## 1 Introduction  
It has been clear in the past few decades how natural disasters are a major source of significant damage and costly repairs around the world. In 2020 alone, more than USD 43 billion of damage resulted from the Atlantic hurricane season in North America [1]. In perspective, according to _Tite Wall Street Journal_, 31% of all hurricane damages from 1980 to 2018 occurred in 2017, with a total of USD 268 billion in damages [2]. This shows how the occurrence of natural disasters is constantly increasing, especially in the last decade. Taking this information into consideration, the need for the means to quickly detect damages and respond to these disasters has also significantly increased recently. Post-disaster damage detection is usually performed manually by human surveyors. Considering all the areas one has to closely look into, as well as the difficult terrain and places with hard access, it becomes easy to understand how incredibly difficult it is for a surveyor to identify and annotate every single possible amount of damage out there. It is also reasonably understandable to assume that the surveyor will miss some type of information in the field that could be essential. Hence, this method of damage identification has become obsolete, making it a slow process and an inefficient and inconsistent way of securing human resources. In this study, we propose to utilize an Unmanned Aerial Vehicle (UAV) with an attached standard color camera to capture footage of the post-disaster conditions of structures using computer vision techniques. In the literature, UAVs have been used frequently for damage detection and identification applications after natural disasters. In this study, we use a quadrotor UAV, as they possess higher mobility that allows the capturing of different points of view for the scene of interest. The main focus of our study is to perform proof-of-concept analysis for the Visual Question Answering (VQA) approach. The concept of VQA is depicted in Figure 1. For the \"Detection\" part, we utilize a Convolutional Neural Network (CNN), and for the \"Language Embedding\" part, we utilize a Bag of Words (BoW). In order to evaluate the feasibility and performance of VQAs, we also present damage detection approaches, using a Cascade Classifier (CC) that is described in Section 3, and the CNN (only CNN, without BoW) that is described in Section 4. Our goal is to analyze and provide the positive and negative aspects of each approach by comparing the results from each one. This includes a detailed analysis of each model and provides reasoning for using one model instead of the other when trying to perform the detection in specific scenarios. For that goal, post-disaster damage detection analysis is conducted on University of West Florida (UWF) campus aerial footage that is unique and gathered after Hurricane Sally (Sally-UWF), which was a slow-moving category two hurricane that made a landfall in 2020 around Pensacola, FL. Additionally, custom annotations and images created for roof damages, and three different classes demonstrating low, medium, and heavy damages, as well as the instance and frequency of the presence of roof damages, are introduced. Different approaches are studied throughout this paper in order to have a good comparison base when testing the actual feasibility of implementing the VQA model for damage detection. The original contributions of this study can be summarized as (i) the use of custom aerial footage data taken from the UWF campus after Hurricane Sally, (ii) the development of custom-annotated data for roof damage detection, (iii) the development of the VQA pipeline and implementation on aerial footage data in order to validate its feasibility for post-disaster damage detection, and (iv) performing a comparison between the VQA-, single CNN-, and single CC-based detection methods to highlight the feasibility of the VQA approach.