Difference between revisions of "Torchgeo"
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TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data. | TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data. | ||
| − | The goal of this library is to make it simple: | + | The goal of this library is to make it simple: for machine learning experts to work with geospatial data, and for remote sensing experts to explore machine learning solutions. |
| − | |||
| − | for machine learning experts to work with geospatial data, and | ||
| − | for remote sensing experts to explore machine learning solutions. | ||
== Core Features == | == Core Features == | ||
Latest revision as of 22:06, 5 May 2024
torchGeo is an incubating OSGeo project.
Description
From the OSGeo project page:
[1] TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
The goal of this library is to make it simple: for machine learning experts to work with geospatial data, and for remote sensing experts to explore machine learning solutions.
Core Features
- Geospatial Datasets and Samplers
- 20+ data loaders for satellite imagery and masks
- Automatic CRS reprojection and resampling
- Support for multimodal learning and data fusion
- Benchmark Datasets
- 50+ data loaders for benchmark datasets
- Quickly experiment with a variety of models
- Automatically download most datasets
- Pre-Trained Weights
- 40+ models pre-trained on geospatial data
- Native support for multispectral imagery
- Enables transfer learning on smaller datasets
- Reproducibility with Lightning
- Builtin train/val/test splits and data augmentation
- Classification, regression, detection, segmentation
- Command-line interface, support for YAML files