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)
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

Fingerprint

Senegal
population estimation
satellite imagery
Satellite imagery
Remote sensing
high resolution
population density
remote sensing
census
land cover
spatial variation
proportion
prediction
estimates
predictions
performance

Keywords

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

Cite this

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. In 2019 Joint Urban Remote Sensing Event (JURSE): 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
Georganos, Stefanos ; Grippa, Tais ; Gadiaga, Assane ; Vanhuysse, Sabine ; Kalogirou, Stamatis ; Lennert, Moritz ; Linard, Catherine. / An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery. 2019 Joint Urban Remote Sensing Event (JURSE): May, 22-24, 2019, Vannes, France. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 Joint Urban Remote Sensing Event, JURSE 2019).
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title = "An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery",
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.",
keywords = "population estimation, random forest, spatial heterogeneity, very-high-resolution remote sensing",
author = "Stefanos Georganos and Tais Grippa and Assane Gadiaga and Sabine Vanhuysse and Stamatis Kalogirou and Moritz Lennert and Catherine Linard",
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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. in 2019 Joint Urban Remote Sensing Event (JURSE): May, 22-24, 2019, Vannes, France., 8809049, 2019 Joint Urban Remote Sensing Event, JURSE 2019, Institute of Electrical and Electronics Engineers Inc., 2019 Joint Urban Remote Sensing Event, JURSE 2019, Vannes, France, 22/05/19. https://doi.org/10.1109/JURSE.2019.8809049

An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery. / Georganos, Stefanos; Grippa, Tais; Gadiaga, Assane; Vanhuysse, Sabine; Kalogirou, Stamatis; Lennert, Moritz; Linard, Catherine.

2019 Joint Urban Remote Sensing Event (JURSE): May, 22-24, 2019, Vannes, France. Institute of Electrical and Electronics Engineers Inc., 2019. 8809049 (2019 Joint Urban Remote Sensing Event, JURSE 2019).

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

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T1 - An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery

AU - Georganos, Stefanos

AU - Grippa, Tais

AU - Gadiaga, Assane

AU - Vanhuysse, Sabine

AU - Kalogirou, Stamatis

AU - Lennert, Moritz

AU - Linard, Catherine

PY - 2019/5/1

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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