DOI: 10.5593/SGEM2014/B21/S8.065


V. Hron,V. Kostin,L. Halounova
Wednesday 1 October 2014 by Libadmin2014

References: 14th International Multidisciplinary Scientific GeoConference SGEM 2014, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619-7105-10-0 / ISSN 1314-2704, June 19-25, 2014, Book 2, Vol. 1, 513-520 pp

The fields of application of 3D building models are quite various such as 3D city models (visualizations, urban planning), intelligent transportation (smart navigation, augmented reality), environmental monitoring (propagation of road traffic noise, air pollution), special application (propagation of electromagnetic waves for telecommunication applications) and risk management (generation of flood maps). 3D building models have a great importance for specialists from a wide range of disciplines, therefore they are very important. The most common form of spatial data for the generation of 3D building models is a point cloud. Point clouds can be obtained by active sensors like airborne laser scanning (ALS) systems which use the Light Detection and Ranging (LiDAR) principle or it can be derived by image matching techniques using satellite or aerial images. Both techniques are very modern and progressive methods of non-selective collection of spatial elevation data. Point cloud data represents the surface geometry of an object via independent distribution of points with uniform quality, however, the representation of buildings through point clouds is not appropriate for many applications. Handling with a set of data points covering large areas is difficult and very consuming on hardware. For more sophisticated tasks, a generalization and simplification of the point cloud is necessary. The generation of 3D building models is just such the case. A major influence on the generation of 3D building models is the density and quality of the point cloud, which is determined by scanning parameters (LiDAR principle) or ground sampling distances and overlaps between images (image matching techniques). The manual processing of point clouds is extremely time consuming and it is impossible to repeat it with the same result due to the human factor. Fully automatic methods of processing are used with increasing amounts of data that can be processed in shorter time periods. These methods of processing are very popular and in demand nowadays. There are several different commercial software products on the market that solve the problem of fully automatic generation of 3D building models. This paper will be a comparison of current commercial software products (ENVI LiDAR and INPHO Building Generator) that process this task. These software products have not yet been compared. Software testing will be performed on three datasets with different densities of point clouds.

Keywords: airborne laser scanning, automatic generation, building models

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