Extraction and Digital Modeling of Road Geometric Information Using LiDAR Data Point Clouds

A general framework from semantic segmentation to geometric information extraction and integrated modeling was proposed based on LiDAR data to rapidly and automatically extract road geometric information and complete digital modeling. The local maximum and neighboring point mean features were concatenated as local features based on a fundamental foundation of spatial contextual features. Three-dimensional coordinates and radial distribution were combined to describe the global contextual features, and a semantic segmentation network was established. Additionally, the voxel grid filter and radius outlier removal methods were used to minimize the amount of point cloud data and remove outliers. The adaptive radius variable alpha-shapes method (VA-Shapes) was then employed to extract the road boundary based on semantic segmentation results. Furthermore, the geometric data of the road, including the road width, longitudinal gradient, and cross-slope, were obtained from the horizontal and vertical coordinates of the boundary. The in shape function and interpolation method were then applied to establish a digital elevation model. Subsequently, road routes were generated from the extracted road geometric information using Dynamo for Revit, and adaptive road components and various infrastructure components were constructed using Revit, developing a detailed digital road model. The Semantic3D dataset was utilized for training and testing to analyze and evaluate the extracted road geometric information. The overall accuracy (OA) of the proposed net is 95%, whereas the intersection-over-union (IOU) of segmented pavement is 97.9%, indicating that the proposed net could accomplish superior performance on semantic segmentation of point clouds. Compared with the traditional fixed radius A-Shapes method, the temporal complexity of the VA-Shapes method is low. In addition, the VA-Shapes method can efficiently extract the road boundary. The mean absolute errors between the extracted and manually measured geometric information are slight, demonstrating the effectiveness and accuracy of the proposed methods. The proposed process from semantic segmentation of the point cloud for geometric information extraction and building information modeling for digital modeling has the potential to build a digital model of a road in reverse, which is critical for the intelligent management of existing road infrastructures. © 2023 Xi'an Highway University. All rights reserved.