Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review

被引:21
作者
Matin, Sahar S. [1 ]
Pradhan, Biswajeet [1 ,2 ,3 ]
机构
[1] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst, Ultimo, NSW, Australia
[2] Sejong Univ, Dept Energy & Mineral Resources Engn, Seoul, South Korea
[3] Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi, Selangor, Malaysia
关键词
Earthquakes; building damage mapping; remote sensing; deep learning; CONVOLUTIONAL NEURAL-NETWORK; COLLAPSED BUILDINGS; ROAD EXTRACTION; SATELLITE; CLASSIFICATION; SULAWESI; FEATURES; LIMITS; PALU; NET;
D O I
10.1080/10106049.2021.1933213
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页码:6186 / 6212
页数:27
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