## 1 Introduction
Main existing methods for mapping impervious surfaces are as follows:  
1. Threshold segmentation methods that include: the 1 Empirical threshold method, which is the extraction of urban areas by artificially setting specific thresholds based on the validation of previous studies [35]; 2 Mutation detection method proposed by [PERSON] et al. [36], which is based on the assumption that urban impervious surfaces consist of intact patches, and, by gradually increasing the segmentation nighttime lighting threshold, the obtained polygonal patches represent the urban areas along the edges. When the segmentation threshold reaches a point where a polygonal patch breaks up from within, the perimeter of the polygon representing the urban area suddenly increases. Pixels with values greater than or equal to this threshold are considered part of the impervious surface area. 2. Data comparison methods that include: the 1 Statistical Comparison Method, which uses the impervious surface area statistics released by the government as a reference and compares the difference between the extracted impervious surface area and the statistics. A threshold is generated iteratively until the extracted urban area matches the statistics [37]. 2 Spatial comparison method, which uses high resolution multSDectral remote sensing data or land use data as auxiliary data to achieve urban impervious surface information extraction [38]. However, there are still several outstanding problems with the extraction of impermeable surfaces using nighttime light imagery. These problems are as follows:  
1. The scale and values of lights can become inconsistent with actual conditions due to cloud diffraction, moonlight, etc. [39]. This error may be amplified in topographically fragmented areas, and it becomes impossible to accurately represent the urban morphology of a region [40]. Consequently, there has been considerable discussion on the utility of nighttime light imagery in topographically fragmented areas for truly representing the urban form of complex landscapes. 2. Topographic conditions strongly influence the spatial distribution of nighttime lights and human activities. The spatial correlation between nighttime light images and surface types at fine scales [41] is essential for the application of nighttime light images for impermeable surface extraction. However, because of the late start of research on nighttime remote sensing data in China (a study of population change in important cities in mainland China based on nighttime light data), there are only a few studies on Karst mountains, and the existing empirical studies mainly analyze their relationships with various social environments. Meanwhile, in practical applications, \"remote and inaccessible areas\" are not perfectly distributed within a specific administrative area. From a geographical perspective, economic and social development emphasizes the process and state of achieving or failing to achieve coordinated development of \"people\", \"industry\", and \"land\" in a specific spatial and temporal context [42]. The socio-economic situation of any region has its own specific geographical background. The study of the application of nighttime light images should emphasize its usage in a regional context and not remain limited to a specific administrative area. It should provide the theory and applications that can be applied to different regions in similar natural environment backgrounds. The Karst mountains of southwest China form a typically complex geomorphological region [43], which, together with the cloudy and rainy climatic characteristics, possibly renders the impervious surface extracted by night lighting spatially displaced. Therefore, it is of great significance to analyze the applicability of nighttime light image datasets in impermeable surface extraction in the southwest Karst mountains.