Urban and rural areas can often be devastated by extreme natural disasters.
Towards any disaster event, an initial response is the key to rescuing within
72 hours and prompt recovery. For the stage of initial responses, it is
important to quickly recognize the disaster damage over a wide area and
determine priority areas. Among machine learning algorithms, deep anomaly
detection is effective in detecting devastated features that are different from
ordinary vision everyday life. In addition, explainable computer vision
applications have been expected to justify the initial responses. In this
paper, we propose an anomaly detection application utilizing the deeper
fully-convolutional data descriptions (FCDDs), that enables to localize
devastated features and visualize damage-marked heatmaps. More concretely, we
show numerous training and test results to a dataset AIDER with the four
disaster categories: collapsed buildings, traffic accidents, fires, and
flooding areas. We also implement ablation studies of anomalous class imbalance
and the data scale competing against the normal class. Finally, we discuss
future works to improve more robust, explainable applications for effective
initial responses.

本文提出一种利用深度异常检测算法的异常值检测应用，能够将损坏特征定位并可视化为热图，以实现在自然灾害发生的最初阶段快速识别和确定损坏优先区域的目的。