Geospatial data processing for 3D city model generation, management and visualization

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Abstract

Recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in "smart city" applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above - http://seneca.fbk.eu). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform.

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APA

Toschi, I., Nocerino, E., Remondino, F., Revolti, A., Soria, G., & Piffer, S. (2017). Geospatial data processing for 3D city model generation, management and visualization. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 527–534). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-1-W1-527-2017

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