To the untrained eye, zebras in Kenya probably all look alike. But each animals black and white markings are like a fingerprint, distinct and invaluable for scientists who need to track the animals and information about them, including their births, deaths, health and migration patterns.
Traditionally, getting this kind of information has been an invasive and labor-intensive process. But breakthroughs in artificial intelligence (AI) and crowdsourcing of photos of individual animals are beginning to change the conservation game.
Portland, Oregon-based nonprofit Wild Me has developed AI to pick out identifying markers the stripes on a zebra, the spots on a giraffe, the contours of a sperm whales fin and catalog animals much faster than a human can. Photo surveys are increasingly used as the backbone for population estimates, and Wild Mes Wildbooks, which catalog various species, are giving conservation groups, governments and citizen scientists a faster way to monitor animals around the world.
We can use this information to track diseases and poaching threats, look at manifestations of diseases, said Michael Brown, a conservation science fellow at the Giraffe Conservation Foundation and the Smithsonian Conservation Biology Institute, who has been working with Wild Me for the past few years. It lets us piece together an understanding of how these threats to giraffes are spatially situated (and) how the giraffes are utilizing different landscapes over time.
Founder Jason Holmberg launched the first iteration of Wild Me in 2003 after swimming with whale sharks off the coast of Djibouti. He wanted to find a different way to track the animals other than invasive tagging, so he teamed up with a biologist and a NASA astronomer, adapting the algorithm for the Hubble telescope to match the sharks spot patterns.
For years, Holmbergs endeavors were a side project he didnt leave his full-time job in tech until recently. Wild Mes work gradually expanded, then it really kicked into gear with a 2018 grant from Microsofts AI for Earth. Today, Wild Me has a team of six full-time staffers, with plans to add more soon.
Wild Mes process of creating and training algorithms takes serious time. Thousands of photos of the species must be manually annotated so that the algorithm learns what a given animal is, what the distinguishing characteristics are and whats just background noise.
The model relies largely on photographs taken by scientists or everyday people who upload their photos to the corresponding Wildbook. It uses AI to find things in the picture and then hand it to algorithms or machine learning to suggest IDs which whale, which giraffe, etc., Holmberg said.
Christin Khan conducts aerial surveys of North Atlantic right whales for the National Oceanic and Atmospheric Administration and had sought an AI-based solution for years. She said she watched Facebook implement facial recognition and wanted to use similar technology to help identify whales within the endangered species (there are only about 400 North Atlantic right whales left).
We needed a really simple, user-friendly web-based interface where a biologist who knows nothing about AI could upload a photograph and get a result back, she said. Eventually we realized the developers at Wild Me had already done a lot of what we needed, and it wouldnt require us to reinvent the wheel.
The Wildbook for whales, called Flukebook, encourages collaboration, which is particularly useful for whales that travel long distances because it can be difficult for one research group to effectively monitor one area.
The more people on the water, the more photos, the more its decentralized, (the better), said Shane Gero, who founded and runs the Dominica Sperm Whale Project. By doing the matching themselves, by contributing their own data, not only do they get to know the animals, but it creates a locally motivated community of people that can react when conservation actions come up.
Before the introduction of AI, Gero said it would take about a month to process a months worth of photos.
(Now), we have our numbers of individuals sighted and population estimates faster, so we can report (almost) in real time, he said.
That means his group is able to provide the government of Dominica with more up-to-date information and offer better advice on how to shape conservation efforts.
One of Wild Mes more recent innovations is an AI-driven feature that datamines YouTube videos of whale sharks and sea turtles, using user-generated videos (often taken by tourists) to get a better sense of the populations. This has been a great way to increase the amount of photos coming in and provide researchers with more data. But it also creates even more work for people on the ground, who have to manually check the AIs suggestions and accept the results.
Were flooding the whale shark community with more data than it can handle, said Holmberg.
So Wild Me is now building the capacity to automate the identification process and scaling the tech that combs social media for relevant videos.
The nonprofit recently received a two-year grant from the Gordon and Betty Moore Foundation to develop the new algorithm that will make the animal IDs on its own.
Its focusing the initial work on zebras because it already has an incredibly rich dataset. Every two years since 2016, the Great Grvys Rally in Kenya has used hundreds of citizen scientists spread out over thousands of kilometers to photograph Grvys zebras over two days. Wild Mes AI analyzes the zebra markings on all the photos to come up with a total population, which the Kenyan government treats as the official census for Grvys zebras.
This type of work is a huge upgrade from the traditional capture-mark-recapture process, which is both invasive and time consuming, with studies done every five to 10 years.
You can only make very coarse-grained conservation decisions, Holmberg said. The point of going to a fully automated system is to shorten that cycle so we can take all of the data over the past week or two weeks and have a continuous prediction of population size. Its fine-grained, which helps researchers understand and lobby for better conservation activities.
For Khan, meanwhile, the existing technology is still in its early days. The algorithm for North Atlantic right whales became operational in November 2019, and she said theyre still working out the kinks and figuring out how best to use it. But, she said, she sees the incredible potential that it holds.
My dream is that we get to the point where the worlds oceans will be trolled by satellite photos and we can understand the worlds whale population, she said. Combining AI with satellite imagery and drones we have the potential to exponentially understand the worlds oceans thats just not possible with manned aircraft.
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