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arXiv · 2504.11172

TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data

Benedikt Blumenstiel·Paolo Fraccaro·Valerio Marsocci·Johannes Jakubik·Stefano Maurogiovanni·Mikolaj Czerkawski·Rocco Sedona·Gabriele Cavallaro·Thomas Brunschwiler·Juan Bernabe-Moreno·Nicolas Longépé
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Abstract

Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public datasets are often limited in scale, geographic coverage, or sensor variety. We introduce TerraMesh, a new globally diverse, multimodal dataset combining optical, synthetic aperture radar, elevation, and land-cover modalities in an Analysis-Ready Data format. TerraMesh includes over 9~million samples with eight spatiotemporal aligned modalities, enabling large-scale pre-training. We provide detailed data processing steps, comprehensive statistics, and empirical evidence demonstrating improved model performance when pre-trained on TerraMesh. The dataset is hosted at https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.

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Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci, Johannes Jakubik, Stefano Maurogiovanni, Mikolaj Czerkawski, Rocco Sedona, Gabriele Cavallaro, Thomas Brunschwiler, Juan Bernabe-Moreno, Nicolas Longépé. 2025-04-15. TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data. https://arxiv.org/abs/2504.11172

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