## 6 Conclusions and Future Works
### Limitations and Future Works  
While this study provides a valuable benchmark, several limitations and avenues for future research must be acknowledged.  
A key limitation of this study is its reliance on high-resolution optical imagery which is susceptible to performance degradation from low-resolution data and adverse weather conditions such as cloud cover. As such, the performance of the proposed models is expected to degrade when fine-grained damage indicators such as surface cracks or small debris may become undetectable.  
Based on the underlying finding, the most critical area for future research is directly addressing the class imbalance and low recall for damage detection. Future work should prioritize the implementation of advanced techniques such as weighted loss functions (e.g., focal loss) or sophisticated data sampling strategies to force the model to learn the features of rare 'damaged' instances more effectively.  
Further enhancements can be achieved by exploring more advanced model architectures and feature fusion strategies. Future work will include a comprehensive ablation study to dissect the contributions of the model components, such as systematically evaluating different feature fusion mechanisms (e.g., concatenation, attention) for the Siamese architecture. Furthermore, this study plans to compare the U-Net backbone against other state-of-the-art segmentation architectures and integrate next-generation change detection backbones (e.g., SChanger, ChangeMamba) to further validate and potentially improve the framework.  
Another avenue for improvement is the fusion of multimodal data. Combining optical imagery with other sensors, such as Synthetic Aperture Radar (SAR) or LiDAR, could create a robuster all-weather disaster response solution. The development of large-scale multimodal datasets such as BRIGHT signifies a growing trend toward this direction, which will be a key focus of subsequent research [49]. Additionally, a quantitative analysis of the computational costs, including training and inference times for each model, was beyond the scope of this study but represents an important direction for future work to provide practitioners with a comprehensive performance-efficiency trade-off analysis.  
Finally, to improve real-world applicability, future work should incorporate post-processing techniques such as road connectivity analysis to identify completely isolated areas and damage length quantification to better estimate repair costs and timelines.  
Through these enhancements, the proposed framework can evolve into an automated and scalable road damage assessment system, serving as a core infrastructure for urban disaster response planning.  
Conceptualization, S.L. and H.-K.K.; methodology, S.L. and H.-K.K.; software, S.L.; validation, J.C., S.L. and H.-K.K.; formal analysis, J.C. and S.L.; investigation, J.C. and S.L.; writing--original draft preparation, J.C. and H.-K.K.; visualization, S.L.; supervision, H.-K.K.; project administration, H.-K.K.; funding acquisition, H.-K.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Dong-A University in the Republic of Korea.  
Not applicable.  
The source code, which includes all configurations, hyperparameters, and the specific random seeds used for each experimental run, is available at [[https://github.com/seunghyeokleeme/xBD_road_damage_assessment](https://github.com/seunghyeokleeme/xBD_road_damage_assessment)]([https://github.com/seunghyeokleeme/xBD_road_damage_assessment](https://github.com/seunghyeokleeme/xBD_road_damage_assessment)), accessed on 29 June 2025. The curated road mask dataset generated through the hybrid labeling strategy is publicly available via the same repository. Details on access and usage are provided in the README file. For further inquiries, contact the corresponding author.  
The authors declare no competing interests.