## 4 Experimental Datasets and Evaluation
### Results  
To validate the superiority of our self-constructed datasets for rural building extraction model training, we will train and test the proposed model on the self-constructed Building Image Dataset of Typical Rural Areas in China and the open China Rural Building UAV Image Dataset respectively. Test data from four geographic regions with distinct characteristics were selected from each dataset to assess the proposed model's building extraction capabilities. Representative sample images were chosen from each image set for qualitative analysis. The proposed rural building extraction network AGSC-Net was compared to models such as SegNet, AGs-UNet, C\({}^{3}\)Net and CSA-UNet. As shown in Figures 10 and 11, the original remote sensing images and corresponding ground truths occupy the first two rows, while rows 3-7 exhibit the building extraction outputs of the different models. The green and black pixels in the extraction results denote regions classified by the models as buildings and non-buildings, respectively. Figure 11: _Cont._  
Figure 10: Comparison of extraction results of each model building in Building Image Dataset of Typical Rural Areas in China. The first two rows are aerial images and ground truth, respectively. Rows 3–7 are building extraction results of SegNet, AGs-UNet, C\({}^{3}\)Net, CSA-UNet, and our proposed AGSC-Net, respectively. The green and black pixels of the maps represent the predictions of true positive and true negative, respectively. The red, purple, yellow, and blue rectangles represent the parts of the extraction results that need to be focused on in the cases of sparse building, moderately dense building, dense building, and dense vegetation, respectively. In Figure 10, the red and purple rectangles in the first two columns contain small buildings. The extraction results of SegNet and AGs-UNet display varying degrees of internal structural deficiencies. C3 Net exhibits more complete internal information but insufficiently defined edges. CSA-UNet has sharper yet less smooth and natural edges. By contrast, AGSC-Net accurately and seamlessly extracts small building edges, contours and internal structures. The yellow rectangle in the third column encapsulates a densely populated large-scale building area. All models misdetect or omit some regions, but AGSC-Net has fewer missed detections, uniquely and accurately extracting even the buildings inside the yellow box. The blue rectangle area in the fourth column suffers from vegetation interference. AGs-UNet and C3 Net display localized contour deficiencies. SegNet and CSA-UNet also contain some misdetections. Comparatively, AGSC-Net obtained more precise extraction results. Overall, the AGSC-Net model demonstrates superior performance across diverse complex scenarios, validating the efficacy of its designed modules and network architecture. Figure 11 exhibits the extraction outputs on the UAV image dataset. With higher resolution, this dataset harbors more complex spectral and texture traits alongside profound detail. Moreover, with a smaller volume, it poses greater difficulty for extraction. When processing the sharper building contours, SegNet and AGs-UNet's outlines show noticeable deficiencies and misclassifications. The contours from C3 Net and CSA-UNet appear more complete yet risk false positives with higher predictive values due to vegetation. Accounting for improvements conferred by the AG module and Contextual Correlation Network (CC-Net) module, AGSC-Net surpasses the other models, extracting building edges more accurately with fewer internal gaps. From the quantitative evaluation, compared with other models, AGSC-Net achieved the best performance in all metrics. From the quantitative results of the first dataset, as illustrated in Table 2, it can be seen that AGSC-Net outperforms the other models in all metrics. Specifically, compared with other models, AGSC-Net improved the OA metric  
Figure 11: Comparison of extraction results of each model building in China Rural Building UAV Imagery Dataset.