Research on Landscape Design Optimization and Spatial Layout Planning Method Based on AI Algorithm
Bibliographic record
Abstract
Traditional landscape design methods have low efficiency, poor subjectivity and insufficient goal optimization.This paper proposes a landscape design optimization and spatial layout method based on artificial intelligence (AI) algorithms to achieve scientific and efficient landscape design through the combination of collected information data and algorithms.The optimization design of landscape facility paths and spatial dimensions is carried out by adopting a heuristic polygonal layout algorithm, establishing a data model based on the database and scene templates, and combining the landscapes in the polygonal space after landscape matching.The optimal sequence of the landscape is obtained by using the scoring function, and then combined with the particle swarm algorithm to realize the optimization of the landscape layout.The Hypervolume index is stable to about 0.815 in 30 generations, which has a good quality of Pareto optimal solution set.In this paper, the algorithm formulates three groups of landscape design optimization and spatial layout planning schemes for different situations, making full use of the land that is utilized for a certain place.The implementation of the sustainable development scenarios improves the local environmental and social benefits significantly, and the average annual growth rate of employment in related industries reaches 3.16%.Satisfaction survey results show that local residents are most satisfied with the green environment and cultural atmosphere after the implementation of the program, respectively 80.03, 79.35, through the smart management to improve the local environmental quality and cultural atmosphere.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".