  Knowledge of population distribution is critical for building infrastructure,
distributing resources, and monitoring the progress of sustainable development
goals. Although censuses can provide this information, they are typically
conducted every ten years with some countries having forgone the process for
several decades. Population can change in the intercensal period due to rapid
migration, development, urbanisation, natural disasters, and conflicts.
Census-independent population estimation approaches using alternative data
sources, such as satellite imagery, have shown promise in providing frequent
and reliable population estimates locally. Existing approaches, however,
require significant human supervision, for example annotating buildings and
accessing various public datasets, and therefore, are not easily reproducible.
We explore recent representation learning approaches, and assess the
transferability of representations to population estimation in Mozambique.
Using representation learning reduces required human supervision, since
features are extracted automatically, making the process of population
estimation more sustainable and likely to be transferable to other regions or
countries. We compare the resulting population estimates to existing population
products from GRID3, Facebook (HRSL) and WorldPop. We observe that our approach
matches the most accurate of these maps, and is interpretable in the sense that
it recognises built-up areas to be an informative indicator of population.
