Viewpoint: Unlocking Europe’s wildfire data

Дата публикации: 10-09-2026 08:51:19

Viewpoint: Unlocking Europe’s wildfire data
By
Jean-Claude Burgelman
Juliette
10 Sep 2026




Jean-Claude Burgelman, professor emeritus at the Free University of Brussels, editor-in-chief at Frontiers Policy Lab and director of the Frontiers Planet Prize.

Europe is already investing in the science, technology and infrastructure needed to understand, prevent and fight wildfires. Copernicus provides satellite-based Earth observation and environmental information, while the European Forest Fire Information System provides information on fire danger and forest fires across Europe. Weather and climate systems add forecasts of heat, drought, wind and humidity; historical fire databases show where fires have occurred; and researchers generate increasingly detailed knowledge about fuels, fire behaviour, forecasting, modelling and risk management. Meanwhile, new European research projects are adding digital twins and other tools to this growing ecosystem.However, these data do not necessarily sit together, nor are they necessarily described, structured or governed in ways that allow them to be used together. Much of the information is public, but some is commercially held. Some is sensitive. Some is subject to legal, security or operational restrictions. Even when data can be legally accessed, different systems may use different formats, terminology and rules.Europe therefore faces an unusual paradox: we have vast amounts of relevant information to respond to the wildfire challenge, but not the means to bring it together. For example, a wildfire researcher or emergency service might need to ask whether a particular combination of vegetation, drought, wind and topography has historically been associated with rapid fire spread. The relevant information may exist in several institutions, using different systems and subject to different conditions of access.The challenge is to conduct an authorised analysis that interacts with these sources, combines the results and respects the rules imposed by each data custodian.An important part of the answer can be found in the Fair principles, the idea that data should be findable, accessible, interoperable and reusable. Developed more than a decade ago, these principles have become a central reference point for modern data stewardship, making it easier for people and machines to find data, understand what it means and determine whether it can be reused.Readability by machines is essential, since AI systems will be key to meeting the data challenge. Bring analysis to the dataThe proposition is not to give an AI system unrestricted access to sensitive databases. Quite the opposite. The goal is to make it possible for approved algorithms to go to the data, rather than requiring sensitive data or large datasets to be copied to wherever the analysis happens.Imagine an analysis asking a series of questions of different data stations. At one station it might examine weather and climate data; at another, historical fire records; at another, experimental measurements of fire behaviour. The algorithm does not need to download everything. It can operate within the environment permitted by each data custodian and return only the results that the rules allow to leave.This is sometimes described as data visiting, and related approaches already exist in areas such as federated analysis and federated learning.Sensitive data should therefore not be treated as a free-for-all for machines. This requires appropriate governance at multiple levels. Access needs to be authenticated and authorised. The purpose of an analysis should be defined. The code or model being run should be controlled. Data custodians should be able to specify what can be queried and what results can leave their environment. Activities should be auditable and human oversight should remain where the consequences require it.In other words, the objective is not to trust AI with the data. It is to design systems in which AI never has more access than it needs.As open as possible, as closed as necessaryFair data should also not be confused with open data. Some wildfire data should be openly available. Other information may legitimately need to remain restricted because it concerns critical infrastructure, security, commercial interests, privacy or operational capabilities. The principle should be: as open as possible, as closed as necessary.The important point is that restricted data should not automatically become unusable data. If machines can discover what information exists, understand its meaning, establish where it came from and determine the conditions under which it can be used, authorised analyses can potentially be carried out without the underlying data ever leaving the environment in which it is protected.Restricted data can still be Fair and machine-actionable.This is also an important question of data sovereignty. European institutions should not have to surrender control of their data to a handful of technology platforms in order to benefit from AI. Nor does Europe need to start again by building one giant repository for every piece of wildfire information. The better approach is to connect what already exists.This is the principle behind emerging approaches such as Tryangle, developed through the Leiden Initiative for Fair and Equitable Science. Rather than creating another centralised data warehouse, the approach seeks to provide an interoperable layer through which approved algorithms can visit distributed data sources while respecting the rules of their custodians.In this way, a data station can remain where it is. Its custodians can retain control. Existing databases and applications do not have to be abandoned. What changes is their ability to communicate with other systems through common, machine-readable descriptions, access rules and interoperability mechanisms.But approaches like this need data that meets the Fair principles. Only then can an interoperable ecosystem safely begin to turn fragmented information into collective intelligence.Data infrastructure is wildfire infrastructureThe implications of this approach are entirely practical. Firefighters could gain better intelligence about local and evolving risks. Land managers could test prevention strategies using more comprehensive information about fuels, weather and past fires. Researchers could compare evidence across ecosystems without repeatedly moving huge datasets between institutions. Smaller regions and resource-constrained teams could access analytical capabilities without having to build large data infrastructures themselves.Europe is already investing heavily in satellites, databases, research programmes, forecasting systems and scientific expertise. The question is whether we will connect those investments well enough to turn them into actionable intelligence.That requires policymakers and funders to treat data infrastructure as seriously as physical infrastructure. Data generated or supported through European public funding should be Fair, verifiable and machine actionable by design, wherever legally and ethically possible.Related articlesData Corner: Europe’s burning need for wildfire mitigation researchClimate monitoring must remain a global enterprise, experts sayIn practice, that means funding not only the production of data, but also the infrastructure that makes data usable across systems: metadata, provenance, interoperability, access mechanisms and governance. Where data must remain restricted, we should ensure that it can still be securely discovered, verified and used for authorised analysis.Initiatives such as the European Open Science Cloud offer an existing framework through which these principles can be put into practice. What is needed now is to accelerate implementation and make them a standard part of how Europe funds and builds research and innovation systems.We do not need another generation of disconnected databases. The data is already there. The opportunity is to make it work together, securely, responsibly and quickly enough to help prevent the next fire, rather than simply document the last one.Jean-Claude Burgelman is professor emeritus at the Free University of Brussels (VUB), editor-in-chief at Frontiers Policy Lab and director of the Frontiers Planet Prize. For more than 20 years he held leading roles in research and innovation policy at the European Commission.

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