Disaster Management and Mapping Using Satellite Images

Disaster Management and Mapping Using Satellite Images

ABSTRACT:

Disaster detection using satellite imagery has become an essential tool in disaster management, providing valuable insights into affected areas in real-time. This project focuses on the development of a deep learning-based system to classify regions into two categories: Damage and No Damage in the aftermath of natural disasters. Utilizing convolutional neural networks (CNN) and ResNet architectures, the system is designed to automatically analyze satellite images and identify areas affected by disasters, such as floods, earthquakes, and hurricanes. The backend of the system is built using Python, which handles the data processing, model training, and prediction tasks. The front-end, created with HTML, CSS, and JavaScript, provides an intuitive interface for users to upload satellite images and view the classification results. This framework allows for rapid response and effective decision-making during disaster management, aiding in resource allocation and relief efforts.

Keywords:
Disaster Detection, Satellite Images, Deep Learning, Convolutional Neural Networks (CNN), ResNet.