## 6 Conclusions  
In the study, we constructed a global dynamic landslide susceptibility model employing ResNet18. By combining this with the GEE platform, we obtained spatial distribution data for the dynamic conditioning factors in the Chinese region from 2001 to 2022. We then employed the SHAP method to elucidate the effect of the input features on the output results generated by the ResNet18 model, thereby highlighting the influence of LCFs on the model. Finally, this study assessed large-scale landslide susceptibility in China from 2001 to2022 and used the MK test to examine spatial trend changes in landslide susceptibility over 22 years. A time-series analysis of large-scale landslide susceptibility in typical Chinese regions was carried out. The principal findings are summarized as follows. 1. Testing the spatial distribution of precipitation, NDVI, and land cover type in China from 2001 to 2022 revealed that precipitation did not show any significant spatial change trends over the 22-year period. In contrast, the NDVI exhibited a significant increasing trend, while the changes in land-cover types were complex and diverse. 2. In the model performance evaluation, the SVM, CNN, and ResNet18 models demonstrated excellent performance (AUC > 0.9), with the ResNet18 model outperforming the others with an AUC of 0.9362. 3. The findings from the SHAP analysis demonstrate that precipitation is the most influential factor affecting landslides, exhibiting a substantial positive spatial correlation with the likelihood of landslide events. Subsequently, other factors, including profile curvature, NDVI, and distance to faults, exert a pronounced influence on landslide occurrence. 4. Based on the spatial distribution of LSM evaluated in China from 2001 to 2022 and statistics on the area share of large-scale landslide susceptibility, research indicates that the zones in China exhibiting high and extremely high susceptibility to landslides are predominantly located in the southwest regions of the country. Over a span of 22 years, the average area shares of the high and extremely high landslide susceptibility zones were recorded at 12.97 percent and 10.22 percent, respectively. Furthermore, the spatial trend test of large-scale landslide susceptibility and the time series analysis of typical regional points indicate that the areas with high landslide susceptibility in Chongqing, Guizhou, and Sichuan have a significant increasing trend, with landslide susceptibility increasing by more than 10 percentage points over 22 years. In contrast, the areas with high landslide susceptibility in Shaanxi and Shanxi showed a significant decreasing trend, with landslide susceptibility decreasing by more than 10 percentage points over 22 years. These findings may provide a valuable reference for subsequent investigations into landslide hazards in China. Conceptualization, H.J., M.D. and W.H. ; methodology, H.J., M.D. and W.H. ; software, H.J. ; validation, H.J. and W.H. ; formal analysis, H.J. and M.D. ; investigation, L.L. ; resources, M.D. and L.L. ; data curation, H.J. and L.L. ; writing--original draft, H.J. ; writing--review and editing, M.D. and W.H. ; visualization, H.J. and W.H. ; supervision, L.L. ; project administration, M.D. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the National Natural Science Foundation of China under Grants 42374027; the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022); the Opening Fund of Key Laboratory of Smart Earth under Grant KF2023 YB04-01; the Key R&D Program Projects in Zhejiang Province under Grant 2023C03177. Not applicable. The article provides a comprehensive description of all data sources utilized in the study. Interested parties may request access to the established dynamic model of landslide susceptibility mapping by contacting the corresponding author via email.