In the context of smart cities and digital twins, three-dimensional semantic city models are increasingly used for the analyses of large urban areas. While the representation of buildings, terrain, and vegetation has become standard for most city models, detailed spatio-semantic representations of streetspace have played a minor role so far. This is now changing (1) because of data availability, and (2) because recent and emerging applications require having detailed data about the streetspace. The upcoming version 3.0 of the international standard CityGML provides a substantially updated data model regarding the transportation infrastructure, including the representation of the streetspace. However, there already exist a number of other standards and data formats dealing with the representation and exchange of streetspace data. Thus, based on an extensive literature review of potential applications as well as discussions and collaborations with relevant stakeholders, seven key modelling aspects of detailed streetspace models are identified. This allows a structured discussion of representational capabilities of the proposed CityGML3.0 Transportation Model with respect to these aspects and in comparison to the other standards. Subsequently, it is shown that CityGML3.0 meets most of these aspects and that streetspace models can be derived from various data sources and for different cities. Models generated compliant to the CityGML standard are immediately usable for a number of applications. This is demonstrated for some applications, such as land use management, solar potential analyses, and traffic and pedestrian simulations.

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.

Structured semantic 3D city models are pivotal in creating urban 3D digital twins. The wide adoption of such models has been primarily enabled by robust, model-based, and automatic 3D reconstruction methods. However, these methods impose requirements on the reconstruction, mainly restricting the solution space to several model types and relying on accurate 2D footprints. Recent research shows that deep-learning-based methods promise highly generic solution space and are footprint-free. Yet, the current training and test datasets are limited, hindering the methods’ development. In this work, we analyze the ubiquity of already existing, open 3D city model datasets and their potential to serve as a large-scale training and test set for 3D reconstruction, where 27 potential dataset collections have been identified. Our review shows that more than 215 million building models are readily available. We firmly believe that this review will facilitate further research on robust automatic 3D city model reconstruction and serve as a reference for benchmarking 3D city models.

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms ‘application domain’, ‘use case’, ‘functionality’ and ‘software application’. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace.

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.

The development of digital urban twins has led to efforts of multiple cities to gather detailed and highly accurate data on many parts of urban environments, including road and railway infrastructure as well as areas used by pedestrians. This paper presents concepts for representing and segmenting this data semantically, geometrically and topologically ensuring usability for various multimodal applications in the context of digital 3D city models. First, methods for modelling multiple transportation types of several standards such as OpenDRIVE, GDF and INSPIRE are examined, including a discussion of different types of information integration such as functional and topographical representations. Then, concepts proposed to the CityGML Standards Working Group for the CityGML 3.0 Transportation model are presented. This includes detailed methods for modelling multiple transportation modes within a common city model while avoiding redundant geometric representations. A section / intersection concept in combination with links used to model these relations is described. Linear as well as areal models of level crossings, pedestrian crosswalks or areas shared by multiple transportation infrastructure at the same time (e.g. a tramway within a road) are presented. Subsequently, examples and applications that benefit from accurate representations of multiple transportation types are described.