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86 строки
4.6 KiB
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86 строки
4.6 KiB
Markdown
<img src="https://raw.githubusercontent.com/microsoft/torchgeo/main/logo/logo-color.svg" width="400" alt="TorchGeo"/>
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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.
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The goal of this library is to make it simple:
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1. for machine learning experts to use geospatial data in their workflows, and
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2. for remote sensing experts to use their data in machine learning workflows.
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See our [installation instructions](#installation-instructions), [documentation](#documentation), and [examples](#example-usage) to learn how to use torchgeo.
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External links:
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[](https://torchgeo.readthedocs.io/en/latest/?badge=latest)
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[](https://codecov.io/gh/microsoft/torchgeo)
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[](https://pypi.org/project/torchgeo/)
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[](https://anaconda.org/conda-forge/torchgeo)
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[](https://spack.readthedocs.io/en/latest/package_list.html#py-torchgeo)
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Tests:
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[](https://github.com/microsoft/torchgeo/actions/workflows/docs.yaml)
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[](https://github.com/microsoft/torchgeo/actions/workflows/style.yaml)
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[](https://github.com/microsoft/torchgeo/actions/workflows/tests.yaml)
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## Installation instructions
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The recommended way to install TorchGeo is with [pip](https://pip.pypa.io/):
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```console
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$ pip install torchgeo
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```
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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).
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## Documentation
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You can find the documentation for torchgeo on [ReadTheDocs](https://torchgeo.readthedocs.io).
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## Example usage
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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).
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### Train and test models using our PyTorch Lightning based training script
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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.
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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.
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```console
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$ python train.py config_file=conf/landcoverai.yaml
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```
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### Download and use the Tropical Cyclone Wind Estimation Competition dataset
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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.
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Using this dataset in torchgeo is as simple as importing and instantiating the appropriate class.
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```python
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import torchgeo.datasets
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dataset = torchgeo.datasets.TropicalCycloneWindEstimation(split="train", download=True)
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print(dataset[0]["image"].shape)
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print(dataset[0]["label"])
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```
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## Citation
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If you use this software in your work, please cite [our paper](https://arxiv.org/abs/2111.08872):
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```
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@article{Stewart_TorchGeo_deep_learning_2021,
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author = {Stewart, Adam J. and Robinson, Caleb and Corley, Isaac A. and Ortiz, Anthony and Lavista Ferres, Juan M. and Banerjee, Arindam},
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journal = {arXiv preprint arXiv:2111.08872},
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month = {11},
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title = {{TorchGeo: deep learning with geospatial data}},
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url = {https://github.com/microsoft/torchgeo},
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year = {2021}
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}
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```
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## Contributing
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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.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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