FOREST FIRE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Forest fires pose significant threats to ecosystems, wildlife, and human life, necessitating rapid detection and response mechanisms. This paper presents a novel approach utilizing Convolutional Neural Networks (CNNs) for the detection of forest fires or the onset of fire activity within images. The proposed model is designed to analyze aerial surveillance footage, enabling real-time monitoring of forested areas. Leveraging the spatial hierarchies learned by CNNs, the model learns to discern patterns indicative of fire presence or initiation. By training on annotated datasets comprising images with and without fire occurrences, the CNN is capable of distinguishing subtle visual cues associated with fire, even in low-framerate video streams. Through extensive experimentation and evaluation, we demonstrate the efficacy of the proposedCNN- based approach in accurately detecting forest fires, with promising results in real-world scenarios. The model’s ability to provide timely alerts upon fire detection makes it a valuable tool for proactive fire management and emergency response systems, ultimately contributing to the preservation of forests and the protection of lives and property.