An algorithm can spot beaver ponds from satellite imagery. //

Corwin’s beaver obsession met a receptive corporate culture. Google’s employees are famously encouraged to devote time to passion projects, the policy that produced Gmail; Corwin decided his passion was beavers. But how best to assist the buck-toothed architects? Corwin knew that beaver infrastructure—their sinuous dams, sprawling ponds, and spidery canals—is often so epic it can be seen from space. In 2010, a Canadian researcher discovered the world’s longest beaver dam, a stick-and-mud bulwark that stretches more than a half-mile across an Alberta park, by perusing Google Earth. Corwin and Ackerstein began to wonder whether they could contribute to beaver research by training a machine-learning algorithm to automatically detect beaver dams and ponds on satellite imagery—not one by one, but thousands at a time, across the surface of an entire state. //

According to Fairfax, EEAGER’s use cases are many. The model could be used to estimate beaver numbers, monitor population trends, and calculate beaver-provided ecosystem services like water storage and fire prevention. It could help states figure out where to reintroduce beavers, where to target stream and wetland restoration, and where to create conservation areas. It could allow researchers to track beavers’ spread in the Arctic as the rodents move north with climate change; or their movements in South America, where beavers were introduced in the 1940s and have since proliferated. “We literally cannot handle all the requests we’re getting,” says Fairfax, who serves as EEAGER’s scientific adviser.