Now saves uint16 values with 0-1 for crop/no-crop and 0-100 for proba…

…bilities

scripts/inference/cropland_mapping.py

@@ -8,6 +8,7 @@
 from openeo_gfmap.backend import Backend, BackendContext
 from openeo_gfmap.features.feature_extractor import apply_feature_extractor
 from openeo_gfmap.inference.model_inference import apply_model_inference
+from openeo_gfmap.preprocessing.scaling import compress_uint16
 
 from worldcereal.openeo.feature_extractor import PrestoFeatureExtractor
 from worldcereal.openeo.inference import CroplandClassifier
@@ -105,6 +106,9 @@
         ],
     )
 
+    # Cast to uint16
+    classes = compress_uint16(classes)
+
     classes.execute_batch(
         outputfile=args.output_path,
         out_format="GTiff",

src/worldcereal/openeo/inference.py

@@ -48,9 +48,12 @@ def predict(self, features: np.ndarray) -> np.ndarray:
         # Extract all prediction values and convert them to binary labels
         prediction_values = [sublist["True"] for sublist in outputs[1]]
         binary_labels = np.array(prediction_values) >= threshold
-        binary_labels = binary_labels.astype(int)
+        binary_labels = binary_labels.astype("uint8")
 
-        return np.stack([binary_labels, prediction_values], axis=0).astype(np.float32)
+        prediction_values = np.array(prediction_values) * 100.0
+        prediction_values = np.round(prediction_values).astype("uint8")
+
+        return np.stack([binary_labels, prediction_values], axis=0)
 
     def execute(self, inarr: xr.DataArray) -> xr.DataArray:
         classifier_url = self._parameters.get("classifier_url", self.CATBOOST_PATH)
@@ -62,9 +65,9 @@ def execute(self, inarr: xr.DataArray) -> xr.DataArray:
         self.onnx_session = self.load_ort_session(classifier_url)
 
         # Run catboost classification
-        self.logger.info(f"Catboost classification with input shape: {inarr.shape}")
+        self.logger.info("Catboost classification with input shape: %s", inarr.shape)
         classification = self.predict(inarr.values)
-        self.logger.info(f"Classification done with shape: {classification.shape}")
+        self.logger.info("Classification done with shape: %s", inarr.shape)
 
         classification = xr.DataArray(
             classification.reshape((2, len(x_coords), len(y_coords))),