Semantic 3D City Models Research Articles

INTEGRATION OF 3D POINT CLOUDS WITH SEMANTIC 3D CITY MODELS – PROVIDING SEMANTIC INFORMATION BEYOND CLASSIFICATION

Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.

MODELLING CHANGES, STAKEHOLDERS AND THEIR RELATIONS IN SEMANTIC 3D CITY MODELS

Abstract. Urban digital twins have been increasingly adopted by cities worldwide. Digital twins, especially semantic 3D city models as key components, have quickly become a crucial platform for urban monitoring, planning, analyses and visualization. However, as the massive influx of data collected from cities accumulates quickly over time, one major problem arises as how to handle different temporal versions of a virtual city model. Many current city modelling deployments lack the capability for automatic and efficient change detection and often replace older city models completely with newer ones. Another crucial task is then to make sense of the detected changes to provide a deep understanding of the progresses made in the cities. Therefore, this research aims to provide a conceptual framework to better assist change detection and interpretation in virtual city models. Firstly, a detailed hierarchical model of all potential changes in semantic 3D city models is proposed. This includes appearance, semantic, geometric, topological, structural, Level of Detail (LoD), auxiliary and scoped changes. In addition, a conceptual approach to modelling most relevant stakeholders in smart cities is presented. Then, a model - reality graph is used to represent both the different groups of stakeholders and types of changes based on their relative interest and relevance. Finally, the study introduces two mathematical methods to represent the relevance relations between stakeholders and changes, namely the relevance graph and the relevance matrix.

UNLOCKING POINT CLOUD POTENTIAL: FUSING MLS POINT CLOUDS WITH SEMANTIC 3D BUILDING MODELS WHILE CONSIDERING UNCERTAINTY

Abstract. Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

THE IMPACT OF LEVEL OF DETAIL IN 3D CITY MODELS FOR CFD-BASED WIND FLOW SIMULATIONS

Abstract. Climate change and urbanization rates are transforming urban environments, making the use of 3D city models in computational fluid dynamics (CFD) a fundamental ingredient to evaluate urban layouts before construction. However, current geometries used in CFD simulations tend to be built by CFD experts to test specific cases, most of the times oversimplifying their designs due to lack of information or in order to reduce complexity. In this work we explore what are the effects of oversimplifying geometries by comparing wind simulations of different level of detail geometries. We use semantic 3D city models automatically built and adjust them to their suitable use in CFD. For the first test, we explore wind simulations within a troublesome section of the TUDelft campus, the passage next to the EWI building (the tallest building in our domain), where the use of 3D city model variants show how differences in geometry and surface properties affect local wind conditions. Finally we analyze what these differences in velocity magnitude could mean for practitioners in terms of pedestrian wind comfort.

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

Worldwide, cities are nowadays formulating their own sustainability goals, including ambitious targets related to the generation and consumption of energy. In order to support decision makers in reaching these goals, energy experts typically rely on simulation models of urban energy systems, which provide a cheap and efficient way to analyze potential solutions. The availability of high-quality, well-formatted and semantically structured data is a crucial prerequisite for such simulation-based assessments. Unfortunately, best practices for data modelling are rarely utilized in the context of energy-related simulations, so data management and data access often become tedious and cumbersome tasks. However, with the steady progress of digitalization, more and more spatial and semantic city data also become available and accessible. This paper addresses the challenge to represent these data in a way that ensures simulation tools can make use of them in an efficient and user-friendly way. Requirements for an effective linking of semantic 3D city models with domain-specific simulation tools are presented and discussed. Based on these requirements, a software prototype implementing the required functionality has been developed on top of the CityGML standard. This prototype has been applied to a simple yet realistic use case, which combines data from various sources to analyze the operating conditions of a gas network in a city district. The aim of the presented approach is to foster a stronger collaboration between experts for urban data modelling and energy simulations, based on a concrete proof-of-concept implementation that may serve as an inspiration for future developments.

3D CITY MODEL AS A FIRST STEP TOWARDS DIGITAL TWIN OF SOFIA CITY

Abstract. Semantic 3D city models are increasingly applied for a wide range of analysis and simulations of large urban areas. Such models are used as a foundation for development of city digital twins, representing with high accuracy the landscapes and urban areas as well as dynamic of the city in terms of processes and events. In this context, this paper presents a 3D city model, which is a starting point for development of digital twin of Sofia city. The 3D model is compliant with CityGML 2.0 in LOD1, supporting integration of the buildings and terrain and enriching the buildings’ attributes with address information. District Lozenets of Sofia city is chosen as a pilot area for modelling. An approach for 3D transformation of proprietary geospatial data into CityGML schemas is presented. The integration of the buildings and terrain is an essential part of it, since the buildings often partially float over or sink into the terrain. A web application for user interaction with the 3D city model is developed. Its main features include silhouetting a single building, showing relevant overlay content, displaying shadows and styling of buildings depending on their attributes.

