## 1 Introduction  
Automatic building extraction from high-resolution aerial imagery is of great significance in a wide range of application domains, such as disaster warning and processing, economic development assessment, and urban land use analysis [1; 2; 3; 4; 5]. In recent years, the improvement of remote sensing image resolution has made the spectral features of buildings more obvious and has provided richer semantic and texture features and other information for building extraction. However, high resolution can also lead to an increase in interference and redundant information. Therefore, the automatic extraction of buildings with high accuracy is a challenge to be tackled. In the last few decades, to make full use of high-resolution remotely sensed imagery features for building information extraction, methods such as edge extraction, image segmentation, and digital morphology have been applied. These methods have achieved definite research results [6; 7; 8; 9; 10]. For example, the voting method was used to determine the orientation of the house edges, and then least squares were used to refine the house edges [11]. Buildings can be extracted by a multi-scale segmentation method based on area growth, and the parameters of the best segmentation results are determined by multiple trials [12]. Building extraction can also be achieved by adding a priori knowledge of the building shape to the active contour model [13]. In addition, excellent machine learning classifiers have been used for the extraction of buildings, including boosting [14], support vector machines (SVM) [15] and random forest [16]. However, the above methods rely heavily on parameter selection and manually predefined features, leading to some limitations in practical building extraction. With the rapid development of computer computing power and an increase in available data sources, deep learning techniques have been more widely and deeply developed, especially convolutional neural networks (CNNs) have played an important role in many fields [17]. Many scholars have used CNN models such as LeNet [18], AlexNet [19], VGGNet [20], GoogleNet [21], and ResNet [22], which outperform traditional machine learning methods, to conduct related studies. However, CNNs perform region division and full convolution operations for each image element, which is computationally expensive. Meanwhile, the CNN processes the feature map into a fixed length output vector and outputs the result in the form of a numerical description [23]. Therefore, CNNs are not suitable for semantic segmentation tasks such as building extraction. In 2015, [PERSON] proposed the Full Convolutional Network (FCN) for classification at the semantic level [24], which can be adapted to predict each pixel of the original image to accomplish the task of image semantic segmentation, and became the paradigm for many classical segmentation networks, mainly including the following. The first is (1) encoder-decoder neural networks. In the encoder part, the feature maps with smaller sizes are obtained by multiple downsampling to obtain multi-level semantic information, and in the decoder part, the final semantic segmentation maps are obtained by decoding the feature maps of each size. For example, The U-Net [25] method improves image segmentation accuracy by fusing multiscale information of images based on the symmetric coding structure through skip-connections. The SegNet [26] method is designed with a convolutional encoder with pooling and a decoder with deconvolution, which improves the edge portrayal and reduces the training parameters. The second is (2) image pyramid neural network, such as the DeepLab series models [27; 28; 29; 30]. To better learn the features at different scales, DeepLabv3+ designs atrous spatial pyramid pooling (ASPP) to obtain more levels of semantic details, which effectively improves the segmentation accuracy. Meanwhile, these FCN-based models and their variants are widely used in tasks such as target extraction and image classification for remote sensing image processing.