https://github.com/torchgeo/terratorch
A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
https://github.com/torchgeo/terratorch
ai4good ai4science computer-vision deep-learning earth-observation foundation-models geospatial solar-physics weather-models
Last synced: 12 days ago
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A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
- Host: GitHub
- URL: https://github.com/torchgeo/terratorch
- Owner: torchgeo
- License: apache-2.0
- Created: 2024-05-15T12:43:28.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2026-05-29T19:41:43.000Z (25 days ago)
- Last Synced: 2026-05-29T20:17:28.500Z (25 days ago)
- Topics: ai4good, ai4science, computer-vision, deep-learning, earth-observation, foundation-models, geospatial, solar-physics, weather-models
- Language: Python
- Homepage: https://torchgeo.github.io/terratorch/
- Size: 263 MB
- Stars: 812
- Watchers: 25
- Forks: 155
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Governance: .github/GOVERNANCE.md
- Maintainers: .github/MAINTAINERS.md
Awesome Lists containing this project
README
[](https://huggingface.co/ibm-nasa-geospatial)
[](https://pypi.org/project/terratorch)
[](https://github.com/torchgeo/terratorch/actions/workflows/tests.yaml)
[](https://torchgeo.github.io/terratorch/)
[](https://pypi.org/project/terratorch/)
[](https://anaconda.org/conda-forge/terratorch)
Please read the contribution guidelines (see `Contribution` below) if you want to contribute to
TerraTorch.
## Overview
TerraTorch is a PyTorch domain library based on [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/) and the [TorchGeo](https://github.com/microsoft/torchgeo) domain library
for geospatial data.
Please also try our HPO/NAS tool: [Iterate](https://github.com/terrastackai/iterate)
## Disclaimer
TerraTorch provides tools for fine-tuning and using pretrained models.
No models are hosted by TerraTorch. TerraTorch only provides the training and inference framework.
User responsibility: It is the sole responsibility of the user to verify that the license of any model they download, fine-tune, or deploy allows their intended use.
The TerraTorch maintainers do not provide legal advice and are not liable for any misuse of third-party models.
Video: Introduction to TerraTorch
TerraTorch’s main purpose is to provide a flexible fine-tuning framework for Geospatial Foundation Models, which can be interacted with at different abstraction levels. The library provides:
- Convenient modelling tools:
- Flexible trainers for Image Segmentation, Classification and Pixel Wise Regression fine-tuning tasks
- Model factories that allow to easily combine backbones and decoders for different tasks
- Ready-to-go datasets and datamodules that require only to point to your data with no need of creating new custom classes
- Launching of fine-tuning tasks through CLI and flexible configuration files, or via jupyter notebooks
- Easy access to:
- Open source pre-trained Geospatial Foundation Model backbones:
* [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M)
* [TerraMind](https://research.ibm.com/blog/terramind-esa-earth-observation-model)
* [SatMAE](https://sustainlab-group.github.io/SatMAE/)
* [ScaleMAE](https://github.com/bair-climate-initiative/scale-mae)
* Satlas (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
* DOFA (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
* SSL4EO-L and SSL4EO-S12 models (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
* [Clay](https://github.com/Clay-foundation/model)
- Backbones available in the [timm](https://github.com/huggingface/pytorch-image-models) (Pytorch image models)
- Decoders available in [SMP](https://github.com/qubvel/segmentation_models.pytorch) (Pytorch Segmentation models with pre-training backbones) and [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) packages
- Fine-tuned models such as [granite-geospatial-biomass](https://huggingface.co/ibm-granite/granite-geospatial-biomass)
- All GEO-Bench datasets and datamodules
- All [TorchGeo](https://github.com/microsoft/torchgeo) datasets and datamodules
## Installation
### Pip
In order to use the file `pyproject.toml` it is necessary to guarantee `pip>=21.8`. If necessary upgrade `pip` using `python -m pip install --upgrade pip`.
For a stable point-release, use `pip install terratorch==`.
[comment]:
To get the most recent version of the branch `main`, install the library with `pip install git+https://github.com/torchgeo/terratorch.git`.
### Conda
TerraTorch is also available on `conda-forge`, to install from there do `conda install -c conda-forge terratorch`.
