# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 1 Introduction  
As an essential component of transportation, roadways exert a substantial impact on various aspects of contemporary life, encompassing urban and rural development, traffic management, and autonomous vehicle navigation [1]. In tandem with the evolution of satellite remote sensing technology, high-resolution satellite imagery has arisen as a crucial asset for digital image processing in the modern era [2]. As a result, the pursuit of high-precision road extraction from high-resolution satellite imagery has attracted considerable scholarly attention in recent years. However, complications from factors such as illumination, shadow occlusion [3], and noise yield divergent features among identical road targets, while spectral ambiguity resulting from the influence of neighboring materials(non-road targets displaying road-like properties) intensifies the difficulty of accurately and comprehensively extracting roads from high-resolution satellite imagery [4]. Lately, in conjunction with the swift advancement of deep learning, an increasing number of researchers have employed deep learning methods within the domains of image categorization and semantic partitioning [5]. Convolutional neural networks have demonstrated their efficacy in road extraction tasks due to their remarkable feature extraction capabilities [6]. In comparison to conventional approaches requiring the manual extraction of superficial features, deep learning methods not only enhance the precision of road extraction but also display resilient performance in large-scale extraction efforts [7]. [PERSON] et al. [8] pioneered the implementation of deep learning techniques in road extraction, proposing a method employing restricted Boltzmann machines for identifying roads in high-resolution satellite imagery. Subsequently, [PERSON] et al. [9] presented fully convolutional networks (FCNs), effectively addressing semantic segmentation in images. By replacing fully connected layers with standard convolutions, FCNs enabled the shift from elementary classification to pixel-level classification. Unfortunately, the continuous downsampling operations in FCNs lead to the loss of numerous fine-grained details on small feature maps, complicating the restoration of the original resolution and resulting in segmentation challenges, such as blurred boundaries, indistinct edges, and insufficient feature representation [10]. To counter these limitations, [PERSON] et al. introduced the U-Net, integrating supplementary skip connections into an FCN to bolster the network's ability to manage intricate details. U-Net pioneered the encoder-decoder architecture, in which the encoder reduces spatial dimensions and captures spatial detail information, while the decoder reuses low-level features from the encoding stage to progressively restore input size and recover spatial position information [11]. This approach adeptly retrieves a wealth of spatial detail information and optimally exploits road texture information. However, as the depth of the U-Net increases, issues such as gradient vanishing and explosion may occur. Consequently, existing research focuses on enhancing network performance and stability by leveraging the U-Net's unique encoding and decoding architecture, while simultaneously refining the network model [12]. For example, [PERSON] et al. [13] combined the advantages of U-Net and ResNet to create the ResUNet, promoting information propagation through abundant contract connections while fully capitalizing on the superior gradient propagation stability of the residual connection structure. [PERSON] et al. [14] incorporated an attention mechanism into U-Net, yielding the attention U-Net model. This model highlights segmented targets by suppressing feature responses in irrelevant background areas, thereby improving segmentation accuracy and robustness. Furthermore, [PERSON] et al.