## 3 Results and Analysis
### SNR-Based Robustness Verification  
OMRoadNet demonstrated remarkable robustness across diverse SNR conditions, as validated through systematic analysis of 540 test samples grouped by road propor  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **IoU (\%)** \\ \hline no-EMAU & 67.81 & 71.80 & 69.75 & 53.55 \\ no-\(\mathcal{L}_{identity}\) & 87.83 & 65.55 & 75.08 & 60.10 \\ no-UDA & 90.43 & 59.31 & 71.64 & 55.81 \\ no-self-training & 87.11 & 62.43 & 72.74 & 57.15 \\ no-TTUR & 91.48 & 65.67 & 76.45 & 61.88 \\ OMRoadNet & 92.16 & 71.89 & 80.77 & 67.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ablation study results of the open-pit mine road network extraction.  
Figure 6: The ablation study results of the open-pit mine road network extraction.  
tion (Figure 7). The SNR metric, calculated as 10_-log\({}_{10}\)(\(S^{2}\)/\(N^{2}\))_, where \(S\) and \(N\) represent road and background pixels, respectively, effectively captured mining-specific challenges, where haul roads occupied 8-35% of the total image area. Frequency distribution analysis (Figure 7, left) revealed that test data predominantly clustered within 12.2-12.6 dB SNR, representing typical operational scenarios with moderate road density.  
Evaluation metrics exhibited exceptional stability across the full SNR spectrum (8-15 dB), with F1-score fluctuations constrained to \(\pm\)3.2% and IoU variance limited to 6.8%. Notably, precision remained consistently above 90% even under extreme low-SNR conditions (8 dB, road proportion < 10%), where competing models suffered >15% performance degradation due to spectral interference. The morphology-aware augmentation strategy proved critical in handling multi-scale road geometries, with deformable convolution kernels (3-35 m receptive fields) reducing IoU variability by 41.7% compared to fixed-kernel approaches.  
Case studies revealed two key adaptive behaviors: (1) In high-SNR scenarios (>14 dB, road proportion > 30%), the model suppressed false detections between parallel haul routes through directional attention masking, achieving 92.4% precision. (2) Under low-SNR conditions, the exponential moving average unit (EMAU) maintained feature continuity across intermittent road segments, recovering 78.3% of fragmented paths undetected by baseline methods. These capabilities stemmed from OMRoadNet's dual-path spectral-spatial processing, which decoupled material-specific reflectance patterns from invariant topological features through adversarial feature disentanglement.  
The OMRoadNet model proposed in this study has made preliminary explorations in achieving three objectives: centimeter accuracy, attribute extraction, and update time less than 24 h. Due to limitations in remote sensing image resolution, centimeter-level localization and fine-grained road attribute extraction are currently not achievable. However, through structural constraints and a high-confidence pseudo-label strategy, the model has demonstrated good performance in pixel-level accuracy and structural reconstruction. In a standard GPU environment, the model can complete a full training and update cycle within 8 h, laying the foundation for a high-frequency automatic extraction mechanism.