Abstract

Title

Machine Learning for Building-Level Heat Risk Mapping

Abstract

Climate change is intensifying the frequency and severity of heat waves, increasing risks to public health and energy systems worldwide. However, many existing heat vulnerability assessments focus primarily on outdoor temperatures, overlooking indoor conditions that directly affect occupants. Although building simulations can reveal the types of buildings whose occupants are most at risk, they rarely pinpoint the exact locations of these vulnerable buildings. In this presentation, I will present a data-driven workflow that locates high-risk buildings and discuss the labeling strategies we have explored for classifying real-world structures using satellite imagery.

Bio

Andrea is a first-year PhD student in the Department of Computer Science and Technology at the University of Cambridge. She is supervised by Prof Srinivasan Keshav. Her research bridges machine learning with civil and environmental engineering, focusing particularly on its applications within the built environment.