Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence

TL;DR

This paper explores how artificial intelligence can enhance urban planning by addressing key challenges like land-use configuration and environmental sustainability, proposing new AI-driven methods for smarter city development.

Contribution

It reviews the integration of AI techniques with urban planning, highlighting open problems and proposing research directions for autonomous urban intelligence.

Findings

AI can improve land-use planning through generative models.

Automated configuration of urban environments is feasible with geospatial data.

Key research areas include AI-driven sustainability and disaster management.

Abstract

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.