## 4 Results
### Experimental Results
#### 4.4.2 Qualitative Analysis  
To facilitate visual comparison of the results, we have provided visualizations of prediction outputs (as shown in Figure 7). From Figure 7, it is evident that Model_4 (BPDNet) exhibited more accurate positioning and boundary delineation of building polygons. BPDNet demonstrated fewer instances of missed detections compared to other methods, which is consistent with the findings of Table 1. In Row (a), Model_4 accurately identified the boundaries of the buildings within the red box, while other methods mistakenly recognized the background as buildings, resulting in inaccurate building polygon results. The Hisup-based methods (Model_1, Model_2, Model_3, Model_4) were able to recognize the complete building polygon in the yellow box, but the Frame-field model mistakenly identified the one building polygon as two parts in the yellow box. In Row (b), based on the imagery and labels in the second column, it can be observed that the building within the red box is a composite structure consisting of three high-rise buildings and a low-rise building. The extracted contour should only represent the entire complex, and only BPDNet and Model_3 successfully identified it as a single entity. Regarding the small building within the yellow box in Row (b), both BPDNet and Model_3 successfully detected it, while Frame-field, Model_1, and Model_2 overlooked this small building. In Row (c), Frame-field, Model_1, Model_2, and Model_3 exhibited varying degrees of issues with building polygon merging within the yellow box, where buildings are incorrectly connected or misclassified as part of the background. However, BPDNet accurately identified the boundaries of the buildings. In Row (d), only BPDNet recognized and precisely extracted the polygon of the small building within the red box, while other models failed to detect it. In the yellow box region, Model_2 mistakenly identified the water body as a building, indicating the impact of the difference in sample distribution between WHU-Mix and the study area on the accuracy of building recognition. In conclusion, the results of building polygon extraction demonstrated that BPDNet effectively tackles the challenges posed by diverse building structures and complex backgrounds. It accurately identifies the positions of building polygon and alleviates issues such as boundary blurring and merging.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **OA** & **IoU** & \(\mathbf{P_{50}^{IoU}}\) & \(\mathbf{P_{50}^{Boundary}}\) & \(\mathbf{R_{50}^{IoU}}\) & \(\mathbf{R_{50}^{Boundary}}\) \\ \hline Frame-field & 88.43 & 59.84 & 50.17 & 43.25 & 54.24 & 47.11 \\ Model\_1 (Baseline) & 90.88 & 62.37 & 52.50 & 45.71 & 56.89 & 49.34 \\ Model\_2 (w/o Fine-tune) & 90.34 & 61.89 & 52.01 & 45.18 & 56.03 & 48.17 \\ Model\_3 (w/Fine-tune) & 91.47 & 63.54 & 53.22 & 49.98 & 57.65 & 51.29 \\ Model\_4 (Ours) & **92.28** & **66.54** & **56.01** & **54.19** & **60.18** & **57.35** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy results on the test set using different methods. “w/Fine-tune ” denotes that fine-tune was employed. “w/o Fine-tune” denotes that fine-tune was not employed.  
Figure 7: Comparison of visualization results of different methods for buildings with various structures. (**a–d**) denote different samples and results of the different methods.