## 4 Discussion  
Traditional methods of landslide detection primarily rely on satellite remote sensing images for identification. However, the accuracy of landslide extraction is limited by the low spatial resolution of medium- and low-resolution satellite remote sensing imagery. Pixels are used as processing units, which leads to the prevalent salt-and-pepper effect. Moreover, obtaining high-resolution satellite remote sensing imagery poses a challenge. Meanwhile, in recent years, the use of UAVs has become more widespread. The data obtained from UAVs' DOM have a high spatial resolution and contain rich spectral characteristics and texture information. However, the utilization of DOM data in the context of landslides is still less extensive than the use of satellite remote sensing. In this study, a method for extracting landslides based on high-resolution DOMs and DEMs has been proposed. This method includes various steps, such as DOM classification, DEM terrain information constraints, filtering algorithms, and morphological processing. Figure 10: Landslide information extraction accuracy results. Figure 9: Landslide boundary extracted with blue line after superimposed with DOM. This method was validated using the Jiangdingya landslide in Gansu Province, China. The results show the capability of this method in identifying and extracting landslides. Optical images are commonly used to identify spatial, spectral, and temporal changes evident on the Earth's surface, such as vegetation clearance, cracks, and inconsistencies in material distribution [29; 30]. This method may result in noisy and spectrally heterogeneous images, which causes difficulty in accurately distinguishing landslide areas from the surrounding environment and precisely comparing images across multiple time periods. This complexity may impede the analysis in capturing complex or subtle clues related to landslides [31]. Ultimately, optical images cannot acquire information about subsurface deformations, whereas subsurface deformations can indicate the severity of slope failures [30]. Compared with traditional methods, this approach preserves effective spectral and texture information in high-resolution DOMs. It also integrates spectral, textural, and morphological characteristics of the studied landforms to extract landslide information. In cases where the terrain variation is significant, the method incorporates DEM terrain information to remove features such as rivers, buildings, and roads. This way improves the accuracy of landslide extraction in complex terrains. This approach also incorporates median filtering and morphological operations. These techniques help achieve adaptive smoothing of classified images, which leads to a more objective and reliable result. The method also has some limitations. First, selecting the optimal threshold is a challenging problem. The optimal threshold values varied across complex terrain. This method is designed for complex terrain and utilizes supervised classification and slope impact factor constraints to eliminate features and geomorphological complexities in such terrain. Eliminating the interference factors that may be encountered during the selection of optimal thresholds for automatic extraction is essential to achieve accurate landslide extraction. Nanyu Township has experienced multiple instances of landslides in the past, which led to the existence of ancient landslide-prone areas. These factors can significantly impact sample selection and classification. The process of supervised classification is significantly influenced by the selection of regions of interest. Consequently, achieving the more accurate classification of landslide-prone areas can be significantly improved. The enhancement in supervised classification can be attained through the meticulous selection of samples that contribute to the model for labeling. The accuracy of the maximum likelihood method is slightly lower than that of other supervisory sub-methods.