ImageB4Act to Optimize Contingencies and Crisis Relief Operations: Insights from Imagery Data and Computer Vision Approaches to Regenerate Dataset through AI-Powered Analysis
Keywords:
Computer Vision, Artificial Intelligence, AI-technologies, Machine learning, Emergency management, natural catastrophes, ImageB4Act dataset, Humanitarian tasksAbstract
Natural disasters and emergencies constitute significant sources of both human suffering and economic losses. The need for effective emergency management has never been more critical. In recent years, there has been a noteworthy shift towards the usage of social media channels by individuals to disseminate immediate updates in both natural and manufactured catastrophes. The internet-generated data has proven to be immensely valuable for humanitarian organizations, enabling them to swiftly comprehend the evolving situation and efficiently coordinate relief efforts. In this work, highlights the findings of numerous studies that emphasize the significance of rapid and efficient processing of this online data for the benefit of humanitarian endeavors. This work provides an overview of the works carried out through computer vision in terms of given objectives: identified studies on required dataset for further research in emergency management, recent studies on various approaches to disaster management, further majorly aimed to process ImageB4Act dataset that helps to perform object detection on post disaster aerial images in order support humanitarian tasks during disaster management.
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