Landsat 8 has been operational since the spring of 2013. Its inclusion helps the new map contain newer data—and, thus, newer structures. In the new map’s version of Toyko, the “D” runway of Haneda Airport can be seen in the bottom center of the frame. The standalone runway was built on reclaimed land in 2010.

Google / Landsat

In the older version of the same scene, the D runway seems more translucent. The map overall is also blurrier, less saturated, and more generically gray.

Google / Landsat

This “ghostly” runway effect points to how Google makes its maps cloudless in the first place. Neither of the images above were captured on a single shoot by a solitary satellite, the way that a camera might capture a snapshot. Instead, Google engineers used a recently developed cartography technique called mosaicking.

Mosaicking draws upon the vast archives of imagery that have been created by the U.S. government’s Landsat program, a series of satellites that have photographed the Earth’s surface every 16 days since the 1970s. (Landsat 7 and Landsat 8 are only the most recent of these craft.)

Taken individually, most of these pictures captured by the Landsat sensors include some clouds. This makes sense: About 70 percent of Earth’s land surface is covered by clouds every day. Over time, though, very few places are completely obscured by clouds. Mosaicking joins the cloudless parts together through the power of surprising, elegant math.

A mosaicking algorithm inspects each pixel of imagery individually—across all of the images of that particular pixel collected by Landsat 7 and 8. (If the archive is properly calibrated, that one pixel should describe the same spot of Earth no matter when it was taken.) In essence, the algorithm takes an initial average color value for that pixel over time. Then it drops the images that are much lighter than that average—since they likely include clouds—and averages the most recent set of remaining, now-cloudless photos to find a final color value.

Then it runs that program for the next pixel. Eventually, these “best pixels” are stitched together into a single map—a mosaic. The team analyzed more than 700 trillion pixels of satellite data in the entire mosaicking process.

Google’s version of this algorithm factors in other special circumstances, like seasonal effects. Matt Hancher, an engineer, called the process a “glorified computation of the median.” The company’s Maps and Earth team has used a mosaicking algorithm to generate its satellite map since June 2013.

An odd characteristic of the mosaicking process is that it captures the essential character of what a place looks like even though it actually encompasses years of data. When writing about a mosaicked map of the United Kingdom produced by the startup Mapbox in May 2013, the journalist Tim Maly wrote: “At no point in the history of the United Kingdom has it looked like this [map]. Yet this [map] is exactly what it looks like.”