Records |
Author |
Anastasia Moumtzidou; Marios Bakratsas; Stelios Andreadis; Anastasios Karakostas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris |
Title |
Flood detection with Sentinel-2 satellite images in crisis management systems |
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 |
1049-1059 |
Keywords |
Floods, Change Detection, Bi-temporal Analysis, Sentinel-2, Deep Neural Networks. |
Abstract |
The increasing amount of falling rain may cause several problems especially in urban areas, which drainage system can often not handle this large amount in a short time. Confirming a flooded scene in a timely manner can help the authorities to take further actions to counter the crisis event or to get prepared for future relevant incidents. This paper studies the detection of flood events comparing two successive in time Sentinel-2 images, a method that can be extended for detecting floods in a time-series. For the flood detection, fine-tuned pre-trained Deep Convolutional Neural Networks are used, testing as input different sets of three water sensitive satellite bands. The proposed approach is evaluated against different change detection baseline methods, based on remote sensing. Experiments showed that the proposed method with the augmentation technique applied, improved significantly the performance of the neural network, resulting to an F-Score of 62% compared to 22% of the traditional remote sensing techniques. The proposed method supports the crisis management authority to better estimate and evaluate the flood impact. |
Address |
Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece; |
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 |
978-1-949373-27-95 |
ISBN |
2411-3481 |
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 |
moumtzid@iti.gr |
Approved |
no |
Call Number |
|
Serial |
2296 |
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Author |
Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini |
Title |
Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks |
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 |
1060-1071 |
Keywords |
Burned Area Delineation, Sentinel-2, U-Net, CuMedVision1, Convolutional Neural Network, Deep Learning, Supervised Learning, Pixel-wise Segmentation. |
Abstract |
Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods. |
Address |
Politecnico di Torino – DAUIN dept., and LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept.; 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 |
978-1-949373-27-96 |
ISBN |
2411-3482 |
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 |
alessandro.farasin@polito.it |
Approved |
no |
Call Number |
|
Serial |
2297 |
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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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