## 2 Dataset and Methodology
### Methodology
#### 2.2.1 Framework Overview  
1. Cyclic GAN Architecture  
OMRoadNet adopts a dual-path adversarial architecture rooted in CycleGAN principles [34], establishing bidirectional translation pathways between mining VHR imagery and road masks. The forward cycle (\(X\)\(\rightarrow\)\(Y\)\(\rightarrow\)\(X^{\prime}\)) employs generator G to transform raw mining scenes into road predictions, while generator F reconstructs the original spatial features from these pseudo-labels. Conversely, the backward cycle (\(Y\)\(\rightarrow\)\(X\)\(\rightarrow\)\(Y^{\prime}\)) synthesizes mining-like imagery from initial mask templates and iteratively refines label predictions. Paired adversarial discriminators (\(D_{X}\) for mining domain validation and \(D_{Y}\) for road domain verification) enforce cross-domain consistency through competitive learning, effectively resolving the unpaired data dilemma inherent in mining road extraction. This cyclic mechanism preserves 92.4% of critical topological features across translation cycles, as quantified by structural similarity index (SSIM) evaluations on validation datasets.
2. Cross-Domain Adaptation Strategy  
The framework implements a phased domain adaptation protocol to bridge urban-to-mining feature disparities. Initialization begins with source pre-training on the DeepGlobe dataset, where generators learn universal road patterns through supervised learning. Domain bridging then activates adversarial dynamics between generators and discriminators, incorporating spectral normalization in discriminators to stabilize gradient propagation and gradient reversal layers for feature space alignment. Final mining-specific tuning employs progressive histogram matching to reconcile spectral disparities between ore-rich surfaces and urban asphalt, coupled with morphology-aware dropout that selectively obscures 15-20% of road pixels to simulate real-world discontinuities. This phased approach achieves 23.7% higher domain similarity scores compared to baseline UDA methods, as measured by Frechet inception distance (FID) metrics.
3. Self-Training Mechanism  
A dynamic self-training loop integrates adversarial learning with pseudo-label evolution, addressing annotation scarcity in mining domains. High-confidence predictions (probability threshold: 0.85) from iterative inference replace initial DeepGlobe masks in Subset Y, with spatial voting across 5 \(\times\) 5 neighboring patches suppressing 68.9% of isolated false positives. Geometric verification modules enforce mining-specific topology constraints: 8-neighborhood connectivity checks rectify fragmented road segments, adaptive kernel resizing (3-35 m) accommodates width variations, and angular validation (15-75\({}^{\circ}\) tolerance) maintains plausible branch configurations. This self-supervised paradigm demonstrates 14.2% higher label consistency across training epochs compared to static pseudo-labeling approaches, effectively balancing feature preservation and domain adaptation.  
The tripartite architecture synergizes cyclic adversarial learning with geometric-constrained self-training, achieving a 0.817 cross-domain correlation coefficient while reducing annotation dependency by 94.6% compared to fully supervised baselines.