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

Author

Listed:
  • Quoc Dung Cao

    (University of Washington)

  • Youngjun Choe

    (University of Washington)

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.

Suggested Citation

  • Quoc Dung Cao & Youngjun Choe, 2020. "Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3357-3376, September.
  • Handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04133-2
    DOI: 10.1007/s11069-020-04133-2
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    References listed on IDEAS

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    1. Jiazheng Lu & Yu Liu & Guoyong Zhang & Bo Li & Lifu He & Jing Luo, 2018. "Partition dynamic threshold monitoring technology of wildfires near overhead transmission lines by satellite," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1327-1340, December.
    2. Hamid Reza Ranjbar & Alireza A. Ardalan & Hamid Dehghani & Mohammad Reza Saradjian, 2018. "Using high-resolution satellite imagery to provide a relief priority map after earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1087-1113, February.
    3. Akansha Mehrotra & Krishna Singh & M. Nigam & Kirat Pal, 2015. "Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(1), pages 367-381, May.
    4. Metehan Ada & B. Taner San, 2018. "Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 237-263, January.
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