@inproceedings{6d1db456db8740e3af0fe67efab42092,
title = "Rapid damage assessment using social media images by combining human and machine intelligence",
abstract = "Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.",
keywords = "Artificial Intelligence, Damage Assessment, Image Processing, Social Media",
author = "Muhammad Imran and Firoj Alam and Umair Qazi and Steve Peterson and Ferda Ofli",
note = "Publisher Copyright: {\textcopyright} 2020 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.; 17th Annual International Conference on Information Systems for Crisis Response and Management, ISCRAM 2020 ; Conference date: 23-05-2021",
year = "2020",
language = "English",
series = "Proceedings of the International ISCRAM Conference",
publisher = "Information Systems for Crisis Response and Management, ISCRAM",
pages = "761--773",
editor = "Hughes, {Amanda Lee} and Fiona McNeill and Zobel, {Christopher W.}",
booktitle = "ISCRAM 2020 - Proceedings",
address = "Spain",
}