## 5 Conclusions  
In this paper, we propose an anomaly detection-based domain adaptation framework for cross-domain building extraction called AD-DAF. Our proposed framework is composed of source domain-based pre-training, anomaly detection for the target domain, and local region reclassification. Our proposed framework leverages the anomaly detection idea to make full use of the feature knowledge of the target domain objects with the pre-classification knowledge of the source domain. Our framework has the advantage that a single pre-training can be used for multiple target domains at the same time. This is completely different from the mainstream domain adaptation methods that require retraining when the target domain is changed. The first three experimental results demonstrate that our framework can significantly improve the cross-domain building extraction of the pre-trained model, with the F1 improving by 8.7%, and the intersection over union improving by 9.94% when migrating from the Inria Aerial Image Labeling Dataset to the Wuhan University dataset. The last experimental result shows that our framework is comparable to the current mainstream domain adaptation methods. Despite the advantages of the proposed framework, there is still much room for improvement in the accuracy and applicability of the method. First, the accuracy of our method is slightly lower than that of some adversarial learning-based domain adaptation methods. In fact, our newly proposed framework is not intended to replace the current domain adaptation methods but to complement them. On the one hand, our framework can provide initial pseudo-label samples for the mainstream supervised domain adaptation methods. On the other hand, our framework can be used as a post-processing step for mainstream deep learning semantic segmentation models and domain adaptation methods to further improve the effectiveness of cross-domain building extraction. Therefore, one of our future works will be to explore the possibility of integrating our framework with adversarial learning-based domain adaptation methods for a higher accuracy. Second, although we have performed method validation on different scenarios and benchmark datasets, these validations do not include all remote sensing data types because of the richness and diversity of remote sensing data types. Therefore, we will also validate the method on more benchmark datasets in the future. Finally, our method has more anomaly detection and reclassification steps than the mainstream deep semantic segmentation or \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Potsdam-\(>\)WHU**} \\ \cline{2-3}  & **F1** & **IoU** \\ \hline DAugNet [10] & 0.7363 & 0.5827 \\ DATA [10] & 0.7951 & 0.6599 \\ JPRNet [10] & 0.7713 & 0.6277 \\ FDANet [10] & 0.8337 & 0.7148 \\ Our AD-DAF & 0.7862 & 0.6713 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison results from the source domain of the Postdam datasets to the target domain of the WHU aerial building dataset. domain adaptation methods. To simplify the usage process, we intend to develop a building extraction web system using dynamic service computing technology in the future [46; 47]. Conceptualization, S.Z., X.Z. and D.H.; methodology, S.Z., X.Z. and D.H.; validation, S.Z. ; writing--original draft preparation, S.Z. and D.H.; funding acquisition, X.Z. and D.H. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China under Grant 41971360 and Grant 42171457, in part by the Hunan Provincial Natural Science Foundation of China under Grant 2021J40721. Not applicable.