## 8 Conclusions and Future Research Directions  
In this work, we have developed 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. This is novel compared to other models in the literature as they do not provide any measure for the degree of damage magnitude to buildings from natural disasters. They only provide labels to tell whether a building still exists after a disaster.  
Of equal importance, we outlined parameters that have a large impact in model training, particularly when using deep learning-based techniques in computer vision to generate models for inferring damage from satellite images. We found image augmentation, decaying learning rates, and transfer learning to be of high importance. This does not discount the importance of other often neglected parameters such as initial deep learning algorithm selection.  
We believe that this work will pave the way for continued research in the automated assessment of damage magnitude with deep learning-based computer vision techniques. These techniques are perfectly poised to alleviate the barrier of entry imposed by other techniques requiring specialty knowledge or tools. They also alleviate many of the complicated steps required by other methods to expedite the creation of models and simplify design. In this way, one can create the solutions that will best aid the quick decision making and planning required in disaster management.  
Now that we have proven we can predict the magnitude of damage to buildings with a moderate to high degree of certainty, there are several logical next steps. The first step would be to change our metric from the F1 score to the mean average precision (maP). We used F1 score for this work because it is the metric commonly used in the literature, and we wanted to use a metric that would provide a direct comparison between our work and others. However, in semantic segmentation-based models like ours, the maP is a significantly more common metric, and one that provides a better measurement of performance. In addition to measuring how well we can label buildings, the maP also measures how well we are able to locate the buildings. This will provide more meaningful information for determining the model that performs best as we are performing our training cycles.  
Another opportunity exists in additional data. The dataset [21] only contains a subset of the satellite images from the original source [22]. Therefore, opportunities to extract additional training data potentially exist from these sets so we can make a stronger model. The authors of the dataset have also released an additional dataset since our experiments that we could also use for additional training.  
Lastly, more complex models may prove beneficial now that we have developed a baseline model. The baseline model performs classification of damage magnitude based on the after-disaster image. More advanced algorithms exist that can provide sequential comparisons of images to learn better representations of change between images. Work with Siamese networks to directly enter and compare pre- and post-disaster images in the same network would be a logic first step in this direction.  
Conceptualization, B.J.W.; methodology, B.J.W.; software, B.J.W.; validation, B.J.W.; formal analysis, H.A.K.; writing--original draft preparation, B.J.W.; writing--review and editing, B.J.W. and H.A.K.; visualization, B.J.W.; supervision, H.A.K.; project administration, B.J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.