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.
In this article, we delve into the use of deep neural models for automatic image classification and tagging in the context of crisis response. During emergencies, crisis response agencies often face a lack of timely and comprehensive information, hindering their ability to make informed decisions. However, citizen journalism through social media has emerged as a valuable source of data, particularly through the widespread use of smartphones with high-quality cameras.
The challenge lies in analyzing the large volume of images posted by citizens, which can be a time-consuming and resource-intensive task. To address this, the authors propose the use of state-of-the-art deep neural models, specifically transformer-based architectures, for crisis image classification. They develop and test a range of models using the Incidents1M crisis image dataset, showcasing the effectiveness of these models in various classification tasks such as emergency type, image relevance, humanitarian category, and damage severity.
The adoption of transformer-based architectures, such as CrisisViT, in crisis image classification signifies the multi-disciplinary nature of this concept. By leveraging advancements in deep learning and computer vision, these models enable automated analysis of crisis-related images, augmenting the capabilities of crisis response agencies.
From a broader perspective, this content aligns closely with the field of multimedia information systems. Multimedia refers to the integration of different forms of media like images, videos, and audio. The analysis of crisis-related images falls under this purview, contributing to the development of more comprehensive multimedia information systems for crisis response.
Furthermore, the article highlights the relevance of artificial reality technologies such as augmented reality (AR) and virtual reality (VR) in crisis response. These technologies enable users to immerse themselves in simulated crisis scenarios and gain valuable experience without being physically present. The accuracy and efficiency gained from improving crisis image classification can enhance the realism and effectiveness of AR and VR-based training programs for first responders and crisis management professionals.
Overall, this research showcases the power of deep neural models in automating crisis image analysis and classification. By leveraging transformer-based architectures and datasets like Incidents1M, significant improvements in accuracy and efficiency can be achieved. These advancements contribute to the wider field of multimedia information systems, as well as align closely with the applications of artificial reality technologies in crisis response.