Evaluation of data subsampling and neighbourhood selection for mobile LiDAR data classification

Abstract

Road features extraction is essential for autonomous driving vehicles and road maintenance. Mobile Laser Scanning (MLS) systems have proven their capability for dense and accurate LiDAR point cloud data acquisition of road features. Usually, MLS data are received in the format of XYZ coordinates and sometimes with intensity values. Thus, the first step in MLS data processing is point classification, which mainly relays on the geometric distribution of surrounding points. However, processing such huge data is costly and time- consuming. Therefore, in this research, different neighborhood selection methods, including k nearest neighbors, spherical and cylindrical methods are evaluated to reveal the suitable method for MLS data classification. In addition, a data sub-sampling method based on minimum point spacing is applied in order to reduce the processing time. A set of point features, including covariance, moment and height was first extracted based on the three neighborhood selection methods. Random forest classifier was then used to classify a part of the benchmark dataset of Paris–Lille-3D, which belongs to NPM3D Benchmark suite research project. The dataset is divided into three main parts; Lille 1, Lille 2 and Paris. Lille 1 and Lille 2 were used in this research with about 1.5 km longitudinal road and about 98.1 million total number of points. Six scenarios were evaluated; three for the full dataset and three for the sub-sampled dataset using the aforementioned neighborhood selection methods. The results showed that the cylindrical neighborhood selection method achieved the highest classification accuracy of 92.39% and 90.26% for the full and sub-sampled datasets, respectively. The data sub-sampling has showed a good performance, whereas the dataset was reduced by about half and processing time was reduced by almost half with close classification accuracy using the cylindrical neighborhood selection method.