###### Abstract  
Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the rego growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. mobile laser scanning; point cloud; supervoxel; region growing; road surface +
Footnote †: journal: Medicalquality of highways, conducting road surface maintenance and repair, designing expansion projects, etc. [3]. Traditional road surveying methods, such as total station, GNSS-RTK, etc., usually involve a large amount of data calculation and processing before the collected information can be converted into digital records; therefore, they heavily rely on various professional testing equipment and on-site manual investigations. Moreover, these methods are inefficient, costly, disruptive, pose safety hazards for personnel, and even cause potential damage to road structures [4]. In addition, there is excessive manual intervention in the detection process, which makes it difficult to ensure the objectivity and accuracy of the evaluation results, and it is difficult to provide fast, efficient, and accurate data support for large-scale highway maintenance tasks. Therefore, there is an urgent need for fast and accurate measurement methods to replace traditional technologies and realize efficient and accurate 3D road information acquisition. With the emergence of laser scanning technology, three-dimensional (3D) terrain data of road networks can be captured accurately and quickly [5]. This new measurement method can significantly improve the efficiency and accuracy of road surface information collection, reduce traffic and personnel interference, and provide reliable data support for road maintenance and management tasks. Mobile laser scanning (MLS) integrates multiple sensors, including a 3D laser, the Global Navigation Satellite System (GNSS), the inertial navigation system (INS), and panoramic cameras, into a mobile or airborne platform. This integration allows for the accurate and rapid acquisition of target information with features such as real-time capability, high precision, high density, non-contact operation, and automation [6]. Compared with traditional surveying methods, MLS offers significant efficiency and cost advantages.