# Census-Independent Population Estimation using Representation Learning

arXiv: https://arxiv.org/abs/2110.02839 · HTML: https://mlsanity.com/paper/2110.02839/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [A Deep Learning Approach for Population Estimation from Satellite Imagery](https://mlsanity.com/paper/1708.09086/) (2017)
- [Mapping the world population one building at a time](https://mlsanity.com/paper/1712.05839/) (2017)
- [Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation](https://mlsanity.com/paper/1703.10757/) (2017)
- [Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery](https://mlsanity.com/paper/1905.02196/) (2019)

## Built on by (most cited first, 4 of 4)

- [Fine-grained Population Mapping from Coarse Census Counts and Open Geodata](https://mlsanity.com/paper/2211.04039/) (2022)
- [Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda](https://mlsanity.com/paper/2303.08511/) (2023)
- [Population Estimation using Deep Learning over Gandhinagar Urban Area](https://mlsanity.com/paper/2509.12926/) (2025)
- [A Hybrid State-Space Approach for Census-Tract Population Estimation](https://mlsanity.com/paper/2608.30094/) (2026)

## Abstract (arXiv)

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.