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Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller, Dorothee and Wurm, Michael and Stark, Thomas and Angelo, Pablo and Stebner, Karsten and Dech, Stefan and Taubenböck, Hannes (2021) Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing. Journal of Transport and Land Use, 14 (1), pp. 777-803. University of Minnesota. doi: 10.5198/jtlu.2021.1855. ISSN 1938-7849.

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Official URL: http://jtlu.org/index.php/jtlu/article/view/1855/1571

Abstract

A significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution.

Item URL in elib:https://elib.dlr.de/138834/
Document Type:Article
Title:Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Stiller, DorotheeDorothee.Stiller (at) dlr.dehttps://orcid.org/0000-0002-8681-6144
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Stark, ThomasThomas.Stark (at) dlr.dehttps://orcid.org/0000-0002-6166-7541
Angelo, PabloPablo.Angelo (at) dlr.dehttps://orcid.org/0000-0001-8541-3856
Stebner, KarstenKarsten.Stebner (at) dlr.deUNSPECIFIED
Dech, StefanStefan.Dech (at) dlr.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:2021
Journal or Publication Title:Journal of Transport and Land Use
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.5198/jtlu.2021.1855
Page Range:pp. 777-803
Publisher:University of Minnesota
ISSN:1938-7849
Status:Published
Keywords:urban spatial structure, built environment, 3D city model, land-use model, intra-urban population, data fusion
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, V - Transport und Klima
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Institute of Optical Sensor Systems > Security Research and Applications
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Stiller, Dorothee
Deposited On:07 Jul 2021 13:03
Last Modified:13 Jul 2021 14:03

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