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Wenjie Du, Qian Sheng, Xiaodong Fu, Jian Chen, Jingyu Kang, Xin Pang, Daochun Wan, Wei Yuan. Application of unmanned aerial vehicle remote sensing on dangerous rock mass identification and deformation analysis: case study of a high-steep slope in an open pit mine. Journal of Earth Science. doi: 10.1007/s12583-023-1813-7
Citation: Wenjie Du, Qian Sheng, Xiaodong Fu, Jian Chen, Jingyu Kang, Xin Pang, Daochun Wan, Wei Yuan. Application of unmanned aerial vehicle remote sensing on dangerous rock mass identification and deformation analysis: case study of a high-steep slope in an open pit mine. Journal of Earth Science. doi: 10.1007/s12583-023-1813-7

Application of unmanned aerial vehicle remote sensing on dangerous rock mass identification and deformation analysis: case study of a high-steep slope in an open pit mine

doi: 10.1007/s12583-023-1813-7
  • Received Date: 10 Oct 2022
  • Rev Recd Date: 03 Jan 2023
  • Accepted Date: 07 Jan 2023
  • Available Online: 07 Feb 2023
  • Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment, the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work. In this study, based on the UAV remote sensing technology in acquiring refined model and quantitative parameters, a semi-automatic dangerous rock identification method based on multi-source data is proposed; In terms of the periodicity UAV-based deformation monitoring, the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud. Taking a high-steep slope as research object, the UAV equipped with special sensors was used to obtain multi-source and multi-temporal data, including high-precision DOM and multi-temporal 3D point clouds. The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass, realizes the closed-loop of identification and accuracy verification; changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy. The results show that the multi-source data-based semi-automatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification, and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.

     

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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