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.
Full documentation is available at https://allenai.github.io/rslearn/.
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 the Usage overview, which provides an overview of the typical workflow in rslearn, from defining windows to training models.
- Finally, walk through the Quickstart, 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.
- Compositors documents built-in and custom raster compositing methods, including cloud-aware ranking compositors.
- 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.
Metadata
Release files for rslearn 0.1.16
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| rslearn-0.1.16-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.1 MB
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| Tags | Python 3 |
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