COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails
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2504.08548
โข
Published
โข
1
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mikonvergenceย
authored
2
papers
13 days ago
mikonvergenceย
posted
an
update
16 days ago
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729
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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- 1 reply
mikonvergenceย
posted
an
update
21 days ago
Post
1588
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
- 2 replies
mikonvergenceย
authored
a
paper
21 days ago
mikonvergenceย
authored
a
paper
5 months ago
mikonvergenceย
posted
an
update
8 months ago
Post
2342
๐๐๐ฐ ๐๐๐ฅ๐๐๐ฌ๐: ๐๐๐ฃ๐จ๐ซ ๐๐๐ ๐๐ข๐ ๐ข๐ญ๐๐ฅ ๐๐ฅ๐๐ฏ๐๐ญ๐ข๐จ๐ง ๐๐จ๐๐๐ฅ ๐๐ฑ๐ฉ๐๐ง๐ฌ๐ข๐จ๐ง ๐บ๏ธ
Dataset: Major-TOM/Core-DEM
Today with European Space Agency - ESA and Adobe Research, we release a global expansion to Major TOM with GLO-30 DEM data.
You can now instantly access nearly 2M of Major TOM samples with elevation data to build your next AI model for EO. ๐
๐ Browse the data in our usual viewer app: Major-TOM/MajorTOM-Core-Viewer
Fantastic work championed by Paul Borne--Pons @NewtNewt ๐
Dataset: Major-TOM/Core-DEM
Today with European Space Agency - ESA and Adobe Research, we release a global expansion to Major TOM with GLO-30 DEM data.
You can now instantly access nearly 2M of Major TOM samples with elevation data to build your next AI model for EO. ๐
๐ Browse the data in our usual viewer app: Major-TOM/MajorTOM-Core-Viewer
Fantastic work championed by Paul Borne--Pons @NewtNewt ๐
robmarkcoleย
updated
a
dataset
11 months ago
mikonvergenceย
posted
an
update
about 1 year ago
Post
1565
๐ ๐ฎ๐ท๐ผ๐ฟ ๐ง๐ข๐ : ๐ฃ๐น๐ฎ๐ป๐ฒ๐ ๐๐ฎ๐ฟ๐๐ต ๐ถ๐ ๐ฏฬถ๐นฬถ๐ฬถ๐ฒฬถ ๐ฑ.๐ฐ๐ฌ๐ฑ ๐๐๐
๐จ EXPANSION RELEASE: ๐ฆ๐ฒ๐ป๐๐ถ๐ป๐ฒ๐น-๐ญ ๐ถ๐ ๐ป๐ผ๐ ๐ฎ๐๐ฎ๐ถ๐น๐ฎ๐ฏ๐น๐ฒ in the MajorTOM-Core!
Major-TOM/Core-S1RTC
๐ Together with @aliFrancis we've been racing to release the first official expansion to the Major TOM project.
MajorTOM-Core-S1RTC contains 1,469,955 of SAR images paired to Sentinel-2 images from Core-S2.
๐We cover more than 65% of the optical coverage with an average time shift of 7 days.
16 TB of radiometrically calibrated SAR imagery, available in the exact same format as the existing Major-TOM data.
๐บ๏ธ You can explore instantly in our viewing app:
Major-TOM/MajorTOM-Core-Viewer
So, what now?
๐งฑ ๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ฐ๐ญ๐ก: our community continues to grow! To coordinate the upcoming expansions as well as use cases of the open data, we will organise a meet up on 23 April, you can ๐ซ๐๐ ๐ข๐ฌ๐ญ๐๐ซ ๐ฒ๐จ๐ฎ๐ซ ๐ข๐ง๐ญ๐๐ซ๐๐ฌ๐ญ here: https://forms.gle/eBj8JvibJx9b6PLf9
๐ ๐๐ฉ๐๐ง ๐๐๐ญ๐ ๐๐จ๐ซ ๐๐ฉ๐๐ง ๐๐จ๐๐๐ฅ๐ฌ: Major-TOM Core dataset is currently supporting several strands of ongoing research within and outwith our lab and we are looking forward to the time when we can release models that take advantage of that data!
