000281819 001__ 281819
000281819 005__ 20251028094210.0
000281819 0247_ $$2CORDIS$$aG:(EU-Grant)101131841$$d101131841
000281819 0247_ $$2CORDIS$$aG:(EU-Call)HORIZON-EUSPA-2022-SPACE$$dHORIZON-EUSPA-2022-SPACE
000281819 0247_ $$2originalID$$acorda_____he::101131841
000281819 0247_ $$2doi$$a10.3030/101131841
000281819 035__ $$aG:(EU-Grant)101131841
000281819 150__ $$aEarth Observation & Weather Data Federation with AI Embeddings$$bAI compressing for Earth observation and weather data exchange$$y2024-01-01 - 2026-12-31
000281819 371__ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$s20240101$$t20261231
000281819 450__ $$aEmbed2Scale$$wd$$y2024-01-01 - 2026-12-31
000281819 5101_ $$0I:(DE-588b)5098525-5$$aEuropean Union$$bCORDIS
000281819 680__ $$aThe Copernicus programme, weather models, and Global Navigation Satellite Systems (GNSS) provide extensive geospatial data applicable to various scientific sectors. However, the volume of this data makes it impractical for a single platform to host. As a result, service providers face challenges in accessing data from different archives due to cost constraints. The EU-funded Embed2Scale project will address this issue by leveraging AI-based data compression techniques to facilitate efficient data exchange. The project will investigate deep neural network training methods and introduce innovations in data management and portability. The outcome will be groundbreaking research in AI-driven data compression, leading to more accessible and efficient access to earth observation and weather data.
000281819 8564_ $$uhttps://cordis.europa.eu/project/id/101131841$$yHomepage
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000281819 909CO $$ooai:juser.fz-juelich.de:1047384
000281819 980__ $$aG
000281819 980__ $$aAUTHORITY
000281819 980__ $$aCORDIS