Model-Based Analysis-Synthesis for Realistic Tree Reconstruction and Growth Simulation

Corina Iovan 1, 2, * Paul-Henry Cournède 2 Thomas Guyard 1 Benoît Bayol 2 Dider Boldo 3 Matthieu Cord 4
* Corresponding author
1 DIGIPLANTE - Modélisation de la croissance et de l'architecture des plantes
MAS - Mathématiques Appliquées aux Systèmes - EA 4037, CIRAD - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Inria Saclay - Ile de France, Ecole Centrale Paris
2 MAS - Mathématiques Appliquées aux Systèmes - EA 4037
3 EDF R&D - EDF R&D
4 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Vegetation analysis and reconstruction of remote sensing data are challenging problems. Using architectural tree models combined with model inputs estimated from aerial image analysis, this paper presents an analysis-synthesis approach for urban vegetation detection, modeling, and reconstruction. Tree species, height, and crown size information are extracted by aerial image analysis. These variables serve for model inversion to retrieve plant age, climatic growth conditions, and competition with neighbors. Functional-structural individual-based tree models are used to reconstruct and visualize virtual trees and their time evolutions realistically in a 3-D viewer rendering the models with geographical coordinates in the reconstructed scene. Our main contributions are: 1) a novel approach for generating plant models in 3-D reconstructed scenes based on the analysis of the geometric properties of the data, and 2) a modeling workflow for the reconstruction and growth simulation of vegetation in urban or natural environments
Keywords : vegetation detection species classification Dynamic scene evolution functional-structural tree growth model image-based vegetation modeling
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Corina Iovan, Paul-Henry Cournède, Thomas Guyard, Benoît Bayol, Dider Boldo, et al.. Model-Based Analysis-Synthesis for Realistic Tree Reconstruction and Growth Simulation. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2014, 52 (2), pp.1438-1450. ⟨10.1109/TGRS.2013.2251467⟩. ⟨hal-00826023⟩

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