  Reliable and frequent population estimation is key for making policies around
vaccination and planning infrastructure delivery. Since censuses lack the
spatio-temporal resolution required for these tasks, census-independent
approaches, using remote sensing and microcensus data, have become popular. We
estimate intercensal population count in two pilot districts in Mozambique. To
encourage sustainability, we assess the feasibility of using publicly available
datasets to estimate population. We also explore transfer learning with
existing annotated datasets for predicting building footprints, and training
with additional `dot' annotations from regions of interest to enhance these
estimations. We observe that population predictions improve when using
footprint area estimated with this approach versus only publicly available
features.
