Lockheed Martin announced it has developed a satellite imagery recognition system named Global Automated Target Recognition (GATR). This system uses open-source deep learning libraries to quickly identify and classify objects or targets in large areas across the world. With this it might save image analysts countless hours of manually categorizing and labeling items within an image. GATR runs in the cloud, using Maxar’s Geospatial Big Data platform (GBDX) to access Maxar’s 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms.
GATR teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system scales quickly to scan large areas, including entire countries. GATR uses deep learning techniques common in the commercial sector and can identify ships, airplanes, buildings, seaports, and many other categories. So far the system has shown a high accuracy rate with well over 90% on the models the company has tested so far. It only took two hours to search the entire state of Pennsylvania for fracking sites – that is 120,000 square kilometers, Lockheed Martin stated.
“There is more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process,” said Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions. “Artificial Intelligence models like GATR keep analysts in control while letting them focus on higher-level tasks.”
“I am not an expert on what oil production sites are, and I don’t have to be,” added Mark Pritt, senior fellow at Lockheed Martin and principle investigator for GATR. “This system teaches itself the defining characteristics of an object, saving valuable time training an algorithm and ultimately letting an image analyst focus more on their mission.” GATR builds on research Pritt’s team pioneered during a Intelligence Advanced Research Projects Activity (IARPA) challenge, called the “Functional Map of the World.”