## 3 Experiments
### Comparative Experiments
It demonstrates significant advantages, particularly in complex scenarios where buildings are small, sparsely distributed, and overlap with vegetation. In the first, third, and fourth images, the buildings are small and scattered, with vegetation intertwining with the structures, making accurate segmentation more challenging for the model. By incorporating the attention mechanism, REU-Net(2 EEAM) enhances its focus on building edge information, allowing it to clearly segment the building outlines and accurately present their shapes. In contrast, other comparison models, lacking the specific handling of edge information, suffer from a significant shape distortion of the buildings and are more sensitive to image noise, leading to the misidentification of non-existent buildings. In the second and fifth images, which feature dense clusters of small buildings, REU-Net(2 EEAM) accurately segments the clear outlines of the buildings and shows a high success rate in identifying smaller structures. Other comparison models, particularly the FCN8s model, fail to effectively capture the edge details of the buildings, resulting in blurry segmentation and a significant distortion of the building shapes. In the sixth, seventh, and eighth images, which feature mixed scenes of large and small buildings, REU-Net(2 EEAM) also performs exceptionally well. Whether accurately capturing the shape of large buildings or recognizing the details of smaller ones, REU-Net(2 EEAM) demonstrates its powerful segmentation capabilities. In contrast, other models \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **P** & **MPA** & **MIoU** & **FWIoU** & **Border IoU** & **HD** \\ \hline FCN8s & 0.7404 & 0.5629 & 0.3337 & 0.5085 & 0.4923 & 12.8472 \\ SegNet & 0.7345 & 0.5528 & 0.4166 & 0.6781 & 0.5894 & 10.2834 \\ BiSeNet & 0.7372 & 0.5575 & 0.4606 & 0.6546 & 0.5749 & 10.3762 \\ DANet & 0.7824 & 0.6860 & 0.6128 & 0.7147 & 0.6022 & 10.1485 \\ PSPNet & 0.8656 & 0.7763 & 0.7008 & 0.8241 & 0.6793 & 9.8376 \\ UNet & 0.8803 & 0.8113 & 0.7276 & 0.8378 & 0.7363 & 8.3764 \\ DeepLabV3+ & 0.8958 & 0.8130 & 0.7223 & 0.8340 & 0.7409 & 8.7465 \\ Unet++ & 0.8995 & 0.8035 & 0.7281 & 0.8535 & 0.7781 & 7.8547 \\ MAFF-HRNet & 0.9143 & 0.7939 & 0.7639 & 0.8854 & 0.7957 & 7.2376 \\ NPSFF-Net & 0.9209 & 0.8024 & 0.7655 & 0.8799 & 0.8173 & 6.9487 \\ REU-Net & 0.9267 & 0.8318 & 0.7632 & 0.8725 & 0.8241 & 6.7364 \\ REU-Net(2 EEAM) & 0.9408 & 0.8641 & 0.7952 & 0.8897 & 0.9295 & 1.0893 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative experiments quantitative comparison of remote sensing building image segmentation results. fail to effectively distinguish between buildings of different scales in complex architectural structures, resulting in imprecise segmentation. In summary, REU-Net(2 EEAM) successfully improves building segmentation accuracy through enhanced edge information, demonstrating superior performance in complex scenarios compared to other models. In terms of a detailed comparison, as shown in Figure 8, the first image demonstrates that REU-Net(2 EEAM) accurately identifies the small building, while REU-Net and UNet, although able to segment the building within the frame, lose varying degrees of detail. PSPNet, BiSeNet, and FCN8s completely fail to segment the building. In the second image, which contains both a large and a small building, the gap between the two buildings is precisely identified by REU-Net(2 EEAM). While REU-Net also recognizes it, the boundaries are somewhat blurred. In contrast, UNet, PSPNet, BiSeNet, and FCN8s fail to fully distinguish the boundaries between the two. Similarly, in the third and fourth images, REU-Net(2 EEAM) accurately identifies the gaps between buildings, while the other comparison models fail to fully distinguish the two buildings. This clearly demonstrates that the introduction of the Edge Enhancement Attention Module significantly improves the accuracy of building segmentation by REU-Net(2 EEAM). Figure 7: Visual comparison of building segmentation results in ablation experiment.