Separating natural forests from other tree cover with AI for deforestation-free supply chains

Forests are vital for our planet as they regulate rainfall, mitigate floods, store and sequester carbon, and help sustain the majority of the planet’s land-based species. Despite their importance, deforestation continues at an alarming rate. A key challenge in conservation efforts is differentiating centuries-old natural ecosystems from newly planted forests or tree crop plantations with satellite data. Most existing maps simply show "tree cover," a basic measure of any woody vegetation, leading to an "apples-to-oranges" comparison. This conflates the harvesting of a short-term plantation with the permanent loss of an irreplaceable, biodiversity-rich natural forest.

The need for this distinction is more important than ever due to new global regulations, like the European Union Regulation on Deforestation-free Products (EUDR). This regulation mandates that products like coffee, cocoa, rubber, timber, and palm oil sold in the EU cannot come from land that was deforested or degraded after December 31, 2020, with the goal of protecting natural forests, like primary and naturally regenerating forests. This policy creates a need for a reliable, high-resolution, and globally-consistent map of natural forests as they existed in 2020. The protection of these forests is also a central pillar for COP30, which recognizes their crucial role in climate stability and human well-being.

In an effort to help meet this need, together with Google DeepMind, we’re releasing Natural Forests of the World 2020, a new map and dataset, published in Nature Scientific Data. This project stems from a collaboration with the World Resources Institute and the International Institute for Applied Systems Analysis, and provides a critical baseline for deforestation and degradation monitoring. We provide the first globally consistent, 10-meter resolution map that differentiates natural forests from other tree cover and achieves a best-in-class accuracy of 92.2% when validated against a global independent dataset. We hope that this publicly available baseline can help companies conduct due diligence, support governments in monitoring deforestation, and empower conservation groups to target their efforts to protect what matters most.

Distinguishing a natural forest from a complex agroforestry system or a 50-year-old planted forest is difficult using a single satellite image. To overcome this, we developed an AI model that acts like a forester, observing a patch of land over the course of a year, segmenting a 1280 x 1280 meter patch and estimating the likelihood that each 10 x 10 meter pixel within it is a natural forest. This allows the model to make assessments based on the surrounding context, rather than a single snapshot. This novel multi-modal temporal-spatial vision transformer (MTSViT) model analyzes seasonal Sentinel-2 satellite imagery and topographical data (e.g., elevation and slope), along with the sample’s geographical coordinate. By observing satellite imagery over time, the model identifies distinct spectral, temporal, and texture signatures (i.e., data patterns used to recognize different forest types) that differentiate complex, natural forests from uniform, fast-growing commercial plantations and other land use and land cover.

To build the Natural Forests of the World 2020 map, we sampled over 1.2 million global 1280 x 1280 meter patch locations at 10-meter resolution to create a massive, multi-source training dataset. We used this data to train the MTSViT model to recognize complex patterns of natural forests and other land types. We then applied the trained MTSViT model across all land on Earth, generating a seamless, globally consistent 10-meter probability map. To rigorously validate the map, we created an evaluation dataset by repurposing an independent dataset focused on global forest management for 2015 and updating its labels to focus on natural forests for 2020. See more details in the paper.

We hope that the Natural Forests of the World 2020 baseline proves to be a valuable resource for policymakers, auditors, and companies seeking to comply with new deforestation-free regulations such as the EUDR. But forests are not static. To truly support global conservation and sustainability, we need to distinguish between more classes of forest and, crucially, understand how they change over time. This involves differentiating between and locating key forest types: natural forests (carbon-dense and biodiversity-rich forests), planted forests, plantations, and commercial tree crops (such as ecosystem-friendly coffee and cocoa agroforestry systems).

To advance this effort, we’re developing a new multi-year series of global forest type maps, powered by next-generation AI models. These maps will categorize the world's land into six distinct types: Primary Forest, Naturally Regenerating Forest, Planted Forest, Plantation Forest, Tree Crops, and Other Land Cover. We expect to release these comprehensive maps in 2026.

To encourage the broader research community to contribute to this effort, we have also released two large-scale benchmark datasets. These datasets are important for developing and rigorously testing the next generation of AI models designed to analyze the world’s forests. The Planted dataset is a global, multi-sensor long-temporal collection featuring over 2.3 million time-series classification examples. It is specifically designed to help AI models recognize 64 different (species or genera) types of planted forests and tree crops worldwide. The Forest Typology (ForTy) benchmark provides a truly global-scale dataset with 200,000 multi-source and multi-temporal image patches with per-pixel labels for semantic segmentation models. This resource is tailored for the core task of mapping the key classes: natural forest, planted forest, and tree crops.

Turning climate and nature ambitions into action requires transparent, trusted, and high-resolution data. We are committed to making these tools as accessible as possible. We hope these new datasets and tools will help governments, companies, and communities work together to meet their deforestation-free goals and protect the critical ecosystems on which we all depend.

Learn more about our AI and sustainability efforts by checking out Google Earth AI, Google Earth Engine, and AlphaEarth Foundations.

This research was co-developed by Google Deepmind and Google Research in collaboration with WRI and IIASA.

We thank our collaborators at Google, World Resources Institute (WRI) / Global Forest Watch (GFW), and International Institute for Applied Systems Analysis (IIASA): Anton Raichuk, Charlotte Stanton, Dan Morris, Drew Purves, Elizabeth Goldman, Katelyn Tarrio, Keith Anderson, Maxim Neumann, Mélanie Rey, Michelle J. Sims, Myroslava Lesiv, Nicholas Clinton, Petra Poklukar, Radost Stanimirova, Sarah Carter, Steffen Fritz, Yuchang Jiang.

Special thanks to early map reviewers: Andrew Lister (United States Forest Service), Astrid Verheggen (Joint Research Centre), Clement Bourgoin (Joint Research Centre), Erin Glen (WRI), Frederic Achard (Joint Research Centre), Jonas Fridman (Swedish University of Agricultural Sciences), Jukka Meiteninen (VTT), Karen Saunders (World Wildlife Fund Canada), Louis Reymondin (Alliance Bioversity International - CIAT), Martin Herold (GFZ Helmholtz Centre for Geosciences), Olga Nepomshina (GFZ Helmholtz Centre for Geosciences), Peter Potapov (University of Maryland/WRI), Rene Colditz (Joint Research Centre), Thibaud Vantalon (Alliance Bioversity International - CIAT), and Viviana Zalles (WRI).