Extreme natural disasters can have devastating effects on both urban and rural areas. In any disaster event, an initial response is the key to rescue within 72 hours and prompt recovery. During the initial stage of disaster response, it is important to quickly assess the damage over a wide area and identify priority areas. Among machine learning algorithms, deep anomaly detection is effective in detecting devastation features that are different from everyday features. In addition, explainable computer vision applications should justify the initial responses. In this paper, we propose an anomaly detection application utilizing deeper fully convolutional data descriptions (FCDDs), that enables the localization of devastation features and visualization of damage-marked heatmaps. More specifically, we show numerous training and test results for a dataset AIDER with the four disaster categories: collapsed buildings, traffic incidents, fires, and flooded areas. We also implement ablation studies of anomalous class imbalance and the data scale competing against the normal class. Our experiments provide results of high accuracies over 95% for F1. Furthermore, we found that the deeper FCDD with a VGG16 backbone consistently outperformed other baselines CNN27, ResNet101, and Inceptionv3. This study presents a new solution that offers a disaster anomaly detection application for initial responses with higher accuracy and devastation explainability, providing a novel contribution to the prompt disaster recovery problem in the research area of anomaly scene understanding. Finally, we discuss future works to improve more robust, explainable applications for effective initial responses.

翻译：极端自然灾害可能对城市和农村地区造成毁灭性影响。在任何灾害事件中，初始响应是72小时内救援和快速恢复的关键。在灾害响应的初始阶段，快速评估大面积损害并识别优先区域至关重要。在机器学习算法中，深度异常检测能有效检测与日常特征不同的破坏特征。此外，可解释的计算机视觉应用应为初始响应提供依据。本文提出了一种利用更深层全卷积数据描述（FCDD）的异常检测应用，该应用能够定位破坏特征并可视化带损害标记的热力图。具体来说，我们展示了数据集AIDER的众多训练和测试结果，该数据集包含四种灾害类别：建筑物倒塌、交通事故、火灾和洪水区域。我们还进行了异常类别不平衡和数据规模与正常类别竞争的消融研究。实验结果表明，F1得分超过95%的高准确率。此外，我们发现基于VGG16骨干网络的更深层FCDD始终优于其他基线模型CNN27、ResNet101和Inceptionv3。本研究提出了一种新解决方案，为初始响应提供了更高准确率和破坏可解释性的灾难异常检测应用，为异常场景理解研究领域中快速灾后恢复问题做出了新颖贡献。最后，我们讨论了未来工作方向，以改进更鲁棒、可解释的应用，从而支持有效的初始响应。