Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-4/W25, 73-79, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
30 Aug 2012
M. Ziems1, J. Beyen2, S. Mueller1, S. Roovers2, and C. Heipke1 1IPI – Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, Germany
2NGI – National Geographical Institute, Abdij Ter Kameren 13, B-1000 Brussels, Belgium
Keywords: Road database, Quality, Updating, Aerial, DEM/DTM, Classification, Model, Change detection Abstract. This paper describes a semi-automatic system for road update based on high resolution orthophotos and 3D surface models. Potential update regions are identified by an object-wise verification of all existing database records, followed by a scene-wide detection of redevelopment regions. The proposed system combines several road detection and road verification approaches from current literature to form a more general solution. Each road detection / verification approach is realized as an independent module representing a unique road model combined with a corresponding processing strategy. The object-wise verification result of each module is formulated as a binary decision between the classes "correct road" and "incorrect road". These individual decisions are combined by Dempster-Shafer fusion, which provides tools for dealing with uncertain and incomplete knowledge about the statistical properties of the data. For each road detection / verification module a confidence function for the result is introduced that reflects the degree of correspondence of an actual test situation with an optimal situation according to the underlying road model of that module. Experimental results achieved with data from the national Belgian road database in a test site of about 134 km2 demonstrate the potential of the method.
Conference paper (PDF, 1711 KB)

Citation: Ziems, M., Beyen, J., Mueller, S., Roovers, S., and Heipke, C.: MULTIPLE-MODEL BASED UPDATE OF BELGIAN REFERENCE ROAD DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-4/W25, 73-79,, 2011.

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