Combining AI with urban resilience planning: A bibliometric analysis of literature

• A bibliometric analysis uncovers the emerging AI applications in urban resilience. • Use of AI technologies in urban resilience studies has expanded over the past decade. • Environmental and critical infrastructure resilience are addressed extensively. • Human, cultural, organizational, and participatory dimensions are understudied. • Algorithmic bias and uncertainty are ignored in resilience decision-support technologies. The rise of artificial intelligence (AI) technologies shows great promise for reshaping urban planning and decision-making processes. While recent studies have examined AI implications broadly for local government, research remains limited in understanding its implications for urban resilience planning for climate change adaptation and mitigation. This study presents a comprehensive bibliometric analysis of 3941 publications spanning 1975 to 2024 to address this knowledge gap. The goal of this study is to examine the trends, evolution, and modalities of AI integration in urban resilience research and identify the key knowledge clusters developing in this critical area. Several notable findings emerge from this study: (a) AI adoption in the urban resilience field significantly expanded in the last decade, ushering in a new era of algorithmic urban planning; (b) AI applications in urban resilience studies are primarily geared towards climate change-related disaster risk mitigation, critical infrastructure management, environmental resilience, land use and urban growth management, and improved health outcomes; (c) the most prolific use cases of AI technologies are observed in the areas of extreme heat mitigation, flood forecasting, traffic congestion and air pollution management, and scenario-based generative adaptive urban design; (d) human, cultural, organizational, and participatory dimensions of urban resilience, including issues of equity, algorithmic bias, and governance, remain significantly underexplored in the current literature; and (e) while AI technologies are used effectively for enhancing rapid simulation, prediction, forecasting, and automated decision-support capabilities, greater emphasis on algorithmic bias and climate uncertainty is necessary to plan for a dynamic and equitable future. This study outlines future research and development avenues, providing resilience planners and policymakers with strategic foresight to advance AI-based applications for urban resilience.