  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.
