# Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery

arXiv: https://arxiv.org/abs/1905.02196 · HTML: https://mlsanity.com/paper/1905.02196/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 9 of 9)

- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases](https://mlsanity.com/paper/2103.02155/) (2021)
- [Census-Independent Population Estimation using Representation Learning](https://mlsanity.com/paper/2110.02839/) (2021)
- [So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale](https://mlsanity.com/paper/2204.08524/) (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)
- [Tracking the industrial growth of modern China with high-resolution panchromatic imagery: A sequential convolutional approach](https://mlsanity.com/paper/2301.09620/) (2023)
- [Mesh-Wise Prediction of Demographic Composition from Satellite Images Using Multi-Head Convolutional Neural Network](https://mlsanity.com/paper/2308.13441/) (2023)
- [Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators from High-Resolution Orthographic Imagery and Hybrid Learning](https://mlsanity.com/paper/2309.16808/) (2023)
- [A Hybrid State-Space Approach for Census-Tract Population Estimation](https://mlsanity.com/paper/2608.30094/) (2026)

## Abstract (arXiv)

Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census. We present two Convolutional Neural Network (CNN) architectures which efficiently and effectively combine satellite imagery inputs from multiple sources to accurately predict the population density of a region. In this paper, we use satellite imagery from rural villages in India and population labels from the 2011 SECC census. Our best model achieves better performance than previous papers as well as LandScan, a community standard for global population distribution.