Deep learning identification technology of InSAR significant deformation zone of potential landslide hazard at large scale

doi:10.11947/j.AGCS.2022.20220303

Abstract

Abstract: Comprehensive identification and discovery of potential landslide hazards has become a major practical demand of geological hazard prevention and control in China. At present, the application effect and applicability of the combination of InSAR technology and deep learning for the intelligent identification of geological hazards at large scale are still worthy of further exploration and research, this paper obtained the phase data of surface deformation based on stacking interferometric synthetic aperture radar (Stacking InSAR) technology, used deep learning to identify the location and distribution of the deforming landslide hazards, and determined the boundary of the significant deformation zone of potential landslide hazards. The above technical methods were exploratively applied to test and dynamic update data sets. The average identification precision, recall and F₁ score value of the test data set were 0.69, 0.67 and 0.67, respectively. The identification precision, recall and F₁ score value of the dynamic update data set were 0.85, 0.58 and 0.68, respectively. The results showed that the technical method used in this paper is feasible in the identification of potential landslide hazards in a wide area, and can provide theory and technical support for geological disaster monitoring and early warning.

Key words: potential landslide hazard, significant deformation zone, deep learning

CLC Number:

P237

WU Qiong, GE Daqing, YU Junchuan, ZHANG Ling, LI Man, LIU Bin, WANG Yan, MA Yanni, LIU Hongjuan. Deep learning identification technology of InSAR significant deformation zone of potential landslide hazard at large scale[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2046-2055.

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