A graph convolutional neural network for classification of building patterns using spatial vector data

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 150; pp. 259 - 273
• Main Authors: Yan, Xiongfeng, Ai, Tinghua, Yang, Min, Yin, Hongmei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2019
• Subjects: artificial intelligence; Building pattern classification; buildings; Deep learning; Graph convolutional neural network; Graph Fourier transform; graphs; Machine learning; neural networks; Spatial vector data; vector data; Graph Fourier transform; Deep learning; Spatial vector data; Graph convolutional neural network; Building pattern classification; Machine learning
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2019.02.010

Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. However, these powerful methods often fail to perform pattern analysis and knowledge mining with spatial vector data because in most cases, such data are not underlying grid-like or array structures but can only be modeled as graph structures. The present study introduces a novel graph convolution by converting it from the vertex domain into a point-wise product in the Fourier domain using the graph Fourier transform and convolution theorem. In addition, the graph convolutional neural network (GCNN) architecture is proposed to analyze graph-structured spatial vector data. The focus of this study is the classical task of building pattern classification, which remains limited by the use of design rules and manually extracted features for specific patterns. The spatial vector data representing grouped buildings are modeled as graphs, and indices for the characteristics of individual buildings are investigated to collect the input variables. The pattern features of these graphs are directly extracted by training labeled data. Experiments confirmed that the GCNN produces satisfactory results in terms of identifying regular and irregular patterns, and thus achieves a significant improvement over existing methods. In summary, the GCNN has considerable potential for the analysis of graph-structured spatial vector data as well as scope for further improvement.