## 5 Conclusions  
This study presented OMRoadNet, a novel self-training-based UDA framework that achieved state-of-the-art performance in open-pit mine road extraction through three key innovations. First, the bidirectional cyclic translation architecture with parametric sharing enabled 92.4% structural consistency preservation during urban-to-mining domain adaptation, resolving feature dissociation challenges through material-invariant attention mechanisms. Second, the dynamic self-training paradigm reduced annotation dependency by 94.6% while maintaining 92.16% precision and 67.75% IoU through confidence-guided label refinement and geometric verification. Third, morphology-aware augmentation with deformable kernels (3-35 m adaptive receptive fields) ensured robust performance across extreme width variations (8-35 m roads), demonstrating <6.8% IoU fluctuation under diverse SNR conditions (8-15 dB).  
The framework established critical advantages over conventional solutions: (1) 78% reduction in per-kilometer operational costs compared to LiDAR alternatives, (2) 23.7% higher domain similarity scores than baseline UDA methods (FID metrics), and (3) compatibility with industrial GIS platforms through sub-meter geolocation accuracy (0.85 m RMSE). Field validations confirmed practical efficacy in transient mining environments, recovering 68.9% of occluded road segments through multi-temporal fusion and maintaining a 80.77% F1-score under high dust interference (PM\({}_{10}\geq\) 800 \(\upmu\)g/m\({}^{3}\)).  
Current limitations of the framework include tile-wise prediction discontinuities, sensitivity to spectral noise, and the need for manual calibration. Specifically, fixed-size inputs (500 \(\times\) 500 pixels) resulted in 13.8% boundary discontinuities during scene mosaicking, though mitigated via overlapping and blending. High dust levels (PM\({}_{10}\) > 800 \(\upmu\)g/m\({}^{3}\)) reduced temporal fusion stability, requiring more iterations to converge. Manual cross-sensor calibration and pseudo-label verification still took 12-15 min/km\({}^{2}\), though this involved only lightweight validation, not annotation. Prospective research directions include FPGA-accelerated edge deployment for real-time processing, hyperspectral material disambiguation using PRISMA data, and digital twin integration for autonomous haulage system navigation. These advancements promise to revolutionize geospatial intelligence frameworks in smart mining operations, establishing OMRoadNet as a foundational technology for next-generation mine infrastructure digitization.  
Conceptualization, S.T., Z.R., X.X. and Z.H.; funding acquisition, X.X. and Z.H.; methodology, S.T., Z.R., X.X. and Z.H.; project administration, X.X.; supervision, X.X.; validation, Z.R., Z.H. and W.L.; visualization, W.L., Z.L. and Y.S.; writing--original draft, S.T., Z.R. and Z.H.; writing--review and editing, Z.H., W.L., Z.L. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by the Fundamental Research Funds for the Central Universities under grant number 2023 QN1056.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank the editor, assistant editor, and anonymous reviewers for their careful reviews and insightful remarks.  
**Conflicts of Interest:** The authors declare no conflict of interest.