## 1 Introduction  
Landslides, as a common natural disaster in China, mainly occur in mountainous areas. The analysis and monitoring of landslide areas have always been research hotspots and emphasized in the field of disaster studies to reduce losses and predict preventive and control measures [1]. On 12 July 2018, a large landslide occurred in Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province, as triggered by continuous rainfall and geological factors. A substantial amount of debris rushed into the Bailong River basin, which blocked the river in Nanyu Township. This event posed a serious threat to the life, property, and safety of the surrounding residents. Simultaneously, various safety measures, including landslide monitoring and prevention, were implemented [2]. The development of technology has enabled the monitoring of landslides by GNSS (Global Navigation Satellite System) technology, UAV (Unmanned Aerial Vehicle) remote sensing, and GB-InSAR (Ground-based Interferometric synthetic aperture radar) technology [3, 4, 5, 6]. According to statistics, landslides constitute the largest portion of geological disasters. Therefore, the study of landslide area extraction, monitoring, and early warning is important. At present, the visual interpretation of remote sensing images and automatic computer recognition acquisition are two ways to identify and extract landslides. Visual interpretation requires the translator to have rich geographical professional knowledge and interpret based on image characteristics [7]. [PERSON] et al. [8] used 112 seismic landslides with an area of more than 50,000 m\({}^{2}\) to study the development and distribution of large landslides induced by the Hanchuan earthquake. [PERSON] et al. [9] visually interpreted 196,007 Wenchuan landslides which induced a landslide with a total area of 1150.622 km\({}^{2}\) within 110,000 km\({}^{2}\). Computer automatic identification mainly includes landslide extraction methods based on image elements and object-oriented approaches, as well as deep learning-based landslide extraction methods. [PERSON] et al. [10] proposed a combination of an adaptive model with random forest to build a mixed model for the automatic identification of landslides. [PERSON] et al. [11] introduced a digital elevation model (DEM) in the object-oriented method, where the topographic information, such as slope, is constrained, identified, and extracted from the landslide. [PERSON] et al. [12] established a model for characterizing the hidden danger terrain of the same earthquake landslide using the morphological characteristics of fine DEMs. They also used a morphological algorithm to automatically identify seismic landslides. [PERSON] et al. [13] introduced morphological operations and a regional level-set algorithm to realize an object-oriented classification method for the automatic extraction of landslides. [PERSON] et al. [14] automatically extracted UAV image landslides by combining an improved optimal segmentation scale model with a deep convolutional neural network. Automatic landslide recognition involves identifying and extracting landslides based on feature information, such as texture and morphology, or by constructing deep learning network models using extensive datasets and training samples [15]. The abovementioned methods effectively enhance the accuracy of landslide extraction. However, visual interpretation is greatly influenced by subjective factors and relies on landslide images, with limited use of topographic features and other data sources. Automatic computer recognition algorithms effectively improve the precision of landslide extraction but require the construction of a large number of training samples. The low spatial resolution of medium- and low-resolution satellite remote sensing imagery, where pixels are used as processing units, limits the accuracy of landslide extraction. [PERSON] et al.