### Pipx
Alternatively, it is possible to install using [pipx](https://github.com/pypa/pipx) via `pipx install terratorch`, which creates an isolated environment and allows the user to run the application as a common CLI tool, with no need of installing dependencies or activating environments.
### Gdal
TerraTorch requires gdal to be installed, which can be quite a complex process.
If you don't have GDAL set up on your system, we recommend using a conda
environment and installing it with `conda install -c conda-forge gdal`. If you
are installing from `conda-forge` it probably won't be a problem.
### Install as a developer
To install as a developer (e.g. to extend the library):
```
git clone https://github.com/torchgeo/terratorch.git
cd terratorch
pip install -e .[test]
```
### Optional Dependencies
TerraTorch supports several optional features that can be installed separately:
- **VLLM support**: `pip install terratorch[vllm]`
- **Weather Foundation Models**: `pip install terratorch[wxc]` (Python >= 3.11 only)
- **PEFT (Parameter-Efficient Fine-Tuning)**: `pip install terratorch[peft]`
- **Visualization tools**: `pip install terratorch[visualize]`
- **GeoBench v2**: `pip install terratorch[geobenchv2]`
- **Logging with Weights & Biases**: `pip install terratorch[logging]`
- **MMSegmentation support**: `pip install terratorch[mmseg]`
- **Surya support**: `pip install terratorch[surya]`
- **Tortilla file support**: `pip install terratorch[tortilla]` - Required for loading datasets from tortilla files
You can install multiple optional dependencies at once: `pip install terratorch[vllm,peft,logging]`
## Documentation
To get started, check out the [quick start guide](https://torchgeo.github.io/terratorch/quick_start).
Developers, check out the [architecture overview](https://torchgeo.github.io/terratorch/architecture).
[TerraTorch: The Geospatial Foundation Models Toolkit on arXiv](https://arxiv.org/abs/2503.20563)
## Contributing
This project welcomes contributions and suggestions. Ways to contribute or get involved:
- Join our [Discord](https://discord.gg/vQXTNmrkTM)
- Create an [Issue](https://github.com/torchgeo/terratorch/issues) (for bugs or feature requests)
- Contribute via [PR](https://github.com/torchgeo/terratorch/pulls)
- Join our [duoweekly](https://romeokienzler.medium.com/the-duoweekly-manifesto-eaa6c1f542c8) community calls taking place [Tuesdays 4:30 PM - 5 PM CEST](https://teams.microsoft.com/l/meetup-join/19%3ameeting_MWJhMThhMTMtMjc3MS00YjAyLWI3NTMtYTI0NDQ3NWY3ZGU2%40thread.v2/0?context=%7b%22Tid%22%3a%22fcf67057-50c9-4ad4-98f3-ffca64add9e9%22%2c%22Oid%22%3a%227f7ab87a-680c-4c93-acc5-fbd7ec80823a%22%7d) and [Thursdays 2:30 PM - 3 PM CEST](https://teams.microsoft.com/l/meetup-join/19%3ameeting_MWJhMThhMTMtMjc3MS00YjAyLWI3NTMtYTI0NDQ3NWY3ZGU2%40thread.v2/0?context=%7b%22Tid%22%3a%22fcf67057-50c9-4ad4-98f3-ffca64add9e9%22%2c%22Oid%22%3a%227f7ab87a-680c-4c93-acc5-fbd7ec80823a%22%7d).
You can find more detailed contribution guidelines [here](https://torchgeo.github.io/terratorch/stable/contributing/).
If you want to meet the GitHub DCO checks, you **need** to do your commits as below:
```
git commit -s -m
```
It will sign the commit with your ID and the check will be met.
## Credits
Embed2Scale.
The embedding workflow integration and maintenance in TerraTorch are carried out as part of the Embed2Scale project
(Earth Observation & Weather Data Federation with AI Embeddings), funded by the EU’s Horizon Europe programme
(Grant Agreement No. 101131841), with additional support from SERI and UKRI.
## License
This project is primarily licensed under the **Apache License 2.0**.
However, some files contain code licensed under the **MIT License**. These files are explicitly listed in [`MIT_FILES.txt`](./MIT_FILES.txt).
By contributing to this repository, you agree that your contributions will be licensed under the Apache 2.0 License unless otherwise stated.
For more details, see the [LICENSE](./LICENSE) file.