Assessment of Landslide Susceptibility Considering the Factors of Human Engineering Activities

Landslides, as a significant geological hazard widely distributed globally, require precise risk assessment to ensure the utmost protection of human lives and property. Existing studies typically rely on historical landslide inventories and consider factors such as topography, geology, geomorphology, and hydrology for landslide susceptibility evaluation. However, these inventories often need more timely updates and seldom account for the impact of human engineering activities. Therefore, this study focused on Yongping County in Yunnan Province, China, a region prone to frequent landslides. It updated and supplemented the landslide inventory using geolocalization methods. Additionally, the study incorporated various human engineering factors in the evaluation process and employed a convolutional neural network (CNN) model to assess landslide susceptibility. The results align more closely with field survey realities, demonstrating the reliability of this method. This method can potentially be applied to the susceptibility assessment of other geological disasters in the future.