Classification of building group patterns using graph residual neural network

doi:10.13474/j.cnki.11-2246.2022.0001

Abstract

Abstract: Buildings are important features in the city, the analysis of building group patterns is of great significance in many fields such as map generalization, navigation, municipal planning and so on. Traditional methods for the recognition of building group patterns can be roughly divided into two categories:rule-based methods and machine-learning methods which require a lot of manual processing. In recent years, deep learning, especially the graph convolution neural network's emerging that does not require prior manual processing and can improve the automation degree of the analysis of building group patterns. Traditional graph convolutional neural network model is prone to degradation when training deep networks, which makes it difficult to extract deep features. To solve this problem, a graph residual neural network (GResNet) model is proposed for the classification of building group patterns. Firstly, the roads and rivers are used as constraints, and the K-means method is used to cluster the buildings. Secondly, many indices are used to compute Bertin's visual variables. In each building group, the centroids of the buildings are taken as nodes, and the minimum spanning tree is used to generate edges connecting nodes, after that, the graph representation for building group is constructed. Finally, the building graphs are taken as the input of the proposed GResNet model, and two building group patterns are obtained, namely, regular groups and irregular groups. Experiment results confirm that the proposed model can solve the degradation problem of the traditional graph convolutional neural network model, and obtain higher accuracy.

Key words: building groups, pattern classification, GResNet model, machine learning, deep learning

CLC Number:

P237

ZHANG Ziqiang, LIU Tao, DU Ping, SUO Xuhong, YANG Guolin. Classification of building group patterns using graph residual neural network[J]. Bulletin of Surveying and Mapping, 2022, 0(1): 1-7.

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