[Paper Review] 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 ecosystems8 citations

TL;DR

A systematic survey of AI-enabled UAV technologies across pre-fire, active-fire, and post-fire management, analyzing UAV advancements, sensor tech, and AI methods for wildfire tasks.

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.

Motivation & Objective

• Survey the state-of-the-art AI-enabled UAV systems for wildfire management across pre-, active-, and post-fire stages.
• Analyze remote sensing, UAV hardware specifications, and sensor technologies relevant to wildfire management.
• Summarize machine learning, reinforcement learning, and deep learning techniques for wildfire classification, segmentation, detection, and monitoring.

Proposed method

• Systematic review of recent state-of-the-art technologies in AI-enabled UAVs for wildfires.
• Analysis of UAV advancements, device specifications, and sensor technologies for wildfire management.
• Review of pre-fire and post-fire management approaches including fuel monitoring, prevention strategies, evacuation planning, damage assessment, and operation strategies.
• Summarization of computer vision techniques in active-fire management with focus on ML, RL, and DL for classification, segmentation, detection, and monitoring.

Experimental results

Research questions

• RQ1 What are the latest AI-enabled UAV technologies and sensor configurations used for wildfire management across pre-, active-, and post-fire stages?
• RQ2 How do AI models (ML/DL/RL) contribute to wildfire classification, segmentation, detection, monitoring, and predictive modeling when integrated with UAV data?
• RQ3 What are the gaps and challenges in UAV-based wildfire management, and what future directions are suggested for improved predictive capabilities and response?
• RQ4 How do pre-fire and post-fire strategies (fuel monitoring, prevention, evacuation planning, damage assessment) integrate with active-fire AI-enabled UAV systems?

Key findings

• AI-enabled UAV systems and AI models have progressed to support multi-stage wildfire management.
• There is a focus on UAV hardware, sensor technologies, and remote sensing relevant to wildfire tasks.
• Active-fire management emphasizes computer vision techniques using ML, RL, and DL for classification, segmentation, detection, and monitoring tasks.
• The survey highlights advancements in wildfire modeling through integrating AI techniques with UAV-based data to improve predictive capabilities.