## 5 Discussion  
In addition to the quantitative metrics' evaluation, our study also presents visual road-extraction depictions from our model, including comparisons to other models. Figure 6 presents the road extraction performed using the validation set of the Massachusetts dataset. As depicted in the diagram, our proposed model achieves accurate road extraction in several parts of the images, as highlighted with red-colored squares, compared to most of the other models. Similarly, our model demonstrated comparable performance using the DeepGlobe road dataset, as shown in Figure 7. Accurate road construction and connection can be seen in the red-colored squares in the diagram. An interesting observation is that our model tends to connect roads when there are trees present, as highlighted with blue-colored squares in the second row of Figure 6 and the fourth row of Figure 7. In the ground truth images, these road connections are not visible. Upon closer inspection, it can be assumed \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **IOU (\%)** & **Precision (\%)** & \(F_{1}\) **Score (\%)** \\ \hline U-Net [21] & 62.82 & 80.36 & 71.83 \\ \hline DeepLabV3+ [13] & 69.05 & 83.16 & 77.96 \\ \hline SegNet [34] & 57.66 & 80.67 & 72.09 \\ \hline Attention-UNet [25] & 67.42 & 79.95 & 80.54 \\ \hline D-LinkNet [27] & 63.94 & 78.54 & 0.7659 \\ \hline RFE-Link Net [28] & 70.72 & 83.09 & **82.85** \\ \hline Proposed Model & **71.61** & **83.19** & 81.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative observation of results obtained from all models in DeepGlobe road dataset. Bold values indicate the best performance in the respective metric. Figure 6: **Comparative results of road extraction from the Massachusetts dataset**. The figure presents a side-by-side comparison of road extracted by various models, including the proposed model, against the ground truth, highlighting the effectiveness of each approach in synthesizing accurate road extraction. that there is a road occluded by trees, and our model may be able to achieve correct road extraction even in such occluded scenarios. Based on the quantitative and visualization results, our model outperformed various state-of-the-art models in road extraction. Our study not only demonstrated better performance but also presented an efficient computational approach by adopting the depthwise separable mechanism and increasing the field of view with the dilation mechanism. To provide a more intuitive and mathematical understanding of how depthwise separable dilated convolution used in our work brought efficiency compared to standard convolution, we can express the computational savings mathematically. Given an image of the size \(512\times 512\times 3\) and using a \(3\times 3\) kernel, the standard convolution operation can be expressed as  
\[\text{Operations}_{\text{standard}}=H\times W\times K\times K\times C_{\text{ in}}\times C_{\text{out}}\]  
where \(H=512\) (the height of the image), \(W=512\) (the width of the image), \(K=3\) (the size of the kernel), \(C_{\text{in}}=3\) (the number of input channels), and \(C_{\text{out}}=64\) (the number of output channels). Substituting these values, we obtain  
\[\text{Operations}_{\text{standard}}=512\times 512\times 3\times 3\times 3\times 64 =151,165,440\]  
Since depthwise separable convolution involves two steps, the operations can be split into two parts, depthwise convolution and pointwise convolution, and their operations are expressed below as  
\[\text{Operations}_{\text{depthwise}}=H\times W\times K\times K\times C_{ \text{in}}\]  
\[\text{Operations}_{\text{pointwise}}=H\times W\times C_{\text{in}}\times C_{ \text{out}}\]  
Figure 7: **Comparative results of road extraction from the DeepGlobe road dataset**. The figure presents a side-by-side comparison of road extracted by various models, including the proposed model, against the ground truth.