It’s obviously important to Australians to make sure their koala population is closely tracked — but how can you do so when the suckers live in forests and climb trees all the time? With drones and AI, of course.

A new project from Queensland University of Technology combines some well-known techniques in a new way to help keep an eye on wild populations of the famous and soft marsupials. They used a drone equipped with a heat-sensing camera, then ran the footage through a deep learning model trained to look for koala-like heat signatures.

It’s similar in some ways to an earlier project from QUT in which dugongs — endangered sea cows —[1] were counted along the shore via aerial imagery and machine learning. But this is considerably harder.

A koala

“A seal on a beach is a very different thing to a koala in a tree,” said study co-author Grant Hamilton in a news release[2], perhaps choosing not to use dugongs as an example because comparatively few know what one is.

“The complexity is part of the science here, which is really exciting,” he continued. “This is not just somebody counting animals with a drone, we’ve managed to do it in a very complex environment.”

The team sent their drone out in the early morning, when they expected to see the greatest contrast between the temperature of the air (cool) and tree-bound koalas (warm and furry). It traveled as if it was a lawnmower trimming the tops of the trees, collecting data from a large area.

Infrared image, left, and output of the neural network highlighting areas of interest

This footage was then put through a deep...

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