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docs/tutorials/basic_usage.rst

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The following tutorials introduce the basic concepts and components of TorchGeo:
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* `Transforms <transforms>`_: Preprocessing and data augmentation transforms for geospatial data
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* `Indices <indices>`_: Spectral indices
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* `Pretrained Weights <pretrained_weights>`_: Models and pretrained weights
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* `Lightning Trainers <trainers>`: PyTorch Lightning data modules and trainers
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* `Transforms <transforms.ipynb>`_: Preprocessing and data augmentation transforms for geospatial data
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* `Indices <indices.ipynb>`_: Spectral indices
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* `Pretrained Weights <pretrained_weights.ipynb>`_: Models and pretrained weights
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* `Lightning Trainers <trainers.ipynb>`_: PyTorch Lightning data modules and trainers
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.. toctree::
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docs/tutorials/customization.rst

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Customization and Contributing
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Customization
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=============
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Is TorchGeo missing a dataset or model you need? Would you like to modify the default augmentations for a data module or extend a builtin trainer?
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The following tutorials will teach you how to customize TorchGeo to meet your needs:
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* `Custom Raster Datasets <custom_raster_datasets>`_: How to create a new RasterDataset
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* `Custom Raster Datasets <custom_raster_datasets.ipynb>`_: How to create a new RasterDataset
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TorchGeo is a community-driven open source library. If there is a feature missing that you would like to add, please open a pull request to add it. See the ref:`contributing` guidelines to get started.
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docs/tutorials/getting_started.rst

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The following tutorials will teach you enough to get started:
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* `Introduction to TorchGeo <torchgeo>`_: A brief overview of the design of TorchGeo
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* `Introduction to TorchGeo <torchgeo.ipynb>`_: A brief overview of the design of TorchGeo
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.. toctree::
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