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ISPRS Int. J. Geo-Inf. 2016, 5(8), 145; doi:10.3390/ijgi5080145

Continuous Road Network Generalization throughout All Scales

GIS Technology, OTB—Research for the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands
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Academic Editors: Monika Sester and Wolfgang Kainz
Received: 25 April 2016 / Revised: 30 June 2016 / Accepted: 29 July 2016 / Published: 13 August 2016
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Abstract

Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to lines, which may confuse users. Therefore, we aim at the continuous generalization of a road network for the whole range, from the large scale, where roads are represented as areas, to mid- and small scales, where roads are represented progressively more frequently as lines. As a consequence of this process, there is an intermediate scale range where at the same time some roads will be represented as areas, while others will be represented as lines. We propose a new data model together with a specific data structure where for all map objects, a range of valid map scales is stored. This model is based on the integrated and explicit representation of: (1) a planar area partition; and (2) a linear road network. This enables the generalization process to include the knowledge and understanding of a linear network. This paper further discusses the actual generalization options and algorithms for populating this data structure with high quality vario-scale cartographic content. View Full-Text
Keywords: vario-scale; smooth; continuous map generalization; road network; scales vario-scale; smooth; continuous map generalization; road network; scales
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Šuba, R.; Meijers, M.; Oosterom, P.V. Continuous Road Network Generalization throughout All Scales. ISPRS Int. J. Geo-Inf. 2016, 5, 145.

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