“Machine learning is turning the data into an omnipresent source of business intelligence”
Facebook has a vested interest in helping the 4.2 billion people who still lack reliable Internet access find their way online. But when the social media company launched an effort in 2013 to provide connectivity to some of the world’s most remote and disconnected regions, it immediately ran into a problem. Facebook knew these disconnected billions existed—just not precisely where in the world they were.
So the Facebook Connectivity Lab team set out to find them. Using technology similar to what allows Facebook to recognize faces in photos uploaded to its service, the company sifted through more than 14 billion geospatial images captured by satellite imagery provider DigitalGlobe. The resulting maps (shown below) reveal the locations of more than 2 billion disconnected people spread across 20 countries, many of them developing nations where even basic mapping data is scarce.
(Image Courtesy: DigitalGlobe)
“There’s a lot of location data out there, but there hasn’t been a good way to use it to answer questions,” says Kevin Lausten, director of geospatial big data at DigitalGlobe. “If you can start to correlate all this information, you can uncover business opportunities.”
Discovering correlations within reams of visual data requires technology that can both see and comprehend. To turn DigitalGlobe’s raw satellite images into meaningful insights, Facebook engineers had to teach their image-recognition engines what to look for—in this case, man-made structures and other infrastructure indicative of human activity. Then they set the software to work on roughly 14.6 billion DigitalGlobe images, documenting the location of every building across some 21.6 million square kilometers of the earth’s surface.