PriMera Scientific Engineering (ISSN: 2834-2550)

Literature Review

Volume 4 Issue 1

Multi-Scalar Urban Digital Twin Design: Architecture and OpenUSD Standards Based Methodology

Igor AGBOSSOU*

December 28, 2023

DOI : 10.56831/PSEN-04-100

Abstract

Digital twins have become indispensable tools in urban planning, providing dynamic and interactive portrayals of urban landscapes. This paper presents an innovative perspective on urban digital twin design, placing a pronounced emphasis on a multi-scalar framework that captures the intricate dynamics of urban systems across macro, meso, and micro scales, enriched by the concept of varying levels of detail. The proposed architectural model is rooted in OpenUSD standards, harnessing the Universal Scene Description format to amplify interoperability, facilitate seamless data exchange, and enable nuanced capture of urban visual features. Our exhaustive methodology addresses the constraints observed in existing urban digital twin frameworks and showcases the effectiveness of our approach through practical implementation in real-world urban settings. The outcomes underscore the critical role of multi-scalar representation and the integration of OpenUSD standards in propelling the capabilities of urban digital twins, thereby fostering more enlightened and responsive urban planning.

Keywords: Urban digital twin; Multi-Scale representation; OpenUSD standards; Urban planning; Level of detail; Urban visual features

