## 3 A Cascade Classifier Model Using Haar Features  
The first approach that is analyzed in this paper is the Cascade Classifier (CC). Proposed by [PERSON] and [PERSON] in 2001 [6], the Cascade Classifier is a machine-learning method that uses positive and negative images to train its model and obtain the final detection. Positive images are considered to be all the images containing the object the model is searching for, while negative images are considered to be all the images that do not contain the object that the model is searching for. The algorithm for the Cascade Classifier requires a significant number of positive images and negative images, and separates them into two files. It then extracts the features from each file; for this paper, the Haar features are used in order to detect and extract the features from the images. The Haar features are a sequence of rescaled square-shape functions first introduced in the literature by [PERSON] in 1909 [6]. The next step in this approach is to define a threshold. In a perfect scenario, when detecting an object of interest in an image, the algorithm would return a perfect match. However, since it is very unlikely that the model will find the final result with 100% certainty every time the object is in the image, a threshold is determined. For example, setting the threshold to 0.6 (60%) would mean that every time the algorithm returns a value equal to or greater than 0.6, the model would consider that the object has been detected. On the other hand, if it returns a value below 0.6, that means the model has not detected the object it has been looking for. Setting the threshold too low could lead to many false positives, while setting the threshold too high could lead to false negatives. After identifying all the features, the algorithm starts a process that applies the features to all the collected images, then the algorithm then classifies each image into positive or negative, with the positive ones being the ones that contain the \"object\" and negative ones being the ones that do not. The more data used, the better the prediction will be, and therefore, the final results will be more accurate. In our study, it is expected that the majority of areas in an image will not have visible damages. Therefore, the Cascade Classifier is modified to speed up the process of detection, as well as to locate the detected damage inside a positive image. Thus, instead of applying all the features to an image, the features are grouped into different stages of the classifier and applied individually. If an image fails the first \"layer\" of features, that image is discarded. If an image passes the first stage, it means that it is possible that the image being analyzed potentially has the damage the algorithm is trying to find. Therefore, the second stage is applied to it, followed by the third stage, and so on, until the algorithm can detect, with certainty, if there is damage on that image or not. For the training of our model, it is necessary to first separate the training data into positive and negative images. The Sally-UWF dataset consists of aerial footage that was recorded following the hurricane using an UAV with an attached camera. The videos were captured at 3840 \(\times\) 2160 p with 24 frames per second. All frames have been downsized to 1920 \(\times\) 1080 p for analysis in this study. In total, 24 videos ranging from 15 s to 5 min were captured. From this collection, two videos with high damage instance counts were selected and used for testing. Some examples of the positive images obtained from the Sally-UWF dataset are depicted in Figure 2.  
Contrary to the positive images, the negative images are all the images that do not contain any type of damage. The same Sally-UWF dataset was used to separate and identify the negative images. Some examples of the negative images obtained from the Sally-UWF dataset are depicted in Figure 3.  
In total, 1000 negative images and 1100 positive images were used.