DOI: 10.1007/s11069-020-04133-2
论文题名: Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks
作者: Cao Q.D. ; Choe Y.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 103, 期: 3 起始页码: 3357
结束页码: 3376
语种: 英语
中文关键词: Building
; Damage assessment
; Image classification
; Neural network
; Remote sensing
英文关键词: algorithm
; artificial neural network
; building
; hurricane event
; image classification
; satellite imagery
英文摘要: After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to create training, validation, and test datasets. Each square-sized image contains a building to be classified as either ‘Flooded/Damaged’ (labeled by volunteers in a crowd-sourcing project) or ‘Undamaged’. We design and train a convolutional neural network from scratch and compare it with an existing neural network used widely for common object classification. We demonstrate the promise of our damage annotation model (over 97% accuracy) in the case study of building damage assessment in the Greater Houston area affected by 2017 Hurricane Harvey. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168417
Appears in Collections: 气候变化与战略
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作者单位: Department of Industrial and Systems Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, United States
Recommended Citation:
Cao Q.D.,Choe Y.. Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks[J]. Natural Hazards,2020-01-01,103(3)