References

  1. Grieves M., et al. “Virtually Intelligent Product Systems: Digital and Physical Twins”. In Complex Systems Engineering: Theory and Practice, edited by S. Flumerfelt, et al., 175-200. American Institute of Aeronautics and Astronautics (2015).
  2. Grieves M and Vickers J. “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems”. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds) Transdisciplinary Perspectives on Complex Systems. Springer, Cham (2017).
  3. USDZ: 3D interoperability around the Augmented Reality format (2023).
  4. OGC CityGML 3.0 Conceptual Model (2022).
  5. Kutzner T, Chaturvedi K and Kolbe TH. “CityGML 3.0: New Functions Open Up New Applications”. PFG J. Photogramm. Remote Sens. Geoinf. Sci 88 (2020): 43-61.
  6. Pixar Animation Studios (2020).
  7. Ledoux H. “val3dity: validation of 3D GIS primitives according to the international standards”. Open geospatial data, softw. stand (2018).
  8. Ledoux H., et al. “CityJSON: a compact and easy-to-use encoding of the CityGML data model”. Open geospatial data, softw. stand (2019).
  9. Liao T. “Standards and Their (Recurring) Stories: How Augmented Reality Markup Language Was Built on Stories of Past Standards”. Science, Technology, & Human Values 45.4 (2020): 712-737.
  10. Chicago area transportation study. Final report 3 (2020).
  11. Boyce DE and Williams HCWL. “Forecasting urban travel: Past, present and future”. Edward Elgar Publishing.
  12. Batty M. “Fifty years of urban modeling: Macro-statics to micro-dynamics”. In The dynamics of complex urban systems. Physica-Verlag HD (2008): 1-20.
  13. Boyce DE. Urban transportation network-equilibrium and design models: Recent achievements and future prospects: Environment and planning A: Economyand space 16.11 (1984): 1445-1474.
  14. Echenique M. “The use of integrated land use and transport models: The cases of Sao Paulo, Brazil and Bilbao”. In The practice of transportation planning. Amsterdam: Elsevier (1985).
  15. Waddell P. “UrbanSim: Modeling urban development for land use, transportation, and environmental planning”. Journal of the American Planning Association 68.3 (2002): 97-314.
  16. Allen PM. “Cities and regions as self-organizing systems: Models of complexity”. Amsterdam: Gordon and Breach Science (1997).
  17. Couclelis H. “Cellular worlds: A framework for modeling Micro—Macro dynamics”. Environment and Planning 17.5 (1985): 585-596.
  18. Tobler WR. “A computer movie simulating urban growth in the detroit region”. Economic Geography 46 (1970): 234.
  19. White RW and Engelen G. “Cellular automata as the basis of integrated dynamic regional modelling”. Environment and Planning 24.2 (1997): 235-246.
  20. White G., et al. “A digital twin smart city for citizen feedback”. Cities 110 (2021).
  21. Alonso W. Location and land use: Toward a general theory of land rent (1964).
  22. Anas A. “A dynamic disequilibrium model of residential location”. Environment and Planning 5.5 (1973): 633-647.
  23. Whyte J., et al. Analysing systems interdependencies using a digital twin (2019).
  24. Ding K., et al. “Smart steel bridge construction enabled by BIM and Internet of Things in industry 4.0: A framework”. In 2018 IEEE 15th international conference on networking, sensing and control (ICNSC) (2018): 1-5.
  25. Nebiker S., et al. “Fusion of airborne and terrestrial image-based 3d modelling for road infrastructure management - vision and first experiments”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B4 (2012): 79-84.
  26. Melo HC., et al. “City information modeling (CIM) concepts applied to the management of the sewage network”. IOP Conference Series: Earth and Environmental Science 588.4 (2020).
  27. Farruggio D and Glattfelder AH. “Modeling and control of electric power trans-mission lines”. In 2001 European control conference (ECC 2001) (2001): 2322-2327.
  28. Biljecki F., et al. “The variants of an LOD of a 3D building model and their influence on spatial analyses”. ISPRS Journal of Photogrammetry and Remote Sensing 116 (2016): 42-54,
  29. Sinyabe E., et al. “Shapefile-based multiagent geosimulation and visualization of building evacuation scenario”. Procedia Computer Science 220 (2023): 519-526.
  30. Kalogianni E., et al. “3D land administration: A re-view and a future vision in the context of the spatial development lifecycle”. ISPRS Int. J. Geo-Inf 9.2 (2020): 107.
  31. Biljecki F., et al. “Applications of 3D City Models: State of the art review”. ISPRS Int. J. Geo-Inf 4.4 (2015): 2842-2889.
  32. Li L., et al. “Semantic 3D modeling based on CityGML for ancient Chinese-style architectural roofs of digital heritage”. ISPRS Int. J. Geo-Inf 6.5 (2017): 132.
  33. Nys G-A, Poux F and Billen R. “CityJSON Building Generation from Airborne LiDAR 3D Point Clouds”. ISPRS International Journal of Geo-Information 9.9 (2020): 521.
  34. Huang MQ, Ninić J and Zhang QB. “BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives”. Tunnelling and Under-ground Space Technology 108 (2021).
  35. Zheng Y., et al. “Urban computing: Concepts, methodologies, and applications”. ACM Trans. Intell. Syst. Technol (2014).
  36. Agbossou I. “Urban Resilience Key Metrics Thinking and Computing Using 3D Spatio-Temporal Forecasting Algorithms”. In: Gervasi, O., et al. Computational Science and Its Applications - ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, Springer, Cham 13957 (2023): 332-350.
  37. Agbossou I. “Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model”. Artificial Intelligence. IntechOpen (2023).
  38. Cherdo L. The 8 Best 3D Scanning Apps for Smartphones and IPads in (2019).
  39. Ming H., et al. “A topological enabled three-dimensional model based on constructive solid geometry and boundary representation”. Cluster Comput 19 (2016): 2027-2037.
  40. Kang TW and Hong CH. “IFC-CityGML LOD mapping automation using multiprocessing-based screen-buffer scanning including mapping rule”. KSCE J Civ Eng 22 (2018): 373-383.
  41. Stoter JE., et al. “State of the Art in 3D City Modelling: Six Challenges Facing 3D Data as a Platform”. GIM Interna-tional: the worldwide magazine for geomatics 34 (2020).
  42. Vázquez-Canteli JR., et al. “Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities”. Sustainable Cities and Society 45 (2019): 243-257.
  43. Yigitcanlar T., et al. “Can cities become smart without being sustainable?”. A systematic review of the literature Sustain Cities Soc 45 (2019).
  44. Agbossou I. “Urban Augmented Reality for 3D Geosimulation and Prospective Analysis”. In: Pierre Boulanger. Applications of Augmented Reality - Current State of the Art. [Working Title] IntechOpen (2023).
  45. Zheng Y., et al. “Visual Analytics in Urban Computing: An Over-view”. IEEE Transactions on Big Data 2.03 (2016): 276-296.
  46. Gautier J, Brédif M and Christophe S. “Co-Visualization of Air Temperature and Urban Data for Visual Exploration”. in 2020 IEEE Visualization Conference (VIS), Salt Lake City, UT, USA (2020): 71-75.
  47. Li C., et al. “DDLVis: Real-time Visual Query of Spatiotemporal Data Distribution via Density Dictionary Learning". IEEE Transactions on Visualization & Computer Graphics 28.01 (2022): 1062-1072.
  48. Rosen R., et al. “About the Importance of Autonomy and Digital Twins for the Future of Manufacturing”. IFAC-PapersOnLine 48.3 (2015): 567-572.
  49. Wang S., et al. “Fog manufacturing: New paradigm of industrial internet manufacturing based on hierarchical digital twin”. Computer Intergrated Manufacturing Systems 25 (2019): 3070-3080.
  50. Pan YH., et al. “Digital Twin Based Real-time Production Logistics Synchronization System in a Multi-level Computing Architecture”. Journal of Manufacturing Systems 58.B (2021): 246-260.
  51. USDZ: 3D interoperability around the Augmented Reality format (2023).
  52. OGC CityGML 3.0 Conceptual Model (2022).
  53. Semeraro C., et al. “Digital twin paradigm: A systematic literature review”. Computers in Industry 130 (2021): 103469.