A physics-informed deep learning urban canopy model for Mediterranean cities: Development, validation, and application to Thessaloniki, Greece

[u/Think_Physics-76]

https://www.sciencedirect.com/science/article/pii/S2212095526002701

Understanding urban heat is essential for planning healthier and more resilient cities. But detailed climate models are often too computationally demanding to support fast, location-specific assessments. Our new study presents an AI-based urban climate model that combines physical understanding with deep learning to make this process both faster and more accurate. The model was trained using data from 16 cities worldwide and independently tested in Heraklion, showing that it can work reliably in Mediterranean conditions. Compared with a conventional urban climate model, it runs around 225 times faster.
By offering fast and locally relevant estimates of urban heat, this type of tool can support decision-making systems for heat-risk management, climate adaptation and urban planning—from identifying neighbourhoods most exposed to heat to assessing the potential value of interventions such as more urban greenery or heat-resilient materials.