Leveraging urban AI for high-resolution urban heat mapping: Towards climate resilient cities

Urban heat island (UHI) effects are increasingly recognised as a significant challenge arising from urbanisation, leading to elevated temperatures within urban areas that pose risks to public health and undermine the sustainability of cities. Effective UHI management requires high-resolution and timely mapping of urban temperature patterns to guide interventions. Traditional methods for urban heat mapping often lack the spatial accuracy and efficiency necessary for detailed analysis, especially in complex urban environments. This study integrates Urban artificial intelligence (Urban AI) by presenting a U-Net model tailored for urban heat mapping within the metropolitan area of Adelaide, South Australia. Trained on high-resolution thermal and spatial data from the South Australian Government Data Directory, the model captures pixel-level temperature variations across diverse urban landscapes, including densely built areas, suburban zones, and green spaces. Achieving a low Mean Squared Error (MSE) of 0.0029 and processing each map in less than 30 seconds, the model demonstrates exceptional accuracy and computational efficiency. The U-Net model, as an Urban AI agent, offers a scalable tool for urban heat analysis, supporting real-time assessments and facilitating targeted UHI mitigation efforts. By bridging the gap between advanced geospatial modelling and practical urban planning, it enables data-driven decisions that enhance climate resilience, optimise green infrastructure, and improve public health in rapidly urbanising regions. This approach highlights the transformative potential of Urban AI in addressing urban heat challenges, delivering precise and actionable insights to support sustainable and climate-adaptive urban environments.