Marcin Kluczek's picture
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Marcin Kluczek

mkluczek

AI & ML interests

Earth Observation (EO), Remote sensing

Recent Activity

published a dataset 8 days ago
Major-TOM/Core-S2L1C-DeCUR
updated a dataset 8 days ago
Major-TOM/Core-S2L1C-DeCUR
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Organizations

ONNXConfig for all's profile picture Gradio-Themes-Party's profile picture scikit-learn's profile picture Open-Source AI Meetup's profile picture Multi๐Ÿค–Transformers's profile picture Major TOM's profile picture MLX Community's profile picture CloudFerro S.A.'s profile picture Hugging Face Discord Community's profile picture

mkluczek's activity

posted an update 5 days ago
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Expansion of Global and Dense Open Embeddings Dataset of Earth ๐ŸŒ

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 , Jฤ™drzej 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
reacted to mikonvergence's post with ๐Ÿš€๐Ÿ‘ 11 days ago
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๐Ÿ”ต ๐‚๐Ž๐-๐†๐„๐-๐๐ž๐ญ๐š: ๐”๐ง๐ข๐Ÿ๐ข๐ž๐ ๐†๐ž๐ง๐ž๐ซ๐š๐ญ๐ข๐ฏ๐ž ๐Œ๐จ๐๐ž๐ฅ๐ฅ๐ข๐ง๐  ๐จ๐Ÿ ๐‚๐Ž๐๐ž๐ซ๐ง๐ข๐œ๐ฎ๐ฌ ๐ˆ๐ฆ๐š๐ ๐ž๐ซ๐ฒ ๐“๐ก๐ฎ๐ฆ๐›๐ง๐š๐ข๐ฅ๐ฌ

Today we release a prototype of COP-GEN - a universal generative model for Copernicus data. ๐‚๐Ž๐-๐†๐„๐-๐๐ž๐ญ๐š 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.

โš–๏ธ ๐Œ๐จ๐๐ž๐ฅ mespinosami/COP-GEN-Beta

๐Ÿ“ฑ ๐ƒ๐ž๐ฆ๐จ 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. ๐–๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ญ๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐ฌ๐ฉ๐ž๐œ๐ข๐Ÿ๐ข๐œ๐š๐ฅ๐ฅ๐ฒ to perform any of these tasks it can be used to approximate:

โœ… Sentinel-1 to Sentinel-2 translation

โœ… Elevation estimation from Sentinel-2 or Sentinel-1

โœ… Atmospheric Correction (L1C to L2A pipeline)

โœ… Atmospheric Generation (L2A to L1C)

โœ… ...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!

๐ŸŒ ๐–๐ž๐›๐ฌ๐ข๐ญ๐ž https://miquel-espinosa.github.io/cop-gen

๐Ÿ’ป ๐‚๐จ๐๐ž https://github.com/miquel-espinosa/COP-GEN-Beta

๐Ÿ“„ ๐๐š๐ฉ๐ž๐ซ https://arxiv.org/pdf/2504.08548
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reacted to mikonvergence's post with ๐Ÿง  11 days ago
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๐Œ๐„๐’๐€ ๐Ÿ”๏ธ ๐“๐ž๐ฑ๐ญ-๐›๐š๐ฌ๐ž๐ ๐ญ๐ž๐ซ๐ซ๐š๐ข๐ง ๐ ๐ž๐ง๐ž๐ซ๐š๐ญ๐ข๐จ๐ง ๐ฆ๐จ๐๐ž๐ฅ

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 Borneโ€“Pons ( @NewtNewt ) during his joint internship at
Adobe & ESA, and in collaboration with asterisk labs.

๐Ÿ”๏ธ ๐๐ซ๐จ๐ฃ๐ž๐œ๐ญ ๐๐š๐ ๐ž : https://paulbornep.github.io/mesa-terrain/

๐Ÿ“ ๐๐ซ๐ž๐ฉ๐ซ๐ข๐ง๐ญ : https://arxiv.org/abs/2504.07210
๐Ÿค— ๐Œ๐จ๐๐ž๐ฅ ๐–๐ž๐ข๐ ๐ก๐ญ๐ฌ : NewtNewt/MESA
๐Ÿ’พ ๐ƒ๐š๐ญ๐š๐ฌ๐ž๐ญ : Major-TOM/Core-DEM
๐Ÿง‘๐Ÿปโ€๐Ÿ’ปโ€‹๐‚๐จ๐๐ž : https://github.com/PaulBorneP/MESA

๐‡๐… ๐’๐ฉ๐š๐œ๐ž: mikonvergence/MESA
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