Where wild things roam: Identifying wildlife with SpeciesNet

Motion-triggered cameras, or “camera traps”, are giving everyone from homeowners to parks managers an unprecedented view of their local wildlife. While a curious backyard user might be able to identify a critter by eye, larger projects are now collecting thousands or even millions of wildlife images that could take decades to identify manually.

Today, more people than ever are using AI to identify the animals in their images with SpeciesNet. This Google-developed AI model can classify nearly 2,500 animal categories in camera trap images, thanks to conservation partners who have provided 65M labelled images to train the model. Originally part of the online platform Wildlife Insights, a year ago we released SpeciesNet into the wild as an open-source tool for others to download, adapt and refine.

Over the past 12 months, research groups around the world have used the open-source SpeciesNet model to spot pumas and ocelots in Colombia, elk and black bears in Idaho, cassowaries and musky rat-kangaroos in Australia, and lions and elephants in Tanzania’s Serengeti National Park. The AI model is allowing more people to ask broader questions about wildlife patterns and conservation.

SpeciesNet is part of Google Earth AI, a collection of geospatial tools, datasets and AI models for deep planetary intelligence. Earth AI empowers communities and nonprofits to address some of the planet’s most pressing needs.

A new era for wildlife monitoring

Today, almost all effective wildlife monitoring relies on motion-triggered wildlife camera traps. Cameras are typically mounted on trees. In most cases, motion by heat-radiating bodies triggers a few-seconds burst of imagery. Increasingly affordable technology is letting projects deploy dozens or even hundreds of cameras, generating vast amounts of data.

SpeciesNet’s training and performance

SpeciesNet partner projects around the world

Over the past year, some standout projects include: