + assert truth_im.profile == test_im.profile + assert np.array_equal(truth_im.read(1), test_im.read(1)) + + truth_im.close() # need to close the rasterio datasetreader objects + test_im.close() + + +class TestCRS(object): + """Test CRS parsing.""" + + def test_proj_CRS_object(self): + input_crs = pyproj.crs.CRS.from_epsg(4326) + crs = _check_crs(input_crs) + assert crs == rasterio.crs.CRS.from_epsg(4326) diff --git a/docker/solaris/tests/test_utils/test_data.py b/docker/solaris/tests/test_utils/test_data.py new file mode 100644 index 00000000..dbb4f5bd --- /dev/null +++ b/docker/solaris/tests/test_utils/test_data.py @@ -0,0 +1,66 @@ +import os +import pandas as pd +from solaris.data import data_dir +from solaris.utils.data import make_dataset_csv + + +class TestMakeDatasetCSV(object): + """Test sol.utils.data.make_dataset_csv().""" + + def test_with_regex(self): + output_df = make_dataset_csv( + im_dir=os.path.join(data_dir, 'rastertile_test_expected'), + label_dir=os.path.join(data_dir, 'vectortile_test_expected'), + match_re=r'([0-9]{6}_[0-9]{7})', + output_path=os.path.join(data_dir, 'tmp.csv')) + assert len(output_df) == 100 + im_substrs = output_df['image'].str.extract(r'([0-9]{6}_[0-9]{7})') + label_substrs = output_df['label'].str.extract(r'([0-9]{6}_[0-9]{7})') + assert im_substrs.equals(label_substrs) + os.remove(os.path.join(data_dir, 'tmp.csv')) + + def test_no_regex_get_error(self): + try: + # this *should* throw a ValueError + _ = make_dataset_csv( + im_dir=os.path.join(data_dir, 'rastertile_test_expected'), + label_dir=os.path.join(data_dir, 'vectortile_test_expected')) + assert False # it should never get here + except ValueError: + assert True + + def test_no_regex_skip_mismatch(self): + # this should generate an empty df because it doesn't use a regex to + # match images, it uses the full filename, which is different between + # the two sets. + output_df = make_dataset_csv( + im_dir=os.path.join(data_dir, 'rastertile_test_expected'), + label_dir=os.path.join(data_dir, 'vectortile_test_expected'), + ignore_mismatch='skip', + output_path=os.path.join(data_dir, 'tmp.csv')) + + assert len(output_df) == 0 + os.remove(os.path.join(data_dir, 'tmp.csv')) + + def test_catch_no_labels(self): + # make sure it generates an error if you call the function but don't + # give it labels for a training set + try: + _ = make_dataset_csv( + im_dir=os.path.join(data_dir, 'rastertile_test_expected'), + ignore_mismatch='skip', stage='train', + output_path=os.path.join(data_dir, 'tmp.csv')) + assert False + except ValueError: + assert True + + def test_infer_dataset(self): + + output_df = make_dataset_csv( + im_dir=os.path.join(data_dir, 'rastertile_test_expected'), + ignore_mismatch='skip', stage='infer', + output_path=os.path.join(data_dir, 'tmp.csv')) + + assert len(output_df) == 100 + assert len(output_df.columns) == 1 + os.remove(os.path.join(data_dir, 'tmp.csv')) diff --git a/docker/solaris/tests/test_utils/test_geo.py b/docker/solaris/tests/test_utils/test_geo.py new file mode 100644 index 00000000..285909c5 --- /dev/null +++ b/docker/solaris/tests/test_utils/test_geo.py @@ -0,0 +1,174 @@ +import os +import pandas as pd +import geopandas as gpd +import shapely +from affine import Affine +from shapely.wkt import loads +from shapely.ops import cascaded_union +from solaris.data import data_dir +from solaris.utils.core import _check_gdf_load +from solaris.utils.geo import list_to_affine, split_multi_geometries +from solaris.utils.geo import geometries_internal_intersection, split_geom +from solaris.utils.geo import reproject, reproject_geometry +import rasterio + + +class TestCoordTransformer(object): + """Tests for the utils.geo.CoordTransformer.""" + + def test_convert_image_crs(self): + pass + + +class TestListToAffine(object): + """Tests for utils.geo.list_to_affine().""" + + def test_rasterio_order_list(self): + truth_affine_obj = Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) + affine_list = [0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0] + test_affine = list_to_affine(affine_list) + + assert truth_affine_obj == test_affine + + def test_gdal_order_list(self): + truth_affine_obj = Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) + gdal_affine_list = [733601.0, 0.5, 0.0, 3725139.0, 0.0, -0.5] + test_affine = list_to_affine(gdal_affine_list) + + assert truth_affine_obj == test_affine + + +class TestGeometriesInternalIntersection(object): + """Tests for utils.geo.geometries_internal_intersection.""" + + def test_no_overlap(self): + """Test creation of an overlap object with no intersection.""" + poly_df = pd.read_csv(os.path.join(data_dir, 'sample.csv')) + polygons = poly_df['PolygonWKT_Pix'].apply(loads).values + preds = geometries_internal_intersection(polygons) + # there's no overlap, so result should be an empty GeometryCollection + assert preds == shapely.geometry.collection.GeometryCollection() + + def test_with_overlap(self): + poly_df = pd.read_csv(os.path.join(data_dir, 'sample.csv')) + # expand the polygons to generate some overlap + polygons = poly_df['PolygonWKT_Pix'].apply( + lambda x: loads(x).buffer(15)).values + preds = geometries_internal_intersection(polygons) + with open(os.path.join(data_dir, 'test_overlap_output.txt'), 'r') as f: + truth = f.read() + f.close() + truth = loads(truth) + # set a threshold for how good overlap with truth has to be in case of + # rounding errors + assert truth.intersection(preds).area/truth.area > 0.99 + + +class TestSplitMultiGeometries(object): + """Test for splittling MultiPolygons.""" + + def test_simple_split_multipolygon(self): + output = split_multi_geometries(os.path.join(data_dir, + 'w_multipolygon.csv')) + expected = gpd.read_file(os.path.join( + data_dir, 'split_multi_result.json')) + + assert expected.equals(output) + + def test_grouped_split_multipolygon(self): + output = split_multi_geometries( + os.path.join(data_dir, 