async-geotiff

Fast, async GeoTIFF and Cloud-Optimized GeoTIFF (COG) reader for Python, wrapping the Rust-based Async-TIFF library.

Features

• Read-only support for GeoTIFF and COG formats.
• High-level, familiar, easy to use API.
• Performance-focused:
  • Rust core ensures native performance.
  • CPU-bound tasks like image decoding happen in a thread pool, without blocking the async executor.
  • Buffer protocol integration for zero-copy data sharing between Rust and Python.
• Lightweight with no GDAL dependency.
• Integration with obstore for efficient data access on object stores.
• Full type hinting for all operations.
• Broad decompression support: Deflate, LZW, JPEG, JPEG2000, WebP, ZSTD.

Anti-Features (features explicitly not in scope):

• No pixel resampling.
• No warping/reprojection.

Resampling and warping bring significant additional complexity and are out of scope for this library.

Example

First create a "store", such as an S3Store, GCSStore, AzureStore, or LocalStore for reading data from AWS S3, Google Cloud, Azure Storage, or local files. Refer to obstore documentation for more information.

from obstore.store import S3Store

store = S3Store("sentinel-cogs", region="us-west-2", skip_signature=True)
path = "sentinel-s2-l2a-cogs/12/S/UF/2022/6/S2B_12SUF_20220609_0_L2A/TCI.tif"

Then open a GeoTIFF:

from async_geotiff import GeoTIFF

geotiff = await GeoTIFF.open(path, store=store)

On the GeoTIFF instance you have metadata about the image, such as its affine transform and Coordinate Reference System:

geotiff.transform
# Affine(10.0, 0.0, 300000.0,
#        0.0, -10.0, 4100040.0)

geotiff.crs
# <Projected CRS: EPSG:32612>
# Name: WGS 84 / UTM zone 12N

For a COG, you can access the overviews, or reduced resolution versions, of the image:

# Overviews are ordered from finest to coarsest resolution
# In this case, access the second-coarsest resolution version of the image
overview = geotiff.overviews[-2]

Then we can read data from the image. This loads the tile in the upper-left corner of the selected overview.

array = await overview.fetch_tile(0, 0)

This Array instance has data, mask, and some other metadata about the fetched array data.

Plot, using rasterio.plot.show (requires matplotlib):

import rasterio.plot

rasterio.plot.show(array.data)