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001     1046797
005     20251202203134.0
024 7 _ |a 10.1109/CVPRW67362.2025.00225
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037 _ _ |a FZJ-2025-03964
100 1 _ |a Blumenstiel, Benedikt
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111 2 _ |a 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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|d 2025-06-11 - 2025-06-12
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245 _ _ |a Terramesh: A Planetary Mosaic of Multimodal Earth Observation Data
260 _ _ |c 2025
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520 _ _ |a 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. The dataset spans nearly all terrestrial ecosystems and is stored with Zarr to facilitate efficient, HPC-friendly loading at scale. 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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536 _ _ |a AI Foundation - AI Foundation Models for EO (AIFM4EO, FAST-EO) (D/564/67338791)
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700 1 _ |a Fraccaro, Paolo
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700 1 _ |a Marsocci, Valerio
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700 1 _ |a Jakubik, Johannes
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700 1 _ |a Maurogiovanni, Stefano
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700 1 _ |a Czerkawski, Mikolaj
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700 1 _ |a Sedona, Rocco
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700 1 _ |a Cavallaro, Gabriele
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700 1 _ |a Brunschwiler, Thomas
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700 1 _ |a Bernabe-Moreno, Juan
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700 1 _ |a Longépé, Nicolas
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773 _ _ |a 10.1109/CVPRW67362.2025.00225
856 4 _ |u http://doi.org/10.1109/CVPRW67362.2025.00225
856 4 _ |u https://juser.fz-juelich.de/record/1046797/files/_CVPR_EV__TerraMesh.pdf
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