cs.GROct 29, 2026
SaveGreenscapes: Optimized Vegetation Configurations in Cities
Organizations: Adam Mickiewicz University, Poznań, Poland · Purdue University, United States of America · King Abdullah University of Science and Technology (KAUST), Saudi Arabia · Christian-Albrechts-Universität zu Kiel, Germany
Abstract
Vegetation plays a central role in cities, serving functional roles such as balancing shade and sky exposure, shaping the microclimate, carbon sequestration, and providing acoustic screening. Furthermore, aesthetic choices in landscape styles (formal, modernist, picturesque) are important for citizens' well-being and should be considered in the design of urban landscapes. In this paper, we present a novel method for placing vegetation, ranging from shrubs to large trees, in 3D urban scenes. Our method uses individual lots as input and employs a conditioned Vision-Language Model (VLM) to generate a top-down rendering of vegetation, placing large trees, small trees, and shrubs on lawn areas. This rendering is then converted into a 3D scene by detecting individual plants and selecting appropriate 3D assets. To refine the proposed configurations, we define an optimization loop that keeps the VLM-generated positions fixed after a deterministic feasibility repair and uses CMA-ES to optimize per-instance scale transformations along with discrete vegetation asset substitutions. To define the objective function for the optimization, we evaluate the generated 3D scenes with efficient surrogate models of shade and path-based acoustic exposure. Furthermore, we render in-scene views at each optimization step to compute view-dependent perceptual descriptors, enabling the generation of both aesthetic and functional vegetation layouts for urban scenes.
Figures & tables
Figure 1. Three complete park designs synthesized from identical Open Street Map site constraints but different style prompts. Left: formal design and regular spacing. Middle: picturesque -asymmetric composition and clustered planting. Right: modernist -clear geometry. All scenes are generated 3D layouts optimized under perceptual and functional objectives using a non-convex approach.
Figure 2. Overview of our pipeline for prompt-driven park vegetation design. Inputs are a user text prompt (a) and OSM-derived site context (b). An image-to-image constrained editor generates multiple top-down layout proposals (c), which are converted to a metric top-down representation (d) and processed by instance extraction (e) to obtain candidate pedestrian viewpoints (f) and vegetation instance positions and coarse types (g). These instances are projected into world coordinates and assembled into a 3D park scene (h). We evaluate candidate scenes by rendering multiple in-scene views to compute perceptual descriptors and biomass-related quantities (i) and by running physics-inspired field models for functional measures such as shade and sound (j). CMA-ES then refines continuous instance parameters and triggers discrete species substitutions under a Gaussian-mixture asset prior using the combined perceptual and functional objectives (k), yielding the final optimized park layout (l).
Figure 3. Feasibility and constraint adherence of the first stages of our pipeline. In (a), we show an example conditioning raster obtained from OSM data. In (b), a constrained-edit proposal of the VLM is depicted for that input raster. In (c), we show the VLM-generated bounding box annotation overlay.
Figure 4. Color-mapped visualization of the ear-height slice of the simulated 3D sound-energy voxel space. A sound source is placed near the upper-right corner (red marker), and a linear tree belt (green outline) separates the source from receiver samples (red markers) below. Increasing the per-vegetation attenuation parameter slows the diffusion update through the tree belt, reducing and delaying energy propagation toward the receivers; the three panels differ only in this attenuation coefficient, while keeping the geometry and discretization fixed.
Figure 5. Example park scenes before and after CMA-ES refinement of the VLM prior. Path shading is visualized as a scalar field over the walkable network (plasma colormap), and vegetation biomass is visualized per instance (viridis colormap). Optimization increases functional shading where needed while reallocating plant mass to meet biomass/coverage targets under the same geometric constraints; the images show the resulting redistribution of canopy placement along paths.
Figure 6. Sky-view exposure before and after CMA-ES refinement from a fixed in-park viewpoint. The fisheye sky segmentation (white = sky) shows increased open-sky visibility after optimization, raising the sky view factor (SVF) to while preserving the surrounding layout constraints.
Figure 7. Skyline periodicity matching before (left) and after (right) CMA-ES refinement from a fixed viewpoint. The green curve is the target sinusoidal skyline profile; CMA-ES improves agreement by jointly adjusting per-instance scales and performing discrete species substitutions to introduce canopies with more suitable sizes and crown shapes, yielding a more periodic silhouette while maintaining feasibility constraints.
Figure 8. Visualization of the CMA-ES trajectory in the 6D attribute space after PCA projection to 2D. Elliptical contours indicate projected Gaussian components (species priors) used by the GMM-based substitution rule; the polyline shows successive CMA-ES proposals and the induced discrete component assignments.
| Metric (Target) | Real Park (Ref) | VLM Prior | Optimized |
| SVF ( ) | |||
| Shade ( ) | |||
| Acoustics ( ) | |||
| Biomass ( ) | |||
| Skyline Fit ( ) | |||
| Total Cost |
Table 1. Quantitative error analysis over experimental runs reports the mean absolute deviation from design targets ( ) to demonstrate robustness. While the VLM Prior exhibits high variance, the Optimized result shows significantly lower standard deviation, indicating that our method reliably converges to the design goals regardless of initialization. Real Park is a fixed reference geometry.
