While this approach achieved sub-meter geolocation accuracy (0.8-1.2 m RMSE (root mean square error)), its intrinsic limitations became apparent: (1) inability to capture road surface conditions critical for autonomous vehicle navigation and (2) dependency on active mining operations for data collection. Subsequent hybrid methodologies, such as [PERSON]'s raster-satellite fusion framework [13], demonstrated 15.7% precision gains over GPS-only approaches by correlating trajectory heatmaps (30 cm resolution) with WorldView-3 imagery [14]. Nevertheless, these sensor-fusion solutions still necessitate (a) continuous GPS signal coverage (\(\geq\)85% availability), (b) labor-intensive coordinate system alignment, and (c) specialized LiDAR validation for 3D road profiling--requirements often unattainable in developing mining regions. This technological progression reveals an emerging research nexus: the optimal mining road mapping solution must simultaneously satisfy (1) centimeter-level accuracy without ground instrumentation, (2) full attribute extraction (geometry, surface integrity, and drainage status), and (3) near-real-time update capacity (\(\leq\)24 h latency). Current computer vision approaches, however, remain inadequate in addressing the 'feature dissociation' phenomenon--where general road detection models fail to recognize mining-specific patterns like temporary haulage branches and overburden conveyor corridors. Contemporary sensing solutions for mining road mapping have bifurcated into two technical trajectories, each presenting unique cost-performance tradeoffs. The vision-centric UAV (unmanned aerial vehicle) paradigm, exemplified by [PERSON]'s MD-LinkNeSt architecture [15], achieves sub-decimeter planimetric accuracy (0.05 m RMSE) through synergistic integration of ResNeSt backbone and D-LinkNet topology. While this approach attains 0.6914 IoU on orthorectified UAV imagery (5 cm GSD (ground sampling distance)), operational constraints emerge: (1) 120,000\(-\)250,000 UAV systems with multispectral payloads [16], (2) 14-18 h/km\({}^{2}\) photogrammetric processing time, and (3) mandatory [17] certified operators. Conversely, LiDAR-based solutions like [PERSON]'s point cloud analytics [18] demonstrate superior vertical precision (\(\sigma\_z\) = 3.2 cm) through spatial-temporal filtering of 3D LiDAR data (256-beam, 1.2 M pts/s), achieving 95.61% precision on haul road boundary detection. However, the computational intensity manifests in two dimensions: (a) 72-core GPU clusters required for real-time (\(\leq\)5 s) point cloud registration, and (b) 3.4 GB/km memory consumption for road feature extraction. Field validations reveal critical operational limitations: both modalities struggle with dynamic occlusion patterns from haul trucks (38-42% point cloud loss per vehicle pass [19]) and require weekly sensor recalibration in high-dust environments (PM10 > 800 ug/m\({}^{3}\)). A recent lifecycle cost analysis (LCCA) by the International Council on Mining and Metals indicates that UAV-LiDAR hybrid solutions incur USD 18.7/km annual maintenance costs, with only 23% developing-country mines possessing requisite computational infrastructure. Thiseconomic reality necessitates alternative solutions that decouple mapping accuracy from hardware dependency. The evolution of road segmentation in remote sensing has witnessed paradigm shifts from fully supervised to weakly supervised learning frameworks. While [PERSON]'s seminal work [20] demonstrated 89.2% mIoU on urban roads using pixel-level annotations, the prohibitive labeling costs (USD 12.7/km\({}^{2}\) for 0.3 m imagery) spurred innovation in annotation-efficient approaches. [PERSON]'s scribble-based WR2E [21] reduced annotation effort by 78% through road trimap propagation, achieving 86.9% IoU on SpaceNet data--a 14.3% improvement over conventional weakly supervised methods.