Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City
Lade...
Dateien
[Full text]
Autor:in (Körperschaft)
Publikationsdatum
26.10.2015
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
ISPRS International Journal of Geo-Information
Themenheft
DOI der Originalpublikation
Reihe / Serie
Reihennummer
Jahrgang / Band
4
Ausgabe / Nummer
4
Seiten / Dauer
2267-2291
Patentnummer
Verlag / Herausgebende Institution
MDPI
Verlagsort / Veranstaltungsort
Basel
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
In 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.
Schlagwörter
smart city, urban modeling, mobile mapping, stereovision, image matching, georeferencing, cloud computing, 3d monoplotting, augmentation
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
2220-9964
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Lizenz
Zitation
NEBIKER, Stephan, Stefan CAVEGN und Benjamin LOESCH, 2015. Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City. ISPRS International Journal of Geo-Information. 26 Oktober 2015. Bd. 4, Nr. 4, S. 2267–2291. DOI 10.26041/fhnw-145. Verfügbar unter: https://doi.org/10.26041/fhnw-145