## 1 Introduction  
The cadastral survey is a long and tedious task that comprises both legal and technical aspects [1, 2]. The aim is to accurately measure the location, size, and boundary of various land types, and satisfy the needs of the land management or other national economy departments. Especially for rural buildings, keeping the cadastral databases precise and up-to-date is the basic guarantee to ensure the users' land rights and interests, and reduce the disputes when dealing with illegal construction or house-pulling and paying by government. This requires transparency and searchable information in the whole workflow, from data collection to processing, storage, and publication. In recent years, considering that rural buildings often lack unified design and planning, UAV stereo imagery-based approaches are widely used to record and survey cadastral dates [3, 4]. However, due to the complexity of the rural building environments and the high accuracy requirements, huge extensive manual editing is often required, which is both expensive and time-consuming [5]. Even when only considering the 2D vector diagrams for the buildings and courtyard space, current automatic approaches still confront huge challenges [6]. Traditional image-based approaches first extract the building pixels via high-resolution image classification or segmentation, and then track the building outline instances [7, 8]. The key is to fully exploit the salient features like geometric, spectral, or textural, and combine them with the a priori regularized constraints. However, due to the complexity of building types and the interference of various background elements, these methods usually require targeted parameter settings when dealing with different datasets, which is difficult to adjust and difficult to meet the needs of practical applications. Most of the mainstream image segmentation methods can be used for the extraction of buildings, such as region growing by seed points, or using various classification and clustering methods including Mean-shift and K-means [9]. Their main difficulty lies in the limited detection accuracy when dealing with scenes having complex background environments, which are prone to mis-detection and omission [10], especially when the direct transition among roads, elevated areas, and buildings are not obvious. With the continuous development of deep convolutional neural networks, a large number of learning-based methods have also been proposed in recent years. Classical networks turn to adopt an encoder-decoder structure, i.e., the SegNet [11] and UNet [12]. The encoder part continuously performs the convolutional and pooling operations to extract image features, and the decoder part up-samples the image to the original size. The limitations are the loss of spatial details during feature extraction images, the boundary quality, and the accuracy of the final segmentation result. To enhance the adaptation of segmentation algorithms to building scales, the coarse-to-fine boundary refinement network (CBR-Net) [13,14] and the multi-path hybrid attention network (MHA-Net) [15] are also proposed. Moreover, there are some contour description networks that directly extract the polygon boundaries, including the Curve-GCN [16], Polymapper [17], and Polyworld [18]. A significant limitation of these methods is that, according to the regulation of building estate measurement and register, the building range should be measured based on the space enclosed by the outer walls, but not that of the rooftop. The same situation is also confronted by the methods using point clouds from the building rooftop, i.e., [19,20]. Since rural buildings often have significant protruding roofeaves, unenclosed balconies, and concave doorways, the precision of the results detected from the building's roof remain problematic [21]. What is more, the detection results often lack building topology, which means the adjacent or connected roofs are often not separated, requiring extensive editing to achieve the basic requirements.