| Run | Shade | Biomass | Cost |
| Target | 2.00 | 56,317 | 0.0000 |
| Random Init. | 1.65 | 54,473 | 0.2012 |
| Random+CMA-ES | |||
| VLM Prior | 1.70 | 56,317 | 0.0227 |
| VLM+CMA-ES |
Table 2. Ablation of the two components of our method: initialization (random vs. VLM-derived prior) and CMA-ES refinement (on vs. off)-reported on Shade, Biomass, and the optimization Cost , the weighted squared distance to the Shade and Biomass targets (lower is better). CMA-ES on the VLM prior achieves the lowest cost ( ), reaching the shade target ( vs. ) while keeping biomass within 2.3% of baseline; CMA-ES on random initialization falls short of the shade target ( ) and overshoots biomass by , ending further from the joint target.
Figure 9. City-scale vegetation placement examples generated from OSM-derived urban context. Panels show the area around (a) Sydney Park, Australia, (b) the Mausoleum Charlottenburg area in Berlin, Germany, and (c) the Sołacz district in Poznań, Poland, with over 4000 placed tree instances per scene. Panel (d) shows an in-scene rendering of a generated layout.
Figure 12. Objective-ablation results for Joyce Kilmer Park. Panel (a) shows the reconstructed reference layout used as the input condition. Panels (b–f) show CMA-ES refinements obtained by assigning one objective term a dominant weight while keeping the remaining terms at low or zero weights: (b) skyline sine-function fit, (c) SVF ratio, (d) minimal path shading, (e) acoustic/noise-reduction objective, and (f) maximum biomass. All runs use the same site boundary, path network, feasibility constraints, asset library, and optimizer settings; the visual differences therefore illustrate the qualitative effect of each dominant objective.
Figure 13. Examples of generated planting layouts for different site lots. For each lot geometry, our method produces distinct plant configurations, demonstrating its ability to adapt planting structure and spatial organization to varying parcel shapes and constraints.
Figure 14. We plot reconstructed reference parks with known architectural styles and generated parks produced from OSM sites and style prompts. Colors indicate the intended style ( formal blue, picturesque green, modernist red). Disks versus crosses distinguish generated from reconstructed references. The resulting clustering shows that the proposed entropy and linearity metrics separate the three strategies and that the generated parks align with the corresponding reference distributions.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
| Setting | Value |
| VLM model/version | gemini-3-pro-image-preview |
| Layout-generation input | OSM-derived top-down conditioning image plus fixed filled prompt |
| Layout-generation output | Complete edited top-down image with vegetation added only on grass/editable pixels |
| Image resolution | |
| Aspect ratio | |
| Accepted candidates | One accepted generated image per prompt instance |
Table 1. VLM settings used for the reported experiments. “Prompt instance” denotes a fixed filled prompt, i.e., a template together with the corresponding arrangement rules.
| Parameter | Setting |
| VLM model/version | gemini-3-pro-image-preview |
| VLM output resolution | , aspect ratio |
| Prompt protocol | Fixed prompt instances; no interactive prompt tuning during reported runs |
| Accepted VLM candidates | One accepted image per prompt instance; failed API calls retried up to five times; the image API exposes no random seed |
| Annotation labels | bigtree , smalltree , shrub |
| Maximum generated plant count requested in prompt | Approximately 300 plants for park prompts |
Table 2. Reproducibility table for the reported GreenScapes experiments. Values are fixed unless explicitly stated by a specific experiment. Per-experiment weights, targets, and step sizes are given in the released configuration files ( configs/optimizer/ ).
| Main-paper panel | Configuration | Dominant term | Optimization direction | Remaining objective weights |
| Fig. 12(b) | Skyline sine-function criterion | Minimize deviation from the target sinusoidal skyline profile | SVF, path shading, acoustic exposure, and biomass use | |
| Fig. 12(c) | SVF ratio | Match the target sky-view factor / increase openness when the target requires higher SVF | Skyline, path shading, acoustic exposure, and biomass use | |
| Fig. 12(d) | Minimal path shading | Minimize path-level shading according to the ablation target | SVF, skyline, acoustic exposure, and biomass use | |
| Fig. 12(e) | Acoustic objective | Reduce acoustic exposure / increase excess attenuation at receiver viewpoints | SVF, skyline, path shading, and biomass use | |
| Fig. 12(f) | Maximum biomass | Maximize normalized vegetation biomass under feasibility constraints | SVF, skyline, path shading, and acoustic exposure use |
Table 3. Objective configurations used for the objective-ablation figure in the main paper (Fig. 12). Each ablation assigns one dominant weight to the selected term and low or zero weights to the remaining terms. The exact weight vectors and targets for each panel are provided in the released configuration files ( configs/optimizer/ ). This preserves weak regularization from the other objectives while visualizing the qualitative effect of the dominant criterion.
Figure 1. CMA-ES optimization trace for Joyce Kilmer Park. Normalized target error (0 = at target, 1 = worst) over generations for SVF, skyline fit, shade, biomass, and noise reduction. Most terms drop rapidly in the first generations, then refine more slowly with occasional non-monotonic trade-offs. Noise reduction decreases more gradually, indicating a harder objective under the same geometric constraints.
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