As cities continue to grow rapidly, urban open spaces are increasingly recognized for their contribution to sustainability, well-being and environmental quality. Yet, they remain underexplored compared to built environments, particularly regarding spatial metrics such as sky view factor (SVF) and visibility, which influence perceived thermal comfort, safety and openness. These metrics are fully morphology-based and ideal for early-stage planning, but current tools often lack support for localized, interpretable optimization. This study focuses on green spaces such as parks and introduces an AI-driven framework for optimizing SVF and visibility, to enhance the usability and spatial performance of these spaces.
The framework combines Machine Learning Models (MLMs), explainable AI techniques such as SHapley Adaptive Explanations (SHAP), and Counterfactual Explanations (CFXs) to enable localized, interpretable, and efficient optimization. A dataset of 1,152 simulated urban block configurations based on Tehran's morphological patterns was used to train and test five predictive models. SHAP was used to assess the contribution of each feature, while CFXs proposed minimal design interventions to optimize spatial performance.
The results show that park area and building height on the east and west sides are critical factors for SVF, while southern building distance and building width most strongly affect visibility. XGBoost achieved an R2 of 0.91 for SVF prediction and F1-scores up to 0.89 for visibility classification. The CFX approach produced optimized results in approximately 1 min with an average RMSE of 5%, whereas genetic algorithms required 15–30 min to converge. This represents a ∼90% reduction in computation time.
Unlike global optimization methods, which are computationally intensive and impractical for localized adjustments, this framework supports incremental design improvements with lower computational costs and greater flexibility. This study offers a novel application of CFXs for spatial metric optimization in urban open space design, bridging a gap in current literature. The integration of explainable AI not only improves transparency but also supports real-time decision-making in sustainable urban design.
