Damage assessment from social media imagery data during disasters

Dat T. Nguyen, Ferda Ofli, Muhammad Imran, Prasenjit Mitra

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

38 Scopus citations

Abstract

Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
EditorsJana Diesner, Elena Ferrari, Guandong Xu
PublisherAssociation for Computing Machinery, Inc
Pages569-576
Number of pages8
ISBN (Electronic)9781450349932
DOIs
StatePublished - Jul 31 2017
Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
Duration: Jul 31 2017Aug 3 2017

Publication series

NameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Other

Other9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
CountryAustralia
CitySydney
Period7/31/178/3/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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