[M-GI31] Earth and planetary informatics and data utilization

[MGI31-08] Development of slope failure risk prediction system by high-precision topographic model based on point cloud data

*Taiki Kubo¹, Satoshi Kagamihara², Hiroyoshi Tachikawa², Tomoki Shiotani³, Hisafumi Asaue³, Yoshihiko Fukuchi⁴ (1.Graduate School of Engineering, Kyoto University, 2.Dia Nippon Engineering Consultants Co., Ltd., 3.iTi Laboratory, Office of Institutional Advancement and Communications (IAC), Kyoto University, 4.Autodesk Ltd.)

Keywords:digital transformation, disaster prevention, facility maintenance, reality capture

For effective risk assessment and continuous monitoring of slope failures, we analyzed a high-resolution elevation model derived from point cloud data using a quick and simple method. A case study was conducted around the Sabo Dam in Hyogo Prefecture, an area prone to landslides. Point cloud data were generated using reality capture technology (Lin et al., 2020) from 360-degree video footage recorded by a photographer walking through the monitoring area. To detect temporal changes, the randomly distributed point cloud data were converted into a grid format. Since the raw data contained missing areas, as well as noise from vegetation and buildings, interpolation and correction processes were applied to extract accurate ground surface information. We evaluated various data processing methods and identified that extracting the minimum value within each grid unit, combined with RGB-based filtering, was effective. Topographic analysis based on the processed elevation data revealed changes in terrain and slope over time. The results were validated through statistical analysis of the point cloud data, and the associated slope failure risks were assessed. This non-destructive, non-contact monitoring approach demonstrates the potential to reduce labor requirements and maintenance costs, contributing to digital transformation in the regional disaster prevention and facility maintenance.

Lin, J.J., Ibrahim, A., Sarwade, S., Golparvar-Fard. (2021) Bridge Inspection with Aerial Robots: Automating the Entire Pipeline of Visual Data Capture, 3D Mapping, Defect Detection, Analysis, and Reporting. Journal of Computing in Civil Engineering, vol. 35(2). https://doi-org.kyoto-u.idm.oclc.org/10.1061/(ASCE)CP.1943-5487.000095.