A method of processing mobile laser scanning data for road repair design

Abstract

Aim: To develop and present a methodology for processing mobile laser scanning (MLS) data that allows highly precise and reliable determination of the scope of work for justified and optimized road repair design, in contrast to existing approaches. Materials and methods: MLS data were obtained in .las format; control points measured with a total station at 50-meter intervals; software: Microstation, Terrascan, Robur, and AutoCAD. The processing methodology included preliminary point cloud cropping and classification, scanner calibration against the asphalt concrete surface, data adjustment using control points with vector coordinate offsets, semi-automated vectorization of road markings based on reflectance intensity, and noise filtering through solid surface extraction. Results: The method yielded a calibrated and filtered point cloud with a discrepancy in heights at control points of less than 2 cm. Road markings were identified based on reflectance intensity, and 3D polylines of the road edges and centerline were constructed. The solid surface operation eliminated noise, creating a smooth triangulated terrain model. ROBUR calculated rut depths, cross-sections, and a table of values. The final output determined the milling area, asphalt paving volume, and site repair plan. Conclusion: The developed methodology for processing MLS data ensures vertical accuracy of less than 2 cm and reliable determination of milling and asphalt concrete paving volumes. It allows optimizing financial and labor costs of road repairs, unlike existing approaches. The methodology is recommended for implementation in road surveying for highway repairs.