## 4 Discussion  
The proposed OMRoadNet framework represents a significant advancement in unsupervised domain adaptation for mining road extraction, achieving 92.16% precision and 67.75% IoU without target-domain annotations--surpassing prior art by 14.2% and 8.3%, respectively, under comparable test conditions. These improvements stemmed from three key innovations: (1) a self-training mechanism with geometric-prior-guided pseudo-label refinement, (2) an attention-based feature disentanglement module that decouples material-invariant road patterns from spectral noise, and (3) morphology-aware augmentation simulating dynamic mining road deformations. Notably, the framework reduced annotation costs by 94.6% while maintaining compliance with industrial GIS accuracy thresholds (sub-meter RMSE).  
When contextualized with existing literature, these results address critical gaps in mining geospatial analytics. Compared to UAV-based approaches, OMRoadNet eliminated the need for aerial photogrammetry (14-18 h/km\({}^{2}\) processing) while achieving comparable planimetric accuracy (0.85 m vs. 0.82 m RMSE). Relative to LiDAR solutions, our framework achieved superior temporal resolution (<24 h latency) without requiring GPU clusters for real-time processing. Unlike CycleGAN-based domain adaptation (F1-score 74.66%) that struggles with mining-specific topology, OMRoadNet's hybrid adversarial-geometric learning improved the F1-score by 6.11% through explicit road connectivity constraints. However, certain challenges persist. Like [PERSON]'s scribble annotation method, our framework still necessitates initial source-domain labels--a dependency avoided by purely self-supervised approaches like MapGen-GAN.  
Three operational limitations merit attention. First, the 500 \(\times\) 500-pixel input constraints introduced 13.8% boundary discontinuities in full-scene mosaics due to tile-wise prediction averaging. Second, high atmospheric dust levels (PM10 > 800 ug/m\({}^{3}\)) degraded temporal fusion efficacy, requiring 4.5\(\times\) more iterations to stabilize pseudo-labels. Third, while reducing LiDAR dependency, initial cross-domain alignment still demanded specialist validation (3-5 min/km\({}^{2}\)). To address these, future iterations could integrate adaptive tile sizing algorithms and multimodal dust correction models using Sentinel-2 SWIR bands.  
Active learning strategies may further reduce human validation efforts by 68-72% through confidence-based sampling.  
Future research should explore three directions: (1) edge deployment optimization through model pruning and FPGA acceleration to enable on-device processing in resource-limited mines, (2) hyperspectral adaptation leveraging PRISMA or EnMap data to resolve material ambiguities between roads and exposed ones, and (3) cross-continental generalization testing across iron, copper, and bauxite mining regions to establish universal extraction protocols. A promising avenue lies in synthesizing physics-based digital twins of haul roads through OMRoadNet-Sim, combining extracted geometries with discrete element modeling of surface degradation patterns--an integration that could revolutionize autonomous haulage system navigation and predictive maintenance scheduling in the era of Industry 4.0 mining.