Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1091–1096, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1091–1096, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1091-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

DETECTING PROBABLE ROCKFALLS IN OPEN PIT MINES BASED ON UAV POINT CLOUDS

S. M. Yousefi1, H. Arefi1, and A. Bahroudi2 S. M. Yousefi et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: Disaster Management, Rock Face, UAV Based Processing, Open Pit Mines, Rockfall Hazard, Point Cloud Analysis

Abstract. Stability analysis and studying the geological features of rocks and mines have been active research topic for many years. Consequently, it is very important being prepared for probable hazards and having the ability to rescue from earth disasters, in particular in rocks and open pit mines. For this purpose, several methods have been used to measure fractures of a rock face. Among these methods are manual techniques, photogrammetric measurements, and laser scanning based techniques. With the proliferation of unmanned aerial vehicles (UAVs), these systems have been widely used in geological projects recently. Especially in the situation that the case study is very hard to be reached. In this paper, a method is developed to detect the most probable rock fall. After doing some pre-processing, RANSAC algorithm is used to fit planes to the point cloud. Then, intersections of these planes with the point cloud are computed. After some refinements on these intersections, the probable rockfalls are obtained. Point cloud analysis have some advantages over conventional image-based methods; especially in case of probable rock falls, which might be hard to detect using the rock images. However, analyzing point cloud data usually is complicated and computationally expensive.