![]() ![]() The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people’s lives. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity ≥ Ⅷ) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the identification efficiency is low, which seriously delays the earthquake emergency response. ![]() At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. ![]() Continued abuse of our services will cause your IP address to be blocked indefinitely.The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. Please fill out the CAPTCHA below and then click the button to indicate that you agree to these terms. If you wish to be unblocked, you must agree that you will take immediate steps to rectify this issue. If you do not understand what is causing this behavior, please contact us here. If you promise to stop (by clicking the Agree button below), we'll unblock your connection for now, but we will immediately re-block it if we detect additional bad behavior.
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