Citation: Calton, L.; Wei, Z. Using
Artificial Neural Network Models to
Assess Hurricane Damage through
Transfer Learning. Appl. Sci. 2022, 12,
1466. https://doi.org/10.3390/
app12031466
Academic Editors: Amerigo Capria,
Nikos D. Lagaros and Vagelis Plevris
Received: 23 December 2021
Accepted: 20 January 2022
Published: 29 January 2022
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Article
Using Artificial Neural Network Models to Assess Hurricane
Damage through Transfer Learning
Landon Calton
†
and Zhangping Wei *
Department of Physics & Physical Oceanography, University of North Carolina Wilmington,
Wilmington, NC 28403, USA; lcalton@ncsu.edu
* Correspondence: weiz@uncw.edu
† Current address: Department of Electrical and Computer Engineering, North Carolina State University,
Raleigh, NC 27606, USA.
Abstract:
Coastal hazard events such as hurricanes pose a significant threat to coastal communities.
Disaster relief is essential to mitigating damage from these catastrophes; therefore, accurate and
efficient damage assessment is key to evaluating the extent of damage inflicted on coastal cities and
structures. Historically, this process has been carried out by human task forces that manually take
post-disaster images and identify the damaged areas. While this method has been well established,
current digital tools used for computer vision tasks such as artificial intelligence and machine
learning put forth a more efficient and reliable method for assessing post-disaster damage. Using
transfer learning on three advanced neural networks, ResNet, MobileNet, and EfficientNet, we
applied techniques for damage classification and damaged object detection to our post-hurricane
image dataset comprised of damaged buildings from the coastal region of the southeastern United
States. Our dataset included 1000 images for the classification model with a binary classification
structure containing classes of floods and non-floods and 800 images for the object detection model
with four damaged object classes damaged roof, damaged wall, flood damage, and structural damage. Our
damage classification model achieved 76% overall accuracy for ResNet and 87% overall accuracy
for MobileNet. The F1 score for MobileNet was also 9% higher than the F1 score of ResNet at 0.88.
Our damaged object detection model achieved predominant predictions of the four damaged object
classes, with MobileNet attaining the highest overall confidence score of 97.58% in its predictions.
The object detection results highlight the model’s ability to successfully identify damaged areas of
buildings and structures from images in a time span of seconds, which is necessary for more efficient
damage assessment. Thus, we show that this level of accuracy for our damage assessment using
artificial intelligence is akin to the accuracy of manual damage assessments while also completing the
assessment in a drastically shorter time span.
Keywords:
hurricane; building damage; damage classification; damage detection; artificial
intelligence; transfer learning
1. Introduction
Coastal storms and hazard events are often analyzed to address dangers faced by
coastal communities around the world. Many potential threats to communities resid-
ing in coastal areas are captured with a comprehensive plan for risk analysis. In 2018,
a preliminary risk analysis estimated almost $17 billion in damages across the state of
North Carolina in the wake of Hurricane Florence [
1
]. As a result, accurate and efficient
evaluations of damage from coastal hazards such as hurricanes are necessary to provide
data for addressing post-disaster relief efforts. Damage assessment is a primary tool for
understanding the levels of damage to coastal populations in the aftermath of a hazard
event. Knowledge of damage is further applied to models for risk assessment to mitigate
damage from future hazards [2].
Appl. Sci. 2022, 12, 1466. https://doi.org/10.3390/app12031466 https://www.mdpi.com/journal/applsci
