  In the last several years, remote sensing technology has opened up the
possibility of performing large scale building detection from satellite
imagery. Our work is some of the first to create population density maps from
building detection on a large scale. The scale of our work on population
density estimation via high resolution satellite images raises many issues,
that we will address in this paper. The first was data acquisition. Labeling
buildings from satellite images is a hard problem, one where we found our
labelers to only be about 85% accurate at. There is a tradeoff of quantity vs.
quality of labels, so we designed two separate policies for labels meant for
training sets and those meant for test sets, since our requirements of the two
set types are quite different. We also trained weakly supervised footprint
detection models with the classification labels, and semi-supervised approaches
with a small number of pixel-level labels, which are very expensive to procure.
