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