## 3 Experiments and Results
### Results and Analysis of Datasets
#### 3.3.1 Results and Analysis of the Massachusetts Road Dataset  
On the Massachusetts road dataset, we employed three pivotal metrics--precision, recall, and intersection over union (IoU)--to evaluate the performance of each model. The precision outcomes of the models are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Model** & **Precision** & **Recall** & **IoU** \\ \hline a & U-Net & 77.62 & 81.67 & 76.85 \\ \hline b & HRNet & 77.16 & 83.41 & 77.15 \\ \hline c & ResNet & 78.25 & 82.97 & 77.57 \\ \hline d & ResUnet & 79.11 & 83.35 & 77.89 \\ \hline e & DeepLabV3 & 79.58 & 83.76 & 77.93 \\ \hline f & RADANet & 80.9 & 83.81 & 78.01 \\ \hline g & SDUNet & 81.5 & 83.6 & 78.24 \\ \hline h & MixerNet-SAGA(Ours) & 82.62 & 84.41 & 78.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy evaluation of eight models on the Massachusetts road dataset, where a represents U-Net network, b represents HRNet network, c represents ResNet network, d represents Resume network, e represents DeepLabV3 network, f represents RADANet, g represents SDUNet, and h represents the model MixerNet-SAGA in this paper. Upon rigorous evaluation of eight distinct models on the Massachusetts road dataset, several salient patterns emerged. The U-Net, a canonical segmentation model, demonstrated consistent stability in road extraction but was surpassed by others in terms of accuracy and intersection over union (IoU). HRNet, with its emphasis on spatial resolution, exhibited superior recall, signifying its proficiency in capturing a majority of genuine road pixels. ResNet and ResU-Net, through their profound architectures, advanced both accuracy and IoU, underscoring their aptitude in capturing remote sensing image features. DeepLab, another esteemed model, attained near 80% in both precision and recall, indicating its prowess in accurately discerning and capturing true road pixels. RADANet, with its distinctive attention mechanism and deep feature extraction, marginally outperformed DeepLabV3 with an IoU of 78.01%. While SDUNet enhanced precision to 81.5%, its IoU was comparable to that of RADANet. Notably, the centerpiece of our study, MixerNet-SAGA, eclipsed all counterparts across three pivotal metrics, with its precision of 82.62% and IoU of 78.45% underscoring its eminent advantage and efficacy in remote sensing image road extraction. Delving deeper into qualitative analyses on the Massachusetts road dataset, we inspected the performances of eight models across diverse scenarios, encompassing U-Net (a), HRNet (b), ResNet (c), ResUnet (d), DeepLabV3 (e), RADANet (f), SDUNet (g), and the proposed MixerNet-SAGA (h), as visualized in Figure 5.  
Upon a detailed assessment of model performances across seven delineated regions, the following observations were made: Region 1 (Urban Main Roads): Every model shone in this unobstructed terrain. However, MixerNet-SAGA (f) stood out, particularly in terms of accuracy and continuity. Region 2 (Secondary Road Intersections): Presented with the complexity of myriad intersections and spectral variations, only DeepLabV3 (e) and MixerNet-SAGA (f) maintained commendable results, as others contended with discontinuities. Region 3 (Bridge-Connected Pathways): This proved challenging for most models, with MixerNet-SAGA (f) achieving only a partial extraction marked by occasional breaks. Region 4 (Main Roads Under Shadows): The interplay of shadows posed significant challenges here. Yet, MixerNet-SAGA (f) managed a seamless road extraction, unlike its  
Figure 5: The qualitative extraction results of eight models on the Massachusetts road dataset, where a, b, c, d, e, f, g, and h correspond to the eight methods used in the article, and 1 to 7 represent those we selected from 7 different areas, using an elliptical marquee to mark the different detailed areas in the area. counterparts. Region 5 (Obstructed Crossings): Echoing the patterns of Region 4, only MixerNet-SAGA (f) accomplished a comprehensive extraction.