TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data

• URL: http://arxiv.org/abs/2504.11172v1
• Date: Tue, 15 Apr 2025 13:20:35 GMT
• Title: TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
• Authors: Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci, Johannes Jakubik, Stefano Maurogiovanni, Mikolaj Czerkawski, Rocco Sedona, Gabriele Cavallaro, Thomas Brunschwiler, Juan Bernabe-Moreno, Nicolas Longépé,
• Abstract summary: We introduce TerraMesh, a new globally diverse, multimodal dataset combining optical, radar, elevation, and land-cover modalities in a single format.<n>We provide detailed data processing steps, comprehensive statistics, and empirical evidence demonstrating improved model performance when pre-trained on TerraMesh.<n>The dataset will be made publicly available with a permissive license.
• Score: 3.674991996196602
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 and fostering robust cross-modal correlation learning. We provide detailed data processing steps, comprehensive statistics, and empirical evidence demonstrating improved model performance when pre-trained on TerraMesh. The dataset will be made publicly available with a permissive license.

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