## 4 Experimental Design
### Dataset Introduction and Labeling  
The primary dataset used in this study is xBD, consisting of a total of 9330 high-resolution satellite image pairs (i.e., pre- and post-disaster). Individual images were resized to 1024 \(\times\) 1024 resolution. The dataset is split into training, validation, and test sets at a 7.0:1.5:1.5 ratio, that is, 5598 images, 1866 images, and 1866 images, respectively. In addition, pre-processing includes three steps such as radiometric normalization to standardize brightness levels, histogram matching to improve temporal consistency between image pairs. To prepare the images for the model input, each original 1024 \(\times\) 1024 image was divided into four non-overlapping 512 \(\times\) 512 patches via cropping. To address the challenge of non-disaster-related changes (e.g., road repaving) occurring between image acquisition dates, a specific labeling methodology was adopted. For each post-disaster image, the corresponding ground-truth mask was created by starting with the pre-disaster road mask as a template. Annotators then specifically marked only the sections that showed clear evidence of disaster-induced damage. Consequently, a repaved but structurally intact road would appear different in the imagery but remain labeled as 'undamaged' in the ground truth of this study. This process implicitly teaches the model to associate only labeled structural changes with the 'damaged' class while learning to tolerate mere appearance variations. While this strategy mitigates the issue, it is acknowledged that extreme non-disaster changes could still present a challenge, which remains a topic for future investigation.  
Hybrid Labeling Strategy for Ground-Truth Generation  
As the xBD dataset lacks official road annotations, a hybrid labeling strategy was developed to generate high-quality ground-truth masks. This approach consists of three sequential steps:  
1. Baseline Generation via Transfer Learning: A U-Net segmentation model was first trained using the DeepGlobe Road Extraction dataset, which provides manually annotated, high-fidelity road masks. This pre-trained model was then applied to the pre-disaster images from the xBD dataset to infer baseline road labels.
2. Semi-Automated Labeling Pipeline: Concurrently, a semi-automated labeling process was implemented, combining YOLOv9 for initial road detection and the Segment Anything Model (SAM) for pixel-level refinement. This enabled large-scale label generation across xBD imagery.
3. Hybrid Validation and Quality Enhancement: To ensure label quality, the outputs from both pipelines were compared. Cases with significant discrepancies were flagged for manual expert review, wherein the label was either corrected manually or selected based on fidelity (e.g., choosing the DeepGlobe-informed mask when SAM results were noisy). This hybrid pipeline allowed us to construct a reliable, large-scale road mask dataset for training damage detection models while minimizing annotation noise.  
The final ground-truth masks generated through this process consist of three distinct classes at the pixel level: background (class 0), undamaged road (class 1), and damaged road (class 2). This three-class definition serves as the foundational ground truth for the training and evaluation of all models presented in this study.