Golan Levin designed Terrapattern as a piece of contemporary art. Pick a satellite image, find others like it, tile them into a pattern. It was created to inspire others to make better use of satellite images, but was never itself meant for practical purposes.
But in just a few months of existence, it’s become a first iteration of what could be an essential digital weapon for humanitarian agencies, environmentalists, and civic activists.
Billed as the first open-source tool to perform “similar-image searches” for satellite photos, Terrapattern works intuitively. Browse a Google Earth-like map, click on something you’re interested in, and Terrapattern will return similar images.
My first test was a baseball diamond. I zoomed straight into Yankee Stadium in the Bronx, and clicked on home plate. Then I was looking at dozens of fields throughout the New York metro area.
But Levin, a Carnegie Mellon University professor, artist, and engineer, now thinks the best use of Terrapattern is to find more hidden features—things “that might be of interest to journalists, citizen scientists or NGOs.”
For example, he offers, we could use it to uncover heretofore hidden logging roads in the Amazon. These roads are just 10 or 12 feet (3-4 meters) across, but with Terrapattern’s high resolution—at one foot per pixel, it’s working with the most detailed satellite images available today—the tool could spot the thin lines that are harbingers of devastating deforestation to come.
It’s an ambitious vision, but not grandiose. Already, satellite images have become a key tool in managing displaced population camps. But though satellite imagery from private companies is now plentiful, finding patterns in it is impossible without heavy-duty computer algorithms to analyze it.