## 3 Experiments
### Comparative Experiments  
To evaluate the effectiveness of the proposed improved network REU-UNet(2 EEAM), we first selected several classic network models, including FCN8s [15], SegNet [24], BiSeNet [26], DANet [27], PSPNet [28], UNet [16], DeepLabV3+ [29], UNet++ [17], MAFF-HRNet [30], and NPSFF-Net [31], as well as the REU-Net network without the edge-enhancement attention module for building image segmentation experiments. The results were then compared with those of REU-Net(2 EEAM) to systematically validate the improvements in segmentation performance. This comparison aims to systematically validate the performance improvement of the modified network in terms of segmentation accuracy. Table 11 presents a quantitative comparison of REU-Net(2 EEAM) with other classic networks in the building image segmentation task. The experimental results show that traditional networks like FCN8s, SegNet, BiSeNet, and DANet perform poorly in segmentation accuracy metrics. Although PSPNet, UNet, and DeepLabV3+ show some improvements, they still fall short of UNet++. Further observation reveals that MAFF-HRNet, NPSFF-Net, REU-Net, and REU-Net(2 EEAM) show more significant progress in segmentation accuracy. Specifically, REU-Net(2 EEAM) achieved the best results in key evaluation metrics, such as P, MPA, MIoU, FWIoU, border IoU and HD, with improvements of 0.9408, 0.8641, 0.7952, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **P** & **MPA** & **MIoU** & **FWIoU** & **Border IoU** & **HD** \\ \hline REU-Net(SE-Net) & 0.7781 & 0.8129 & 0.7375 & 0.8260 & 0.7264 & 8.3284 \\ REU-Net(CBAM) & 0.7864 & 0.8315 & 0.7409 & 0.8488 & 0.7583 & 7.8565 \\ REU-Net(Transformer) & 0.8027 & 0.8374 & 0.7638 & 0.8453 & 0.8471 & 5.4759 \\ REU-Net(2 EEAM) & 0.9408 & 0.8641 & 0.7952 & 0.8897 & 0.9495 & 1.0893 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of models under different attention mechanisms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **P** & **MPA** & **MIoU** & **FWIoU** & **Border IoU** & **HD** & **Parameters** & **FLOPs (G)** \\ \hline REU-Net & 0.9267 & 0.8318 & 0.7632 & 0.8725 & 0.7937 & 5.3994 & 34.5 & 65.2 \\ REU-Net(1 EEAM) & 0.7958 & 0.7937 & 0.7116 & 0.8315 & 0.8634 & 3.8762 & 35.0 & 67.8 \\ REU-Net(2 EEAM) & 0.9408 & 0.8641 & 0.7952 & 0.8897 & 0.9295 & 1.0893 & 35.6 & 69.3 \\ REU-Net(3 EEAM) & 0.7431 & 0.8002 & 0.7214 & 0.8376 & 0.8265 & 3.4238 & 36.3 & 72.5 \\ REU-Net(4 EEAM) & 0.7853 & 0.7940 & 0.7080 & 0.8453 & 0.8839 & 1.9930 & 37.1 & 75.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation experiment quantitative comparison of remote sensing building image segmentation results. 0.8897, 0.9295, and 1.0893, respectively, compared to the baseline UNet model. These results strongly demonstrate the superiority of the proposed improved algorithm in terms of accuracy, while also indicating that the implemented network improvement strategy is both reliable and effective. The enhanced segmentation accuracy of REU-Net(2 EEAM) compared to other U-Net variants, such as UNet++ or DeepLabV3+, can be attributed to the replacement of traditional 3 \(\times\) 3 and 1 \(\times\) 1 convolution blocks with residual blocks (ResBlocks). This modification not only deepens the network architecture but also mitigates the vanishing gradient problem, effectively transferring features from one layer to the next and enabling the extraction of deeper semantic information. Furthermore, direct skip connections are substituted with edge enhancement attention modules. These modules enhance the extraction of building contours and edge information by calculating the difference between positional features and original features, thereby improving segmentation accuracy. Consequently, these improvements enable REU-Net(2 EEAM) to achieve superior performance in accurately identifying building edges and enhancing overall segmentation precision. Figure 7 shows that REU-Net(2 EEAM) excels in building segmentation tasks across various scenarios.