# A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

arXiv: https://arxiv.org/abs/2401.02456 · HTML: https://mlsanity.com/paper/2401.02456/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 314 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 28 of 28)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Dynamic ReLU](https://mlsanity.com/paper/2003.10027/) (2020)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)
- [Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study](https://mlsanity.com/paper/2101.03409/) (2021)
- [FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics](https://mlsanity.com/paper/1612.05360/) (2016)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles](https://mlsanity.com/paper/1905.00492/) (2019)
- [Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires](https://mlsanity.com/paper/2006.07969/) (2020)
- [A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook](https://mlsanity.com/paper/2104.01283/) (2021)
- [Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications](https://mlsanity.com/paper/2306.16481/) (2023)
- [A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking](https://mlsanity.com/paper/1704.02630/) (2017)
- [A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networks](https://mlsanity.com/paper/1808.00058/) (2018)
- [Multimodal Wildland Fire Smoke Detection](https://mlsanity.com/paper/2212.14143/) (2022)
- [Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning](https://mlsanity.com/paper/2308.12843/) (2023)
- [A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks](https://mlsanity.com/paper/1904.07380/) (2019)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [An FPGA smart camera implementation of segmentation models for drone wildfire imagery](https://mlsanity.com/paper/2309.01318/) (2023)
- [Attention on Classification for Fire Segmentation](https://mlsanity.com/paper/2111.03129/) (2021)
- [A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems](https://mlsanity.com/paper/2302.08623/) (2023)
- [A Coalition Formation Approach to Coordinated Task Allocation in Heterogeneous UAV Networks](https://mlsanity.com/paper/1711.00214/) (2017)
- [Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions](https://mlsanity.com/paper/2110.05551/) (2021)
- [An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification](https://mlsanity.com/paper/2308.09791/) (2023)

## Built on by (most cited first, 25 of 25)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management](https://mlsanity.com/paper/2412.02831/) (2024)
- [Cross-View Image Set Geo-Localization](https://mlsanity.com/paper/2412.18852/) (2024)
- [FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion](https://mlsanity.com/paper/2403.03463/) (2024)
- [Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control](https://mlsanity.com/paper/2505.09029/) (2025)
- [PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments](https://mlsanity.com/paper/2403.11095/) (2024)
- [FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning](https://mlsanity.com/paper/2404.06653/) (2024)
- [Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments](https://mlsanity.com/paper/2410.10843/) (2024)
- [Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets](https://mlsanity.com/paper/2501.15343/) (2025)
- [Seeing Heat with Color -- RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth](https://mlsanity.com/paper/2505.01638/) (2025)
- [An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue](https://mlsanity.com/paper/2511.04042/) (2025)
- [Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks](https://mlsanity.com/paper/2401.08105/) (2024)
- [Driving Towards Inclusion: A Systematic Review of AI-powered Accessibility Enhancements for People with Disability in Autonomous Vehicles](https://mlsanity.com/paper/2401.14571/) (2024)
- [Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication](https://mlsanity.com/paper/2404.08013/) (2024)
- [Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation](https://mlsanity.com/paper/2504.08195/) (2025)
- [TCMA: Text-Conditioned Multi-granularity Alignment for Drone Cross-Modal Text-Video Retrieval](https://mlsanity.com/paper/2510.10180/) (2025)
- [Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning](https://mlsanity.com/paper/2512.07925/) (2025)
- [GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface](https://mlsanity.com/paper/2512.20813/) (2025)
- [Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery](https://mlsanity.com/paper/2602.14929/) (2026)
- [Large-scale Photorealistic Outdoor 3D Scene Reconstruction from UAV Imagery Using Gaussian Splatting Techniques](https://mlsanity.com/paper/2602.20342/) (2026)
- [Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances](https://mlsanity.com/paper/2603.26612/) (2026)
- [Impact of Dataset Composition on Embedded Real-Time UAV Wildfire Detection Using Compact YOLO Models](https://mlsanity.com/paper/2608.07554/) (2026)
- [Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3](https://mlsanity.com/paper/2609.01390/) (2026)

## Abstract (arXiv)

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.