## 4 Experiments and Results
### Ablation Experiment and Result  
To systematically evaluate the effectiveness of the core components in the ShadeNet model, this study designs hierarchical ablation experiments based on the Mask2 Former framework [18]:  
1. The ablation experiment of the backbone network. The Mask2 Former model [18] with ResNet [40] as the backbone is selected as the baseline model. This choice is justified by the following two points: (1) ResNet [40], as a classic convolutional neural network architecture, has been extensively studied and validated in the semantic segmentation field, especially in combination with Transformer decoders. It is widely used in benchmark solutions such as CMNeXt [42] and Mask-RCNN [43]; (2) by keeping the decoder structure unchanged and only replacing the backbone network, the impact of the Swin Transformer's [17] unique hierarchical attention mechanism and long-range dependency modeling capabilities on segmentation performance can be effectively isolated. 2. The validation experiment of the GCLSA Module. The GCLSA module is introduced in the improved model based on the Swin Transformer to quantitatively analyze its contribution to enhancing the model's ability to learn contextual information. The experiment employs a controlled variable approach, keeping other hyperparameters consistent and comparing performance by activating and deactivating the GCLSA module. In the ablation experiment section, we used the mIoU introduced in Section 4.2 as the accuracy evaluation metric. This study verified the effectiveness of each component through a hierarchical experimental design. The accuracy results are shown in Table 2, where \"ResNet Backbone\" refers to the Mask2 Former model with ResNet as the backbone network, \"Swin Transformer Backbone\" refers to the Mask2 Former model with Swin Transformer as the backbone network, and \"Swin Transformer Backbone + GCLSA Module\" refers to the ShadeNet model proposed in this study. Based on these results, we can draw the following conclusions:  
1. The baseline model with ResNet as the backbone achieves an mIoU of 89.21%, which is consistent with the benchmark performance of similar studies under the Mask2 Former framework [18], thereby validating the reliability of the experimental setup. 2. After adopting the Swin Transformer backbone, the model's mIoU improves to 90.05%, indicating that the self-attention mechanism architecture, which incorporates a sliding window attention, significantly enhances the feature representation capability compared to traditional convolutional structures. 3. After introducing the GCLSA module, the model's performance significantly improves to 92.42%, which validates that the GCLSA module, by reinforcing the attention to interrelations, effectively enhances the model's ability to capture global contextual information. To provide a more intuitive comparison of the performance of each model, we present the visualization results of the models in Figure 11. In the following analysis, for convenience, we refer to the Mask2 Former model with ResNet as the backbone as \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mIOU (\%)** \\ \hline ResNet Backbone (Baseline) & 89.21 \\ Swin Transformer Backbone & 90.05 \\ Swin Transformer Backbone + GCLSA & 92.42 \\ Module(our) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mIoU results of the ablation experiment. Mask2 Former(ResNet), the Mask2 Former model with Swin Transformer as the backbone as Mask2 Former (Swin Transformer), and the Swin Transformer Backbone + GCLSA Module as the ShadeNet model proposed in this study. As shown in Figure 11, ShadeNet demonstrates outstanding segmentation performance in complex scenarios, particularly in handling roads, dark-colored water bodies, deep-colored vegetation, black roofs, shadowed regions, and other similar objects, with its accuracy significantly surpassing that of the other models involved in the ablation experiments.