## 3 Materials and Methods
### The Proposal and Integration of DenseDDSSPP into the Network
#### 3.5.2 Incorporation of Squeeze-and-Excitation (SE) Block  
With the advanced processing mentioned above, we hypothesize that learning the feature map \(D\) will enable our model to understand the intricate features of roads and accurately extract high-quality road structures. However, it may encounter limitations in certain edge cases, such as occlusions and the presence of shadows. In these scenarios, an attention mechanism to focus on relevant patterns in edge cases could be invaluable. Previous studies [17; 23] have suggested that initial layers primarily capture the rough structure of roads, while deeper layers encode spatial features connected to roads. Building on this understanding, and to leverage both the deeper layers and the DenseDDSSPP module, the resultant feature map \(D\) is further enhanced using the Squeeze-and-Excitation (SE) block [30], a technique capable of effectively refining channelwise feature responses (as depicted in Figure 5). This procedure enriches the feature map \(D\) to better capture edge cases.  
Figure 4: A visualization of Xception’s layers. The dotted line indicates the omission of intermediate layers for conciseness.  
Let \(D\) have \(C\) channels, where each channel, \(c\), has the height \(H\) and width \(W\). The _squeeze_ operation is applied, which can be mathematically expressed as  
\[z_{c}=F_{\text{sq}}(d_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}d_{c} (i,j) \tag{5}\]  
This is the global average pooling operation.  
To distinguish between the important channels that lead to better road extraction, we perform the _excitation_ operation on the information obtained from the squeeze operation. Similarly to the original work of [PERSON] et al. [30], which acts as an attention mechanism in a channelwise manner, the _excitation_ operation can be written as  
\[e=F_{\text{ex}}(z,W)=\sigma(W_{2}\delta(W_{1}z)) \tag{6}\]  
where \(W_{1}\in\mathbb{R}^{C/r\times C}\) is the reduction to the dimension \(C/r\), \(W_{2}\in\mathbb{R}^{C\times C/r}\) scales back to the dimension \(C\), \(\sigma\) is the sigmoid activation, and \(\delta\) is the ReLU activation.  
Subsequently, we obtain the final feature volume that depicts the importance of one channel over another by a scaling operation. Given the feature map \(D\) and the scalar \(e\), the scaling operation can be represented as  
\[x_{c}=F_{\text{scale}}(d_{c},s_{c})=s_{c}\cdot d_{c} \tag{7}\]  
where \(X=[x_{1},x_{2},\dots,x_{C}]\) and \(F_{\text{scale}}\) refer to channelwise multiplication.  
This completes the enhancement of features through the Squeeze-and-Excitation block in our architecture. Integrating the SE block after the decoder allows the model to capture comprehensive spatial and contextual information while supporting an attention mechanism to focus on relevant patterns, especially in edge cases, resulting in improved road extraction accuracy.  
Finally, consistent with the original DeepLabV3+ [13], the above operation is followed by convolution operations and bilinear upsampling, concluding with a sigmoid activation function to ensure the accurate extraction of road structures from satellite imagery.