'w_multipolygon.csv'), obj_id_col='field_1', + group_col='truncated') + expected = gpd.read_file(os.path.join( + data_dir, 'split_multi_grouped_result.json')) + + assert expected.equals(output) + + +class TestReproject(object): + """Test reprojection functionality.""" + + def test_reproject_rasterio_dataset(self): + input_data = os.path.join(data_dir, 'sample_geotiff.tif') + output = reproject(input_data, target_crs=4326, + dest_path=os.path.join(data_dir, 'tmp.tiff')) + with rasterio.open(input_data) as input_rio: + input_bounds = input_rio.bounds + expected_bounds = rasterio.warp.transform_bounds(input_rio.crs, + 'EPSG:4326', + *input_bounds) + expected_bounds = tuple([round(i, 4) for i in tuple(expected_bounds)]) + output_bounds = tuple([round(i, 4) for i in tuple(output.bounds)]) + + assert expected_bounds == output_bounds + assert output.crs.to_epsg() == 4326 + + os.remove(os.path.join(data_dir, 'tmp.tiff')) + + def test_reproject_gdf(self): + input_data = os.path.join(data_dir, 'gt.geojson') + output = reproject(input_data, target_crs=4326, + dest_path=os.path.join(data_dir, 'tmp.json')) + expected_result = gpd.read_file(os.path.join(data_dir, + 'gt_epsg4326.json')) + out_geoms = cascaded_union(output.geometry) + exp_geoms = cascaded_union(expected_result.geometry) + + assert out_geoms.intersection(exp_geoms).area/out_geoms.area > 0.99999 + os.remove(os.path.join(data_dir, 'tmp.json')) + + def test_reproject_gdf_utm_default(self): + input_data = os.path.join(data_dir, 'gt_epsg4326.json') + output = reproject(input_data) + expected_result = gpd.read_file(os.path.join(data_dir, 'gt.geojson')) + out_geoms = cascaded_union(output.geometry) + exp_geoms = cascaded_union(expected_result.geometry) + + assert out_geoms.intersection(exp_geoms).area/out_geoms.area > 0.99999 + + +class TestReprojectGeometry(object): + """Test reprojection of single geometries.""" + + def test_reproject_from_wkt(self): + input_str = "POLYGON ((736687.5456353347 3722455.06780279, 736686.9301210654 3722464.96326352, 736691.6397869177 3722470.9059681, 736705.5443059544 3722472.614050498, 736706.8992101226 3722462.858909504, 736704.866059878 3722459.457111885, 736713.1443474176 3722452.103498172, 736710.0312805283 3722447.309985571, 736700.3886167214 3722454.263705271, 736698.4577440721 3722451.98534527, 736690.1272768064 3722451.291527834, 736689.4108667439 3722455.113813923, 736687.5456353347 3722455.06780279))" + result_str = "POLYGON ((-84.4487639 33.6156071, -84.44876790000001 33.6156964, -84.4487156 33.61574889999999, -84.44856540000001 33.6157612, -84.44855339999999 33.61567300000001, -84.44857620000001 33.6156428, -84.448489 33.6155747, -84.4485238 33.6155322, -84.4486258 33.615597, -84.4486472 33.61557689999999, -84.4487371 33.6155725, -84.4487438 33.6156071, -84.4487639 33.6156071))" + result_geom = loads(result_str) + reproj_geom = reproject_geometry(input_str, input_crs=32616, + target_crs=4326) + area_sim = result_geom.intersection(reproj_geom).area/result_geom.area + + assert area_sim > 0.99999 + + def test_reproject_from_wkt_to_utm(self): + result_str = "POLYGON ((736687.5456353347 3722455.06780279, 736686.9301210654 3722464.96326352, 736691.6397869177 3722470.9059681, 736705.5443059544 3722472.614050498, 736706.8992101226 3722462.858909504, 736704.866059878 3722459.457111885, 736713.1443474176 3722452.103498172, 736710.0312805283 3722447.309985571, 736700.3886167214 3722454.263705271, 736698.4577440721 3722451.98534527, 736690.1272768064 3722451.291527834, 736689.4108667439 3722455.113813923, 736687.5456353347 3722455.06780279))" + input_str = "POLYGON ((-84.4487639 33.6156071, -84.44876790000001 33.6156964, -84.4487156 33.61574889999999, -84.44856540000001 33.6157612, -84.44855339999999 33.61567300000001, -84.44857620000001 33.6156428, -84.448489 33.6155747, -84.4485238 33.6155322, -84.4486258 33.615597, -84.4486472 33.61557689999999, -84.4487371 33.6155725, -84.4487438 33.6156071, -84.4487639 33.6156071))" + result_geom = loads(result_str) + reproj_geom = reproject_geometry(input_str, input_crs=4326, + target_crs=32616) + area_sim = result_geom.intersection(reproj_geom).area/result_geom.area + + assert area_sim > 0.99999 + + +class TestSplitGeometry(object): + """Test splitting of single geometries. Used in RasterTiler""" + + def test_split_polygon(self): + + poly = gpd.read_file(os.path.join( + data_dir, 'test_polygon_split.geojson')).iloc[0]['geometry'] + reproj_poly = reproject_geometry(poly, input_crs=4326, + target_crs=32611) + split_geom_list = split_geom(reproj_poly, (1024,1024), resolution=30) + assert len(split_geom_list) == 47 + + def test_split_multigeom_gdf(self): + + multi_gdf = _check_gdf_load( + os.path.join(data_dir, 'multigeom.geojson')) + expected_result = _check_gdf_load( + os.path.join(data_dir, 'multigeom_split_result.geojson')) + single_gdf = split_multi_geometries(multi_gdf) + + assert single_gdf.equals(expected_result) diff --git a/docker/solaris/tests/test_utils/test_io.py b/docker/solaris/tests/test_utils/test_io.py new file mode 100644 index 00000000..cd270fc9 --- /dev/null +++ b/docker/solaris/tests/test_utils/test_io.py @@ -0,0 +1,150 @@ +import numpy as np +from solaris.utils.io import preprocess_im_arr + + +class TestPreprocessImArr(object): + """Test image pre-processing.""" + + def test_rescale_auto(self): + expected_result = np.array([[[ 0, 0, 0], + [ 10, 10, 10], + [ 21, 21, 21], + [ 31, 31, 31], + [ 42, 42, 42]], + + [[ 53, 53, 53], + [ 63, 63, 63], + [ 74, 74, 74], + [ 85, 85, 85], + [ 95, 95, 95]], + + [[106, 106, 106], + [116, 116, 116], + [127, 127, 127], + [138, 138, 138], + [148, 148, 148]], + + [[159, 159, 159], + [170, 170, 170], + [180, 180, 180], + [191, 191, 191], + [201, 201, 201]], + + [[212, 212, 212], + [223, 223, 223], + [233, 233, 233], + [244, 244, 244], + [255, 255, 255]]], dtype='uint8') + im_arr = np.arange(5*5*3, 5*5*6).reshape(5, 