Wildfire Detection

Abstract

Wildfires are becoming one of the most severe environmental challenges, posing significant threats to ecosystems, infrastructure, and human lives. The increasing frequency and intensity of wildfires, driven by climate change, deforestation, and human activities, demand efficient detection and mitigation strategies. This study Keywords on leveraging advanced deep learning techniques combined with satellite imagery to improve wildfire detection. By utilizing cutting-edge convolutional neural networks (CNNs), the proposed approach analyzes multi-spectral satellite data to identify fire-prone areas, detect active fires, and predict potential spread patterns. Real-time data integration from satellites such as Sentinel and MODIS enhances the system’s accuracy, scalability, and response time. The model aims to revolutionize wildfire management by offering a robust, scalable, and efficient approach to early detection and prediction, ultimately reducing the devastating impacts of wildfires.