Wildfire Detection : Review of Emerging Technologies and Methodologies

Abstract

Wildfires are uncontrolled fires that very quickly spread across forests, vegetation and grasslands powered or intensified by dry weather conditions, high temperatures and strong winds. These fires not only devastate ecosystems but also threaten human lives and infrastructure, which can lead to potential significant economic and environmental losses. With the climate change adding to their increasing frequency and intensity, the need for effective wildfire detection methods is becoming more crucial.Motivated on exploring solutions for this huge challenge in front of humans , in this study we explore the evolution of wildfire detection technologies, from traditional methods like manual surveillance to advanced systems incorporating satellite imagery, UAV (drones) monitoring, sensor networks, and analysis done by Artificial Intelligence. Each approach offers unique strengths and faces specific challenges, making it crucial to understand their roles in modern wildfire management. We explore how wildfire detection methods have developed over time, offering a clear and practical look at the various different solutions. By taking this approach, we hope to help spark fresh perspectives and inspire innovations potentially contributing to faster, more accurate and proactive wildfire detection strategies that help protect both natural ecosystems and human lives.

Introduction

Summary:

Wildfires, once a natural occurrence, have increased in frequency and severity due to climate change and human activities, posing significant risks to ecosystems and human life. Early detection is critical for effective containment and mitigation.

Traditional wildfire detection relied on human observation and early sensor networks, which were limited by slow responses and false alarms. Advances in technology over the past two decades—especially satellite remote sensing, UAV (drone) technology, and artificial intelligence (AI)—have enabled faster, more accurate, and automated detection systems. Modern methods combine multiple data sources and advanced AI models for improved wildfire monitoring.

Despite progress, key challenges remain: high false alarm rates, delays in real-time processing due to large data volumes, scarcity of comprehensive labeled datasets for AI training, energy limitations of sensor and UAV systems, and lack of integration and standardization among diverse detection technologies.

Four main wildfire detection methodologies are highlighted:

1. Sensor-based Systems: Distributed ground sensors and IoT networks monitor environmental conditions for early fire signs but face issues with range, maintenance, and costs.

2. Satellite-based Detection: Satellites provide wide-area, frequent monitoring using thermal and multispectral imaging, though cloud cover and data processing delays can limit effectiveness.

3. UAV-based Detection: Drones offer real-time aerial surveillance with advanced sensors and deep learning integration, but face operational constraints like battery life and regulations.

4. AI-driven Approaches: Deep learning models automate fire and smoke detection by fusing data from satellites, UAVs, and sensors, improving accuracy but demanding substantial computational resources.

Future improvements focus on enhancing model robustness across environments, reducing false alarms, improving energy efficiency, creating standardized integration frameworks, and expanding high-quality datasets to make wildfire detection faster, more reliable, and scalable.

Conclusion

The evolution of wildfire detection methods—from manual observation and early sensor networks up to sophisticated AI-powered systems—demonstrates a significant advancement in our ability to monitor and respond to the wildfires. Modern detection systems now integrate satellite imagery, UAV-based surveillance, ground sensors, and deep learning models to provide near real-time detection and improved accuracy. However, despite these advancements, several challenges continue to persist. Issues such as false positives, real-time processing limitations, data scarcity, energy efficiency, and the lack of standardized integration frameworks throughout the world continue to affect the performance and reliability of wildfire detection systems.The future research in wildfire detection can benifit by exploring several key areas to enhance the efficiency and reliability of the systems. One important aspect is the development of multi-modal integration, in which standardized platforms can seamlessly combine the data from satellites, UAVs, and ground sensors. Using this integration will be vital in making a unified and comprehensive wildfire monitoring system. Also since acquiring diverse and annotated wildfire datasets remains a challenge, leveraging synthetic data generation and transfer learning techniques can help address data scarcity in turn improving the accuracy of AI-driven detection models. Energy-efficient design is also a very critical factor, particularly for sensor networks and UAVs used in remote locations. Advancements in battery management and low-power sensor technology will be very essential to ensure the continuous and long-term monitoring without their frequent maintenance or power limitations. Additionally, real-time analytics should be enhanced to accelerate decision-making in wildfire detection. And finally, the standardization and interoperability of various detection systems must be prioritized. Establishing universal data-sharing protocols and integration frameworks can allow different technologies to work together, improving coordination and creating a more effective wildfire management strategy. Addressing these research areas will be helpful in advancing wildfire detection capabilities and minimizing their devastating impact.

References

[1] M. Antunes, L. M. Ferreira, C. Viegas, A. P. Coimbra, and A. T. de Almeida, “Low-Cost System for Early Detection and Deployment of Countermeasures Against Wild Fires,” in 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, Apr. 2019, pp. 418–423. doi: 10.1109/WF-IoT.2019.8767331. [2] X. Wang and W. Yao, “GNSS-R-Based wildfire detection: a novel and accurate method,” Eur J Remote Sens, 2024, doi: 10.1080/22797254.2024.2413993. [3] C. Phan and H. H. T. Liu, “A cooperative UAV/UGV platform for wildfire detection and fighting,” in 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing, IEEE, Oct. 2008, pp. 494–498. doi: 10.1109/ASC-ICSC.2008.4675411.

Copyright

Copyright © 2025 Atharv Bhome, Prem Chavan, Lavanya Chaudhary, Pratiksha Sawant. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.