Stanford researchers use AI to empower environmental regulators

Stanford researchers locate sources

Monitoring environmental compliance is a particular challenge for governments in poor countries. A new machine learning approach that uses satellite imagery to pinpoint highly polluting brick kilns in Bangladesh could provide a low-cost solution…

Brick production, a major industry in South Asia, is a source of pollution that threatens health. Regulating brick kilns is difficult because there is no database of kiln locations. To tackle this issue, Stanford researchers have developed a machine learning algorithm that can identify and locate brick kilns from satellite images…

“Brick kilns have proliferated across Bangladesh to supply the growing economy with construction materials, which makes it really hard for regulators to keep up with new kilns that are constructed,” said co-lead author Nina Brooks, a postdoctoral associate at the University of Minnesota’s Institute for Social Research and Data Innovation who did the research while a PhD student at Stanford.

While previous research has shown the potential to use machine learning and satellite observations for environmental regulation, most studies have focused on wealthy countries with dependable data on industrial locations and activities. To explore the feasibility in developing countries, the Stanford-led research focused on Bangladesh, where government regulators struggle to locate highly pollutive informal brick kilns, let alone enforce rules…

By Rob Jordan

Stanford News: Apr 19, 2021
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