Open-Earth-Monitor Global Workshop 2026

Forests, as a land use, are central to climate mitigation, biodiversity conservation, and international policy reporting, including the Sustainable Development Goals, the FAO Global Forest Resources Assessment (FRA), and the EU Regulation on Deforestation-free Products (EUDR). Yet, global monitoring based on Earth Observation (EO) primarily captures land cover, while policy frameworks often require land use distinctions.
Here, we present FLUM 2024 (Forest and other Land Use Map), the first global, wall-to-wall land use map at 10 m resolution aligned with FAO FRA definitions. FLUM distinguishes 18 land use classes—including naturally regenerating versus planted forests, stocked versus temporarily unstocked forests, forest versus other tree-covered land uses—leveraging the largest expert-annotated dataset to date for this purpose. The deep learning model is trained on over 350,000 reference samples collected through the FAO FRA Remote Sensing Survey, annotated by more than 800 experts across 120+ countries, and on time series of satellite embeddings. FLUM achieves an overall accuracy of 71.7 ± 0.7% across 18 classes, increasing to 85.9 ± 0.6% for the four main FRA RSS classes and 93.4 ± 0.2% for forest/non-forest discrimination. The product includes spatially explicit uncertainty estimates that reflect annotation complexity.
Beyond the base map, we elaborate on the critical importance of reference data quality based on local expert knowledge, currently the main constraint on the highest achievable model performance, and on synergetic integration of expert knowledge and artificial intelligence. For example, by identifying high-uncertainty regions, trained deep learning models enable targeted quality control and adaptive sampling in expert surveys, improving both data reliability and model performance over time. We further show how FLUM can be deployed and fine-tuned on open platforms, including FAO SEPAL and Google Earth Engine, supporting transparent, scalable, and country-driven monitoring.
As the first global EO product trained and evaluated directly on FRA-compliant data, FLUM represents a critical step toward operational, policy-relevant land use monitoring.