## 3 Experimental Results and Analysis
### Ablation Study
#### 3.4.2 Evaluation of Different Modules' Effectiveness  
To assess the individual contributions of each module, we examined the results on the WBDS dataset when various combinations of modules were employed. The different module combinations were as follows:  
* **Baseline**: The TransUNet network is employed as the baseline for processing small-sized image patches, and the outcomes from these smaller images are subsequently stitched together directly to produce large-format results.
* **+Global Feature Extraction Module (GFEM)**: TransUNet serves as the global feature extraction module, ResNet-50 acts as the feature coding model, and concatenation operations are employed to replace the feature aggregation module in the global-local feature aggregation process.
* **+Feature Aggregation Module (FAM)**: We added the feature aggregation module to integrate global and local features, without employing lightweight convolution. The output was the prediction result of image patches, and we stitched these predictions to obtain large-format predictions.
* **+Lightweight Convolution Module (LCM)**: The lightweight convolution module was introduced to process large-format feature maps stitched from small patches. The small patch feature maps were produced by the feature aggregation module.  
Table 3 showcases the results of these different module combinations. Table 3 demonstrates that, using TransUNet as the baseline, the model's performance is incrementally enhanced as the global feature extraction module, the feature aggregation module, and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**Direct Stitching**} & \multicolumn{2}{c}{**Ours**} & \multicolumn{2}{c}{**Improvement**} \\ \cline{3-8}  & & **IoU** & **F1** & **IoU** & **F1** & **IoU** & **F1** \\ \hline \multirow{4}{*}{AIDS} & ViT & 84.29 & 91.51 & – & – & – & – \\  & U-Net & 83.53 & 91.19 & 90.00 & 94.73 & 6.47 & 3.54 \\  & TransUNet & 87.11 & 93.12 & 90.12 & 94.80 & 3.01 & 1.68 \\  & ConvNeXt V2 & 87.17 & 93.14 & 89.98 & 94.73 & 2.81 & 1.59 \\  & ViT & 83.78 & 91.56 & 91.74 & 95.69 & 7.96 & 4.13 \\  & U-Net & 85.17 & 91.99 & 91.17 & 95.38 & 6.00 & 3.39 \\  & TransUNet & 87.89 & 93.60 & 92.07 & 95.87 & 4.18 & 2.27 \\  & ConvNeXt V2 & 87.81 & 93.51 & 89.65 & 94.54 & 1.84 & 1.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance improvements of the proposed framework with different global feature extraction modules.  
the lightweight convolution module are incorporated one by one. Compared to the baseline TransUNet, the IoU metrics increased by 0.78%, 0.91%, and 2.49% respectively. This demonstrates the effectiveness of the modules within the network structure designed in this paper.  
To further illustrate the efficacy of the modules used in the ablation study, we selected several images from the dataset to visualize the semantic segmentation outcomes of different module combinations, as depicted in Figure 3. It is evident that the addition of the global information extraction module leads to a slight improvement in performance. However, without advanced feature aggregation module, the enhancement is not substantial. Upon incorporating the feature aggregation module, there is a full integration of global and local information, resulting in a significantly improved outcome. The inclusion of the lightweight convolution module effectively enhances the edge areas of buildings. Hence, this further affirms the validity of the modules presented in this paper.