GIS, AI, and Environmental Data in Fire Susceptibility Mapping: A Survey

Wildfires pose a significant threat to both the environment and human settlements, causing extensive damage and loss. Understanding and predicting fire susceptibility are paramount in crafting effective mitigation and response strategies. In this paper we will provide a comprehensive overview of data sources and methodologies employed in Fire Susceptibility Mapping (FSM), considering various Environmental Factors. It delves into the integration of Geographic Information Systems (GIS) and advanced Machine Learning (ML) techniques, including Statistical Models and Neural Networks, for precise classification of areas based on their susceptibility to wildfires. Through an exploration of various research works, this review aims to illustrate the practicality and effectiveness of the approaches used, contributing to a better understanding of wildfire risk assessment and mitigation strategies.