Adding AI that detects whether a person is in the photo reduces the error rate to a fraction of previous attempts—and it improves its accuracy with time. Capturing and sending fewer images means TrailGuard cameras now last up to 18 months without having to swap the batteries out.

The AI behind the smart cameras has been used in urban settings for years but hasn’t made an appearance to stop poaching until now. “It’s never been used in national parks where we need it the most,” says Eric Dinerstein, the Director of Biodiversity and Wildlife at RESOLVE, a conservation nonprofit and a partner on the TrailGuard AI project.

For cash-strapped national parks trying to stop well-funded poaching forces that can include anything from machine guns to helicopters, any help is appreciated.

“We think it’s going to be a game changer,” Dinerstein says.

TrailGuard AI, developed by Intel and conservation nonprofit RESOLVE, is a system of small cameras that can help park rangers identify possible poachers quickly, without human image analysis. Image credit: RESOLVE

Humanity is making it tough for the rest of the inhabitants on Earth to thrive. Deforestation, poaching, and urban sprawl are just some of the problems we’ve unleashed on our non-homo sapiens neighbors. Those pressures, as well as others like climate change and pollution (which aren’t good for humans either), are contributing to a loss of plant and animal species that scientists argue is nothing less than a massive and human-made sixth extinction event.

Conservationists have long turned to technology—such as remote sensors and animal I.D. tags—to learn more about the species they study, bring attention to the worst problems, and inform solutions like protected refuges that can help species return to health. AI and machine learning are the latest technologies at their disposal. And luckily for perennially strapped conservation efforts, better-funded AI research is creating free tools, cheap cloud services, and open source resources that put cutting edge technology within reach for even small nonprofits.

The need for large datasets, however, still holds backs many ecological AI projects. Most of the data that does exist for conservation isn’t digitized, is incomplete, or is housed in proprietary databases locked down at universities.

Another data creation effort is Conservify, a lab dedicated to using technology to democratize data gathering to make an impact on conservation efforts. One way they’re doing that is by building low-cost sensors that can be placed in the wild to gather new data. They are also developing an online platform called FieldKit that will help researchers, students, and weekend naturalists share data.

Trying out new technology does come with hazards. Communities aren’t going to be happy with tech that doesn’t work or affects them negatively. It might also disturb wildlife unaccustomed to its presence.

“Once you go put it out in the field, that’s where the real risk happens,” says Shah Selbe, Conservify’s CEO and founder.

“In terms of the work that I do, everything has absolutely changed,” he says.

Discovering Endangered Plants Before it’s Too Late

In a fluorescent-lit room lined with gray filing cabinets at the University of Maryland (UMD), a hundred thousand plant specimens are tidily housed in manila folders. The samples in the Norton-Brown Herbarium have been carefully collected for more than a century from across the globe, although the majority are from the mid-Atlantic region. One such sample is Phacelia covillei, a flowering plant with bottle-shaped blue blossoms found in scattered pockets in states like Maryland, North Carolina, Indiana, and Missouri.

Due to factors like urban development and climate change, Phacelia covillei is at risk of extinction, according to NatureServe, a ranking system used to denote the relative imperilment of different plant and animal species in North America. It made the list thanks to a new machine learning tool that tries to identify threatened plants before it’s too late to save them.

A new machine learning tool aims to speed up the identification of threatened plants like P. covillei, pictured here from the University of Maryland’s Norton-Brown’s Herbarium. Image credit: Jackie Snow

It’s a big challenge. The vast majority of plants haven’t been assessed at all: Less than 10 percent of plant species around the world have been checked for the IUCN Red List, the premier directory for global extinction risks that is often used to make conservation decisions. Anahí Espíndola, a professor of evolutionary ecology at UMD who worked on the machine learning tool, wanted to see if she could find ways to zero in on those most in need of classification.

Espíndola and her team trained a machine learning algorithm on details known about plants that are already on the IUCN Red List—like their location, range, and physical traits—and used it to assess 150,000 plant species whose vulnerability is currently unknown. Their method found that more than 10 percent of those unassessed plants were at risk.

This AI tool could speed up conservation efforts and direct limited resources more effectively than current practices. Geographic bias is one problem: Plants are better studied in the U.S. and Europe because of bigger government and university research budgets. Espíndola says AI can quickly pinpoint the plants most at risk, regardless of where they might be. AI might also prevent another problem that plagues animal conservation: The cutest species get an outsized amount of the attention.

“You don’t get that bias [with AI],” she says of our preference for the cute and cuddly. “It tells you where to go first.”

Seeing the Forest for the Trees

Conservation AI isn’t just for remote areas; it could also make cities greener. For example, trees reduce the urban heat island effect, reduce stress in humans, and reduce flooding during storms. Despite all of these benefits, U.S. cities are losing 36 million trees a year, according to a study from the Forest Service.

Knowing where the trees are (or aren’t) isn’t as easy as it sounds. Counting from the ground takes years and the results are often incomplete due to difficulties distinguishing trees on private property, or using satellite images that aren’t crisp or detailed enough for the human eye. Geospatial analytics startup Descartes Labs built a machine learning tool that can take one-meter resolution satellite imagery and count individual trees, without getting tricked by non-tree greenery or shadows that might appear to be trees. This would point out areas that are “tree deserts” and, over time, show changing areas that might need renewed attention.

Descartes Labs originally developed the tool to predict corn harvests but could see the system being used as a deforestation prevention tool. Eventually, Descartes Labs might create layers of different informational maps that would be useful to people trying to manage land for all sorts of reasons, including conservation efforts.

“You can be surprised by what you could see by combining layers,” says Tim Wallace, a geographer at Descartes Labs. “The more we combine, the more powerful it becomes.”

Era of AI-Powered Conservation

It’s not just ecologists and startups working on saving the world. Some of the biggest companies in the world like Microsoft, Intel, and Google all have AI for good projects, including some that target conservation issues. Not only is this work potentially good for the planet, but it’s good for business, too. Creating cutting-edge tools and putting money into conservation programs is a great way to recruit top talent that doesn’t want to just work on improving advertising click-through rates. And there is an additional bonus: If technology can work in the field, it should also work in less rugged office and factory settings.

“[Conservationists] always set the bar super high,” says Lucas Joppa, Microsoft’s Chief Environmental Scientist. “And if you can jump that bar, you passed the bar for many, many, many other use cases by far.”

Joppa has experience with clearing those bars with Microsoft’s AI for Earth, a five-year, $50 million initiative to back projects by environmental groups and researchers working on sustainable plans using AI. Joppa, who helps oversee the program, says that there is nothing harder or more important than creating technology that can solve these environmental problems.

“People think that the hardest problem is how to route an autonomous car to the closest latte,” he says. “We have other problems we need to tackle.”

Of course, there is always the concern that AI could do more harm than good for the environment. Previous industrial revolutions were driven by new technologies that ended up being harmful to the planet, from factories spewing smog to encouraging mining of precious metals for smartphones. This AI revolution, Joppa says, will have to be carefully designed to benefit the planet and not create problems, unanticipated or otherwise, for future generations.

“That’s not easy to do,” he says.

To learn more about researchers’ testing of TrailGuard AI, which could someday help rangers better protect elephants and other endangered species, watch this video:

This content was originally published here.