mlsanityData to 7 Oct 2026

TerraTorch: The Geospatial Foundation Models Toolkit

arXiv 2503.20563 ↗First posted 26 Mar 2025cs.CV cs.LGcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

15 of 35 references.

References · 15

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  6. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  7. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  8. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  9. 2019Tackling Climate Change with Machine Learningcited by 264
  10. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  11. 2023Foundation Models for Generalist Geospatial Artificial Intelligencecited by 143
  12. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
3 more
  1. 2023GEO-Bench: Toward Foundation Models for Earth Monitoringcited by 122
  2. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  3. 2023Decoupling Common and Unique Representations for Multimodal Self-supervised Learningcited by 36

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 17.

24 papers.

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  1. 2025GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AIcited by 19
  2. 2025Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Modelscited by 9
  3. 2026No One Knows the State of the Art in Geospatial Foundation Modelscited by 9
  4. 2025CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Expertscited by 3
  5. 2026TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observationcited by 2
  6. 2026Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Datacited by 2
  7. 2026Benchmarking Geospatial Foundation Models for Agriculture Applicationscited by 2
  8. 2025Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-userscited by 1
  9. 2025The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2cited by 1
  10. 2026Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mappingcited by 1
  11. 2026Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMindcited by 1
  12. 2026How to Embed Matters: Evaluation of EO Embedding Design Choicescited by 1
12 more
  1. 2026Emerging Flexible Designs for Geospatial Multimodal Foundation Modelscited by 1
  2. 2026GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentationcited by 1
  3. 2025Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Modelcited by 0
  4. 2025Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architecturescited by 0
  5. 2026Aligned explanations in neural networkscited by 0
  6. 2026Towards Onboard Continuous Change Detection for Floodscited by 0
  7. 2026How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Modelscited by 0
  8. 2026Ecological mapping with geospatial foundation modelscited by 0
  9. 2026Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbonescited by 0
  10. 2026Now We Know? A Systematic Comparison of TerraMind and THORcited by 0
  11. 2026SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Sciencecited by 0
  12. 2026Multimodal-Multiresolution Foundation Model for Lunar Remote Sensingcited by 0