Google and the World Resources Institute released a comprehensive research paper that outlines how artificial intelligence can be applied to nature conservation and ecosystem protection. The paper frames the work against an urgent backdrop: wildlife populations have declined 73% since 1970, and the World Economic Forum ranks biodiversity loss among the greatest near-term global risks. The partnership draws on decades of collaboration, including platforms such as Global Forest Watch, and consolidates findings from dozens of expert interviews and global case studies.
The teams identified three breakthrough areas where artificial intelligence is already proving valuable. First, real-time planetary monitoring uses machine processing of massive satellite and signal datasets to track human and ecological activity; Global Fishing Watch is cited as an example of scanning billions of satellite signals to map activity at sea. Second, democratized nature knowledge turns smartphones and community science apps into data sources and identification tools; iNaturalist is highlighted as a way to put species identification into the hands of millions. Third, ecosystem-level pattern recognition combines satellite imagery, audio recordings, and field observations to reveal system-scale trends and priorities that were previously invisible.
The paper pairs these technical advances with an actionable strategy. Recommendations include dramatically expanding biodiversity data collection and building accessible infrastructure so data becomes a global public good, prioritizing open and transparent artificial intelligence models to address monitoring gaps with platforms such as Wildlife Insights, and fostering two-way exchanges between developers and on-the-ground practitioners, including indigenous and local communities. Google emphasizes making tools accessible and culturally appropriate and cites Kate Brandt on artificial intelligence’s ability to process vast information and identify hidden patterns.
Authors note limitations and trade-offs, including the rising energy demands of more powerful models and the need to align model development with sustainability goals. The roadmap is presented as more than a corporate sustainability statement: it is a technical playbook intended to translate artificial intelligence capabilities into measurable conservation outcomes through open tools, community partnerships, and targeted data infrastructure.
