Abstract
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.
基金项目
National Natural Science Foundation of China(42271078)
Key Research and Development Program of Shaanxi(2024SF-YBXM-669)
Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(2019QZKK0902)
万方数据