# Research on Automatic Generation of Park Road Network Based on Skeleton Algorithm
## 2 Related Research Status
The skeleton not only possesses the fundamental geometric traits of the shape but also encompasses the topological structure of the shape. It can represent the shape hierarchically, mirroring the local and overall relationship of the shape, and is extensively utilized in fields like image analysis, shape matching, and pattern recognition. For instance, [PERSON] et al. [13] utilized the skeleton algorithm to identify the healthy posture of the human body; [PERSON] et al. [14] detected pavement cracks via the binocular ranging enhancement algorithm combined with edge detection and the skeleton extraction algorithm; [PERSON] et al. [15] integrated the skeleton algorithm with a convolutional neural network for the land cover mapping of remote sensing images. This kind of research focuses on using the skeleton algorithm to accomplish the recognition of graphic images instead of the design of graphics. An increasing number of researchers have also employed the skeleton algorithm in path planning. For example, [PERSON] et al. [16] devised a skeleton-based approach for constructing the global path for mobile robots; [PERSON] et al. [17] exploited the skeleton graph to offer a continuous traffic path optimization scheme; [PERSON] and [PERSON] [18] proposed a path planning method for the autonomous movement of robots in unknown environments by utilizing skeletonization; [PERSON] et al. [19] put forward a path planning method based on skeleton extraction and the greedy algorithm to guide the flight of unmanned aerial vehicles. Path planning bears certain similarities to road network generation, yet road planning and design entail the consideration of more factors and demonstrate stronger functionality. Nevertheless, in current projects when planning road networks, few can comprehensively take into account these diverse design requirements. Some scholars have also employed the skeleton algorithm in the research of topological structure extraction and spatial analysis within the realm of architecture and related fields. [PERSON] et al. [20] utilized the skeleton to define the convexity and feasibility of the outdoor public space of buildings; [PERSON] et al. [21] exploited the graphical skeleton to analyze the plane layout axis and design techniques of Japanese dry landscape gardens. Nevertheless, this kind of research imposes considerable form restrictions on architectural plans. The cases adopted in their studies are mainly architectural plans, and complex environments have not been verified. Through the aforementioned analysis, it becomes evident that the issue of road network generation has garnered sustained attention from individuals. However, the majority of the efforts have not completely addressed the aforementioned challenges. Currently, researchers have achieved certain research advancements in both skeleton extraction and pruning. Nevertheless, when applying them to the domains of landscape or road design, some problems persist. Firstly, they are primarily manifested in the hardship of handling complex environmental shapes, particularly the generation of road networks in intricate environments like parks. Secondly, there are diverse elements such as buildings and water bodies in parks. The questions of how to reflect design requirements while extracting the skeleton and how to incorporate domain knowledge into the design scheme constitute important research contents. This study utilizes the vector terrain of a park as the input data, incorporates the graphic skeleton algorithm into the automatic generation of the park road network, and combines the relevant requirements of the park road network design to optimize the algorithm for diverse environmental textures, thereby attaining the automatic generation of the park road network. This research approach enables designers to comprehensively contemplate environmental constraints in conjunction with domain knowledge, possesses precise operability and high flexibility, and compensates for the shortcomings of deep learning methods.