VOLUME COMPARISON OF AUTOMATICALLY RECONSTRUCTED MULTI-LOD BUILDING MODELS FOR URBAN PLANNING APPLICATIONS

Abstract. 3D city models are playing a growing role worldwide as sources of integrated information upon which different urban applications are developed. In the context of urban planning and design, semantic 3D city models can provide plenty of qualitative and quantitative information about the urban context and of the area(s) to be transformed. This paper takes inspiration and continues a work recently published in which several design parameters and Key Performance Indicators are computed from a semantic 3D city model, and later used in a GIS-supported urban design process to develop a new area. As many of such parameters are derived from the gross volume of the building stock, this paper investigates whether and to which extent different building stock models might affect the estimation of the gross volume. The study is carried out in anticipation of the upcoming LoD2-based, country-wide model of the Netherlands that is being finalised by our team. At the same time, the paper investigates whether and which information can be obtained regarding the quality of the LoD2 model from a comparison with the LoD1 one, with a focus on volume calculation.

PLASTIC SURGERY FOR 3D CITY MODELS: A PIPELINE FOR AUTOMATIC GEOMETRY REFINEMENT AND SEMANTIC ENRICHMENT

Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.

The City of Tomorrow from… the Data of Today

In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This article proposes a methodology to estimate the average size of a dwelling in existing urban areas from available open data, and to use it as one of the design parameters for new urban-development projects. The proposed unit of measure, called “living space”, includes outdoor and indoor spaces. The idea is to quantitatively analyze the city of today to help design the city of tomorrow. First, the “typical”-dwelling size and a series of Key Performance Indicators are computed for all neighborhoods from a semantic 3D city model and other spatial and non-spatial datasets. A limited number of neighborhoods is selected based on their similarities with the envisioned development plan. The size of the living space of the selected neighborhoods is successively used as a design parameter to support the computer-assisted generation of several design proposals. Each proposal can be exported, shared, and visualized online. As a test case, a to-be-planned neighborhood in Amsterdam, called “Sloterdijk One”, has been chosen.

LIFE CYCLE ASSESSMENT OF TECHNICAL BUILDING SERVICES OF LARGE RESIDENTIAL BUILDING STOCKS USING SEMANTIC 3D CITY MODELS

Abstract. Reducing the demand for non-renewable resources and the resulting environmental impact is an objective of sustainable development, to which buildings contribute significantly. In order to realize the goal of reaching a climate-neutral building stock, it must first be analyzed and evaluated in order to develop optimization strategies. The life cycle based consideration and assessment of buildings plays a key role in this process. Approaches and tools already exist for this purpose, but they mainly take the operational energy demand of buildings and not a life cycle based approach into account, especially when assessing technical building services (TBS). Therefore, this paper presents and applies a methodical approach for the life cycle based assessment of the TBS of large residential building stocks, based on semantic 3D city models (CityGML). The methodical approach developed for this purpose describes the procedure for calculating the operational energy demand (already validated) and the heating load of the building, the dimensioning of the TBS components and the calculation of the life cycle assessment. The application of the methodology is illustrated in a case study with over 115,000 residential buildings from Munich, Germany. The study shows that the methodology calculates reliable results and that a significant reduction of the life cycle based energy demand can be achieved by refurbishment measures/scenarios. Nevertheless, the goal of achieving a climate-neutral building stock is a challenge from a life cycle perspective.

A SEMANTICALLY ENRICHED AND WEB-BASED 3D ENERGY MODEL VISUALIZATION AND RETRIEVAL FOR SMART BUILDING IMPLEMENTATION USING CITYGML AND DYNAMIZER ADE

