Next-Gen Forest Fire Management: Integrating Deep Learning with GIS and Earth Observation for Early Warning Systems

Change in Climate, environmental mismanagement and human invasion, caused wildfires to transform from unusual natural disturbances into devastating worldwide dangers. Innovative, data-driven solutions are required for early detection, prediction, and proactive mitigation of wildfires, as traditional methods that rely on manual observation and static risk indicators are becoming less and less effective. The split data, slow response times, and poor forecasting accuracy of current wildfire control systems make it unable to handle the growing size and unpredictability of contemporary wildfires. This study presents a next-generation forest fire management approach that combines Earth Observation (EO) and Geographic Information Systems (GIS) data with deep learning (XGBoost, Deep Neural Networks). The approach creates dynamic risk assessments by integrating multi-source datasets, such as terrain, fuel, climate, and real-time IoT sensor inputs, within an explainable AI (XAI) framework (SHAP, LIME). Drone-based remote sensing and high-resolution satellite imagery, enable continuous monitoring, while ensemble models improve prediction reliability through historical and real-time data calibration. The proposed system demonstrates enhanced accuracy in early fire detection, dynamic risk mapping, and predictive analytics, enabling timely interventions. The integration of AI with geospatial technologies also supports long-term resilience planning, including post-fire recovery and adaptive land-use strategies. By bridging advanced computational intelligence with geospatial and Earth Observation technologies, this research advances the transition from reactive firefighting to proactive, intelligent wildfire management. The framework offers a scalable, real-time early warning system, contributing to global efforts in mitigating wildfire risks amid escalating climate challenges.