Google releases SpeciesNet, an AI model designed to identify wildlife

Google has open-sourced an AI model, SpeciesNet, designed to identify animal species by analyzing photos from camera traps.

Researchers around the world use camera traps — digital cameras connected to infrared sensors — to study wildlife populations. But while these traps can provide valuable insights, they generate massive volumes of data that take days to weeks to sift through.

In a bid to help, Google launched Wildlife Insights, an initiative of the company’s Google Earth Outreach philanthropy program, around six years ago. Wildlife Insights provides a platform where researchers can share, identify, and analyze wildlife images online, collaborating to speed up camera trap data analysis.

Many of Wildlife Insights’ analysis tools are powered by SpeciesNet, which Google claims was trained on over 65 million publicly available images and images from organizations like the Smithsonian Conservation Biology Institute, the Wildlife Conservation Society, the North Carolina Museum of Natural Sciences, and the Zoological Society of London.

Google says that SpeciesNet can classify images into one of more than 2,000 labels, covering animal species, taxa like “mammalian” or “Felidae,” and non-animal objects (e.g. “vehicle”).

“The SpeciesNet AI model release will enable tool developers, academics, and biodiversity-related startups to scale monitoring of biodiversity in natural areas,” Google wrote in a blog post published Monday.

SpeciesNet is available on GitHub under an Apache 2.0 license, meaning it can be used commercially largely sans restrictions.

It’s worth noting that Google’s isn’t the only open source tool for automating the analysis of camera trap images. Microsoft’s AI for Good Lab maintains PyTorch Wildlife, an AI framework that offers pre-trained models fine-tuned for animal detection and classification.