Data Collection and Wrangling Towards Machine Learning in LoD2+ Urban Models Generation
Paper in proceeding, 2024
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.
Machine Learning
Artificial Intelligence in Urban Models
Level of Detail 2+ (LoD2+)
Open-Source Platforms for Urban Modeling
3D City Modeling
Urban Planning
Urban Digital Twins
Geographic Information Systems (GIS)
Data Fusion
Data Fusion in Urban Environments
Author
Vasilis Alexandros Naserentin
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Aristotle University of Thessaloniki
Georgios Spaias
Aristotle University of Thessaloniki
Anestis Kaimakamidis
Aristotle University of Thessaloniki
Sanjay Somanath
Chalmers, Architecture and Civil Engineering, Building Technology
Mariya Pantusheva
Big Data for Smart Society Institute (GATE)
Radostin Mitkov
Big Data for Smart Society Institute (GATE)
Asimina Dimara
University of the Aegean
International Hellenic University (IHU)
Dessislava Petrova-Antonova
Big Data for Smart Society Institute (GATE)
Christos-Nikolaos Anagnostopoulos
Big Data for Smart Society Institute (GATE)
University of the Aegean
International Hellenic University (IHU)
Aristotle University of Thessaloniki
Anders Logg
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Stelios Krinidis
International Hellenic University (IHU)
IFIP Advances in Information and Communication Technology
1868-4238 (ISSN) 1868-422X (eISSN)
Vol. 715 IFIPAICT 391-4049783031632266 (ISBN)
13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024
Corfu, Greece,
Corfu, Greece,
Big data for smart society (GATE)
European Commission (EC) (EC/H2020/857155), 2019-09-01 -- 2026-08-31.
Digital Twin Cities Centre
VINNOVA (2019-00041), 2020-02-29 -- 2024-12-31.
Subject Categories (SSIF 2025)
Computer Sciences
Civil Engineering
DOI
10.1007/978-3-031-63227-3_28