Utilizing Robotic System for Disaster Scene Classification - A Deep Learning Approach

Disaster scene classification plays a vital role in emergency management by facilitating rapid assessment and response to scenarios such as floods, earthquakes, and wildfires. Traditional image classification methods face challenges due to the complexity and variability of disaster scenes, which often include irregular patterns and diverse environmental factors. In recent years, deep learning, particularly convolutional neural networks (CNNs), has demonstrated significant potential in improving the accuracy of disaster scene classification. This project integrates a CNN-based approach with a remote-controlled robot equipped with real-time image-capturing capabilities. The robot navigates disaster zones, capturing images that are processed using CNN architecture like VGG16 and VGG19 to classify disaster scenes efficiently. The robotic system enhances situational awareness by autonomously collecting vital information in hazardous environments, transmitting real-time data for classification, and providing timely insights for emergency response. This integration of robotics with deep learning not only automates disaster scene classification but also reduces reliance on large, labeled datasets, improving performance and response effectiveness.