Terrapattern is an interface for finding "more like this, please" in satellite photos. Simply click an interesting spot on Terrapattern's map, and it will find other locations that look similar. Terrapattern is particularly useful for locating things that aren't usually indicated on maps. We think Terrapattern is important because it's open-source, open-access, and open-ended.
Terrapattern can be briefly described as "a visual search engine for satellite imagery", "similar-image search for satellite photos", or "a prototype for visual query-by-example in satellite imagery". We hope it will help citizen scientists, humanitarians, journalists, and other curious people to discover "patterns of interest".
Technically, the project uses a Deep Convolutional Neural Net (DCNN) to assist with image recognition. For more information, please see terrapattern.com.
Terrapattern is an open-source project created by a collaborative team of artists, creative technologists, and students. Terrapattern is not a company or startup; it is an experimental research prototype, developed in a university setting, whose purpose is to present a new way of exploring, understanding, and organizing the world. Terrapattern went live on May 24th, 2016.
Terrapattern was created by Golan Levin, David Newbury, and Kyle McDonald with the assistance of Irene Alvarado, Aman Tiwari, and Manzil Zaheer. The project was developed at the CMU Frank-Ratchye STUDIO for Creative Inquiry with support from the John S. and James L. Knight Foundation.