## 3 Experiments and Results
### Ablation Study  
In the context of this investigation, we sought to discern the relative contributions of various components within the MixerNet-SAGA model. As such, a structured series of ablation experiments was devised. The detailed experimental design and protocols are elucidated below:  
1. Baseline Model: A streamlined version of the U-Net architecture was selected as the starting point for our experiments. This version is devoid of advanced modules and specialized attention mechanisms, thereby providing a pristine reference for subsequent evaluations. 2. Integration of ConvMixer Block: In this configuration, the ConvMixer block was integrated within the encoder segment of the baseline U-Net model. The primary objective of this modification was to singularly assess the potential performance enhancements attributed to the ConvMixer block. 3. Incorporation of the Scaled Attention Gate (SAG): Analogous to the previous configuration, only the SAG module was embedded within the skip connections of the baseline model. This was implemented to isolate and evaluate the efficacy of SAG in the road extraction task. 4. ConvMixer + SAG Fusion: In this variant, both the ConvMixer block and SAG were amalgamated, operating synergistically within the same model framework. Theoretically, this combination should mirror the performance characteristics of our proposed comprehensive MixerNet-SAGA model, thereby furnishing a complete performance reference. Figure 6: Extraction results of eight models on the DeepGlobe road dataset, where a stands for U-Net network, b stands for HRNet network, c stands for ResNet network, d stands for ResUnet network, e stands for DeepLabV3 network, f stands for RADANet, g stands for SDUNet, and h stands for our model, the MixerNet-SAGA. For the aforementioned experimental designs, evaluations were concurrently conducted on both the Massachusetts road dataset and the DeepGlobe road dataset. The quantitative outcomes are encapsulated in Table 3.  
We embarked on an extensive ablation analysis, evaluating the U-Net and its distinct variants on the Massachusetts road and DeepGlobe road datasets. Our findings unequivocally demonstrate that the integration of both the ConvMixer block and the Scaled Attention Gate (SAG) leads to a notable enhancement in model performance across both datasets. Specifically, the baseline U-Net model achieved an accuracy of 77.62%, a recall of 81.67%, and an IoU of 76.85% on the Massachusetts road dataset. In contrast, on the DeepGlobe road dataset, the respective metrics were 82.59%, 83.67%, and 74.63%. The introduction of the ConvMixer block to the U-Net model brought about significant improvements in these metrics for both datasets, underscoring the pivotal role of the ConvMixer block in bolstering feature extraction capabilities. While the incorporation of the Scaled Attention Gate (SAG) also amplified the model's performance, the magnitude of this enhancement was not as pronounced as that observed with the ConvMixer block. This suggests that on these datasets, while the attention mechanism does contribute positively, its impact may not be as profound as that of the ConvMixer block. Ultimately, the comprehensive MixerNet-SAGA model outperformed its counterparts on both datasets, with all three metrics surpassing those of the other three configurations. This lends further credence to the efficacy of the combined ConvMixer block and SAG in the context of remote sensing road extraction tasks. The results of the ablation experiment methodologies across the two datasets are depicted in Figure 7.  
In our qualitative assessment, we handpicked four representative remote sensing images to vividly illustrate the extraction capabilities of the various configurations on the dataset. Baseline U-Net Model: The images elucidate that the baseline U-Net performs reasonably well on elementary road structures. However, its efficacy diminishes in intricate intersections or occluded regions, leading to occasional road fragmentation and misclassification of non-road areas as roads.