1st Place Winner in I-GUIDE's 2025-26 Spatial AI Challenge!

Urban retrofitting, such as modifying existing areas for greater sustainability, is essential for climate mitigation, yet remains difficult to detect due to its subtle, microscale nature. This project introduces an AI-driven framework that uses high-performance computing and urban big data to quantify and analyze retrofitting. By integrating large-scale street view imagery and socioeconomic data, a Vision Transformer model identifies when, where, and what types of retrofitting occur. Transfer learning enables application across cities globally. The project also assesses retrofitting’s climate impact by tracking changes in urban heat island intensity and evaluates social equity by examining disparities in access and benefits across neighborhoods. Ultimately, this work provides scalable tools to support data-informed, equitable, and climate-resilient urban planning, offering valuable insights to policymakers and practitioners navigating sustainable urban transformation worldwide.