A Pattern Recognition Method for Road Network Combining Road Sections and Mesh Structures
The road network has a distinct spatial distribution pattern,and its pattern recognition plays a crucial role in many fields such as mapping,map matching,and spatial queries.The current road network pattern recognition methods are mostly based on two models:mesh structure and road segment structure,which have achieved good recognition results,but also have certain limitations.Existing mesh based recognition algorithms are often limited to the grid pattern of the smallest unit,and cannot recognize patterns of globally regular and locally fragmented mesh groups;Pattern recognition based on road segment structure involves complex preprocessing and recognition processes,and can only recognize a single road network pattern.As a branch of information entropy,directional entropy can effectively describe the spatial distribution characteristics and patterns of geographic data.Therefore,this article combines the recognition advantages of mesh structure in the case of regular mesh and the advantages of road section structure in analyzing the arrangement of road sections.Based on this,feature parameters such as directional entropy,rectangularity,and concavity are introduced to construct an effective road network pattern recognition algorithm.This article selects the road networks of Wuhan,Shanghai,Nanjing,and Xi'an as experimental objects for sample construction.The sample sets of Nanjing and Xi'an are used to calculate the parameter classification thresholds corresponding to different road network modes and determine the road network pattern recognition rules.The sample sets of Wuhan and Shanghai are used to verify the feasibility and transferability of the algorithm proposed in this article.This method not only completes the pattern recognition of grid networks and irregular road networks,but also divides them into linear mesh groups and grid mesh groups based on the different arrangement of mesh groups in grid networks by constructing a minimum mesh centroid spanning tree and using directional entropy.The experimental results show that the pattern classification accuracy of the road network pattern recognition algorithm combining road segments and mesh structures reaches over 97％,which can effectively complete the pattern recognition of grid networks and irregular road networks,and can well subdivide grid patterns according to the arrangement of mesh groups in the grid network.The algorithm in this paper has good transferability and the recognition results are consistent with human cognition.Compared with existing road network pattern recognition methods,this paper constructs a simple,fast,and relatively accurate road network pattern recognition algorithm,providing a new approach for subsequent research in fields such as map synthesis,pattern recognition,and urban planning.
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