基于约束的城市街道网自动综合方法

An approach to constraint-based urban street network automated generalization

近年来,基于约束的地图自动综合成为研究的热点。本研究在基于约束的地图自动综合理论的基础上,提出了一种城市街道网自动综合方法。该方法依赖于街道网综合中的约束和改进的动态决策树结构,它很好地将约束融入了特定的数据结构,实现了街道网的渐进式综合。实验证明了方法具备可行性。

The constraint - based automated generalization is an active issue in the field of map generalization. Based on the theories of constraint - based automated map generalization, this paper proposes an approach to the generalization of an urban street network. It relies on the constraints of the street network generalization and a novel data structure, called ameliorated dynamic decision tree. This method makes a good fusion of constraint and specific data structure. Thus the generalization process can be implemented progressively. The proposed approach is validated by a case study.