Fine-grained population mapping from coarse census counts and open geodata

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We p...

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Bibliographic Details
Published in:Scientific reports 2022-11, Vol.12 (1), p.20085-14, Article 20085
Main Authors: Metzger, Nando, Vargas-Muñoz, John E., Daudt, Rodrigo C., Kellenberger, Benjamin, Whelan, Thao Ton-That, Ofli, Ferda, Imran, Muhammad, Schindler, Konrad, Tuia, Devis
Format: Article
Language:English
Subjects:
704/172
704/844
Censuses
Environmental Monitoring
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
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Summary:Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present P omelo , a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100 m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with P omelo are in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R 2 values of 85–89%; unconstrained prediction in the absence of any counts reaches 48–69%.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-24495-w