NASA | JPL-Caltech | MRO | 2020 Oct 01
It's the first time machine learning has been used to find previously unknown craters on the Red Planet.
Sometime between March 2010 and May 2012, a meteor streaked across the Martian sky and broke into pieces, slamming into the planet’s surface. The resulting craters were relatively small -- just 13 feet (4 meters) in diameter. The smaller the features, the more difficult they are to spot using Mars orbiters. But in this case -- and for the first time -- scientists spotted them with a little extra help: artificial intelligence (AI).
It’s a milestone for planetary scientists and AI researchers at NASA’s Jet Propulsion Laboratory in Southern California, who worked together to develop the machine-learning tool that helped make the discovery. The accomplishment offers hope for both saving time and increasing the volume of findings.
Typically, scientists spend hours each day studying images captured by NASA’s Mars Reconnaissance Orbiter (MRO), looking for changing surface phenomena like dust devils, avalanches, and shifting dunes. In the orbiter’s 14 years at Mars, scientists have relied on MRO data to find over 1,000 new craters. They’re usually first detected with the spacecraft’s Context Camera (CTX), which takes low-resolution images covering hundreds of miles at a time.
Only the blast marks around an impact will stand out in these images, not the individual craters, so the next step is to take a closer look with the High-Resolution Imaging Science Experiment, or HiRISE. The instrument is so powerful that it can see details as fine as the tracks left by the Curiosity Mars rover. ...
