A comprehensive survey of research towards AI-enabled unmanned aerial
  systems in pre-, active-, and post-wildfire management
Sayed Pedram Haeri Boroujeni Abolfazl Razi Sahand Khoshdel Fatemeh Afghah Janice L. Coen Leo ONeill Peter Z. Fule Adam Watts Nick-Marios T. Kokolakis Kyriakos G. Vamvoudakis
Wildfires have emerged as one of the most destructive natural disasters
worldwide, causing catastrophic losses in both human lives and forest wildlife.
Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by
the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models,
has created an unprecedented momentum to implement and develop more effective
wildfire management. Although some of the existing survey papers have explored
various learning-based approaches, a comprehensive review emphasizing the
application of AI-enabled UAV systems and their subsequent impact on
multi-stage wildfire management is notably lacking. This survey aims to bridge
these gaps by offering a systematic review of the recent state-of-the-art
technologies, highlighting the advancements of UAV systems and AI models 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.