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.

翻译：在人道主义援助与灾难响应（HADR）应用中，定量化灾损评估（DA）至关重要。本文为实现HADR中高效且可扩展的灾损评估，提出了一种图像先验与后验条件概率（IP2CP）表示方法，将其作为有效的计算成像表征手段。借助IP2CP表示，灾前与灾后匹配图像可被高效编码为单张图像，进而利用深度学习方法处理以判定损伤等级。针对HADR应用中灾损评估实际使用的两个关键场景展开研究：像素级语义分割与基于补丁对比学习的全局损伤分类。IP2CP在两个场景下的实验结果均展现出优异性能，表明基于IP2CP的深度学习方法能够有效实现数据与计算效率，这对HADR应用中的灾损评估至关重要。