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Water Segmentation with Deep Learning Models for Flood Detection and Monitoring
Mirko Zaffaroni
Claudio Rossi
Amanda Hughes
Fiona McNeill
Christopher W. Zobel
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.
urn:ISBN:2411-3393
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Water%20Segmentation%20with%20Deep%20Learning%20Models%20for%20Flood%20Detection%20and%20Monitoring&stitle=Iscram%202020&issn=978-1-949373-27-7&isbn=2411-3393&date=2020&spage=66&epage=74&aulast=Mirko%20Zaffaroni&au=Claudio%20Rossi&pub=Virginia%20Tech&place=Blacksburg%2C%20VA%20%28USA%29&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2208
citekey:MirkoZaffaroni+ClaudioRossi2020
citation:Mirko Zaffaroni, & Claudio Rossi. (2020). Water Segmentation with Deep Learning Models for Flood Detection and Monitoring. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 66-74). Blacksburg, VA (USA): Virginia Tech.
2020
ConferencePaper
text
Deep Learning, Water Segmentation, Data Validation.
file:http://idl.iscram.org/files/mirkozaffaroni/2020/2208_MirkoZaffaroni+ClaudioRossi2020.pdf
Virginia Tech
English
978-1-949373-27-7
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
2020
66
74
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