## 4 Results
### Experimental Results  
#### 4.4.1 Quantitative Analysis  
The experimental results are presented in Table 1. From the results, we can observe the following findings. Firstly, our proposed BPDNet method achieved improvements compared to the frame-field and HiSup. It was shown that BPDNet is superior to the current state-of-the-art building polygon extraction algorithms when facing the problem of limited training sample sizes. Secondly, compared with HiSup, BPDNet showed an increase of 1.40% in OA, 4.17% in IoU, 3.51% in \(P_{50}^{IoU}\), 8.48% in \(P_{50}^{Boundary}\), 3.29% in \(R_{50}^{IoU}\) and 8.01% in \(R_{50}^{Boundary}\). The significant improvement in \(P_{50}^{Boundary}\) and \(R_{50}^{Boundary}\) indicated that BPDNet reduces missed and false extraction of building polygons and enhances the accuracy of building polygon extraction, including the precise positioning of building boundaries. This improvement effectively mitigates issues related to incorrect building polygon localization and blurry boundaries caused by diverse building structures and complex backgrounds. Thirdly, Model_2 performed the worst among the four comparative models, as indicated by the lowest evaluation scores, where the OA was 90.34%, IoU was 61.89%, \(P_{50}^{IoU}\) was 52.01%, \(P_{50}^{Boundary}\) was 45.18%, \(R_{50}^{IoU}\) was 56.03% and \(R_{50}^{Boundary}\) was 48.17%. This result indicates that the significant difference in the sample distribution between WHU-Mix and the study area greatly affected the performance of Model_2. Directly applying a model trained on WHU-Mix to predict the test set from the study area shows that the generalization capability obtained from a large dataset is still insufficient to overcome the impact of sample distribution differences. To address this issue, it is necessary to enable the model to learn the distribution characteristics of the samples in the task area. Compared to Model_2, Model_3 incorporates fine-tuning on the training set from the study area, enabling the model to learn the distribution characteristics of the samples in the task area. Although Model_3 showed improved performance compared to both Model_1 and Model_2, its evaluation metrics were still lower than those of Model_4 (BPDNet). Model_4 (BPDNet) achieved an OA of 92.28%, IoU of 66.54%, \(P_{50}^{IoU}\) of 56.01%, \(P_{50}^{Boundary}\) of 54.19%, \(R_{50}^{IoU}\) of 60.18%, \(R_{50}^{Boundary}\) of 57.35%. This indicates that although fine-tuning allows the model to learn the distribution characteristics of the task-specific dataset and partially overcome the differences in sample distribution, it does not effectively preserve the generalization performance obtained from the large dataset training. Model_4 (BPDNet) effectively distills the building feature representation capability obtained from diverse structured building samples in the large dataset to the student model through the CWD method. This enables the student model to achieve the best prediction results on the test set of the study area. Additionally, the inclusion of Dice Loss in the mask branch of Model_4 (BPDNet) further optimizes the boundaries of buildings, allowing for better differentiation between buildings and complex backgrounds. Therefore, based on the aforementioned analysis, the BPDNet method not only improves the pixel-level extraction results of buildings but also enhances the accuracy of the shape and position of building polygons. It reduces missed and false extraction of building polygons and demonstrates superior generalization ability compared to other methods for extracting building polygons with few samples in the Wenzhou area.