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Knowledge Extracted from Copernicus Satellite Data

Dumitru, Corneliu Octavian and Schwarz, Gottfried and Eltoft, Torbjørn and Kræmer, Thomas and Wegner, Penelope and Hughes, Nick and Arthurs, David and Koubarakis, Manolis and Datcu, Mihai (2019) Knowledge Extracted from Copernicus Satellite Data. 11th International Symposium on Digital Earth, 24. - 27. Sept. 2019, Florence, Italy.

Full text not available from this repository.

Official URL: http://www.digitalearth2019.eu

Abstract

ExtremeEarth is a European H2020 project; it aims at developing analytics techniques and technologies that combine Copernicus satellite data with information and knowledge extraction, and exploiting them on ESA’s Food Security and Polar Thematic Exploitation Platforms. In this paper, we focus on the Polar case which requires the selection of validation areas, the generation of a training dataset, the development and testing of deep learning algorithms, and the demonstration of regional results. During the development of deep learning algorithms, a key activity is to establish a large amount of referenced Earth Observation data. They need to be sufficiently diverse to cover the major target areas of satellite images under varying imaging conditions and across all seasons. For doing this, we propose to select overlapping target areas from Synthetic Aperture Radar and multispectral images acquired with rapid succession. Such a combination approach already demonstrated its applicability for monitoring seasonal snow cover. By applying an already established active learning approach based on a Support Vector Machine with relevance feedback, we can limit ourselves to a limited number of typical satellite images to extract their information content, and to generate semantic annotations for them. This approach is also a simple way to generate benchmarking datasets that can be used for testing and validating different algorithms, and for creating additional bigger datasets for large-scale demonstrations. The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts.

Item URL in elib:https://elib.dlr.de/130277/
Document Type:Conference or Workshop Item (Poster)
Title:Knowledge Extracted from Copernicus Satellite Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deUNSPECIFIED
Eltoft, TorbjørnUiT The Arctic University of Norway, Department of Physics and Technology, NO-9037, Tromso, NorwayUNSPECIFIED
Kræmer, ThomasUiT The Arctic University of Norway, Department of Physics and Technology, NO-9037, Tromso, NorwayUNSPECIFIED
Wegner, PenelopeMET NorwayUNSPECIFIED
Hughes, NickMET NorwayUNSPECIFIED
Arthurs, DavidPolarView CanadaUNSPECIFIED
Koubarakis, ManolisUniversity of AthensUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:March 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Knowledge extraction, Copericus,Generative Adversarial Networks
Event Title:11th International Symposium on Digital Earth
Event Location:Florence, Italy
Event Type:international Conference
Event Dates:24. - 27. Sept. 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:03 Dec 2019 09:12
Last Modified:04 Dec 2019 13:25

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