Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence
It proposes cross-disciplinary research to apply AI in urban planning for sustainability and disaster management, but it is incremental as it reviews existing concepts without presenting new results.
The paper reviews how AI techniques, such as generative neural networks and adversarial learning, can address urban planning challenges like automated land-use configuration, aiming to integrate these fields for sustainable and intelligent cities.
The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics.