research-article

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

Authors:

Jay Harshadbhai Patel,

Zoe-Alanah Robert,

Marshall Burke,

David Lobell, and

Stefano ErmonAuthors Info & Claims

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

January 2019

Pages 353 - 359

https://doi.org/10.1145/3306618.3314263

Published: 27 January 2019 Publication History

Abstract

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.

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Cited By

Brewer EValdrighi GSolunke PRulff JPiadyk YLv ZPoco JSilva C(2024)Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid LearningIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.336801817(5668-5679)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3368018
Doda SKahl MOuan KObadic IWang YTaubenböck HZhu X(2024)Interpretable deep learning for consistent large-scale urban population estimation using Earth observation dataInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.103731128(103731)Online publication date: Apr-2024
https://doi.org/10.1016/j.jag.2024.103731
David LZohar MShimshoni I(2023)Geo-Referencing and Analysis of Entities Extracted from Old Drawings and Photos Using Computer Vision and Deep Learning AlgorithmsISPRS International Journal of Geo-Information10.3390/ijgi1212050012:12(500)Online publication date: 13-Dec-2023
https://doi.org/10.3390/ijgi12120500
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Index Terms

Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
  2. Physical sciences and engineering
    1. Aerospace
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

January 2019

577 pages

ISBN:9781450363242

DOI:10.1145/3306618

General Chairs:
Vincent Conitzer
Duke University, USA
,
Gillian Hadfield
University of Toronto + Vector Institute, Canada; OpenAI, USA
,
Shannon Vallor
Santa Clara University, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence
AAAI: American Association for Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2019

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Stanford Center on Global Poverty and Development

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AIES '19

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SIGAI
AAAI

AIES '19: AAAI/ACM Conference on AI, Ethics, and Society

January 27 - 28, 2019

HI, Honolulu, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Brewer EValdrighi GSolunke PRulff JPiadyk YLv ZPoco JSilva C(2024)Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid LearningIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.336801817(5668-5679)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3368018
Doda SKahl MOuan KObadic IWang YTaubenböck HZhu X(2024)Interpretable deep learning for consistent large-scale urban population estimation using Earth observation dataInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.103731128(103731)Online publication date: Apr-2024
https://doi.org/10.1016/j.jag.2024.103731
David LZohar MShimshoni I(2023)Geo-Referencing and Analysis of Entities Extracted from Old Drawings and Photos Using Computer Vision and Deep Learning AlgorithmsISPRS International Journal of Geo-Information10.3390/ijgi1212050012:12(500)Online publication date: 13-Dec-2023
https://doi.org/10.3390/ijgi12120500
Gulgulia AGupta ASarashetti ASinha ASeth A(2023)Tracking Socio-Economic Development in Rural India over Two Decades Using Satellite ImageryACM Journal on Computing and Sustainable Societies10.1145/36153611:2(1-31)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3615361
Hafner SGeorganos SMugiraneza TBan Y(2023)Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda2023 Joint Urban Remote Sensing Event (JURSE)10.1109/JURSE57346.2023.10144139(1-4)Online publication date: 17-May-2023
https://doi.org/10.1109/JURSE57346.2023.10144139
Cartuyvels RFierens TCoppieters EMoens MSileo D(2023)Spatiotemporal self-supervised pre-training on satellite imagery improves food insecurity predictionEnvironmental Data Science10.1017/eds.2023.422Online publication date: 18-Dec-2023
https://doi.org/10.1017/eds.2023.42
Pareek SBhatnagar VPoonia RSharma SSamanta D(2023)A Review on Rural Women’s Entrepreneurship Using Machine Learning ModelsInnovations in Computational Intelligence and Computer Vision10.1007/978-981-99-2602-2_29(375-395)Online publication date: 13-Oct-2023
https://doi.org/10.1007/978-981-99-2602-2_29
Tabar MJung WYadav AWilson Chavez OFlores ALee DAl Hasan MXiong L(2022)WARNER: Weakly-Supervised Neural Network to Identify Eviction Filing Hotspots in the Absence of Court RecordsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557128(3514-3523)Online publication date: 17-Oct-2022
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Runfola DBaier HMills LNaughton‐Rockwell MStefanidis A(2022)Deep learning fusion of satellite and social information to estimate human migratory flowsTransactions in GIS10.1111/tgis.1295326:6(2495-2518)Online publication date: 27-Jun-2022
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Neal ISeth SWatmough GDiallo M(2022)Census-independent population estimation using representation learningScientific Reports10.1038/s41598-022-08935-112:1Online publication date: 25-Mar-2022
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