A Photorealistic 3D City Modeling Framework for Smart City Digital Twin

Adreani, Lorenzo; Colombo, Carlo; Fanfani, Marco; Nesi, Paolo; Pantaleo, Gianni; Pisanu, Riccardo

doi:10.1109/smartcomp55677.2022.00071

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Regarding roof and façade patterns, they are respectively extracted from orthomaps and street level images. To obtain an accurate orthomap segmentation, so as to extract the roof texture, a deep net was used [70] to find any similarity transformation required to locally warp the orthomaps and make them accurately fit the building plant shapes [71], [72]. On the other hand, façade patterns are extracted by segmenting the building façade and then rectifying it using planar homographies.…”

Section: ) Façade and Roof Pattern Extractionmentioning

confidence: 99%

Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution

Adreani,

Bellini,

Fanfani

et al. 2024

IEEE Access

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Digital Twins are becoming fundamental tools to monitor the status of entities, predict their future evolution and simulate alternative scenarios to understand the impact of possible changes for planning and design. More recently, Digital Twin solutions have been applied in the context of Smart Cities. Thanks to the large deployment of sensors, together with the increasing amount of information available for municipalities and governmental organizations, it is possible to build wide virtual reproductions of urban environments including structural data and real-time information that can undoubtfully help decision makers to face future challenges in urban development and improve the citizens' quality of life. In this paper, the Snap4City Smart City Digital Twin framework is presented, which can respond to the requirements identified in recent literature and by international forums. The proposed architecture provides an integrated solution for data gathering, indexing, computing, and information distribution, thus realizing a continuously updated digital twin of the urban environment at global and local scales for monitoring operation and planning. It addresses 3D building models, road networks, Internet of Things entities, points of interest, paths, as well as results from analytical processes for traffic density reconstruction, pollutant dispersion, predictions, and what-if analysis for assessing impact of changes, all integrated into a freely accessible interactive 3D web interface, enabling stakeholder and citizen participation to city decision processes. As case study, the digital twin of the city of Florence (Italy) is presented, including what-if analysis. The solution is released on top of the Snap4City platform as open-source and made available through our GitHub repository (https://github.com/disit) and as Docker compose.

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Section: ) Façade and Roof Pattern Extractionmentioning

confidence: 99%

Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution

Adreani,

Bellini,

Fanfani

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Without POI data, it would be difficult to represent places and human-place interactions in computational models, especially those models focusing on urbanized areas. Accordingly, POI data have been increasingly used in smart city projects to build digital twins and support urban management decisions (Bilal et al, 2020;Adreani et al, 2022;Ferré-Bigorra et al, 2022).…”

Section: Introductionmentioning

confidence: 99%