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

Authors Xiongfeng Yan , Tinghua Ai



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Xiongfeng Yan
  • School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Tinghua Ai
  • School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

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Xiongfeng Yan and Tinghua Ai. Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 69:1-69:7, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.69

Abstract

Machine learning methods such as Convolutional Neural Network (CNN) are becoming an integral part of scientific research in many disciplines, the analysis of spatial data often failed to these powerful methods because of its irregularity. By using the graph Fourier transform and convolution theorem, we try to convert the convolution operation into a point-wise product in Fourier domain and build a learning architecture of graph CNN for the classification of building patterns. Experiments showed that this method has achieved outstanding results in identifying regular and irregular patterns, and has significantly improved in comparing with other methods.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
Keywords
  • Building pattern
  • Graph CNN
  • Spatial analysis
  • Machine learning

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References

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