## 3 Experiments and Results
### Ablation Study
U-Net with ConvMixer: In juxtaposition with the baseline, this configuration showcases evident enhancements in road continuity and integrity. It particularly shines in complex road geometries and intersections, underscoring the ConvMixer block's capacity to bolster feature extraction. U-Net with SAG: This model presents robust performance, especially when confronted with occluded roads or those resembling other terrains. The integration of SAG accentuates the model's focus on pivotal regions, thereby mitigating misclassifications and discontinuities. MixerNet-SAGA: This embodies our holistic model. Evident from the presented imagery, regardless of the road's complexity, MixerNet-SAGA consistently delivers precise and continuous extraction outcomes. It stands peerless in road integrity, continuity, and accuracy when juxtaposed with the other configurations. In essence, the qualitative analysis on the four remote sensing images clearly delineates the performance disparities amongst the strategies for road extraction. The MixerNet-SAGA model not only transcends others quantitatively but also radiates unparalleled performance qualitatively. In this section, we del \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Massachusetts Road Dataset**} & \multicolumn{3}{c}{**DeepGlobe Road Dataset**} \\ \cline{3-8}  & & **Precision** & **ReCall** & **IoU** & **Precision** & **ReCall** & **IoU** \\ \hline a & U-Net & 77.62 & 81.67 & 76.85 & 82.59 & 83.67 & 74.63 \\ \hline b & U-Net+ConvMixer & 81.87 & 83.26 & 77.85 & 87.28 & 89.3 & 80.66 \\ \hline c & U-Net+SAG & 80.11 & 81.65 & 77.322 & 86.64 & 88.32 & 80.28 \\ \hline d & MixerNet-SAGA & 82.62 & 84.41 & 78.45 & 87.81 & 89.26 & 81.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics for the four ablation experiment configurations on the Massachusetts road dataset and the DeepGlobe road dataset. the comparative performances of the MixerNet-SAGA model against other state-of-the-art models across two significant remote sensing image datasets. Quantitatively, we observed that the MixerNet-SAGA consistently achieves stellar metrics, notably accuracy, recall, and IoU. Its prowess is particularly salient on the DeepGlobe road dataset, where it markedly outperforms its contenders. Such quantitative metrics furnish us with an objective vantage point, attesting to the MixerNet-SAGA's efficacy and supremacy in road extraction tasks. Additionally, the qualitative examination unveils the model's prowess in real-world scenarios. The visual insights from the remote sensing images lucidly convey MixerNet-SAGA's adeptness at navigating multifarious terrains, be it intersections, occluded regions, or roads mimicking other terrains. Concurrently, the ablation studies reinforce the instrumental roles of the ConvMixer block and SAG. Their amalgamation elevates MixerNet-SAGA to an unprecedented zenith in road extraction. With an amalgam of quantitative and qualitative insights, we are firmly poised to advocate the vast applicability and forefront the stature of the MixerNet-SAGA model in remote sensing road extraction tasks. Figure 7: Extraction results of ablation experiments on remote sensing images. The letters under the image correspond to the experimental scheme in Table 3. Among them, Image represents the original image, a represents the extraction result of the baseline model U-Net network, b represents the extraction result of U-Net network plus ConvMixer block model, c represents the extraction result of U-Net network plus SAG block model, d represents The model extraction results of this paper.