Census-Independent Population Estimation using Representation Learning

TL;DR

This paper presents a novel, census-independent population estimation method using representation learning on satellite imagery, reducing human supervision and improving transferability across regions.

Contribution

It introduces a representation learning approach for population estimation that minimizes human annotation and enhances adaptability to different geographic areas.

Findings

Matches the accuracy of existing population maps

Automatically extracts features related to built-up areas

Improves transferability of population estimates

Abstract

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.