Update COG overviews, output datatypes

intertidal/elevation.py

@@ -1232,7 +1232,8 @@ def intertidal_cli(
         ds_prepared = prepare_for_export(ds)  # sets correct dtypes and nodata
 
         # Calculate additional tile-level tidal metadata attributes
-        metadata_dict = tidal_metadata(ds)
+        # (requires exposure/offsets to have been calculated)
+        metadata_dict = tidal_metadata(ds) if exposure_offsets else None
 
         # Export data and metadata
         export_dataset_metadata(

intertidal/io.py

@@ -7,6 +7,7 @@
 import xarray as xr
 from pathlib import Path
 from urllib.parse import urlparse
+from rasterio.enums import Resampling
 from rasterio.errors import NotGeoreferencedWarning
 
 import datacube
@@ -486,7 +487,7 @@ def load_topobathy_mask(
         The name of the topo-bathymetric DEM product to load from the
         datacube. Defaults to "ga_ausbathytopo250m_2023".
     elevation_band : str, optional
-        The name of the band containing elevation data. Defaults to 
+        The name of the band containing elevation data. Defaults to
         "height_depth".
     resampling : str, optional
         The resampling method to use, by default "bilinear".
@@ -497,7 +498,7 @@ def load_topobathy_mask(
         The elevation value used to create the mask; all pixels with
         elevations above this value will be given a value of True.
     mask_filters : list of tuples, optional
-        An optional list of morphological processing steps to pass to 
+        An optional list of morphological processing steps to pass to
         the `mask_cleanup` function. The default is `[("dilation", 25)]`,
         which will dilate True pixels by a radius of 25 pixels (~250 m).
 
@@ -562,13 +563,13 @@ def load_aclum_mask(
     Returns
     -------
     reclassified_aclum : xarray.DataArray
-        An output boolean mask, where True equals intensive urban and 
+        An output boolean mask, where True equals intensive urban and
         False equals all other classes.
     """
     # Load from datacube, reprojecting to GeoBox of input satellite data
-    aclum_ds = dc.load(
-        product=product, like=geobox, resampling=resampling
-    ).squeeze("time")
+    aclum_ds = dc.load(product=product, like=geobox, resampling=resampling).squeeze(
+        "time"
+    )
 
     # Mask invalid data
     if mask_invalid:
@@ -815,8 +816,8 @@ def _ls_platform_instrument(year):
 def prepare_for_export(
     ds,
     int_bands=None,
-    int_nodata=-999,
-    int_dtype=np.int16,
+    int_nodata=255,
+    int_dtype=np.uint8,
     float_dtype=np.float32,
     output_location=None,
     overwrite=True,
@@ -837,10 +838,10 @@ def prepare_for_export(
         "offset_hightide", "offset_lowtide", "spread")
     int_nodata : int, optional
         An integer that represents nodata values for integer bands
-        (default is -999).
+        (default is 255).
     int_dtype : string or numpy data type, optional
         The data type to use for integer layers (default is
-        np.int16).
+        np.uint8).
     float_dtype : string or numpy data type, optional
         The data type to use for floating point layers (default is
         np.float32).
@@ -860,7 +861,7 @@ def prepare_for_export(
     def _prepare_band(
         band, int_bands, int_nodata, int_dtype, float_dtype, output_location, overwrite
     ):
-        # Export specific bands as integer16 data types by first filling
+        # Export specific bands as integer data types by first filling
         # NaN with nodata value before converting to int, then setting
         # nodata attribute on layer
         if band.name in int_bands:
@@ -1026,14 +1027,14 @@ def export_dataset_metadata(
             label_parts[-2] = time_convention
             dataset_assembler.names.dataset_label = "_".join(label_parts)
 
-            # Write measurements from xarray, extracting nodata values
-            # from each input array and assigning these on the outputs
-            for dataarray in ds:
-                log.info(f"{run_id}: Writing array {dataarray}")
-                nodata = ds[dataarray].attrs.get("nodata", None)
-                dataset_assembler.write_measurements_odc_xarray(
-                    ds[[dataarray]], nodata=nodata
-                )
+            # Write measurements from xarray (this will loop through each
+            # array in the dataset and export them with correct nodata values)
+            log.info(f"{run_id}: Writing output arrays")
+            dataset_assembler.write_measurements_odc_xarray(
+                ds,
+                overviews=(2, 4, 8, 16, 32),
+                overview_resampling=Resampling.nearest,
+            )
 
             # Add lineage
             s2_set = set(d.id for d in s2_lineage) if s2_lineage else []

metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml

@@ -27,25 +27,25 @@ measurements:
       - uncertainty
 
   - name: exposure
-    dtype: int16
+    dtype: uint8
     units: "percent"
-    nodata: -999
+    nodata: 255
 
   - name: extents
-    dtype: int16
+    dtype: uint8
     units: "class"
-    nodata: -999
+    nodata: 255
     flags_definition:
       extents:
         description: Intertidal extents class
         bits: [0, 1, 2, 3, 4, 5, 6, 7]
         values:
-          0: test0
-          1: test1
-          2: test2
-          3: test3
-          4: test4
-          5: test4
+          0: Dry
+          1: Inland intermittent wet
+          2: Inland persistent wet
+          3: Tidal influenced persistent wet
+          4: Intertidal low confidence
+          5: Intertidal high confidence
 
   - name: ta_hat
     dtype: float32
@@ -75,23 +75,23 @@ measurements:
     aliases:
       - lat
   - name: ta_offset_high
-    dtype: int16
+    dtype: uint8
     units: "percent"
-    nodata: -999
+    nodata: 255
     aliases:
       - offset_high
 
   - name: ta_offset_low
-    dtype: int16
+    dtype: uint8
     units: "percent"
-    nodata: -999
+    nodata: 255
     aliases:
       - offset_low
 
   - name: ta_spread
-    dtype: int16
+    dtype: uint8
     units: "percent"
-    nodata: -999
+    nodata: 255
     aliases:
       - spread
 
@@ -103,9 +103,9 @@ measurements:
       - ndwi_corr
 
   - name: qa_ndwi_freq
-    dtype: int16
+    dtype: uint8
     units: "percent"
-    nodata: -999
+    nodata: 255
     aliases:
       - ndwi_freq
 

tests/README.md

@@ -10,7 +10,7 @@ Integration tests
 
 This directory contains tests that are run to verify that DEA Intertidal code runs correctly. The ``test_intertidal.py`` file runs a small-scale full workflow analysis over an intertidal flat in the Gulf of Carpentaria using the DEA Intertidal [Command Line Interface (CLI) tools](../notebooks/Intertidal_CLI.ipynb), and compares these results against a LiDAR validation DEM to produce some simple accuracy metrics.
 
-The latest integration test completed at **2024-03-12 16:16**. Compared to the previous run, it had an:
+The latest integration test completed at **2024-03-13 11:54**. Compared to the previous run, it had an:
 - RMSE accuracy of **0.14 m ( :heavy_minus_sign: no change)**
 - MAE accuracy of **0.12 m ( :heavy_minus_sign: no change)**
 - Bias of **0.12 m ( :heavy_minus_sign: no change)**

tests/validation.csv

@@ -44,3 +44,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope
 2024-03-07 07:52:01.276183+00:00,0.977,0.147,0.126,0.955,0.121,1.125
 2024-03-08 04:13:32.992746+00:00,0.975,0.141,0.121,0.95,0.116,1.11
 2024-03-12 05:16:34.997844+00:00,0.975,0.141,0.121,0.95,0.116,1.11
+2024-03-13 00:54:01.731099+00:00,0.975,0.141,0.121,0.95,0.116,1.11