An Operational Framework for Dynamic ML-informed Fuel Mapping for Wildfire Risk Management

Fuel characterization plays a central role in every phase of wildfire risk management, from identifying priority areas for prevention to supporting wildfire danger evaluation and simulating fire spread. Despite their importance, producing fuel maps that are both up to date and spatially extensive remains a persistent difficulty in fire science. Highly detailed information on fuel structure and composition would improve fire behavior simulations and danger assessment, but collecting such data is often difficult or impractical at the required level of detail. As a result, wildfire management must often rely on simplified representations that trade detail for feasibility. This raises a critical operational question: can fuel classification systems be designed to remain effective and reliable while being simple enough for large-scale, operational applications? 

In response to this need, the CIMA Foundation has developed an operational fuel classification methodology tailored to civil protection requirements. The approach integrates land cover data, vegetation typologies, and environmental variables with expert-driven rules and machine learning–based wildfire susceptibility analyses. Rather than aiming for exhaustive fuel descriptions, the method focuses on capturing the most relevant characteristics for operational decision-making that is, the susceptibility of the territory to wildfire spreading. 

Originally conceived for static fire susceptibility mapping at multiple spatial scales - ranging from regional to pan-European - the methodology has since been expanded to account for drought conditions. This enhancement allows fuel susceptibility to vary over time, producing dynamic maps that better represent seasonal changes in vegetation flammability. Such temporal variability is especially important in the context of climate change, where prolonged droughts combined with extreme weather can amplify wildfire severity. Addressing these compound drivers is a key requirement for operational wildfire forecasting in civil protection systems. 

The resulting fuel maps serve as a core input for the RISICO wildfire danger forecasting model, developed by CIMA Foundation and used by the Italian Civil Protection Department, regional authorities, and international partners. Dynamic fuel representations have been tested in pre-operational settings at the regional level in Italy, as well as in international applications, demonstrating their usefulness in supporting wildfire danger bulletins. In parallel, the static fuel map has been employed as an input for the PROPAGATOR fire spread model, extending its applicability across different components of the wildfire risk management cycle. 

Although intentionally less detailed than some advanced fuel classification schemes, this approach has proven fit for purpose in operational contexts. It offers a pragmatic compromise between scientific rigor and usability, enabling the effective integration of scientific knowledge into decision-support tools for wildfire management.