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Usage of rslearn typically follows this workflow:

  1. Specify the dataset configuration file.
  2. Create windows.
  3. Import raster and vector data from the configured data sources that align with the windows.
  4. If needed, programmatically add additional raster and vector data to the dataset (e.g., labels from an external annotation tool).
  5. Specify the model configuration file and train a model.
  6. Get model predictions in new regions.

Steps 1-3 are sufficient if you are using rslearn to obtain remote sensing data, but not to train remote sensing models.

Specify the Dataset Configuration File

The dataset configuration file specifies the raster and vector layers in the rslearn dataset. Each layer can be programmatically populated with data, or can specify a data source from which the layer can be automatically populated.

  • DatasetConfig provides a reference for the dataset configuration file.
  • DataSources details the data sources that are built-in to rslearn. For each data source, example usage is provided, including an example dataset configuration file.
  • Each example in Examples includes a dataset configuration file.

Create Windows

rslearn datasets consist of windows. Each window is a spatiotemporal box.

Import from Data Sources

Once windows are created, data can be imported from configured data sources by running the prepare (match data source items to windows), ingest (download items), and materialize (re-project and crop items to align with windows) stages:

rslearn dataset prepare --root /path/to/dataset
rslearn dataset ingest --root /path/to/dataset
rslearn dataset materialize --root /path/to/dataset

Add Additional Raster and Vector Data

If you have vector annotations that you want to use as targets during training, or you already have your own remote sensing images, then you can add these to your rslearn dataset instead of importing it from a data source.

ProgrammaticWindows shows how to programmatically add additional raster and vector data to a dataset. Alternatively, if you have a local collection of GeoTIFF or GeoJSON files, you can use the LocalFiles data source to import them.

Specify the Model Configuration File

The model configuration file specifies the model architecture, the machine learning task (e.g. segmentation or object detection), the dataset layers to use as inputs and targets, and training hyperparameters.

  • ModelConfig provides a reference for the model configuration file.
  • TasksAndModels details the built-in tasks and model components.
  • The examples in Examples that involve training a model each include a model configuration file.
  • See OlmoEarth.md for details on fine-tuning OlmoEarth in particular.

Get Model Predictions

Getting model predictions in a new region involves creating windows corresponding to that region, and running the rslearn model predict command. The examples in the README and Examples show how to do that.