The model owes a lot to the volume of open Copernicus data distributed on HuggingFace via ๐บ Major TOM org:
https://huggingface.co/Major-TOM
It's a fantastic resource for those looking to experiment with Earth observation applications! ๐
Mikolaj Czerkawski
mikonvergence
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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 https://huggingface.co/mespinosami/COP-GEN-Beta
๐ฑ ๐๐๐ฆ๐จ https://huggingface.co/spaces/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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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 https://huggingface.co/mespinosami/COP-GEN-Beta
๐ฑ ๐๐๐ฆ๐จ https://huggingface.co/spaces/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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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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Sorting modalities in generate_output() backend.py for consistent generations
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๐๐๐๐ ๐๏ธ ๐๐๐ฑ๐ญ-๐๐๐ฌ๐๐ ๐ญ๐๐ซ๐ซ๐๐ข๐ง ๐ ๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง ๐ฆ๐จ๐๐๐ฅ
MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations (co-registered colour and depth maps) of terrains based on text prompt conditioning.
Work developed by Paul BorneโPons ( @NewtNewt ) during his joint internship at
Adobe & ESA, and in collaboration with asterisk labs.
๐๏ธ ๐๐ซ๐จ๐ฃ๐๐๐ญ ๐๐๐ ๐ : https://paulbornep.github.io/mesa-terrain/
๐ ๐๐ซ๐๐ฉ๐ซ๐ข๐ง๐ญ : https://arxiv.org/abs/2504.07210
๐ค ๐๐จ๐๐๐ฅ ๐๐๐ข๐ ๐ก๐ญ๐ฌ : NewtNewt/MESA
๐พ ๐๐๐ญ๐๐ฌ๐๐ญ : Major-TOM/Core-DEM
๐ง๐ปโ๐ปโ๐๐จ๐๐ : https://github.com/PaulBorneP/MESA
๐๐ ๐๐ฉ๐๐๐: mikonvergence/MESA
MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations (co-registered colour and depth maps) of terrains based on text prompt conditioning.
Work developed by Paul BorneโPons ( @NewtNewt ) during his joint internship at
Adobe & ESA, and in collaboration with asterisk labs.
๐๏ธ ๐๐ซ๐จ๐ฃ๐๐๐ญ ๐๐๐ ๐ : https://paulbornep.github.io/mesa-terrain/
๐ ๐๐ซ๐๐ฉ๐ซ๐ข๐ง๐ญ : https://arxiv.org/abs/2504.07210
๐ค ๐๐จ๐๐๐ฅ ๐๐๐ข๐ ๐ก๐ญ๐ฌ : NewtNewt/MESA
๐พ ๐๐๐ญ๐๐ฌ๐๐ญ : Major-TOM/Core-DEM
๐ง๐ปโ๐ปโ๐๐จ๐๐ : https://github.com/PaulBorneP/MESA
๐๐ ๐๐ฉ๐๐๐: mikonvergence/MESA
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