  This study uses the challenging and publicly available SpaceNet dataset to
establish a performance baseline for a state-of-the-art object detector in
satellite imagery. Specifically, we examine how various features of the data
affect building detection accuracy with respect to the Intersection over Union
metric. We demonstrate that the performance of the R-FCN detection algorithm on
imagery with a 1.5 meter ground sample distance and three spectral bands
increases by over 32% by using 13-bit data, as opposed to 8-bit data at the
same spatial and spectral resolution. We also establish accuracy trends with
respect to building size and scene density. Finally, we propose and evaluate
multiple methods for integrating additional spectral information into
off-the-shelf deep learning architectures. Interestingly, our methods are
robust to the choice of spectral bands and we note no significant performance
improvement when adding additional bands.
