TerraMesh
Field |
Value |
|---|---|
Folder |
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Discipline |
GeoAI / Remote Sensing / Earth Science |
DOI |
|
Link |
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Public |
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Publication Date |
2025-09-05 |
Downloaded |
2025-09-05 |
Data Type |
tar |
Dataset Size |
31T |
Number of Files |
12618 |
Usage Policy Link |
Description
TerraMesh<br><br>Dataset Summary<br>TerraMesh is a planetary-scale, multimodal analysis-ready dataset for Earth Observation foundation models. It merges Sentinel-1 SAR, Sentinel-2 optical, Copernicus DEM, NDVI, and land-cover sources into more than nine million co-registered patches for large-scale representation learning.<br><br>Dataset Structure<br>The dataset includes two top-level splits (train/ and val/), each containing sub-folders for modalities: DEM, LULC, NDVI, S1GRD, S1RTC, S2L1C, S2L2A, and S2RGB. Each folder contains up to 889 shard files, each storing up to 10,240 samples as compressed Zarr archives.<br><br>Data Characteristics<br>Each sample contains seven spatially aligned modalities (optical, radar, topographic, vegetation, and land-cover). Metadata fields include center latitude/longitude, timestamps, CRS, and cloud masks.<br><br>Intended Use<br>TerraMesh enables multimodal pretraining, global geospatial feature extraction, and benchmarking of foundation models for planetary surface understanding.<br><br>Performance & Evaluation<br>Pretraining on TerraMesh led to TerraMind-B achieving 66.6% mIoU across PANGAEA benchmark tasks, outperforming CROMA and SSL4EO-S12 models.<br><br>Acknowledgments<br>Developed under ESA Φ-Lab’s FAST-EO project. Source data include SSL4EO-S12 (CC-BY-4.0), MajorTOM-Core (CC-BY-SA-4.0), and Copernicus DEM (© DLR / Airbus / ESA).
Usage
$ module avail
$ module load datasets
$ module load geoai/ibm-esa-geospatial/TerraMesh/2025-09-05
Usage Policy
Citation
Blumenstiel, B., Fraccaro, P., Marsocci, V., Jakubik, J., Maurogiovanni, S., Czerkawski, M., Sedona, R., Cavallaro, G., Brunschwiler, T., Bernabe-Moreno, J., et al. (2025). TerraMesh: A planetary mosaic of multimodal Earth observation data. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
BibTeX
@article{blumenstiel2025terramesh,
title={Terramesh: A planetary mosaic of multimodal earth observation data},
author={Blumenstiel, Benedikt and Fraccaro, Paolo and Marsocci, Valerio and Jakubik, Johannes and Maurogiovanni, Stefano and Czerkawski, Mikolaj and Sedona, Rocco and Cavallaro, Gabriele and Brunschwiler, Thomas and Bernabe-Moreno, Juan and others},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
year={2025},
}