WEF & MIT: Earth Observation Tech for Climate Intelligence

How can earth observation technologies impact climate intelligence?

That is the leading question behind The World Economic Forum’s (WEF)’s research produced in partnership with the MIT Media Lab – Charting the Future of Earth Observation: Technology Innovation for Climate Intelligence.

The whitepaper presents a vision of transformative leaps in climate intelligence through Earth observation (EO) technology, and demonstrates how rapid advancements in satellite data, AI and synergistic digital tools are recreating the landscape of climate action and resilience. 

“This white paper, written in collaboration with the Massachusetts Institute of Technology (MIT) Media Lab, highlights the transformative potential of EO for climate intelligence and forecasting,” explain Sebastian Buckup, MD at the WEF and Dava Newman, Director at MIT's Media Lab, in the foreword.

“By combining the research capabilities of the MIT Media Lab with the global platform of the World Economic Forum, the paper identifies technology pipelines accelerating the processing and analysis of satellite EO data to provide unparalleled insights into climate change.

"The paper also highlights the need for accessible and inclusive climate insights, especially for communities most vulnerable to the effects of climate change.”

The future of earth observation

EO encompasses the collection and analysis of data on Earth’s physical, chemical, biological and human systems, primarily via satellite-based remote sensing. 

The whitepaper underscores that more than half of essential climate variables can only be accurately measured from space, ensuring EO’s place at the centre of climate intelligence strategies. 

By 2032, satellite EO is forecast to generate more than two exabytes of data – more than two billion gigabytes – accounting for 86% of all data from the space applications segment. Yet EO’s potential has long been underutilised due to prohibitive processing times, limited accessibility and a lack of timely, actionable insights.​

Recent breakthroughs, however, are breaking down these barriers and enabling new pipelines – from bytes to insights. These advances are built upon several interconnected technology shifts.

Enhanced satellite sensor capabilities now provide near-continuous, high-resolution, multi-spectral imagery of Earth at unprecedented speed and granularity.

For instance, the upcoming Landsat Next mission, set to launch in 2030, will collect 26 ‘superspectral’ bands – more than double previous generations – enabling far more detailed and frequent observations.​

Modern AI, machine learning (ML) and advanced visualisation platforms can process massive EO datasets almost in real time, turning raw satellite images into climate insights in minutes rather than weeks.

ML models trained on historic and live EO data can deliver predictions up to 1,000 times faster than previous techniques, bringing damage assessments and forecasts to those who need them most when it matters.​

The dual evolution of small and large EO satellites means more nations and organisations can access, launch and benefit from satellites.

While miniaturised sensors and lower launch costs have opened EO participation to small and medium enterprises (SMEs) and emerging economies, larger and more capable satellites are delivering ever-more powerful and reliable EO data streams for critical climate applications.​

Revolutionising disaster response and climate adaptation

With climate-driven disasters on the rise, decision-makers depend on timely information for everything from wildfire detection to post-hurricane recovery.

New low Earth orbit satellite constellations, such as the planned Muon Space system, will soon deliver near real-time, multispectral data capable of detecting fire ignition sites as small as 25m², with a revisit time of just 20 minutes – a capability that can greatly improve rapid response, minimise damages and protect lives.​

When it comes to post-disaster assessment, AI-powered ML models can now analyse satellite imagery of hurricane or earthquake zones at the pixel level, categorising the extent and severity of damage across millions of buildings in hours, not years.

For example, following Hurricane Beryl in 2024, Microsoft partnered with Planet to rapidly assess and map building damage in Carriacou, Grenada, supporting more effective and targeted emergency response.​

These advances are supported by innovations in edge computing. Satellites equipped with onboard AI can now process images and detect anomalies in orbit, downlinking only salient insights and reducing data latency – a key benefit for emergency responders and planners.

A new era for climate forecasting

EO’s revolution in data is paralleled by advances in high-resolution climate forecasting and digital twins.

