# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
###### Abstract
[10] developed a multi-class deep learning model with an attention mechanism to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disaster using the xView2 dataset [11]. [PERSON] et al. [12] developed a stacked convolutional neural network architecture to train on an in-house visual dataset from Hurricane Dorian that was collected using an unmanned aerial vehicle. An effective hurricane damage assessment model should train on both aerial and ground-level image data to increase adaptability for emergency damage assessment of a future coastal hazard. Social media has been explored as a primary source of data for hurricane damage assessment because of the swift integrability these platforms provide to automated damage assessments (e.g., [13, 14, 15]). [PERSON] and [PERSON] [16] used five machine learning classifiers that take social networking platform images and output the damage types and severity levels presented in images. Leveraging social media platforms to train damage assessment models has shown success with rapid operation capabilities. The transfer learning approach to developing artificial neural network models for hurricane damage assessment has also been recently explored. Most of these studies focus on using transfer learning on pre-trained convolutional neural network (CNN) models with aerial images of hurricane damage to buildings (e.g., [17, 18, 19, 20]). [PERSON] et al. [21] uses transfer learning on two well-established CNNs, AlexNet and VGGNet, to create classification models for the two-dimensional orthomosaic images gathered from unpiloted aerial systems. These and other similar studies limit the source of the training dataset, making the classification models useful only for functional datasets comprised of aerial images taken by satellite or drone. Our work incorporates both aerial and ground-level images for hurricane damage classification and detection of damaged buildings to create a more operational damage assessment framework to apply to future coastal hazards. Incorporating transfer learning for building damage assessment is affected by the transferability of the learned features and information from the source domain to the target domain used for testing the model. Domain adaption when using transfer learning arises when there are discrepancies among images in the source domain and between the source and target domains (e.g., [22,23]). These discrepancies are a result of how remote sensing captures images with varying sensors, locations, times, and perspectives. This issue with domain invariance extends to the transferability of information derived from different coastal hazards. A CNN-based model was shown to reach high classification performance when training on the same damage type for different disasters [24]. The source and target domains in our study do not present any major discrepancies. Rather, our damage classification and damage detection models focus on a single coastal hazard that causes multiple types of damage to enhance the efficacy of damage assessment. There are several challenges in the area of building damage assessment using artificial neural network models. First, machine learning training requires a considerable amount of input data in order to sufficiently assess the damage or classify the damage levels from images (e.g., [5; 6]). Second, in-house machine learning model development requires a significant amount of effort to achieve high accuracy. This study focuses on the building damages due to hurricanes in the U.S. southeast area, and we improve the efficiency of assessing hurricane damage to buildings by applying neural network models for damage classification and object detection. We address the first challenge by developing our in-house building damage dataset using internet search engines, and we address the second challenge by utilizing the advanced artificial intelligence models for computer vision, MobileNet [25], ResNet [26], and EfficientNet [27], through transfer learning.