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

Sayed Pedram Haeri Boroujeni, Abolfazl Razi+8 more|arXiv (Cornell University)|Jan 4, 2024

Fire effects on ecosystems被引用 8

一句话总结

系统性综述AI驱动无人机在火灾前期、火灾中期和火灾后管理中的技术，分析无人机进展、传感器技术，以及用于野火任务的AI方法。

ABSTRACT

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.

研究动机与目标

• 调查针对火灾管理，在火灾前、火灾中、火灾后各阶段的AI驱动无人机系统的最新技术状态。
• 分析与野火管理相关的遥感、无人机硬件规格与传感器技术。
• 总结用于野火分类、分割、检测和监测的机器学习、强化学习和深度学习技术。

提出的方法

• 针对野火的AI驱动无人机的最近前沿技术进行系统综述。
• 分析野火管理中的无人机进展、设备规格与传感器技术。
• 综述火灾前后管理方法，包括燃料监测、预防策略、撤离规划、损害评估和运营策略。
• 对火灾中管理中的计算机视觉技术进行总结，重点是用于分类、分割、检测和监控的机器学习、强化学习和深度学习。

实验结果

研究问题

• RQ1 在火灾前、火灾中、火灾后阶段用于野火管理的最新AI驱动无人机技术和传感器配置有哪些？
• RQ2 将AI模型（ML/DL/RL）与无人机数据整合后，对野火分类、分割、检测、监测和预测建模有何贡献？
• RQ3 基于无人机的野火管理存在哪些差距与挑战？为提升预测能力与响应，未来有哪些方向？
• RQ4 火灾前后策略（燃料监测、预防、撤离规划、损害评估）如何与火灾中AI驱动无人机系统整合？

主要发现

• AI驱动无人机系统和AI模型已进展以支持多阶段野火管理。
• 人们聚焦于无人机硬件、传感器技术和与野火任务相关的遥感。
• 火灾中管理强调使用ML、RL和DL的计算机视觉技术用于分类、分割、检测和监控等任务。
• 综述强调通过将AI技术与基于无人机的数据整合来改进野火建模和预测能力的进展。