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

    Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

    Résumé

    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.

    langue originaleAnglais
    titre2019 Joint Urban Remote Sensing Event, JURSE 2019
    Sous-titreMay, 22-24, 2019, Vannes, France
    EditeurInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronique)9781728100098
    Les DOIs
    Etat de la publicationPublié - 1 mai 2019
    Evénement2019 Joint Urban Remote Sensing Event, JURSE 2019 - Vannes, France
    Durée: 22 mai 201924 mai 2019

    Série de publications

    Nom2019 Joint Urban Remote Sensing Event, JURSE 2019

    Une conférence

    Une conférence2019 Joint Urban Remote Sensing Event, JURSE 2019
    PaysFrance
    La villeVannes
    période22/05/1924/05/19

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  • Contient cette citation

    Georganos, S., Grippa, T., Gadiaga, A., Vanhuysse, S., Kalogirou, S., Lennert, M., & Linard, C. (2019). An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery. Dans 2019 Joint Urban Remote Sensing Event, JURSE 2019: May, 22-24, 2019, Vannes, France [8809049] (2019 Joint Urban Remote Sensing Event, JURSE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/JURSE.2019.8809049