Artificial Intelligence in urban design: A systematic review

Abstract

Artificial Intelligence (AI) is playing an increasingly transformative role in urban design by enhancing the efficiency, scalability, and adaptability of design processes. This study presents a systematic review of AI applications in urban design, with a particular focus on the design generation phase, encompassing data analysis, scheme generation and optimization, and design visualization. AI-driven methodologies facilitate rapid data processing, automated design iterations, and advanced visualizations, thereby mitigating some key limitations in conventional urban design workflows that often rely on manual and time-consuming processes. Despite these advancements, several challenges persist. These include the fragmented integration of AI tools into existing workflows, the incomplete automation of the design process, and the potential for algorithmic bias in AI-generated outcomes. Such shortcomings underscore the importance of developing structured AI workflows, fostering effective human-AI collaboration, and curating diverse, inclusive datasets to promote equitable and context-sensitive design solutions. This review advocates for a balanced approach that leverages AI's computational power while retaining human creativity and contextual judgment. By doing so, AI-enhanced urban design holds the potential to support the creation of more sustainable, efficient, and resilient cities, better equipped to meet the complex challenges of contemporary urbanization.