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TerraMesh

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Field Value
Description TerraMesh

Dataset Summary
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.

Dataset Structure
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.

Data Characteristics
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.

Intended Use
TerraMesh enables multimodal pretraining, global geospatial feature extraction, and benchmarking of foundation models for planetary surface understanding.

Performance & Evaluation
Pretraining on TerraMesh led to TerraMind-B achieving 66.6% mIoU across PANGAEA benchmark tasks, outperforming CROMA and SSL4EO-S12 models.

Acknowledgments
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).
Folder /datasets/geoai/ibm-esa-geospatial/TerraMesh
Discipline GeoAI / Remote Sensing / Earth Science
DOI 10.48550/arXiv.2504.11172
Link Access Data
Public True
Publication Date 2025-09-05
Downloaded 2025-09-05
Data Type tar
Dataset Size 31T
Number of Files 12618
Usage 
$ module avail
$ module load datasets
$ module load geoai/ibm-esa-geospatial/TerraMesh/2025-09-05
Usage Policy Link https://choosealicense.com/licenses/cc-by-sa-4.0/
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
📜 View BibTeX citation
@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},
}