# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 2 Building Damage Dataset
### Data Statistics  
The next step required dividing the dataset into a set for the classification model and a second set for the object detection model. The main difference among the two datasets was that the set applied to object detection required images only containing buildings, and the set applied to classification was independent from only using images containing buildings. Images contained in both sets of data are of varying pixel resolution and unaltered from the original source. Historical hurricanes usually brought about significant flood damage due to storm surges and heavy precipitation. Thus, the damage classification research in this work aims to determine if there are floods in the image. To this purpose, we selected 1000 images from our dataset and divided them into two categories, _ floods_ and _non-floods_, as indicated in Table 1. The motivation is to examine if neural network models can perform binary classification on our dataset. Flood damage is characterized by flood waters in the images, and it can occur in various ways. Typical _floods_ damages in our dataset include (1) flooded buildings, houses, and communities, (2) flooded streets, (3) flooded vehicles, and (4) flooded coastal areas. The _non-floods_ images are related to hurricane damage, but they do not include floods in them; these images needed to be characteristic of areas and buildings damaged from hurricanes because the purpose of our classification models is to exemplify their success learning from data that would be used for traditional hurricane damage assessment. Finally, the binary classification task does not require additional data processing other than sorting the images into two categories. Unlike the data preparation for damage classification, machine learning object detection requires the preparation of labeled data, which guides the model to learn common features in a specific type of object. The pre-processing image labeling in this work was accomplished by using the open-source annotation tool, LabelImg [28]. This tool allowed us to take an input image in our dataset and create bounding boxes around the areas of interest in the image corresponding to an annotation label. The position of the bounding box and the label were then exported for neural network model training. The object detection dataset consisted of 800 images that were annotated, and annotation labels are the damaged objects as listed in Table 2. Four categories of objects were identified from our hurricane damage dataset, and the features associated with each of them are briefly explained below. * _Damaged roof_. The bounding box label highlights a roof that has the whole roof, some shingles, or parts of the roof damaged. The bounding box label typically encompasses the entire roof in the image; however, if the entire roof is not visible then the damaged area and any additional parts of the roof that are visible were included. * _Damaged wall_. The labeling bounding box highlights a damaged building wall or windows within a wall. Damage to walls/windows could range from areas with minor disintegration of brick or glass structure to entire loss of the wall or window structure. * _Flood damage_. The bounding box label highlights flood waters in an image. The flood water can occur in various places as explained in the binary classification dataset. Due to this sporadic nature, in some images, multiple bounding box labels were used to encapsulate the entirety of the flood water. * _Structural damage_. The bounding box label highlights a building suffering from structural damage, e.g., the disintegration of the roof and/or any floor(s) within the building, complete loss of multiple walls/structures, or the collapse of the whole building. It should be pointed out that the total number of samples in Table 2 is 958, which is greater than the total number of annotated images, i.e., 800. The difference is due to the fact that multiple objects were annotated/observed in a single image, resulting in a larger number of objects than the number of images.