Scavenger hunts just got significantly more tactical with Descartes Labs' new GeoVisual Search. Finding shipping containers, runways and even parking lots on a global scale is no problem with the free tool made available today. Descartes Labs is a geospatial analytics startup based in Los Alamos, New Mexico. The company specializes in analyzing satellite imagery and other global data with machine learning to power predictive analytics for agriculture and other key industries. Users can scour the earth's surface by placing a provided bounding box around any object they would like to search for.

Terrapattern finds places that look the same using machine learning. Terrapattern is a prototype for helping people quickly scan extremely large geographical areas for specific visual features. We are particularly keen to help people identify, characterize and track indicators which have not been detected or measured previously, and which have sociological, humanitarian, scientific, or cultural significance. Terrapattern provides an open-ended interface for visual query-by-example. Simply click an interesting spot on Terrapattern's map, and it will find other locations that look similar.

Terrapattern is a visual search engine that, from the first moment you use it, you wonder: Why didn't Google come up with this 10 years ago? Click on a feature on the map -- a baseball diamond, a marina, a roundabout -- and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. And how did a handful of digital artists and developers create it -- and for under 35,000?

Right now, Terrapattern only covers four American cities: Pittsburgh, Detroit, San Francisco, and New York City. Terrapattern is so computing-hungry that it is effectively a proof of concept right now, at least for a team of artists working with less than 35,000. Each metro region takes about 10 gigabytes of RAM--not storage, but active memory. That said, Terrapattern is relatively technically straightforward. It's constructed from a convolutional neural network and CoverTree, an algorithm that remembers some descriptions and allows the searches to happen quickly.

In 2008, through something of a happy accident, a team of zoologists from the University of Duisburg-Essen in Germany discovered that grazing cows and deer tend to align their bodies with magnetic north. It was an odd thing to notice, particularly because the researchers had been perusing satellite imagery for something else entirely. But that's what happens when you look at something from 400 miles above the Earth's surface--change your perspective, and you'll change what you see. When Golan Levin, a professor of new media art at Carnegie Mellon University, heard about the cow discovery, he found it "to be simultaneously wonderful and very inspiring and totally useless." He was also overcome, he says, by the desire to make similar discoveries.

Golan Levin, an associate professor of art at Carnegie Mellon University, suggested. I was looking at a satellite image of the school's campus in Pittsburgh, embedded in the home page of Levin's latest online project, Terrapattern. "What you should immediately see are all the most tennis-court-ish patches of Allegheny County," he said. With gratifying speed, the right-hand side of my screen filled with dozens and dozens of tennis courts--solo or in pairs or in clusters of six, white on green, purple on green, green on red. A confusingly painted parking lot ended up in the mix, too.

Terrapattern is a visual search engine that, from the first moment you use it, you wonder: why didn't Google come up with this ten years ago? Click on a feature on the map -- a baseball diamond, a marina, a roundabout -- and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use, and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. And how did a handful of digital artists and developers create it -- and for under 35,000?

Golan Levin, an associate professor of art at Carnegie Mellon University, suggested. I was looking at a satellite image of the school's campus in Pittsburgh, embedded in the home page of Levin's latest online project, Terrapattern. "What you should immediately see are all the most tennis-court-ish patches of Allegheny County," he said. With gratifying speed, the right-hand side of my screen filled with dozens and dozens of tennis courts--solo or in pairs or in clusters of six, white on green, purple on green, green on red. A confusingly painted parking lot ended up in the mix, too.