This study aims to improve post-disaster preliminary damage assessment (PDA) using artificial intelligence (AI) and unmanned aerial vehicle (UAV) imagery. In particular, a stacked convolutional neural network (CNN) architecture is introduced and trained on an in-house visual dataset from Hurricane Dorian. To account for the ordinality of damage level classes, the cross-entropy classification loss function is replaced with the square of earth mover's distance (EMD²) loss. The trained model achieves 65.6% building localization precision and 61% (90% considering ±1 class deviation from ground-truth) classification accuracy. It also exhibits a positive accuracy–confidence correlation, which is valuable for model assessment in situations where ground-truth information is not readily available. Finally, the outcome of damage assessment is compared with the literature by examining the relationship between building size and number of stories, and severity of induced disaster damage.

中文翻译：

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深度学习用于建筑物的飓风后空中破坏评估

这项研究旨在利用人工智能（AI）和无人机（UAV）图像来改善灾后初步损害评估（PDA）。特别是，引入了堆叠式卷积神经网络（CNN）架构，并在来自Dorian飓风的内部视觉数据集上对其进行了训练。考虑到损坏等级的一般性，将交叉熵分类损失函数替换为推土机距离的平方（EMD ²） 失利。经过训练的模型可实现65.6％的建筑物定位精度和61％（90％的考虑到与实地的±1级偏差）的分类精度。它还显示出正的准确度－置信度相关性，这对于不易获得地面真相信息的情况下的模型评估非常有价值。最后，通过检查建筑物的大小和层数以及诱发灾害破坏的严重性之间的关系，将破坏评估的结果与文献进行比较。