Disaster Damage Assessment and Response Framework

Abstract

In recent years, the frequency and impact of natural disasters have escalated, underscoring the need for efficient damage assessment and response frameworks. This paper proposes a novel approach that combines advanced image processing techniques, machine learning algorithms, and real-time data aggregation to create a comprehensive disaster damage assessment and response system. Using input from camera-based imagery and multi-source data streams, our framework rapidly identifies damage levels, enabling swift decision-making. This system aims to optimize resource allocation, support first responders, and improve situational awareness during crisis events. The proposed framework is evaluated on various disaster scenarios to assess its effectiveness in enhancing response efficiency and accuracy.