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Machine learning methods such as convolutional neural networks (CNNs) arebecoming an integral part of scientific research in many disciplines, spatialvector data often fail to be analyzed using these powerful learning methodsbecause of its irregularities. With the aid of graph Fourier transform andconvolution theorem, it is possible to convert the convolution as a point-wiseproduct in Fourier domain and construct a learning architecture of CNN on graphfor the analysis task of irregular spatial data. In this study, we used theclassification task of building patterns as a case study to test this method,and experiments showed that this method has achieved outstanding results inidentifying regular and irregular patterns, and has significantly improved incomparing with other methods.

Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network