From Point Clouds to Porous Crowns: A Scalable Approach for CFD-Ready Urban Tree Reconstruction
N.P. Alting (TU Delft - Architecture and the Built Environment)
H. Ledoux – Mentor (TU Delft - Urban Data Science)
C. Garcia Sanchez – Graduation committee member (TU Delft - Urban Data Science)
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Abstract
Urban climate simulations increasingly rely on digital twins of cities, yet vegetation remains largely absent or oversimplified despite its strong influence on wind flow and heat exchange. Existing lidar-based tree studies mainly target forests or small plots, and no scalable method currently exists to reconstruct detailed urban trees for computational fluid dynamics (CFD) analysis.
This thesis presents a scalable, automated pipeline for reconstructing CFD-ready urban tree models from open-access airborne lidar. The workflow operates directly on unstructured point clouds and comprises three main components. The first introduces the High-Order Multi-Echo Density (HOMED) vegetation filter, a new approach for distinguishing vegetation from non-vegetation in airborne lidar point clouds. Combined with TreeSeparation, a cuboid-based tree instance segmentation algorithm, it produces clean per-tree clusters for subsequent analysis. The second explores taxonomy-based classification to assess structural separability between species. The third abstracts each segmented point cloud into CFD-suitable crown and trunk geometries and derives per-tree volumetric porosity values, ensuring watertightness, manifoldness, and controlled mesh complexity. Designed to be dataset-agnostic, the pipeline generalises to any comparable airborne laser scanning data.
The workflow was applied to several major Dutch cities—Amsterdam, Rotterdam, Utrecht, and Delft—processing hundreds of thousands of trees from raw point clouds to 3D models within practical runtimes (≈ 13 hours for Amsterdam on 16 CPU cores). Segmentation and reconstruction proved robust and consistent, yielding α-wrapped crowns, cylindrical trunk approximations, and physically meaningful porosity estimates. Taxonomic classification, however, was unreliable due to label noise, class imbalance, and limited structural separability in lidar-only data; supplementary optical features from RGB and infrared imagery were evaluated but proved unreliable for consistent integration.
The results demonstrate that the pipeline enables scalable reconstruction of CFD-ready tree models that preserve canopy structure and aerodynamic properties, allowing their explicit representation in urban digital twins and supporting more realistic urban climate simulations.