Optimizing urban greening and densification in the context of outdoor heat: Opportunities for AI-supported urban adaptation

• Weighing up urban greening and densification in established neighbourhood is complex. • Urban land use and heat-based adaptation planning can be expedited with AI support. • AI-supported methods are best combined, to balance different urban planning goals. • Transdisciplinary methods can be used to test AI-supported methods for usability. Confronted with increasing urban heat stress risks, local governments need to reconcile expanding green infrastructure for urban cooling with urban densification goals. However, the impacts of incremental urban development in established neighborhoods on urban heat stress risks remain poorly understood. We demonstrate how decision support tools using Artificial Intelligence (AI) can assist complex urban land use and climate adaptation planning. Our findings are based on an inter- and transdisciplinary research project that developed and combined novel AI-supported simulation and prediction methods, namely 3D semantic models, AI-based outdoor thermal comfort models, and optimization and scenario-based AI models. Tool development was combined with transdisciplinary research to assess the real-world application potentials of AI-supported approaches in the City of Freiburg, Germany. The article demonstrates how AI-supported methods can aide and expedite urban land use and adaptation planning to support complex decision-making that needs to balance different strategic goals and interests.