ArticleInternational journal of health geographics2019
Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa.
Article in International journal of health geographics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 23 citations in OpenAlex.
- Census-independent population estimation using representation learning.Scientific reports · 2022Article
- Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery.
methodsBayesian methods were used to sample the large solution space of candidate regression models for estimating population density.
resultsWe accurately estimated the population densities and counts of 20 neighborhoods in the city of Bo, Sierra Leone, using statistical measures derived from Landsat multi-band satellite imagery. The best regression model proposed estimated the latter with an absolute median proportional error of 8.0%, while the total population of the 20 neighborhoods was estimated with an error of less than 1.0%. We also compare our results with those obtained using an empirical Bayes approach.
conclusionsOur approach provides a rapid and effective method for constructing predictive models for population densities and counts utilizing remote sensing imagery. Our results, including cross-validation analysis, suggest that masking non-urban areas in the Landsat section images prior to computing the candidate covariate regressors should further improve model generality.
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