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Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
Content Provider | MDPI |
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Author | Calton, Landon Wei, Zhangping |
Copyright Year | 2022 |
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 |
Starting Page | 1466 |
e-ISSN | 20763417 |
DOI | 10.3390/app12031466 |
Journal | Applied Sciences |
Issue Number | 3 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-01-29 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Remote Sensing Hurricane Building Damage Damage Classification Damage Detection Artificial Intelligence Transfer Learning |
Content Type | Text |
Resource Type | Article |