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


  • Dorothee Stiller Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany
  • Michael Wurm Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen 82234, Germany
  • Thomas Stark Signal processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich 80331, Germany
  • 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



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


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.


Aljoufie, M., Zuidgeest, M., Brussel, M., & van Maarseveen, M. (2013). Spatial–temporal analysis of urban growth and transportation in Jeddah City, Saudi Arabia. Cities, 31, 57–68. https://doi.10.1016/j.cities.2012.04.008

Angel, S., Parent, J., Civco, D. L., Blei, A., & Potere, D. (2011). The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in Planning, 75(2), 53–107. htpps://doi.10.1016/j.progress.2011.04.001

Arsanjani, J. J., Mooney, P., Zipf, A., & Schauss, A. (2015). Quality assessment of the contributed land-use information from OpenStreetMap versus authoritative datasets. Lecture Notes in Geoinformation and Cartography (pp. 37–58). Basel, Switzerland: Springer International Publishing. https://doi.10.1007/978-3-319-14280-7_3

Barrington-Leigh, C., & Millard-Ball, A. (2017). The world’s user-generated road map is more than 80% complete. PLOS One, 12(8), e0180698. https://doi.10.1371/journal.pone.0180698

Biljecki, F., Ohori, K. A., Ledoux, H., Peters, R., & Stoter, J. (2016). Population estimation using a 3D city model: A multi-scale country-wide study in the Netherlands. PLOS One, 11(6). https://doi.10.1371/journal.pone.0156808

Bittner, K., Cui, S., & Reinartz, P. (2017). Building extraction from remote sensing data using fully convolutional networks. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 481–486. https://doi.10.5194/isprs-archives-xlii-1-w1-481-2017

Bowen, B., Vlasek, K., & Webb, C. (2004). An assessment of remote sensing applications in transportation. Paper presented at the 2004 Annual Forum of the Transportation Research Forum.

Brovelli, M., & Zamboni, G. (2018). A new method for the assessment of spatial accuracy and completeness of OpenStreetMap building footprints. ISPRS International Journal of Geo-Information, 7(8), 289. https://doi.10.3390/ijgi7080289

Cao, X., Næss, P., & Wolday, F. (2019). Examining the effects of the built environment on auto ownership in two Norwegian urban regions. Transportation Research Part D: Transport and Environment, 67, 464–474. https://doi.10.1016/j.trd.2018.12.020

Cervero, R. B. (2013). Linking urban transport and land use in developing countries. Journal of Transport and Land Use, 6(1), 7. https://doi.10.5198/jtlu.v6i1.425

Chen, T., Hui, E. C. M., Wu, J., Lang, W., & Li, X. (2019). Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data. Habitat International, 89, 102005. https://doi.10.1016/j.habitatint.2019.102005

Cheng, L., Chen, X., Yang, S., Cao, Z., Vos, J. D., & Witlox, F. (2019). Active travel for active aging in China: The role of built environment. Journal of Transport Geography, 76, 142–152. https://doi.10.1016/j.jtrangeo.2019.03.010

Choi, K., & Zhang, M. (2017). The impact of metropolitan, county, and local land use on driving emissions in US metropolitan areas: Mediator effects of vehicle travel characteristics. Journal of Transport Geography, 64, 195–202. https://doi.10.1016/j.jtrangeo.2017.09.004

Choupani, A.-A., & Mamdoohi, A. R. (2016). Population synthesis using iterative proportional fitting (IPF): A review and future research. Transportation Research Procedia, 17, 223–233. https://doi.10.1016/j.trpro.2016.11.078

Cichosz, P. (2015). Data mining algorithms: Explained. Hoboken, NJ: John Wiley & Sons.

d'Angelo, P., & Reinartz, P. (2011). Semiglobal matching results on the ISPRS stereo matching benchmark. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4), 79–84. https://doi.10.5194/isprsarchives-xxxviii-4-w19-79-2011

de Sherbinin, A., Balk, D., Yager, K., Jaiteh, M., Pozzi, F., Giri, C., & Wannebo, A. (2002). Social science applications of remote sensing. A CIESIN thematic guide. Palisades, NY: Center for International Earth Science Information Network of Columbia University.

