Abstract

Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain.

摘要

评估建筑物损坏的程度和水平对于支持地震后的救援和救济活动至关重要。大量文献提出了使用高分辨率遥感影像来自动化地震引起的建筑物破坏图的新颖框架。但是，其在实际场景中的部署在很大程度上仅限于图像的手动解释。尽管人工解释成本高昂且费力，但由于其可靠性，它比自动和半自动建筑物损坏映射框架（例如机器学习和深度学习）更受青睐。因此，本文旨在探索各种自动和半自动建筑物损坏映射技术，以期了解不同方法的利弊，以缩小研究与实践之间的差距。更多，确定了现实情况的未来发展中的研究差距和机会，地震引起的建筑物破坏图。该评论可以作为应急管理服务领域的研究人员，决策者和从业人员的指南。