Major-TOM
๐ ๐๐จ๐ฌ๐ญ๐๐ซ ๐๐ญ ๐๐๐๐๐๐: We will present Major TOM project as a poster at IGARSS in Athens (July) - come talk to us if you're there! You can access the paper here: Major TOM: Expandable Datasets for Earth Observation (2402.12095)
๐ Developed at European Space Agency ฮฆ-lab in partnership with Hugging Face
๐จ EXPANSION RELEASE: ๐ฆ๐ฒ๐ป๐๐ถ๐ป๐ฒ๐น-๐ญ ๐ถ๐ ๐ป๐ผ๐ ๐ฎ๐๐ฎ๐ถ๐น๐ฎ๐ฏ๐น๐ฒ in the MajorTOM-Core!
Major-TOM/Core-S1RTC
๐ Together with @aliFrancis we've been racing to release the first official expansion to the Major TOM project.
MajorTOM-Core-S1RTC contains 1,469,955 of SAR images paired to Sentinel-2 images from Core-S2.
๐We cover more than 65% of the optical coverage with an average time shift of 7 days.
16 TB of radiometrically calibrated SAR imagery, available in the exact same format as the existing Major-TOM data.
๐บ๏ธ You can explore instantly in our viewing app:
Major-TOM/MajorTOM-Core-Viewer
So, what now?
๐งฑ ๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ฐ๐ญ๐ก: our community continues to grow! To coordinate the upcoming expansions as well as use cases of the open data, we will organise a meet up on 23 April, you can ๐ซ๐๐ ๐ข๐ฌ๐ญ๐๐ซ ๐ฒ๐จ๐ฎ๐ซ ๐ข๐ง๐ญ๐๐ซ๐๐ฌ๐ญ here: https://forms.gle/eBj8JvibJx9b6PLf9
๐ ๐๐ฉ๐๐ง ๐๐๐ญ๐ ๐๐จ๐ซ ๐๐ฉ๐๐ง ๐๐จ๐๐๐ฅ๐ฌ: Major-TOM Core dataset is currently supporting several strands of ongoing research within and outwith our lab and we are looking forward to the time when we can release models that take advantage of that data!
Major-TOM
๐ ๐๐จ๐ฌ๐ญ๐๐ซ ๐๐ญ ๐๐๐๐๐๐: We will present Major TOM project as a poster at IGARSS in Athens (July) - come talk to us if you're there! You can access the paper here: Major TOM: Expandable Datasets for Earth Observation (2402.12095)
๐ Developed at European Space Agency ฮฆ-lab in partnership with Hugging Face
robmarkcoleย
posted
an
update
about 1 year ago
Post
As promised, a video on the new Major TOM dataset on HF: https://youtu.be/KonWxQ1mCpA?si=tp3Cz6-F0exfgdx3
If you prefer audio, there is a podcast too: https://www.satellite-image-deep-learning.com/p/major-tom-expandable-eo-datasets
I think this is just the start for remote sensing datasets on HuggingFace..!
If you prefer audio, there is a podcast too: https://www.satellite-image-deep-learning.com/p/major-tom-expandable-eo-datasets
I think this is just the start for remote sensing datasets on HuggingFace..!
-
- 1 reply
robmarkcoleย
posted
an
update
about 1 year ago
Post
Huge new dataset for remote sensing on HF: Major TOM
More info in this thread https://x.com/mikonvergence/status/1764913256102031402?s=20
Note: video coming soon
More info in this thread https://x.com/mikonvergence/status/1764913256102031402?s=20
Note: video coming soon
- 1 reply
mikonvergenceย
authored
3
papers
about 1 year ago
Detecting Cloud Presence in Satellite Images Using the RGB-based CLIP Vision-Language Model
Paper
โข
2308.00541
โข
Published
From LAION-5B to LAION-EO: Filtering Billions of Images Using Anchor Datasets for Satellite Image Extraction
Paper
โข
2309.15535
โข
Published
Major TOM: Expandable Datasets for Earth Observation
Paper
โข
2402.12095
โข
Published
โข
9
robmarkcoleย
posted
an
update
over 1 year ago
Post
I've just published the 23rd edition of the satellite-image-deep-learning newsletter to 8,188 subscribers
This edition: METEOR, Seeing the roads through the trees ๐ด, A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection & Globe230k dataset
https://www.satellite-image-deep-learning.com/p/new-discoveries-23
This edition: METEOR, Seeing the roads through the trees ๐ด, A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection & Globe230k dataset
https://www.satellite-image-deep-learning.com/p/new-discoveries-23