Disaster Image Classification Using Capsule Networks

When a disaster happens, affected individuals may use social media platforms, such as Twitter or Facebook, to ask for help or post information about the disaster. From a disaster response point of view, it is important to filter posts, in particular, text and images that provide situational awareness information, in a timely manner. For image classification, capsule networks have shown superiority over convolutional neural networks (CNN). Given their success in other application domains, in this study, we used capsule networks to classify disaster images as Informative or Non-informative. Using publicly available images collected from several disasters, we compared capsule network models with ResNet-18 models, for both in-domain and cross-domain settings. The results showed that the capsule network models had better performance for all the disaster datasets considered in the in-domain experiments, and also for most of the cross-domain pairs of disasters used in the study.