  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.
