Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks

After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms. From the building coordinates, we extract their aerial-view windows of appropriate size and classify whether a building is damaged or not. We demonstrate the result of our method in the case study of 2017 Hurricane Harvey.

在飓风过后，对于应急管理者和第一响应人员而言，灾害评估至关重要，以便能够合理规划和分配资源。评估灾害程度的一种方法是通过检测和量化受损建筑的数目，这一过程传统上是通过在受影响区域驾车进行。这一流程既耗时又费力。在本文中，我们利用卫星影像的可用性和准备就绪状态，提出利用图像分类算法来提高灾害检测的效率和准确性。从建筑坐标中，我们提取适当大小的空中视角窗口，并对其是否受损进行分类。我们以2017年哈维飓风的案例研究展示了我们方法的结果。