## 5 Experiments
### High-Level Experiments  
As mentioned in our methods section, we selected AlexNet, VGG, DenseNet, GoogLeNet, and ResNet as the algorithms to evaluate our semantic classification model. These are all recent algorithms [23] that can be adapted to work well in computer vision satellite object classification tasks and are readily available in Pytorch [24]. They also fully take advantage of developments in deep learning as described in our related works section and can be used to fill the current gaps that exist in the literature for inferring damage magnitude from satellite images. All of these models also come with the option to apply pretrained weights to them from models carefully trained on the ImageNet [25] dataset. As mentioned in our methods section above, this enables us to take advantage of transfer learning techniques from models that were carefully crafted on millions of precisely annotated images, including images of buildings [25].  
The data we utilized were a small random subset from the xDB [7] dataset. We chose random samples from each of the 17 different disasters uniformly. In all, we used 1000 training images, 400 validation images and 400 testing images. Each set had an equal number of images from each of the no damage, some damage, significant damage, and destroyed classification labels. Therefore, in our high-level training, no class weighting was required, and we could iterate through experiments quickly.  
For the models, some parameters remained static. This choice was made for several parameters to either provide an equal comparison between models, limit the amount of time required for training and inference, or by convention. All models used cross-entropy to measure their loss and stochastic gradient descent to converge to a global minimum. Setting these metrics across all models allowed us to equally compare their outcomes and is also a convention in computer vision. All models were trained over 50 epochs with a 32-image batch size. This allowed us to create models that performed better than prior models in the literature [7] but could still be run quickly enough that we could execute many tests successively.  
Of the parameters we changed, we spent the most time studying the impact that different image augmentation and learning rate techniques had on the model training process. These were parameters that we could quickly change and had a visible impact we could measure in the training loss over the first few epochs of training. We did not have to wait for all the training to complete to see if the change made a positive impact in training performance. We could not find works that included extensive use of these parameters in the literature, yet found the parameters to be the most significant ones outside of transfer learning for developing strong predictive models for semantic inference of damage magnitude in satellite imaging. In our experiments, we utilized decaying learning rates. This allowed us to decrease the step in the stochastic gradient descent procedure accordingly as we trained to avoid overshooting a global minimum and not converging. We started with a learning rate between 0.01 and 0.001 and divided the learning rate by 10 somewhere between one and four times over an entire training sequence. We adjusted the image augmentation procedure as outlined in our methods section.