| Name | terratorch JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | TerraTorch - A model training toolkit for geospatial tasks |
| upload_time | 2025-05-15 14:56:27 |
| maintainer | None |
| docs_url | None |
| author | Romeo Kienzler, Benedikt Blumenstiel, Carlos Gomes, Francesc Martí Escofet, Paolo Fraccaro, Pedro Henrique Conrado, Jaione Tirapu Azpiroz, Bianca Zadrozny, Daniela Szwarcman, Þorsteinn Elí Gíslason, Raunak Bhansali, Takao Moriyama |
| requires_python | >=3.10 |
| license | Apache License, Version 2.0 |
| keywords |
fine-tuning
geospatial foundation models
artificial intelligence
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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[](https://huggingface.co/ibm-nasa-geospatial)
[](https://pypi.org/project/terratorch)
[](https://github.com/ibm/terratorch/actions/workflows/test.yaml)
[](https://ibm.github.io/terratorch/)
[](https://pypi.org/project/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.
<hr>
<a href="https://www.youtube.com/watch?v=CB3FKtmuPI8">
<img src="https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png" alt="YouTube" width="20">
Watch the latest recording on YouTube: Earth observation foundation models with Prithvi-EO-2.0 and TerraTorch
<img src="https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png" alt="YouTube" width="20">
</a>
<hr>
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
## Install
### Pip
In order to use th 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==<version>`.
[comment]: <If you prefer to get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.>
To get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.
[comment]: <Another alternative is 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.>
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`.
To install as a developer (e.g. to extend the library):
```
git clone https://github.com/IBM/terratorch.git
cd terratorch
pip install -r requirements_test.txt
conda install -c conda-forge gdal
pip install -e .
```
To install terratorch with partial (work in development) support for Weather Foundation Models, `pip install -e .[wxc]`, which currently works just for `Python >= 3.11`.
## Documentation
To get started, check out the [quick start guide](https://ibm.github.io/terratorch/quick_start).
Developers, check out the [architecture overview](https://ibm.github.io/terratorch/architecture).
## Contributing
This project welcomes contributions and suggestions. Ways to contribute or get involved:
- Join our [Slack](https://join.slack.com/t/terratorch/shared_invite/zt-34uzp28xx-xz1VHvu9vCN1ffx7fd~dGw)
- Create an [Issue](https://github.com/IBM/terratorch/issues) (for bugs or feature requests)
- Contribute via [PR](https://github.com/IBM/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://ibm.github.io/terratorch/stable/contributing/).
A simple hint for any contributor. If you want to meet the GitHub DCO checks, just do your commits as below:
```
git commit -s -m <message>
```
It will sign the commit with your ID and the check will be met.
## 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.
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"description": "<!---\n<img src=\"https://github.com/user-attachments/assets/f7c9586f-6220-4a53-9669-2aee3300b492#light-only\" alt=\"TerraTorch\" width=\"400\"/>\n<img src=\"assets/logo_white.png#dark-only\" alt=\"TerraTorch\" width=\"400\"/>\n-->\n<picture>\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/user-attachments/assets/f8c9586f-6220-4a53-9669-2aee3300b492\">\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"assets/logo_white.png\">\n <center><img style=\"display: block; margin-left: auto; margin-right: auto\"; src=\"https://github.com/user-attachments/assets/f7c9586f-6220-4a53-9669-2aee3300b492\" alt=\"TerraTorch\" width=\"400\"/></center>\n</picture>\n\n<!--\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/figs/logo_inv.png\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"docs/figs/logo.png\">\n</picture>\n-->\n\n[](https://huggingface.co/ibm-nasa-geospatial)\n[](https://pypi.org/project/terratorch)\n[](https://github.com/ibm/terratorch/actions/workflows/test.yaml)\n[](https://ibm.github.io/terratorch/)\n\n[](https://pypi.org/project/terratorch/)\n\n## Overview\nTerraTorch 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\nfor geospatial data. \n\n<hr>\n<a href=\"https://www.youtube.com/watch?v=CB3FKtmuPI8\">\n <img src=\"https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png\" alt=\"YouTube\" width=\"20\">\n Watch the latest recording on YouTube: Earth observation foundation models with Prithvi-EO-2.0 and TerraTorch\n <img src=\"https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png\" alt=\"YouTube\" width=\"20\">\n</a>\n<hr>\n\n\nTerraTorch\u2019s 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:\n\n- Convenient modelling tools:\n - Flexible trainers for Image Segmentation, Classification and Pixel Wise Regression fine-tuning tasks\n - Model factories that allow to easily combine backbones and decoders for different tasks\n - Ready-to-go datasets and datamodules that require only to point to your data with no need of creating new custom classes\n - Launching of fine-tuning tasks through CLI and flexible configuration files, or via jupyter notebooks\n- Easy access to:\n - Open source pre-trained Geospatial Foundation Model backbones:\n * [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M)\n * [TerraMind](https://research.ibm.com/blog/terramind-esa-earth-observation-model)\n * [SatMAE](https://sustainlab-group.github.io/SatMAE/)\n * [ScaleMAE](https://github.com/bair-climate-initiative/scale-mae)\n * Satlas (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))\n * DOFA (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))\n * SSL4EO-L and SSL4EO-S12 models (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))\n * [Clay](https://github.com/Clay-foundation/model)\n - Backbones available in the [timm](https://github.com/huggingface/pytorch-image-models)\u00a0(Pytorch image models)\n - Decoders available in [SMP](https://github.com/qubvel/segmentation_models.pytorch) (Pytorch Segmentation models with pre-training backbones)\u00a0and [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) packages\n - Fine-tuned models such as [granite-geospatial-biomass](https://huggingface.co/ibm-granite/granite-geospatial-biomass)\n - All GEO-Bench datasets and datamodules\n - All [TorchGeo](https://github.com/microsoft/torchgeo) datasets and datamodules \n\n## Install\n### Pip\nIn order to use th file `pyproject.toml` it is necessary to guarantee `pip>=21.8`. If necessary upgrade `pip` using `python -m pip install --upgrade pip`. \n\nFor a stable point-release, use `pip install terratorch==<version>`.\n\n[comment]: <If you prefer to get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.>\nTo get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.\n\n[comment]: <Another alternative is 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.>\n\nTerraTorch 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`.\n\nTo install as a developer (e.g. to extend the library):\n```\ngit clone https://github.com/IBM/terratorch.git\ncd terratorch\npip install -r requirements_test.txt\nconda install -c conda-forge gdal\npip install -e .\n```\n\nTo install terratorch with partial (work in development) support for Weather Foundation Models, `pip install -e .[wxc]`, which currently works just for `Python >= 3.11`. \n\n## Documentation\n\nTo get started, check out the [quick start guide](https://ibm.github.io/terratorch/quick_start).\n\nDevelopers, check out the [architecture overview](https://ibm.github.io/terratorch/architecture).\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Ways to contribute or get involved:\n\n- Join our [Slack](https://join.slack.com/t/terratorch/shared_invite/zt-34uzp28xx-xz1VHvu9vCN1ffx7fd~dGw)\n- Create an [Issue](https://github.com/IBM/terratorch/issues) (for bugs or feature requests)\n- Contribute via [PR](https://github.com/IBM/terratorch/pulls)\n- 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).\n\nYou can find more detailed contribution guidelines [here](https://ibm.github.io/terratorch/stable/contributing/). \n\nA simple hint for any contributor. 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