## 3 Experiments
### Dataset  
The dataset used in this study is the Massachusetts Building Dataset. This publicly available dataset contains 151 high-resolution aerial images, each with a size of 1500 \(\times\) 1500 pixels, covering an area of approximately 2.25 square kilometers per image, with the entire dataset covering about 340 square kilometers. The data labels are generated by OpenStreetMap and have been manually verified to ensure accuracy.  
To ensure stable convergence and prevent overfitting, we adopt a learning rate scheduling strategy where the initial learning rate (0.0001) is multiplied by 0.5 every 50 epochs. Although the residual structure increases the network depth, its parameter-efficient design (e.g., identity mapping and 1 \(\times\) 1 convolutions) avoids significant complexity growth. As a  
\begin{table}
\begin{tabular}{c c} \hline
**Environment** & **Implement Parameters** \\ \hline CPU & Intel Core i5 \\ GPU & NVDIA RTX 4060 TI 16G \\ python & 3.11 \\ torch & 2.20 \\ opencv-python & 4.10.0.84 \\ \hline \end{tabular}
\end{table}
Table 1: Building segmentation experiment environment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Batch Size** & **Training Time/Epoch (min)** & **GPU Memory Usage (GB)** \\ \hline
16 & 25.3 & 10.2 \\
32 & 18.5 & 14.8 \\
64 & — & Out of Memory \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of computational costs for different batch sizes.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyper Parameters** & **Implement** \\ \hline batch\_size & 32 \\ epochs & 200 \\ optimizer & SGD \\ loss function & Loss\({}_{\text{new}}\) \\ learning rate & 0.0001 \\ \hline \end{tabular}
\end{table}
Table 3: Experiment training parameters.  
result, the training process remains stable without requiring gradient clipping or adaptive optimizers like AdamW.  
To evaluate the effect of image resolution, we trained REU-Net(2 EEAM) on the Massachusetts dataset with input sizes of 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512. Performance comparison is shown in Table 4.  
Although increased resolutions (512 \(\times\) 512) improve MIoU and Border IoU over 256 \(\times\) 256, their computational cost is much higher than the 256 \(\times\) 256 baseline. Thus, we selected 256 \(\times\) 256 as the default resolution to balance accuracy (0.7952 MIoU) and efficiency (48.9 ms per image), making it suitable for real-time applications.  
To increase the diversity of the data and improve computational efficiency, the images in the dataset were divided into smaller 256 \(\times\) 256 pixel patches from the original 1500 \(\times\) 1500 pixel images. This transformation expanded the dataset from 151 to 3775 images, significantly increasing the variety of building scenes, such as urban buildings along roads, densely arranged small buildings, circular building clusters, lakeside buildings, scattered buildings in dense vegetation, and large buildings under extensive shadows. The images of different scenarios are shown in Figure 5. In the experiment, the dataset was randomly split into training, testing, and validation sets in a specific ratio, with 3075 images used for training, 350 images for testing, and 350 images for validation.