Quick and automatic detection of co-seismic landslides with multifeature deep learning model

Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.

More Catastrophic Flow Events may Follow the Chamoli Rock and Ice Avalanche under Climate Change

0 INTRODUCTION Global warming not only exacerbates the instability of the climate system,causing more weather extremes but also has far-reaching impacts on surface systems,changing disaster environments(Qiu et al.,2024;Ye et al.,2024).The key medium between climate warming and the mountain environment is the cryosphere,whose recession increases the occurrence of chain disasters such as rock-ice avalanches and other flow events(Yang et al.,2023).