## 6 Conclusions  
This paper proposed a knowledge distillation method called BPDNet for building polygon extraction. BPDNet utilizes the structural knowledge of buildings distilled from a teacher network trained on a large dataset containing diverse building polygons. By leveraging the generalization capability of the teacher network in extracting structurally diverse building polygons, the proposed method enables the student network to learn the characteristics of building polygons in the target region, thereby improving the student network to represent the features of buildings in the target region. This approach addresses the challenge of limited samples and diverse building structures, which can lead to insufficient generalization performance of the model. Moreover, the incorporation of Dice Loss in the model enhances the accuracy of the boundaries and reduces the blurring of building edges caused by complex backgrounds such as building shadows and impermeable surfaces. The experimental results demonstrated that BPDNet exhibits superior performance in terms of evaluation metrics compared to models trained solely on the target region samples or models fine-tuned using large datasets. The ablation experiments further validated the effectiveness of our knowledge distillation method and the use of Dice Loss. The discussion on hyperparameters also confirmed the rationality of the parameter settings in BPDNet. In addition, the method has the following limitation: the method embeds sample knowledge obtained from a large dataset on the task areas with an insufficient sample size for enhancing the model's feature representation. However, if the sample size of the task areas is sufficient, the model can be trained without the sample knowledge from the large dataset to obtain a better representation of various styles of buildings. Therefore, the method is not applicable when the sample size of the task areas is sufficient. When the sample size is large enough, the extraction results of this method may not exceed the SOTA method by much.  
In future work, our method can be further enhanced by incorporating prior knowledge about the structural regularities of buildings, such as the symmetry of building structures and the presence of right angles at building corners. By leveraging this prior knowledge, we can assist the inference process of building polygons and extract more accurate building polygons.  
Conceptualization, G.X. and J.C.; methodology, H.X. and G.S.; software, J.H.; validation, J.H.; formal analysis, H.X.; investigation, H.X.; data curation, J.H.; writing--original draft preparation, [PERSON] and G.S.; writing--review and editing, [PERSON] and J.C.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Wenzhou Basic Scientific Research Project, grant number S20210017, and the Science and Technology Project of the Department of Natural Resources of Zhejiang Province, grant number 2021-38.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weight of Dice Loss** & **OA** & **IoU** & \(\mathbf{P_{50}^{IoU}}\) & \(\mathbf{P_{50}^{Boundary}}\) \\ \hline \(\theta=0.25\) & 90.95 & 62.78 & 53.01 & 51.52 \\ \(\theta=0.50\) & 91.38 & 64.64 & 54.13 & 52.09 \\ \(\theta=0.75\) & 92.10 & 66.25 & 55.05 & 53.13 \\ \(\theta=1.00\) & **92.28** & **66.54** & **56.01** & **54.19** \\ \(\theta=1.25\) & 92.11 & 66.26 & 55.87 & 53.62 \\ \(\theta=1.50\) & 92.16 & 66.32 & 55.46 & 54.01 \\ \(\theta=1.75\) & 89.57 & 60.45 & 51.42 & 49.47 \\ \(\theta=2.00\) & 91.44 & 64.73 & 54.18 & 52.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dice Loss weights and experimental results.