Near-real-time gradually expanding 3D land surface reconstruction in disaster areas by sequential drone imagery

The ability of drones to access disaster areas has been proven powerful and flexible for acquiring first-hand optical imagery data for environmental observation. However, such imagery data usually undergo post processing, and the three-dimensional (3D) products are mainly for accurate land surveys. The postprocessing procedure is too time-consuming to meet instant decision support and rescue response requirements. Therefore, this paper intends to develop a systematic workflow that is able to achieve on-the-fly 3D reconstruction in disaster areas by optical imagery sequentially acquired by drones. This study proposes a strategy to spatially link sequential images (SLSI) for image localization and suitable stereopair selection. In addition, the criteria for valid epipolar stereoapair determination are developed to make the 3D dense reconstruction more automatic and effective. The 3D digital land surface can be gradually reconstructed and expanded in the computer system while the drone is capturing new images. This paper utilizes the imagery dataset of collapsed buildings induced by the 2016 Kumamoto earthquake in Japan to simulate the more effective 3D reconstruction. Although the accuracy of the consequence is reported to be closely one meter, the mean data processing time for every image can achieve the level by approximately ten seconds while performing the proposed scheme on an iMac with Intel Core i5 and 16 GB random access memory (RAM). As a result, the efficiency and computational power needed are significantly reduced to support emergency applications soon after a disaster occurs.

Automated summary

This paper introduces a systematic workflow for on-the-fly 3D reconstruction in disaster areas using optical imagery acquired by drones. It proposes a strategy for spatially linking sequential images and determining suitable stereopair selection. The study also develops criteria for valid epipolar stereoapair determination to improve the effectiveness of 3D dense reconstruction. The research utilizes a dataset from collapsed buildings induced by the 2016 Kumamoto earthquake in Japan to simulate more effective 3D reconstruction, achieving a mean data processing time of approximately ten seconds per image.