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Rapid damage assessment is one of the core tasks that response organizationsperform at the onset of a disaster to understand the scale of damage toinfrastructures such as roads, bridges, and buildings. This work analyzes theusefulness of social media imagery content to perform rapid damage assessmentduring a real-world disaster. An automatic image processing system, which wasactivated in collaboration with a volunteer response organization, processed~280K images to understand the extent of damage caused by the disaster. Thesystem achieved an accuracy of 76% computed based on the feedback received fromthe domain experts who analyzed ~29K system-processed images during thedisaster. An extensive error analysis reveals several insights and challengesfaced by the system, which are vital for the research community to advance thisline of research.

Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence