首页|Quick and automatic detection of co-seismic landslides with multi-feature deep learning model
Quick and automatic detection of co-seismic landslides with multi-feature deep learning model
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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 com-parative 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 co-seismic landslide detection to ensure a rapid response to co-seismic landslide disasters.
Co-seismic landslideAutomatic detectionDeep learningLandslide gain indexPlanetScope images
College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China
Institute of Earth Surface System and Hazards,Northwest University,Xi'an 710127,China
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,Northwest University,Xi'an 710127,China
Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China
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National Natural Science Foundation of ChinaKey Research and Development Program of ShaanxiSecond Tibetan Plateau Scientific Expedition and Research Program(STEP)
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