  Images shared on social media help crisis managers gain situational awareness
and assess incurred damages, among other response tasks. As the volume and
velocity of such content are typically high, real-time image classification has
become an urgent need for a faster disaster 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. To develop
robust real-time models, it is necessary to understand the capability of the
publicly available pre-trained models for these tasks, which remains to be
under-explored in the crisis informatics literature. In this study, we address
such limitations by investigating ten different network architectures for four
different tasks using the largest publicly available datasets for these tasks.
We also explore various data augmentation strategies, semi-supervised
techniques, and a multitask learning setup. In our extensive experiments, we
achieve promising results.
