Of particular interest to scientists are the orbiter’s crater photos, which can provide a window into the planet’s deep history. NASA engineers are still working on a mission to return samples from Mars; without the rocks that will help them calibrate remote satellite data with conditions on the surface, they must do a lot of educated guesswork when it comes to determining each crater’s age and composition.
For now, they need other ways to tease out that information. One tried and true method is to extrapolate the age of the oldest craters from the characteristics of the planet’s newest ones. Since scientists can know the age of some recent impact sites within a few years—or even weeks—they can use them as a baseline to determine the age and composition of much older craters. The problem is finding them. Combing through a planet’s worth of image data looking for the telltale signs of fresh impact is tedious work, but it’s exactly the sort of problem that an AI was made to solve.
Late last year, researchers at NASA used a machine-learning algorithm to discover fresh Martian craters for the first time. The AI discovered dozens of them hiding in image data from the Mars Reconnaissance Orbiter and revealed a promising new way to study planets throughout our solar system. “From a science perspective, that’s exciting because it’s increasing our knowledge of those features,” says Kiri Wagstaff, a computer scientist at NASA’s Jet Propulsion Laboratory and one of the leaders of the research team. “The data was there all the time, it’s just that we hadn’t seen it ourselves.”
