Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
This work addresses the problem of timely infrastructure damage assessment for disaster response organizations, but it is incremental as it applies existing methods to new data.
The study tackled rapid damage assessment during disasters by analyzing social media images, processing ~280K images with an automatic system that achieved 76% accuracy based on expert feedback on ~29K images.
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.