worldcereal WorldCereal

rslearn.data_sources.worldcereal.WorldCereal

This data source is for the ESA WorldCereal 2021 agricultural land cover map. For details about the land cover map, see https://esa-worldcereal.org/en.

This data source will download and extract all of the WorldCereal GeoTIFFs to a local directory. Since different regions are covered with different bands, the data source is designed to only be configured with one band per layer; to materialize multiple bands, repeat the data source across multiple layers (with different bands).

Configuration

{
  "class_path": "rslearn.data_sources.worldcereal.WorldCereal",
  "init_args": {
    // Required local path to extract the WorldCereal GeoTIFF files. For high performance,
    // this should be a local directory; if the dataset is remote, prefix with a protocol
    // ("file://") to use a local directory.
    "worldcereal_dir": "cache/worldcereal"
  }
}

Available Bands

Specify one per layer, with a single-band band set: - tc-annual_temporarycrops_confidence - tc-annual_temporarycrops_classification - tc-maize-main_irrigation_confidence - tc-maize-main_irrigation_classification - tc-maize-main_maize_confidence - tc-maize-main_maize_classification - tc-maize-second_irrigation_confidence - tc-maize-second_irrigation_classification - tc-maize-second_maize_confidence - tc-maize-second_maize_classification - tc-springcereals_springcereals_confidence - tc-springcereals_springcereals_classification - tc-wintercereals_irrigation_confidence - tc-wintercereals_irrigation_classification - tc-wintercereals_wintercereals_confidence - tc-wintercereals_wintercereals_classification

Example

Here is an example data source configuration to obtain the maize-main and wintercereals classification. We configure the tile store to use the given raster files directly. We use SINGLE_COMPOSITE due to an issue where the items in the data source are large enough to cause re-projection issues, and so rslearn instead treats all of them as global, but this means that during prepare it won't be able to determine which files match with the window; with SINGLE_COMPOSITE, all of those files will be combined into one item group, and during materialization we will use the first one that is not NODATA.

{
  "layers": {
    "maize": {
      "band_sets": [
        {
          "bands": [
            "tc-maize-main-maize-classification"
          ],
          "dtype": "float32"
        }
      ],
      "data_source": {
        "class_path": "rslearn.data_sources.worldcereal.WorldCereal",
        "init_args": {
          "worldcereal_dir": "source_data/worldcereal/"
        },
        "query_config": {
          "space_mode": "SINGLE_COMPOSITE"
        }
      },
      "resampling_method": "nearest",
      "type": "raster"
    },
    "wintercereals": {
      "band_sets": [
        {
          "bands": [
            "tc-wintercereals-wintercereals-classification"
          ],
          "dtype": "float32"
        }
      ],
      "data_source": {
        "class_path": "rslearn.data_sources.worldcereal.WorldCereal",
        "init_args": {
          "worldcereal_dir": "source_data/worldcereal/"
        },
        "query_config": {
          "space_mode": "SINGLE_COMPOSITE"
        }
      },
      "resampling_method": "nearest",
      "type": "raster"
    }
  },
  "tile_store": {
    "class_path": "rslearn.tile_stores.default.DefaultTileStore",
    "init_args": {
      "convert_rasters_to_cogs": false
    }
  }
}

Save this to a dataset folder like /path/to/dataset/config.json. Then we can create a sample window, and then run prepare, ingest, and materialize.

export DATASET_PATH=/path/to/dataset
# This will create one 1024x1024 window at 10 m/pixel in Ohio.
# WorldCereal data is treated as static-in-time so the time range of the window will be
# ignored.
rslearn dataset add_windows --root $DATASET_PATH --group default --name ohio --box=-80.626,41.758,-80.626,41.758 --src_crs EPSG:4326 --window_size 1024 --utm --resolution 10 --start 2025-01-01T00:00:00Z --end 2026-01-01T00:00:00Z
rslearn dataset prepare --root $DATASET_PATH
rslearn dataset ingest --root $DATASET_PATH
rslearn dataset materialize --root $DATASET_PATH

You can then visualize the image in qgis:

qgis $DATASET_PATH/windows/default/ohio/layers/maize/tc-maize-main-maize-classification/geotiff.tif