ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 59-66, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-59-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
30 May 2017
CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION
D. Bulatov1, S. Wenzel2, G. Häufel1, and J. Meidow1 1Fraunhofer IOSB, Ettlingen, Germany
2Institute of Geodesy and Geoinformation, Photogrammetry Group, University of Bonn, Germany
Keywords: Road Extraction, Generalization, Polyline Simplification, Model Selection, Traffic Roundabout Abstract. Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.
Conference paper (PDF, 9156 KB)


Citation: Bulatov, D., Wenzel, S., Häufel, G., and Meidow, J.: CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 59-66, https://doi.org/10.5194/isprs-annals-IV-1-W1-59-2017, 2017.

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