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

TL;DR

This paper presents a system that leverages social media images and combines human and machine intelligence to rapidly assess disaster damage, processing nearly 280,000 images with 76% accuracy during a real event.

Contribution

It introduces an automatic image processing system integrated with volunteer efforts for real-time damage assessment using social media imagery.

Findings

Processed ~280K images during a disaster

Achieved 76% accuracy based on expert feedback

Identified key challenges and insights for future research

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.