---
license: cc-by-sa-4.0
pretty_name: TerraMesh
size_categories:
- 10M<n<100M
viewer: false
---

# TerraMesh

> **A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models.**


**TerraMesh** merges data from **Sentinel‑1 SAR, Sentinel‑2 optical, Copernicus DEM, NDVI and land‑cover** sources into more than **9 million co‑registered patches** ready for large‑scale representation learning.

**Dataset release – End of June.**

![Examples from TerraMesh](assets%2Fexamples.png)

Samples from the TerraMesh dataset with seven spatiotemporal aligned modalities. Sentinel-2 L2A uses IRRG pseudo-coloring and Sentinel-1 RTC is visualized in db scale as VH-VV-VV/VH. Copernicus DEM is scaled based on the image value range with an additional 10 meter buffer to highlight flat scenes.

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## Dataset organisation

The archive ships two top‑level splits `train/` and `val/`, each holding one folder per modality. More details follow with the dataset release end of June.

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## Description 

TerraMesh fuses complementary optical, radar, topographic and thematic layers into pixel‑aligned 10 m cubes, allowing models to learn joint representations of land cover, vegetation dynamics and surface structure at planetary scale. 
The dataset is globally distributed and covers multiple years.

Heat map of the sample count in a one-degree grid. |  Monthly distribution of all S-2 timestamps.
:-------------------------:|:-------------------------:
![one_degree_map.png](assets%2Fone_degree_map.png) | ![timestamps.png](assets%2Ftimestamps.png)

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## Performance evaluation

![radar.png](assets%2Fradar.png)

TerraMesh was used to pre-train [TerraMind-B](https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base).
On the six evaluated segmentation tasks from PANGAEA bench, TerraMind‑B reaches an average mIoU of 66.6%, the best overall score with an average rank of 2.33. This amounts to roughly a 3pp improvement over the next‑best open model (CROMA), underscoring the benefits of pre‑training on TerraMesh.
Compared to an ablation model pre-trained only on SSL4EO-S12 locations TerraMind-B performs overall 1pp better with better global generalization on more remote tasks like CTM-SS.
More details in our [paper](https://arxiv.org/abs/2504.11172).

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## Citation

If you use TerraMesh, please cite:

```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}
}
```

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## License

TerraMesh is released under the **Creative Commons Attribution‑ShareAlike 4.0 (CC‑BY‑SA‑4.0)** license.

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## Acknowledgements

TerraMesh is part of the **FAST‑EO** project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT) and builds upon SSL4EO‑S12 and MajorTOM‑Core datasets.

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