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

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