A team of researchers have found a just-fallen meteorite in the West Australian desert – using drone footage and an artificial intelligence.
They say that their machine learning program will slash the amount of time scientists have to spend combing the desert for meteorites. (Don’t you just hate it when you have to spend days searching through the desert for a space rock?)
Seamus Anderson, a graduate student at Curtin University’s Space Science and Technology Centre, and lead solver of this highly relatable problem, says that meteorites are important to study because they can tell us more about the geology of the solar system.
“Beyond increasing our understanding of the solar system, the study of meteorites is useful for many reasons,” he says. “For example, meteorites often contain a higher concentration of rare and valuable elements such as cobalt, which is crucial to the construction of modern batteries.”
This is particularly important when we’ve seen the meteorites falling. “We can actually observe their fall from the upper atmosphere,” says Anderson.
“And from that information, we can essentially figure out the orbits of where it came from in the solar system.”
But typically, identifying and finding meteorites is a laborious process. They can be tracked while falling through the Desert Fireball Network, but this still leaves a large area of ground that needs to be manually surveyed.
“We figured it takes about 350 or so labour days to recover meteorites, give or take,” says Anderson.
To address this, Anderson and colleagues trained a convolutional neural network – a type of machine learning program – to identify meteorites from drone footage.
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“Basically, what we did was we went outside with a few meteorites that were on loan to us from the Western Australian Museum, and we were able to take images of those,” says Anderson. These images were then used to teach the neural network.
“The hardest part, I think, was trying to limit the number of false positives just because of how much area we had to cover […] We had to train it to kind of ignore stuff,” says Anderson.
The researchers had previously published a description of their AI, but until last year they hadn’t been able to test it on a freshly fallen meteorite. On 1April 2021, the Desert Fireball Network spotted a meteor over the Nullarbor, and the Curtin researchers headed out to retrieve it.
They first used a large drone to survey the 5.1 square kilometre area where the meteor was predicted to fall.
“After each flight we offload it onto our computer, and we’ll process it with all of our programming software,” says Anderson. “And basically from those images, which are all geotagged, we can figure out where a meteorite candidate likely is.
“Then we go out with a smaller drone to go and see what it looks like up close. And then if it still looks good, we’ll send people out to go and check it out.”
The program wasn’t perfect – it had a penchant for mistaking kangaroos, and a purple flower that was in bloom, for meteorites – but it dramatically narrowed the search time.
Using this method, four researchers were able to find the rock after three days of searching – or 12 labour days, down from 350.
Anderson says it’s a “huge improvement in how much effort you have to put in”.
With some more training, the program should work in other environments as well.
“We’re hoping to export this methodology to a whole bunch of our partners from all over the globe – from Morocco, to Manitoba, to the UK,” says Anderson.
“It’s designed to basically take in local training data, which is really nice.”
Amateur meteorite spotters with the right equipment could also use the software – although smaller amateur drones might take longer to perform the same task.
The drone and AI-assisted discovery of the meteorite has been reported in a pre-print (not peer-reviewed) paper, currently hosted on arXiv.
Ellen Phiddian is a science journalist at Cosmos. She has a BSc (Honours) in chemistry and science communication, and an MSc in science communication, both from the Australian National University.
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