A milestone has been achieved for planetary scientists and artificial intelligence researchers at NASA's Jet Propulsion Laboratory (JPL): identify craters on other planets thanks to machine learning algorithms.
The image had been captured by the HiRISE camera team aboard NASA's Mars orbiter MRO. AND, thanks to these algorithms, scientists could save the hours they spend each day studying images captured by the MRO.
Mars Reconnaissance Orbiter
In the Mars orbiter's 14 years, scientists have relied on MRO data to find more than 1,000 new craters. But it is hard work. The process requires patience, requiring approximately 40 minutes for a researcher to carefully scan a single image from the context camera.
To save time, we now have the so-called automated fresh impact crater classifier, as part of a broader JPL effort called COSMIC (Capturing Onboard Summarization to Monitor Image Change) that develops technologies for future generations of Mars orbiters.
What takes a human 40 minutes, this classifier did it in 5 seconds.
To train the crater classifier, the researchers fed it 6,830 context camera images, including those from locations with previously discovered impacts that had already been confirmed through HiRISE. The tool was also fed images with no new hits to show the classifier what not to look for.
The first discovery of a crater made by artificial intelligence took place by exploring around 112,000 images.
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The news
This is the first discovery of a crater on Mars with the help of Artificial Intelligence
was originally published in
Xataka Science
by
Sergio Parra
.