## 2 Related Work  
Currently, methods for detecting road surfaces from MLS point clouds can be roughly divided into two categories: (1) extracting road surface point clouds based on the detected curbs as boundaries and (2) directly extracting road surface point clouds based on road surface geometric features. In the first type, the extraction of road surface information is mainly based on the geometric constraints of the MLS point cloud, which first detects the edge of the road, and all points located within the edge of the road are extracted as road surface data. [PERSON] et al. [19] proposed a boundary recognition algorithm based on scan lines to extract the road surface and detect the curb (or road boundary) by identifying sudden changes in slope or elevation. However, this method has some difficulties when the number of MLS scan lines changes or the quality of point cloud data is not high. [PERSON] et al. [20] developed a method based on the slope and elevation to extract road curbs and surfaces. This method forms multiple cross-sectional profiles by connecting pairs of curb points to extract road edges. However, it cannot accurately extract the road edges with significant curvature changes. [PERSON] et al. [21] proposed a method for curb extraction based on the local normal saliency. They partitioned the road into overlapping segments and extracted dense curb points from each segment. However, their experimental data were limited to flat urban roads, and owing to the influence of curved trajectory data, their method could not fully extract the road edges at T intersections. [PERSON] et al. [22] applied the concept of linear clouds to extract highway road surfaces automatically. However, to generate linear clouds from 3D data points, their method requires strictly ordered point cloud data based on timestamps. The second method for extracting road surface information is mostly based on the statistical rules and semantic information of MLS point clouds. [PERSON] et al. [23] proposed a method for extracting road surfaces from MLS point cloud data by combining topological and geometric information. However, their method is limited by the uneven and irregular terrain of the road surfaces. [PERSON] et al. [24] used a line-based region growing method to create road surfaces, assuming that most road surfaces are planar. [PERSON] et al. [25] proposed a step-wise method for off-ground point removal. First, the raw point clouds were vertically partitioned along the trajectory of the vehicle. Subsequently the Random Sample Consensus (RANSAC) method was employed for ground point extraction by determining the average height of the ground points. [PERSON] et al. [26] presented a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment. However, these methods were designed for relatively flat and simple road scenes. When dealing with complex highways with large-scale data and significant elevation variations, these methods often suffer from low efficiency and poor robustness when extracting road surfaces from MLS data. Furthermore, it is worth mentioning that in recent years, with the rapid development of deep learning and neural networks, many researchers have used them to study the semantic classification of point clouds [27]. Some image-based deep learning methods are used for point cloud classification, which mainly convert 3D point cloud data into 2D image data through projection or direct conversion, extract image features, and finally use the correlation between image data and point cloud data to complete the classification task. Such as [PERSON] et al. [28] proposed Point cloud Classification based on image-based Instance Segmentation (PCIS), to classify point clouds automatically; this method is based on two-dimensional digital images from a daily work basis. [PERSON] et al.