TY - GEN
T1 - Preparing detailed 3D building models for Google earth integration
AU - Truong-Hong, Linh
AU - Pham Thi, Thanh Thoa
AU - Yin, Junjun
AU - Carswell, James D.
PY - 2013
Y1 - 2013
N2 - Today's spatially aware users are becoming more interested in retrieving personalised and task relevant information, requiring detailed 3D city models linked to non-spatial attribute data. However, current implementations of 3D city models are typically LoD2 that don't include geometric or attribute details about many visible features (e.g. rooms) of a building. As such, value-added applications developed for web-based and wireless platforms are limited to querying for available non-spatial business data at the building level only. To overcome this, geometrically accurate 3D building models are necessary to enable users to visualize, interact, and query for task specific non-spatial business data. This paper proposes a workflow for creating detailed 3D building models with LoD3 from TLS data and uploading these models into Google Earth so that users can then explore the non-spatial business data of a building and its sub-components (e.g. windows, doors, rooms). Processing bottlenecks of the proposed workflow for detailed 3D building reconstruction are also discussed.
AB - Today's spatially aware users are becoming more interested in retrieving personalised and task relevant information, requiring detailed 3D city models linked to non-spatial attribute data. However, current implementations of 3D city models are typically LoD2 that don't include geometric or attribute details about many visible features (e.g. rooms) of a building. As such, value-added applications developed for web-based and wireless platforms are limited to querying for available non-spatial business data at the building level only. To overcome this, geometrically accurate 3D building models are necessary to enable users to visualize, interact, and query for task specific non-spatial business data. This paper proposes a workflow for creating detailed 3D building models with LoD3 from TLS data and uploading these models into Google Earth so that users can then explore the non-spatial business data of a building and its sub-components (e.g. windows, doors, rooms). Processing bottlenecks of the proposed workflow for detailed 3D building reconstruction are also discussed.
UR - http://www.scopus.com/inward/record.url?scp=84880745373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880745373&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39649-6_5
DO - 10.1007/978-3-642-39649-6_5
M3 - Conference contribution
AN - SCOPUS:84880745373
SN - 9783642396489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 76
BT - Computational Science and Its Applications, ICCSA 2013 - 13th International Conference, Proceedings
PB - Springer Verlag
T2 - 13th International Conference on Computational Science and Its Applications, ICCSA 2013
Y2 - 24 June 2013 through 27 June 2013
ER -