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Inferring the Scale of OpenStreetMap Features

Abstract : Traditionally, national mapping agencies produced datasets and map products for a low number of specified and internally consistent scales, i.e. at a common level of detail (LoD). With the advent of projects like OpenStreetMap, data users are increasingly confronted with the task of dealing with heterogeneously detailed and scaled geodata. Knowing the scale of geodata is very important for mapping processes such as for generalization of label placement or land-cover studies for instance. In the following chapter, we review and compare two concurrent approaches at automatically assigning scale to OSM objects. The first approach is based on a multi-criteria decision making model, with a rationalist approach for defining and parameterizing the respective criteria, yielding five broad LoD classes. The second approach attempts to identify a single metric from an analysis process, which is then used to interpolate a scale equivalence. Both approaches are combined and tested against well-known Corine data, resulting in an improvement of the scale inference process. The chapter closes with a presentation of the most pressing open problems
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Submitted on : Thursday, August 29, 2019 - 5:20:45 PM
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Guillaume Touya, Andreas Reimer. Inferring the Scale of OpenStreetMap Features. Jokar Arsanjani, Jamal; Zipf, Alexander; Mooney, Peter; Helbich, Marco. OpenStreetMap in GIScience, Springer International Publishing, pp.81-99, 2015, Lecture Notes in Geoinformation and Cartography, 978-3-319-14280-7. ⟨10.1007/978-3-319-14280-7_5⟩. ⟨hal-02274434⟩



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