A Systematic Review of Generative Artificial Intelligence Applications in Urban Planning: Opportunities, Challenges, and Future Directions

Generative Artificial Intelligence (GenAI) has rapidly emerged as one of the most transformative technological developments influencing urban planning, spatial analysis, public administration, and smart city governance. Unlike conventional Artificial Intelligence systems that primarily focus on prediction, classification, or optimization, Generative AI creates new content including textual documents, planning scenarios, maps, urban layouts, visualizations, simulation outputs, programming code, and policy recommendations based on extensive training datasets and advanced deep learning architectures. The recent development of Large Language Models (LLMs), diffusion models, multimodal foundation models, and generative design algorithms has significantly expanded opportunities for evidence-based urban planning, participatory governance, infrastructure management, climate adaptation, and sustainable city development. Urban planners increasingly employ Generative AI to automate planning reports, generate multiple land-use alternatives, support public engagement, analyze planning regulations, improve Geographic Information System (GIS) workflows, develop Digital Twins, and accelerate decision-making processes. Despite these advantages, concerns remain regarding data quality, hallucination, algorithmic bias, transparency, privacy, legal accountability, intellectual property, and ethical governance. This systematic review synthesizes recent scientific literature regarding the application of Generative Artificial Intelligence within urban planning. The review discusses theoretical foundations, enabling technologies, planning applications, integration with GIS and Digital Twins, opportunities for sustainable development, implementation challenges, governance considerations, and future research directions. The findings indicate that Generative AI has the potential to fundamentally transform urban planning practice by improving efficiency, creativity, interdisciplinary collaboration, and evidence-based policymaking. Nevertheless, responsible implementation requires transparent governance frameworks, human oversight, reliable datasets, and rigorous validation methodologies.

کلیدواژه ها:

Generative Artificial Intelligence ، Large Language Models ، Urban Planning ، Smart Cities.