Building an Urban Heat Island Detector with Python, Satellite Data, and Machine Learning
Every summer, cities get hotter than the countryside around them. This isn't just a feeling; it's a measurable phenomenon called the Urban Heat Island (UHI) effect, where concrete, asphalt, and dense buildings trap heat that green spaces and water bodies would otherwise dissipate [1]. UHIs are linked to higher energy costs, worse air quality, and real public health risks during heat waves.
As a data analyst, and I also lecture on machine learning at university level, so this is the kind of question I like poking at outside of work: can we use free satellite data and a fairly ordinary machine learning pipeline to map heat islands in any city, without expensive proprietary tools?
The answer is yes, and this article walks through exactly how, using Python, open satellite imagery, and a lightweight regression model. I've run the full pipeline myself on a synthetic city grid to sanity-check every step...
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