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docs/satlas_marine_infra.md

@@ -30,6 +30,7 @@ can use the code below to identify a suitable bounding box:
 
     longitude = 120.148
     latitude = 24.007
+    window_size = 4096
 
     import json
     import shapely
@@ -40,11 +41,15 @@ can use the code below to identify a suitable bounding box:
     src_geom = STGeometry(WGS84_PROJECTION, shapely.Point(longitude, latitude), None)
     dst_projection = get_utm_ups_projection(longitude, latitude, 10, -10)
     dst_geom = src_geom.to_projection(dst_projection)
+    center_point = (
+        int(dst_geom.shp.x) // 2048 * 2048,
+        int(dst_geom.shp.y) // 2048 * 2048,
+    )
     bounds = (
-        int(dst_geom.shp.x) - 2048,
-        int(dst_geom.shp.y) - 2048,
-        int(dst_geom.shp.x) + 2048,
-        int(dst_geom.shp.y) + 2048,
+        center_point[0] - window_size // 2,
+        center_point[1] - window_size // 2,
+        center_point[0] + window_size // 2,
+        center_point[1] + window_size // 2,
     )
     print(json.dumps(dst_projection.serialize()))
     print(json.dumps(bounds))
@@ -54,8 +59,10 @@ time range, it should be a seven month range to give enough options to pick the
 30-day mosaics, note that the timestamps are ISO 8601 formatted.
 
     mkdir out_dir
-    mkdir scratch_dir
-    python -m rslp.main satlas predict MARINE_INFRA '{"crs": "EPSG:32651", "x_resolution": 10, "y_resolution": -10}' '[18937, -267842, 23033, -263746]' '["2024-01-01T00:00:00+00:00", "2024-08-01T00:00:00+00:00"]' out_dir/ scratch_dir/ --use_rtree_index false
+    python -m rslp.main satlas predict MARINE_INFRA '{"crs": "EPSG:32651", "x_resolution": 10, "y_resolution": -10}' '[18432, -268288, 22528, -264192]' '["2024-01-01T00:00:00+00:00", "2024-08-01T00:00:00+00:00"]' out_dir/ scratch_dir/ --use_rtree_index false
+
+You may need to delete the "scratch_dir" directory if it exists already. This is used
+to store a temporary rslearn dataset for ingesting the Sentinel-2 input images.
 
 This generates a GeoJSON in out_dir but it is in pixel coordinates. Convert to
 longitude/latitude coordinates using this script (which can also be used to merge
@@ -76,24 +83,31 @@ Training
 First, download the training dataset:
 
     cd rslearn_projects
-    mkdir -p project_data/datasets/sentinel2_vessels/
-    wget https://storage.googleapis.com/ai2-rslearn-projects-data/sentinel2_vessels/sentinel2_vessels.tar -O project_data/datasets/sentinel2_vessels.tar
-    tar xvf project_data/datasets/sentinel2_vessels.tar --directory project_data/datasets/sentinel2_vessels/
+    mkdir -p project_data/datasets/satlas_marine_infra/
+    wget https://storage.googleapis.com/ai2-rslearn-projects-data/satlas_marine_infra/satlas_marine_infra.tar -O project_data/datasets/satlas_marine_infra.tar
+    tar xvf project_data/datasets/satlas_marine_infra.tar --directory project_data/datasets/satlas_marine_infra/
 
 It is an rslearn dataset consisting of window folders like
-`windows/sargassum_train/1186117_1897173_158907/`. Inside each window folder:
+`windows/label/2102272_1262592/`. Inside each window folder:
 
-- `layers/sentinel2/` contains different Sentinel-2 bands used by the model, such as
-  `layers/sentinel2/R_G_B/image.png`.
-- `layers/label/data.geojson` contains the positions of ships. These are offset from
-  the bounds of the window which are in `metadata.json`, so subtract the window's
-  bounds to get pixel coordinates relative to the image.
+- `layers/sentinel2{.1,.2,.3}/` contains the four input Sentinel-2 mosaics.
+- `layers/label/data.geojson` contains the positions of marine infrastructure. These
+  are offset from the bounds of the window which are in `metadata.json`, so subtract
+  the window's bounds to get pixel coordinates relative to the image.
+- `layers/mask/mask/image.png` contains a mask specifying the valid portion of the
+  window. The labels were originally annotated in WebMercator projection, but have been
+  re-projected to UTM in this dataset; the transformation results in a non-rectangular
+  extent, so the window corresponds to the rectangular bounds of that extent while the
+  mask specifies the extent within those bounds. This is used in the mask step in the
+  model configuration file `data/satlas_marine_infra/config.yaml` to black out the
+  other parts of the input image.
 
-To train the model, run:
+Use the command below to train the model. Note that Weights & Biases is needed. You can
+disable W&B with `--no_log true` but then it may be difficult to track the metrics.
 
-    python -m rslp.rslearn_main model fit --config data/sentinel2_vessels/config.yaml --data.init_args.path project_data/datasets/sentinel2_vessels/
+    python -m rslp.rslearn_main model fit --config data/satlas_marine_infra/config.yaml --data.init_args.path project_data/datasets/satlas_marine_infra/
 
 To visualize outputs on the validation set:
 
     mkdir vis
-    python -m rslp.rslearn_main model test --config data/sentinel2_vessels/config.yaml --data.init_args.path project_data/datasets/sentinel2_vessels/ --model.init_args.visualize_dir=vis/
+    python -m rslp.rslearn_main model test --config data/satlas_marine_infra/config.yaml --data.init_args.path project_data/datasets/satlas_marine_infra/ --model.init_args.visualize_dir=vis/

docs/sentinel2_vessels.md

@@ -51,7 +51,8 @@ It is an rslearn dataset consisting of window folders like
   the bounds of the window which are in `metadata.json`, so subtract the window's
   bounds to get pixel coordinates relative to the image.
 
-To train the model, run:
+Use the command below to train the model. Note that Weights & Biases is needed. You can
+disable W&B with `--no_log true` but then it may be difficult to track the metrics.
 
     python -m rslp.rslearn_main model fit --config data/sentinel2_vessels/config.yaml --data.init_args.path project_data/datasets/sentinel2_vessels/