Publications du COGIT

Inferring the Scale of OpenStreetMap Features

Guillaume Touya, Andreas Reimer
OpenStreetMap in GIScience, Springer International Publishing, page 81--99 - 2015

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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Références BibTex

@InCollection\{Touya15,
  author       = "Touya, Guillaume and Reimer, Andreas",
  title        = "Inferring the Scale of OpenStreetMap Features",
  booktitle    = "OpenStreetMap in GIScience",
  series       = "Lecture Notes in Geoinformation and Cartography",
  pages        = "81--99",
  year         = "2015",
  editor       = "Jokar Arsanjani, Jamal and Zipf, Alexander and Mooney, Peter and Helbich, Marco",
  publisher    = "Springer International Publishing",
  keywords     = "lod, osm, scale, vgi",
}
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