Separating natural forests from other tree cover with AI for deforestation-free supply chains (opens in new tab)

Researchers from Google DeepMind and Google Research have developed "Natural Forests of the World 2020," an AI-powered global map that distinguishes natural ecosystems from commercial tree plantations. By utilizing high-resolution satellite data and machine learning, the project provides a critical 10-meter resolution baseline to support deforestation-free supply chain regulations like the EUDR. This tool enables governments and companies to monitor biodiversity-rich areas with unprecedented accuracy, ensuring that natural forests are protected from industrial degradation.

The Limitation of Traditional Tree Cover Maps

• Existing maps frequently conflate all woody vegetation into a generic "tree cover" category, leading to "apples-to-oranges" comparisons between different land types.
• This lack of distinction makes it difficult to differentiate between the harvesting of short-term plantations and the permanent loss of ancient, biodiversity-rich natural forests.
• Precise mapping is now a legal necessity due to regulations like the European Union Regulation on Deforestation-free Products (EUDR), which bans products from land deforested or degraded after December 31, 2020.

The MTSViT Modeling Approach

• To accurately identify forest types, researchers developed the Multi-modal Temporal-Spatial Vision Transformer (MTSViT).
• Rather than relying on a single snapshot, the AI "observes" 1280 x 1280 meter patches over the course of a year to identify seasonal, spectral, and textural signatures.
• The model integrates multi-modal data, including Sentinel-2 satellite imagery, topographical information (such as elevation and slope), and specific geographical coordinates.
• This temporal-spatial analysis allows the AI to recognize the complex patterns of natural forests that distinguish them from the uniform, fast-growing structures of commercial plantations.

Dataset Scale and Global Validation

• The model was trained on a massive dataset comprising over 1.2 million global patches at 10-meter resolution.
• The final map provides seamless global coverage, achieving a best-in-class validation accuracy of 92.2% against an independent global dataset.
• The research was a collaborative effort involving the World Resources Institute and the International Institute for Applied Systems Analysis to ensure scientific rigor and practical utility.

The "Natural Forests of the World 2020" dataset is publicly available via Google Earth Engine and other open repositories. Organizations should leverage this high-resolution baseline to conduct environmental due diligence, support government monitoring, and target conservation efforts in preparation for global climate milestones like COP30.