Damage Estimation and Localization from Sparse Aerial Imagery

TL;DR

This paper presents a method for damage detection and localization in aerial images from small aircraft, focusing on flooding, using structure from motion and class activation mapping, achieving high precision in post-disaster scenarios.

Contribution

It introduces a novel approach combining structure from motion and class activation mapping for damage localization in aerial imagery without IMU data.

Findings

Achieves 88% precision in damage localization

Effective for flood damage detection from handheld aerial images

Viable for rapid disaster response using limited data

Abstract

Aerial images provide important situational awareness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As such, DEL from such imagery is still a highly manual and time-consuming process. We propose an approach to both detect damage in aerial images and localize it in world coordinates, with specific focus on detecting and localizing flooding. The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates. We evaluate the performance of our approach on post-event data from the 2016 Louisiana floods, and find that our approach achieves a precision of 88%. Given this high precision using limited data, we argue that this approach is currently viable for fast and effective DEL from handheld aerial imagery for disaster response.