Road generalization data & code

As a complex decision-making process, roadnetwork simplification involves stroke recognition, mesh density relatively preserving, and network structure abstracting. Such a multi-factor decision and scaling operation traditionally applied rule-based methods. The construction and adjusting of these rules contain many human-set parameters and conditions, which makes generalized results closely related to the cartographer’sexperience and habits. On the other hand, the existing methods tend to consider individual structures, for example,strokes, meshes, graph networks, etc.,separately in differentalgorithms lacking a solution that bringsthe advantages of these pattern structure handlingstogether. Aiming at the above problems, this study designs a simplification method using the Mesh-Line Structure Unit (MLSU) to simultaneously account for polyline and polygon properties. A graph-based deep learning network is built to use data-driven ideas to realize road selection decisions. The MLSU model can extract22 kinds of polyline features, 5 kinds of polygon features, and 3 interactivefeatures. In order to make generalization decisions,a model based on graph convolutional network is constructed,and the network model is trained with real data from partial areas in the southern United States, thus realizing automatic generalization of the road network. The experimental results show that the proposed method effectively realizes the automatic generalization of road data, and the simplified results have better performance in terms of visual representation, quantity maintenance, and average connectivity compared with other methods. This study also demonstrates the advantages and potential of using graph deep learning techniquesfor map generalization problems.