RFInject
Collection
Data compendium of the RFInject project • 3 items • Updated
AI & ML interests
ESA’s Φ-lab mission is to accelerate the future of Earth Observation (EO) by means of transformational innovations, i.e. innovations that completely transform or create entire industries via new technologies, with the aim to strengthen the world-leading competitiveness of the European EO industrial and research sectors.
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sirbastiano94
updated a
collection 7 days ago
mikonvergence
posted an update 15 days ago
Post
179
🥕 Introducing BetaEarth - your own Earth embedding emulator [𝐏𝐫𝐞-𝐑𝐞𝐥𝐞𝐚𝐬𝐞]
The past year has brought many notable embedding products, like AlphaEarth, TESSERA or OlmoEarth. We are entering a phase where embeddings begin to act as a substitute for real observation data.
BetaEarth is an attempt to explore how much one can learn from a model based on its embeddings alone, and whether those embeddings can serve as a useful training target for other models. Huge credit to the AlphaEarth team for releasing the embedding archive openly — it's what made this kind of community-built extension possible.
[𝐁𝐞𝐭𝐚𝐄𝐚𝐫𝐭𝐡 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥 𝐛𝐮𝐭 𝐢𝐭 𝐭𝐫𝐢𝐞𝐬 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭]
BetaEarth is a flexible (and relatively lightweight) emulator of the AlphaEarth annual product. It doesn't reproduce AlphaEarth's exact outputs, nor the product, but it reaches ~0.87 cosine similarity on held-out data and retains 97% of downstream land-cover classification accuracy. It only took 1-2 days to train.
It can encode any combination (including multi-temporal) of:
- Sentinel-2 L1C
- Sentinel-2 L2A
- Sentinel-1 RTC
- COP-DEM 30 product
The model weights are open, just like its training data (built exclusively using Major TOM). The GitHub repository provides a script for automated generation of embeddings across any footprint.
You can also try the workflow over small bounding boxes on the free Hugging Face web app!
⚙️ GitHub: https://github.com/asterisk-labs/beta-earth
🖥️ Web App: asterisk-labs/betaearth
🏭 Models: https://huggingface.co/collections/asterisk-labs/beta-earth
🟨 Colab: https://colab.research.google.com/github/asterisk-labs/beta-earth/blob/main/examples/generate_demo.ipynb
🗞️ Pre-print: https://github.com/asterisk-labs/beta-earth/blob/main/docs/beta_earth_preprint.pdf
The past year has brought many notable embedding products, like AlphaEarth, TESSERA or OlmoEarth. We are entering a phase where embeddings begin to act as a substitute for real observation data.
BetaEarth is an attempt to explore how much one can learn from a model based on its embeddings alone, and whether those embeddings can serve as a useful training target for other models. Huge credit to the AlphaEarth team for releasing the embedding archive openly — it's what made this kind of community-built extension possible.
[𝐁𝐞𝐭𝐚𝐄𝐚𝐫𝐭𝐡 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥 𝐛𝐮𝐭 𝐢𝐭 𝐭𝐫𝐢𝐞𝐬 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭]
BetaEarth is a flexible (and relatively lightweight) emulator of the AlphaEarth annual product. It doesn't reproduce AlphaEarth's exact outputs, nor the product, but it reaches ~0.87 cosine similarity on held-out data and retains 97% of downstream land-cover classification accuracy. It only took 1-2 days to train.
It can encode any combination (including multi-temporal) of:
- Sentinel-2 L1C
- Sentinel-2 L2A
- Sentinel-1 RTC
- COP-DEM 30 product
The model weights are open, just like its training data (built exclusively using Major TOM). The GitHub repository provides a script for automated generation of embeddings across any footprint.
You can also try the workflow over small bounding boxes on the free Hugging Face web app!
