Classification methods for point clouds in rock slope monitoring: A novel machine learning approach and comparative analysis

Abstract

Abstract High-resolution remote monitoring of slopes using terrestrial LiDAR and photogrammetry is a valuable tool for the management of civil and mining geotechnical asset hazards, but accurately classifying regions of interest in the data is sometimes a difficult and time-consuming task. Filtering unwanted areas of a point cloud, such as vegetation and talus, is often a necessary step before rockfall change detection results can be further processed into actionable information. In addition, long-term monitoring through seasonal vegetation changes and snow presents unique challenges to the goal of accurate classification in an automated workflow. This study presents a Random Forest machine learning approach to improve the classification accuracy and efficiency of terrestrial LiDAR monitoring of complex natural slopes. The algorithm classifies points as vegetation, talus, snow, and bedrock using multi-scale neighborhood geometry, slope, change, and intensity features. The classifier was trained on two manually labeled scans from summer and winter, then tested on three other unseen times. We find that F Score generally remains above 0.9 for talus and vegetation, and above 0.95 for bedrock and snow, indicating very high accuracy and an ability to adapt to changing seasonal conditions. In comparing this approach to CANUPO, an existing classification tool, we find our approach to be generally more accurate and flexible, at the expense of increased complexity and computation time. Comparisons with manual masking and a hybrid approach indicate that a machine learning solution is useful primarily in cases of rapidly changing rock slopes or in climates with significant seasonal variability and snow.