• Divya R
• Mr. Rakesh Sharma

Lightning remains a significant atmospheric hazard, posing critical threats to human safety, infrastructure, and environmental stability. Despite advances in meteorological monitoring, the need for highly accurate, real-time lightning detection systems continues to grow, especially in relation to climate variability and urban expansion. This study focuses on the creation and implementation of a comprehensive lightning detection framework aimed at improving public safety, environmental surveillance, and emergency response efficacy. The suggested system integrates high-resolution imaging sensors with a centralized data processing architecture capable of detecting and localizing lightning strikes with high spatial and temporal precision. A core component of the framework includes Utilizing machine learning methods, for analysis of image data, enabling automated identification and classification of lightning events. This data-driven approach improves detection accuracy and minimizes false alarms, offering a robust solution for real-time monitoring. Furthermore, the system incorporates a user-centric interface designed to facilitate intuitive access to alerts, visualizations, and historical data. The interface is tailored for both operational responders and researchers, promoting efficient dissemination of actionable information. The study adds to the larger field of atmospheric sensing by proposing a scalable, intelligent solution with potential applications in disaster preparedness, smart city infrastructure, and climatological research.

CNN.

"ADVANCED LIGHTNING DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.965-970, July-2025, Available :http://www.jetir.org/papers/JETIRGX06179.pdf

"ADVANCED LIGHTNING DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp965-970, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06179.pdf