# Fine-grained Population Mapping from Coarse Census Counts and Open Geodata

arXiv: https://arxiv.org/abs/2211.04039 · HTML: https://mlsanity.com/paper/2211.04039/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Mapping the world population one building at a time](https://mlsanity.com/paper/1712.05839/) (2017)
- [Correcting rural building annotations in OpenStreetMap using convolutional neural networks](https://mlsanity.com/paper/1901.08190/) (2019)
- [A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery](https://mlsanity.com/paper/1810.09528/) (2018)
- [Census-Independent Population Estimation using Representation Learning](https://mlsanity.com/paper/2110.02839/) (2021)

## Built on by (most cited first, 6 of 6)

- [High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2](https://mlsanity.com/paper/2311.14006/) (2023)
- [DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery](https://mlsanity.com/paper/2502.10060/) (2025)
- [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)
- [A Geolocation-Aware Multimodal Approach for Ecological Prediction](https://mlsanity.com/paper/2601.08750/) (2026)
- [A Hybrid State-Space Approach for Census-Tract Population Estimation](https://mlsanity.com/paper/2608.30094/) (2026)
- [Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations](https://mlsanity.com/paper/2610.11914/) (2026)

## Abstract (arXiv)

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R2 values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.