## 5 Conclusions  
This paper begins by conducting a thorough examination of existing datasets and summarizing the limitations observed in experimental results from current methodologies. Building on this foundation, we propose the Parallel CNN-Transformer Encoder-Decoder Model.  
In our method, the CEM leverages locality and translation equivariance to allow neighboring pixels to contribute to target pixel classification, enhancing consistent feature recognition and local feature extraction. The TEM integrates global context via attention mechanisms without stacking layers, enabling whole-image feature assistance in pixel classification. Multi-head attention optimizes this process, reducing computation time and increasing efficiency. To maximize CEM and TEM integration, we adopt a parallel structure instead of the common serial pipeline. This approach mitigates each module's limitations during feature learning, improving real-time feature extraction and overall robustness. For road segmentation, the LCM adjusts pixel importance within regions, ensuring continuous segmentation and complete edges. Compared with traditional stacked convolution kernels, parallel use of linear convolution in multiple directions achieves a larger receiving field, is more suitable for road shapes, and reduces parameter and computational complexity.  
Experiments on the German Street dataset and Massachusetts Roads dataset, featuring diverse urban, suburban, and rural scenes, demonstrate our method's superior accuracy in distinguishing roads from backgrounds with enhanced completeness and continuity. This robust performance facilitates rapid and efficient road network extraction from satellite imagery, potentially reducing manual labor costs and accelerating follow-up tasks.  
In summary, our method significantly improves road extraction accuracy, serving as a valuable reference for existing research. Future work will focus on advancing high-resolution remote sensing road extraction.  
Conceptualization, X.G. and L.G.; methodology, X.G. and L.G.; validation, X.G., L.G. and F.H.; formal analysis, L.G.; investigation, X.G.; resources, S.R. and C.F.; data curation, X.G., L.G. and F.H.; writing--original draft preparation, X.G. and L.G.; writing--review and editing, F.H.; visualization, H.Q.; supervision, S.R. and C.F.; project administration, H.Q. and C.F.; funding acquisition, C.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Defense Science and Technology Outstanding Youth Science Fund (2021-JCJCQ-ZQ-017).  
Not applicable.  
Data Availability Statement: The datasets used in this study are publicly available and can be accessed as follows: (1) The German-Street dataset: [[https://zenodo.org/records/1154821?utm_source](https://zenodo.org/records/1154821?utm_source)]([https://zenodo.org/records/1154821?utm_source](https://zenodo.org/records/1154821?utm_source)); (accessed on 31 October 2024) (2) The Massachusetts Roads dataset: [[https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)]([https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)) (accessed on 31 October 2024).  
The authors would like to express their gratitude for the valuable feedback and suggestions provided by all the anonymous reviewers and the editorial team.  
The authors declare no conflicts of interest.