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SUMMARY:Machine Learning for Building-Level Heat Risk Mapping - Andrea Dom
 iter\, University of Cambridge
DTSTART:20250508T120000Z
DTEND:20250508T130000Z
UID:TALK229579@talks.cam.ac.uk
CONTACT:114742
DESCRIPTION:*Title*\n\nMachine Learning for Building-Level Heat Risk Mappi
 ng\n\n*Abstract*\n\nClimate change is intensifying the frequency and sever
 ity of heat waves\, increasing risks to public health and energy systems w
 orldwide. However\, many existing heat vulnerability assessments focus pri
 marily on outdoor temperatures\, overlooking indoor conditions that direct
 ly affect occupants. Although building simulations can reveal the types of
  buildings whose occupants are most at risk\, they rarely pinpoint the exa
 ct locations of these vulnerable buildings. In this presentation\, I will 
 present a data-driven workflow that locates high-risk buildings and discus
 s the labeling strategies we have explored for classifying real-world stru
 ctures using satellite imagery.\n\n*Bio*\n\nAndrea is a first-year PhD stu
 dent in the Department of Computer Science and Technology at the Universit
 y of Cambridge. She is supervised by Prof Srinivasan Keshav. Her research 
 bridges machine learning with civil and environmental engineering\, focusi
 ng particularly on its applications within the built environment.
LOCATION:Room SS03 at the William Gates Building. Zoom link: https://cl-ca
 m-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&amp\;fro
 m=addon 
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