## 4 Experiments and Analysis
### Experiment I: Semantic Segmentation Model Applicability  
In this experiment, three dominant models, UNet, PSPNet, and LinkNet, are chosen to test the applicability of our AD-DAF framework. The Inria Aerial Image Labeling Dataset (IAILD) is selected as the source domain for the pre-training model. It is an open access dataset for urban building detection with building and non-building semantic classes. The dataset consists of 180 images with 5000 \(\times\) 5000 pixels and a pixel spatial resolution of 0.3 m. It mainly covers different forms of urban settlements in the United States and Australia [33], which makes it often used to test the generalization capabilities of deep semantic segmentation models. In the 180 images, 150 images are selected for training and the remaining 30 images for validation. Specifically, each training image with 5000 \(\times\) 5000 pixels is split into 384 \(\times\) 384 pixel patches under the condition of a 20% overlap, and finally 40,836 training samples for deep semantic segmentation models are obtained. A dense residential image in the urban area and a sparse residential image in the suburban area from different regions are selected as the target domains to verify the domain adaptation effect of our AD-DAF framework. Figure 2 shows the two images from different regions. The first image is selected from Google Images at Shenzhen, China, with 500 \(\times\) 500 pixels and a spatial resolution of 1 m. The second image is selected from the ISPRS (International Society for Photogrammetry and Remote Sensing) semantic segmentation dataset [45] at Postdam, Germany, with 6000 \(\times\) 6000 pixels and a spatial resolution of 0.05 m. The building labels of the first image are obtained by manual vectorization, and the building labels of the third image are directly from the ISPRS dataset. Compared with the source domain of IAILD, the above two images are from different regions with different resolutions; thus, these two images can be considered as two different target domains in the experiments. Table 1 represents the Recall, Precision, and F1 of our AD-DAF framework on the three semantic segmentation models in two different target domains. It can be seen from this table that three models behave differently in different target domains. Specifically, the LinkNet model in our AD-DAF framework shows the largest increase in F1 metrics in the two target domains compared to the other two models, while the UNet model in our AD-DAF framework has the largest average F1 value of 87.05%. This indicates that our AD-DAF framework with the UNet model has the most stable effect for building extraction. Therefore, we choose the UNet model as our pre-trained model in the remaining three experiments. For the Precision metrics, our AD-DAF framework with the three models is mostly lower than their only pre-trained models. This is mainly because our method introduces some error classes while substantially increasing the recall. In addition, it also can be seen that the recall and F1 metrics using only the pre-trained model of the three semantic segmentation models are all lower than those of our proposed AD-DAF framework, with the value of F1 improving by at least 7.89% and at most 16.96%. These experimental results indicate that our AD-DAF framework is able to improve the migration ability of different deep segmentation models and is also applicable to different target domains.