# An Arch-Bridge Topology-Based Expressway Network Structure and Automatic Generation
## 2 Literature Review  
### Remote Sensing Image Recognition Method  
Research on generating road networks based on remote sensing images can be categorized into semi-automatic and automatic methods depending on the degree of human intervention [16]. In the semi-automatic approach, seeds are typically the center points or contours of road segments, and are manually provided or verified after extraction. On the other hand, the automatic method involves adjusting parameters based on different source images before the program starts, and the subsequent process does not require human intervention. In the earlier days of computer vision, bottom-up processes such as stereo matching and motion tracking required advanced knowledge to correctly interpret low-level events. To address this challenge, [PERSON] et al. [17] proposed the snake model, which defines an energy function on the user-defined image contour and iteratively adjusts the energy minimum to converge the image contour. The snake model has been widely adopted in various fields of computer vision and has also been introduced into the research on semi-automatic road network generation based on remote sensing images [18; 19; 20]. Additionally, the snake model can solve the problem of road image noise and large gaps between roads [21]. Some scholars have also incorporated dynamic programming methods. [PERSON] and [PERSON] [22] proposed a semi-automatic road extraction scheme that combines wavelet decomposition for road sharpening with a model-driven linear feature extraction method based on dynamic programming. Based on this research, [PERSON] and [PERSON] [23] introduced road width into the metric function, which can be utilized for road network extraction in remote sensing images. Furthermore, [PERSON] et al. [24] automatically estimated seeds using the maximum road probability, connected seeds using the minimum cost path, and constructed a road network through mixed integer programming, thereby resolving the problem of low efficiency due to human intervention. Although the semi-automatic method has achieved some results, the manual intervention mode greatly increases the time cost. Therefore, many scholars are also exploring automatic methods for extracting road networks. Artificial neural networks are complex network structures composed of a large number of neurons, which are an abstract simulation of the human brain mechanism that developed rapidly after the introduction of backpropagation. [PERSON] and [PERSON] [25] proposed a method for road detection from high-resolution satellite images using artificial neural networks and found the best network structure by verifying the impact of different input parameters on the network's ability. Based on this, some scholars have improved the road and background detection capabilities of neural networks by adding different combinations of texture and spectral parameters during road extraction based on an artificial neural network classifier [26]. Support vector machines (SVMs) are a type of linear binary classifier that assigns one of two possible labels to a given test sample. SVMs are particularly attractive in remote sensing because they have good generalization ability, even with limited training samples [27]. [PERSON] and [PERSON] [28] first used Support Vector Machines (SVMs) to extract roads from remote sensing images based on road edges, achieving high completeness but relatively low accuracy. Based on this, [PERSON] et al. [29] combined support vector machines with the output of differential matrices to extract roads more accurately. In recent years, the rapid improvement of GPU performance has facilitated the wide application of deep learning techniques in road network generation. [PERSON] and [PERSON] [30] pioneered the use of deep learning techniques for this purpose, proposing a method that utilizes restricted Boltzmann machines (RBMs) to detect road areas from high-resolution aerial images. Following their lead, [PERSON] et al.