Dincer, S. E., Akdemir, F., Ulvi, H., & Duzkaya, H. (2019). Assessing urban sprawl effect of transportation investments using remote sensing data and GIS methods: The case of Ankara protocol road. IOP Conference Series: Materials Science and Engineering, 471, 092079. https://doi.10.1088/1757-899x/471/9/092079

Duncan, M. J., Winkler, E., Sugiyama, T., Cerin, E., duToit, L., Leslie, E., & Owen, N. (2010). Relationships of land-use mix with walking for transport: Do land uses and geographical scale matter? Journal of Urban Health, 87(5), 782–795. https://doi.10.1007/s11524-010-9488-7

Estima, J., & Painho, M. (2015). Investigating the potential of OpenStreetMap for land use/land cover production: A case study for continental Portugal. In Lecture notes in Geoinformation and Cartography (pp. 273–293). Basel, Switzerland: Springer International Publishing. https://doi.10.1007/978-3-319-14280-7_14

Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314. https://doi.10.1016/j.jtrangeo.2014.01.013

Fan, H., Zipf, A., Fu, Q., & Neis, P. (2014). Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science, 28(4), 700–719. https://doi.10.1080/13658816.2013.867495

Farber, S., & Li, X. (2013). Urban sprawl and social interaction potential: An empirical analysis of large metropolitan regions in the United States. Journal of Transport Geography, 31, 267–277. https://doi.10.1016/j.jtrangeo.2013.03.002

Fonte, C., Minghini, M., Patriarca, J., Antoniou, V., See, L., & Skopeliti, A. (2017). Generating up-to-date and detailed land use and land cover maps using OpenStreetMap and GlobeLand30. ISPRS International Journal of Geo-Information, 6(4), 125. https://doi.10.3390/ijgi6040125

Frasco, M., Hamner, B., & LeDell, E. (2018). Metrics: Evaluation metrics for machine learning.

Ghanea, M., Moallem, P., & Momeni, M. (2016). Building extraction from high-resolution satellite images in urban areas: Recent methods and strategies against significant challenges. International Journal of Remote Sensing, 37(21), 5234–5248. https://doi.10.1080/01431161.2016.1230287

Glaeser, E. (2011). Triumph of the city: How our greatest invention makes us richer, smarter, greener, healthier, and happier. London: Penguin Press HC.

Grange, L. de, & Troncoso, R. (2011). Impacts of vehicle restrictions on urban transport flows: The case of Santiago, Chile. Transport Policy. https://doi.10.1016/j.tranpol.2011.06.001

Guindon, B., & Zhang, Y. (2007). Using satellite remote sensing to survey transport-related urban sustainability. International Journal of Applied Earth Observation and Geoinformation, 9(3), 276–293. https://doi.10.1016/j.jag.2006.09.006

Harvey, J. T. (2002). Estimating census district populations from satellite imagery: Some approaches and limitations. International Journal of Remote Sensing, 23(10), 2071–2095. doi.10.1080/01431160110075901

Hecht, R., Kunze, C., & Hahmann, S. (2013). Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS International Journal of Geo-Information, 2(4), 1066–1091. https://doi.10.3390/ijgi2041066

Hu, H., Xu, J., Shen, Q., Shi, F., & Chen, Y. (2018). Travel mode choices in small cities of China: A case study of Changting. Transportation Research Part D: Transport and Environment, 59, 361–374. https://doi.10.1016/j.trd.2018.01.013

Hui, J., Du, M., Ye, X., Qin, Q., & Sui, J. (2019). Effective building extraction from high-resolution remote sensing images with multitask driven deep neural network. IEEE Geoscience and Remote Sensing Letters, 16(5), 786–790. https://doi.10.1109/lgrs.2018.2880986

Humanity & Inclusion. (2019). Mapping challenge. Building missing maps with machine learning. CrowdAI/Mapping Challenge.

