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 m...
The promise and peril of using visual AI to study cities
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