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Computational Intelligence for Multi-Source Remote Sensing Data Analytics

Project description

Deep learning in earth observation for better data

Earth observation (EO) is changing considerably because of the large amounts of observations obtained from remote sensing and in-situ sensor networks that acquire very precise localised measurements. Novel solutions are needed to obtain data from spaceborne and ground-based instruments for estimating geophysical parameters. To better understand multisource EO data, the EU-funded CALCHAS project will gather observations from different sources, combine sampling scales associated with spaceborne and in-situ measurements and analyse time series of dynamic observations. Mathematical tools will be used to extend the present capacity of single-source data analysis. The project will analyse time series of measurements from active and passive microwave and multispectral spaceborne imaging instruments, and in-situ sensor measurements.

Coordinator

IDRYMA TECHNOLOGIAS KAI EREVNAS
Net EU contribution
€ 215 492,16
Address
N Plastira Str 100
70013 Irakleio
Greece

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Region
Ηράκλειο Κρήτη Νησιά Αιγαίου
Activity type
Research Organisations
Non-EU contribution
€ 0,00

Partners (1)

Partner

Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.

UNIVERSITY OF SOUTHERN CALIFORNIA
United States
Net EU contribution
€ 0,00
Address
3551 Trousdale Pkwy Adm 352
90089 5013 Los Angeles Ca

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Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 132 949,44