Although level of detail (LoD) is a central concept in 3D city modelling, specifying different LoDs in an unambiguous manner is not straightforward. To resolve this, a set of frameworks have been developed. This paper evaluates the suitability of the LoD framework of (Biljecki et al. 2016) for 3D building models that have been generated directly from BIM models. The output of two BIM shell extractors are tested on how well they can be defined by the framework. It was found that although BIM-derived models can be specified by the framework to a certain degree, the framework is not fully capable to also specify lower quality models and to support all the output that may come from BIM shell extractors. This can be resolved by either addressing issues in the shell extractors’ output or in the framework itself. The results of this research can be used to improve the LoD framework and to adjust the shell extractors output to better comply with unambiguous definitions of building models at different LoDs and could be a first step to standardise the conversion of BIM models at different LoDs to be used in urban applications.@en

The integration of 3D city models and Building Information Models (BIM) in the context of GeoBIM has gained significant attention from both academia and industry. Harmonizing the distinct characteristics and goals of these models is crucial for successful integration. In this paper, we present the development of a plugin for Autodesk Revit, a popular BIM platform, which allows for the incorporation of 3D Geo-data encoded in CityJSON. The plugin, published as open source, enables the generation of individual geometries with associated city model attributes as parameters, facilitating analysing the impact of new or changed buildings (modelled in BIM) on the environment (captured in geo-data). Challenges addressed during development include georeferencing, data format import, handling different geometry approaches, hierarchy of attributes, code optimization, user-friendliness, and enhanced visualization. The plugin contributes to the seamless integration of geo- and BIM data, enhancing interoperability and supporting informed decision-making in the Architecture, Engineering, and Construction and urban domains.@en

The integration of geoinformation with Building Information Models (BIM), termed GeoBIM, has garnered significant attention across academic and non-academic sectors due to its potential for analyzing the reciprocal impacts of new designs on their environment. However, achieving integration between 3D city models and BIM necessitates ensuring consistency and alignment between their respective features and specifications. Georeferencing, a fundamental task in GeoBIM, involves establishing a connection between digital models and the Earth’s surface through coordinate transformations. Despite its importance, accurate georeferencing of BIM models has often been overlooked, resulting in challenges for integrating BIM models and geographical data. To address this gap, our study proposes a novel approach to enhance the georeferencing accuracy of BIM models by integrating surveyed points, considering the varying levels of georeferencing precision applicable to Industry Foundation Classes (IFC) models. We explore the potential benefits and challenges associated with this integrated surveyed point methodology, providing insights to improve georeferencing within the GeoBIM framework.@en

In the evolving landscape of digitalization, automating building permit processes are crucial for municipalities and other governmental bodies. Our research addresses the complexities of modeling digital building permit regulations, considering the level of the information needs (LoIN) for geometry-based regulations in four municipalities (Prague, Lisbon, Vila Nova de Gaia, and Ascoli Piceno) as case study. We propose a methodology and guidelines for geometrical building modeling, addressing challenges of integrating geoinformation with Building Information Modeling (BIM) for environmental based digital building permit (DBP) checks. This paper provides BIM-IFC geometrical interpretation choices, ensuring a seamless conversion into 3D city models. It offers insights for software companies, developers, and standardization organizations towards in implementing DBP checks, and preliminary results for a future scalable approach.@en

This paper introduces Property Valuation Application Domain Extension (ADE) within CityGML 3.0, aiming to integrate relevant indoor and outdoor 3D variables (cost estimation, view quality, etc.) for accurate property valuation. Current models lack the necessary features for this specific application. Leveraging IFC data for indoor elements, this ADE extends CityGML, addressing the existing gap. This paper identifies and categorizes data requirements, leading to the conceptualization and development of the model. By enriching CityGML 3.0 with IFC data, the approach introduces new features like the "Property Unit" to ensure adaptability across diverse valuation scenarios. Despite encountering data integrability challenges, we here commit to refining the model and overcoming these obstacles. A preliminary implementation using CityJSON demonstrates successful integration and paves the way for future implementation. These include developing an API platform and establishing an official repository to facilitate practical usability and scalability. This research significantly contributes to advancing property valuation processes by providing accurate valuations for stakeholders and promoting the use of 3D urban data in domain-specific extensions.@en

