The conversation started over a fence dividing two backyards. On one side, an ecologist remarked that surveying animals is a pain. His neighbor, an astronomer, said he could see objects in space billions of light years away.
And so began an unusual partnership to adapt tools originally developed to detect stars in the sky to monitor animals on the ground.
The neighbors, Steven Longmore, the astronomer, and Serge Wich, the ecologist, both of Liverpool John Moores University in England, made their backyard banter a reality that may contribute to conservation and the fight against poaching.
The scientists developed a system of drones and special cameras that can record rare and endangered species on the ground, day or night. Computer-vision and machine-learning techniques that help researchers study the universe’s oldest and most distant galaxies can now be used to find animals in video footage.
Keeping track of elusive animals, especially those that are endangered, isn’t trivial. First, it takes time and money to conduct manual counts on the ground or to shoot photos from planes in the sky. With video, cheaper drones and software, identifying animals has become more efficient.
But cameras made for daylight can miss animals or poachers moving through vegetation, and the devices don’t work at night. Infrared cameras can help: Dr. Wich had been using them for decades to study orangutans.
These cameras yield large amounts of footage that can’t be analyzed fast enough. So what do animals and stars have in common? They both emit heat. And much like stars, every species has a recognizable thermal footprint.
“They look like really bright, shining objects in the infrared footage,” said Dr. Burke. So the software used to find stars and galaxies in space can be used to seek out thermal footprints and the animals that produce them.
To build up a reference library of different animals in various environments, the team is working with a safari park and zoo to film and photograph animals. With these thermal images — and they’ll need thousands — they’ll be able to better calibrate algorithms to identify target species in ecosystems around the world…
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