Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Accurate, detailed, and up-to-date 3D building models are important for several applications such as telecommunication network planning, urban planning, and military simulation. Existing building reconstruction approaches can be classified according to the data sources they use (i.e., single versus multi-sensor approaches), the processing strategy (i.e., data-driven, model-driven, or hybrid), or the amount of user interaction (i.e., manual, semiautomatic, or fully automated). While it is obvious that 3D building models are important components for many applications, they still lack the economical and automatic techniques for their generation while taking advantage of the available multi-sensory data and combining processing strategies. In this research, an automatic methodology for building modelling by integrating multiple images and LiDAR data is proposed. The objective of this research work is to establish a framework for automatic building generation by integrating datadriven and model-driven approaches while combining the advantages of image and LiDAR datasets
Rocznik
Tom
Strony
187--200
Opis fizyczny
Bibliogr. 5 poz.
Twórcy
autor
- Department of Geomatics Engineering, University of Calgary, 2500 University Drive, Calgary, T2N 1N4, AB, Canada
autor
- Department of Geomatics Engineering, University of Calgary, 2500 University Drive, Calgary, T2N 1N4, AB, Canada
autor
- Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, M5S 1A4, ON, Canada
Bibliografia
- 1.Awrangjeb, M., Ravanbakhsh, M., and Fraser, C.S., 2010. Automatic detection of residential buildings using LiDAR data and multispectral imagery, ISPRS Journal of Photogrammetry & Remote Sensing, 65, pp. 457-467.
- 2.Brenner, C., 2005. Building reconstruction from images and laser scanning, International
- 3.Journal of Applied Earth Observation and Geoinformation, Vol 6, Issues 3-4, pp. 187-198.
- 4.Canny, J., 1986. A computational approach to edge detection, IEEE Transactions on Pattern
- 5. Analysis and Machine Intelligence, Vol 8, pp. 679–690.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c9d0e2a2-3124-4c22-852c-3b6956b9cfa2