Automatic Road Inventory Using a Low-Cost Mobile Mapping System and Based on a Semantic Segmentation Deep Learning Model

Abstract

Road maintenance is crucial for ensuring safety and government compliance, but manual measurement methods can be time-consuming and hazardous. This work proposes an automated approach for road inventory using a deep learning model and a 3D point cloud acquired by a low-cost mobile mapping system. The road inventory includes the road width, number of lanes, individual lane widths, superelevation, and safety barrier height. The results are compared with a ground truth on a 1.5 km subset of road, showing an overall intersection-over-union score of 84% for point cloud segmentation and centimetric errors for road inventory parameters. The number of lanes is correctly estimated in 81% of cases. This proposed method offers a safer and more automated approach to road inventory tasks and can be extended to more complex objects and rules for road maintenance and digitalization. The proposed approach has the potential to pave the way for building digital models from as-built infrastructure acquired by mobile mapping systems, making the road inventory process more efficient and accurate.