Technology has been both a savior and a destroyer throughout history, solving ancient problems, causing new ones, and then progressing enough to solve those too. Every year brings with it new innovations and inventions that promise to change the lives of everyday people, solving problems long thought unsolvable. The advent of modern AI and machine learning technology represents another earth-shaking point of change, and it may be able to help avert or minimize the damage of the largest looming crisis of today.
To say that people today are worried about the state of the environment would be an understatement. On average, 63% of adults in the United States are worried or very worried about global warming, and 67% believe that protecting biodiversity should be a national priority. The long-term effects of fossil fuels on the environment, long credited as a cause of both climate change and shrinking biodiversity across the globe, represent an ongoing crisis and point of social and political tension for many. While a range of options for changing the tide of these destructive trends are being explored globally, new AI technology may provide an avenue for everyday citizens to contribute to biodiversity tracking and conservation work.
AI-Powered Citizen Conservationism
Through tools like EarthSnap or other AI-powered platforms, citizens around the country can participate in conservation by gathering user-generated data in real time, and sending it off to be added to a massive open data set that can be tracked and analyzed by researchers anywhere. These models often assist users in identifying animal and plant life via pictures taken on their phone, letting them contribute to field research. The public is invested in protecting the environment and protecting biodiversity, and AI tools can be the answer.
Because of this intersection of public will and technological capability, there is an opportunity available for citizen conservation on a level never seen before. The only thing left is to make that technology easily available to that public. That’s precisely the mission objective that spurred environmental tech founder Eric Ralls to develop EarthSnap, an AI-powered phone app that lets users take pictures of the flora and fauna around them. The subject of the photo is cross-referenced with an ever-growing encyclopedia of more than two million species on Earth, and detailed information is delivered instantly to the user—all at the touch of a button. Paired with its sharing and interaction functions, it presents a way of building community around a shared love of the environment.
“The idea is simple: identify what you’re seeing, learn about it, and contribute to a growing knowledge base at the same time,” says Eric Ralls. “The community doesn’t just use the product—it helps make it smarter and more valuable over time. When millions of people photograph plants, animals, fungi, and ecosystems—and those observations are responsibly aggregated—you suddenly get a real-time, ground-level view of life on Earth.”
By leveraging AI tools to functionally deputize everyday citizens, conservationists and environmental researchers can access a wealth of real-time, on the ground information. From tracking migration shifts or the spread of invasive species, to monitoring changes in flowering seasons in the wake of climate shifts, these tools can provide insights on a scope and scale to-date unheard of.
However, it’s important to balance the environmental conservation benefits of data collection and AI analysis with the realities of data privacy and ethical application. Artificial intelligence tools and models are hotly debated and as-of-yet untouched by significant regulations, so leveraging these tools effectively also comes with real responsibility.
Tech-Powered Environmental Infrastructure
This kind of citizen-enabled data collection isn’t the only area that AI tools can assist in. Even basic data analytics can be massively improved through the use of artificial intelligence, according to a recent study from McGill University. Most biodiversity-related applications of AI have focused primarily on tracking and monitoring wildlife populations (whether through citizen conservation platforms or via professional fieldwork), but there is much untapped potential for the technology to analyze the complex image data, satellite imagery, and more.
New technologies are rapidly forming a new kind of infrastructure for environmental stewardship and conservation work. Technology like drones for monitoring or reforestation efforts, or tracking tools for mapping species through DNA or bioacoustics were already in practice, but AI tools represent the next frontier for the trend. Massive biodiversity datasets made up of satellite images are ripe processing targets for AI, and may soon become essential for environment assessments and management. This all goes before considering the standard strengths and benefits of AI tools for data analysis, automation, and associated cost reduction and efficiency gains.
Indeed, powerful and accurate data processing and interpretation are both essential for modern conservation and restoration efforts because of the sheer amount of available data. As environmentalists are leveraging camera traps, drones, satellites, microphones, and environmental DNA to track the movements and health of wildlife, they’re generating truly incomprehensible amounts of data. Modern artificial intelligence and machine learning algorithms are the secret to turning this treasure trove of data into actionable insights.
With the data gathered, AI tools can also serve in a predictive capacity. With such a wealth of data at researcher’s fingertips, the ability to model the current (and future) state of a given ecosystem becomes possible, and AI makes it feasible. Modeling a single ecosystem requires tracking and monitoring an unbelievably complex series of interactions, and only modern AI tools have the processing power and analytical capacity to do so. Events like species interactions and migrations, biodiversity tipping points, and the impact of invasive species can be modeled, which then allows conservationists and researchers to propose proactive solutions or anticipate disasters.
Outside of ecosystem modeling, AI tools can also help predict other trends, like environmental crimes. Wildlife poaching, setting fires, and illegal deforestation, are examples of human-caused environmental disruptions that can be tracked. Satellite data can be used with AI to predict deforestation, for example, which can provide actionable data for businesses and environmentalists alike.
