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Author
Giulio Palomba
;
Alessandro Farasin
;
Claudio Rossi
Title
Sentinel-1 Flood Delineation with Supervised Machine Learning
Type
Conference Article
Year
2020
Publication
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
Abbreviated Journal
Iscram 2020
Volume
Issue
Pages
1072-1083
Keywords
Floods, Mapping, Deep Learning, Copernicus EMS, Sentinel-1, SAR.
Abstract
Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.
Address
LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept. and LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept.
Corporate Author
Thesis
Publisher
Virginia Tech
Place of Publication
Blacksburg, VA (USA)
Editor
Amanda Hughes; Fiona McNeill; Christopher W. Zobel
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
978-1-949373-27-97
ISBN
2411-3483
Medium
Track
Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management
Expedition
Conference
17th International Conference on Information Systems for Crisis Response and Management
Notes
giulio.palomba@linksfoundation.com
Approved
no
Call Number
Serial
2298
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