Update crop type mapping workflow

environment.yml

@@ -16,7 +16,7 @@ dependencies:
   - openeo=0.29.0
   - pyarrow=16.1.0
   - python=3.10.0
-  - pytorch=2.3.0
+  - pytorch=2.3.1
   - rasterio=1.3.10
   - rioxarray=0.15.5
   - scikit-image=0.22.0

scripts/inference/cropland_mapping.py

@@ -11,8 +11,8 @@
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        prog="WC - Cropland Inference",
-        description="Cropland inference using GFMAP, Presto and WorldCereal classifiers",
+        prog="WC - Crop Mapping Inference",
+        description="Crop Mapping inference using GFMAP, Presto and WorldCereal classifiers",
     )
 
     parser.add_argument("minx", type=float, help="Minimum X coordinate (west)")
@@ -25,6 +25,11 @@
         default=4326,
         help="EPSG code of the input `minx`, `miny`, `maxx`, `maxy` parameters.",
     )
+    parser.add_argument(
+        "product",
+        type=str,
+        help="Product to generate. One of ['cropland', 'croptype']",
+    )
     parser.add_argument(
         "start_date", type=str, help="Starting date for data extraction."
     )
@@ -46,6 +51,15 @@
     start_date = args.start_date
     end_date = args.end_date
 
+    product = args.product
+
+    # minx, miny, maxx, maxy = (664000, 5611134, 665000, 5612134)  # Small test
+    # minx, miny, maxx, maxy = (664000, 5611134, 684000, 5631134)  # Large test
+    # epsg = 32631
+    # start_date = "2020-11-01"
+    # end_date = "2021-10-31"
+    # product = "croptype"
+
     spatial_extent = BoundingBoxExtent(minx, miny, maxx, maxy, epsg)
     temporal_extent = TemporalContext(start_date, end_date)
 
@@ -56,7 +70,7 @@
         temporal_extent,
         backend_context,
         args.output_path,
-        product_type=WorldCerealProduct.CROPLAND,
+        product_type=WorldCerealProduct(product),
         out_format="GTiff",
     )
     logger.success("Job finished:\n\t%s", job_results)

scripts/inference/croptype_mapping_local.py

@@ -15,7 +15,7 @@
 from openeo_gfmap.inference.model_inference import apply_model_inference_local
 
 from worldcereal.openeo.feature_extractor import PrestoFeatureExtractor
-from worldcereal.openeo.inference import CroptypeClassifier
+from worldcereal.openeo.inference import CroplandClassifier, CroptypeClassifier
 
 TEST_FILE_URL = "https://artifactory.vgt.vito.be/artifactory/auxdata-public/worldcereal/presto/localtestdata/local_presto_inputs.nc"
 TEST_FILE_PATH = Path.cwd() / "presto_test_inputs.nc"
@@ -40,29 +40,53 @@
         .astype("uint16")
     )
 
-    print("Running presto UDF locally")
-    features = apply_feature_extractor_local(
+    print("Get Presto cropland features")
+    cropland_features = apply_feature_extractor_local(
         PrestoFeatureExtractor,
         arr,
+        parameters={EPSG_HARMONIZED_NAME: 32631, "ignore_dependencies": True},
+    )
+
+    print("Running cropland classification inference UDF locally")
+
+    cropland_classification = apply_model_inference_local(
+        CroplandClassifier,
+        cropland_features,
         parameters={
             EPSG_HARMONIZED_NAME: 32631,
             "ignore_dependencies": True,
-            "presto_model_url": PRESTO_URL,
         },
     )
 
-    features.to_netcdf(Path.cwd() / "presto_test_features_croptype.nc")
+    print("Get Presto croptype features")
+    croptype_features = apply_feature_extractor_local(
+        PrestoFeatureExtractor,
+        arr,
+        parameters={
+            EPSG_HARMONIZED_NAME: 32631,
+            "ignore_dependencies": True,
+            "presto_model_url": PRESTO_URL,
+        },
+    )
 
-    print("Running classification inference UDF locally")
+    print("Running croptype classification inference UDF locally")
 
-    classification = apply_model_inference_local(
+    croptype_classification = apply_model_inference_local(
         CroptypeClassifier,
-        features,
+        croptype_features,
         parameters={
             EPSG_HARMONIZED_NAME: 32631,
             "ignore_dependencies": True,
             "classifier_url": CATBOOST_URL,
         },
     )
 
-    classification.to_netcdf(Path.cwd() / "test_classification_croptype.nc")
+    # Apply cropland mask -> on the backend this is done with mask process
+    croptype_classification = croptype_classification.where(
+        cropland_classification.sel(bands="classification") == 1, 0
+    )
+
+    croptype_classification.to_netcdf(
+        Path("/vitodata/worldcereal/validation/internal_validation/")
+        / "test_classification_croptype_local.nc"
+    )