{"id":31977611,"url":"https://github.com/IBM/terratorch","last_synced_at":"2025-10-14T21:49:04.125Z","repository":{"id":239935997,"uuid":"801016442","full_name":"IBM/terratorch","owner":"IBM","description":"A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).","archived":false,"fork":false,"pushed_at":"2025-10-04T21:00:30.000Z","size":152515,"stargazers_count":609,"open_issues_count":98,"forks_count":102,"subscribers_count":24,"default_branch":"main","last_synced_at":"2025-10-04T22:25:49.582Z","etag":null,"topics":["ai4good","ai4science","computer-vision","deep-learning","earth-observation","foundation-models","geospatial","weather-models"],"latest_commit_sha":null,"homepage":"https://ibm.github.io/terratorch/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/IBM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-05-15T12:43:28.000Z","updated_at":"2025-10-04T10:14:06.000Z","dependencies_parsed_at":"2025-01-02T19:27:33.691Z","dependency_job_id":"3861adb8-1ae3-4c8e-bfa0-3d7555f61647","html_url":"https://github.com/IBM/terratorch","commit_stats":null,"previous_names":["ibm/terratorch"],"tags_count":30,"template":false,"template_full_name":null,"purl":"pkg:github/IBM/terratorch","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2Fterratorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2Fterratorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2Fterratorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2Fterratorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IBM","download_url":"https://codeload.github.com/IBM/terratorch/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2Fterratorch/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279021374,"owners_count":26087023,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-14T02:00:06.444Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai4good","ai4science","computer-vision","deep-learning","earth-observation","foundation-models","geospatial","weather-models"],"created_at":"2025-10-14T21:48:34.587Z","updated_at":"2025-10-14T21:49:04.115Z","avatar_url":"https://github.com/IBM.png","language":"Python","funding_links":[],"categories":["🔬 Domain-Specific Applications"],"sub_categories":["🌍 Earth \u0026 Climate Science"],"readme":"\u003c!---\n\u003cimg src=\"https://github.com/user-attachments/assets/f7c9586f-6220-4a53-9669-2aee3300b492#light-only\" alt=\"TerraTorch\"  width=\"400\"/\u003e\n\u003cimg src=\"assets/logo_white.png#dark-only\" alt=\"TerraTorch\"  width=\"400\"/\u003e\n--\u003e\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/user-attachments/assets/f8c9586f-6220-4a53-9669-2aee3300b492\"\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"assets/logo_white.png\"\u003e\n  \u003ccenter\u003e\u003cimg 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\"/\u003e\u003c/center\u003e\n\u003c/picture\u003e\n\n\u003c!--\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs/figs/logo_inv.png\"\u003e\n  \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"docs/figs/logo.png\"\u003e\n\u003c/picture\u003e\n--\u003e\n\n[![huggingface](https://img.shields.io/badge/Hugging_Face-join-FFD21E?logo=huggingface)](https://huggingface.co/ibm-nasa-geospatial)\n[![pypi](https://badge.fury.io/py/terratorch.svg)](https://pypi.org/project/terratorch)\n[![tests](https://github.com/IBM/terratorch/actions/workflows/test.yaml/badge.svg)](https://github.com/ibm/terratorch/actions/workflows/test.yaml)\n[![MkDocs](https://img.shields.io/badge/MkDocs-526CFE?logo=materialformkdocs\u0026logoColor=fff)](https://ibm.github.io/terratorch/)\n![cov](https://github.com/IBM/terratorch/raw/main/assets/coverage-badge.svg)\n[![PyPI Downloads](https://img.shields.io/pypi/dm/terratorch.svg?label=PyPI%20downloads)](https://pypi.org/project/terratorch/)\n[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/terratorch.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/terratorch)\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## Disclaimer\nTerraTorch provides tools for fine-tuning and using pretrained models.\nNo models are hosted by TerraTorch. TerraTorch only provides the training and inference framework.  \n\nUser 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.\nThe TerraTorch maintainers do not provide legal advice and are not liable for any misuse of third-party models.\n\n\u003chr\u003e\n\u003ca href=\"https://www.youtube.com/watch?v=CB3FKtmuPI8\"\u003e\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png\" alt=\"YouTube\" width=\"20\"\u003e\n  Watch the latest recording on YouTube: Earth observation foundation models with Prithvi-EO-2.0 and TerraTorch\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png\" alt=\"YouTube\" width=\"20\"\u003e\n\u003c/a\u003e\n\u003chr\u003e\n\n\nTerraTorch’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:\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) (Pytorch image models)\n    - 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\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 the file `pyproject.toml` it is necessary to guarantee `pip\u003e=21.8`. If necessary upgrade `pip` using `python -m pip install --upgrade pip`. \n\nFor a stable point-release, use `pip install terratorch==\u003cversion\u003e`.\n\n[comment]: \u003cIf 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`.\u003e\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]: \u003cAnother 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.\u003e\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 \u003e= 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[TerraTorch: The Geospatial Foundation Models Toolkit on arXiv](https://arxiv.org/abs/2503.20563)\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-3e84x0aw2-cMojjUP~2WBXbao9pipWfg)\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. If you want to meet the GitHub DCO checks, just do your commits as below:\n```\ngit commit -s -m \u003cmessage\u003e\n```\nIt will sign the commit with your ID and the check will be met. \n\n## Credits\n\nTerraTorch is supported by the EU’s Horizon Europe program under Grant Agreement number 101131841 and also received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI) and the UK Research and Innovation (UKRI).\n\n\n## License\n\nThis project is primarily licensed under the **Apache License 2.0**. \n\nHowever, some files contain code licensed under the **MIT License**. These files are explicitly listed in [`MIT_FILES.txt`](./MIT_FILES.txt).\n\nBy contributing to this repository, you agree that your contributions will be licensed under the Apache 2.0 License unless otherwise stated.\n\nFor more details, see the [LICENSE](./LICENSE) file.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FIBM%2Fterratorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FIBM%2Fterratorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FIBM%2Fterratorch/lists"}