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.
Understanding the Multi-Disciplinary Nature of AI-Enabled UAV Systems in Wildfire Management
Wildfires have long been a devastating force, causing immense destruction to both human lives and the environment. However, recent advancements in Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) have given us new tools to tackle this problem more effectively. This survey aims to provide a comprehensive review of the state-of-the-art technologies in AI-enabled UAV systems and their impact on multi-stage wildfire management.
Advancements in UAV Systems for Wildfire Management
One of the key areas of focus in this survey is the analysis of remote sensing systems, specifically those integrated with UAVs. These systems play a crucial role in gathering data that aids in wildfire management. The review delves into the advancements in UAV technology, device specifications, and sensor technologies that are relevant to this field. By understanding the capabilities and limitations of these systems, we can harness their potential to gather accurate and timely information.
Pre-Fire and Post-Fire Management Approaches
The survey also explores pre-fire and post-fire management approaches. In terms of pre-fire management, the focus is on fuel monitoring and prevention strategies. By effectively monitoring vegetation and fuel levels, authorities can take proactive measures to reduce the risk of wildfires. On the other hand, post-fire management involves damage assessment and operation strategies. Evacuation planning is also an integral part of this stage, ensuring the safety of affected communities.
Computer Vision Techniques for Active-Fire Management
Computer vision techniques using Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms have become powerful tools in active-fire management. These techniques enable wildfire classification, segmentation, detection, and monitoring tasks with higher accuracy and efficiency. The survey reviews and summarizes the wide range of computer vision techniques utilized in this field, emphasizing their contribution to real-time decision-making.
The Integration of AI Techniques and UAV-Based Data in Wildfire Modeling
Finally, the survey highlights the substantial advancements in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data. By combining real-time data from UAVs with AI models, we gain novel insights and enhanced predictive capabilities to understand the dynamic behavior of wildfires. This knowledge can aid in the development of more effective strategies for firefighting and containment.
In conclusion, this survey serves as a comprehensive resource for understanding the multi-disciplinary nature of AI-enabled UAV systems in wildfire management. By analyzing the advancements in technology, remote sensing systems, computer vision techniques, and wildfire modeling, we can pave the way for more efficient and proactive approaches to combating wildfires. The integration of AI and UAVs offers unprecedented potential in protecting lives and the environment from the destructive force of wildfires.