Digital Twins for cities: Analyzing the gap between concepts and current implementations with a specific focus on data integration

Jeddoub, Imane; Nys, Gilles-Antoine; Hajji, Rafika; Billen, Roland

doi:10.1016/j.jag.2023.103440

Cited by 30 publications

(15 citation statements)

References 78 publications

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“…Digital twin [47,48] is hot topic in the field of building smart city platform, by applying technological advances of data integration and machine learning. In the realm of cultural heritage preservation, the paper [49] contributed to maintenance or visualization from perspective of architectural style or structure analysis.…”

Section: Digital Twin Platformmentioning

confidence: 99%

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

Pan,

Lin,

et al. 2024

Herit Sci

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This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

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Section: Digital Twin Platformmentioning

confidence: 99%

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

Pan,

Lin,

et al. 2024

Herit Sci

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“…Furthermore, as discussed in the work of Ferre (2022) [36], visualization tools in urban DTs play a pivotal role not only in data representation but also in facilitating interaction between the DT and urban management processes. These tools, such as maps and 3D models, serve as a medium for indirect interaction, influencing decision making, and can facilitate effective communication among different stakeholders, such as citizens, businesses, and government agencies [37][38][39][40]. By presenting data in an accessible and engaging manner, it can help build awareness and support for various initiatives to improve the quality of life in cities.…”

Section: Large-scale Data Visualization In the Production Of Dtsmentioning

confidence: 99%

Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data

Somanath,

Naserentin,

Eleftheriou

et al. 2024

Remote Sensing

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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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“…Digital Twins (DTs) for cities have become an efficient and collaborative decision-making tool that helps overcome cities' challenges (Ketzler et al 2020;Jeddoub et al 2023). The Urban Digital Twins (UDTs) serve city needs by integrating data, models, and processes into a one-stop platform, enabling twoway flows from the physical world to the digital replica and vice versa (Lehtola et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

Ballouch,

Jeddoub,

Hajji

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The emergence of Digital Twins in city planning and management marks a contemporary trend, elevating the realm of 3D modeling and simulation for cities. In this context, the use of semantic point clouds to generate 3D city models for Digital Twins proves instrumental in addressing this evolving need. This article introduces a processing pipeline for the automatic modeling of buildings, roads, and vegetation based on the semantic segmentation results of 3D LiDAR point clouds. It employs a semantic segmentation approach that integrates multiple training datasets to achieve precise extraction of target objects. Open-source reconstruction tools have been adapted for building and road modeling, while a Python code was optimized for tree modeling, leveraging a foundational code. The case study was conducted in the city of Liège, Belgium. The obtained results were satisfactory, and the schemas and geometry of the developed models were validated. An evaluation of the adopted reconstruction methods was conducted, along with their comparison to other methods from the literature.

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Digital Twins for cities: Analyzing the gap between concepts and current implementations with a specific focus on data integration

Cited by 30 publications

References 78 publications

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

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