Ai Powered Disaster Monitoring and Assessment Using Satellite Images

Abstract

AI Powered Disaster Monitoring and Assessment Using Satellite Images is an intelligent system developed to automate the detection and assessment of natural disasters, including infrastructure damage, volcanic eruptions, and wildfires. Traditional manual analysis of satellite imagery is often time-consuming and prone to inaccuracies, leading to delays in emergency response and resource allocation. To overcome these limitations, the proposed system employs Convolutional Neural Networks (CNNs) for accurate image classification and disaster identification. The system is developed using Python as the backend programming language and Flask as the web framework. TensorFlow and Keras are utilized for deep learning model development, while OpenCV and NumPy perform image preprocessing and numerical computations. Matplotlib is used for performance visualization, SQLite manages user authentication and data storage, and HTML, CSS, and JavaScript provide an interactive and user-friendly frontend interface. The trained CNN model classifies satellite images into multiple disaster categories with high accuracy, enabling rapid disaster assessment. The proposed solution assists emergency response teams, disaster management agencies, and government authorities in making informed decisions, optimizing resource allocation, reducing response time, and minimizing human and economic losses. The system is scalable, reliable, and suitable for real-time disaster monitoring applications.