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Author (up) Xukun Li; Doina Caragea; Cornelia Caragea; Muhammad Imran; Ferda Ofli pdf 
  Title Identifying Disaster Damage Images Using a Domain Adaptation Approach Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages 633-645  
  Keywords image classification; disaster damage; domain adaptation; domain adversarial neural networks.  
  Abstract Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters, in real time, are greatly needed. While many studies have focused on filtering textual information, the research on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster. To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks (DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone, and uses the adversarial training to find a transformation that makes the source and target data indistinguishable. Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better results as compared to the VGG-19 model fine-tuned on the source labeled data.  
  Address (1) Department of Computer Science, Kansas State University, United States of America; (2) Department of Computer Science, University of Illinois at Chicago, United States of America; (3) Qatar Computing Research Institute, Hamad Bin Khalifa University, Q  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes xukun@ksu.edu; dcaragea@ksu.edu; cornelia@uic.edu; mimran@hbku.edu.qa; fofli@hbku.edu.qa Approved no  
  Call Number Serial 1749  
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