## 1 Introduction
Deep learning, an advanced method within the field of machine learning, has found widespread applications in remote sensing and computer vision. Numerous researchers have conducted extensive studies in these fields using this technology. [PERSON], [PERSON] et al. [14] introduced the AlexNet model, which was the first to integrate the ReLU activation function and the Dropout strategy into the architecture of convolutional neural networks (CNNs). They also applied GPU acceleration to the training and testing processes of the network. [PERSON], [PERSON] et al. [15] proposed the Fully Convolutional Network (FCN), an innovative architecture. The breakthrough of this method lies in its ability to achieve end-to-end learning for image segmentation tasks, where feature images are directly input, and the corresponding outputs are generated. This innovation not only improves the computational efficiency of semantic image segmentation but also enhances segmentation accuracy. However, Fully Convolutional Networks (FCNs) have certain limitations in image segmentation tasks. While the network performs well in pixel-level classification tasks, it primarily focuses on the independent classification of individual pixels. It does not fully consider the spatial relationships between pixels and the correlation of pixel values, leading to segmentation results that lack spatial consistency. To address this issue, researchers have focused on optimizing the network architecture, improving the model's ability to capture the spatial relationships and value correlations between pixels to enhance segmentation performance. U-Net, proposed by [PERSON], [PERSON] et al. [16]in 2015, is a variant of the Fully Convolutional Network (FCN). The network features a symmetric architecture, extracting both low-level and high-level features. By introducing skip connections, U-Net enables the fusion of feature maps at the same scale between the encoder and decoder. However, this method requires strict consistency in feature scales and is challenging to directly determine the optimal depth in practical applications. [PERSON], [PERSON] et al. [17] proposed UNet++ to address the limitations of traditional U-Net in segmenting objects of varying scales. By introducing nested and dense skip connections, UNet++ significantly improves segmentation accuracy and achieves faster convergence. In addition to improving the network architecture, some researchers have actively explored new methods and techniques to combine with the U-Net model to enhance its performance and efficiency. [PERSON], [PERSON] et al. [18] addressed the overfitting issue in U-Net segmentation results by proposing a random weighted averaging method, which achieved a broader optimal solution and improved generalization. He, [PERSON] et al. [19] tackled the issue of CNNs neglecting the correlations between intermediate features in building segmentation by proposing a hybrid first- and second-order attention network (HFSA). This network adaptively re-scales intermediate features by leveraging global averages and inner products between different channels, making the features more representative. The key challenge in high-resolution remote sensing image segmentation for buildings lies in distinguishing building pixels from background pixels. However, during segmentation, interference from adjacent factors, such as bridges, shadows, riverbanks, and road-like structures makes it difficult to establish an efficient segmentation model, especially at the boundaries of the segmented targets where accurate results are hard to achieve. To address these issues, this study applies deep learning to building extraction from remote sensing images and proposes a building segmentation network named REU-Net. This network combines residual structures with an Edge Enhancement Attention Module to improve segmentation performance. The network is based on U-Net, with the following three main optimization strategies:  
1.