  Images shared on social media help crisis managers in terms of gaining
situational awareness and assessing incurred damages, among other response
tasks. As the volume and velocity of such content are really high, therefore,
real-time image classification became an urgent need in order to take a faster
response. Recent advances in computer vision and deep neural networks have
enabled the development of models for real-time image classification for a
number of tasks, including detecting crisis incidents, filtering irrelevant
images, classifying images into specific humanitarian categories, and assessing
the severity of the damage. For developing real-time robust models, it is
necessary to understand the capability of the publicly available pretrained
models for these tasks. In the current state-of-art of crisis informatics, it
is under-explored. In this study, we address such limitations. We investigate
ten different architectures for four different tasks using the largest publicly
available datasets for these tasks. We also explore the data augmentation,
semi-supervised techniques, and a multitask setup. In our extensive
experiments, we achieve promising results.
