  In times of emergency, crisis response agencies need to quickly and
accurately assess the situation on the ground in order to deploy relevant
services and resources. However, authorities often have to make decisions based
on limited information, as data on affected regions can be scarce until local
response services can provide first-hand reports. Fortunately, the widespread
availability of smartphones with high-quality cameras has made citizen
journalism through social media a valuable source of information for crisis
responders. However, analyzing the large volume of images posted by citizens
requires more time and effort than is typically available. To address this
issue, this paper proposes the use of state-of-the-art deep neural models for
automatic image classification/tagging, specifically by adapting
transformer-based architectures for crisis image classification (CrisisViT). We
leverage the new Incidents1M crisis image dataset to develop a range of new
transformer-based image classification models. Through experimentation over the
standard Crisis image benchmark dataset, we demonstrate that the CrisisViT
models significantly outperform previous approaches in emergency type, image
relevance, humanitarian category, and damage severity classification.
Additionally, we show that the new Incidents1M dataset can further augment the
CrisisViT models resulting in an additional 1.25% absolute accuracy gain.
