## 3 Experiments
### Comparative Experiments
(**a**) Original image; (**b**) label; (**c**) FCN8s; (**d**) BiSeNet; (**e**) PSPNe; (**f**) UNet; (**g**) REU-Net; (**h**) REU-Net(2 EEAM). To validate the generalization capability of REU-Net(2 EEAM), we conducted comparative experiments on three remote sensing datasets: Massachusetts Building Dataset (Dataset 1), WHU Building Dataset (Dataset 2), and Inria Aerial Dataset (Dataset 3). The performance of baseline models (UNet, DeepLabV3+, UNet++), recent state-of-the-art models (NPSFF-Net, REU-Net), and REU-Net(2 EEAM) is summarized in Table 12.  
As shown in Table 12, REU-Net(2 EEAM) achieves the highest MIoU and Border IoU across all datasets, demonstrating robust generalization. For instance, on Dataset 3 (Inria), it outperforms UNet++ by 13.5% in MIoU and 29.1% in Border IoU, highlighting its ability to adapt to diverse building distributions and resolutions. The significant improvement in Border IoU (e.g., 0.939 on Inria) is attributed to the Edge Enhancement Attention Module (EEAM), which explicitly extracts directional gradients (Sobel operators) and applies spatial weighting to amplify boundary features. This design effectively suppresses background noise while preserving fine-grained edges. To evaluate whether REU-Net(2 EEAM) is suitable for real-time applications, this paper compares the computational costs of several models, including UNet, DeepLabV3+, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Dataset 1**} & \multicolumn{2}{c}{**Dataset 2**} & \multicolumn{2}{c}{**Dataset 3**} \\ \cline{2-7}  & **MIoU** & **Border IoU** & **MIoU** & **Border IoU** & **MIoU** & **Border IoU** \\ \hline UNet & 0.7281 & 0.7363 & 0.6431 & 0.6792 & 0.6103 & 0.6497 \\ DeepLabV3+ & 0.7223 & 0.7409 & 0.6486 & 0.6925 & 0.6593 & 0.7142 \\ UNet++ & 0.7281 & 0.7781 & 0.7083 & 0.7496 & 0.6940 & 0.7271 \\ NPSFF-Net & 0.7655 & 0.8173 & 0.7298 & 0.7736 & 0.7284 & 0.7603 \\ REU-Net & 0.7632 & 0.8241 & 0.7129 & 0.7583 & 0.7453 & 0.8097 \\ REU-Net(2 EEAM) & 0.7952 & 0.9295 & 0.7584 & 0.8621 & 0.8294 & 0.9386 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance comparison across datasets (MIoU and Border IoU). Figure 8: Detailed comparison of building segmentation results in ablation experiment. (**a**) Original image; **(b)** FCN8s; **(c)** BiSeNet; **(d)** PSPNet; **(e)** UNet; **(f)** REU-Net; **(g)** REU-Net(2 EEAM). (Green boxes highlight the proposed model’s improved accuracy in capturing building edges. Red boxes indicate segmentation inaccuracies by other models). UNet++, NPSFF-Net, REU-Net, and REU-Net(2 EEAM). The computational cost is assessed across the following dimensions. * Parameters: the number of model parameters (in millions);
* FLOPs: the forward computation cost for a single image (in billions of floating-point operations);
* Inference Time: the inference time for a single image with a batch size of 1 (in milliseconds);
* FPS: Frames Per Second, representing the number of frames processed per second. The calculation formula is as follows: \(\text{FPS}\ =\ 1000\) / Inference Time. The experimental results are presented in Table 13. For fairness, all inference time measurements were conducted on an NVIDIA RTX 4060 Ti GPU with 16 GB of memory. Input images were resized to 256 \(\times\) 256 pixels, and the batch size was set to 1 to simulate real-time processing scenarios. As shown in Table 13, REU-Net(2 EEAM) achieves a competitive inference time of 48.9 ms per image (20.5 FPS), which meets the real-time threshold (\(\leq\)100 ms). Compared to UNet++ (58.1 ms) and DeepLabV3+ (62.7 ms), our model reduces inference latency by 15.8% and 22.0%, respectively, while maintaining higher segmentation accuracy (see Table 11). The minor increase in FLOPs (69.3 G vs. UNet's 65.2 G) is attributed to the EEMM's edge-enhancement operations, but its parameter-efficient design (35.6 M parameters) ensures compatibility with resource-constrained devices.