Citizen Scientists Help Explore the Moon 60
Pickens writes "NPR reports that NASA's Lunar Reconnaissance Orbiter is doing such a good job photographing every bit of the moon's surface that scientists can't keep up, so Oxford astrophysicist Chris Lintott is asking amateur astronomers to help review, measure, and classify tens of thousands of moon photos streaming to Earth using the website Moon Zoo, where anyone can log on, get trained, and become a space explorer. 'We ask people to count the craters that they can see ... and that tells us all sorts of things about the history and the age of that bit of surface,' says Lintott. Volunteers are also asked to identify boulders, measure the craters, and generally classify what is found in the images. If one person does the classification — even if they're an expert — then anything odd or interesting can be blamed on them. But with multiple independent classifications, the team can statistically calculate the confidence in the classification. That's a large part of the power of Moon Zoo. Lintott adds the British and American scientists heading up the LRO project have been randomly checking the amateur research being sent in and find it as good as you would get from an expert. 'There are a whole host of scientists ... who are waiting for these results, who've already committed to using them in their own research.'"
A better use of the public's time... (Score:5, Interesting)
...would be to use the statistically-validated user input in a feed-forward image recognition neural network utilizing error feedback that would "learn" to identify the various features of interest. Use edge detection to identify the features of interest (for instance, by number just like a paint-by-number canvas), and have users "identify" what they see. We're talking about invariant scale here, which vastly simplifies the learning process as well as automated feature measurement.
I was doing this in the '90s using multi-band spectral imagery from LANDSAT with good success. I would imagine there have been some advances in this area since that time.
Comment removed (Score:3, Interesting)