Automatic Non-Urban Road Surface Point Extraction Based on Geometric Features Using Neural Networks and Raster Structure Approach

Abstract. The automatic segmentation of road surface points from 3D point cloud data has recently gained significant attention. However, it remains challenging due to the variability of road characteristics and the complexity of surrounding environments, especially in non-urban areas. This study introduces a comprehensive methodology leveraging neural networks to segment road surface points from non-urban point cloud data, supporting autonomous driving applications. The proposed approach computes multiple geometric features of the point cloud at two resolutions, 0.2 and 0.4 meters, to enhance segmentation accuracy. The features are projected onto a regular grid and converted into a raster format where each pixel's value represents the averaged features of points within its space. The rasterized values serve as structured inputs for a feature-based Neural Network (NN), which classifies road pixels based on intensity, density, curvature, planarity, roughness, surface variation, and verticality properties. Classified road pixels are further refined through morphological operations, distinguishing main road and road border pixels. The created masks are then used to extract the corresponding point cloud data of each category. The same neural network model is applied to extract road points within the border point clouds, where a precise road surface point cloud is obtained by merging the inside-road and filtered border points. The proposed method was evaluated on a MLS-acquired road point cloud dataset, achieving high performance with average completeness, correctness, quality, and overall accuracy rates of 98.9%, 97.6%, 96.6%, and 98.2%, respectively. Its key advantage lies in the reduced computational requirements demand by operating on rasterized inputs rather than traditional raw point cloud data.