5, 3).astype('uint16') + normalized_arr = preprocess_im_arr(im_arr, 'uint16', rescale=True) + + assert np.array_equal(normalized_arr, expected_result) + + def test_rescale_single_vals(self): + expected_result = np.array([[[ 77, 79, 80], + [ 82, 83, 85], + [ 86, 87, 89], + [ 90, 92, 93], + [ 94, 96, 97]], + + [[ 99, 100, 102], + [103, 104, 106], + [107, 109, 110], + [111, 113, 114], + [116, 117, 119]], + + [[120, 121, 123], + [124, 126, 127], + [128, 130, 131], + [133, 134, 136], + [137, 138, 140]], + + [[141, 143, 144], + [145, 147, 148], + [150, 151, 153], + [154, 155, 157], + [158, 160, 161]], + + [[162, 164, 165], + [167, 168, 170], + [171, 172, 174], + [175, 177, 178], + [179, 181, 182]]], dtype='uint8') + im_arr = np.arange(5*5*3, 5*5*6).reshape(5, 5, 3).astype('uint16') + normalized_arr = preprocess_im_arr(im_arr, 'uint16', rescale=True, + rescale_min=20, rescale_max=200) + + assert np.array_equal(normalized_arr, expected_result) + + def test_rescale_limit_range(self): + expected_result = np.array([[[ 0, 0, 0], + [ 0, 0, 0], + [ 0, 0, 0], + [ 0, 0, 0], + [ 0, 0, 0]], + + [[ 0, 5, 10], + [ 15, 20, 25], + [ 30, 35, 40], + [ 45, 51, 56], + [ 61, 66, 71]], + + [[ 76, 81, 86], + [ 91, 96, 102], + [107, 112, 117], + [122, 127, 132], + [137, 142, 147]], + + [[153, 158, 163], + [168, 173, 178], + [183, 188, 193], + [198, 204, 209], + [214, 219, 224]], + + [[229, 234, 239], + [244, 249, 255], + [255, 255, 255], + [255, 255, 255], + [255, 255, 255]]], dtype='uint8') + im_arr = np.arange(5*5*3, 5*5*6).reshape(5, 5, 3).astype('uint16') + normalized_arr = preprocess_im_arr(im_arr, 'uint16', rescale=True, + rescale_min=90, rescale_max=140) + + assert np.array_equal(normalized_arr, expected_result) + + def test_rescale_channel_lists(self): + expected_result = np.array([[[100, 83, 67], + [105, 89, 72], + [111, 94, 78], + [116, 100, 83], + [122, 105, 89]], + + [[127, 111, 94], + [132, 116, 100], + [138, 122, 105], + [143, 127, 111], + [149, 132, 116]], + + [[154, 138, 122], + [160, 143, 127], + [165, 149, 132], + [171, 154, 138], + [176, 160, 143]], + + [[182, 165, 149], + [187, 171, 154], + [193, 176, 160], + [198, 182, 165], + [204, 187, 171]], + + [[209, 193, 176], + [214, 198, 182], + [220, 204, 187], + [225, 209, 193], + [231, 214, 198]]], dtype='uint8') + im_arr = np.arange(5*5*3, 5*5*6).reshape(5, 5, 3).astype('uint16') + normalized_arr = preprocess_im_arr(im_arr, 'uint16', rescale=True, + rescale_min=[20, 30, 40], + rescale_max=[160, 170, 180]) + + assert np.array_equal(normalized_arr, expected_result) diff --git a/docker/solaris/tests/test_vector/__init__.py b/docker/solaris/tests/test_vector/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docker/solaris/tests/test_vector/test_graph.py b/docker/solaris/tests/test_vector/test_graph.py new file mode 100644 index 00000000..9342a87d --- /dev/null +++ b/docker/solaris/tests/test_vector/test_graph.py @@ -0,0 +1,19 @@ +import os +from solaris.data import data_dir +from solaris.vector.graph import geojson_to_graph +import pickle +import networkx as nx + + +class TestGeojsonToGraph(object): + """Tests for cw_geodata.vector_label.graph.geojson_to_graph.""" + + def test_graph_creation(self): + """Test if a newly created graph is identical to an existing one.""" + with open(os.path.join(data_dir, 'sample_graph.pkl'), 'rb') as f: + truth_graph = pickle.load(f) + f.close() + output_graph = geojson_to_graph(os.path.join(data_dir, + 'sample_roads.geojson')) + + assert nx.is_isomorphic(truth_graph, output_graph) diff --git a/docker/solaris/tests/test_vector/test_mask.py b/docker/solaris/tests/test_vector/test_mask.py new file mode 100644 index 00000000..7d250b76 --- /dev/null +++ b/docker/solaris/tests/test_vector/test_mask.py @@ -0,0 +1,301 @@ +import os +import numpy as np +import geopandas as gpd +import skimage +from solaris.data import data_dir +from solaris.vector.mask import footprint_mask, boundary_mask, \ + contact_mask, df_to_px_mask, mask_to_poly_geojson, road_mask, \ + preds_to_binary, instance_mask + + +class TestFootprintMask(object): + """Tests for solaris.vector.mask.footprint_mask.""" + + def test_make_mask(self): + """test creating a basic mask using a csv input.""" + output_mask = footprint_mask(os.path.join(data_dir, 'sample.csv'), + geom_col="PolygonWKT_Pix") + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_mask.tif')) + + assert np.array_equal(output_mask, truth_mask) + + def test_make_mask_w_output_file(self): + """test creating a basic mask and saving the output to a file.""" + output_mask = footprint_mask( + os.path.join(data_dir, 'sample.csv'), + geom_col="PolygonWKT_Pix", + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_mask.tif')) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + # clean up + os.remove(os.path.join(data_dir, 'test_out.tif')) + + def test_make_mask_w_file_and_transform(self): + """Test creating a mask using a geojson and an affine xform.""" + output_mask = footprint_mask( + os.path.join(data_dir, 'geotiff_labels.geojson'), + reference_im=os.path.join(data_dir, 'sample_geotiff.tif'), + do_transform=True, + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread( + os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + # clean up + os.remove(os.path.join(data_dir, 'test_out.tif')) + + def test_make_mask_infer_do_transform_true(self): + output_mask = footprint_mask( + os.path.join(data_dir, 'geotiff_labels.geojson'), + reference_im=os.path.join(data_dir, 'sample_geotiff.tif'), + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread( + os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + # clean up + os.remove(os.path.join(data_dir, 'test_out.tif')) + + +class TestBoundaryMask(object): + """Tests for solaris.vector.mask.boundary_mask.""" + + def test_make_inner_mask_from_fp(self): + """test creating a boundary mask using an existing footprint mask.""" + fp_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_mask.tif')) + output_mask = boundary_mask(fp_mask) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_b_mask_inner.tif')) + + assert np.array_equal(output_mask, truth_mask) + + def test_make_outer_mask_from_fp(self): + """test creating a boundary mask using an existing footprint mask.""" + fp_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_mask.tif')) + output_mask = boundary_mask(fp_mask, boundary_type="outer") + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_b_mask_outer.tif')) + + assert np.array_equal(output_mask, truth_mask) + + def test_make_thick_outer_mask_from_fp(self): + """test creating a 10-px thick boundary mask.""" + fp_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_mask.tif')) + output_mask = boundary_mask(fp_mask, boundary_type="outer", + boundary_width=10) + truth_mask = skimage.io.imread( + os.path.join(data_dir, 'sample_b_mask_outer_10.tif') + ) + + assert np.array_equal(output_mask, truth_mask) + + def test_make_binary_and_fp(self): + """test creating a boundary mask and a fp mask together.""" + output_mask = boundary_mask(df=os.path.join(data_dir, 'sample.csv'), + geom_col="PolygonWKT_Pix") + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_b_mask_inner.tif')) + + assert np.array_equal(output_mask, truth_mask) + + +class TestContactMask(object): + """Tests for solaris.vector.mask.contact_mask.""" + + def test_make_contact_mask_w_save(self): + """test creating a contact point mask.""" + output_mask = contact_mask( + os.path.join(data_dir, 'sample.csv'), geom_col="PolygonWKT_Pix", + contact_spacing=10, + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_c_mask.tif')) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after + + +class TestDFToPxMask(object): + """Tests for solaris.vector.mask.df_to_px_mask.""" + + def test_basic_footprint_w_save(self): + output_mask = df_to_px_mask( + os.path.join(data_dir, 'sample.csv'), + geom_col='PolygonWKT_Pix', + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif')) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_fp_from_df2px.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask[:, :, 0]) + os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after + + def test_border_footprint_w_save(self): + output_mask = df_to_px_mask( + os.path.join(data_dir, 'sample.csv'), channels=['boundary'], + geom_col='PolygonWKT_Pix', + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif')) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_b_from_df2px.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask[:, :, 0]) + os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after + + def test_contact_footprint_w_save(self): + output_mask = df_to_px_mask( + os.path.join(data_dir, 'sample.csv'), channels=['contact'], + geom_col='PolygonWKT_Pix', + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif')) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_c_from_df2px.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask[:, :, 0]) + os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after + + def test_all_three_w_save(self): + """Test creating a 3-channel mask.""" + output_mask = df_to_px_mask( + os.path.join(data_dir, 'sample.csv'), + channels=['footprint', 'boundary', 'contact'], + boundary_type='outer', boundary_width=5, contact_spacing=15, + geom_col='PolygonWKT_Pix', + reference_im=os.path.join(data_dir, "sample_geotiff.tif"), + out_file=os.path.join(data_dir, 'test_out.tif')) + truth_mask = skimage.io.imread( + os.path.join(data_dir, 'sample_fbc_from_df2px.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + os.remove(os.path.join(data_dir, 'test_out.tif')) # clean up after + + +class TestMaskToGDF(object): + """Tests for converting pixel masks to geodataframes or geojsons.""" + + def test_mask_to_gdf_basic(self): + gdf = mask_to_poly_geojson( + os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif')) + truth_gdf = gpd.read_file(os.path.join(data_dir, + 'gdf_from_mask_1.geojson')) + assert truth_gdf[['geometry', 'value']].equals(gdf) + + def test_mask_to_gdf_geoxform_simplified(self): + gdf = mask_to_poly_geojson( + os.path.join(data_dir, 'sample_fp_mask_from_geojson.tif'), + reference_im=os.path.join(data_dir, 'sample_geotiff.tif'), + do_transform=True, + min_area=100, + simplify=True + ) + truth_gdf = gpd.read_file(os.path.join(data_dir, + 'gdf_from_mask_2.geojson')) + assert truth_gdf[['geometry', 'value']].equals(gdf) + + def test_flatten_multichannel_mask(self): + anarr = np.array([[[0, 0, 0, 1], + [0, 0, 1, 0], + [0, 0, 0, 1], + [0, 0, 0, 0]], + [[1, 1, 0, 0], + [1, 1, 1, 0], + [0, 0, 0, 0], + [0, 0, 0, 1]], + [[1, 0, 0, 1], + [0, 1, 0, 1], + [0, 1, 1, 0], + [0, 0, 0, 0]]], dtype='float') + scaling_vector = [0.25, 1., 2.] + result = preds_to_binary(anarr, scaling_vector, bg_threshold=0.5) + assert np.array_equal(result, + np.array([[255, 255, 0, 255], + [255, 255, 255, 255], + [0, 255, 255, 0], + [0, 0, 0, 255]], dtype='uint8')) + + +class TestRoadMask(object): + """Test(s) for solaris.vector.mask.road_mask.""" + + def test_make_mask_w_file_and_transform(self): + """Test creating a mask using a geojson and an affine xform.""" + output_mask = road_mask( + os.path.join(data_dir, 'sample_roads_for_masking.geojson'), + reference_im=os.path.join(data_dir, 'road_mask_input.tif'), + width=4, meters=True, do_transform=True, + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread( + os.path.join(data_dir, 'sample_road_raster_mask.tif') + ) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(output_mask, truth_mask) + assert np.array_equal(saved_output_mask, truth_mask) + # clean up + os.remove(os.path.join(data_dir, 'test_out.tif')) + + +class TestInstanceMask(object): + """Tests for solaris.vector.mask.instance_mask.""" + + def test_make_mask_w_ref_image(self): + """Test creating a multichannel instance mask with geojson + ref im.""" + output_mask = instance_mask( + os.path.join(data_dir, 'geotiff_labels.geojson'), + reference_im=os.path.join(data_dir, 'sample_geotiff.tif'), + do_transform=True, + out_file=os.path.join(data_dir, 'test_out.tif') + ) + truth_mask = skimage.io.imread(os.path.join(data_dir, + 'sample_inst_mask.tif')) + saved_output_mask = skimage.io.imread(os.path.join(data_dir, + 'test_out.tif')) + + assert np.array_equal(saved_output_mask, truth_mask) + # clean up + os.remove(os.path.join(data_dir, 'test_out.tif')) + assert np.array_equal(output_mask, truth_mask) diff --git a/docker/solaris/tests/test_vector/test_polygon.py b/docker/solaris/tests/test_vector/test_polygon.py new file mode 100644 index 00000000..ed13e63a --- /dev/null +++ b/docker/solaris/tests/test_vector/test_polygon.py @@ -0,0 +1,141 @@ +import os +import pandas as pd +from affine import Affine +from shapely.geometry import Polygon +from shapely.wkt import loads, dumps +import geopandas as gpd +import rasterio +from solaris.data import data_dir +from solaris.vector.polygon import convert_poly_coords, \ + affine_transform_gdf, georegister_px_df, geojson_to_px_gdf, \ + gdf_to_yolo + +square = Polygon([(10, 20), (10, 10), (20, 10), (20, 20)]) +forward_result = loads("POLYGON ((733606 3725129, 733606 3725134, 733611 3725134, 733611 3725129, 733606 3725129))") +reverse_result = loads("POLYGON ((-1467182 7450238, -1467182 7450258, -1467162 7450258, -1467162 7450238, -1467182 7450238))") +# note that the xform below is the same as in cw_geodata/data/sample_geotiff.tif +aff = Affine(0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0) +affine_list = [0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0] +long_affine_list = [0.5, 0.0, 733601.0, 0.0, -0.5, 3725139.0, + 0.0, 0.0, 1.0] +gdal_affine_list = [733601.0, 0.5, 0.0, 3725139.0, 0.0, -0.5] + + +class TestConvertPolyCoords(object): + """Test the convert_poly_coords functionality.""" + + def test_square_pass_affine(self): + """Test both forward and inverse transforms when passed affine obj.""" + xform_result = convert_poly_coords(square, affine_obj=aff) + assert xform_result == forward_result + rev_xform_result = convert_poly_coords(square, + affine_obj=aff, + inverse=True) + assert rev_xform_result == reverse_result + + def test_square_pass_raster(self): + """Test forward affine transform when passed a raster reference.""" + raster_src = os.path.join(data_dir, 'sample_geotiff.tif') + xform_result = convert_poly_coords(square, raster_src=raster_src) + assert xform_result == forward_result + + def test_square_pass_list(self): + """Test forward and reverse affine transform when passed a list.""" + fwd_xform_result = convert_poly_coords(square, + affine_obj=affine_list) + assert fwd_xform_result == forward_result + rev_xform_result = convert_poly_coords(square, + affine_obj=affine_list, + inverse=True) + assert rev_xform_result == reverse_result + + def test_square_pass_gdal_list(self): + """Test forward affine transform when passed a list in gdal order.""" + fwd_xform_result = convert_poly_coords(square, + affine_obj=gdal_affine_list + ) + assert fwd_xform_result == forward_result + + def test_square_pass_long_list(self): + """Test forward affine transform when passed a full 9-element xform.""" + fwd_xform_result = convert_poly_coords( + square, affine_obj=long_affine_list + ) + assert fwd_xform_result == forward_result + + +class TestAffineTransformGDF(object): + """Test the affine_transform_gdf functionality.""" + + def test_transform_csv(self): + truth_gdf = pd.read_csv(os.path.join(data_dir, 'aff_gdf_result.csv')) + input_df = os.path.join(data_dir, 'sample.csv') + output_gdf = affine_transform_gdf(input_df, aff, + geom_col="PolygonWKT_Pix", + precision=0) + output_gdf['geometry'] = output_gdf['geometry'].apply(dumps, trim=True) + assert output_gdf.equals(truth_gdf) + + +class TestGeoregisterPxDF(object): + """Test the georegister_px_df functionality.""" + + def test_transform_using_raster(self): + input_df = os.path.join(data_dir, 'sample.csv') + input_im = os.path.join(data_dir, 'sample_geotiff.tif') + output_gdf = georegister_px_df(input_df, im_path=input_im, + geom_col='PolygonWKT_Pix', precision=0) + truth_df = pd.read_csv(os.path.join(data_dir, 'aff_gdf_result.csv')) + truth_df['geometry'] = truth_df['geometry'].apply(loads) + truth_gdf = gpd.GeoDataFrame( + truth_df, + crs=rasterio.open(os.path.join(data_dir, 'sample_geotiff.tif')).crs + ) + + assert truth_gdf.equals(output_gdf) + + def test_transform_using_aff_crs(self): + input_df = os.path.join(data_dir, 'sample.csv') + crs = rasterio.open(os.path.join(data_dir, 'sample_geotiff.tif')).crs + output_gdf = georegister_px_df(input_df, affine_obj=aff, crs=crs, + geom_col='PolygonWKT_Pix', precision=0) + truth_df = pd.read_csv(os.path.join(data_dir, 'aff_gdf_result.csv')) + truth_df['geometry'] = truth_df['geometry'].apply(loads) + truth_gdf = gpd.GeoDataFrame( + truth_df, + crs=rasterio.open(os.path.join(data_dir, 'sample_geotiff.tif')).crs + ) + + assert truth_gdf.equals(output_gdf) + + +class TestGeojsonToPxGDF(object): + """Tests for geojson_to_px_gdf.""" + + def test_transform_to_px_coords(self): + output_gdf = geojson_to_px_gdf( + os.path.join(data_dir, 'geotiff_labels.geojson'), + os.path.join(data_dir, 'sample_geotiff.tif'), + precision=0 + ) + truth_gdf = gpd.read_file(os.path.join(data_dir, + 'gj_to_px_result.geojson')) + truth_subset = truth_gdf[['geometry']] + output_subset = output_gdf[['geometry']].reset_index(drop=True) + + assert truth_subset.equals(output_subset) + + +class TestGDFToYOLO(object): + """Test the gdf_to_yolo function.""" + + def test_gdf_to_yolo(self): + gdf = gpd.read_file(os.path.join(data_dir, 'geotiff_labels.geojson')) + image = os.path.join(data_dir, 'sample_geotiff.tif') + output_gdf = gdf_to_yolo(gdf, image, data_dir, column='origlen') + truth_gdf = pd.read_csv(os.path.join(data_dir, 'yolo_gdf_result.csv')) + truth_gdf = truth_gdf.sort_values(by='area').reset_index(drop=True) + output_gdf = output_gdf.sort_values(by='area').reset_index(drop=True) + truth_gdf = truth_gdf['w'].round(4) + output_gdf = output_gdf['w'].round(4) + assert truth_gdf.equals(output_gdf) diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 2fe396e1..00000000 --- a/environment.yml +++ /dev/null @@ -1,515 +0,0 @@ -name: fair -channels: - - conda-forge - - defaults -dependencies: - - _ipyw_jlab_nb_ext_conf=0.1.0 - - _libgcc_mutex=0.1 - - alabaster=0.7.12 - - anaconda=2022.10 - - anaconda-client=1.11.0 - - anaconda-navigator=2.3.1 - - anaconda-project=0.11.1 - - anyio=3.5.0 - - appdirs=1.4.4 - - argon2-cffi=21.3.0 - - argon2-cffi-bindings=21.2.0 - - arrow=1.2.2 - - astroid=2.11.7 - - astropy=5.1 - - atomicwrites=1.4.0 - - attrs=21.4.0 - - automat=20.2.0 - - autopep8=1.6.0 - - babel=2.9.1 - - backcall=0.2.0 - - backports=1.1 - - backports.functools_lru_cache=1.6.4 - - backports.tempfile=1.0 - - backports.weakref=1.0.post1 - - bcrypt=3.2.0 - - beautifulsoup4=4.11.1 - - binaryornot=0.4.4 - - bitarray=2.5.1 - - bkcharts=0.2 - - black=22.6.0 - - blas=1.0 - - bleach=4.1.0 - - blosc=1.21.0 - - bokeh=2.4.3 - - boost-cpp=1.70.0 - - boto3=1.24.28 - - botocore=1.27.28 - - bottleneck=1.3.5 - - brotli=1.0.9 - - brotli-bin=1.0.9 - - brotlipy=0.7.0 - - brunsli=0.1 - - bzip2=1.0.8 - - c-ares=1.18.1 - - ca-certificates=2022.07.19 - - cairo=1.16.0 - - cfitsio=3.470 - - chardet=4.0.0 - - charls=2.2.0 - - charset-normalizer=2.0.4 - - cloudpickle=2.0.0 - - clyent=1.2.2 - - colorama=0.4.5 - - colorcet=3.0.0 - - conda=22.11.1 - - conda-build=3.22.0 - - conda-content-trust=0.1.3 - - conda-env=2.6.0 - - conda-pack=0.6.0 - - conda-package-handling=1.9.0 - - conda-repo-cli=1.0.20 - - conda-token=0.4.0 - - conda-verify=3.4.2 - - constantly=15.1.0 - - cookiecutter=1.7.3 - - cssselect=1.1.0 - - curl=7.84.0 - - cycler=0.11.0 - - cython=0.29.32 - - daal4py=2021.6.0 - - dal=2021.6.0 - - dataclasses=0.8 - - datashader=0.14.1 - - datashape=0.5.4 - - dbus=1.13.18 - - debugpy=1.5.1 - - decorator=5.1.1 - - defusedxml=0.7.1 - - diff-match-patch=20200713 - - dill=0.3.4 - - distributed=2022.7.0 - - docutils=0.18.1 - - entrypoints=0.4 - - et_xmlfile=1.1.0 - - expat=2.4.9 - - fftw=3.3.9 - - filelock=3.6.0 - - flake8=4.0.1 - - flask=1.1.2 - - fontconfig=2.13.1 - - freetype=2.11.0 - - freexl=1.0.6 - - future=0.18.2 - - gensim=4.1.2 - - geos=3.8.0 - - geotiff=1.7.0 - - giflib=5.2.1 - - glib=2.69.1 - - glob2=0.7 - - gmp=6.2.1 - - gmpy2=2.1.2 - - greenlet=1.1.1 - - gst-plugins-base=1.14.0 - - gstreamer=1.14.0 - - hdf4=4.2.13 - - hdf5=1.10.6 - - heapdict=1.0.1 - - holoviews=1.15.0 - - hvplot=0.8.0 - - hyperlink=21.0.0 - - icu=58.2 - - idna=3.3 - - imagesize=1.4.1 - - importlib-metadata=4.11.3 - - importlib_metadata=4.11.3 - - incremental=21.3.0 - - inflection=0.5.1 - - iniconfig=1.1.1 - - intake=0.6.5 - - intel-openmp=2021.4.0 - - intervaltree=3.1.0 - - ipykernel=6.15.2 - - ipython=7.31.1 - - ipython_genutils=0.2.0 - - ipywidgets=7.6.5 - - isort=5.9.3 - - itemadapter=0.3.0 - - itemloaders=1.0.4 - - itsdangerous=2.0.1 - - jdcal=1.4.1 - - jedi=0.18.1 - - jeepney=0.7.1 - - jellyfish=0.9.0 - - jinja2-time=0.2.0 - - jmespath=0.10.0 - - joblib=1.1.0 - - jpeg=9e - - jq=1.6 - - json-c=0.13.1 - - json5=0.9.6 - - jsonschema=4.16.0 - - jupyter=1.0.0 - - jupyter_client=7.3.4 - - jupyter_console=6.4.3 - - jupyter_core=4.11.1 - - jupyter_server=1.18.1 - - jupyterlab=3.4.4 - - jupyterlab_pygments=0.1.2 - - jupyterlab_server=2.10.3 - - jupyterlab_widgets=1.0.0 - - jxrlib=1.1 - - kealib=1.4.14 - - keyring=23.4.0 - - kiwisolver=1.4.2 - - krb5=1.19.2 - - lazy-object-proxy=1.6.0 - - lcms2=2.12 - - lerc=3.0 - - libaec=1.0.4 - - libarchive=3.6.1 - - libbrotlicommon=1.0.9 - - libbrotlidec=1.0.9 - - libbrotlienc=1.0.9 - - libcurl=7.84.0 - - libdap4=3.19.1 - - libdeflate=1.8 - - libedit=3.1.20210910 - - libev=4.33 - - libevent=2.1.12 - - libffi=3.3 - - libgdal=3.4.1 - - libgfortran5=11.2.0 - - libidn2=2.3.2 - - libkml=1.3.0 - - liblief=0.11.5 - - libllvm10=10.0.1 - - libllvm11=11.1.0 - - libnetcdf=4.8.1 - - libnghttp2=1.46.0 - - libpng=1.6.37 - - libpq=12.9 - - libsodium=1.0.18 - - libspatialindex=1.9.3 - - libspatialite=4.3.0a - - libssh2=1.10.0 - - libtiff=4.4.0 - - libunistring=0.9.10 - - libuuid=1.0.3 - - libwebp=1.2.2 - - libwebp-base=1.2.2 - - libxml2=2.9.14 - - libxslt=1.1.35 - - libzip=1.8.0 - - libzopfli=1.0.3 - - llvmlite=0.38.0 - - lxml=4.9.1 - - lz4=3.1.3 - - lz4-c=1.9.3 - - lzo=2.10 - - markdown=3.3.4 - - matplotlib-inline=0.1.6 - - mccabe=0.7.0 - - mistune=0.8.4 - - mkl=2021.4.0 - - mkl-service=2.4.0 - - mkl_fft=1.3.1 - - mkl_random=1.2.2 - - mock=4.0.3 - - mpc=1.1.0 - - mpfr=4.0.2 - - mpi=1.0 - - mpich=3.3.2 - - mpmath=1.2.1 - - msgpack-python=1.0.3 - - multipledispatch=0.6.0 - - munkres=1.1.4 - - mypy_extensions=0.4.3 - - navigator-updater=0.3.0 - - nbclassic=0.3.5 - - nbclient=0.5.13 - - nbconvert=6.4.4 - - nbformat=5.5.0 - - ncurses=6.3 - - nest-asyncio=1.5.5 - - nltk=3.7 - - nose=1.3.7 - - notebook=6.4.12 - - nspr=4.33 - - nss=3.74 - - numba=0.55.1 - - numexpr=2.8.3 - - numpydoc=1.4.0 - - olefile=0.46 - - oniguruma=6.9.7.1 - - openjpeg=2.4.0 - - openpyxl=3.0.10 - - openssl=1.1.1q - - packaging=21.3 - - pandocfilters=1.5.0 - - panel=0.13.1 - - param=1.12.0 - - parsel=1.6.0 - - parso=0.8.3 - - partd=1.2.0 - - patch=2.7.6 - - patchelf=0.13 - - pathlib=1.0.1 - - pathspec=0.9.0 - - patsy=0.5.2 - - pcre=8.45 - - pep8=1.7.1 - - pexpect=4.8.0 - - pickleshare=0.7.5 - - pixman=0.40.0 - - pkginfo=1.8.2 - - platformdirs=2.5.2 - - plotly=5.9.0 - - pluggy=1.0.0 - - ply=3.11 - - poppler=0.81.0 - - poppler-data=0.4.11 - - poyo=0.5.0 - - proj=6.2.1 - - prometheus_client=0.14.1 - - prompt-toolkit=3.0.20 - - prompt_toolkit=3.0.20 - - protego=0.1.16 - - psutil=5.9.0 - - ptyprocess=0.7.0 - - py=1.11.0 - - py-lief=0.11.5 - - pyasn1=0.4.8 - - pyasn1-modules=0.2.8 - - pycodestyle=2.8.0 - - pycosat=0.6.3 - - pycparser=2.21 - - pyct=0.4.8 - - pycurl=7.45.1 - - pydispatcher=2.0.5 - - pydocstyle=6.1.1 - - pyerfa=2.0.0 - - pyflakes=2.4.0 - - pygments=2.11.2 - - pyhamcrest=2.0.2 - - pyjwt=2.4.0 - - pylint=2.14.5 - - pyls-spyder=0.4.0 - - pyodbc=4.0.34 - - pyqt=5.15.7 - - pyqt5-sip=12.11.0 - - pyqtwebengine=5.15.7 - - pyrsistent=0.18.0 - - pysocks=1.7.1 - - pytables=3.6.1 - - pytest=7.1.2 - - python=3.9.13 - - python-dateutil=2.8.2 - - python-fastjsonschema=2.16.2 - - python-libarchive-c=2.9 - - python-lsp-black=1.2.1 - - python-lsp-jsonrpc=1.0.0 - - python-lsp-server=1.5.0 - - python-slugify=5.0.2 - - python-snappy=0.6.0 - - python_abi=3.9 - - pytz=2022.1 - - pyviz_comms=2.0.2 - - pywavelets=1.3.0 - - pyxdg=0.27 - - pyyaml=6.0 - - pyzmq=23.2.0 - - qdarkstyle=3.0.2 - - qstylizer=0.1.10 - - qt=5.15.9 - - qt-main=5.15.2 - - qt-webengine=5.15.9 - - qtawesome=1.0.3 - - qtconsole=5.3.2 - - qtpy=2.2.0 - - qtwebkit=5.212 - - queuelib=1.5.0 - - readline=8.1.2 - - regex=2022.7.9 - - requests-file=1.5.1 - - ripgrep=13.0.0 - - rope=0.22.0 - - rtree=0.9.7 - - ruamel.yaml=0.17.21 - - ruamel.yaml.clib=0.2.6 - - ruamel_yaml=0.15.100 - - s3transfer=0.6.0 - - scikit-image=0.19.2 - - scikit-learn=1.0.2 - - scikit-learn-intelex=2021.6.0 - - scrapy=2.6.2 - - seaborn=0.11.2 - - send2trash=1.8.0 - - service_identity=18.1.0 - - sip=6.6.2 - - six=1.16.0 - - smart_open=5.2.1 - - snappy=1.1.9 - - sniffio=1.2.0 - - snowballstemmer=2.2.0 - - sortedcollections=2.1.0 - - sortedcontainers=2.4.0 - - soupsieve=2.3.1 - - sphinx=5.0.2 - - sphinxcontrib-applehelp=1.0.2 - - sphinxcontrib-devhelp=1.0.2 - - sphinxcontrib-htmlhelp=2.0.0 - - sphinxcontrib-jsmath=1.0.1 - - sphinxcontrib-qthelp=1.0.3 - - sphinxcontrib-serializinghtml=1.1.5 - - spyder=5.3.3 - - spyder-kernels=2.3.3 - - sqlalchemy=1.4.39 - - sqlite=3.39.3 - - statsmodels=0.13.2 - - sympy=1.10.1 - - tabulate=0.8.10 - - tbb=2021.6.0 - - tbb4py=2021.6.0 - - tblib=1.7.0 - - tenacity=8.0.1 - - terminado=0.13.1 - - testpath=0.6.0 - - text-unidecode=1.3 - - textdistance=4.2.1 - - three-merge=0.1.1 - - tiledb=2.3.3 - - tinycss=0.4 - - tk=8.6.12 - - tldextract=3.2.0 - - toml=0.10.2 - - tomli=2.0.1 - - tomlkit=0.11.1 - - toolz=0.11.2 - - tornado=6.1 - - traitlets=5.1.1 - - twisted=22.2.0 - - typing-extensions=4.3.0 - - typing_extensions=4.3.0 - - tzdata=2022c - - ujson=5.4.0 - - unidecode=1.2.0 - - unixodbc=2.3.11 - - w3lib=1.21.0 - - watchdog=2.1.6 - - wcwidth=0.2.5 - - webencodings=0.5.1 - - websocket-client=0.58.0 - - werkzeug=2.0.3 - - wget=1.21.3 - - whatthepatch=1.0.2 - - wheel=0.37.1 - - widgetsnbextension=3.5.2 - - wrapt=1.14.1 - - wurlitzer=3.0.2 - - xarray=0.20.1 - - xerces-c=3.2.4 - - xlrd=2.0.1 - - xlsxwriter=3.0.3 - - xz=5.2.6 - - yaml=0.2.5 - - yapf=0.31.0 - - zeromq=4.3.4 - - zfp=0.5.5 - - zict=2.1.0 - - zipp=3.8.0 - - zlib=1.2.12 - - zope=1.0 - - zope.interface=5.4.0 - - zstd=1.5.2 - - pip: - - absl-py==1.3.0 - - affine==2.3.1 - - albumentations==1.0.3 - - astunparse==1.6.3 - - branca==0.4.2 - - build==0.9.0 - - bumpver==2022.1120 - - cachetools==5.2.0 - - certifi==2021.10.8 - - cffi==1.15.0 - - click==8.1.0 - - click-plugins==1.1.1 - - cligj==0.7.2 - - commonmark==0.9.1 - - cryptography==36.0.0 - - cytoolz==0.11.2 - - dask==2022.3.0 - - efficientnet==1.0.0 - - fiona==1.8.21 - - flatbuffers==1.12 - - folium==0.12.1.post1 - - fonttools==4.31.2 - - fsspec==2022.2.0 - - gast==0.4.0 - - gdal==3.3.2 - - geojson==2.5.0 - - geopandas==0.10.2 - - google-auth==2.15.0 - - google-auth-oauthlib==0.4.6 - - google-pasta==0.2.0 - - grpcio==1.51.1 - - h5py==3.6.0 - - image-classifiers==1.0.0 - - imagecodecs==2022.2.22 - - imageio==2.16.1 - - imgaug==0.4.0 - - jinja2==3.1.1 - - keras==2.9.0 - - keras-applications==1.0.8 - - keras-preprocessing==1.1.2 - - lexid==2021.1006 - - libclang==14.0.6 - - locket==0.2.0 - - mapclassify==2.4.3 - - markupsafe==2.1.1 - - matplotlib==3.5.1 - - mercantile==1.2.1 - - munch==2.5.0 - - networkx==2.7.1 - - numpy==1.22.3 - - oauthlib==3.2.2 - - opencv-python==4.5.5.64 - - opencv-python-headless==4.6.0.66 - - opt-einsum==3.3.0 - - pandas==1.4.1 - - pathlib2==2.3.7.post1 - - pep517==0.13.0 - - pillow==9.0.1 - - pip==21.2.4 - - protobuf==3.19.6 - - pyopenssl==21.0.0 - - pyparsing==3.0.7 - - pyproj==3.3.0 - - ramp-fair==0.1.1 - - rasterio==1.2.10 - - readme-renderer==37.3 - - requests==2.27.1 - - requests-oauthlib==1.3.1 - - requests-toolbelt==0.10.1 - - rfc3986==2.0.0 - - rich==12.6.0 - - rsa==4.9 - - scikit-fmm==2022.8.15 - - scipy==1.8.0 - - segmentation-models==1.0.1 - - setuptools==58.0.4 - - shapely==1.8.0 - - snuggs==1.4.7 - - solaris==0.4.0 - - tensorboard==2.9.1 - - tensorboard-data-server==0.6.1 - - tensorboard-plugin-wit==1.8.1 - - tensorflow==2.9.2 - - tensorflow-estimator==2.9.0 - - tensorflow-io-gcs-filesystem==0.28.0 - - termcolor==2.1.1 - - tf-estimator-nightly==2.8.0.dev2021122109 - - threadpoolctl==3.1.0 - - tifffile==2022.3.25 - - tinydb==4.7.0 - - tqdm==4.62.3 - - twine==4.0.2 - - unicodedata2==14.0.0 - - urllib3==1.26.7 - - xyzservices==2022.3.0 diff --git a/package_test.ipynb b/package_test.ipynb deleted file mode 100644 index 4a15480c..00000000 --- a/package_test.ipynb +++ /dev/null @@ -1,324 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num GPUs Available: 1\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WI8nslrSgROI", - "outputId": "2cbad97c-765f-4d2b-87df-773447500332" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/kshitij/hotosm/fAIr-utilities\n", - "/Users/kshitij/hotosm/fAIr-utilities\n" - ] - } - ], - "source": [ - "import os \n", - "print(os.getcwd())\n", - "os.environ.update(os.environ)\n", - " # Add a new environment variable to the operating system\n", - "os.environ[\"RAMP_HOME\"] = os.getcwd()\n", - "# Print the environment variables to verify that the new variable was added\n", - "print(os.environ[\"RAMP_HOME\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ramp.utils\n", - "import hot_fair_utilities\n", - "base_path = f\"{os.getcwd()}/ramp-data/sample_2\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from hot_fair_utilities import preprocess\n", - "model_input_image_path = f\"{base_path}/input\"\n", - "preprocess_output=f\"/{base_path}/preprocessed\"\n", - "preprocess(\n", - " input_path = model_input_image_path,\n", - " output_path = preprocess_output,\n", - " rasterize=True,\n", - " rasterize_options=[\"binary\"],\n", - " georeference_images=True,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from hot_fair_utilities import train" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Writing /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/fair_split_train.csv\n", - "Writing /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/fair_split_val.csv\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting to prepare data for training\n", - "Data is ready for training\n", - "Metric constructor function: get_sparse_categorical_accuracy_fn\n", - "Model: importing saved model /home/kshitij/hotosm/fAIr-lib-python/ramp-code/ramp/checkpoint.tf\n", - "\n", - "Starting Training with 2 epochs , 2 batch size , 62 steps per epoch , 11 validation steps......\n", - "Epoch 1/2\n", - "62/62 [==============================] - ETA: 0s - loss: 0.7531 - sparse_categorical_accuracy: 0.8088" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 92). These functions will not be directly callable after loading.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819/model_20221212-183819_001_0.803.tf/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819/model_20221212-183819_001_0.803.tf/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 1s 1s/step\n", - "62/62 [==============================] - 106s 2s/step - loss: 0.7531 - sparse_categorical_accuracy: 0.8088 - val_loss: 0.6061 - val_sparse_categorical_accuracy: 0.8031\n", - "Epoch 2/2\n", - "62/62 [==============================] - ETA: 0s - loss: 0.4104 - sparse_categorical_accuracy: 0.8492" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 92). These functions will not be directly callable after loading.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819/model_20221212-183819_002_0.893.tf/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819/model_20221212-183819_002_0.893.tf/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 0s 257ms/step\n", - "62/62 [==============================] - 102s 2s/step - loss: 0.4104 - sparse_categorical_accuracy: 0.8492 - val_loss: 0.4541 - val_sparse_categorical_accuracy: 0.8934\n", - "Training Finished , Time taken to train : 208.0741161549995 seconds\n", - "Generating graphs ....\n", - "Graph generated at : /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/graphs\n", - "extracting highest accuracy model\n", - "/home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819\n", - "model_20221212-183819_002_0.893.tf\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_output = f\"{base_path}/train\"\n", - "final_accuracy, final_model_path = train(\n", - " input_path=preprocess_output,\n", - " output_path=train_output,\n", - " epoch_size=2,\n", - " batch_size=2,\n", - " model=\"ramp\",\n", - " model_home=os.environ[\"RAMP_HOME\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "89.3 /home/kshitij/hotosm/fAIr-lib-python/ramp-data/sample_2/train/model-checkpts/20221212-183819/model_20221212-183819_002_0.893.tf\n" - ] - } - ], - "source": [ - "print(final_accuracy,final_model_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "w1PLe9S9BL8L", - "outputId": "e4f3ce64-bbd6-4969-e49d-0f47dc9d6c0a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 1s 1s/step\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Georeferencing for output: 100%|██████████████████████████████████████████████| 2/2 [00:00<00:00, 59.06it/s]\n" - ] - } - ], - "source": [ - "from hot_fair_utilities import predict\n", - "prediction_output = f\"{base_path}/prediction/output\"\n", - "predict(\n", - " checkpoint_path=final_model_path,\n", - " input_path=f\"{base_path}/prediction/input\",\n", - " prediction_path=prediction_output,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ho4zn_5UBgS3", - "outputId": "afdec49e-4172-45a6-e7b6-cd34522ca7a0" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 47.26mask/s]\n", - "/home/kshitij/anaconda3/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " pd.Int64Index,\n", - "ERROR:fiona._env:PROJ: proj_create_from_database: /home/kshitij/anaconda3/share/proj/proj.db lacks DATABASE.LAYOUT.VERSION.MAJOR / DATABASE.LAYOUT.VERSION.MINOR metadata. It comes from another PROJ installation.\n", - "Building graph: 100%|███████████████████████████████████████████████████| 66/66 [00:02<00:00, 28.58shapes/s]\n", - "Merging components: 100%|████████████████████████████████████████████| 59/59 [00:04<00:00, 14.34component/s]\n", - "/home/kshitij/anaconda3/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " pd.Int64Index,\n" - ] - } - ], - "source": [ - "from hot_fair_utilities import polygonize\n", - "geojson_output= f\"{prediction_output}/prediction.geojson\"\n", - "polygonize(\n", - " input_path=prediction_output, \n", - " output_path=geojson_output,\n", - " remove_inputs = True,\n", - ")" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0 (main, Oct 26 2022, 19:06:18) [Clang 14.0.0 (clang-1400.0.29.202)]" - }, - "vscode": { - "interpreter": { - "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/pyproject.toml b/pyproject.toml index ad1ae168..47553c55 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "hot-fair-utilities" version = "1.0.52" description = "Utilities for AI - Assisted Mapping fAIr" readme = "README.md" -authors = [{ name = "Omdena", email = "project@omdena.com" },{ name = "Hot Tech Team", email = "sysadmin@hotosm.org" }] +authors = [{ name = "Hot Tech Team", email = "sysadmin@hotosm.org" }] license = { file = "LICENSE" } classifiers = [ "License :: OSI Approved :: BSD License",