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      Utilizing Post-Hurricane Satellite Imagery to Identify Flooding Damage with Convolutional Neural Networks

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          Abstract

          Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead, in this paper we furthered the idea of implementing deep learning through convolutional neural networks in order to classify post-hurricane satellite imagery of buildings as Flooded/Damaged or Undamaged. The experimentation was conducted employing a dataset containing post-hurricane satellite imagery from the Greater Houston area after Hurricane Harvey in 2017. This paper implemented three convolutional neural network model architectures paired with additional model considerations in order to achieve high accuracies (over 99%), reinforcing the effective use of machine learning in post-hurricane disaster assessment.

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          Author and article information

          Journal
          05 September 2022
          Article
          2209.02124
          ac9c8c7e-7946-43d9-8c32-3f7cf154b97f

          http://creativecommons.org/licenses/by/4.0/

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          Custom metadata
          18 pages without figures/references, 12 figures. Patrick Emedom-Nnamdi is the Editor
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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