Abstract
Creating vegetation contents for a digital twin city entails generating dynamic 3D plant models in a large scale to represent the actual vegetation in the city. To enable high-fidelity environmental simulations and analysis applications, we model individual trees at a species level of detail. The 3D models are generated procedurally based on their botanical species profiles within the constraints of measurements and growth spaces derived from laser-scanned point cloud data. Users can conveniently define the known profile of a species by using a species profile template that we formulated based on species growth processes and patterns. Based on the given species profile and solving for the unknowns within the growth space constraints, a species model will be grown through iterations of our formulated growth rules. We show that this methodology produces structurally-representative species models with respect to their actual physical and species characteristics.
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Acknowledgments
This work is supported by National Research Foundation Singapore, Virtual Singapore Award no. NRF2015VSG-AA3DCM001-034. Authors thank colleagues at IHPC (A*STAR), NParks, and GovTech for their valuable input and support.
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Gobeawan, L., Wise, D.J., Wong, S.T., Yee, A.T.K., Lim, C.W., Su, Y. (2021). Tree Species Modelling for Digital Twin Cities. In: Gavrilova, M.L., Tan, C.K. (eds) Transactions on Computational Science XXXVIII. Lecture Notes in Computer Science(), vol 12620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63170-6_2
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