Recently, 3D city modelling has attracted a growing interest as a building block for creating city Digital Twins. They are complex representations that include interactive representations of buildings and infrastructures, integrated with the wide range of data typically useful in a Smart City environment. This paper presents an automatic method for producing 3D city models from a various set of data, as well as its integration into the open-source Smart City framework, Snap4City. The proposed solution offers a method for creating effective integrated data visualizations of 3D city entities coupled with a large variety of Smart City data (e.g., IoT Devices which generate time-series data, heatmaps, geometries and shapes related to traffic flows, bus routes, cycling paths). The solution is based on a deep learning approach for rooftop detection and alignment based on a U-Net architecture. The implementation has been enforced into the open-source Snap4City Smart City platform, and has been validated by using a manually created ground-truth of 200 buildings scattered uniformly in the central area of Florence, plus a number of meshes representing a number of facades (not detailed in this paper), and traffic flows, pins, heatmaps, etc.

Digital Twin Framework for Smart City Solutions / Adreani L.; Bellini P.; Colombo C.; Fanfani M.; Nesi P.; Pantaleo G.; Pisanu R.. - ELETTRONICO. - (2022), pp. 1-8. (Intervento presentato al convegno 28th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2022 tenutosi a KSIR Virtual Conference Center, usa nel 2022) [10.18293/DMSVIVA22-012].

Digital Twin Framework for Smart City Solutions

Adreani L.;Bellini P.;Colombo C.;Fanfani M.;Nesi P.;Pantaleo G.;Pisanu R.
2022

Abstract

Recently, 3D city modelling has attracted a growing interest as a building block for creating city Digital Twins. They are complex representations that include interactive representations of buildings and infrastructures, integrated with the wide range of data typically useful in a Smart City environment. This paper presents an automatic method for producing 3D city models from a various set of data, as well as its integration into the open-source Smart City framework, Snap4City. The proposed solution offers a method for creating effective integrated data visualizations of 3D city entities coupled with a large variety of Smart City data (e.g., IoT Devices which generate time-series data, heatmaps, geometries and shapes related to traffic flows, bus routes, cycling paths). The solution is based on a deep learning approach for rooftop detection and alignment based on a U-Net architecture. The implementation has been enforced into the open-source Snap4City Smart City platform, and has been validated by using a manually created ground-truth of 200 buildings scattered uniformly in the central area of Florence, plus a number of meshes representing a number of facades (not detailed in this paper), and traffic flows, pins, heatmaps, etc.
2022
DMSVIVA 2022 - Proceedings of the 28th International DMS Conference on Visualization and Visual Languages
28th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2022
KSIR Virtual Conference Center, usa
2022
Adreani L.; Bellini P.; Colombo C.; Fanfani M.; Nesi P.; Pantaleo G.; Pisanu R.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1295204
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