TerraTorch: The Geospatial Foundation Models Toolkit

TL;DR

TerraTorch is a comprehensive, open-source toolkit built on PyTorch Lightning that simplifies fine-tuning and benchmarking geospatial foundation models for satellite, weather, and climate data applications.

Contribution

It introduces a modular, no-code framework with integrated hyperparameter optimization and benchmarking capabilities tailored for geospatial data.

Findings

Enables easy fine-tuning of models with minimal expertise.

Supports systematic benchmarking with GEO-Bench integration.

Reduces time and effort in developing geospatial AI models.

Abstract

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.