Innovations in Wildfire and Smoke Detection: A Comprehensive Survey

Wildfires are a growing threat due to climate change, causing significant damage to ecosystems, property, and human lives. Effective detection systems are critical for prompt intervention and damage mitigation. This survey explores advances in fire and smoke detection, emphasizing the role of machine learning and computer vision techniques such as Convolutional Neural Networks (CNNs) and YOLO object detection models. Recent approaches leverage multi-source data, including satellite imagery, drone feeds, and ground sensors, to enhance detection accuracy and scalability. Key contributions include lightweight models such as FireNet for IoT applications, real-time smoke detection frameworks, and hybrid systems integrating traditional and AI-based techniques. Despite notable progress, challenges remain, such as false positives, environmental variability, and computational limitations in resource-constrained environments. This paper reviews these advancements, evaluates their limitations, and identifies promising research directions for developing robust and scalable wildfire detection systems. Key Words Wildfire Detection, Smoke Detection, Machine Learning, Deep Learning, CNN, YOLO, Disaster Management, IoT, Remote Sensing