Automatic Detection and Interpretation of Changes in Massive Semantic 3D City Models

Son H. Nguyen

Automatic Detection and Interpretation of Changes in Massive Semantic 3D City Models

Titel in Übersetzung: Automatische Erkennung und Interpretation von Änderungen in massiven semantischen 3D-Stadtmodellen

Son H. Nguyen

Computational Methods

Abstract

This thesis focuses on the digital aspect of urban digital twins, with semantic 3D city models as a key component. The study addresses the current lack of effective methods for tracking und comprehending changes in large city models. It proposes methods for automatic detection and interpretation of changes in these models by leveraging the graph-like structure of city objects and representing them as graphs. This allows for efficient comparison of complex objects and the identification of change patterns. The proposed methods were tested using the datasets of Hamburg as a case study.

Titel in Übersetzung	Automatische Erkennung und Interpretation von Änderungen in massiven semantischen 3D-Stadtmodellen
Originalsprache	Englisch
Qualifikation	Doctor of Philosophy
Gradverleihende Hochschule	Technische Universität München
Betreuer/-in / Berater/-in	Kolbe, Thomas H., Betreuer, Externe Person
Datum der Bewilligung	19 Nov. 2024
Publikationsstatus	Veröffentlicht - 2024

Zugriff auf Dokument

https://mediatum.ub.tum.de/node?id=1765978

Dieses zitieren

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title = "Automatic Detection and Interpretation of Changes in Massive Semantic 3D City Models",

abstract = "This thesis focuses on the digital aspect of urban digital twins, with semantic 3D city models as a key component. The study addresses the current lack of effective methods for tracking und comprehending changes in large city models. It proposes methods for automatic detection and interpretation of changes in these models by leveraging the graph-like structure of city objects and representing them as graphs. This allows for efficient comparison of complex objects and the identification of change patterns. The proposed methods were tested using the datasets of Hamburg as a case study. ",

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note = "The dissertation was graded with summa cum laude; Excenllent Teaching in Project Work in Geoinformatics (2020); Excenllent Teaching in Exercise for Computer Science 2 (2020); Outstanding Paper Award at the 3DGeoInfo Conference at the New York University (2021); Best Young Researcher Paper Award at the 3DGeoInfo at the University of New South Wales Sydney (2022)",

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N2 - This thesis focuses on the digital aspect of urban digital twins, with semantic 3D city models as a key component. The study addresses the current lack of effective methods for tracking und comprehending changes in large city models. It proposes methods for automatic detection and interpretation of changes in these models by leveraging the graph-like structure of city objects and representing them as graphs. This allows for efficient comparison of complex objects and the identification of change patterns. The proposed methods were tested using the datasets of Hamburg as a case study.

AB - This thesis focuses on the digital aspect of urban digital twins, with semantic 3D city models as a key component. The study addresses the current lack of effective methods for tracking und comprehending changes in large city models. It proposes methods for automatic detection and interpretation of changes in these models by leveraging the graph-like structure of city objects and representing them as graphs. This allows for efficient comparison of complex objects and the identification of change patterns. The proposed methods were tested using the datasets of Hamburg as a case study.

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