AI Mapping for Rapid Disaster Assessment

The Disaster poses significant threats to human life, infrastructure, and socioeconomic stability. Using deep learning-based image analysis techniques, we provide a novel strategy in this research to improve catastrophe management. Three major components comprise our methodology: date: Evaluating the extent of building damage involves assessing the type of disaster and determining the disaster occurrence. Our approach, which makes use of Convolutional Neural Networks (CNNs), can reliably identify catastrophic events from pictures, allowing for quick mitigation and response actions. Further supporting customized response plans and resource allocation is Mobile Net V2's ability to precisely classify different sorts of disasters. To prioritize rescue efforts and infrastructure restoration, we also used U-Net architecture for building damage level assessment. Through the integration of various models into a cohesive system, we provide a thorough approach to catastrophe management, equipping stakeholders with useful information for effective coordination of responses. By highlighting the effectiveness of deep learning in tackling difficult social issues and boosting resilience in the event of calamities, our research advances plans for disaster planning and response.