ESA-philab/RFInject-v1-L0
479 GB
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
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sirbastiano94
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mikonvergence
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blumenstiel
authored 2
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mikonvergence
posted an update 8 months ago
Post
493
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 4
papers 9 months ago
Foundation Models for Generalist Geospatial Artificial Intelligence
Paper • 2310.18660 • Published • 11
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
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
Paper • 2504.11172 • Published
nlongepe
updated a
collection 11 months ago