Using AI to count craters on the moon at U of T's Centre for Planetary Sciences
A new technique developed by researchers at the University of Toronto is using the 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 researcher at the Centre for Planetary Sciences (CPS) at U of T Scarborough.
"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."
[...] 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.
Also at New Scientist and Science News.
Lunar Crater Identification via Deep Learning (arXiv:1803.02192)
(Score: 2) by maxwell demon on Saturday March 17 2018, @10:10AM (1 child)
You know the difference between 2/3 and 1/500? Moreover I'm sure the scientists know how to select an unbiased sample, which the Daily Mail sample obviously was not.
The Tao of math: The numbers you can count are not the real numbers.
(Score: 0) by Anonymous Coward on Saturday March 17 2018, @10:24PM
Whoosh!