TerraMind: A Breakthrough Multimodal Model for Global-Scale Earth Observation
What does our planet really look like – and how is it changing? TerraMind is a groundbreaking AI model that intelligently connects various Earth observation data, including satellite imagery, elevation models, and land use maps. Developed by researchers at the Jülich Supercomputing Centre, it is designed to help better understand environmental changes and support informed decision-making in the future.
The FAST-EO project has released the TerraMind AI foundation model, the first any-to-any generative, multimodal foundation model for Earth observation, integrating satellite imagery, elevation data, land use maps, and natural language descriptions. TerraMind was pretrained both on token-level and pixel-level data across nine modalities using over 500 billion tokens on the JUWELS Booster HPC system at JSC.
TerraMind achieved top performance on the PANGAEA benchmark, outperforming all previously published geospatial foundation models across diverse Earth observation tasks. Model weights are now available on Hugging Face for fine-tuning and the paper is available on arXiv.
JSC’s SDL (Simulation and Data Lab) AI and ML for Remote Sensing aims to increase the adoption of interdisciplinary research combining remote sensing applications, large-scale AI, and high-performance and innovative computing. Being an integral part of the work on TerraMind is an important step towards that aim.
Dr. Rocco Sedona said, “With an eye on the future, by leveraging the JUPITER AI Factory—a cornerstone of Europe’s AI infrastructure—we are committed to providing the Earth observation community with powerful, transformative tools that will drive their downstream applications forward.” Prof. Gabriele Cavallaro added “It’s remarkable to witness how rapidly AI research and achievements are evolving across numerous domains. Geospatial foundation models are emerging as the backbone of geoscience and remote sensing, driving progress in addressing the many challenges we face, particularly those tied to climate change.”
Jülich Supercomputing Centre – News flashes, 22.04.2025
The original press release can be found at:
TerraMind: A Breakthrough Multimodal Model for Global-Scale Earth Observation
The original publication can be found at:
TerraMind: Large-Scale Generative Multimodality for Earth Observation: Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Longépé. https://doi.org/10.48550/arXiv.2504.11171
Localization in Helmholtz Information:
Helmholtz Information, Program 1: Engineering Digital Futures, Topic 1: Enabling Computational- & Data-Intensive Science and Engineering
Contact:
Dr. Rocco Sedona
Deputy Head of Simulation and Data Lab (SDL) Artificial Intelligence and Machine Learning for Remote Sensing
Institute for Advanced Simulation (IAS)
Jülich Supercomputing Centre (JSC)
Forschungszentrum Jülich
Phone: +49 2461/61-1497
E-Mail: r.sedona@fz-juelich.de
Prof. Dr. -Ing. Gabriele Cavallaro
Head of Simulation and Data Lab (SDL) Artificial Intelligence and Machine Learning for Remote Sensing
Institute for Advanced Simulation (IAS)
Jülich Supercomputing Centre (JSC)
Forschungszentrum Jülich
Phone: +49 2461/61-3858
E-Mail: g.cavallaro@fz-juelich.de
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