Facebook fb 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 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.
Facebook’s ambitious foray into geospatial big data comes at a time when commercial satellite operators are poised to turn the data spigot wide open. Over the next 18 months, companies dealing in satellite imagery plan to place several dozen new satellites in orbit. By the middle of next year, many of those same companies expect to achieve daily “refresh rates”—new imagery of the same parts of the planet—at least once every 24 hours. Humans will soon be able to see huge swaths of the planet change daily.
With the effort, Facebook joins a growing cast of Silicon Valley companies scrambling to perfect “machine learning”—a newly popular form of artificial intelligence—that could unlock value in petabytes of satellite imagery.
“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.;