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Author Anastasia Moumtzidou; Marios Bakratsas; Stelios Andreadis; Anastasios Karakostas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris pdf  isbn
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  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 (up) 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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