Estimating Displaced Populations from Overhead

Armin Hadzic; Gordon Christie; Jeffrey Freeman; Amber Dismer; Stevan Bullard; Ashley Greiner; Nathan Jacobs; Ryan Mukherjee

doi:10.1109/IGARSS39084.2020.9324617

Estimating Displaced Populations from Overhead

Armin Hadzic, Gordon Christie, Jeffrey Freeman, Amber Dismer, Stevan Bullard, Ashley Greiner, Nathan Jacobs, Ryan Mukherjee

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 7.41% 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.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Pages	1121-1124
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9324617
State	Published - Sep 26 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	9/26/20 → 10/2/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

CNN
Deep Learning
Machine Learning
Population Estimation
Regression
Remote Sensing

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS39084.2020.9324617

Cite this

Hadzic, A., Christie, G., Freeman, J., Dismer, A., Bullard, S., Greiner, A., Jacobs, N., & Mukherjee, R. (2020). Estimating Displaced Populations from Overhead. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1121-1124). Article 9324617 (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS39084.2020.9324617

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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 7.41% 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.",

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author = "Armin Hadzic and Gordon Christie and Jeffrey Freeman and Amber Dismer and Stevan Bullard and Ashley Greiner and Nathan Jacobs and Ryan Mukherjee",

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doi = "10.1109/IGARSS39084.2020.9324617",

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Hadzic, A, Christie, G, Freeman, J, Dismer, A, Bullard, S, Greiner, A, Jacobs, N & Mukherjee, R 2020, Estimating Displaced Populations from Overhead. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9324617, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1121-1124, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 9/26/20. https://doi.org/10.1109/IGARSS39084.2020.9324617

Estimating Displaced Populations from Overhead. / Hadzic, Armin; Christie, Gordon; Freeman, Jeffrey et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. 2020. p. 1121-1124 9324617 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Christie, Gordon

AU - Freeman, Jeffrey

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AB - 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 7.41% 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.

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Estimating Displaced Populations from Overhead

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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