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Author (up) Mirko Zaffaroni; Claudio Rossi pdf  isbn
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  Title Water Segmentation with Deep Learning Models for Flood Detection and Monitoring 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 66-74  
  Keywords Deep Learning, Water Segmentation, Data Validation.  
  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.  
  Address LINKS Foundation, University of Turin, Computer Science Department; LINKS Foundation  
  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 978-1-949373-27-7 ISBN 2411-3393 Medium  
  Track AI Systems for Crisis and Risks Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes mirko.zaffaroni@linksfoundation.com Approved no  
  Call Number Serial 2208  
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