TorchGeo is a [PyTorch](https://pytorch.org/) domain library, similar to [torchvision](https://pytorch.org/vision), that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data. The goal of this library is to make it simple: 1. for machine learning experts to use geospatial data in their workflows, and 2. for remote sensing experts to use their data in machine learning workflows. See our [installation instructions](#installation-instructions), [documentation](#documentation), and [examples](#example-usage) to learn how to use torchgeo. External links: [](https://torchgeo.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/microsoft/torchgeo) [](https://pypi.org/project/torchgeo/) [](https://anaconda.org/conda-forge/torchgeo) [](https://spack.readthedocs.io/en/latest/package_list.html#py-torchgeo) Tests: [](https://github.com/microsoft/torchgeo/actions/workflows/docs.yaml) [](https://github.com/microsoft/torchgeo/actions/workflows/style.yaml) [](https://github.com/microsoft/torchgeo/actions/workflows/tests.yaml) ## Installation instructions The recommended way to install TorchGeo is with [pip](https://pip.pypa.io/): ```console $ pip install torchgeo ``` For [conda](https://docs.conda.io/) and [spack](https://spack.io/) installation instructions, see the [documentation](https://torchgeo.readthedocs.io/en/latest/user/installation.html). ## Documentation You can find the documentation for torchgeo on [ReadTheDocs](https://torchgeo.readthedocs.io). ## Example usage The following sections give basic examples of what you can do with torchgeo. For more examples, check out our [tutorials](https://torchgeo.readthedocs.io/en/latest/tutorials/getting_started.html). ### Train and test models using our PyTorch Lightning based training script We provide a script, `train.py` for training models using a subset of the datasets. We do this with the PyTorch Lightning `LightningModule`s and `LightningDataModule`s implemented under the `torchgeo.trainers` namespace. The `train.py` script is configurable via the command line and/or via YAML configuration files. See the [conf/](conf/) directory for example configuration files that can be customized for different training runs. ```console $ python train.py config_file=conf/landcoverai.yaml ``` ### Download and use the Tropical Cyclone Wind Estimation Competition dataset This dataset is from a competition hosted by [Driven Data](https://www.drivendata.org/) in collaboration with [Radiant Earth](https://www.radiant.earth/). See [here](https://www.drivendata.org/competitions/72/predict-wind-speeds/) for more information. Using this dataset in torchgeo is as simple as importing and instantiating the appropriate class. ```python import torchgeo.datasets dataset = torchgeo.datasets.TropicalCycloneWindEstimation(split="train", download=True) print(dataset[0]["image"].shape) print(dataset[0]["label"]) ``` ## Citation If you use this software in your work, please cite [our paper](https://arxiv.org/abs/2111.08872): ``` @article{Stewart_TorchGeo_deep_learning_2021, author = {Stewart, Adam J. and Robinson, Caleb and Corley, Isaac A. and Ortiz, Anthony and Lavista Ferres, Juan M. and Banerjee, Arindam}, journal = {arXiv preprint arXiv:2111.08872}, month = {11}, title = {{TorchGeo: deep learning with geospatial data}}, url = {https://github.com/microsoft/torchgeo}, year = {2021} } ``` ## Contributing This project welcomes contributions and suggestions. If you would like to submit a pull request, see our [Contribution Guide](https://torchgeo.readthedocs.io/en/latest/user/contributing.html) for more information. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.