TerraTorch: The Geospatial Foundation Models Toolkit
This toolkit addresses the problem of efficient model adaptation for researchers and practitioners working with Earth Observation data, though it is incremental as it builds on existing frameworks.
The paper introduces TerraTorch, a toolkit for fine-tuning and benchmarking geospatial foundation models on satellite, weather, and climate data, which reduces the expertise and time needed for model development by integrating domain-specific components and automated optimization.
TerraTorch is a fine-tuning and benchmarking toolkit for Geospatial Foundation Models built on PyTorch Lightning and tailored for satellite, weather, and climate data. It integrates domain-specific data modules, pre-defined tasks, and a modular model factory that pairs any backbone with diverse decoder heads. These components allow researchers and practitioners to fine-tune supported models in a no-code fashion by simply editing a training configuration. By consolidating best practices for model development and incorporating the automated hyperparameter optimization extension Iterate, TerraTorch reduces the expertise and time required to fine-tune or benchmark models on new Earth Observation use cases. Furthermore, TerraTorch directly integrates with GEO-Bench, allowing for systematic and reproducible benchmarking of Geospatial Foundation Models. TerraTorch is open sourced under Apache 2.0, available at https://github.com/IBM/terratorch, and can be installed via pip install terratorch.