## 1 Introduction  
In the current informationization era, the extraction of road information from high-resolution remote sensing images (HR-RSIs) has become a hot issue in the research field. Thanks to the application of high-resolution remote sensing image technology, the description of road information has become more detailed, covering not only basic attributes such as road geometry, radial characteristics, topology, and context but also detailed features such as texture and structure. As the resolution of the image increases, the road is more clearly visualized as a feature. These rich road targets and detail features provide strong support for the automatic extraction of road information [1]. These are the following difficulties in the road decoding of HR-RSIs: (1) Confusing image elements. When performing the road extraction task, some image elements (pixels) may be incorrectly categorized as roads or non-roads in remote sensing images or other types of images. (2) There is less inter-class variation and more intra-class variation. Spectral features of roads and some non-road objects (e.g., parking lots, buildings, shadows, etc.) may be very similar [2, 3], making it difficult for classification algorithms to distinguish between them. (3) Shadow occlusion and visual occlusion in remotely sensed imagery can lead to missing information, thus affecting the accuracy [4] and completeness of roadway extraction. In this paper, road extraction algorithms based on deep learning methods are further categorized into convolutional neural network methods based on \"encoding-decoding\" and neural network methods based on attention mechanisms. Based on the \"encoding-decoding\" convolutional neural network method, U-Net [5] usually consists of an encoder and a decoder, and its unique \"U\"-shaped network structure gives it this name. [PERSON] [6] et al. proposed a U-Net model that specifically emphasizes the importance of connectivity. The model repaired the problem of incomplete road information extraction due to occlusion by iteratively correcting the lines of road interruptions. [PERSON] [7] et al. designed the Res-UNet model, which incorporates the strengths of residual learning and the U-Net structure, which in turn optimizes and enhances the accuracy of road network extraction. In order to improve the accuracy and reliability of the extraction of small-sized roads in high-resolution remote sensing images, [PERSON] [8] et al. proposed an improved deep neural network model, DDU-Net. This model adds an auxiliary small decoder to U-Net to construct a dual-decoder structure for finer feature acquisition. [PERSON] [9] et al. proposed an improved U-Net model by incorporating deep residuals and dilation-aware mechanisms for the road extraction task. In the encoder stage of the model, they introduced a residual module to mitigate the degradation problem during training. Meanwhile, in order to enhance the model's ability to extract multi-scale features, they added an expansion-aware unit between the encoder and the decoder, and this improvement expanded the model's sensory field. The extraction of unpaved roads in rural areas is more challenging compared to urban roads. [PERSON] et al. successfully identified and extracted unpaved roads in rural areas by using the SegNet model, which was particularly effective. [PERSON] [10] et al. proposed a convolutional neural network, which is based on a down-sampling followed by up-sampling architecture, with the aim of extracting roads from aerial images. The proposed encoder-decoder structure allows the network to retain boundary information and is less complex in terms of depth, number of parameters, and memory size. A neural network method based on attention mechanism.