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A Multi-parameter Approach to Automated Building Grouping and Generalization

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Abstract

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,’ ‘average’ or ‘weak.’ The ‘weak’ groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in blocks.

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Acknowledgements

We are grateful to the anonymous reviewers whose comments helped to improve our paper. We would also like to thank the Shenzhen Municipal Bureau of Land Resource of Guangdong province, China, and the Institut Géographique National (IGN), France for providing the data used in our experiments. Research on this paper was partially funded by the Chinese Scholarship Council, by the Natural Science Foundation Committee of China (40301037), and by the Key Laboratory of Geographically Spatial Information Engineering of the National Surveying and Mapping Bureau of China.

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Authors and Affiliations

  1. School of Mathematics, Physics and Software Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Haowen Yan

  2. GIS Division, Department of Geography, University of Zurich, Zurich, Switzerland

    Haowen Yan & Robert Weibel

  3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Bisheng Yang

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  1. Haowen Yan
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  2. Robert Weibel
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  3. Bisheng Yang
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Correspondence to Haowen Yan.

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Yan, H., Weibel, R. & Yang, B. A Multi-parameter Approach to Automated Building Grouping and Generalization. Geoinformatica 12, 73–89 (2008). https://doi.org/10.1007/s10707-007-0020-5

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  • DOI: https://doi.org/10.1007/s10707-007-0020-5

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