  We propose a novel convolutional neural network architecture for estimating
geospatial functions such as population density, land cover, or land use. In
our approach, we combine overhead and ground-level images in an end-to-end
trainable neural network, which uses kernel regression and density estimation
to convert features extracted from the ground-level images into a dense feature
map. The output of this network is a dense estimate of the geospatial function
in the form of a pixel-level labeling of the overhead image. To evaluate our
approach, we created a large dataset of overhead and ground-level images from a
major urban area with three sets of labels: land use, building function, and
building age. We find that our approach is more accurate for all tasks, in some
cases dramatically so.
