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Automatic classification of building types in 3D city models

Using SVMs for semantic enrichment of low resolution building data

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Abstract

This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.

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Acknowledgements

The authors appreciate the helpful and detailed comments given by the anonymous reviewers. We thank Rosemarie Schlager for proof-reading and improving the English language and style of this text. Furthermore, we thank Michael Kneuper for his assistance in preparing the illustrations.

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Authors and Affiliations

  1. Institute for Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115, Bonn, Germany

    André Henn, Christoph Römer, Gerhard Gröger & Lutz Plümer

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  1. André Henn
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  3. Gerhard Gröger
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Correspondence to André Henn.

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Henn, A., Römer, C., Gröger, G. et al. Automatic classification of building types in 3D city models. Geoinformatica 16, 281–306 (2012). https://doi.org/10.1007/s10707-011-0131-x

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  • DOI: https://doi.org/10.1007/s10707-011-0131-x

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