标题: A point selection method in map generalization using graph convolutional network model

作者: Xiao, TY (Xiao, Tianyuan); Ai, TH (Ai, Tinghua); Yu, HF (Yu, Huafei); Yang, M (Yang, Min); Liu, PC (Liu, Pengcheng)

来源出版物: CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE DOI: 10.1080/15230406.2023.2187886 提前访问日期: MAR 2023

摘要: For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manual setting of conditions or thresholds and sometimes encounter special cases that make it difficult to directly match certain rules or integrate different rules together. An alternative method is using a data-driven strategy under AI technology background to simulate cartographer behaviors through typical sample training, such as deep learning. The integration of cartography domain knowledge and deep learning is a better choice to settle generalization decisions. This study uses a combination of domain knowledge and a data-driven approach to introduce graph neural networks into point cluster generalization. First, we construct a virtual graph structure of point clusters using Delaunay triangulation, secondly, we extract spatial features, contextual features, and attributes of each point separately, and then propose a generalization model based on the TAGCN network. Finally, this model is trained with the manually generalized sample to realize the automatic point cluster generalization. The results demonstrate that the proposed model is valid and efficient for point cluster generalization and that this algorithm can better maintain various characteristics of the point cluster in both the local area and the overall map compared to other methods.

作者关键词: Map generalization; point cluster; data-driven; graph convolutional network; context

地址: [Xiao, Tianyuan; Ai, Tinghua; Yu, Huafei; Yang, Min] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Liu, Pengcheng] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.

通讯作者地址: Ai, TH (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

电子邮件地址: tinghuaai@whu.edu.cn

影响因子：2.354