## 5 The Visual Question Answering Model  
The next approach to be analyzed in this study is the Visual Question Answering (VQA). VQA is a relatively more recent method in computer vision literature, and different from previously presented approaches, the VQA method needs to demonstrate a more - profound knowledge of images. It needs to be able to answer completely different questions based on an image. With that, the user does not have to predetermine anymore what the algorithm will be looking for in an image. Using this method, the user can just ask questions based on an image, and the algorithm runs an analysis based on the user's question and builds an answer upon that question, for instance, to find the object or person asked by the question. A VQA approach integrated with a UAV can be fundamental in the advancement of damage detection and assessment in the case of natural disasters, such as hurricanes. Since aiding damaged areas is an activity that is heavily dependent on real-time evaluation and estimation, the introduction of a VQA model can prove to be essential when dealing with high-risk situations. The VQA model is considered a complicated, multimodal research problem in which the aim is to address an image-specified question [32; 33; 34]. VQA can be considered a type of comprehensible approach that differentiates itself from other types of approaches, of which most are based on the identification of images. Since the VQA model needs to have a higher understanding of the attributes of an image, and it also needs to find all the relevant objects based on natural language questions, thus, it can prove to be an important aspect in the support for damage detection and identification after natural disasters. An example of the VQA approach is depicted in Figure 4.  
Considering what is essential after a hurricane passes by, some of the first questions that come to mind are \"Is there anyone in the area?\" or \"How many houses were destroyed?\" among other questions. Being able to answer these questions in real-time is one of the many benefits provided by the VQA approach. The same dataset (Sally-UWF) is used in the analysis of VQA. Overall, 1000 frames were selected to be analyzed. In total, 1000 training annotations and 30 training questions were used to compose this approach. Furthermore, two videos with high damage instance counts were selected and used for testing. For the annotations, this study based its approach on the VQA API, introduced on the visualqa website [35]. In this approach, a few sets of requirements need to be met in order to have a model working. First, there is the image step in which images are processed in order to train the model. Next, there is the question step, in which questions need to be input and paired with an image. Lastly, there is the annotation step, in which an answer is introduced and put together with both an image and a question. To facilitate this process, identification numbers (ID) are provided for all questions, answers, and images on the dataset. After all steps, the features, the image, and the question are combined, and the probabilities for each possible answer are assigned. Figure 4: Visual Question Answering basic application example. There are many different ways to utilize the VQA model. The one focused on in this study uses the CNN paired with the BoW. To be able to answer open-ended questions, it is necessary to combine both visual and language understanding. With that in mind, the most common approach to this problem is to use two parts, the first one is to analyze the images (which is the visual aspect), and the second part is to analyze the language (which is the question and answering aspect). The VQA model needs to be able to detect what is being displayed in an image, in order to effectively give an appropriate answer to what is being asked. The CNN part, which will be utilized for analyzing the image, is already discussed in Section 4.