Datasets:
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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.
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You find more information about the data sampling and preprocessing in our paper: [TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data](https://arxiv.org/abs/2504.11172).
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**Unsafe files warning:** The file scanners picked up some compressed data in the Zarr Zip binaries as broken executables. We checked the files and they work correctly. Hugging Face deployed a fix but it takes some time until the files get scanned again and the warning is removed.
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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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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.
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You find more information about the data sampling and preprocessing in our paper: [TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data](https://arxiv.org/abs/2504.11172).
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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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