Ihlanfeldt, K. (2020). Vehicle miles traveled and the built environment: New evidence from panel data. Journal of Transport and Land Use, 13(1), 23–48. https://doi.10.5198/jtlu.2020.1647

Infraestructura de Datos Geoespeciales (IDE) Chile. (2019). Fotografía aérea del Gran Santiago año 2014.

Instituto Nacional de Estadísticas (INE) Chile. (2017). Censo de población y vivienda.

Jin, J. (2019). The effects of labor market spatial structure and the built environment on commuting behavior: Considering spatial effects and self-selection. Cities, 95, 102392. https://doi.10.1016/j.cities.2019.102392

Kaddoura, I., Kröger, L., & Nagel, K. (2016). User-specific and dynamic internalization of road traffic noise exposures. Networks and Spatial Economics, 17(1), 153–172. https://doi.10.1007/s11067-016-9321-2

Kopsiaftis, G., & Karantzalos, K. (2015). Vehicle detection and traffic density monitoring from very high resolution satellite video data. Paper presented at the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.10.1109/igarss.2015.7326160

Krauß, T. (2014). Six years operational processing of satellite data using CATENA at DLR: Experiences and recommendations. Kartographische Nachrichten, 64, 74–80. https://doi.10.1007/BF03544117

Krehl, A., Siedentop, S., Taubenböck, H., & Wurm, M. (2016). A comprehensive view on urban spatial structure: Urban density patterns of German city regions. ISPRS International Journal of Geo-Information, 5(6), 76. https://doi.10.3390/ijgi5060076

Lee, S., & Lee, B. (2020). Comparing the impacts of local land use and urban spatial structure on household VMT and GHG emissions. Journal of Transport Geography, 84, 102694. https://doi.10.1016/j.jtrangeo.2020.102694

Lehmann, F., Berger, R., Brauchle, J., Hein, D., Meissner, H., Pless, S.,… Wieden, A. (2011). MACS – Modular Airborne Camera System for generating photogrammetric high-resolution products. Photogrammetrie - Fernerkundung - Geoinformation, 2011(6), 435–446. https://doi.10.1127/1432-8364/2011/0096

Levashev, A. (2017). Application of geoinformation technologies for the transportation demand estimation. Transportation Research Procedia, 20, 406–411. https://doi.10.1016/j.trpro.2017.01.066

Liddle, B. (2013). Urban density and climate change: A STIRPAT analysis using city-level data. Journal of Transport Geography, 28, 22–29. https://doi.10.1016/j.jtrangeo.2012.10.010

Linton, C., Grant-Muller, S., & Gale, W. F. (2015). Approaches and techniques for modelling CO₂ emissions from road transport. Transport Reviews, 35(4), 533–553. https://doi.10.1080/01441647.2015.1030004

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.10.1016/j.isprsjprs.2019.04.015

Machado, C. A. S., & Quintanilha, J. A. (2019). Identification of trip generators using remote sensing and geographic information system. Transportation Research Interdisciplinary Perspectives, 3, 100069. https://doi.10.1016/j.trip.2019.100069

María, H. S., Hube, M. A., Rivera, F., Yepes-Estrada, C., & Valcárcel, J. A. (2016). Development of national and local exposure models of residential structures in Chile. Natural Hazards, 86(S1), 55–79. https://doi.10.1007/s11069-016-2518-3

Martínez, L. M., Viegas, J. M., & Silva, E. A. (2009). A traffic analysis zone definition: A new methodology and algorithm. Transportation, 36(5), 581–599. https://doi.10.1007/s11116-009-9214-z

Nordenholz, F., Metzler, S., & Winkler, C. (2019). An automated gradual zoning approach for large-scale transport models. Procedia Computer Science, 151, 147–154. https://doi.10.1016/j.procs.2019.04.023

Nuhn, H., & Hesse, M. (2006). Verkehrsgeographie. Paderborn: Ferdinand Schöningh GmbH.

