It is of great importance to obtain precise trace data, as traces are frequently the sole visible and measurable parameter in most outcrops. The manual recognition and detection of traces on high-resolution three-dimensional (3D) models are relatively straightforward but time-consuming. One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces. In this study, a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model. A virtual digital image rendering was then employed to capture virtual images of selected regions. This technique helps to overcome limitations caused by the surface morphology of the rock mass, such as restricted access, lighting conditions, and shading effects. After AI-powered trace detection on two-dimensional (2D) images, a 3D data structuring technique was applied to the selected trace pixels. In the 3D data structuring, the trace data were structured through 2D thinning, 3D reprojection, clustering, segmentation, and segment linking. Finally, the linked segments were exported as 3D polylines, with each polyline in the output corresponding to a trace. The efficacy of the proposed method was assessed using a 3D model of a real-world case study, which was used to compare the results of artificial intelligence (AI)-aided and human intelligence trace detection. Rosette diagrams, which visualize the distribution of trace orientations, confirmed the high similarity between the automatically and manually generated trace maps. In conclusion, the proposed semi-automatic method was easy to use, fast, and accurate in detecting the dominant jointing system of the rock mass.

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Keywords: Automatic trace detection, Digital joint mapping, Rock discontinuities characterization, Three-dimensional (3D) trace network