Building Polygon Extraction from High-Resolution Remote Sensing Imagery Using Knowledge Distillation  
[PERSON]  
1 Zhejiang College of Security Technology, No. 2555 Ouhai Main Road, Wenzhou 325016, China; 2009534@zjcs. example, firstly, remote sensing imagery is initially interpreted manually or through semi-automatic methods to extract the raster mask [7]. Secondly, mathematical morphology [8], edge detection operators such as Canny and Sobel [9; 10] and Hough transform [11] are employed to extract building features, which are used to design rules for the transformation process. Finally, a rule set is used to determine the boundary positions of building raster masks and convert them into vector polygons. However, these methods can only extract accurate building polygons when predefined rules are satisfied. Besides, the methods are complex, resource-intensive, and have high labor costs, resulting in low automation levels [12]. In recent years, deep learning techniques have been widely applied in the field of remote sensing and have achieved remarkable results in building extraction [13]. For instance, using high-resolution remote sensing imagery, accurate building regions can be extracted using techniques such as semantic segmentation [14; 15; 16], object detection [17; 18; 19], and instance segmentation [20; 21]. Building polygon extraction techniques have also gradually shifted towards deep learning-based methods. The basic workflow of such methods involves first obtaining a raster mask for the building class using deep learning-based semantic segmentation techniques and then vectorizing the raster results. Furthermore, researchers have integrated strategies such as multi-scale information [22], attention mechanisms [23], and multi-feature fusion [24] to extract more precise and effective features from high-resolution imagery to obtain more accurate raster masks. In the vectorization process, some studies have incorporated building corner and edge information to optimize building boundaries [25; 26] or used Frame-Field to align the building segmentation maps for better edge representation [27; 28]. To achieve end-to-end automatic building polygon extraction, some methods adopt the idea of multi-task learning by combining building segmentation with the vectorization process [29]. Certain approaches first detect the bounding boxes of building objects and then use LSTM to predict the building corners [30; 31]. Additionally, some researchers treat building polygons as graphs [32], detecting building vertices and computing their adjacent connections to obtain polygonal contours. However, deep learning methods require a large number of well-annotated labeled samples for support [33], and obtaining building polygon labels through manual annotation incurs significant labeling costs [34]. In practical applications, government agencies can only provide a small number of building polygon-labeled samples due to data confidentiality requirements. Training with a small number of samples leads to insufficient model generalization, making it difficult to handle the challenges of extracting diverse building polygons against complex backgrounds [35]. It is worth noting that some large and high-quality building extraction datasets are publicly available [26; 36; 37]. These datasets include building samples from different regions, encompassing various building styles, structures, and diverse backgrounds. Therefore, it is possible to extract generalization knowledge from these abundant and diverse building samples. However, directly applying models trained on other datasets to predict the target area may not yield satisfactory results. To account for the building characteristics in the target area, it is possible to incorporate samples from the target area during the training process. A commonly used approach in current research is to first pre-train the model on publicly available datasets and then fine-tune the model on samples from the target area [38; 39; 40; 41].