rslearn

rslearn is a tool for developing remote sensing datasets and models.

rslearn helps with:

• Developing remote sensing datasets, starting with defining spatiotemporal windows (3D boxes in height/width/time) that are roughly equivalent to training examples.
• Importing raster and vector data from various online or local data sources into the dataset.
• Fine-tuning remote sensing foundation models on these datasets.
• Applying models on new locations and times.

Quickstart

If you are new to rslearn, we suggest starting here:

• First, read CoreConcepts, which summarizes key concepts in rslearn, including datasets, windows, layers, and data sources.
• Second, read WorkflowOverview, which provides an overview of the typical workflow in rslearn, from defining windows to training models.
• Finally, walk through the IntroExample, or find another example in Examples.md that can most readily be adapted for your project.

Other links:

• DatasetConfig documents the dataset configuration file.
• DataSources details the built-in data sources in rslearn, from which raster and vector data can be imported into rslearn dataset layers.
• ModelConfig documents the model configuration file.
• TasksAndModels details the training tasks and model components available in rslearn.

Setup

rslearn requires Python 3.11+ (Python 3.12 is recommended).

git clone https://github.com/allenai/rslearn.git
cd rslearn
pip install .[extra]

For linting and tests:

pip install .[dev]
pre-commit install
pre-commit run --all-files
pytest tests/unit tests/integration
# For online data source tests, you can store credentials in .env and they will be
# loaded by pytest-dotenv.
pytest tests/online

Contact

For questions and suggestions, please open an issue on GitHub.