Comparison and evaluation of ground surface classification methods for LiDAR point clouds

Abstract

In recent years, the utilization of three-dimensional point cloud data has been advancing across various fields, becoming one of the essential means for obtaining Digital Terrain Models (DTMs) used in terrain analysis. Methods for acquiring three-dimensional point clouds include Mobile Laser Scanners (MLS), Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), and Airborne Laser Scanners (ALS). In this study, point cloud data obtained from these scanners were used to describe, compare, and evaluate three ground surface classification methods: the Simple Morphological Filter (SMRF), Cloth Simulation Filtering (CSF), and Improved Adaptive Triangulation (IAT). Precision, Recall, Accuracy, and F1-Score were employed as evaluation metrics. To quantitatively assess the effects of datasets and methods on each evaluation metric, a two-way analysis of variance (ANOVA) and the Friedman test were applied to verify statistical significance. As a result, it was found that the main effect of the dataset was statistically highly significant across multiple evaluation metrics.