⚙️ GitHub: https://github.com/asterisk-labs/beta-earth
🖥️ Web App: asterisk-labs/betaearth
🏭 Models: https://huggingface.co/collections/asterisk-labs/beta-earth
🟨 Colab: https://colab.research.google.com/github/asterisk-labs/beta-earth/blob/main/examples/generate_demo.ipynb
🗞️ Pre-print: https://github.com/asterisk-labs/beta-earth/blob/main/docs/beta_earth_preprint.pdf
sirbastiano94
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mikonvergence
authored 2
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sirbastiano94
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blumenstiel
authored 2
papers about 2 months ago
mikonvergence
posted an update 9 months ago
Post
499
𝐌𝐚𝐣𝐨𝐫 𝐓𝐎𝐌 ➕ 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐞𝐞𝐩𝐌𝐢𝐧𝐝'𝐬 𝐀𝐥𝐩𝐡𝐚𝐄𝐚𝐫𝐭𝐡 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 𝐚𝐫𝐞 𝐧𝐨𝐰 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 ‼️
This is a tiny (about 6 TB of data, but only 62,489 grid cells of ~100 sqkm) prototype dataset that allows to instantly connect existing Major TOM data with AlphaEarth embeddings.
Major-TOM/Core-AlphaEarth-Embeddings
I curated it to support several relevant research projects, but I figured it could help more people in the community to experiment and explore new applications of AlphaEarth.
𝐃𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐔𝐬𝐞
Each embedding sample comes from the original annual dataset produced by Google DeepMind. It means that, unlike samples from Sentinel-2 or Sentinel-1, it contains aggregated annual information from a particular year and is not linked to one particular observation. The existing Major TOM samples from physical sensors provide information potentially (and likely) contained in the AlphaEarth embedding sample, but they miss the temporal component represented within AEF embedding fields.
For more information, please check the dataset card on HuggingFace.
⚠️ 𝐖𝐀𝐑𝐍𝐈𝐍𝐆: 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐝𝐨 𝐧𝐨𝐭 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐬𝐚𝐦𝐩𝐥𝐞𝐬, 𝐛𝐮𝐭 𝐚 𝐰𝐡𝐨𝐥𝐞 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐦𝐮𝐥𝐭𝐢-𝐦𝐨𝐝𝐚𝐥 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬. 𝐇𝐚𝐯𝐞 𝐟𝐮𝐧!
🙏 Built on top of fantastic work of
Christopher Brown, Michal Kazmierski, Valerie Pasquarella, Emily Schechter and others at Google DeepMind.
This is a tiny (about 6 TB of data, but only 62,489 grid cells of ~100 sqkm) prototype dataset that allows to instantly connect existing Major TOM data with AlphaEarth embeddings.
Major-TOM/Core-AlphaEarth-Embeddings
I curated it to support several relevant research projects, but I figured it could help more people in the community to experiment and explore new applications of AlphaEarth.
𝐃𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐔𝐬𝐞
Each embedding sample comes from the original annual dataset produced by Google DeepMind. It means that, unlike samples from Sentinel-2 or Sentinel-1, it contains aggregated annual information from a particular year and is not linked to one particular observation. The existing Major TOM samples from physical sensors provide information potentially (and likely) contained in the AlphaEarth embedding sample, but they miss the temporal component represented within AEF embedding fields.
For more information, please check the dataset card on HuggingFace.
⚠️ 𝐖𝐀𝐑𝐍𝐈𝐍𝐆: 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐝𝐨 𝐧𝐨𝐭 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐬𝐚𝐦𝐩𝐥𝐞𝐬, 𝐛𝐮𝐭 𝐚 𝐰𝐡𝐨𝐥𝐞 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐦𝐮𝐥𝐭𝐢-𝐦𝐨𝐝𝐚𝐥 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬. 𝐇𝐚𝐯𝐞 𝐟𝐮𝐧!
🙏 Built on top of fantastic work of
Christopher Brown, Michal Kazmierski, Valerie Pasquarella, Emily Schechter and others at Google DeepMind.
jhnnsjkbk
authored 3
papers 10 months ago
Foundation Models for Generalist Geospatial Artificial Intelligence
Paper • 2310.18660 • Published • 12
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation
Paper • 2503.15969 • Published
TerraMind: Large-Scale Generative Multimodality for Earth Observation
Paper • 2504.11171 • Published • 2