Mapping urban complexity AI, facades, and the future of environmental risk assessment

Buildings account for 40% of global energy consumption and emissions. Building façades determine thermal efficiency, daylighting, and occupant wellbeing, yet traditional assessment methodologies prove prohibitively expensive and time-intensive at urban scales. This research develops an integrated framework addressing four interconnected research questions. First, it establishes a comprehensive definition of high-performance façades integrating energy efficiency, occupant comfort, environmental sustainability, and durability, validated against occupant satisfaction data. Second, it develops an artificial intelligence pipeline for automated façade assessment from street-level imagery, enabling unprecedented urban-scale measurement capability. Third, it rigorously validates this AI approach through systematic error analysis and accuracy stratification across diverse building typologies. Fourth, it develops urban integration frameworks connecting building- level performance data to municipal decision-making systems supporting retrofit prioritization. Implementation across European urban districts demonstrates practical feasibility of evidence-based retrofit strategies and policy integration. The research demonstrates that urban sustainability challenges require simultaneous advancement in definitional clarity, technological innovation, rigorous validation, and governance integration. Results provide essential infrastructure for building decarbonization and climate action implementation.