## 4 Conclusions  
In this paper, a Multi-spectral image-guided fusion of Spatial and Channel Features for road extraction algorithm (SC-FMNet) is proposed. The SCConv module is first introduced in the encoding part in both branches, respectively, which exhibits higher accuracy of road extraction as well as reduced computational complexity. Then, the SAFMM module is introduced in the decoding structure to improve the image quality by spatially localizing the image features for adaptive adjustment. And the network SC-FMNet in this paper is compared with several network models for experiments on the GF2-FC road dataset and the CHN6-CUG road dataset, respectively. The experimental results show that SC-FMNet exhibits good extraction results when performing the road extraction task.  
In summary, applying deep learning to road information extraction from high-resolution remote sensing images can significantly improve the recognition and understanding of road information using the intelligent interpretation system. However, its drawbacks and limitations should not be ignored, so it is necessary to continuously explore and optimize the structure and algorithms of the deep learning model to improve its performance and adaptability in practical applications. In the future, we will continue to study the use of Multi-spectral images to improve the effect of road extraction from remote sensing images.  
Conceptualization, L.G.; Methodology, Y.Z.; Writing--original draft, L.G. and A.J. Foundation, L.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by Liaoning Provincial Department of Education Youth Project, [PERSON] (NO.1030040000560), Liaoning Province Applied Basic Research Program (YouthSpecial Project, 2023 JH2/101600038), Shenyang Youth Science and Technology Innovation Talent Sup-port Program (RC220458),Basic Research Special Funds for Undergraduate Universities in Liaoning Province (Guangxuan Program of Shenyang Ligong University (SYLUGXRC202216)), and Basic Research Special Funds for Undergraduate Universities in Liaoning Province (LJ212410144067). The data presented in this study are openly available in GF2-FC road dataset; CHN6-CUG road dataset at [[http://cugurs5477.mikecrm.com/ZtMn5](http://cugurs5477.mikecrm.com/ZtMn5) tR]([http://cugurs5477.mikecrm.com/ZtMn5](http://cugurs5477.mikecrm.com/ZtMn5) tR) (accessed on 2 February 2025), references number [25,26]. Thanks to all the editors and reviewers. The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline A2 FPN & 95.38 & 77.22 & 59.20 & 63.25 & 55.29 \\ BANet & 94.99 & 54.03 & 52.81 & 58.13 & 47.98 \\ DCSwin & 93.77 & 69.39 & 42.50 & 52.71 & 45.79 \\ MANet & 94.11 & 72.98 & 44.19 & 55.05 & 49.20 \\ UNetFormer & 93.10 & 72.39 & 45.11 & 55.58 & 48.49 \\ ABCNet & 94.04 & 70.18 & 47.03 & 56.32 & 40.98 \\ MSNet & 95.46 & 78.83 & 62.91 & 66.93 & 56.00 \\ SC-FMNet & **96.16** & **79.60** & **66.86** & **72.61** & **63.10** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiments of multiple models in the CHN6-CUG dataset.