Estimating Displaced Populations from Overhead

TL;DR

This paper presents a deep learning method for accurately estimating populations in displacement camps using high-resolution overhead imagery, aiding humanitarian efforts.

Contribution

It introduces a novel deep learning approach trained on drone imagery and population data for refugee camps, achieving high accuracy in population estimation.

Findings

7.02% mean absolute percent error on camp imagery

Effective population estimation for displacement camps

Potential for rapid humanitarian response tools

Abstract

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.