## 1 Introduction
One scholar [31] added an attention mechanism with a multiple loss method to U-Net and successfully extracted buildings from publicly available aerial image datasets. Some scholars [32] improved the accuracy of small building detection based on residual features and feature pyramidal multiscale prediction. A team [33] proposed the RFA-U-Net model with a joint attention mechanism for building extraction. The FCN-based pixel-by-pixel prediction can ensure the accuracy of individual pixels, but the spatial relationship of the position before the pixel is often ignored. In recent years, the emergence of generative adversarial networks (GANs) has provided an emerging solution thinking for semantic segmentation of building contour extraction. In our problem setup, the generator of the GAN is used to obtain a pixel-level semantic segmentation result map, while the discriminator is used to distinguish the segmentation map from the true coverage. The generator and discriminator are jointly optimized in the \"adversarial\" setting, so that the generator gets the best possible result. Methods based on GAN [34] can enhance the continuity of spatial labels and refine the segmentation results of the image. A team [35] used the DeepLabv3+ architecture as the basic segmentation model and the Pix2 pix architecture as the GAN model to build a network that could perform the semantic segmentation task. [PERSON] et al. [36] propose a GAN model with SegNet and BCovLSTM modules for feature extraction and detection in complex environments,which can effectively detect buildings obscured by features. To test the effectiveness of GAN application, [PERSON] et al. conducted building extraction experiments using Yangon city in Myanmar as a test area and obtained the local building footprint [37]. Although the above techniques made some achievements in solving the building extraction problem, the extracted building boundaries are not precise enough, and the accuracy can be further improved. In order to further improve the accuracy and stability of the results of high-resolution building extraction and optimize the building boundaries more accurately, this paper proposes a new automatic building contour extraction network ASGASN. ASGASN has three main contributions:  
1. ASGASN implements automatic and efficient building segmentation based on GAN. In this algorithm, the segmentation network provides the class-false a priori knowledge for the training of the discriminator network, and the discriminator network corrects the learning of the segmentation network through training to make the classification results more closely match the a posteriori knowledge. 2. The ASGASN architecture, into which ASPP and skip connections are embedded, allows features to be extracted from multiple spatial scales and improves segmentation accuracy by fusing multiscale information. The global convolutional block is also added to make a tight connection between the feature map and the pixel-by-pixel classifier. 3. The segmentation and discriminator network are trained alternately by multiscale L1 loss and multiple cross entropy losses, which finally make the best performance of ASGASN. We conduct relevant experiments on both the WHU building dataset [38] and the Chinese typical city building dataset [39] to verify the advancedness of the present network.