2 Search Results for "Ai, Tinghua"

Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.41
Center Point of Simple Area Feature Based on Triangulation Skeleton Graph (Short Paper)

Authors: Wei Lu and Tinghua Ai

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
In the area of cartography and geographic information science, the center points of area features are related to many fields. The centroid is a conventional choice of center point of area feature. However, it is not suitable for features with a complex shape for the center point may be outside the area or not fit the visual center so well. This paper proposes a novel method to calculate the center point of area feature based on triangulation skeleton graph. This paper defines two kinds of centrality of vertices in skeleton graph according to the centrality theory in graph and network analysis. Through the measurement of vertices centrality, the center points of polygon area features are defined as the vertices with maximum centrality.
Cite as

Wei Lu and Tinghua Ai. Center Point of Simple Area Feature Based on Triangulation Skeleton Graph (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 41:1-41:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{lu_et_al:LIPIcs.GISCIENCE.2018.41,
  author =	{Lu, Wei and Ai, Tinghua},
  title =	{{Center Point of Simple Area Feature Based on Triangulation Skeleton Graph}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{41:1--41:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.41},
  URN =		{urn:nbn:de:0030-drops-93699},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.41},
  annote =	{Keywords: Shape Center, Triangulation Skeleton Graph, Graph Centrality}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.69
Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network (Short Paper)

Authors: Xiongfeng Yan and Tinghua Ai

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

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.
Cite as

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)

Copy BibTex To Clipboard

@InProceedings{yan_et_al:LIPIcs.GISCIENCE.2018.69,
  author =	{Yan, Xiongfeng and Ai, Tinghua},
  title =	{{Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network}},
  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 =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.69},
  URN =		{urn:nbn:de:0030-drops-93973},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.69},
  annote =	{Keywords: Building pattern, Graph CNN, Spatial analysis, Machine learning}
}
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