Okokon, E., Turunen, A., Ung-Lanki, S., Vartiainen, A.-K., Tiittanen, P., & Lanki, T. (2015). Road-traffic noise: Annoyance, risk perception, and noise sensitivity in the Finnish adult population. International Journal of Environmental Research and Public Health, 12(6), 5712–5734. https://doi.10.3390/ijerph120605712

OpenStreetMap contributors. (2017). Planet dump.

Palubinskas, G., Kurz, F., & Reinartz, P. (2010). Model based traffic congestion detection in optical remote sensing imagery. European Transport Research Review, 2(2), 85–92. https://doi.10.1007/s12544-010-0028-z

Parr, J. B. (2013). The regional economy, spatial structure and regional urban systems. Regional Studies, 48(12), 1926–1938. https://doi.10.1080/00343404.2013.799759

Perko, R., Raggam, H., Gutjahr, K. H., & Schardt, M. (2015). Advanced DTM generation from very high resolution satellite stereo images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(3), 165–172. https://doi.10.5194/isprsannals-ii-3-w4-165-2015

Pijl, A., Bailly, J.-S., Feurer, D., Maaoui, M. A. E., Boussema, M. R., & Tarolli, P. (2020). TERRA: Terrain extraction from elevation rasters through repetitive anisotropic filtering. International Journal of Applied Earth Observation and Geoinformation, 84, 101977. https://doi.10.1016/j.jag.2019.101977

Piltz, B., Bayer, S., & Poznanska, A. M. (2016). Volume based DTM generation from very high resolution photogrammetric DSMS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 83–90. https://doi.10.5194/isprs-archives-XLI-B3-83-2016

Puertas, O. L., Henríquez, C., & Meza, F. J. (2014). Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago metropolitan area, 2010–2045. Land Use Policy, 38, 415–425. https://doi.10.1016/j.landusepol.2013.11.024

Resch, E., Bohne, R. A., Kvamsdal, T., & Lohne, J. (2016). Impact of urban density and building height on energy use in cities. Energy Procedia, 96, 800–814. https://doi.10.1016/j.egypro.2016.09.142

Rodrigue, J.-P., Comtois, C., & Slack, B. (2016). The geography of transport systems. Oxfordshire, England: Routledge, Taylor & Francis Ltd.

Salvo, G., Caruso, L., Scordo, A., Guido, G., & Vitale, A. (2017). Traffic data acquirement by unmanned aerial vehicle. European Journal of Remote Sensing, 50(1), 343–351. https://doi.10.1080/22797254.2017.1328978

Sarlas, G., Páez, A., & Axhausen, K. W. (2020). Betweenness-accessibility: Estimating impacts of accessibility on networks. Journal of Transport Geography, 84, 102680. https://doi.10.1016/j.jtrangeo.2020.102680

Schultz, M., Voss, J., Auer, M., Carter, S., & Zipf, A. (2017). Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, 206–213. https://doi.10.1016/j.jag.2017.07.014

Senaratne, H., Mobasheri, A., Ali, A. L., Capineri, C., & Haklay, M. (Muki). (2016). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1), 139–167. https://doi.10.1080/13658816.2016.1189556

Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis. A new toolbox for ArcGIS. Revue internationale de géomatique, 22(2), 287–305. https://doi.10.3166/rig.22.287-305

Sohn, J. (2005). Are commuting patterns a good indicator of urban spatial structure? Journal of Transport Geography, 13(4), 306–317. https://doi.10.1016/j.jtrangeo.2004.07.005

Soltani, A., & Somenahalli, S. (2005). Household vehicle ownership: Does urban structure matter? Paper presented at the 28th Australasian Transport Research Forum, ATRF 05, Curtin University, Australia.

Srinivasan, S., Provost, R., & Steiner, R. (2013). Modeling the land-use correlates of vehicle-trip lengths for assessing the transportation impacts of land developments. Journal of Transport and Land Use, 6(2), 59. https://doi.10.5198/jtlu.v6i2.254

Steinnocher, K., Bono, A. D., Chatenoux, B., Tiede, D., & Wendt, L. (2019). Estimating urban population patterns from stereo-satellite imagery. European Journal of Remote Sensing, 52(sup2), 12–25. https://doi.10.1080/22797254.2019.1604081

Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLOS One, 10(2), e0107042. https://doi.10.1371/journal.pone.0107042

Stevenson, M., Thompson, J., de Sá, T. H., Ewing, R., Mohan, D., McClure, R.,… Woodcock, J. (2016). Land use, transport, and population health: Estimating the health benefits of compact cities. The Lancet, 388(10062), 2925–2935. https://doi.10.1016/s0140-6736(16)30067-8

Stiller, D., Ottinger, M., & Leinenkugel, P. (2019a). Spatio-temporal patterns of coastal aquaculture derived from Sentinel-1 time series data and the full Landsat archive. Remote Sensing, 11(14), 1707. https://doi10.3390/rs11141707

Stiller, D., Stark, T., Wurm, M., Dech, S., & Taubenböck, H. (2019b). Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN. Paper presented at the 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France. https://doi.10.1109/jurse.2019.8808977

Sun, B., Ermagun, A., & Dan, B. (2017). Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D: Transport and Environment, 52, 441–453. https://doi.10.1016/j.trd.2016.06.001

Taubenböck, H., Kraff, N. J., & Wurm, M. (2018). The morphology of the arrival city—A global categorization based on literature surveys and remotely sensed data. Applied Geography, 92, 150–167. https://doi.10.1016/j.apgeog.2018.02.002

Taubenböck, H., Roth, A., & Dech, S. (2008). Linking structural urban characteristics derived from high resolution satellite data to population distribution. In V. Coors, M. Rumor, E. Fendel, & S. Zlatanova (Eds.), Urban and regional data management, proceedings and monographs in engineering, water and earth sciences (pp. 35–45). Stuttgart, Germany: Urban Data Management Society.

Taubenböck, H., Weigand, M., Esch, T., Staab, J., Wurm, M., Mast, J., & Dech, S. (2019). A new ranking of the world’s largest cities—Do administrative units obscure morphological realities? Remote Sensing of Environment, 232, 111353. https://doi/10.1016/j.rse.2019.111353

Tian, Y., Zhou, Q., & Fu, X. (2019). An analysis of the evolution, completeness and spatial patterns of OpenStreetMap building data in China. ISPRS International Journal of Geo-Information, 8(1), 35. https://doi.10.3390/ijgi8010035

Tracy, A. J., Su, P., Sadek, A. W., & Wang, Q. (2011). Assessing the impact of the built environment on travel behavior: A case study of Buffalo, New York. Transportation, 38(4), 663–678. https://doi.10.1007/s11116-011-9337-x

United Nations. (2019). World urbanization prospects: The 2018 revision. New York: United Nations.

Van Acker, V., & Witlox, F. (2010). Commuting trips within tours: How is commuting related to land use? Transportation, 38(3), 465–486. https://doi.10.1007/s11116-010-9309-6

Walker, J., Li, J., Srinivasan, S., & Bolduc, D. (2010). Travel demand models in the developing world: Correcting for measurement errors. Transportation Letters, 2(4), 231–243. https://doi.10.3328/tl.2010.02.04.231-243

Wang, Y., Mishra, S., Ye, X., Li, L., & Wu, B. (2017). The application of integrated multimodal metropolitan transportation model in urban redevelopment for developing countries. Transportation Research Procedia, 25, 2990–3002. https://doi.10.1016/j.trpro.2017.05.378

