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

Authors

  • Dorothee Stiller Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany https://orcid.org/0000-0002-8681-6144
  • Michael Wurm Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany https://orcid.org/0000-0001-5967-1894
  • Thomas Stark Signal processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich 80331, Germany https://orcid.org/0000-0002-6166-7541
  • Pablo d'Angelo Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany
  • Karsten Stebner Institute of Optical Sensor Systems (OS), German Aerospace Center (DLR), Berlin 12489, Germany
  • Stefan Dech Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany; Department of Remote Sensing, Institute for Geography and Geology, University of Wuerzburg, Wuerzburg 97074, Germany
  • Hannes Taubenböck Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany; Department of Remote Sensing, Institute for Geography and Geology, University of Wuerzburg, Wuerzburg 97074, Germany https://orcid.org/0000-0003-4360-9126

DOI:

https://doi.org/10.5198/jtlu.2021.1855

Keywords:

urban spatial structure, built environment, 3D city model, land-use model, intra-urban population, data fusion

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.

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2021-07-05

How to Cite

Stiller, D., Wurm, M., Stark, T., d’Angelo, P., Stebner, K., Dech, S., & Taubenböck, H. (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), 777–803. https://doi.org/10.5198/jtlu.2021.1855

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