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High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent.

Palacios Lopez, Daniela and Bachofer, Felix and Esch, Thomas and Marconcini, Mattia and MacManus, Kytt and Sorichetta, Alessandro and Zeidler, Julian and Dech, Stefan and Tatem, Andrew and Reinartz, Peter (2021) High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent. Remote Sensing, 13 (6), pp. 1-26. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13061142. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/6/1142#


The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa- the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

Item URL in elib:https://elib.dlr.de/141540/
Document Type:Article
Title:High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent.
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Palacios Lopez, DanielaUNSPECIFIEDhttps://orcid.org/0000-0001-6302-2491UNSPECIFIED
Bachofer, FelixUNSPECIFIEDhttps://orcid.org/0000-0001-6181-0187UNSPECIFIED
Esch, ThomasUNSPECIFIEDhttps://orcid.org/0000-0003-4193-9586UNSPECIFIED
Marconcini, MattiaUNSPECIFIEDhttps://orcid.org/0000-0002-5042-5176UNSPECIFIED
Sorichetta, AlessandroUniversity of Southhamptonhttps://orcid.org/0000-0002-3576-5826UNSPECIFIED
Zeidler, JulianUNSPECIFIEDhttps://orcid.org/0000-0001-9444-2296UNSPECIFIED
Tatem, AndrewUniversity of SouthhamptonUNSPECIFIEDUNSPECIFIED
Reinartz, PeterUNSPECIFIEDhttps://orcid.org/0000-0002-8122-1475UNSPECIFIED
Date:17 March 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 1-26
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:MDPI
Keywords:gridded population distribution mapping; large-scale population distribution modelling; World Settlement Footprint; percent of impervious surface; accuracy assessment; dasymetric modelling; sustainable development
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Optical remote sensing, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Palacios Lopez, Daniela
Deposited On:19 Apr 2021 09:17
Last Modified:23 Jul 2022 13:45

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