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It is important to quantify Damage Assessment (DA) for Human Assistance andDisaster Response (HADR) applications. In this paper, to achieve efficient andscalable 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-disasterimages are effectively encoded into one image that is then processed using deeplearning approaches to determine the damage levels. Two scenarios of crucialimportance for the practical use of DA in HADR applications are examined:pixel-wise semantic segmentation and patch-based contrastive learning-basedglobal damage classification. Results achieved by IP2CP in both scenariosdemonstrate promising performances, showing that our IP2CP-based methods withinthe deep learning framework can effectively achieve data and computationalefficiency, which is of utmost importance for the DA in HADR applications.

Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment