Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
Journal article, 2024

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.

urban simulation

geospatial visualization

procedural generation

spatial data analysis

3D reconstruction

digital twin

LiDAR integration

Author

Sanjay Somanath

Chalmers, Architecture and Civil Engineering, Building Technology

Vasilis Naserentin

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Orfeas Eleftheriou

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Daniel Sjölie

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Beata Stahre Wästberg

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Anders Logg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Remote Sensing

20724292 (eISSN)

Vol. 16 11 1939

MiljöVis - Effective representation of environmental information in infrastructure models

Swedish Transport Administration (TRV 2020/9876), 2020-01-01 -- 2021-04-30.

Subject Categories

Other Computer and Information Science

Computer and Information Science

Areas of Advance

Information and Communication Technology

Building Futures (2010-2018)

Driving Forces

Sustainable development

Roots

Basic sciences

DOI

10.3390/rs16111939

More information

Latest update

6/3/2024 1