ML-based models are one of the most impactful trends, as they can significantly accelerate and sharpen climate and weather predictions. Foundation models – large AI systems trained on diverse EO datasets – enable localised and global forecasting at speeds and scale that traditional physics-based models cannot match.

Microsoft’s Aurora, for example, can predict air pollutant levels worldwide in seconds, and NASA’s open-source Prithvi-weather-climate model supports both flood mapping and crop yield projections.​

In terms of Digital Earth Twins, initiatives like the European Commission’s Destination Earth programme aim to create full-scale digital replicas of our planet, incorporating EO data, high-performance computing and AI. Such digital twins enable researchers, planners and policymakers to simulate “what if” climate scenarios in incredible detail, testing strategies for adaptation and resilience against events ranging from floods to droughts or urban heat waves.​

Crucially, these AI and ML-powered models dramatically reduce forecast build times and boost prediction accuracy, especially for extreme weather. This allows for easier and more effective resource allocation, stronger early warning systems, and smarter, locally targeted adaptation measures.

Democratising climate intelligence

Perhaps one of the most important shifts identified in the whitepaper is the focus on democratising access to high-impact climate intelligence. By transforming vast EO datasets – once accessible only to technical experts – into interactive, visual and decision-support tools, technology pipelines are helping bridge the gap between data and action.

Tools using augmented reality (AR) and virtual reality (VR) enable users – from policymakers to grassroots communities – to experience and understand climate insights in intuitive, actionable ways.

These platforms can turn complex EO data into interactive, scenario-based models that support collaborative climate adaptation and advocacy.​

New platforms aggregate multidimensional EO data and make it easy to access, analyse and visualise by location, time or key environmental variables.

The MIT Media Lab’s Earth Mission Control and Africa’s Digital Earth Africa initiative are leading examples, using open-source technology to bring EO-driven water monitoring and flood forecasting to hundreds of thousands of sites and millions of users across Africa.​

Imminent advances in generative AI, such as ESA’s planned digital EO assistant, will seamlessly answer text-based queries and provide tailored visual insights from EO data – making complex climate science ever-more accessible.​

Overcoming challenges and enabling collaboration

The WEF recognises, however, that realizing EO’s full potential for climate intelligence relies on addressing significant challenges. These include:

• Data interoperability and integration: EO data must be standardised and seamlessly integrated across platforms and formats, including in-situ measurements, satellite streams and third-party datasets.
• Infrastructure and access: Investment in cloud-based processing, ground segments, and digital literacy programmes is essential so that vulnerable and less-resourced communities benefit equally.
• Skills and capacity-building: As technology evolves, so do workforce and training needs – especially as new tools demand cross-disciplinary expertise in AI, satellite engineering, geospatial analytics and policy.

Continued cross-sector and public-private partnerships, investment in open-source platforms and collective engagement can foster a scalable, inclusive ecosystem for climate intelligence. Governments, businesses, academia, and communities must coordinate to maintain momentum and share expertise.​

Climate intelligence for a resilient future

The World Economic Forum’s whitepaper ultimately presents an urgent but optimistic vision – through human-centric, technology-enabled approaches, EO can forge the foundation of a more resilient, adaptive and informed society.

By harnessing advances in satellite technology, AI and digital collaboration, we are moving beyond siloed datasets and slow insights to real-time, actionable intelligence for climate action.

This new climate intelligence paradigm is not only about smarter disaster response or improved weather forecasting – it is about empowering everyone to anticipate, plan and act for the future.

From city planners preparing for the next flood, to farmers optimising water use, to international collaborations simulating global risks, the EO revolution promises a step change in our relationship with the planet.

For sustainability leaders and practitioners, the message is clear – the future of climate action will be data-driven, collaborative and deeply technological.

Organisations must embrace the opportunities of next-generation EO, invest in skills and partnerships and advocate for open, equitable access to the insights that will shape our shared future.