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Record |
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Author |
Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini |
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Title |
Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Issue |
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Pages |
1060-1071 |
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Keywords |
Burned Area Delineation, Sentinel-2, U-Net, CuMedVision1, Convolutional Neural Network, Deep Learning, Supervised Learning, Pixel-wise Segmentation. |
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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. |
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Address |
Politecnico di Torino – DAUIN dept., and LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept.; LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept. |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Edition |
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ISSN |
978-1-949373-27-96 |
ISBN |
2411-3482 |
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Track |
Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
alessandro.farasin@polito.it |
Approved |
no |
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Call Number |
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Serial |
2297 |
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