Wildfire Monitoring and Detection Using XGBoost Algorithm and Image Segmentation

Abstract

Highly sensitive to wildfires are ecosystems, human communities, and climatic balance as well as their respective environments. Inefficiencies, delays, and inadequate coverage define conventional approaches of detection include human observation and satellite imaging. This work proposes a novel artificial intelligence based aerial photography, image processing, and machine learning based wildfire detection system. The system uses edge computing to lower latency and the XGBoost algorithm for exact smoke classification in various environmental circumstances, therefore enabling real-time detection. High-resolution images from stationary cameras, satellites, and drones are analyzed using advanced feature extraction techniques in order to improve detection accuracy. Edge computing eliminates cloud infrastructure and speeds responses by supporting local processing. Early testing shows low false positive rates and good detection accuracy, therefore raising system performance confidence. The scalable suggested approach supports several image sources, therefore supporting extensive wildfire monitoring. Reducing response times and real-time warnings in the suggested solution dramatically improves wildfire management initiatives. It’s fit with artificial intelligence and modern computer technology fills in the void between traditional detection techniques and real-time automated solutions.