Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks
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DOI: 10.1007/s11069-020-04133-2
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- 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.
- 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.
- 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.
- 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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Keywords
Image classification; Neural network; Damage assessment; Building; Remote sensing;All these keywords.
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