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EO Spatio-Temporal Patterns Extraction

Datcu, Mihai (2019) EO Spatio-Temporal Patterns Extraction. MULTITEMP19, 6 Aug 2019, Shanghai,China.

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Official URL: https://multitemp2019.tongji.edu.cn/


Since the very beginning of satellite remote sensing the methods and applications the Satellite Image Time Series (SITS) are the main nature of Earth Observation. Presently, with the regular observations and free and open access of the Copernicus data the impact of SITS is largely amplified. The challenges of the EO Big Data are critically accentuated due to joint volume explosion, high acquisition velocity and sensor variety. The presentation emphases on novel Artificial Intelligence (AI) paradigms focuses to convert the SITS in valuable EO products with impact in new applications for understanding of the Erath cover spatio-temporal processes over long periods of time. AI for EO is largely an interdisciplinary field and involves the convergence of very different methods. The lecture overviews and discuss specific topics for SITS regarding the orbit, mission, sensor constellations, intelligent agents, machine learning, deep learning, data indexing, data bases, and DNN.

Item URL in elib:https://elib.dlr.de/130899/
Document Type:Conference or Workshop Item (Keynote)
Title:EO Spatio-Temporal Patterns Extraction
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:August 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Earth Observation, Pattern Extraction, Artificial Intelligence
Event Title:MULTITEMP19
Event Location:Shanghai,China
Event Type:international Conference
Event Dates:6 Aug 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:04 Dec 2019 14:49
Last Modified:04 Dec 2019 14:49

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