# Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/1905.00492 · HTML: https://mlsanity.com/paper/1905.00492/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation](https://mlsanity.com/paper/2211.15414/) (2022)
- [Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2401.11582/) (2024)
- [Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees](https://mlsanity.com/paper/2206.10544/) (2022)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution](https://mlsanity.com/paper/2204.11350/) (2022)
- [A Search and Detection Autonomous Drone System: from Design to Implementation](https://mlsanity.com/paper/2211.15866/) (2022)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning](https://mlsanity.com/paper/2404.06653/) (2024)
- [Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks](https://mlsanity.com/paper/2401.08105/) (2024)
- [Wildfire spread forecasting with Deep Learning](https://mlsanity.com/paper/2505.17556/) (2025)
- [WildFireVQA: A Large-Scale Radiometric Thermal VQA Benchmark for Aerial Wildfire Monitoring](https://mlsanity.com/paper/2604.20190/) (2026)

## Abstract (arXiv)

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone %with longer communication range that employs observer drones potentially with different sensing and imaging %actuation capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system -- without the need for inter-coalition communications -- approaches that of a centrally-optimized system.