Drones are being used to assess the situation in various disasters. In this
study, we investigate a method to automatically estimate the damage status of
people based on their actions in aerial drone images in order to understand
disaster sites faster and save labor. We constructed a new dataset of aerial
images of human actions in a hypothetical disaster that occurred in an urban
area, and classified the human damage status using 3D ResNet. The results
showed that the status with characteristic human actions could be classified
with a recall rate of more than 80%, while other statuses with similar human
actions could only be classified with a recall rate of about 50%. In addition,
a cloud-based VR presentation application suggested the effectiveness of using
drones to understand the disaster site and estimate the human condition.

利用无人机图像中人类行为自动估计人员损伤状况，以更快地了解灾难现场并节省人工。通过构建一个新的数据集，并使用 3D ResNet 对城市地区发生的假想灾难中的人类行为进行分类，结果显示具有特征人类行为的状况可达到 80% 以上的召回率，而其他具有类似人类行为的状况只能达到约 50% 的召回率。此外，基于云端的虚拟现实演示应用提示了利用无人机了解灾难现场和估计人类状况的有效性。