A Bibliometric Analysis of Artificial Intelligence-Based Solutions to Challenges in Wildfire Fuel Mapping

Wildfire fuel mapping plays a vital role in understanding and mitigating the risks associated with wildfires. This study conducts a comprehensive analysis of existing literature to investigate the prevailing trends in wildfire fuel mapping, including an analysis of the satellite sensors commonly used for fuel mapping, the predominant types of fuels mapped, the resolution of fuel maps to assess accuracy and detail, and the publication trends over the years to understand the growth and interest in the field. By leveraging AI techniques including machine learning, we review solutions to overcome challenges such as data scarcity, modalities, mapping understory vegetation, model explainability, and lack of uncertainty-aware models. This manuscript aims to serve as a reference for researchers and practitioners seeking to advance the field of wildfire fuel mapping through data-driven and AI-powered approaches.