Automatic identification of building types based on topographic databases – A comparison of different data sources
ISBN 978-85-88783-11-9
Authors
1Hecht, R.; 2Meinel, G.; 3Buchroithner, M.
1LEIBNIZ INSTITUTE OF ECOLOGICAL URBAN AND REGIONAL DEVELOPME Email: r.hecht@ioer.de
2LEIBNIZ INSTITUTE OF ECOLOGICAL URBAN AND REGIONAL DEVELOPME Email: g.meinel@ioer.de
3DRESDEN UNIVERSITY OF TECHNOLOGY Email: manfred.buchroithner@tu-dresden.de
Abstract
The domains of urban and regional planning, land management, urban studies as well as risk assessment require detailed information about the functional, morphological and socio-economic structure of the built environment. Buildings play a key role as they determine the physical structure of a settlement, which in turn is strongly related to the distribution pattern of housing, workplaces, infrastructure or energy consumption. Data, maps and services of the national mapping and cadastral agencies contain the geometric information on buildings, particularly building footprints. Certainly, detailed information about the building function, the housing form, the number of floors, or building age is often not included. Therefore, during the last years various approaches have been developed to classify and describe the urban structure by means of an analysis of remote sensing imagery and topographic data. In this paper, we propose a data-driven approach for automatic classification of building footprints that make use of pattern recognition and machine learning techniques. Using a Random Forest Classifier the suitability of five different data sources (e.g. topographic raster maps, cadastral databases or digital landscape models) is investigated with respect to the achieved accuracies. The results of this study show that building footprints obtained from topographic databases such as digital landscape models, cadastral databases or 3D city models can be classified with an accuracy of 90 % to 95 %. When classifying building footprints on the basis of topographic maps the accuracy is considerably lower (as of 76 % to 88 %). The automatic classification of building footprints provides an important contribution to the acquisition of new small-scale indicators on settlement structure such as building density, floor space ratio or dwelling/population densities. In addition to its importance for urban research and planning, the results are also relevant for cartographic disciplines such as map generalization, automated mapping and geo-visualization.