## 2 Background  
Research in computer vision and artificial intelligence focused on remote sensing in disaster management has produced a vast corpus of tools and algorithms [11] for providing the analysis and interpretation of data needed by emergency responders. Models and algorithms to infer damage or change have become increasingly popular in aiding decision support as the technology has grown to support such computation. These models and algorithms are now able to accept a range of visual inputs including satellite, drone, plane, and street view images, and can even fuse social media images for their input sources. Processing of the data can be handled with methods ranging from specific rule-based engines, to unsupervised learning techniques, to supervised machine learning techniques, and, more recently, to deep learning techniques. The output of these methods can be utilized to provide the information needed for disaster management decision support. Identifying damage to buildings automatically from satellite images is a particularly rich field of research in remote sensing [4; 8; 12; 13; 14]. In the work of [PERSON] et al. [14], a U-net based algorithm is used to accept pre- and post-disaster satellite images where the output is a damage detection segmentation mask. This mask provides a pixel-level classification of where buildings were either washed away, collapsed, or still standing after a Tsunami. As washed away and collapsed cases both no longer exist,  
Figure 1: Pipeline for classifying damage magnitude to buildings from satellite images. this model can classify undamaged and destroyed buildings for a single disaster type with 70.9% accuracy. [PERSON] et al. (2020) used post-disaster images of buildings after a hurricane already extracted from satellite images to classify the images as damaged or undamaged using a Lenet-5-inspired CNN. They were able to achieve a 97% accuracy, but a higher accuracy is to be expected if part of the pipeline does not require identifying buildings. [PERSON] et al. (2019) is a multi-disaster example using both flood and fire-based disasters. They used CNNs to create segmentation masks where buildings stand in pre- and post-disaster satellite images. They then took the delta of the masks to establish a change mask. Finally, they took the ratio of changed vs. unchanged pixels to establish a quantitative change ration. While this algorithm can create a true quantitative value between zero to one, it indicates changes to an area and not to specific buildings. [PERSON] et al. (2020) is one of the few examples that did not use a neural network. Instead, they used a visual bag-of-words method to create a feature-based representation of small regions in the before- and after-disaster satellite images, concatenated them, and used an SVM to make a prediction of damaged or not damaged for the small region based on the concatenated feature representations. They were able to achieve a 91.7% accuracy, but like [PERSON] et al. (2020), their work assumed knowing where buildings are. The small regions selected are not guaranteed to contain buildings at all. Lastly, [PERSON] et al. (2020) used a CNN-based architecture to detect damaged and undamaged buildings due to earthquakes. In their method, they segmented out buildings from pre- and post-disaster images and passed them into their algorithm. The algorithm extracted features of the pre- and post-disaster images, computed the delta between them, and predicted damaged or not damaged based on the delta with an AUC of 0.8302. As mentioned in the introduction, the large strides made in deep learning-based computer vision models have made them a prime candidate for utilization in remote sensing and disaster management. Deep learning methods have been the top performers in many vision recognition tasks ([PERSON] et al., 2015; [PERSON] et al., 2015).