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Record |
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Author  |
Mirko Zaffaroni; Claudio Rossi |

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Title |
Water Segmentation with Deep Learning Models for Flood Detection and Monitoring |
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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Volume |
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Issue |
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Pages |
66-74 |
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Keywords |
Deep Learning, Water Segmentation, Data Validation. |
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Abstract |
Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media. |
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Address |
LINKS Foundation, University of Turin, Computer Science Department; LINKS Foundation |
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Thesis |
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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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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-7 |
ISBN |
2411-3393 |
Medium |
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Track |
AI Systems for Crisis and Risks |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
mirko.zaffaroni@linksfoundation.com |
Approved |
no |
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Call Number |
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Serial |
2208 |
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