# Mapping the world population one building at a time

arXiv: https://arxiv.org/abs/1712.05839 · HTML: https://mlsanity.com/paper/1712.05839/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2017.

## Built on by (most cited first, 12 of 12)

- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [Micro-Estimates of Wealth for all Low- and Middle-Income Countries](https://mlsanity.com/paper/2104.07761/) (2021)
- [Fine-grained Population Mapping from Coarse Census Counts and Open Geodata](https://mlsanity.com/paper/2211.04039/) (2022)
- [Census-Independent Population Estimation using Representation Learning](https://mlsanity.com/paper/2110.02839/) (2021)
- [TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery](https://mlsanity.com/paper/2511.12104/) (2025)
- [Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward](https://mlsanity.com/paper/2208.04683/) (2022)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [PD-SEG: Population Disaggregation Using Deep Segmentation Networks For Improved Built Settlement Mask](https://mlsanity.com/paper/2307.16084/) (2023)
- [Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents](https://mlsanity.com/paper/2308.16599/) (2023)
- [Gridding Forced Displacement using Semi-Supervised Learning](https://mlsanity.com/paper/2506.08019/) (2025)
- [A Hybrid State-Space Approach for Census-Tract Population Estimation](https://mlsanity.com/paper/2608.30094/) (2026)

## Abstract (arXiv)

High resolution datasets of population density which accurately map sparsely-distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment. By combining this settlement data with census data, we create population maps with ~30 meter resolution for 18 countries. We validate our method, and find that the building identification has an average precision and recall of 0.95 and 0.91, respectively and that the population estimates have a standard error of a factor ~2 or less. Based on our data, we analyze 29 percent of the world population, and show that 99 percent lives within 36 km of the nearest urban cluster. The resulting high-resolution population datasets have applications in infrastructure planning, vaccination campaign planning, disaster response efforts and risk analysis such as high accuracy flood risk analysis.