Abstract. Smart Cities are complex distributed systems which may involve services, applications, sensors and IoT devices. In order to be able to link and use such heterogeneous data, spatial data infrastructures for Smart Cities can play an important role in establishing interoperability between systems and platforms. Semantic 3D city models describe spatial, graphical and thematic aspects of the city objects according to the CityGML international standard, issued by the Open Geospatial Consortium (OGC). The requirement to support energy time-dynamic properties within CityGML objects arises from the fact that cities yield great potential in terms of energy consumption reduction and efficiency increase. In order to support such time-varying energy properties concerning city objects, recent extensions of the CityGML in the form of Application Domain Extensions (ADEs) are researched and developed, such as the Energy ADE and the Dynamizer ADE. Starting from a Building Information Model (BIM) and evaluating energy use of the building through the created Building Energy Model (BEM), the aim of this study is to integrate and visualize the time-based energy simulation results with the 3D building model within a 3D semantic city model. Using the latter ADE, the highly detailed static 3D Building (LOD 4) is extended to support those energy variations of individual feature properties and associations over time. Moreover, the web-based visualization approaches and data retrieval were further researched, and an interoperable web-based application was developed, in order to accomplish an integrated knowledge on how time-series data can be distributed in a virtual 3D environment.

CityJSON in QGIS: Development of an open-source plugin.

When QGIS 3.0 was released in 2018, it added support for 3D visualisation. At the same time, CityJSON has been developing as an easy‐to‐use JavaScript Object Notation (JSON) encoding for 3D city models using the CityGML 2.0 data model. Together, this opened the possibility to support semantic 3D city models in the popular open‐source GIS software for the first time. In order to add support for 3D city models in QGIS, we have developed a plugin that enables CityJSON datasets to be loaded. The plugin parses a CityJSON file and analyses its tree structure to identify all city objects. Then, the geometry and attributes of every city object are transformed into QGIS features and divided into layers according to user preferences. CityJSON parsing was proven to be straightforward and consistent when tested against several open datasets. One of the biggest challenges we faced, though, was mapping CityJSON’s hierarchical data structure to the relational model of QGIS. We undertook this issue by providing various methods on how geometries from the model are loaded as QGIS features. We intend to use the plugin for educational purposes in our university and we believe it can be proven a worthy tool for researchers and practitioners.

Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions.

Bayesian calibration at the urban scale: a case study on a large residential heating demand application in Amsterdam

ABSTRACTA bottom-up building energy modelling at the urban scale based on Geographic Information System and semantic 3D city models can provide quantitative insights to tackle critical urban energy challenges. Nevertheless, incomplete information is a common obstacle to produce reliable modelling results. The residential building heating demand simulation performance gap caused by input uncertainties is discussed in this study. We present a data-driven urban scale energy modelling framework from open-source data harmonization, sensitivity analysis, heating demand simulation at the postcode level to Bayesian calibration with six years of training data and two years of validation data. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity in the study area have significantly improved from 25.0% to 8.3% and from 19.9% to 7.7% in two validation years, while and . The overall methodology is extendable to other urban contexts.

Towards Integrating Heterogeneous Data: A Spatial DBMS Solution from a CRC-LCL Project in Australia

Over recent decades, more and more cities worldwide have created semantic 3D city models of their built environments based on standards across multiple domains. 3D city models, which are often employed for a large range of tasks, go far beyond pure visualization. Due to different spatial scale requirements for planning and managing various built environments, integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) has emerged in recent years. Focus is now shifting to Precinct Information Modeling (PIM) which is in a more general sense to built-environment modeling. As scales change so do options to perform information modeling for different applications. How to implement data interoperability across these digital representations, therefore, becomes an emerging challenge. Moreover, with the growth of multi-source heterogeneous data consisting of semantic and varying 2D/3D spatial representations, data management becomes feasible for facilitating the development and deployment of PIM applications. How to use heterogeneous data in an integrating manner to further express PIM is an open and comprehensive topic. In this paper, we develop a semantic PIM based on multi-source heterogeneous data. Then, we tackle spatial data management problems in a Spatial Database Management System (SDBMS) solution for our defined unified model. Case studies on the University of New South Wales (UNSW) campus demonstrate the efficiency of our solution.

A REQUIREMENT ANALYSIS ON EXTENDING SEMANTIC 3D CITY MODELS FOR SUPPORTING TIME-DEPENDENT PROPERTIES

Abstract. Semantic 3D City Models are used worldwide for different application domains ranging from Smart Cities, Simulations, Planning to History and Archeology. Well-defined data models like CityGML, IFC and INSPIRE Data Themes allow describing spatial, graphical and semantic information of physical objects. However, cities and their properties are not static and change with respect to time. Hence, it is important that such semantic data models handle different types of changes that take place in cities and their attributes over time. This paper provides a systematic analysis and recommendations for extensions of Semantic 3D City Models in order to support time-dependent properties. This paper reviews different application domains in order to identify key requirements for temporal and dynamic extensions and proposes ways to incorporate these extensions. Over the last couple of years, different extensions have been proposed for these standards to deal with temporal attributes. This paper also presents an analysis to which degree these extensions cover the requirements for dynamic city models.

