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🔵 𝐂𝐎𝐏-𝐆𝐄𝐍-𝐁𝐞𝐭𝐚: 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 𝐨𝐟 𝐂𝐎𝐏𝐞𝐫𝐧𝐢𝐜𝐮𝐬 𝐈𝐦𝐚𝐠𝐞𝐫𝐲 𝐓𝐡𝐮𝐦𝐛𝐧𝐚𝐢𝐥𝐬
Today we release a prototype of COP-GEN - a universal generative model for Copernicus data. 𝐂𝐎𝐏-𝐆𝐄𝐍-𝐁𝐞𝐭𝐚 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.
⚖️ 𝐌𝐨𝐝𝐞𝐥 mespinosami/COP-GEN-Beta
📱 𝐃𝐞𝐦𝐨 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. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐥𝐥𝐲 to perform any of these tasks it can be used to approximate:
✅ Sentinel-1 to Sentinel-2 translation
✅ Elevation estimation from Sentinel-2 or Sentinel-1
✅ Atmospheric Correction (L1C to L2A pipeline)
✅ Atmospheric Generation (L2A to L1C)
✅ ...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!
🌐 𝐖𝐞𝐛𝐬𝐢𝐭𝐞 https://miquel-espinosa.github.io/cop-gen
💻 𝐂𝐨𝐝𝐞 https://github.com/miquel-espinosa/COP-GEN-Beta
📄 𝐏𝐚𝐩𝐞𝐫 https://arxiv.org/pdf/2504.08548
Today we release a prototype of COP-GEN - a universal generative model for Copernicus data. 𝐂𝐎𝐏-𝐆𝐄𝐍-𝐁𝐞𝐭𝐚 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.
⚖️ 𝐌𝐨𝐝𝐞𝐥 mespinosami/COP-GEN-Beta
📱 𝐃𝐞𝐦𝐨 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. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐥𝐥𝐲 to perform any of these tasks it can be used to approximate:
✅ Sentinel-1 to Sentinel-2 translation
✅ Elevation estimation from Sentinel-2 or Sentinel-1
✅ Atmospheric Correction (L1C to L2A pipeline)
✅ Atmospheric Generation (L2A to L1C)
✅ ...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!
🌐 𝐖𝐞𝐛𝐬𝐢𝐭𝐞 https://miquel-espinosa.github.io/cop-gen
💻 𝐂𝐨𝐝𝐞 https://github.com/miquel-espinosa/COP-GEN-Beta
📄 𝐏𝐚𝐩𝐞𝐫 https://arxiv.org/pdf/2504.08548
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