Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network
This work addresses the problem of handling irregular spatial data for researchers in fields like geography and urban planning, representing an incremental advancement by adapting existing graph-based methods to a specific domain.
The study tackled the problem of analyzing irregular spatial data, which is challenging for standard convolutional neural networks, by developing a graph convolutional neural network using graph Fourier transform and convolution theorem, achieving outstanding results in classifying building patterns with significant improvements over other methods.
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.