INTEGRATED MANAGEMENT AND VISUALIZATION OF STATIC AND DYNAMIC PROPERTIES OF SEMANTIC 3D CITY MODELS

Abstract. CityGML is an international standard issued by the Open Geospatial Consortium (OGC) for representing and exchanging Semantic 3D City Models. Due to their large scale and deeply nested structures, the management and visualization of CityGML based models require sophisticated solutions such as the 3D City Database (3DCityDB). The research work presented in this article proposes a high level architecture for extending the 3D City Database to store and manage dynamic properties encoded within a new Application Domain Extension (ADE) of CityGML called Dynamizer ADE. The implementation employs the 3DCityDB 4.2 ADE Plugin Manager, which provides an automatic way for dynamically extending the 3DCityDB to support the storage and management of CityGML models with ADEs. The paper introduces a relational database model for storing and managing the Dynamizer ADE within the 3DCityDB. Further, the research work includes the extension of the 3DCityDB Importer/Exporter in order to import and export CityGML documents including Dynamizer ADE data. 3DCityDB already comes with a Web Feature Service (WFS) interface allowing CityGML features to be requested in standardized ways. The proposed framework enables CityGML Viewers to access static data (using OGC WFS interface) and dynamic data (using the OGC SWE interfaces) in an integrated fashion.

AN IMPROVED LOD FRAMEWORK FOR THE TERRAINS IN 3D CITY MODELS

Abstract. The Level of Detail (LOD) concept in CityGML 2.0 is meant to differentiate the multiple representations of semantic 3D city models. Despite the popularity and general acceptance of the concept by the practitioners and stakeholders in 3D city modelling, there are still some limitations. While the CityGML LOD concept is well defined for buildings, bridges, tunnels, and to some extent for roads, there is no clear definition of LODs for terrain/relief, vegetation, land use, water bodies, and generic city objects in CityGML. In addition, extensive research has been done to refine the LOD concept of CityGML for buildings but little is known on requirements and possibilities to model city object types as terrain at different LODs. To address this gap, we focus in this paper on the terrain of a 3D city model and propose a framework for modelling terrains at different LODs in CityGML. As a proof of concept of our framework, we implemented a software prototype to generate terrain models with other city features integrated (e.g. buildings) at different LODs in CityGML.

INTEGRATION OF SEMANTIC 3D CITY MODELS AND 3D MESH MODELS FOR ACCURACY IMPROVEMENTS OF SOLAR POTENTIAL ANALYSES

Abstract. High-resolution 3D mesh models are an inexpensive and increasingly available data source for 3D models of cities and landscapes of high visual quality and rich geometric detail. However, because of their simple data structure, their analytic capabilites are limited. Semantic 3D city model contain rich thematic information and are well suited for analytics due to their deeply structured semantic data model. In this work an approach for the integration of semantic 3D city models with 3D mesh models is presented. The method is based on geometric distance measures between mesh triangles and semantic surfaces and a region growing approach using plane fitting. The resulting semantic segmentation of mesh triangles is stored in a CityGML data set, to enrich the semantic model with an additional detailed geometric representation of its surfaces and a broad range of unrepresented features like technical building installations, balconies, dormers, chimneys, and vegetation. The potential of the approach is demonstrated on the example of a solar potential analysis, which estimation quality is significantly improved due to the mesh integration. The impact of the method is quantified on a case study using open data from the city of Helsinki.

COMBINING URBAN METABOLISM METHODS AND SEMANTIC 3D CITY MODELS

Abstract. The last decade has witnessed increasing interest in the study of urban stocks and flows. This paper provides a literature review for different studies of urban stocks and flows. The review shows that urban stocks and flows studies can be categorized according to their output and spatial resolution: The first type of studies uses a lower scale of spatial resolution and focuses on the city as one unit. The second type of studies uses a higher spatial resolution to analyze stocks and flows of sub-areas of the city and is therefore able to produce maps to visualize their results. Both types of studies can either use recent data in a non-dynamic way, or use a time series of data, which allows for the investigation of the city’s development over time. Information about input and output data per study was determined. The literature review also identifies problems associated with current studies, which are related to data availability and accuracy, assumptions, simplifications, and errors. The paper proposes that stocks and flows studies could benefit greatly from the linkage with a standardized semantic 3D city model, such as defined by the CityGML standard. Feedback on CityGML and its ADE capabilities to model urban metabolism is provided.