  Satellite imagery is important for many applications including disaster
response, law enforcement, and environmental monitoring. These applications
require the manual identification of objects and facilities in the imagery.
Because the geographic expanses to be covered are great and the analysts
available to conduct the searches are few, automation is required. Yet
traditional object detection and classification algorithms are too inaccurate
and unreliable to solve the problem. Deep learning is a family of machine
learning algorithms that have shown promise for the automation of such tasks.
It has achieved success in image understanding by means of convolutional neural
networks. In this paper we apply them to the problem of object and facility
recognition in high-resolution, multi-spectral satellite imagery. We describe a
deep learning system for classifying objects and facilities from the IARPA
Functional Map of the World (fMoW) dataset into 63 different classes. The
system consists of an ensemble of convolutional neural networks and additional
neural networks that integrate satellite metadata with image features. It is
implemented in Python using the Keras and TensorFlow deep learning libraries
and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of
writing the system is in 2nd place in the fMoW TopCoder competition. Its total
accuracy is 83%, the F1 score is 0.797, and it classifies 15 of the classes
with accuracies of 95% or better.
