Estimating Displaced Populations from Overhead
This work addresses the need for rapid and accurate population estimation in humanitarian contexts, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of estimating displaced populations in refugee camps using high-resolution overhead imagery, achieving a mean absolute percent error of 7.02% on drone imagery from Cox's Bazar, Bangladesh.
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.