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Abstract
About one decade has passed since US vice president Al Gore articulated his vision of Digital Earth (DE). Within this decade, a global multi-resolution and three-dimensional (3D) representation of the Earth, which sums up the DE vision, increasingly gained interest in both public and science. Due to the desired high resolution of the available data, highly detailed 3D city models comprise a huge part of DE and they are becoming an essential and useful tool for a range of different applications. In the past as well as at present, 3D models normally come from a range of different sources generated by professionals, such as laser scans or photogrammetry combined with 2D cadaster data. Some models are generated with semi-automated or fully automated approaches, but in most cases manual fine tuning or even manual construction from architectural plans is required. Further beyond outdoor city models, DE additionally envisages the provision of indoor information. That is, the interior structure of public or publically accessible buildings, such as airports or shopping malls, is represented and made available in 3D; however, at the moment, such models are mostly created by hand and essentially based on professional data sources. In contrast to such professional data, which is mainly captured by surveyors or companies, the last few years revealed the phenomenon of crowdsourced geodata, which receives an increasing attractiveness as an alternative data source for many Geographic Information Systems (GIS). Former research already demonstrated the power and richness of such geodata – especially OpenStreetMap (OSM) – and it has also been proved that this non-standardized, crowdsourced geodata can be combined with international standards of the Open Geospatial Consortium (OGC). For example, CityGML Level-of-Detail 1 (LoD1) and LoD2 models have already been created automatically from OSM. The research presented in this article will further continue on the automated generation of CityGML models from OpenStreetMap. Essentially, a method for the creation of highly detailed CityGML LoD4 models with interior structures will be explained. By applying the invented approach on existing OSM data, limitations and restrictions of the IndoorOSM mapping proposal, the available data and the developed approach are revealed and discussed.
Acknowledgments
The author would like to express his thankfulness to the anonymous reviewers. By providing their valuable comments, they contributed toward the improvement of this work. The author would also like to thank his colleagues Julian Hagenauer, Eric Vaz and Alexander Zipf for their proofreading and the fruitful discussions.
Notes
3. The Web 2.0 describes an open community of users who do not only consume data in the web but also create their own data and share it with the corresponding community.
6. A global OSM dataset of 23 July 2012 contains 61,203,592 buildings and 57,941,913 streets footprints (based on our internal OSM database).
7. JOSM is a Java based OSM editor and can be downloaded on josm.openstreetmap.org.
8. Potlatch is the integrated editor on the OSM webpage www.openstreetmap.org