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dc.contributor.authorNebiker, Stephan
dc.contributor.authorCavegn, Stefan
dc.contributor.authorLoesch, Benjamin
dc.date.accessioned2015-11-27T09:12:11Z
dc.date.available2015-11-27T09:12:11Z
dc.date.issued2015-10-26
dc.identifier.urihttp://hdl.handle.net/11654/11502
dc.identifier.urihttp://dx.doi.org/10.26041/fhnw-145
dc.description.abstractIn this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG (“what you see is what you get”) urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.
dc.description.urihttp://www.mdpi.com/2220-9964/4/4/2267
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofISPRS International Journal of Geo-Information
dc.accessRightsAnonymous
dc.subjectsmart city
dc.subjecturban modeling
dc.subjectmobile mapping
dc.subjectstereovision
dc.subjectimage matching
dc.subjectgeoreferencing
dc.subjectcloud computing
dc.subject3d monoplotting
dc.subjectaugmentation
dc.titleCloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City
dc.type01 - Zeitschriftenartikel, Journalartikel oder Magazin
dc.volume4
dc.issue4
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandYes
fhnw.pagination2267-2291
fhnw.IsStudentsWorkno


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