  In recent years, geotagged social media has become popular as a novel source
for geographic knowledge discovery. Ground-level images and videos provide a
different perspective than overhead imagery and can be applied to a range of
applications such as land use mapping, activity detection, pollution mapping,
etc. The sparse and uneven distribution of this data presents a problem,
however, for generating dense maps. We therefore investigate the problem of
spatially interpolating the high-dimensional features extracted from sparse
social media to enable dense labeling using standard classifiers. Further, we
show how prior knowledge about region boundaries can be used to improve the
interpolation through spatial morphing kernel regression. We show that an
interpolate-then-classify framework can produce dense maps from sparse
observations but that care must be taken in choosing the interpolation method.
We also show that the spatial morphing kernel improves the results.
