## 4 Results  
### Experimental Results on the WHU Dataset  
In order to verify the effectiveness of the model for buildings, this paper conducted building extraction experiments based on the WHU dataset and FCN8s, PSPNet, SegNet, and U-Net models, and the extraction results are shown in Figure 11. Overall, PSPNet returns more false negatives (blue) and the fewest true positive (green), while FCN8s got the most false positives (red). Although both SegNet and U-Net return more positives (green) results, SegNet obtains more false negatives (blue) than U-Net. In contrast, ASGASN shows significantly fewer false positives (red) and false negatives (blue) than the other models and the extracted building outline is closer to the real situation on the ground. The following is a comparative analysis of the extraction results for different types of buildings. The red boxes in the first column show the extraction results of different models for irregular buildings. FCN8s, PSPNet, and SegNet only extracted a part of the buildings, and a considerable part was not detected. The U-Net model extracts the integrity of buildings better than these three models but still suffers from the problem of fine blurred edges. ASGASN extraction is relatively complete, with clear building boundaries and basically no voids. The red box in the second column shows the extraction of buildings at the edge of the image by different models. FCN8s, PSPNet, SegNet, and U-Net poorly extract buildings, all failing to accurately extract the boundaries of buildings and, in some cases, not detecting buildings at all. ASGASN can detect all buildings; although there are a few false positives (red), ASGASN still obtains a smoother edge for the building outline. The red box in the third column shows the extraction of different models for small-scale buildings. FCN8s, PSPNet, SegNet, and U-Net are basically unable to extract buildings and only extract sporadic patches in the interior. ASGASN can detect buildings at smaller scales and extract a higher degree of completeness. The red box in the fourth column shows the extraction of regular buildings by different models. The PSPNet building extraction is the worst as basically no edge details are detected. FCN8s, SegNet, and U-Net extracted buildings have more accurate edges, but still result in false negatives (blue). ASGASN extracts the edges of the building outline more smoothly and accurately, and the integrity of the building interior is higher. Therefore, ASGASN achieves the best extraction results. Table 2 shows the comparison of the results of all models for each evaluation metric, and ASGASN achieved the highest scores in four metrics. Among them, the OA of ASGASN is 2.3% better than SegNet, which has the highest accuracy among the other models. For recall, the performance of U-Net and the proposed ASGASN is significantly better than that of the other three methods. ASGASN has the highest performance, which is 4.4% higher than that of U-Net. The ASGASN F1-score reached 94.4%, which is 3.4%, 5.3%, 13.0% and 5.3% higher than those of U-Net, SegNet, PSPNet and FCN8s, respectively. The cross-convergence ratios increased by 3.5%, 9.2%, 17.2% and 9.1% relative to U-Net, SegNet, PSPNet and FCN8s, respectively. Figure 11: _Cont._