  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.
