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Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Palacios Lopez, Daniela and Esch, Thomas and MacManus, Kytt and Marconcini, Mattia and Sorichetta, Alessandro and Yetman, Gregorie and Zeidler, Julian and Dech, Stefan and Tatem, Andrew and Reinartz, Peter (2022) Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling. Remote Sensing, 14 (325), pp. 1-30. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14020325. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/14/2/325/htm

Abstract

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

Item URL in elib:https://elib.dlr.de/185419/
Document Type:Article
Additional Information:This work was supported by the EU-funded ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery of the World Bank (contract nos. 7194331 and 7196541). This work was funded by the German Academic Exchange Service (DAAD) providing the research fellowship to Daniela Palacios Lopez No. 91687956.
Title:Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Palacios Lopez, DanielaUNSPECIFIEDhttps://orcid.org/0000-0001-6302-2491UNSPECIFIED
Esch, ThomasUNSPECIFIEDhttps://orcid.org/0000-0002-5868-9045UNSPECIFIED
MacManus, KyttCIESINUNSPECIFIEDUNSPECIFIED
Marconcini, MattiaUNSPECIFIEDhttps://orcid.org/0000-0002-5042-5176UNSPECIFIED
Sorichetta, AlessandroUniversity of Southhamptonhttps://orcid.org/0000-0002-3576-5826UNSPECIFIED
Yetman, GregorieCIESINUNSPECIFIEDUNSPECIFIED
Zeidler, JulianUNSPECIFIEDhttps://orcid.org/0000-0001-9444-2296UNSPECIFIED
Dech, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Tatem, AndrewUniversity of SouthhamptonUNSPECIFIEDUNSPECIFIED
Reinartz, PeterUNSPECIFIEDhttps://orcid.org/0000-0002-8122-1475UNSPECIFIED
Date:11 January 2022
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI:10.3390/rs14020325
Page Range:pp. 1-30
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:MPDI
ISSN:2072-4292
Status:Published
Keywords:large-scale gridded population dataset; dasymetric modelling; accuracy assessment; World Settlement Footprint-3D; random forest classifier; spatial metrics; 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 - Geoscientific remote sensing and GIS methods, R - Geoproducts and systems, services, R - Remote Sensing and Geo Research, R - Optical remote sensing
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
German Remote Sensing Data Center > Leitungsbereich DFD
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Palacios Lopez, Daniela
Deposited On:16 May 2022 13:37
Last Modified:19 May 2022 16:28

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