Google has open-sourced SpeciesNet, an AI model designed to identify animal species from camera trap images. The model, trained on over 65 million images, helps researchers analyze vast amounts of wildlife data more efficiently.
Camera traps—digital cameras triggered by infrared sensors—are widely used to study wildlife populations. However, these devices generate massive datasets that can take weeks to process manually. To address this challenge, Google launched Wildlife Insights around six years ago as part of its Google Earth Outreach philanthropy program. This platform allows researchers to share, identify, and analyze wildlife images collaboratively, streamlining data processing.
SpeciesNet powers many of Wildlife Insights’ tools and can classify images into over 2,000 labels, including specific animal species, broader taxa like “mammalian” or “Felidae,” and even non-animal objects such as vehicles. The AI model was trained using publicly available images and contributions 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.
In a blog post, Google emphasized that SpeciesNet’s open-source release will benefit tool developers, academics, and biodiversity-focused startups, helping them scale biodiversity monitoring in natural areas. The model is available on GitHub under an Apache 2.0 license, allowing commercial use with minimal restrictions.
However, Google is not alone in this field. Microsoft’s AI for Good Lab also offers an open-source AI tool called PyTorch Wildlife, which provides pre-trained models specifically designed for animal detection and classification.
The model is available on GitHub under an Apache 2.0 license, allowing commercial use with minimal restrictions.While SpeciesNet is a significant step forward.
