AI-Based Forest Fire Detection Using Satellite Imagery: Challenges, Advances, and a Framework for Real-Time Alarm Systems

Abstract

Forest fires are a growing global concern, with increasing frequency and intensity due to climate change. Timely detection and rapid intervention are crucial in mitigating the impact of these disasters. Satellite imagery has long been used for fire detection, but existing methods often suffer from limitations such as false positives and delayed responses. This survey explores the state-of-the-art methods in satellite-based forest fire detection, focusing on the role of artificial intelligence (AI) in improving detection accuracy and speed. We examine various AI techniques, including traditional machine learning models and deep learning approaches, and evaluate their effectiveness in analyzing satellite imagery for fire detection. Based on this survey, we propose a novel solution that integrates AI for verifying detected fire anomalies and triggers immediate alarms, aiming to enhance the accuracy and timeliness of fire detection systems. Our approach aims to improve existing systems by reducing false positives, ensuring faster responses, and ultimately aiding in better disaster management.