  Twenty-three machine learning algorithms were trained then scored to
establish baseline comparison metrics and to select an image classification
algorithm worthy of embedding into mission-critical satellite imaging systems.
The Overhead-MNIST dataset is a collection of satellite images similar in style
to the ubiquitous MNIST hand-written digits found in the machine learning
literature. The CatBoost classifier, Light Gradient Boosting Machine, and
Extreme Gradient Boosting models produced the highest accuracies, Areas Under
the Curve (AUC), and F1 scores in a PyCaret general comparison. Separate
evaluations showed that a deep convolutional architecture was the most
promising. We present results for the overall best performing algorithm as a
baseline for edge deployability and future performance improvement: a
convolutional neural network (CNN) scoring 0.965 categorical accuracy on unseen
test data.