Wu, S., Qiu, X., & Wang, L. (2005). Population estimation methods in GIS and remote sensing: A review. GIScience & Remote Sensing, 42(1), 80–96. https://doi.10.2747/1548-1603.42.1.80

Wu, S., Wang, L., & Qiu, X. (2008). Incorporating GIS building data and census housing statistics for sub-block-level population estimation. The Professional Geographer, 60(1), 121–135. https://doi.10.1080/00330120701724251

Wurm, M., d'Angelo, P., Reinartz, P., & Taubenböck, H. (2014). Investigating the applicability of cartosat-1 DEMs and topographic maps to localize large-area urban mass concentrations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4138–4152. https://doi.10.1109/jstars.2014.2346655

Wurm, M., Droin, A., Stark, T., Geiß, C., Sulzer, W., & Taubenböck, H. (2021). Deep learning-based generation of building stock data from remote sensing for urban heat demand modeling. ISPRS International Journal of Geo-Information, 10(1), 23. https://doi.10.3390/ijgi10010023

Wurm, M., Goebel, J., Wagner, G. G., Weigand, M., Dech, S., & Taubenböck, H. (2019a). Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception. Environment and Planning B: Urban Analytics and City Science, 239980831986934. https://doi.10.1177/2399808319869341

Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H. (2019b). Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 59–69. https://doi.10.1016/j.isprsjprs.2019.02.006

Wurm, M., Taubenböck, H., Krings, S., Birkmann, J., Roth, A., & Dech, S. (2009). Derivation of population distribution for vulnerability assessment in flood-prone German cities using multisensoral remote sensing data. In U. Michel & D. L. Civco (Eds.), Remote sensing for environmental monitoring, GIS applications, and geology IX. Bellingham, WA: SPIE. https://doi.10.1117/12.830318

Wurm, M., Taubenböck, H., Schardt, M., Esch, T., & Dech, S. (2011). Object-based image information fusion using multisensor earth observation data over urban areas. International Journal of Image and Data Fusion, 2(2), 121–147. https://doi.10.1080/19479832.2010.543934

Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R., & Evangelista, P. H. (2017). A survival guide to Landsat preprocessing. Ecology, 98(4), 920–932. https://doi.10.1002/ecy.1730

Yuan, J., Chowdhury, P. K. R., McKee, J., Yang, H. L., Weaver, J., & Bhaduri, B. (2018). Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria. Scientific Data, 5(1) 180217. https://doi.10.1038/sdata.2018.217

Zegras, C. (2010). The built environment and motor vehicle ownership and use: Evidence from Santiago de Chile. Urban Studies, 47(8), 1793–1817. https://doi.10.1177/0042098009356125

Zhang, H., Zhao, F., & Sutherland, J. W. (2013). Manufacturing scheduling for reduced energy cost in a smart grid scenario. In A. Y. C. Nee, B. Song, & S.-K. Ong (Eds.), Re-engineering manufacturing for sustainability (pp. 183–190). Berlin: Springer Science & Business Media.

Zhang, S., Liu, X., Tang, J., Cheng, S., & Wang, Y. (2019). Urban spatial structure and travel patterns: Analysis of workday and holiday travel using inhomogeneous Poisson point process models. Computers, Environment and Urban Systems, 73, 68–84. https://doi.10.1016/j.compenvurbsys.2018.08.005

Zhang, Y., & Guindon, B. (2006). Using satellite remote sensing to survey transport-related urban sustainability. International Journal of Applied Earth Observation and Geoinformation, 8(3), 149–164. https://doi.10.1016/j.jag.2005.08.005

Zhang, Y., Guindon, B., & Sun, K. (2010). Measuring Canadian urban expansion and impacts on work-related travel distance: 1966–2001. Journal of Land Use Science, 5(3), 217–235. https://doi.10.1080/1747423x.2010.500684

Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36. https://doi.10.1109/mgrs.2017.2762307




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.