Major-TOM/Core-S2RGB-SigLIP
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Marcin Kluczek
mkluczek
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Earth Observation (EO), Remote sensing
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Expansion of Global and Dense Open Embeddings Dataset of Earth ๐
We updated our previous embeddings release with three models MMEarth and DeCUR-S2, DeCUR-S1 of the Major TOM embeddings dataset, developed in collaboration with CloudFerro S.A. asterisk labs and ฮฆ-lab, European Space Agency - ESA. Together with @mikonvergence , Jฤdrzej S. Bojanowski, we extend the open-access collection of open dataset of Copernicus embeddings built at global scale, providing dense coverage across the entire acquisition area of Sentinel-1 and Sentinel-2 sensors.
Total embedding resources after the update:
- 51 TB of AI-embeddings generated from processed Sentinel data,
- over 40 billion embedding vectors,
- processing of 147 TB of raw satellite data,
- analysis covering more than 15 million Sentinel-1 and Sentinel-2 scenes and more than 16 trillion pixels.
This project delivers open and free vectorized expansions of Major TOM datasets available on CREODIAS and Hugging Face, setting a new standard for embedding releases and enabling lightweight, scalable ingestion of Earth Observation (EO) data for countless applications.
Datasets:
Major-TOM/Core-S2L2A-MMEarth
Major-TOM/Core-S2L1C-DeCUR
Major-TOM/Core-S1RTC-DeCUR
#EarthObservation #AI #CloudFerro #asterisklabs #ESA
We updated our previous embeddings release with three models MMEarth and DeCUR-S2, DeCUR-S1 of the Major TOM embeddings dataset, developed in collaboration with CloudFerro S.A. asterisk labs and ฮฆ-lab, European Space Agency - ESA. Together with @mikonvergence , Jฤdrzej S. Bojanowski, we extend the open-access collection of open dataset of Copernicus embeddings built at global scale, providing dense coverage across the entire acquisition area of Sentinel-1 and Sentinel-2 sensors.
Total embedding resources after the update:
- 51 TB of AI-embeddings generated from processed Sentinel data,
- over 40 billion embedding vectors,
- processing of 147 TB of raw satellite data,
- analysis covering more than 15 million Sentinel-1 and Sentinel-2 scenes and more than 16 trillion pixels.
This project delivers open and free vectorized expansions of Major TOM datasets available on CREODIAS and Hugging Face, setting a new standard for embedding releases and enabling lightweight, scalable ingestion of Earth Observation (EO) data for countless applications.
Datasets:
Major-TOM/Core-S2L2A-MMEarth
Major-TOM/Core-S2L1C-DeCUR
Major-TOM/Core-S1RTC-DeCUR
#EarthObservation #AI #CloudFerro #asterisklabs #ESA
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๐ต ๐๐๐-๐๐๐-๐๐๐ญ๐: ๐๐ง๐ข๐๐ข๐๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐จ๐๐๐ฅ๐ฅ๐ข๐ง๐ ๐จ๐ ๐๐๐๐๐ซ๐ง๐ข๐๐ฎ๐ฌ ๐๐ฆ๐๐ ๐๐ซ๐ฒ ๐๐ก๐ฎ๐ฆ๐๐ง๐๐ข๐ฅ๐ฌ
Today we release a prototype of COP-GEN - a universal generative model for Copernicus data. ๐๐๐-๐๐๐-๐๐๐ญ๐ is a model trained globally on the thumbnails of the Major TOM Core datasets, including Sentinel-2 L1C, Sentinel-2 L2A, Sentinel-1 RTC, and COP-DEM GLO-30.
โ๏ธ ๐๐จ๐๐๐ฅ mespinosami/COP-GEN-Beta
๐ฑ ๐๐๐ฆ๐จ mikonvergence/COP-GEN-Beta
How is it universal? COP-GEN learns a joint generative process of all modalities, which means that it can reconstruct data from any subset of present observations. ๐๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ญ๐ซ๐๐ข๐ง๐ข๐ง๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐๐ฅ๐ฅ๐ฒ to perform any of these tasks it can be used to approximate:
โ Sentinel-1 to Sentinel-2 translation
โ Elevation estimation from Sentinel-2 or Sentinel-1
โ Atmospheric Correction (L1C to L2A pipeline)
โ Atmospheric Generation (L2A to L1C)
โ ...and any other task involving translation between the supported modalities
On its own, the model can be used as a useful prior for estimating the data likelihood distribution for Copernicus data. COP-GEN-Beta learns joint, conditional, and marginal distributions within a single unified backbone, allowing to flexibly sample any modality given any condition.
Why is it Beta? Because thumbnails are a low-cost representation of the data that scales well and we managed to develop this prototype quite fast. We are currently developing the more costly COP-GEN model that supports the original data. For now, we wanted to showcase the prototype and make it available to the community for a test!
๐ ๐๐๐๐ฌ๐ข๐ญ๐ https://miquel-espinosa.github.io/cop-gen
๐ป ๐๐จ๐๐ https://github.com/miquel-espinosa/COP-GEN-Beta
๐ ๐๐๐ฉ๐๐ซ https://arxiv.org/pdf/2504.08548
Today we release a prototype of COP-GEN - a universal generative model for Copernicus data. ๐๐๐-๐๐๐-๐๐๐ญ๐ is a model trained globally on the thumbnails of the Major TOM Core datasets, including Sentinel-2 L1C, Sentinel-2 L2A, Sentinel-1 RTC, and COP-DEM GLO-30.
โ๏ธ ๐๐จ๐๐๐ฅ mespinosami/COP-GEN-Beta
๐ฑ ๐๐๐ฆ๐จ mikonvergence/COP-GEN-Beta
How is it universal? COP-GEN learns a joint generative process of all modalities, which means that it can reconstruct data from any subset of present observations. ๐๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ญ๐ซ๐๐ข๐ง๐ข๐ง๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐๐๐ฅ๐ฅ๐ฒ to perform any of these tasks it can be used to approximate:
โ Sentinel-1 to Sentinel-2 translation
โ Elevation estimation from Sentinel-2 or Sentinel-1
โ Atmospheric Correction (L1C to L2A pipeline)
โ Atmospheric Generation (L2A to L1C)
โ ...and any other task involving translation between the supported modalities
On its own, the model can be used as a useful prior for estimating the data likelihood distribution for Copernicus data. COP-GEN-Beta learns joint, conditional, and marginal distributions within a single unified backbone, allowing to flexibly sample any modality given any condition.
Why is it Beta? Because thumbnails are a low-cost representation of the data that scales well and we managed to develop this prototype quite fast. We are currently developing the more costly COP-GEN model that supports the original data. For now, we wanted to showcase the prototype and make it available to the community for a test!
๐ ๐๐๐๐ฌ๐ข๐ญ๐ https://miquel-espinosa.github.io/cop-gen
๐ป ๐๐จ๐๐ https://github.com/miquel-espinosa/COP-GEN-Beta
๐ ๐๐๐ฉ๐๐ซ https://arxiv.org/pdf/2504.08548
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