changed the preprocessing of the point extractions to match the prepr…

…ocessing of the inference

scripts/extractions/point_extractions/point_extractions.py

@@ -16,7 +16,10 @@
 from openeo_gfmap.manager.job_splitters import split_job_s2grid
 from openeo_gfmap.preprocessing import linear_interpolation, median_compositing
 
-from worldcereal.openeo.preprocessing import raw_datacube_S2
+from worldcereal.openeo.preprocessing import (
+    raw_datacube_S2,
+    worldcereal_preprocessed_inputs_gfmap,
+)
 
 # Logger for this current pipeline
 pipeline_log: Optional[logging.Logger] = None
@@ -127,8 +130,8 @@ def create_datacube(
     connection: openeo.DataCube,
     provider,
     connection_provider,
-    executor_memory: str = "5G",
-    executor_memory_overhead: str = "2G",
+    executor_memory: str = "3G",
+    executor_memory_overhead: str = "5G",
 ):
     """Creates an OpenEO BatchJob from the given row information."""
 
@@ -147,34 +150,16 @@ def create_datacube(
     backend = Backend(row.backend_name)
     backend_context = BackendContext(backend)
 
-    # TODO: Adjust this to the desired bands to download
-    bands_to_download = [
-        "S2-L2A-B04",
-        "S2-L2A-B08",
-    ]
-
-    fetch_type = FetchType.POINT
-
-    cube = raw_datacube_S2(
+    inputs = worldcereal_preprocessed_inputs_gfmap(
         connection=connection,
         backend_context=backend_context,
         spatial_extent=geometry,
         temporal_extent=temporal_extent,
-        bands=bands_to_download,
-        fetch_type=fetch_type,
-        distance_to_cloud_flag=True,
-        additional_masks_flag=False,
-        apply_mask_flag=True,
+        fetch_type=FetchType.POINT,
     )
 
-    # Create monthly median composites
-    cube = median_compositing(cube=cube, period="month")
-
-    # Perform linear interpolation
-    cube = linear_interpolation(cube)
-
     # Finally, create a vector cube based on the Point geometries
-    cube = cube.aggregate_spatial(geometries=geometry, reducer="mean")
+    cube = inputs.aggregate_spatial(geometries=geometry, reducer="mean")
 
     # Increase the memory of the jobs depending on the number of polygons to extract
     number_points = get_job_nb_points(row)
@@ -183,6 +168,7 @@ def create_datacube(
     job_options = {
         "executor-memory": executor_memory,
         "executor-memoryOverhead": executor_memory_overhead,
+        "soft-error": True,
     }
     return cube.create_job(
         out_format="Parquet",
@@ -204,7 +190,19 @@ def post_job_action(
 
         # Convert band dtype to uint16 (temporary fix)
         # TODO: remove this step when the issue is fixed on the OpenEO backend
-        bands = ["S2-L2A-B04", "S2-L2A-B08"]
+        bands = [
+            "S2-L2A-B02",
+            "S2-L2A-B03",
+            "S2-L2A-B04",
+            "S2-L2A-B05",
+            "S2-L2A-B06",
+            "S2-L2A-B07",
+            "S2-L2A-B08",
+            "S2-L2A-B11",
+            "S2-L2A-B12",
+            "S1-SIGMA0-VH",
+            "S1-SIGMA0-VV",
+        ]
         gdf[bands] = gdf[bands].fillna(65535).astype("uint16")
 
         gdf.to_parquet(item_asset_path, index=False)
@@ -238,7 +236,7 @@ def post_job_action(
     parser.add_argument(
         "--memory-overhead",
         type=str,
-        default="1G",
+        default="5G",
         help="Memory overhead to allocate for the executor.",
     )