Disaster Image

Disaster image analysis focuses on automatically extracting information from images of disaster scenes to improve emergency response and recovery efforts. Current research heavily utilizes convolutional neural networks (CNNs), often ensembled with other models like XGBoost, for tasks such as damage assessment and multi-label classification of disaster types. This field is significant because efficient analysis of large volumes of aerial and social media imagery can accelerate situational awareness, resource allocation, and ultimately, save lives. Furthermore, multimodal approaches integrating pre-disaster imagery and weather data are emerging to enhance prediction and response capabilities.