An AI-driven framework for rapid and localized optimizations of urban open spaces

TL;DR

This paper presents an AI-driven, explainable framework for rapid, localized optimization of urban open spaces, improving thermal comfort and safety with lower computational costs compared to traditional methods.

Contribution

It introduces a novel, scalable approach combining machine learning and explainable AI for urban space optimization, enabling efficient, incremental design improvements.

Findings

XGBoost identified as the most accurate MLM for SVF and visibility.

CFX method optimized urban design in 1 minute with 5% RMSE.

Compared to genetic algorithms, the framework is significantly faster and more practical for retrofitting.

Abstract

As urbanization accelerates, open spaces are increasingly recognized for their role in enhancing sustainability and well-being, yet they remain underexplored compared to built spaces. This study introduces an AI-driven framework that integrates machine learning models (MLMs) and explainable AI techniques to optimize Sky View Factor (SVF) and visibility, key spatial metrics influencing thermal comfort and perceived safety in urban spaces. 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. The framework employs SHapley Adaptive Explanations (SHAP) to analyze feature importance and Counterfactual Explanations (CFXs) to propose minimal design changes. Simulations tested five MLMs, identifying XGBoost as the most accurate, with building width, park area, and heights of surrounding buildings as critical for SVF, and distances from southern buildings as key for visibility. Compared to Genetic Algorithms, which required approximately 15/30 minutes across 3/4 generations to converge, the tested CFX approach achieved optimized results in 1 minute with a 5% RMSE error, demonstrating significantly faster performance and suitability for scalable retrofitting strategies. This interpretable and computationally efficient framework advances urban performance optimization, providing data-driven insights and practical retrofitting solutions for enhancing usability and environmental quality across diverse urban contexts.