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A new technique developed by researchers at U of T Scarborough is using the same technology behind self-driving cars to measure the size and location of crater impacts on the moon.
"When it comes to counting craters on the moon, it's a pretty archaic method," says Mohamad Ali-Dib, a postdoctoral fellow in the Centre for Planetary Sciences (CPS).
Image - Locate - Craters - Size - Image
"Basically we need to manually look at an image, locate and count the craters and then calculate how large they are based off the size of the image. Here we've developed a technique from artificial intelligence that can automate this entire process that saves significant time and effort."
Researchers have tried in the past to develop algorithms that could identify and count lunar craters but when they were used on new, previously unseen patches of craters they tended to perform poorly. By comparison, the technique developed by Ali-Dib and his colleagues can generalize very well to unseen lunar patches, and even other cratered bodies like Mercury.
Time - Algorithm - Craters - Parts - Moon
"It's the first time we have an algorithm that can detect craters really well for not only parts of the moon, but also areas of Mercury," says Ali-Dib, who developed the technique along with Ari Silburt, Chenchong Charles Zhu and a group of researchers at CPS and the Canadian Institute for Theoretical Astrophysics (CITA).
In order to determine its accuracy, the researchers first trained the neural network on a large data set covering two thirds of the moon, and then tested their trained network on the remaining third of the moon. It worked so well that it was able to identify twice as many craters as traditional manual counting. In fact, it was able to identify about 6,000 previously unidentified craters on the moon.
Technique - Network - Class - Machine - Algorithms
The technique itself relies on a convolutional neural network, a class of machine learning algorithms that has been...
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