关键词: 面状居民地, 图卷积神经网络, 形状分类, 制图综合, 图分类

Abstract: Shape recognition and classification is one of the important contents of cartographic generalization. Areal settlement is an important part of geospatial vector data and its shape cognition is a basic technique of cartographic generalization. To solve the shortcomings of traditional geometric and statistical shape classification methods, this paper proposes a novel areal settlements shape classification method based on graph data classification ability of graph convolutional neural network. Firstly, the computation graph is generated according to the contour polygon of areal settlement, and the features of the contour shape are extracted as the attributes of the vertices of computation graph. Secondly, the vertex attributes of the computation graph are aggregated and transmitted for multiple rounds, and the shape information is embedded into a high dimension vector with these vertices attributes. Finally, the graph vectors are input into a fully connected neural network to realize the classification of graphs. The experimental results show that this method can effectively achieve the end-to-end shape information extraction and classification of areal settlements. And it overcomes the deficiency of setting parameters through experience in traditional methods.

Key words: areal settlement, graph convolutional neural network, shape classification, cartographic generalization, graph classification