@inproceedings{7f4d27ad171c41e2a5af6f7ccd4c6c2d,
title = "Image4Act: Online social media image processing for disaster response",
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.",
author = "Firoj Alam and Muhammad Imran and Ferda Ofli",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 ; Conference date: 31-07-2017 Through 03-08-2017",
year = "2017",
month = jul,
day = "31",
doi = "10.1145/3110025.3110164",
language = "English",
series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "601--604",
editor = "Jana Diesner and Elena Ferrari and Guandong Xu",
booktitle = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",
}