An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery

Stefanos Georganos, Tais Grippa, Assane Gadiaga, Sabine Vanhuysse, Stamatis Kalogirou, Moritz Lennert, Catherine Linard

    Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

    Abstract

    In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population density at the neighborhood level from the 2013 census of Dakar, while as explanatory features, the proportions of three different built-up types in each neighborhood derived from a VHHRS land cover classification. The results demonstrated, that by using an appropriate geographic scale to calibrate GRF, we can maximize prediction accuracy due to the incorporation of spatial heterogeneity in the estimates. Additionally, since GRF is an ensemble of local sub-models, the results can be mapped, highlighting local model performance and other interesting spatial variations. Consequently, GRF is suggested as an interesting exploratory and explanatory technique to model remotely-sensed spatially heterogeneous relationships.

    Original languageEnglish
    Title of host publication2019 Joint Urban Remote Sensing Event, JURSE 2019
    Subtitle of host publicationMay, 22-24, 2019, Vannes, France
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728100098
    DOIs
    Publication statusPublished - 1 May 2019
    Event2019 Joint Urban Remote Sensing Event, JURSE 2019 - Vannes, France
    Duration: 22 May 201924 May 2019

    Publication series

    Name2019 Joint Urban Remote Sensing Event, JURSE 2019

    Conference

    Conference2019 Joint Urban Remote Sensing Event, JURSE 2019
    CountryFrance
    CityVannes
    Period22/05/1924/05/19

    Keywords

    • population estimation
    • random forest
    • spatial heterogeneity
    • very-high-resolution remote sensing

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