Metadata-Version: 2.1
Name: torchgeo
Version: 0.1.0
Summary: TorchGeo: datasets, transforms, and models for geospatial data
Home-page: https://github.com/microsoft/torchgeo
Author: Adam J. Stewart
Author-email: ajstewart426@gmail.com
License: UNKNOWN
Description: <img src="https://raw.githubusercontent.com/microsoft/torchgeo/main/logo/logo-color.svg" width="400" alt="TorchGeo"/>
        
        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:
        [![docs](https://readthedocs.org/projects/torchgeo/badge/?version=latest)](https://torchgeo.readthedocs.io/en/latest/?badge=latest)
        [![codecov](https://codecov.io/gh/microsoft/torchgeo/branch/main/graph/badge.svg?token=oa3Z3PMVOg)](https://codecov.io/gh/microsoft/torchgeo)
        
        Tests:
        [![docs](https://github.com/microsoft/torchgeo/actions/workflows/docs.yaml/badge.svg)](https://github.com/microsoft/torchgeo/actions/workflows/docs.yaml)
        [![style](https://github.com/microsoft/torchgeo/actions/workflows/style.yaml/badge.svg)](https://github.com/microsoft/torchgeo/actions/workflows/style.yaml)
        [![tests](https://github.com/microsoft/torchgeo/actions/workflows/tests.yaml/badge.svg)](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"])
        ```
        
        ## 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.
        
Keywords: pytorch,deep learning,machine learning,remote sensing,satellite imagery,geospatial
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: GIS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: datasets
Provides-Extra: train
Provides-Extra: style
Provides-Extra: tests
