Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks

Quoc Dung Cao () and Youngjun Choe ()
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Quoc Dung Cao: University of Washington
Youngjun Choe: University of Washington

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 3, No 33, 3357-3376

Abstract: Abstract 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.

Keywords: Image classification; Neural network; Damage assessment; Building; Remote sensing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-04133-2

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