Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses have underscored the urgent need to improve public knowledge and advance existing techniques in wildfire management. 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 existing survey papers have explored learning-based approaches in wildfire, drone use in disaster management, and wildfire risk assessment, a comprehensive review emphasizing the application of AI-enabled UAV systems and investigating the role of learning-based methods throughout the overall workflow of multi-stage wildfire management, including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire (e.g., fire growth modeling), and post-fire tasks (e.g., evacuation planning) is notably lacking. This survey synthesizes and integrates state-of-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.

中文翻译：

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对人工智能无人机系统在野火前、火中和火后管理中的研究的全面调查

野火已成为全球最具破坏性的自然灾害之一，造成灾难性损失。这些损失凸显了提高公众知识和改进野火管理现有技术的迫切需要。最近，在无人机（UAV）和深度学习模型集成的推动下，人工智能（AI）在野火中的应用为实施和开发更有效的野火管理创造了前所未有的动力。尽管现有的调查论文探讨了基于学习的方法在野火、无人机在灾害管理中的使用以及野火风险评估，但一项全面的综述强调了人工智能无人机系统的应用，并调查了基于学习的方法在整个野火工作流程中的作用。多阶段野火管理，包括火灾前（例如，基于视觉的植被燃料测量）、主动火灾（例如，火势蔓延建模）和火灾后任务（例如，疏散规划）明显缺乏。这项调查综合并整合了野火观测和建模、人工智能和无人机等野火管理进展前沿主题的最新科学评论和研究，阐明了人工智能在执行监测和驱动任务中的作用从火灾前、火灾发生阶段到火灾后管理。为此，我们对现有遥感系统进行了广泛的分析，特别关注与野火管理相关的无人机进步、设备规格和传感器技术。我们还研究了火灾前和火灾后的管理方法，包括燃料监测、预防策略以及疏散计划、损害评估和运营策略。此外，我们回顾并总结了主动火灾管理中的各种计算机视觉技术，重点是用于野火分类、分割、检测、和监控任务。最后，我们强调通过尖端人工智能技术和基于无人机的数据的集成，野火建模取得了实质性进展，为了解动态野火行为提供了新颖的见解和增强的预测能力。