Image4Act: Online social media image processing for disaster response

Firoj Alam, Muhammad Imran, Ferda Ofli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

We present an end-to-end social media image processing system called Image4Act. The system aims at collecting, denoising, and classifying imagery content posted on social media platforms to help humanitarian organizations in gaining situational awareness and launching relief operations. It combines human computation and machine learning techniques to process high-volume social media imagery content in real time during natural and human-made disasters. To cope with the noisy nature of the social media imagery data, we use a deep neural network and perceptual hashing techniques to filter out irrelevant and duplicate images. Furthermore, we present a specific use case to assess the severity of infrastructure damage incurred by a disaster. The evaluations of the system on existing disaster datasets as well as a real-world deployment during a recent cyclone prove the effectiveness of the system.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
PublisherAssociation for Computing Machinery, Inc
Pages601-604
Number of pages4
ISBN (Electronic)9781450349932
DOIs
Publication statusPublished - 31 Jul 2017
Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
Duration: 31 Jul 20173 Aug 2017

Other

Other9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
CountryAustralia
CitySydney
Period31/7/173/8/17

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Alam, F., Imran, M., & Ofli, F. (2017). Image4Act: Online social media image processing for disaster response. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (pp. 601-604). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3110164