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The article centers on computer vision techniques used to analyze urban data for pollution monitoring, traffic optimization, and crowd movement, making AI the primary subject rather than incidental. It critically examines the trade-offs between unprecedented scale in city research and emerging concerns regarding privacy and algorithmic fairness. TechPulse take: This piece highlights how visual AI transforms raw camera feeds into quantifiable datasets for urban planning, offering significant precision in tracking emissions and traffic patterns while raising essential questions about data ethics at scale.
The promise and peril of using visual AI to study cities
In their new book, “How AI Sees the City,” the leaders of MIT’s Senseable City Lab examine the technology’s implications for researching urban life.
A few months ago, researchers from the MIT Senseable City Lab published a study about pollution in New York City featuring some new methods. For instance: With machine learning, they identified the types of vehicles appearing in 331 traffic cameras in the city, and estimated the emissions coming from each automobile. Given enough cameras, these visual artificial intelligence techniques could monitor emissions with an unprecedented combination of precision and scale. For that matter, visual AI today can address all kinds of questions for urban planners. Why exactly is traffic snarling? What are the most dangerous aspects of different intersections? Which parts of plazas or parks attract the most people? Across cities, more images means more data, more insight —…
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