A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

This survey synthesizes and integrates stateof -the -science reviews and research at the nexus of wildfire observations and modeling, AI, and UAVs - topics at the forefront of advances in wildfire management, elucidating the role of AI in performing monitoring and actuation tasks from pre -fire, through the active -fire stage, to post -fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management.

We also examine the pre -fire and post -fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active -fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks.

Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting -edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.