Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support

TL;DR

This paper introduces a department-aware AI approach for localized forest fire risk prediction in France, emphasizing regional differences and operational utility, and presents a new national benchmark dataset.

Contribution

It proposes a novel, region-sensitive fire risk modeling approach tailored to firefighting departments and introduces the first national AI benchmark for France.

Findings

Region-specific models outperform generic ones.

The benchmark dataset enables standardized evaluation.

Supplementary materials are available on GitHub.

Abstract

Forest fire prediction involves estimating the likelihood of fire ignition or related risk levels in a specific area over a defined time period. With climate change intensifying fire behavior and frequency, accurate prediction has become one of the most pressing challenges in Artificial Intelligence (AI). Traditionally, fire ignition is approached as a binary classification task in the literature. However, this formulation oversimplifies the problem, especially from the perspective of end-users such as firefighters. In general, as is the case in France, firefighting units are organized by department, each with its terrain, climate conditions, and historical experience with fire events. Consequently, fire risk should be modeled in a way that is sensitive to local conditions and does not assume uniform risk across all regions. This paper proposes a new approach that tailors fire risk assessment to departmental contexts, offering more actionable and region-specific predictions for operational use. With this, we present the first national-scale AI benchmark for metropolitan France using state-of-the-art AI models on a relatively unexplored dataset. Finally, we offer a summary of important future works that should be taken into account. Supplementary materials are available on GitHub.