Big Data Is for the Birds
In Ithaca, New York, a virtual machine in a laboratory at the Cornell Lab of Ornithology sits in the night, humming. The machine’s name is Bubo, after the genus for horned owls. About every five minutes, Bubo grabs an image from Northeast weather radar stations, and feeds it through a pipeline of artificial-intelligence algorithms. What does this radar image show me? Bubo asks. Is it rain? Are these insects? Could it be pollen? Bubo doesn’t care about those things; all it wants to see are birds in flight. To find them, Bubo analyzes the velocity and direction of targets seen by the radar station. Bubo knows birds have a velocity different from wind and insects, and filters those out. Now Bubo sees only birds. But how dense are they? How fast are they going? How high in the sky are they flying? The machine makes these calculations and creates an image of countless birds in flight, traveling under cover of darkness.
“If we could see at night, we would see millions of birds flying overhead,” says Thomas Dietterich, a professor of computer science at Oregon State University, who works with the Cornell Lab of Ornithology. Black-chinned hummingbirds fly along the Mexican coast on their way to Alaska. Yellow-throated vireos soar over the Gulf Coast, headed to Ontario. Olive-and-yellow flycatchers sail across Central America, bound for the Northwest Territories. “It’s just so awe-inspiring that there’s this huge, secret thing happening that we’re unaware of.”
Scientists have long sought
