气候变化领域集成服务门户(CCPortal): A framework to enhance disaster debris estimation with AI and aerial photogrammetry

CCPortal

DOI	10.1016/j.ijdrr.2024.104468
	A framework to enhance disaster debris estimation with AI and aerial photogrammetry
	Cheng, Chih-Shen; Luo, Linchao; Murphy, Sean; Lee, Yu-Chen; Leite, Fernanda
发表日期	2024
ISSN	2212-4209
起始页码	107
卷号	107
英文摘要	This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.
英文关键词	Disaster response; Artificial intelligence; Photogrammetry; Unmanned aerial vehicle; Debris estimation
语种	英语
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS记录号	WOS:001234654800001
来源期刊	INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299170
作者单位	University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
推荐引用方式 GB/T 7714	Cheng, Chih-Shen,Luo, Linchao,Murphy, Sean,et al. A framework to enhance disaster debris estimation with AI and aerial photogrammetry[J],2024,107.
APA	Cheng, Chih-Shen,Luo, Linchao,Murphy, Sean,Lee, Yu-Chen,&Leite, Fernanda.(2024).A framework to enhance disaster debris estimation with AI and aerial photogrammetry.INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION,107.
MLA	Cheng, Chih-Shen,et al."A framework to enhance disaster debris estimation with AI and aerial photogrammetry".INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 107(2024).