Abstract

Mining-induced surface cracks are direct indicators of ground damage caused by underground coal extraction, yet their accurate three-dimensional (3D) characterization remains challenging because most unmanned aerial vehicle (UAV)-based studies are limited to two-dimensional (2D) detection and planar parameter extraction. This study proposes a UAV–terrestrial laser scanning (TLS) fusion framework for measurable 3D characterization of mining-induced surface cracks under real topographic conditions. High-resolution UAV orthomosaics were used to extract crack semantics with a dual residual-attention U-Net (DRA-UNet). The resulting crack masks were skeletonized and vectorized, and a k-dimensional tree (KDTree)-based spatial matching strategy was applied to link crack centerlines with TLS point clouds, thereby constructing semantic crack point clouds. The framework was evaluated at the Huojitu Mine, Shenmu City, China, using approximately 5000 crack samples. DRA-UNet achieved precision, recall, F1-score, and mean intersection over union values of 85.13%, 77.84%, 81.32%, and 70.26%, respectively. Compared with conventional 2D length measurements, the proposed 3D estimation reduced the mean relative error from 4.49% to 2.64%. Width validation at 24 field points yielded a mean relative error of 4.47%. These results show that UAV–TLS fusion can bridge the gap between planar crack detection and true 3D geometric characterization, providing a practical tool for mining-induced ground damage monitoring.

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