## 4 Experiments and Analysis
### Experiment II: Large Area Applicability  
In this experiment, two images with relatively larger areas (about 1 km\({}^{2}\), 5201 \(\times\) 5201 pixels) and with a spatial resolution of 0.2 m are used as two different target domains. The first image represents an urban area and is obtained from Google Images at Jilin, China, where the building types include residential areas, educational sites, industrial sites, and so on. The second image represents a suburban area and is also obtained from Google Images at Yunnan, China, in which the buildings are mainly low single bungalows. Their details are shown in Figure 4. The UNet-based pre-trained model by the IAILD dataset in experiment I is also used for the pre-trained model in this experiment.  
Figure 4: Two remote sensing images with larger area as target domains. (**a**) The first image for urban area. (**b**) The second image for suburban area.  
Figure 3: Semantic segmentation results of our framework with three different models in two different target domains. The first and second rows correspond to the results of the first image and the second image, respectively. The white area indicates the building footprint. (**a**) Ground truth. (**b**) Results of our framework based on UNet. (**c**) Results of our framework based on PSPNet. (**d**) Results of our framework based on LinkNet.  
Table 2 provides the Recall, Precision, and F1 of our AD-DAF framework with the UNet model on the two relatively larger areas. As can be seen from this table, our AD-DAF framework improves by 41.92% in the Recall metric and 26.93% in the F1 metric on the first image compared with the only pre-trained model. This is because the first image is slightly distorted and its angle is completely different from the angle of the image in the source domain of the IAILD dataset. This causes the pre-trained model based on the IAILD dataset to be less effective in extracting buildings in this image. In contrast, our method uses anomaly detection and reclassification to further improve the building extraction. On the second image, our method only improves by 9.42% in the Recall metric and 8.23% in the F1 metric. The above experimental results show that our AD-DAF framework is also effective under relatively large areas.  
Figure 5 shows the building extraction results of the two methods on the two target domains. It is apparent from Figure 5 that the completeness of the segmentation results by our AD-DAF framework is significantly better than those of the only pre-trained model. This again indicates the advantage of our AD-DAF framework in the cross-domain building extraction. However, it should be noted that there is still a gap between the building outlines of our AD-DAF framework and the ground truth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Images** & **Methods** & **Recall** & **Precision** & **F1** \\ \hline \multirow{3}{*}{(a)} & Only Pre-trained & 42.97\% & 72.94\% & 54.08\% \\  & AD-DAF & 84.89\% & 77.46\% & 81.01\% \\ \cline{1-1}  & Only Pre-trained & 72.12\% & 72.98\% & 72.55\% \\ \cline{1-1}  & AD-DAF & 81.54\% & 80.03\% & 80.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of our framework with the UNet model on the two large areas.  
Figure 5: Semantic segmentation results on the larger areas. The white area indicates the building footprint. (**a**) Ground truth. (**b**) Results by using the only pre-trained model based on the UNet model. (**c**) Results of our framework with the UNet model.