## 2 Dataset and Methodology  
### Study Areas and Data Preparation  
OMRoadNet was developed as an unsupervised domain adaptation (UDA) framework to transfer road segmentation knowledge from labeled urban datasets to unlabeled open-pit mine environments. The framework utilizes two distinct data domains:  
1. Source Domain: The DeepGlobe Road Extraction dataset [31], comprising 8000 satellite images (1024 \(\times\) 1024 pixels, 0.5 m resolution) with pixel-level road masks. These images cover structured urban and rural roads in Thailand, Indonesia, and India, characterized by regular layouts, clear edges, and contextual features like adjacent vegetation.
2. Target Domain: Unlabeled VHR imagery of open-pit mines, divided into two subsets: 1. Subset X: 2700 high-resolution images (cropped to 500 \(\times\) 500 pixels from the original 3840 \(\times\) 2160 scenes) sourced from Google Earth, capturing diverse mining operations. The study area covers typical open-pit mining areas in multiple regions around the world, including coal mines, iron mines, and copper mines in Australia, South Africa, Chile, and other locations. These regions feature significant topographical variations and complex road structures, reflecting the diverse characteristics of open-pit mine roads under different climatic zones and geological conditions, combining both universality and challenges. Open-pit mine roads in remote sensing images usually show irregular structures, blurred boundaries, and complex spectral changes. Road characteristics are significantly affected by differences in mineral types and operational disturbances.
2. Subset Y: 2700 randomly selected road masks from DeepGlobe, resized to 500 \(\times\) 500 pixels. These masks serve as initial pseudo-labels [32] but lack spatial correspondence to Subset X.  
The dataset was partitioned into 80% training data (2160 image-mask pairs) and 20% testing data (540 pairs). Key domain discrepancies arose from fundamental differences in road characteristics:  
Structural Complexity: Urban roads exhibited uniform width (3-5 m) and linear patterns, whereas mining roads displayed irregular branching layouts influenced by dynamic excavation activities.  
Spectral Variability: Open-pit mine roads showed significant color/texture variations due to mineral composition (e.g., coal vs. iron ore) and surface dust accumulation, unlike the stable asphalt spectra of urban roads.  
Annotation Challenges: Manual labeling of mining roads was labor-intensive and error-prone, as ambiguous boundaries and frequent route updates complicated precise annotation.  
To address these challenges, a self-training strategy was implemented. Initially, pseudo-labels were generated by applying the source-pretrained model to Subset X, retaining only high-confidence predictions (threshold: 0.85). Subset Y was iteratively updated by replacing low-quality masks with refined pseudo-labels through training cycles. This process enhanced the model's ability to adapt to mining-specific features, such as irregular geometries and spectral heterogeneity, while reducing reliance on manual annotations [33]. Geometric constraints (e.g., road connectivity checks) were applied to ensure pseudo-label consistency. Figure 1 illustrates representative samples from both subsets, highlighting the structural and spectral contrasts between urban and mining road networks.