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GEM

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Global Earth Monitor

Global Earth Monitor project (GEM) is addressing the challenge of continuous monitoring of large areas in a sustainable cost effective way. The goal of the project is to establish a new disruptive Earth Observation (EO) DATA-EXPLO... ver más

31/08/2023

SINERGISE SOLUTION...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

SINERGISE SOLUTIONS LABORATORIJ ZAGEOGRAFSKE... No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 9 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-08-31

DT-SPACE-25-EO-2020: Big data technologies and Artificia...

I+D

Cerrada hace 4 años

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9 Participantes

SINERGISE SOLUTIONS, GEOGRAP...

0.00€ | Lider

TUM

435.00K€ | Participante

TOM TOM BE

799.56K€ | Participante

TELE ATLAS

N.D. | Participante

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Duración del proyecto: 34 meses Fecha Inicio: 2020-10-30
Fecha Fin: 2023-08-31

Líder del proyecto

SINERGISE SOLUTIONS LABORATORIJ ZAGEOGRAFSKE... No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Global Earth Monitor project (GEM) is addressing the challenge of continuous monitoring of large areas in a sustainable cost effective way. The goal of the project is to establish a new disruptive Earth Observation (EO) DATA-EXPLOITATION MODEL which will dramatically enhance the exploitation of Copernicus data. For the first time a continuous monitoring of the planet on the global/regional scale will be enabled for a sustainable price. Disruptive innovations are planned in the technology and in the methodology domain, where a proprietary concept of Adjustable Data Cubes (a combination of static and dynamic data cubes) will be developed and integrated with EO-oriented open source Machine Learning (ML) framework EO-LEARN. During the project EO-LEARN will be upgraded to consume ML technologies from widely accepted ML frameworks and to adapt/evolve them to specifics of EO-data interpretation. Modern ML technologies and approaches (GAN, RNN, LSTM, Attention & Bayesian Deep Learning, Curriculum Learning, Incremental learning, Meta-learning, Hybrid modelling) will be combined to construct GLOBAL, SCALE-INDEPENDENT interpretation models with the special focus on CAUSALITY and CHANGE DETECTION. Technological and Methodological innovations will be combined into a unique CONTINUOUS MONITORING PROCESS. The process, based on seamless combination of data interpreted with sub-resolution, native resolution and super-resolution methods, will deliver optimal combination of Processing/Storage costs – enabling continuous monitoring of large areas for just a FRACTION OF CURRENT COSTS. The concept of continuous monitoring will be validated through the development of five specific use-cases and through their employment in a 10-month demonstration - operational continuous monitoring of 10 MIO square km area.

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