## 2 Background
Deep learning-based methods have been shown to outperform ([PERSON] et al., 2015; [PERSON] et al., 2015; [PERSON] et al., 2015) other methods in the remote sensing and disaster management space as well, including rule-based/ontological methods ([PERSON] et al., 2015), clustering/bag-of-words methods ([PERSON] et al., 2015), and machine learning-based methods ([PERSON] et al., 2015). Many of the current deep learning-based methods in the literature ([PERSON] et al., 2015; [PERSON] et al., 2015; [PERSON] et al., 2015) operate on variations in CNNs. This enables them to take advantage of many powerful tools available in deep learning including image segmentation, transfer learning, class weighting, and decaying learning rates. Therefore, as part of our work, we will explore primary methods for improving the performance of and tuning such methods. A significant advantage of CNNs is their ability to handle multi-class classification within a single model. This is another distinguishing feature between them and other methods that have been utilized in remote sensing and disaster management. Despite this, multi-class classification models for inferring damage magnitude are currently non-existent or, at best, limited in what they offer. ([PERSON] et al., 2015) conceptualize a framework that would incorporate major damage, moderate damage, minor damage, no damage, washed away, and collapsed semantic labels. However, they only experimented with washed away, collapsed, and survived building classifications. While this is more than a binary comparison, it does not provide a measure of magnitude. The classes can simply be inferred by where a building was (label 1), where nothing is now (label 2), where a pile of rubble exist now (label 3), or where a building remains. The framework lacks the ability to determine the degree of damage to buildings that they conceptually outlined in the beginning of the paper. They also use accuracy as their measurement, which has been shown to be an inappropriate performance metric ([PERSON] et al., 2015) as class sizes are so imbalanced in most disaster assessment cases and artificially inflate the accuracy percentage. ([PERSON] et al., 2015) provide multi-class labels for damage magnitude (i.e., no damage, some damage, significant damage, and destroyed) and correctly choose the F1 score as their metric. However, the model is put forth as a proof of concept to complement the data model they present and only identifies no damage and destroyed cases with a significant degree of certainty. To develop a model that could potentially be used in decision support, disaster managers require a model that can both provide multiple degrees of magnitude and do so with a moderate to high degree of certainty. This is a difficult task as degrees of magnitude between undamaged and destroyedbuildings are subjective, and cases that border between two degrees of magnitude can mislead a model and complicate training [7]. The model we will develop will be trained on a satellite image dataset [21] with ground truth labels for no damage, some damage, moderate damage, and destroyed buildings. We will then use this model in a framework to provide these degrees of magnitude for buildings in a separate testing dataset with a moderate to high degree of certainty.