AI-Powered Forest Fire Detection: Satellite and Sensor Fusion for Early Warning Systems

Authors- Akash T.C

Abstract--The increasing occurrence and intensity of forest fires globally has prompted the need for innovative solutions to prevent and mitigate their destructive impact. This paper presents a novel approach to forest fire detection using Artificial Intelligence (AI)-powered systems, integrating satellite imagery with sensor fusion technology for an early warning system. The proposed model leverages real-time satellite data, including infrared and optical imagery, in combination with data from ground-based sensors, such as temperature, humidity, and smoke detection sensors. By using AI techniques like machine learning and deep learning, the system can rapidly analyze vast amounts of data to identify potential fire hotspots and predict fire behavior with high accuracy. The integration of satellite data with sensor fusion enhances the system’s capability to detect fires in remote or inaccessible regions, significantly improving response times and resource allocation for firefighting efforts. The paper discusses the design of the system, the algorithms employed for data fusion and fire detection, and the results of the model’s performance in comparison to traditional fire detection methods. The outcome suggests that AI-powered detection systems can revolutionize the management of forest fire prevention and response, reducing environmental damage, loss of life, and economic impacts.

DOI: /10.61463/ijset.vol.13.issue2.397