%0 Conference Paper
%A Blumenstiel, Benedikt
%A Fraccaro, Paolo
%A Marsocci, Valerio
%A Jakubik, Johannes
%A Maurogiovanni, Stefano
%A Czerkawski, Mikolaj
%A Sedona, Rocco
%A Cavallaro, Gabriele
%A Brunschwiler, Thomas
%A Bernabe-Moreno, Juan
%A Longépé, Nicolas
%T Terramesh: A Planetary Mosaic of Multimodal Earth Observation Data
%I IEEE
%M FZJ-2025-03964
%P n/a
%D 2025
%X 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.
%B 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
%C 11 Jun 2025 - 12 Jun 2025, Nashville (TN)
Y2 11 Jun 2025 - 12 Jun 2025
M2 Nashville, TN
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%R 10.1109/CVPRW67362.2025.00225
%U https://juser.fz-juelich.de/record/1046797