## 6 Conclusions  
In this paper, we propose a GAN framework called ASGASN for efficient and accurate automatic building segmentation from high-resolution remote sensing images. AS-GASN consists of a segmentation network and a discriminator network, which are used to obtain building segmentation results and to distinguish segmentation results from ground truth, respectively. Model training as well as comparative analysis experiments based on WHU dataset as well as a CHN dataset show that ASGASN extracts smoother and more accurate building contour edges, higher integrity of building interiors, and better performance for different types of buildings. The following conclusions are obtained based on the study.  
1. ASGASN using the adversarial training strategy can pay more attention to the relationship between pixels, improve the continuity of segmentation results, and make the extracted building boundaries clearer.  
2. ASGASN introduces depth-separable convolution and global convolution to im-prove the classification and localization accuracy of the model, and uses ASPP to improve the model's ability to perceive buildings at different scales. These measures allow the network to obtain building extraction results that are closer to the ground truth.
3. The wide applicability of ASGASN for remote sensing images is greatly improved compared with other networks. The building extraction results on the WHU dataset show that ASGASN can get better extraction results for different types of buildings. Additionally, in the quantitative evaluation metrics of both datasets, the method in this paper achieves better score performance.  
In the future, we plan on conducting research related to the field of deep neural network security such as robustness testing of models [48; 49; 50]. Meanwhile, we will consider combining with unsupervised classification and other algorithms to further improve the generalization ability of the model so that our model can be better applied to engineering fields such as urban planning and built-up area change detection.  
Conceptualization, W.Z. and M.Y.; methodology, W.Z.; software, W.Z.; validation, M.Y., W.Z. and X.C.; formal analysis, M.Y.; investigation, W.Z. and J.N.; resources, Y.L.; data curation, X.C.; writing--original draft preparation, M.Y.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (41801308) and the Open Fund Project of Key Laboratory of Earthquake Dynamics in Hebei Province (FZ212203).  
Not applicable.  
With dataset available at [[http://gpcv.whu.edu.cn/data/](http://gpcv.whu.edu.cn/data/)]([http://gpcv.whu.edu.cn/data/](http://gpcv.whu.edu.cn/data/)) (accessed on 21 April 2022); CHN dataset available at [[https://www.scidb.cn/en/detail?dataSetId=806674532768153600&dataSetType=journal](https://www.scidb.cn/en/detail?dataSetId=806674532768153600&dataSetType=journal)]([https://www.scidb.cn/en/detail?dataSetId=806674532768153600&dataSetType=journal](https://www.scidb.cn/en/detail?dataSetId=806674532768153600&dataSetType=journal)) (accessed on 21 April 2022).  
The authors would like to thank the team from Wuhan University and China University of Geo-sciences (Wuhan) for providing the remote sensing dataset used in this study.  
The authors declare no conflict of interest.