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Smart city article details

Title	Data Collection And Wrangling Towards Machine Learning In Lod2+ Urban Models Generation
ID_Doc	17179
Authors	Naserentin V.; Spaias G.; Kaimakamidis A.; Somanath S.; Pantusheva M.; Mitkov R.; Dimara A.; Petrova-Antonova D.; Anagnostopoulos C.-N.; Logg A.; Krinidis S.
Year	2024
Published	IFIP Advances in Information and Communication Technology, 715 IFIPAICT
DOI	http://dx.doi.org/10.1007/978-3-031-63227-3_28
Abstract	This paper presents a novel approach for streamlining data collection and wrangling processes to facilitate Machine Learning (ML) and Artificial Intelligence (AI) applications in generating Level of Detail 2+ (LoD2+) urban models. In the era of rapid urbanization, accurate and dynamic 3D city modeling has become indispensable for urban planning, disaster management, and Geographic Information Systems (GIS). The proposed methodology leverages the capabilities of public domain data sources like Google Street View and OpenStreetMap to assist in creating comprehensive urban digital twins. By integrating diverse datasets within a unified data model, we aim to overcome the limitations posed by traditional urban modeling techniques. The Digital Twin Cities Centre (DTCC), hosted by Chalmers University of Technology, plays a pivotal role in this endeavor, providing an open-source platform for data fusion and urban design. The work presented is a milestone towards automating LoD2+ urban digital twins creation based on non-commercial software and data sources. © IFIP International Federation for Information Processing 2024.
Author Keywords	3D City Modeling; Artificial Intelligence in Urban Models; Data Fusion; Data Fusion in Urban Environments; Geographic Information Systems (GIS); Level of Detail 2+ (LoD2+); Machine Learning; Open-Source Platforms for Urban Modeling; Urban Digital Twins; Urban Planning

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