## 3 Results and Analysis
### Results and Comparison  
(1)GF2-FC road dataset  
As shown in Figure 9, some of the prediction results of the model trained using the GF2-FC dataset in multiple comparison networks are demonstrated. The figure shows, in order, the original high-resolution remote sensing image, the corresponding labeled maps, and the prediction result maps of A2 FPN, BANet, DCSwin, MANet, UNetFormer, ABCNet, and the SC-FMNet network proposed in this chapter, respectively. As can be seen in Figure 9, both in simple and complex backgrounds, the results extracted by the network in Figure 9d,e,h have more isolated points, and the breakage of the road is more obvious. In the process of road segmentation, it is easy to be interfered with by the nearby buildings, which leads to the segmentation result that fails to meet the expected segmentation target. The network model shown in Figure 9c has improved in the accuracy of road segmentation, and the road segmentation results are more accurate, but the phenomenon of breaks and burrs of roads still occurs. Figure 9f,g have higher extraction accuracy in simple backgrounds, but in complex and dense areas, the extracted roads are not smooth, and in the prediction results, it is observed that the integrity of some roads is impaired, as well as the existence of problems such as misrecognition and the omission of recognition, which affect the extraction results. Comparative analysis shows that Figure 9j is the network proposed in this paper, both in simple background and complex background; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline MSNet & 95.46 & 78.83 & 62.91 & 66.93 & 56.00 \\ MSNet + SAFM & 96.08 & 79.44 & 65.38 & 71.80 & 57.00 \\ MSNet + SCConv & 95.54 & 76.98 & 62.96 & 69.37 & 56.25 \\ SC-FMNet & **96.16** & **79.60** & **66.86** & **72.61** & **63.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the ablation experiments were compared in the CHN6-CUG dataset. Figure 8: The ablation effect was extracted from the CHN6-CUG dataset. it can be seen that Figure 9 is not easy to be interfered with by nearby buildings. On the basis of Figure 9, the extracted road information can be more continuous, and at the same time, it can also reduce the situation of misidentification and the omission of identification, which improves the model's robustness as well as the accuracy of the extraction of roads. This section focuses on the comparison of experimental segmentation results using the network model SC-FMNet proposed in this paper with other network models on the GF2-FC road dataset. The experimental results of the commonly used evaluation metrics are shown in Table 3.  
As shown in Table 3, in comparison with the other models selected, the network SC-FMNet proposed in this paper achieves the optimal results in all the metrics, with an OA of 98.57%, a precision of 80.74%, a recall of 72.47%, an F1-score of 76.01%, as well as an IOU of 61.30%. Compared with other methods, the evaluation indexes are all improved. Combined with the prediction result plots in Figure 9, it can be concluded that the network \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline A2 FPN [26] & 94.81 & 79.62 & 61.83 & 70.03 & 53.89 \\ BANet [27] & 93.96 & 79.03 & 51.05 & 62.21 & 45.15 \\ DCSwin [28] & 93.80 & 77.46 & 51.36 & 61.77 & 44.68 \\ MANet [29] & 94.77 & 78.62 & 58.56 & 69.45 & 53.20 \\ UNetFormer [30] & 94.40 & 72.46 & 57.37 & 66.62 & 49.95 \\ ABCNet [31] & 93.71 & 76.74 & 50.82 & 61.15 & 44.04 \\ MSNet & 94.84 & 79.91 & 63.12 & 70.18 & 54.06 \\ A2 FPN [26] & **98.57** & **80.74** & **72.47** & **76.01** & **61.30** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison experiment of multi-model in the GF2-FC dataset. Figure 9: Comparison chart of different network test results in the GF2-FC dataset.