## 1 Introduction  
A remote sensing image is a digital image that is observed and obtained using observation instruments loaded on satellites or airplanes to observe and obtain information about water bodies on the earth's surface [1], the atmospheric environment [2], and the distribution of roads. The extraction of road information from remote sensing images has been broadly applied in various fields, such as map road information updating and maintenance [3], urban road network planning and construction [4], and land use detection [5]. The traditional road extraction process still requires manual intervention, which is time consuming and has high labor costs [6]. Nowadays, an increasing number of researchers and students are using convolutional neural networks (CNNs) designed on high-performance GPU devices to replace manual features. This approach has been shown to greatly improve the efficiency of road extraction and to better capture the global contextual information of roads through automated feature learning and end-to-end optimization, which performs well in complex, high-resolution remote sensing imagery, providing higher accuracy, efficiency, and scalability [7, 8, 9, 10, 11]. FCN [12] achieves end-to-end image segmentation by replacing the fully connected layer with convolution, solves the problem that traditional classification networks cannot handle, full-image segmentation, and introduces an upsampling operation to recover the resolution. U-Net [13] further improves FCN by introducing an encoder-decoder structure and hopping connections, which enhances the capture of details and structural information and is particularly suitable for medical applications, remote sensing images, and other tasks that require high-resolution segmentation. The DeepLab [14; 15; 16; 17], on the other hand, employs techniques such as atrous convolution and Spatial Pyramid Pooling, which enhances the model's ability to deal with features at different scales, improves segmentation accuracy and global context understanding, and performs well, especially in complex scenarios. Based on the LinkNet [18], D-LinkNet [19] introduces dense connections and atrous convolution to improve the ability of the model to capture features of different scales and is more suitable for road extraction tasks in high-resolution remote sensing images, promoting the further development of computer vision in road extraction tasks. Existing problems in real situations include the interference of tree occlusion and terrain shadows, the inconsistency of texture and spectral features of roads in different remotely sensed images, and the complexity of road topology, etc. ; hence, the road extraction methods based on deep learning usually produce broken roads, which affects the practical application of the road extraction model. To solve the above problems, researchers have recently made efforts to improve the model's understanding of long-distance context and multiscale information, and to implement improvements around the shape, connectivity, multiscale, and overall extraction strategies of roads. In terms of road shape-based issues, [PERSON] et al. [20] proposed a connectivity attention network (CoANet) based on the strip convolution module, which jointly learns segmentation and neighboring pixel dependencies, and utilizes the road shape law to reduce the effect of topological complexity such as narrow and elongated roads. [PERSON] et al. [21] designed a dynamic snake convolution, which enables the model to adaptively learn meandering roads, thus improving the model's ability to extract narrow paths. In the connectivity-based aspect, [PERSON] et al. [22] proposed a new loss function that prevents unwanted connections between background regions and promotes the connectivity of network-like structures, thus reducing the topological differences between predicted and real roads. In terms of multiscale, [PERSON] et al.