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

TL;DR

This paper presents a novel graph convolutional neural network approach for classifying irregular spatial data, specifically building patterns, demonstrating significant improvements over existing methods.

Contribution

It introduces a graph-based CNN architecture utilizing graph Fourier transform to effectively analyze irregular spatial vector data.

Findings

Achieved outstanding accuracy in classifying building patterns.

Significantly outperformed existing classification methods.

Validated effectiveness on real-world spatial data.

Abstract

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.