Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 55-62, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018


L. Gobeawan1, E. S. Lin2, A. Tandon2, A. T. K. Yee2, V. H. S. Khoo3, S. N. Teo3, S. Yi1, C. W. Lim1, S. T. Wong1, D. J. Wise1, P. Cheng1, S. C. Liew4, X. Huang4, Q. H. Li5, L. S. Teo5, G. S. Fekete6, and M. T. Poto6 L. Gobeawan et al.
  • 1Institute of High Performance Computing, 1 Fusionopolis Way #16-16 Connexis North Tower, Singapore
  • 2National Parks Board, 1 Cluny Road, Singapore
  • 3Singapore Land Authority, 55 Newton Road, Singapore
  • 4National University of Singapore, Centre for Remote Imaging, Sensing and Processing, Blk S17 Level 2, Lower Kent Ridge Road, Singapore
  • 5Government Technology Agency of Singapore, 1 Fusionopolis View #07-01 Sandcrawler Building, Singapore
  • 6Roadata Global Pte. Ltd., 30 Cecil Street #19-08 Prudential Tower, Singapore

Keywords: LiDAR, virtual city, tree extraction and quantification, procedural tree modeling, CityGML solitary vegetation

Abstract. Singapore, branded as a “City in a Garden”, has a long standing commitment to green the nation, one which has resulted in trees becoming an integral component of the urban environment. Similarly for its digital twin, Virtual Singapore, we undertake the research to automate the population of this virtual city with semantically and biologically representative trees in a CityGML (City Geography Markup Language) format. This paper presents our framework of modeling trees for Virtual Singapore, showcasing an array of methodologies in data acquisition of light detection and ranging (LiDAR) and satellite images, tree extraction and quantification, and 3D tree modeling at LODs (level of details) 1, 2 and 3. The paper will also highlight challenges and chosen methodologies along with the preliminary results of this framework.

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