Image4Act Online Social Media Image Processing for Disaster Response

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...

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Vydáno v:	2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) s. 601 - 604
Hlavní autoři:	Alam, Firoj, Imran, Muhammad, Ofli, Ferda
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	New York, NY, USA ACM 31.07.2017
Edice:	ACM Conferences
Témata:	Computing methodologies Computing methodologies > Machine learning Computing methodologies > Machine learning > Machine learning approaches Computing methodologies > Machine learning > Machine learning approaches > Neural networks Human-centered computing Human-centered computing > Collaborative and social computing Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social media Information systems Information systems > Information retrieval Information systems > Information retrieval > Retrieval tasks and goals Information systems > Information retrieval > Retrieval tasks and goals > Sentiment analysis Information systems > Information retrieval > Specialized information retrieval Information systems > Information retrieval > Specialized information retrieval > Environment-specific retrieval Information systems > Information systems applications Information systems > World Wide Web Information systems > World Wide Web > Web mining Information systems > World Wide Web > Web mining > Data extraction and integration
ISBN:	1450349935, 9781450349932
ISSN:	2473-991X
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Shrnutí:	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.
ISBN:	1450349935 9781450349932
ISSN:	2473-991X
DOI:	10.1145/3110025.3110164