Estimation of Human Condition at Disaster Site Using Aerial Drone Images

TL;DR

This paper presents a method using aerial drone images and 3D ResNet to automatically estimate human damage status in disaster sites, aiding faster assessment and response.

Contribution

Introduces a new dataset and a classification approach for human damage status in aerial drone images during disasters, demonstrating effective use of deep learning.

Findings

Damage status with characteristic actions classified with over 80% recall

Other similar actions classified with about 50% recall

Cloud-based VR application demonstrated for disaster site understanding

Abstract

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.