## 1 Introduction  
Buildings are essential components of cities and the most variable basic geographic data. Accurate extraction of building information holds significant application value in aspects such as urban planning, urban management, urban digitization, and the updating of geodatabases [1, 2, 3]. Buildings in images possess richer detailed information due to the continuous improvement of remote sensing image quality and resolution. Nevertheless,this situation also brings in a greater amount of noise, leading to a more pronounced differentiation between building areas and non-building areas [4]. Additionally, there are issues such as diverse building scales and complex structures. Therefore, accurate and efficient high-resolution remote sensing image-based building extraction is currently a key research area. In large-scale practical applications, employing manual extraction of information will consume a lot of human and material resources, and accuracy cannot be guaranteed. In recent years, many building extraction methods have been proposed by related scholars, including traditional methods and deep learning methods. Traditional methods, such as maximum likelihood algorithms [5; 6], support vector machines [7; 8; 9; 10], decision trees [11; 12], and random forests [13; 14; 15; 16], etc., face challenges. Since top-quality remote sensing images are packed full of diverse feature details, these algorithms rely on a priori knowledge. They utilize the shape, color, and contour of the target objects to obtain feature parameters and establish decision rules. However, they are easily restricted to building areas with specific shapes. As a result, the \"salt-and-pepper\" phenomenon occurs when extracting building information. Moreover, the difference in spectral brightness between features can lead to the situations of \"the same object with different spectra\" and \"different objects with the same spectrum\", which ultimately results in low extraction accuracy and blurred boundaries. Object-oriented classification approaches have the ability to efficiently prevent classification errors that are induced by spectral disparities. These methods mainly encompass the region segmentation method [17], texture-based segmentation [18], and several other techniques. The segmentation parameters play a vital part in determining the precision of image segmentation. Moreover, they also augment the complexity of the classification task. Owing to the intricacy of buildings, their high density, and the occlusion caused by trees, it becomes rather arduous to extract useful information from the images. With the rapid advancement in the realm of computer vision, deep learning models have increasingly come into the spotlight. In particular, the capability of convolutional neural networks (CNNs) to autonomously extract features has been extensively utilized in the domain of image recognition. Prominent examples of such CNN architectures include VGG, GoogLeNet, and ResNet, among others [19; 20; 21]. [PERSON] [22] adopted a convolutional neural network-based method for the extraction of buildings. Nevertheless, the relatively high computational demands associated with this approach, which influenced the segmentation efficiency, had an impact on the quality of the processed images. [PERSON] et al. [23] proposed the fully convolutional neural network (FCN) to solve the problem, and it became the mainstream semantic segmentation framework, making use of convolutional layers in lieu of fully connected layers to bring about the first end-to-end trained network, which effectively improved training efficiency. Generalized convolutional neural network (CNN) models cannot accurately extract the contours of buildings. In an attempt to further enhance the precision of building extraction, scholars have proposed some improved methods based on the FCN, including PSPNet [24], DeepLabV3+ [25], U-Net [26], and other networks. The pyramid pooling module plays the role of the essential component within the PSPNet network.