## 6 Conclusions  
To solve the problems of the low accuracy and efficiency of the automatic extraction of road surfaces from MLS point cloud data when the road scene is complex, in this study, we proposed an improved region growing method based on supervoxel and vehicle trajectory data to automatically extract the road surface using MLS point cloud data with structured or unstructured patterns. From the above discussion, we can draw the following conclusions:  
1. In order to solve the problems of redundant and unstructured MLS point cloud data, in this study, we reorganized the original scattered point cloud data through supervoxelization without changing their characteristics. The optimization of this data structure allows for a better differentiation of boundaries between different objects, significantly improving the quality of the target extraction and producing a more complete road surface. 2. To address the issue of low accuracy caused by the difficulty in selecting seed points on a road surface extracted using the region growing segmentation algorithm, the proposed algorithm combines the trajectory data of a mobile laser scanning system to enhance the selection strategy of seed voxels in the regional growth algorithm. This integration ensures a high-precision extraction of the road surface. 3. The neighborhood definition based on adjacent supervoxels was used in this study to facilitate the calculations of the Euclidean distance and normal vectors and to reduce the need for a time-consuming K-D tree nearest neighbor search for initial point data. This method can quickly and efficiently identify adjacent supervoxels, which can significantly improve the computational efficiency. The experimental results demonstrate that the proposed method achieves a high performance in extracting road surfaces from MLS point cloud data, with an average extraction completeness, accuracy, and quality of 99.1%, 96.0%, and 97.5%, respectively, for both flat and sloped road datasets. In addition, the method utilizes only the xyz coordinates of the point cloud, making it applicable to other datasets. In conclusion, this study provides a good method for infrastructure management departments to monitor and evaluate road surface quality and provides key support for the further exploration of road 3D information processing applications and related engi  
Figure 16: Road boundary extraction results: (**a**) Site 1; (**b**) Site 2.  
neering construction. Moreover, with the development of autonomous driving, intelligent driving, and other technologies, high-resolution 3D maps will become the main source of information support, and excellent MLS point cloud processing technology can provide the necessary navigation data for dynamic autonomous vehicles. The results presented herein are promising. However, the final conclusions regarding the absolute accuracy of the proposed algorithm and its suitability for road applications can be drawn only from extensive tests on ground realities. The authors plan to publish the results of these tests in subsequent studies. At the same time, we are also verifying some methods to solve highway classification on the market, for example, the Pointly.AI point cloud classifier was specially designed for highway classification and has a high degree of automation. However, this kind of solution needs to ensure the accuracy of the extraction through certain classification training, and on the other hand, it also lacks a detailed evaluation of the performance of the solution, such as in terms of the accuracy, recall rate, and other indicators, making it difficult for users to evaluate its applicability and effect in specific practical scenarios. The next research goal of this research is to further explore the application of MLS point cloud data in high-precision maps and optimize the calculation speed of the algorithm to meet the positioning and safety requirements of vehicles while driving. Conceptualization, W.Z. and Y.N. ; methodology, W.Z. ; software, X.J. ; validation, D.C. and X.J. ; formal analysis, F.S.