Disaster Detection Based on Synthetic Aperture Radar (SAR) Images

Natural disasters such as floods, landslides, and earthquakes pose serious risks to human lives and infrastructure and the environment and thus require effective and efficient detection systems to manage and mitigate disasters. Traditional approaches to disaster monitoring are often affected by adverse weather conditions, the lack of real-time data, and the complexity of the terrain in the affected region. Synthetic Aperture Radar (SAR) has emerged as an efficient remote sensing tool because it can operate in any weather condition, both day and night, and thus is extremely useful in the detection of disasters. Experimental analysis clearly indicates the efficiency and potency of the proposed model in achieving high accuracy in the detection of disasters. The system has immense potential in the real-time monitoring of disasters, such that the concerned authorities may make optimal use of the early warning system, resources, and strategies for disaster response. This proposed work contributes significantly to the development of AI-driven remote sensing technology, offering an efficient and scalable solution for multi-disaster detection and management. The proposed methodology will involve data acquisition from SAR satellites such as Sentinel-1 and Terra SAR- X, preprocessing techniques such as speckle noise reduction and feature extraction, and CNN-based classification techniques for disaster detection