An Automated Forest Fire Detection System

DOI

10.2991/978-94-6463-738-0_72How to use a DOI?

Keywords

Thermal Imaging; IoT Sensor Networks; MachineLearningModels

Abstract

Forest fires happen to be the most devastating natural calamities, which have serious ecological, economical, and social implications. Early detection and a timely fight are the crux of remedying this malady. Most of the traditional methods of fire detection in forests, which are human observation and satellite observation, work slowly and provide a limited error-free detections. The emergence of automated forest fire detection systems was a very important solution coming from modern technologies like remote sensing, ground-based sensors, and artificial intelligence. Automated approaches that are the subject of this study are thermal imaging by means of drones, IoT sensor networks, and machine learning models for prediction and detection of fire. Techniques such as Convolutional Neural Networks (CNNs) and predictive algorithms have shown promising results with great levels of accuracy when detecting fire patterns and estimating fire spread. However, issues such as false alarms, high installation costs, and disagreements in integrating the data remain crucial hindrances.Trends that have emerged and will respond to some of these challenges are those such as edge computing, blockchain for secure data sharing, and hybrid systems embedding several detection methods. The scope and state of automated forest fire detection systems, their shortcomings, and the way they are going to develop are thoroughly presented in this research paper, stressing on the need for well-organized interdisciplinary collaboration to develop more feasible and promising solutions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Priyata Mishra
AU  - Sirisha Rokkam
AU  - M. Dikshita
AU  - Khushboo Jha
PY  - 2025
DA  - 2025/06/22
TI  - An Automated Forest Fire Detection System
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 925
EP  - 934
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_72
DO  - 10.2991/978-94-6463-738-0_72
ID  - Mishra2025
ER  -