## 1 Introduction  
Emergency response teams draw from a large range of technical tools for the management of natural disasters [1]. Among them, the use of satellite imagery has proven highly beneficial for collecting the data required for driving detailed disaster management planning [2; 3; 4]. However, these satellites can gather petabytes of data [5], which is significantly more information than humans can analyze manually. Furthermore, manually analyzing such large volumes of data requires subject matter experts to derive meaning from the data [6]. For this, advanced automated methods, mainly in computer vision, are under development.  
To alleviate the massive workload placed on humans for processing large amounts of satellite data, computer vision has been employed to automate the detection of objects and conditions vital to disaster management. However, despite this ability, the problem with current automated solutions is that they are unable to detect all magnitudes of damage to buildings with a high degree of confidence [7]. Therefore, while a high level of automation is possible, the current methods provide solutions at a high level of uncertainty. Furthermore, more can be done to take advantage of methods available prior to and during training to develop better performing models for computer vision satellite imagery tasks.  
To fill this gap (i.e., having a model that can confidently detect all magnitudes of damage to buildings and appropriate methods prior to and during training to develop the best performing model), we propose a deep learning-based computer vision model for detecting the magnitude of damage to buildings from pre- and post-disaster images. Figure 1 shows a pipeline with the model at its core. The pipeline takes pre- and post-disaster images as input, semantically segments the buildings from the images, classifies the magnitude of damage done to each building, and provides a damage magnitude mask color-coded to label the damage detected to each building. This is novel in that current approaches reported in the literature only address the existence or the non-existence of buildings after a natural disaster or only analyze damage for a single disaster type [8; 9].  
Advances in deep learning have driven computer vision to new levels of performance. The creation of AlexNet, for example, noted a marked change in how computer vision tasks were approached [10], and also noted a marked change where a deep learning-based computer vision model outperformed humans on benchmark image classification tasks. For both reasons, in this paper, we propose using a convolutional neural network (CNN). CNNs also afford us many methods to apply before and during training. In our approach, we will use transfer learning, image augmentation, decaying learning rates, and class weighting. We analyze the impact of each of these methods to determine the best performing model through a series of ablation studies and experimentation.  
The contribution of our paper is a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters from pre-and post-disaster satellite images.  
The rest of this paper will be structured as follows. We will provide a background of the problem and some related works (Section 2). We will review the dataset we will be using for our work (Section 3). We will describe the methods used for performing the work (Section 4). We will describe the experiments we performed (Section 5). We will review the results we received from our experiments (Section 6). We will discuss the results and provide analysis (Section 7). We will conclude our work and discuss some future research directions (Section 8).