## 1 Introduction  
Landslides are a common geological hazard with a significant impact on human life. Recent studies have shown that climate change exacerbates the frequency and severity of landslides. Drought-induced soil desiccation and structural weakening [1], coupled with slope destabilization from flood-induced saturation [2], collectively increase the likelihood of landslides. It is estimated that landslides cause billions of dollars in economic losses worldwide each year [3]. An irregular shape for a landslide area poses a considerable challenge to subsequent rescue and investigation efforts. Therefore, accurate identification of a landslide area is essential for effective rescue, mitigation, and exploration. In the field of landslide exploration, remote sensing images have long been employed for the monitoring and analysis of geological hazards due to their extensive coverage, all-weather operability, and high resolution [4, 5]. This is particularly the case in the context of landslide detection [6, 7]. However, it should be noted that there are numerous types of remote sensing imagery, and the relative advantages of different types in landslide detection vary. While visible light is relatively inexpensive, it is not possible to continuously monitor landslide displacements, and long-term dynamic analyses are difficult to perform. Near-infrared and thermal infrared data are capable of accurately capturing environmental information [8]. However, the resolution is typically low, which impairs the observation of landslide details. Synthetic-aperture radar (SAR) [9] and laser radar (LiDAR) are not susceptible to the effects of weather and light [10; 11], yet they require processing through interferometric and polarimetric analyses prior to further analysis. The aforementioned disparate types of remote sensing imagery can all play a role in landslide exploration, providing a reliable basis for real-time monitoring and risk assessment. The objective of this research is to address the issue of landslide image segmentation in the aftermath of disasters. This entails accurately delineating the landslide area and precisely determining the extent of landslide occurrence. Among the various imaging means, the visible light image has significant potential for landslide image segmentation due to its intuitive visualization, low acquisition cost, and other characteristics. Accordingly, this paper selects the visible-light image as the primary subject of investigation, upon which the associated landslide detection and analysis procedures are conducted [12]. Nevertheless, the intricate nature of landslide morphology, the multi-scale distribution of features, and the interference of background features such as rivers, vegetation, and bare soil render the precise delineation of landslide areas a persistently challenging undertaking [13; 14]. It is indubitable that traditional image processing methods have always played an important role in the processing of visible light images. Such methods include threshold segmentation [15; 16], edge detection [17; 18], and region-growing algorithms [19; 20]. These methods are characterized by their simplicity and straightforwardness, as well as their lack of requirement for high computer performance. As a result, they have been the dominant approach in landslide segmentation for an extended period of time. Threshold segmentation is a method that effectively delineates the landslide region from the background by establishing thresholds for intensity or color, particularly in well-lit and high-contrast scenes. However, in complex environments with significant illumination variations or noise, since threshold segmentation is entirely based on pixel gray values, the segmentation effect is unstable when the gray contrast between the landslide region and the background is low or the illumination is uneven [21]. Similarly, edge detection delineates landslide boundaries by recognizing sudden changes in image intensity.