switch to AWS COG for satellite data

inference.py

@@ -199,10 +199,10 @@ def crop_predictions_to_gdf(field_ids, targets, predictions, transform, crs, cla
     return gdf
 
 def perform_inference(lon, lat, model, config, debug=False):
-    features_path = "../data/stacked_features.tif"
-    labels_path = "../data/labels.tif"
-    field_ids_path = "../data/field_ids.tif"
-    stats_path = "../data/chips_stats.yaml"
+    features_path = "./data/stacked_features.tif"
+    labels_path = "./data/labels.tif"
+    field_ids_path = "./data/field_ids.tif"
+    stats_path = "./data/chips_stats.yaml"
 
     loader = InferenceDataLoader(features_path, labels_path, field_ids_path, stats_path, n_timesteps=9, fold_indices=[0], debug=True)
 

pages/2_Perform_Crop_Classification.py

@@ -79,13 +79,22 @@ def perform_inference_step():
         st.success("Inference completed!")
 
     # GeoTIFF Satellite Imagery with selected timestep and band
-    m.add_raster(
-        GEOTIFF_PATH,
-        layer_name="Satellite Image",
+    # m.add_raster(
+    #     GEOTIFF_PATH,
+    #     layer_name="Satellite Image",
+    #     bands=selected_bands,
+    #     fit_bounds=True,
+    #     vmin=vmin_vmax[selected_band][0],
+    #     vmax=vmin_vmax[selected_band][1],
+    # )
+
+    # Add COG
+    m.add_cog_layer(
+        url="https://messis-demo.s3.amazonaws.com/stacked_features_cog.tif",
+        name="AWS COG",
         bands=selected_bands,
-        fit_bounds=True,
-        vmin=vmin_vmax[selected_band][0],
-        vmax=vmin_vmax[selected_band][1],
+        rescale=f"{vmin_vmax[selected_band][0]},{vmin_vmax[selected_band][1]}",
+        zoom_to_layer=True
     )
 
     # Show the POI on the map