Image classification to Support emergency Situation Awareness

Recent advances in image classification methods, along with the availability of associated tools, have seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of Emergency Situation Awareness. We discuss image classification based on low-level features as well as methods built on top of pretrained classifiers. The performance of the classifiers is assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW, where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire-related classes with an accuracy of 86%.