10 Emerging Technology Solutions for Planetary Health 2025

Luca Brocca Director of Research, National Research Council of Italy Illah Nourbakhsh Professor, Robotics Institute, Carnegie Mellon University Masami Onoda Director, International Relations and Research Department, Japan Aerospace Exploration Agency From flooding to drought and deforestation, environmental conditions are shifting faster than they can be tracked. A new wave of Earth observation (EO) tools is closing that gap – moving beyond coarse snapshots to offer high-resolution, near real-time views of planetary change.60 These tools give us the unprecedented ability to measure key variables across multiple orders of magnitude, from satellite-based whole-Earth observations to micro-local, tree-by-tree health evaluations and periodic changes in individual city demographics. By fusing satellite, drone and ground-based data with AI-powered analytics, EO systems now provide metre-scale insights (or finer) on key environmental and human-driven impacts such as precipitation, soil moisture, vegetation health and land-use dynamics. These conditions directly affect planetary boundaries, including climate change, land-system change, freshwater use, biosphere integrity and biogeochemical flows. Multi-spectral and multi-sensor remote measurements, from satellites to drones to local app-based reporting systems, have all begun to enable a vast network of zoomed-in and zoomed- out data parameters to be measured with increasing time regularity and spatial density around the Earth. Combined with computational techniques, including computer vision and image classification, these advances enable vaster quantities of visual and spectral data to be interpreted in near-real-time, transforming data into information that is shareable and actionable for policy and public understanding of science. In addition, open-source visualization platforms enable relevant data and scientific information – both conclusory and predictive – to be shared openly across public engagements and educational venues, dramatically increasing common ground globally. EO foundation models are another technological advancement set to transform how the public engages with Earth data. Large-scale foundation modals trained on extensive geospatial information allow users to explore that data through natural language, much like large language models (LLMs) do with written text. Once these models reach market maturity, anyone with an internet connection will be able to easily access critical information about Earth systems, from changes in land use over time to the location of urban heat islands or levels of atmospheric CO2. Supported by these latest advances, the European Commission’s ambitious Destination Earth initiative aims to develop a digital twin of the entire planet.61,62 The European Space Agency’s Digital Twin Earth programme merges EO data with high-resolution hydrological models to simulate and forecast flood, drought and wildfire risks, helping governments prepare for extreme events.63 Other systems use AI to map surface water loss, monitor deforestation and identify hotspots of land degradation, even under cloud cover – enhancing the local relevance of EO for land restoration, agriculture, climate monitoring and disaster response.64,65 If AI-enhanced EO scales in the coming years, it could reduce response time to environmental pressures while altering how governments, industries and communities monitor planetary changes. Frequent and granular environmental monitoring would improve tracking of freshwater depletion, land degradation and biodiversity loss – thus enabling earlier interventions. In industry, EO systems could assess supply chain impacts with greater specificity, helping companies identify deforestation, water stress or emissions hotspots tied to sourcing and production. Broader adoption could generate new roles in satellite operations, environmental analytics and local land-use planning – but high costs, infrastructure demands, and limited internet access may still constrain uptake in low-resource regions. Societally, EO could improve access to actionable environmental data in communities facing climate risk, which could guide decisions on agriculture, relocation or disaster response. Expanding access to EO tools and training could allow local governments and civil society actors to respond more effectively to environmental threats – especially in regions including Africa, South America and South-East Asia, where timely data can guide life-saving decisions. By fusing satellite, drone and ground-based data with AI- powered analytics, EO systems now provide metre- scale insights (or finer) on key environmental and human-driven impacts. 10 Emerging Technology Solutions for Planetary Health 26