## 1 Introduction  
The application of semantic segmentation in remote sensing image interpretation has become a crucial link between basic and advanced remote sensing image processing and analysis. This technology significantly enhances the accuracy of remote sensing data interpretation and provides critical technical support for interdisciplinary applications. Remote sensing images, as key carriers of land feature information, contain substantial economic and social value within their data. In remote sensing image analysis, the in-depth exploration and utilization of this information is central to research [1]. Digital image processing techniques enable the efficient extraction of rich information from remote sensing images, which is crucial for various fields, such as cadastral surveying, ecological environment monitoring, and urban planning. Semantic segmentation, by dividing images into multiple regions with specific attributes, can accurately identify target objects or features. It has demonstrated significant applications in various fields, such as geographic information systems (GIS), autonomous driving, robotics, and medical image recognition. However, due to the inherent diversity of categories and the complexity of detailed features in remote sensing images, as well as significant redundancy across different spectral bands,traditional shallow models often struggle to achieve optimal feature extraction results. At the same time, the accuracy of traditional remote sensing image semantic segmentation methods remains relatively low. Therefore, improving the accuracy of target extraction in remote sensing images has become a key and challenging topic in current remote sensing research and technological development [2; 3]. Classic remote sensing image segmentation methods primarily rely on low-level image features, such as color and shape, to design a set of features, which are then combined with an existing feature library for automatic target recognition and extraction. Thresholding, edge detection, and region-based segmentation are common methods for image segmentation. Thresholding segmentation [4; 5; 6] selects a threshold based on the image's grayscale features, effectively separating the target from the background with high computational efficiency. Due to the inherent feature of regional discontinuity in remote sensing images, edge detection segmentation can be employed to perform image segmentation based on this characteristic. [PERSON], [PERSON] et al. [7] detected edge features in multi-spectral remote sensing images and combined them with multi-scale segmentation techniques to control the merging process of adjacent objects. [PERSON], [PERSON] et al. [8] added an edge penalty term, which not only ensures the accurate segmentation of large objects in remote sensing images but also effectively identifies smaller objects. [PERSON], [PERSON] et al. [9] used differential operators for edge detection in their study and proposed an image segmentation method based on edge detection. [PERSON], [PERSON] et al. [10] proposed a co-occurrence matrix from the grayscale perspective, combining regional feature information with image edge information to improve segmentation accuracy. However, the information contained in remote sensing images is complex, and these segmentation algorithms are susceptible to interference from factors, such as lighting and sensors, resulting in limited robustness. They are only capable of handling simple, specific data and are not efficient or precise enough for building extraction. Machine learning-based segmentation algorithms have somewhat alleviated these issues. However, traditional machine learning classifiers (such as AdaBoost, support vector machines (SVMs), and random forests) used for building feature extraction typically require post-processing steps to refine the segmentation results [11; 12; 13]. These methods not only have high model complexity and require substantial human-machine interaction but are also often limited by the constraints of human knowledge and experience.