Localizing and quantifying damage in social media images

Xukun Li*, Doina Caragea, Huaiyu Zhang, Muhammad Imran

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

55 Citations (Scopus)

Abstract

Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment, and provides an inexpensive and feasible alternative to the more expensive GIS approach.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages194-201
Number of pages8
ISBN (Electronic)9781538660515
DOIs
Publication statusPublished - 24 Oct 2018
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: 28 Aug 201831 Aug 2018

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Conference

Conference10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country/TerritorySpain
CityBarcelona
Period28/08/1831/08/18

Keywords

  • class activation mapping (CAM)
  • convolutional neural networks (CNN)
  • damage localization
  • image analysis

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