# Estimating Displaced Populations from Overhead

arXiv: https://arxiv.org/abs/2006.14547 · HTML: https://mlsanity.com/paper/2006.14547/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Towards Indirect Top-Down Road Transport Emissions Estimation](https://mlsanity.com/paper/2103.08829/) (2021)
- [Smartflow: Enabling Scalable Spatiotemporal Geospatial Research](https://mlsanity.com/paper/2506.03022/) (2025)

## Abstract (arXiv)

We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery. We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh in 2018 and 2019. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery. We believe our experiments with real-world displacement camp data constitute an important step towards the development of tools that enable the humanitarian community to effectively and rapidly respond to the global displacement crisis.