## 3 Experiments and Results
### Results and Analysis of Datasets
#### 3.3.2 Results and Analysis of DeepGlobe Road Dataset  
Similarly, on the DeepGlobe road dataset, we used three key indicators, namely, precision, recall, and IoU, to evaluate the performance of each model. The accuracy results of each model are shown in Table 2.  
In an exhaustive quantitative evaluation on the DeepGlobe road dataset, MixerNet-SAGA and seven other leading deep learning models were examined. Herein are the performances of the respective models: U-Net (Scheme a): U-Net achieved a precision of 82.59%, a recall of 83.67%, and an intersection over union (IoU) of 74.63%. While U-Net \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Model** & **Precision** & **Recall** & **IoU** \\ \hline a & U-Net & 82.59 & 83.67 & 74.63 \\ \hline b & HRNet & 81.67 & 83.39 & 74.23 \\ \hline c & ResNet & 83.92 & 83.87 & 75.96 \\ \hline d & ResUnet & 84.12 & 84.08 & 77.81 \\ \hline e & DeepLabV3 & 85.47 & 85.35 & 78.13 \\ \hline f & RADANet & 86.8 & 88.2 & 79.7 \\ \hline g & SDUNet & 87.2 & 88.8 & 80.1 \\ \hline h & MixerNet-SAGA (Ours) & 87.81 & 89.26 & 81.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy evaluation of eight models on the DeepGlobe road dataset, where a represents U-Net network, b represents HRNet network, c represents ResNet network, d represents ResNet network, e represents DeepLabV3 network, f represents RADANet, g represents SDUNet, and h represents our model, the MixerNet-SAGA. displayed consistent performances across various tasks, it was slightly outperformed by certain models on this specific dataset. HRNet (Scheme b): HRNet registered a precision of 81.67%, a recall of 83.39%, and an IoU of 74.23%. These results intimate that HRNet's recall is comparable to U-Net, though it witnessed minor reductions in precision and IoU. ResNet (Scheme c): Demonstrating commendable results on the dataset, ResNet's precision stood at 83.92%, with a recall of 83.87% and an IoU of 75.96%. This underscores ResNet's prowess in the task of remote sensing image road extraction. ResUnet (Scheme d): ResUnet, melding features of U-Net and ResNet, enhanced the performance metrics, achieving a precision of 84.12%, a recall of 84.08%, and an IoU of 77.81%. DeepLabV3 (Scheme e): DeepLabV3 recorded a precision of 85.47%, a recall of 85.35%, and an IoU of 78.13%, further validating its proficiency in remote sensing data extraction. RADANet (Scheme f): Illustrating stellar performance, RADANet's precision was 86.8%, with a recall of 88.2% and an IoU of 79.7%, revealing its efficacy and precision in intricate scenarios. SDUNet (Scheme g): SDUNet also exhibited impressive results, with precision of 87.2%, recall of 88.8%, and an IoU of 80.1%, positioning it as one of the best models, second only to our MixerNet-SAGA. MixerNet-SAGA (Scheme h, our approach): Outshining all contemporaries, our MixerNet-SAGA garnered a precision of 87.81%, a recall of 89.26%, and an IoU of 81.02%. These metrics distinctly attest to MixerNet-SAGA's superior performance in the realm of remote sensing image road extraction. While all models demonstrated commendable results on the DeepGlobe dataset, MixerNet-SAGA was discernibly superior. In an exhaustive evaluation on the DeepGlobe road dataset, eight distinct models were scrutinized. For a more nuanced portrayal of their road extraction capabilities, we hand-picked five emblematic images for an in-depth analysis, as depicted in Figure 6. Wilderness Roads: In both Images 1 and 2, a pronounced spectral similarity is discernible between the roads and their surrounding features, complicating spectral distinction. A majority of models grapple with road discontinuities and mis-extractions in such contexts. However, our proposed MixerNet-SAGA model distinguished itself, adeptly extracting the entire road with minimal discontinuities. Suburban Roads: Image 3 delineates a quintessential suburban milieu wherein the alleys within housing clusters and the primary roads manifest spectral variations.