Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
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| Názov: | Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data |
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| Autori: | Somanath, Sanjay, 1994, Naserentin, Vasilis, 1981, Eleftheriou, Orfeas, Sjölie, Daniel, Stahre Wästberg, Beata, 1974, Logg, Anders, 1976 |
| Zdroj: | MiljöVis - Effektiv representation av miljöinformation i infrastrukturmodeller Remote Sensing. 16(11) |
| Predmety: | digital twin, geospatial visualization, 3D reconstruction, procedural generation, LiDAR integration, spatial data analysis, urban simulation |
| Popis: | A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. |
| Popis súboru: | electronic |
| Prístupová URL adresa: | https://research.chalmers.se/publication/541644 https://research.chalmers.se/publication/541420 https://research.chalmers.se/publication/541644/file/541644_Fulltext.pdf |
| Databáza: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs16111939 |