  It is important to quantify Damage Assessment (DA) for Human Assistance and
Disaster Response (HADR) applications. In this paper, to achieve efficient and
scalable DA in HADR, an image prior and posterior conditional probability
(IP2CP) is developed as an effective computational imaging representation.
Equipped with the IP2CP representation, the matching pre- and post-disaster
images are effectively encoded into one image that is then processed using deep
learning approaches to determine the damage levels. Two scenarios of crucial
importance for the practical use of DA in HADR applications are examined:
pixel-wise semantic segmentation and patch-based contrastive learning-based
global damage classification. Results achieved by IP2CP in both scenarios
demonstrate promising performances, showing that our IP2CP-based methods within
the deep learning framework can effectively achieve data and computational
efficiency, which is of utmost importance for the DA in HADR applications.
