Where wild things roam: Identifying wildlife with SpeciesNet

SpeciesNet is an open-source AI tool that identifies wildlife in camera-trap images, making large-scale monitoring faster and more practical. Trained on more than 65 million labeled images, it can classify nearly 2,500 animal categories and process tens of thousands of images per day. Its adoption by researchers, governments, and conservation groups is expanding wildlife research and enabling more responsive conservation efforts.

A New Era for Wildlife Monitoring

• Motion-triggered camera traps generate enormous volumes of images, often far beyond what human teams can classify manually.
• Automated identification helps researchers:
  • Track population health and changes.
  • Study migration and climate-related movement.
  • Estimate population sizes.
  • Detect rare or endangered species.
• SpeciesNet uses deep learning to identify animals in camera-trap photos, accelerating analysis and improving wildlife-management decisions.
• The tool is part of Google Earth AI, a collection of geospatial AI tools intended to support environmental and conservation work.

SpeciesNet’s Training and Performance

• SpeciesNet classifies 2,498 categories of mammals, birds, reptiles, and other animals.
• It works with MegaDetector, another open-source model that identifies which images and pixels contain animals.
• The system provides:
  • Species names.
  • Confidence scores.
  • Multiple identifications when several animals appear in one image.
• Processing capacity is approximately:
  • 30,000 images per day on a standard laptop.
  • 250,000 or more images per day on a low-end gaming GPU.
• SpeciesNet was trained on more than 65 million images from Wildlife Insights and public repositories.
• On held-out camera-trap projects, it:
  • Detected animals in 99.4% of relevant images.
  • Reached species-level classification 83% of the time.
  • Produced correct species-level predictions in 94.5% of those cases.
• Human-verified labels from Wildlife Insights can be reused as additional training data, creating a feedback loop for improving the model.

Conservation Projects Using SpeciesNet

• Snapshot Serengeti: Researchers can analyze roughly 11 million images collected since 2010 in just days, rather than relying exclusively on citizen scientists. Field processing also allows cameras to be redeployed based on recent sightings.
• Wildlife Observatory of Australia: The organization trained a regional version of SpeciesNet to recognize Australian species missing from the original label set, including musky rat-kangaroos and orange-footed scrubfowl.
• Idaho Department of Fish and Game: SpeciesNet serves as a first-pass classifier for images of deer, elk, black bears, coyotes, and other wildlife, speeding up human verification.
• Public and private platforms: Tools including Animl and AddaxAI have integrated SpeciesNet, while companies such as Okala use it alongside Google’s Perch audio model to monitor biodiversity in Africa.
• The model has also supported studies of pumas and ocelots in Colombia, cassowaries in Australia, and lions and elephants in Tanzania.

SpeciesNet demonstrates how open-source AI can turn massive camera-trap datasets into usable scientific evidence. Its strongest role is as a scalable first-pass system combined with human review, while regional adaptations can extend its usefulness to local and threatened species.