Full details of the project: https://cityjson.orgThe international standard CityGML is both a data model and an exchange format to store digital 3D models of cities. While the data model is used by several cities, companies, and governments, in this paper we argue that its XML-based exchange format has several drawbacks. These drawbacks mean that it is difficult for developers to implement parsers for CityGML, and that practitioners have, as a consequence, to convert their data to other formats if they want to exchange them with others. We present CityJSON, a new JSON-based exchange format for the CityGML data model (version 2.0.0). CityJSON was designed with programmers in mind, so that software and APIs supporting it can be quickly built. It was also designed to be compact (a compression factor of around six with real-world datasets), and to be friendly for web and mobile development. We argue that it is considerably easier to use than the CityGML format, both for reading and for creating datasets. We discuss in this paper the main features of CityJSON, briefly present the different software packages to parse/view/edit/create files (including one to automatically convert between the JSON and GML encodings), analyse how real-world datasets compare to those of CityGML, and we also introduce Extensions, which allow us to extend the core data model in a documented manner.
The Application Domain Extension (ADE) is a built-in mechanism of CityGML to augment its data model with additional concepts required by particular use cases. The goal of this paper is to provide an overview of the ADE mechanism and a literature review of developments since its introduction a decade ago. The discovery of publications found that currently there are 44 ADEs supporting a wide range of applications, but also application-agnostic purposes such as harmonisation with national geographic information standards. We hope this paper to double as a reference material for the developers of new ADEs.
LandInfra is a relatively new open standard for modelling and representing land and infrastructure features. As it overlaps with other open standards in BIM (IFC) and 3D GIS (CityGML), it has been recognised as a potential candidate to bridge the gap between the two domains. However, the knowledge of this standard in both communities is low, and there are still no publications which fully explore LandInfra and its possibilities for integrated BIM-GIS applications. In this paper, we review the LandInfra conceptual model and its GML encoding InfraGML, provide a detailed comparison of it with respect to CityGML and IFC, and investigate a few potential use cases where LandInfra and InfraGML are useful for BIM-GIS applications.
Noise is one of the main problems in urban areas. To monitor and manage noise problems, governmental organisations at all levels are obliged to regularly carry out noise studies. The simulation of noise is an important part of these studies. Currently, different organisations collect their own 3D input data as required in noise simulation in a semi-automated way, even if areas overlap. This is not efficient, but also differences in input data may lead to differences in the results of noise simulation which has a negative impact on the reliability of noise studies. To address this problem, this paper presents a methodology to automatically generate 3D input data as required in noise simulations (i.e. buildings, terrain, land coverage, bridges and noise barriers) from current 2D topographic data and point clouds. The generated data can directly be used in existing noise simulation software. A test with the generated data shows that the results of noise simulation obtained from our generated data are comparable to results obtained in a current noise study from practice. Automatically generated input data for noise simulation, as achieved in this paper, can be considered as a major step in noise studies. It does not only significantly improve the efficiency of noise studies, thus reducing their costs, but also assures consistency between different studies and therefore it improves the reliability and reproducibility. In addition, the availability of countrywide, standardised input data can help to advance noise simulation methods since the calculation method can be adopted to improved ways of 3D data acquisition and reconstruction.
CityGML is the most important international standard used to model cities and landscapes in 3D with extensive semantics. Compared to BIM standards such as IFC, CityGML models are usually less detailed but they cover a much greater spatial extent. They are also available in any of five standardized levels of detail. CityGML serves as an exchange format and as a data source for visualizations, either in dedicated applications or in a web browser. It can also be used for a wide range of spatial analyses, such as visibility studies and solar potential. Ongoing research will improve the integration of BIM standards with CityGML, making improved data exchange possible throughout the life-cycle of urban and environmental processes. IntroductionMunicipalities and other governmental organizations are increasingly using 3D city and landscape models to maintain and plan the environment (see Fig. 11.1 for an example). These models contain 3D data about urban objects such as buildings, roads, and waterways, and the data is collected, maintained and used in applications for urban planning and environmental simulations. Examples of such applications are estimating the shadows cast by buildings and vegetation, simulations of floods and noise propagation, and predicting exposure of roof surfaces to sunlight to assess the potential of installing solar panels. An overview of applications of 3D city
While there exist international standards for geospatial metadata (ISO 19115), these are rarely used in practice for 3D datasets, and one of the OGC standards for 3D city models, CityGML, does not offer a mechanism to store metadata in a structured way. Having metadata in CityGML files, which are in practice often very large and complex, would provide us with the ability to quickly understand the nature of a dataset and to determine if it is relevant for a specific task. A lack of metadata introduces uncertainty into models that are already full of assumptions and estimations. In this paper, we first examine the metadata needs that are specific for 3D geographical datasets and propose ISO 19115 compliant categories. We then describe how these can be used within CityGML by defining an Application Domain Extension (ADE), which allows us to store metadata for existing city objects of CityGML, as well as objects in other domain-specific ADEs. Our ADE, its schema in both UML and XSD, and sample datasets is openly accessible, and it can be easily extended to support application specific metadata. In addition the metadata elements have been added to the core of CityJSON. We also offer software to generate automatically many of the metadata categories and we propose coupling it with the source 3D dataset.
ABSTRACT:Road traffic and industrial noise has become a major source of discomfort and annoyance among the residents in urban areas. More than 44% of the EU population is regularly exposed to road traffic noise levels over 55 dB, which is currently the maximum accepted value prescribed by the Environmental Noise Directive for road traffic noise. With continuously increasing population and number of motor vehicles and industries, it is very unlikely to hope for noise levels to diminish in the near future. Therefore, it is necessary to monitor urban noise, so as to make mitigation plans and to deal with its adverse effects. The 2002/49/EC Environmental Noise Directive aims to determine the exposure of an individual to environmental noise through noise mapping. One of the most important steps in noise mapping is the creation of input data for simulation. At present, it is done semi-automatically (and sometimes even manually) by different companies in different ways and is very time consuming and can lead to errors in the data. In this paper, we present our approach for automatically creating input data for noise simulations. Secondly, we focus on using 3D city models for presenting the results of simulation for the noise arising from road traffic and industrial activities in urban areas. We implemented a few noise modelling standards for industrial and road traffic noise in CityGML by extending the existing Noise ADE with new objects and attributes. This research is a steping stone in the direction of standardising the input and output data for noise studies and for reconstructing the 3D data accordingly.
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