Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 195-199, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/195/2016/
doi:10.5194/isprs-archives-XLI-B3-195-2016
 
09 Jun 2016
AN APPROACH TO AUTOMATIC DETECTION AND HAZARD RISK ASSESSMENT OF LARGE PROTRUDING ROCKS IN DENSELY FORESTED HILLY REGION
S. Chhatkuli, K. Kawamura, K. Manno, T. Satoh, and K. Tachibana PASCO CORPORATION, 2-8-10 Higashiyama, Meguro-Ku, Tokyo, Japan
Keywords: LiDAR, Hazard, Rock-fall, Curvature analysis, Simulation Abstract. Rock-fall along highways or railways presents one of the major threats to transportation and human safety. So far, the only feasible way to detect the locations of such protruding rocks located in the densely forested hilly region is by physically visiting the site and assessing the situation. Highways or railways are stretched to hundreds of kilometres; hence, this traditional approach of determining rock-fall risk zones is not practical to assess the safety throughout the highways or railways. In this research, we have utilized a state-of-the-art airborne LiDAR technology and derived a workflow to automatically detect protruding rocks in densely forested hilly regions and analysed the level of hazard risks they pose. Moreover, we also performed a 3D dynamic simulation of rock-fall to envisage the event. We validated that our proposed technique could automatically detect most of the large protruding rocks in the densely forested hilly region. Automatic extraction of protruding rocks and proper risk zoning could be used to identify the most crucial place that needs the proper protection measures. Hence, the proposed technique would provide an invaluable support for the management and planning of highways and railways safety, especially in the forested hilly region.
Conference paper (PDF, 1298 KB)


Citation: Chhatkuli, S., Kawamura, K., Manno, K., Satoh, T., and Tachibana, K.: AN APPROACH TO AUTOMATIC DETECTION AND HAZARD RISK ASSESSMENT OF LARGE PROTRUDING ROCKS IN DENSELY FORESTED HILLY REGION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 195-199, doi:10.5194/isprs-archives-XLI-B3-195-2016, 2016.

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