Parallel advancements in unsupervised domain mapping, exemplified by [PERSON]'s MapGen-GAN [22], attained 0.68 SSIM (structural similarity index measure) in cross-sensor translation tasks, yet revealed critical performance disparities: structured urban roads maintained 0.81 SSIM, versus 0.52 SSIM for mining roads in identical experiments. This performance chasm stems from three mining-specific challenges: (1) geometric heterogeneity (road width variance: 8-35 m vs. urban 3-5 m), (2) spectral ambiguity between haul roads and exposed ore bodies (NDVI difference < 0.15 [23]), and (3) dynamic surface texture changes (daily reflectance variation > 22%). [PERSON]'s RATT-UNet [24] partially addressed these issues through hybrid attention mechanisms, achieving a 0.8376 F1-score on mining roads. However, its structural heterogeneity index (0.9345) remained 18.7% lower than urban benchmarks, underscoring persistent challenges in mining road topology modeling. The integration of deep learning [25; 26] and remote sensing has predominantly advanced structured road extraction [27; 28], with most studies focusing on urban networks where standardized geometries (e.g., 3-5 m width consistency) and stable spectral profiles (e.g., asphalt reflectance characteristics) enable reliable model training. In contrast, open-pit mine roads remain an understudied domain due to their inherent geospatial complexities [29]. Three critical challenges hinder progress: (1) morphological irregularity, characterized by nonlinear road patterns and extreme width variations, (2) spectral ambiguity caused by diverse mineral compositions and surface materials, and (3) dynamic topology driven by frequent operational adjustments. Compounding these issues, manual annotation of mining roads demands significantly higher time and expertise compared to urban road labeling, while domain shifts degrade model performance when transferring urban-trained models to mining environments. To address these challenges, we propose OMRoadNet, an unsupervised domain adaptation (UDA) framework tailored for mining road extraction. The architecture integrates three core innovations: (1) a self-training mechanism leveraging multi-temporal very-high-resolution (VHR) imagery to iteratively refine pseudo-labels [30], (2) an attention-based feature disentanglement module designed to isolate material-invariant road characteristics, and (3) morphology-aware augmentation simulating mining-specific road deformations. By dynamically adapting to spectral and topological variations without target-domain annotations, OMRoadNet bridges the gap between structured urban datasets and unstructured mining environments. This work advances intelligent mining through three key dimensions. Theoretically, it establishes a cross-domain adaptation framework addressing the coupled spectral-morphological challenges unique to mining roads. Technologically, it introduces a novel attention mechanism to enhance feature robustness under complex mining conditions. Practically, the framework demonstrates compatibility with industrial geographic information systems (GISs), providing scalable solutions for autonomous mining operations. These innovations collectively address the critical need for efficient, annotation-free road extraction in dynamic mining environments.