Neural Network-Driven 3D Generation of Urban Trees : Advancing carbon mitigation simulation through detailed tree modeling from point cloud data

Urban digital twins are essential for climate-responsive urban planning but often fail to accurately represent trees, relying instead on oversimplified models that inadequately capture their environmental impact. Traditional methods for tree modeling, notably skeletonization, are both iterative and labor-intensive, leading to inefficiencies in environmental simulation accuracy. Addressing this gap, our study introduces a novel approach using a Block Sparse Convolutional Neural Network (BSCNN) to generate precise 3D tree models from mobile laser-scanned point clouds, significantly enhancing simulations for carbon mitigation efforts. Our method, tested in Helsinki's Jätkäsaari area, leverages pre-defined skeleton data to train the neural network, streamlining the extraction of movement direction and distance, thus bypassing traditional skeletonization's iterative nature. We further refine our model's accuracy and robustness by incorporating point clouds of varying densities and tailoring our approach to account for the morphological diversity of specific tree species. This specificity enables our models to more closely mirror real-world trees, making them invaluable for dynamic environmental modeling within urban digital twins. Moreover, our models support integration with the L-system, a prominent plant growth simulation algorithm, showcasing the potential of advanced neural networks to revolutionize computational architecture and foster precise, sustainable urban environmental simulations.