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001     281819
005     20251028094210.0
024 7 _ |2 CORDIS
|a G:(EU-Grant)101131841
|d 101131841
024 7 _ |2 CORDIS
|a G:(EU-Call)HORIZON-EUSPA-2022-SPACE
|d HORIZON-EUSPA-2022-SPACE
024 7 _ |2 originalID
|a corda_____he::101131841
024 7 _ |2 doi
|a 10.3030/101131841
035 _ _ |a G:(EU-Grant)101131841
150 _ _ |a Earth Observation & Weather Data Federation with AI Embeddings
|b AI compressing for Earth observation and weather data exchange
|y 2024-01-01 - 2026-12-31
371 _ _ |0 P:(DE-Juel1)185654
|a Kesselheim, Stefan
|s 20240101
|t 20261231
450 _ _ |a Embed2Scale
|w d
|y 2024-01-01 - 2026-12-31
510 1 _ |0 I:(DE-588b)5098525-5
|a European Union
|b CORDIS
680 _ _ |a The Copernicus programme, weather models, and Global Navigation Satellite Systems (GNSS) provide extensive geospatial data applicable to various scientific sectors. However, the volume of this data makes it impractical for a single platform to host. As a result, service providers face challenges in accessing data from different archives due to cost constraints. The EU-funded Embed2Scale project will address this issue by leveraging AI-based data compression techniques to facilitate efficient data exchange. The project will investigate deep neural network training methods and introduce innovations in data management and portability. The outcome will be groundbreaking research in AI-driven data compression, leading to more accessible and efficient access to earth observation and weather data.
856 4 _ |u https://cordis.europa.eu/project/id/101131841
|y Homepage
909 C O |o oai:juser.fz-juelich.de:1047384
|p authority:GRANT
|p authority
909 C O |o oai:juser.fz-juelich.de:1047384
980 _ _ |a G
980 _ _ |a AUTHORITY
980 _ _ |a CORDIS

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