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.
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.
R
in terms of gaining
gain
R
assessing
assess
R
really high, therefore,
typically high,
R
became
has become
R
in order to take a faster
for a faster disaster
R
For developing real-time robust
To develop robust real-time
R
pretrained
pre-trained
R
. In the current state-of-art of crisis informatics , it is
, which remains to be
A
in the crisis informatics literature
R
. We investigate ten different
by investigating ten different network
R
the data augmentation
various data augmentation strategies
A
learning