Title: Visual Urban Sensing
Abstract: Street View services have documented the visual appearance of more than 3,000 cities across the world in the past decade. I design computer vision tools that harness Street View imagery to conduct computer-driven automated surveys of the built environment at street-level resolution and global scale. In this talk, I will describe two algorithms that computationally evaluate urban appearance from imagery. The first algorithm, Streetscore, quantifies the perceived safety of a street block, by harnessing data from a crowdsourced game. The second algorithm quantifies the growth or decay of cities from time-series Street View imagery obtained over several years. Finally, I will demonstrate the use of these algorithms for studying important questions in urban economics, sociology, and urban planning.
Bio: Nikhil Naik is a PhD candidate at the MIT Media Laboratory. His research interests are in computer vision and computational imaging. His recent work focuses on harnessing online imagery for applications in the social sciences. He is a recipient of the Harvard Prize Fellowship and Dhirubhai Ambani Scholarship. His research has been featured in The Atlantic, The Economist, The Guardian, and Harvard Business Review, among others. He is also a decent ping-pong player.