License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.69
URN: urn:nbn:de:0030-drops-93973
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9397/
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Yan, Xiongfeng ; Ai, Tinghua

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

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LIPIcs-GISCIENCE-2018-69.pdf (0.7 MB)

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.

BibTeX - Entry

@InProceedings{yan_et_al:LIPIcs:2018:9397,
  author =	{Xiongfeng Yan and Tinghua Ai},
  title =	{{Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{69:1--69:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9397},
  URN =		{urn:nbn:de:0030-drops-93973},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.69},
  annote =	{Keywords: Building pattern, Graph CNN, Spatial analysis, Machine learning}
}

Keywords: Building pattern, Graph CNN, Spatial analysis, Machine learning
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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