But elsewhere, population-distribution maps have dozens of applications in different fields. Urban planners need to estimate city density so they can place and improve roads. Epidemiologists and public-health workers use them to track outbreaks or analyze access to health care. And after a disaster, population maps can be used (along with crisis mapping) to prioritize where emergency aid gets sent.

Facebook has a bottom-line interest in the data. Its future corporate growth depends on Internet access expanding to the roughly 4 billion humans who can’t yet log on, so it’s explicitly interested in the maps for infrastructural reasons. It wants to know how a certain population might gain Internet access most efficiently: Should they run a fiber-optic line, or would the task best be accomplished with drones, satellites, or high-altitude balloons?

That’s part of why Facebook chose the countries it did: 20 nations where the web still does not reach some rural populations. They include Nigeria, Kenya, Uganda, Turkey, Ukraine, Uzbekistan—and India, where the company’s Free Basics product was just rebuffed. Free Basics and Connectivity Labs are both part of Internet.org, an international (and often for-profit) corporate initiative to expand the reach of the web and, with it, Facebook’s services.

But in all of those countries, how did Facebook make better population maps than anyone else, including local governments? It’s not like they paid vans to drive around the landscape like Google did. The answer lies in Facebook’s access to incredible computing power.

Here’s how the maps got made: Facebook’s Connectivity Labs first took the best-available world population information, a dataset from Columbia University called Gridded Population of the World. This is an agglomerated set of local census data, normalized to the same year. It’s the best population map available globally, but it’s fairly low resolution: The grid squares can vary from “a few square kilometers in urban areas to tens of thousands of square kilometers in the rural areas of interest,” according to Facebook.

Then, it bought millions of kilometers of high-resolution satellite imagery from DigitalGlobe, the company that operates most of the private, high-resolution Earth-observing satellites now in space. When you look at your house in Google Maps, you’re usually (but not always) looking though one of DigitalGlobe’s four orbiting lenses.

Most DigitalGlobe imagery is “submetric”; that is, instead of a grid square being hundreds of kilometers to a side, it’s 50 centimeters. Facebook developers taught their neural-net algorithms to recognize what a building looked like from above in this data. Then, they turned the programs loose. The software estimated urban density by the number of buildings it could see, and extrapolated the best-available population data onto the settlements.