Damage Assessment from Social Media Imagery Data During Disasters

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...

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Bibliographic Details
Published in:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) pp. 569 - 576
Main Authors: Nguyen, Dat T., Ofli, Ferda, Imran, Muhammad, Mitra, Prasenjit
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 31.07.2017
Series:ACM Conferences
Subjects:
Computing methodologies
Computing methodologies > Artificial intelligence
Computing methodologies > Artificial intelligence > Natural language processing
Computing methodologies > Artificial intelligence > Natural language processing > Information extraction
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 systems and tools
Human-centered computing > Collaborative and social computing > Collaborative and social computing systems and tools > Social networking sites
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 systems applications
ISBN:1450349935, 9781450349932
ISSN:2473-991X
Online Access:Get full text
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Summary: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.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3110109