Recognition of building group patterns using GCN and knowledge graph

Effective identification of building patterns can improve the quality of automated map generalization. Established methods are limited by complex rule definitions and cannot fully consider the local domain information of buildings patterns, and few studies have focused on recognizing multiple building group patterns using GCN. Therefore, we propose a new model for recognizing building patterns by combining knowledge graph methods and GCN methods. First, the features of individual buildings are acquired, then the graph structure of building is constructed, and finally, by means of GCN and knowledge embedding, the features of buildings are efficiently learned on the basis of the graph structure of building patterns, and the pattern features of building are extracted, so as to realize the recognition of building patterns. The results show that the training accuracy and testing accuracy reach 99.03% and 95.89%, respectively. Compared with other methods, the proposed model can effectively utilize the local spatial information of building patterns and accurately recognize building patterns.