PHOTO BY DIRK DANIEL MANN/SHUTTERSTOCK
warmer than their environment—so, with
thermal imaging, they stand out really brightly
against this cooler background.”
Mounting thermal cameras on drones allows
conservationists like Wich to survey vast
areas of parkland or wilderness that would
be impossible to cover on foot. But, once you
have all this footage, someone still needs to
identify the animal that each thermal blob
represents.
“We were gathering so much data that
it quickly became very laborious to have a
person go through all these thousands and
thousands of images,” Wich says. “When
Steve suggested he could help, I thought he
meant offering to put some volunteer time
into manually labeling the images. But then
he explained how in astrophysics they’ve
developed machine-vision algorithms for
automatically classifying stars and galaxies
from thermal data and that this might be
applicable to identifying animals from our
thermal imagery.”
AUTOMATING ANIMAL
IDENTIFICATION
To retrain these algorithms for species instead
of star clusters, Longmore founded the
Astro-Ecology Group. First, his team gathered
thermal camera footage from a nearby farm
to see if these algorithms could be taught to
identify the difference between the thermal
signature of humans versus cows. When this
was a success, they visited local safari parks
and zoos to build up a database of numerous
thermal images of multiple species in different
environments.
In order to be used for machine learning,
these images would need to be labeled
with the correct identifications. For this,
they turned to the citizen science platform
Zooniverse, which was created in 2009
to allow members of the public to assist
astronomers by hand-classifying types of
galaxies. Since uploading their first set of data
to the platform in February 2019, Longmore’s
group has received the help of over 4,000
volunteers, who’ve scoured around 27,000
images from Knowsley Safari Park for the
“ We were gathering so much
data that it quickly became
very laborious to have a
person go through all these
thousands and thousands
of images.”
—Serge Wich, Liverpool John Moores
University
presence of animals, and tagged them as an
example for the team’s automated system to
follow.
After this initial training with captive animals,
the team next took their system out into the
wild. So far, it has been used to successfully
identify and count orangutans in Borneo, spider
monkeys in Mexico, river dolphins in Brazil, and
riverine rabbits in South Africa.
“The riverine rabbit is the most endangered
animal you’ve never heard of,” Longmore says.
“It’s the 13th most endangered mammal on the
planet, and there are only a few hundred living
in an area of the South African desert the size
of Austria. So the fact that we were able to
get a confirmed identification of one was really
exciting.”
There have been challenges, however. While
endangered animals may be much closer than
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