A new collaboration between the World Economic Forum and the MIT Media Lab is set to redefine climate intelligence, leveraging artificial intelligence to translate vast quantities of satellite data into actionable insights for disaster response, weather forecasting, and environmental monitoring. The joint research initiative addresses a critical bottleneck in climate action: while Earth observation systems generate immense volumes of valuable data, prohibitive processing times have historically limited its real-time application. This partnership aims to unlock the full potential of satellite imagery by applying advanced AI and machine learning models, transforming a passive data collection exercise into a dynamic, predictive system.
The core of this advancement lies in using AI to dramatically accelerate the analysis of data from Earth observation satellites. A whitepaper from the collaboration, titled “Charting the Future of Earth Observation: Technology Innovation for Climate Intelligence,” details how machine learning is the key to activating largely unused archives of satellite information. More than half of the essential variables needed to measure climate conditions can only be captured accurately from space. AI systems can now process these complex datasets in minutes rather than weeks, delivering predictions up to 1,000 times faster than previous methods. This leap in speed and efficiency is turning satellite images into a critical tool for on-the-ground decision-making by governments, communities, and industries facing the escalating frequency and severity of climate-driven disasters.
Transforming Forecasting with Foundation Models
A central element of this technological shift is the application of foundation models—large AI systems trained on diverse, extensive datasets. These models enable both localized and global forecasting at speeds unattainable by traditional physics-based simulations. Two leading examples highlighted by the research are Microsoft’s Aurora system and NASA’s Prithvi-weather-climate model. These platforms are designed to process atmospheric and environmental data with unprecedented speed and accuracy, providing critical insights for a range of applications from air quality monitoring to flood mapping.
Microsoft’s Aurora Platform
Microsoft’s Aurora is a 1.3 billion-parameter foundation model designed for comprehensive atmospheric forecasting. It operates using a two-phase training process. First, it undergoes “pre-training” on a massive and varied dataset of weather and climate data to build a general understanding of atmospheric physics. It is then “fine-tuned” on smaller, higher-quality datasets to excel at specific tasks, such as predicting air pollution levels five days in advance or generating 10-day global weather forecasts. Aurora can generate these high-resolution predictions in seconds, a task that would take hours for conventional supercomputers. Its effectiveness has been demonstrated in accurately predicting extreme events, including forecasting a major sandstorm in Iraq a day in advance and outperforming major global forecasting centers in predicting cyclone paths during the 2022–2023 season.
NASA’s Prithvi-Weather-Climate Model
Developed as a collaboration between NASA and IBM Research, the Prithvi-weather-climate model is an open-source AI tool trained on 40 years of NASA’s Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data. The name “Prithvi” is the Sanskrit word for Earth. Unlike specialized models, Prithvi is built to be a flexible foundation for a wide array of climate and weather applications. It excels at improving the resolution of climate models through a process called downscaling, effectively increasing the detail of long-term projections by a factor of 12. This allows for more precise regional and local forecasts, which is critical for predicting severe weather, mapping flood risk, and projecting crop yields.
Next-Generation Satellite Constellations
The advancements in AI-driven data processing are paralleled by significant upgrades in satellite hardware. New constellations are being deployed into low Earth orbit, equipped with more sophisticated sensors capable of capturing higher-resolution imagery at much more frequent intervals. This new generation of hardware will produce data of unparalleled detail and volume, which would be largely unmanageable without the AI processing power now being developed. These systems are designed to monitor specific environmental threats, such as wildfires and agricultural stress, with near real-time updates.
The Muon Space FireSat System
A planned low Earth orbit constellation from Muon Space will deliver near real-time data specifically for wildfire detection. The completed FireSat system will consist of more than 50 satellites, enabling it to scan every point on Earth every 20 minutes, with revisit times as short as nine minutes for wildfire-prone regions. The satellites will be equipped with 6-band multispectral infrared instruments capable of detecting fire ignition sites as small as 5 meters square (approximately 25 square feet). This high-frequency, high-resolution coverage provides a powerful tool for early detection and for mapping a fire’s perimeter, intensity, and direction of growth, allowing for faster and more effective response from emergency services.
The Landsat Next Mission
The upcoming Landsat Next mission, a collaboration between NASA and the U.S. Geological Survey expected to launch in the early 2030s, represents a major leap in land imaging capabilities. This mission will comprise three identical observatories, providing a collective temporal revisit of six days for any location on Earth. The satellites will be equipped with instruments that collect data across 26 spectral bands, more than double the 11 bands captured by the previous generation (Landsat 8 and 9). These new bands are designed to provide detailed information on water quality, detect harmful algal blooms, measure plant stress, and analyze snow and ice dynamics. The spatial resolution will also be improved to 10–20 meters for visible and shortwave infrared bands, enabling more precise monitoring of land use and environmental changes.
Democratizing Access to Climate Data
A key focus of the WEF and MIT collaboration is ensuring that these advanced climate intelligence tools are accessible to all, especially vulnerable communities that are disproportionately affected by climate change. Open-source technology and user-friendly platforms are being developed to translate complex satellite data into practical information that can be used by policymakers, local communities, and non-technical experts. These initiatives aim to break down barriers of cost and technical expertise, empowering users to make data-driven decisions for climate adaptation and resilience.
MIT’s Earth Mission Control
The MIT Media Lab’s Earth Mission Control (EMC) is an immersive data visualization platform that uses augmented and virtual reality (AR/VR) to make complex climate data more intuitive and understandable. Instead of presenting users with raw numbers or complex charts, EMC creates interactive visualizations, such as global data projected on a sphere or location-based information dashboards. This approach helps decision-makers explore climate scenarios, understand the impact of environmental policies, and communicate climate narratives more effectively. It serves as a bridge between highly technical satellite data and the stakeholders who need to act on it.
Digital Earth Africa’s Initiatives
Digital Earth Africa provides open and accessible Earth observation data tailored to the needs of the African continent. Its Water Observations from Space (WOfS) service translates more than 30 years of satellite imagery into information on the location and recurrence of surface water. The organization also runs a Waterbodies Monitoring service that tracks changes in the surface area of over 700,000 water bodies across Africa, with updates occurring weekly. These tools provide critical data for managing water resources, assessing flood risk, and monitoring drought conditions, offering a comprehensive view of water availability and dynamics for a continent facing significant water security challenges.
Overcoming Implementation Hurdles
Despite the transformative potential of these technologies, significant challenges remain. The research highlights the need for greater data interoperability, requiring standardization across different satellite platforms and data formats to ensure seamless integration and analysis. Furthermore, expanding infrastructure access is critical, which includes investing in cloud-based processing capabilities and promoting digital literacy programs in underserved communities. As the technology continues to evolve, there is a corresponding need to expand workforce training in the specialized fields of AI, satellite engineering, and geospatial analytics. Addressing these hurdles will be essential to fully realize the potential of AI-powered Earth observation for building a more resilient and sustainable future.
