Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ergebnis 8 von 65
Journal of Applied Engineering Sciences, 2018-12, Vol.8 (2), p.59-64
2018

Details

Autor(en) / Beteiligte
Titel
LOD2 Building Generation Experiences and Comparisons
Ist Teil von
  • Journal of Applied Engineering Sciences, 2018-12, Vol.8 (2), p.59-64
Ort / Verlag
Sciendo
Erscheinungsjahr
2018
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
  • The VOLTA project is a RISE Marie-Curie action designed to realize Research & Innovation (R&I) among intersectoral partners to exchange knowledge, methods and workflows in the geospatial field. To accomplish its objectives, the main R&I activities of VOLTA are divided in four interlinked Work Packages with two transversal ones responsible for knowledge transfer & training as well as dissemination of the project results. The research activities and knowledge transfer are performed with a series of secondments between partners. The consortium is composed of 13 partners from academic & research institutions, industrial partners and national mapping agencies. The Romanian National Center of Cartography is part of this research project and in this article the achievements of the secondment at Bruno Kessler Foundation in Trento (Italy) are given. The main goal of the exchange was to generate level of detail - LOD2 building models in an automated manner from photogrammetric point clouds and without any ancillary data. To benchmark existing commercial solutions for the realization of LOD2 building models, we tested Building Reconstruction. This program generates LOD2 models starting from building footprints, digital terrain model (DTM) and digital surface model (DSM). The presented work examined a research and a commercial-based approach to reconstruct LOD2 building models from point clouds. The full paper will report all technical details of the work with insight analyses and comparisons.
Sprache
Englisch
Identifikatoren
ISSN: 2247-3769
eISSN: 2284-7197
DOI: 10.2478/jaes-2018-0019
Titel-ID: cdi_crossref_primary_10_2478_jaes_2018_0019

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX