![]() ![]() The classifier was trained on two manually labeled scans from summer and winter, then tested on three other unseen times. The algorithm classifies points as vegetation, talus, snow, and bedrock using multi-scale neighborhood geometry, slope, change, and intensity features. This study presents a Random Forest machine learning approach to improve the classification accuracy and efficiency of terrestrial LiDAR monitoring of complex natural slopes. In addition, long-term monitoring through seasonal vegetation changes and snow presents unique challenges to the goal of accurate classification in an automated workflow. 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. 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. ![]()
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