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Facebook Leverages Machine Learning to Measure Population Density

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crowdby Angela Guess

Daniel Terdiman reports in Fast Company, “Leveraging in-house data science, machine learning, and artificial intelligence expertise, Facebook has come up with what it says are the most accurate and scalable population density models ever assembled. The work, announced today in conjunction with Mobile World Congress in Barcelona, Spain, was ostensibly inspired by Facebook’s desire to understand how best to deploy satellites, the company’s massive solar-powered Aquila drones, and terrestrial networks in order to most efficiently provide low-cost Internet connectivity in the developing world. ‘The basis of this stems from being able to have accurate models of where people in the world are,’ Yael Maguire, who oversees Facebook’s Connectivity Lab, told Fast Company. ‘The data that (already existed) was somewhat inadequate to build adequate models for where’ drones, satellites, and other assets could be deployed.”

Terdiman goes on, “As part of the project, Maguire’s team at the Connectivity Lab took billions of satellite images and applied computer vision techniques to them, ultimately identifying buildings and other infrastructure ‘as a proxy for where people live,’ Facebook scientists Tobias Tiecke and Andi Gros wrote in a blog post. ‘We then combined our results with existing census counts and created a population dataset with 5-meter resolution’ for 20 countries. Those countries are Algeria, Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, India, Ivory Coast, Kenya, Madagascar, Mexico, Mozambique, Nigeria, South Africa, Sri Lanka, Tanzania, Turkey, Uganda, Ukraine, and Uzbekistan. The ‘crazy idea,’ as Maguire put it, was to train Facebook’s machine learning system to recognize things like houses and other man-made structures and interpret them as evidence of population.”

Read more here.

photo credit: Flickr

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