Google announces SpeciesNet, an AI model to identify animal species

Google SpeciesNet

Google has announced the release of SpeciesNet, an open-source AI model designed to identify animal species by analysing photos from camera traps. The motion-triggered wildlife cameras, or “camera traps”, generate vast quantities of image data. Manual processing of the images taken through camera traps is a “significant bottleneck”, as per Google. Using an AI-based solution can accelerate image processing. The SpeciesNet AI model is claimed to be one such solution.

In a blog post dedicated to the SpeciesNet AI model, Google said: “The species classifier (SpeciesNet) was trained at Google using a large dataset of camera trap images and an EfficientNet V2 M architecture. It is designed to classify images into one of more than 2000 labels, covering diverse animal species, higher-level taxa (like "mammalia" or "felidae"), and non-animal classes ("blank", "vehicle"). SpeciesNet has been trained on a geographically diverse dataset of over 65M images, including curated images from the Wildlife Insights user community, as well as images from publicly available repositories".

The SpeciesNet ensemble combines these two models (object detector and image classifier) using a set of heuristics and, optionally, geographic information to assign each image to a single category.

Google claims that since 2019, thousands of wildlife biologists have used SpeciesNet through the Google Cloud-based tool called Wildlife Insights to streamline biodiversity monitoring and inform conservation decision-making. The SpeciesNet AI model release will enable tool developers, academics and biodiversity-related startups to scale monitoring of biodiversity in natural areas, said the tech giant.