## 3 Experimental Results and Analysis
### Comparisons with the State of the Arts  
To further validate the effectiveness of the proposed method, we conducted comparative experiments on AIDS and WBDS datasets, including DANet [10], DeepLab v3+ [6], U-Net [3], ViT [26], TransUNet [30], CMTFNet [31] and UANet [24]. To make the experimental results more convincing, except for the ViT network, we selected ResNet50 as the feature extraction backbone network for all networks during the experiment. ResNet50 loaded  
Figure 3: Visualization of ablation study on the WBDS dataset. white represents the building area, black represents the background, red represents missed detection, and green represents error detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TransUNet** & **+GFEM** & **+FAM** & **+LCM** & **IoU** & **F1** \\ \hline \(\surd\) & & & & 87.89 & 93.60 \\  & \(\surd\) & & & 88.67 & 93.99 \\  & \(\surd\) & \(\surd\) & & 89.58 & 94.52 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **92.07** & **95.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different module combinations on the WBDS dataset. The items with “\(\surd\)” indicate the options that have been adopted. The boldfaced values indicate the optimal result. the pretrained model on ImageNet-1k and ViT loaded the pretrained model of ViT-B on ImageNet21k as the initialization parameters. Table 4 displays the comparisons on the AIDS and WBDS datasets, with the best results indicated in bold. Table 4 illustrates that the early CNN-based semantic segmentation networks (such as U-Net and DeepLab v3+) are not satisfactory due to the absence of attention mechanisms. Results from the ViT network, relying solely on self-attention mechanisms, also proved unsatisfactory. However, subsequent networks combining CNNs with attention mechanisms (such as TransUNet, CMTFNet and UANet) notably improved results by integrating various attention modules into CNNs. Among them, despite its relatively simple structure, TransUNet exhibited stable segmentation results. Compared with the state of the arts, the proposed method achieves superior semantic segmentation results on both datasets due to the introduction of large-format global features. Additionally, implementing gradient accumulation strategies introduced some fluctuations in accuracy, but still yielded favorable outcomes. This is primarily because our framework effectively combines the global semantic features from large-format images with the local details of image patches. This integration allows for a more precise handling and extraction of objects across various scales, which notably enhances the accuracy and reliability of semantic segmentation in high-resolution remote sensing images. To further verify the computational efficiency of the proposed method, we have detailed the training parameters and Floating Point of Operations (FLOPs) for different models on the AIDS dataset in Table 4. Our network is divided into two parts for statistical purposes: global semantic feature extraction (i.e., Part I) and other components (i.e., Part II). Part I is TransUNet, which is mainly responsible for extracting global semantic features. Part II consists of our four modules: feature coding, feature aggregation, information decoding, and lightweight convolution. As indicated in Table 4, our supplementary processing modules for large-format images (i.e., Part II) impose only a modest increase in computational load, thereby preserving computational efficiency. To further demonstrate the effectiveness of the proposed method, we selected several images from two datasets to visualize the semantic segmentation results of buildings. The results are shown in Figure 4. It can be observed that the segmentation results of the comparison method in some building areas are not ideal, and there are problems such as blurred and incoherent boundaries. The proposed method achieves the best detection results, especially in large building areas, which significantly reduces error detection.