## 4 Results
### Experimental Results on the CHN Dataset  
Figure 12 shows the building extraction results of different models on the CHN dataset. Overall, ASGASN, U-Net and SegNet have significantly higher performance than FCN8s and PSPNet. FCN8s and PSPNet can only obtain the general location of the building, and have difficulty in extracting detailed information of buildings. In particular, for Image 4, both models return too many false positives (blue), indicating that there are missing buildings in the extraction results. Between ASGASN, U-Net and SegNet, ASGASN returns the most negatives (green) and has basically no voids in the extracted buildings. In contrast, ASGASN gives a more accurate building profile and no voids in the results. In order to evaluate the performance of each network more objectively, we calculated their evaluation metrics in this test set as shown in Table 3. Compared to other models, ASGASN has the highest scores on all four metrics. Among them, U-Net has the highest performance apart from ASGASN. Compared to U-Net, ASGASN improved 0.6% in F1 score (0.905 vs. 0.911) and 0.9% in IoU (0.827 vs. 0.836).  
Figure 12: Cont.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Methods** & **Image1** & **Image2** & **Image3** & **Image4** & **Mean** \\ \hline \multirow{4}{*}{OA} & FCN8s & 0.861 & 0.883 & 0.812 & 0.811 & 0.841 \\  & PSPNet & 0.917 & 0.883 & 0.781 & 0.813 & 0.848 \\  & SegNet & 0.967 & 0.932 & 0.818 & 0.882 & 0.899 \\  & U-Net & 0.961 & **0.961** & 0.846 & 0.935 & 0.925 \\  & ASGANN & **0.976** & 0.946 & **0.848** & **0.937** & **0.926** \\ \hline \multirow{4}{*}{Precision} & FCN8s & **0.967** & 0.758 & 0.921 & **0.959** & 0.901 \\  & PSPNet & 0.946 & 0.882 & 0.964 & 0.941 & 0.933 \\  & SegNet & 0.958 & 0.866 & **0.972** & 0.943 & **0.934** \\  & U-Net & 0.949 & **0.883** & 0.963 & 0.932 & 0.931 \\  & ASGANN & 0.961 & 0.852 & 0.953 & 0.919 & 0.921 \\ \hline \multirow{4}{*}{Recall} & FCN8s & 0.644 & 0.848 & 0.801 & 0.641 & 0.733 \\  & PSPNet & 0.741 & 0.695 & 0.744 & 0.674 & 0.713 \\  & SegNet & 0.887 & 0.821 & 0.719 & 0.729 & 0.789 \\  & U-Net & 0.875 & **0.948** & 0.813 & 0.896 & 0.833 \\  & ASGANN & **0.944** & **0.948** & **0.818** & **0.901** & **0.902** \\ \hline \multirow{4}{*}{\(\mathrm{F1}\)-score} & FCN8s & 0.773 & 0.801 & 0.856 & 0.768 & 0.799 \\  & PSPNet & 0.831 & 0.777 & 0.841 & 0.785 & 0.808 \\  & SegNet & 0.921 & 0.843 & 0.827 & 0.822 & 0.853 \\  & U-Net & 0.911 & **0.914** & 0.882 & **0.914** & 0.905 \\  & ASGANN & **0.952** & 0.897 & **0.883** & 0.910 & **0.911** \\ \hline \multirow{4}{*}{IoU} & FCN8s & 0.631 & 0.668 & 0.749 & 0.624 & 0.688 \\  & PSPNet & 0.710 & 0.636 & 0.724 & 0.647 & 0.679 \\  & SegNet & 0.854 & 0.729 & 0.705 & 0.698 & 0.746 \\  & U-Net & 0.836 & **0.843** & 0.789 & **0.842** & 0.827 \\  & ASGANN & **0.908** & 0.814 & **0.791** & 0.834 & **0.836** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison with the state-of-the-art models on the CHN dataset. The highest value for each metric is marked as bold.  
Figure 12: Examples of segmentation results by different models on the CHN dataset. The green, red, blue, and black pixels of the maps represent the true positive, false positive, false negative, and true negative predictions, respectively.