# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
###### 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. h 2022 20  
Efficient relief plans and proper allocation of relief funding to the affected areas are impractical without accurate data. Traditionally, post-disaster data have been collected through methods involving individuals or teams making initial observations and assessments of damage. These people capture photographs of the damage in door-to-door assessments or windshield surveys (e.g., [3,4]). Remote validations are a supplemental tool used during the damage assessment process which increases the swiftness of the manual evaluations. These desktop assessments replace onsite validations when the risk for preliminary damage assessment staff is high and images of the damaged area are readily available [4]. However, these validations still rely on humans to identify damaged structures and verify damage assessments making them prone to a significant level of inaccuracy. In a myriad of classification tasks, artificial neural network technology has proven to be significantly more efficient in performing the same work as a human to a higher level of accuracy. Machine learning techniques possess particular advantages over humans in tasks that incorporate a large data set from multiple events of highly similar situations [2]. Hurricanes provide a multitude of events for data collection that can be used by artificial neural network models to perform damage assessment. There are usually two types of data capturing hurricane damage to buildings. The first type is satellite imagery (e.g., [5,6]), and the other type is ground-level images/photos (e.g., [7]) taken by drones or other similar ways. Both data types have been used for damage assessment. For example, [PERSON] and [PERSON] [8] used the Mask R-CNN [9] to predict both building locations and damage level based on pre-disaster and post-disaster images of xBD database [6]. Furthermore, [PERSON] et al.