Enhancing post-hurricane regional loss assessment with crowdsourcing: A probabilistic inference approach

Abstract

With the emergence of efficient, widespread social networking infrastructure, along with advances in sensing technology, data retrieval, and data processing techniques, crowdsourcing and participatory sensing have garnered attention as promising approaches for leveraging the general public’s decision-making capacity for more effective and scalable disaster management practices. Although these approaches have recently been acknowledged for their potential in technical and domain-specific tasks, such as post-disaster damage assessment, there is still room for exploration and enhancement. This paper represents a significant stride in this direction by introducing a probabilistic approach designed to improve the reliability and quality of data gathered through participatory disaster damage assessments. By rigorously addressing crucial challenges related to task design and implementing sophisticated truth-inference mechanisms, this study aims to minimize uncertainty and subjectivity in the assessment of building damage states from imagery data conducted by non-expert crowd participants. The proposed methodology is demonstrated using a post-disaster damage database from Hurricane Laura, with Amazon Mechanical Turk (M-Turk) serving as the online crowdsourcing platform. With an achieved accuracy of approximately 50% and a ±1-class accuracy of around 95%, the proposed methodology significantly outperforms other inference methods derived from crowd-assigned labels in similar applications. Furthermore, damage assessment is extended to loss estimation, providing a comprehensive view of end-to-end damage and loss assessment. The findings of this study can significantly contribute to the development of efficient and reliable strategies for post-disaster assessments, ultimately facilitating expedited response and recovery efforts in areas affected by disasters.