## 1 Introduction  
Accurate extraction of urban buildings is significant for urban planning, disaster assessment, building area estimation, 3D urban modeling, and so on [1; 2; 3; 4]. Compared with urban buildings, rural buildings have their own characteristics: rural buildings have smaller scales, lower floors, scattered distributions in villages, and higher dispersion degrees; second, building materials, designs and construction times vary greatly, leading to larger internal differences [5]. Therefore, extracting rural buildings is more challenging, and related research is relatively scarce. In addition, the high cloud cover in rural areas increases the extraction difficulty [6]. Accurately identifying rural building roof types is of great importance for rural revitalization, environmental planning, energy assessment and disaster management [7; 8; 9; 10]. Most previous rural building identification studies have focused on some local areas without considering the sparse distribution and differential features of buildings across regions, resulting in limited generalization capabilities [11]. Therefore, it is imperative to develop rural building extraction methods applicable to large areas. Traditional manual measurement methods conduct field surveys on buildings, which are inefficient and costly. The complex rural topography increases the workload, making it difficult to meet modern requirements [12]. In recent years, the spatial resolution of high-resolution remote sensing images has significantly improved to submeter levels, providing richer texture and contour details [13]. Therefore, an increasing number of scholars use high-resolution remote sensing images to extract building information [14]. Traditional simple classifiers mostly rely on spectral and texture features to extract buildings from remote sensing images. Faced with increasingly complex high-resolution remote sensing images with abundant information and details, traditional extraction methods are no longer applicable [15]. Object-oriented and machine learning methods are widely used to identify features, such as buildings, in complex images [16]. [PERSON] and colleagues used classifiers such as AdaBoost, Random Forest and support vector machine (SVM) to identify buildings from remote sensing images by segmenting images and removing shadows and vegetation. The newly introduced features of the edge regularity index (ERI) and shadow line index (SLI) significantly improved the accuracy [17]. Related studies also adopt the morphological building index (MBI) to detect potential building areas and use an SVM for training and self-correction to remove falsely detected buildings [18]. However, object-oriented methods rely on image segmentation results. Machine learning methods such as SVMs require large sample sets, and their simple structures cannot acquire in-depth information on building details [19], leading to poor extraction effects on rural buildings with large internal differences. Deep learning technologies based on big data and computer technology have been widely used in image recognition fields and are particularly suitable for analyzing high-resolution remote sensing images. Typical convolutional neural networks (CNNs), such as FCNs, UNet, and VGG16, can autonomously learn complex spectral and texture features to achieve automatic extraction of buildings, roads and other targets [20]. Many scholars have improved classic semantic segmentation network frameworks to adapt to the feature extraction of different land cover types. [PERSON] et al. [21] integrated attention mechanism modules in the building extraction model AGs-UNet. By optimizing feature selection and focusing on small-scale building feature expression, the quality of building contour extraction in high-resolution remote sensing images has been significantly improved. Transfer learning is an effective method to overcome overfitting in neural network training with small data volumes. [PERSON] et al.