Workflow to accurately model vegetation canopy effects in building energy simulation
Bibliographic record
Abstract
This work develops an efficient and accurate workflow for integrating vegetation canopy effects into building energy simulations through unmanned aerial vehicle (UAV)-based video capture and automated reconstruction techniques. Unlike traditional methods that rely on static vegetation representations, the present approach utilizes a dual-perspective UAV video strategy to simultaneously capture the building exterior and its surrounding vegetation from an interior-facing viewpoint. This method enables precise shading analysis while significantly reducing computational costs compared to full-scale microclimate simulations. The high-resolution UAV data is processed using advanced photogrammetry and 2D Gaussian Splatting to reconstruct a detailed 3D building model with optimized vegetation meshes that accurately preserve canopy geometry. The 2D Gaussians refine the meshes' representation by optimizing mapping calculations. These meshes are then incorporated as shading elements within energy simulation platforms such as Grasshopper/EnergyPlus, thereby enhancing simulation accuracy over conventional coarse approximations. By dynamically integrating real-world vegetation geometry, the present workflow yields context-aware and seasonally adaptable results, bridging the gap between high-fidelity 3D reconstruction and practical energy analysis. This scalable and automated approach offers a promising avenue for urban energy modeling and the optimization of passive solar design in built environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".