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Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks
Zachary D Calhoun · Ziyang Jiang · Mike Bergin · David Carlson

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in Tackling Climate Change with Machine Learning »
Event URL: https://www.climatechange.ai/papers/neurips2022/105 »

Compared to rural areas, urban areas experience higher temperatures for longer periods of time because of the urban heat island (UHI) effect. This increased heat stress leads to greater mortality, increased energy demand, regional changes to precipitation patterns, and increased air pollution. Urban developers can minimize the UHI effect by incorporating features that promote air flow and heat dispersion (e.g., increasing green space). However, understanding which urban features to implement is complex, as local meteorology strongly dictates how the environment responds to changes in urban form. In this proposal we describe a methodology for estimating the causal relationship between changes in urban form and changes in the UHI effect. Changes in urban form and temperature changes are measured using convolutional neural networks, and a causal inference matching approach is proposed to estimate causal relationships. The success of this methodology will enable urban developers to implement city-specific interventions to mitigate the warming planet's impact on cities.

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Author Information

Zachary D Calhoun (Duke University)

I am an environmental engineering PhD student at Duke University. I study how to apply machine learning to better understand air pollution.

Ziyang Jiang (Duke University)
Mike Bergin (Duke University)
David Carlson (Duke University)

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