The role and adoption of 3D city models have been changing from a data endpoint to a centralised data source that is used for a variety of different analyses in different sectors. This change has not yet been fully completed and the transition process is still very noticeable at certain places. For example, data required for city-scale analyses are often missing, incorrect, or not stored in a standard way. A subset of these data (E.g. shell volume, shell area & footprint area) can be approximated from lower LoD shapes (LoD2.2 or lower) in the 3D city models. However, these models frequently simplify reality and therefore these approximations are not accurate. This paper proposes computing these data by voxelising Building Information Modelling (BIM) models representing the same buildings as the 3D city model. It is shown that a subset of these approximations (shell volume & footprint area) are more accurate than values computed from lower LoD shapes. Storing these data as attributes of the building models in 3D city models can improve the ease of use and the outcome of city-scale analyses. The computed values from BIM models can also be assigned to outputs of BIM to Geo conversions. This overturns the accuracy loss of the geometry caused by the conversion in which geometry is significantly generalised and simplified.@en

In this chapter, we start from the typical concepts from Geographic Information System (GIS): data representation, acquisition, querying and analysis. We follow with the transition from 2 to 3D GIS and describe open standards such as CityGML and CityJSON and recent advances on 3D geospatial simulations, computing and real-time GIS and Internet of Things (IoT). Then we discuss the discrepancies in information management and modelling with respect to Building Information Modelling (BIM) and the related open standard, Industry Foundation Classes (IFC). We highlight the difference between Cartesian engineering coordinate systems and geospatial coordinate reference systems, contrast the procedural geometry definitions of IFC with the explicit geometries of GIS and look at implementation mechanisms such as boundary representations and polyhedral surface models and describe the semantic Level of Detail used in CityGML. The section that follows describes relevant processes supporting integration such as georeferencing, conversion of formats using semantic and geometric approaches and linking of heterogeneous information. We also highlight interoperability challenges that stem from consistency and validity of data, by interpreting the results of a recent benchmark on interoperability of the most common involved data formats (CityGML and IFC). We close with a conclusion and perspectives on the future with case studies on geo-enabled building permit checking and geospatial artificial intelligence and machine learning.

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Municipalities invest a lot of time and person-hours into manual building permit checks. With the increase in computational power and the use of Building Information Models (BIM) in the building design life cycle, several municipalities are investing in automating these checks using BIM and geo-data sets. However, few examples exist of tools effectively using geo-information with BIM. In order to address this gap, a project was developed with the municipality of Rotterdam (NL). In a previous phase, a tool was implemented, able to analyse the BIM data to extract the needed information for a few representative regulations. In order to extend and improve the previously developed tool, a web-based interface is now implemented and geo-data sets are integrated to the process allowing more powerful GeoBIM analysis. Three checks using both BIM and GIS data were implemented and tested: (i) The parcel limit check evaluates if the building's footprint derived from BIM falls within the parcel limit provided from the municipalities' parcel data sets. (ii) The height check evaluates the maximum building's relative height to the road's height. Finally, (iii) the road overhang check detects neighbouring roads to the parcel and evaluates the admissible overhang over that road. This paper presents these developments, including the type of input data that is needed for the checks, the tools for the three new GeoBIM checks (parcel limit check, height check, road overhang check) and the implementation in the web-based tool.

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Level of Detail (LoD) is a well known concept in 3D city models, used to designate different geometric detail that can be used in different applications. Nevertheless, multi-LoD datasets are hard to maintain and manage because of their intrinsic complexity. Versioning is a solution that aids in the storage and management of big and complex dataset, with its main goal being to facilitate the tracking of changes and collaboration. In this paper, we investigate the effects of utilising versioning and, more specifically, the concept of branches as a way to manage the evolution of multi-LoD datasets. We propose a framework according to which every LoD is stored in its own branch and can be extracted and updated independently. We tested this framework on a tile from 3D BAG, a dataset of 3D buildings for the whole of the Netherlands containing four LoDs (namely, LoD0, LoD1.2, LoD1.3 and LoD2.2). Our results suggest that there are certain benefits from this solution, such as the efficient tracking of changes for individual LoDs and the ability to extract and update the model using one LoD at a time. Nevertheless, there is a lot of complexity added to the process as a set of rules needs to be enforced when managing the model.

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Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ.

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Urban morphology is important in a broad range of investigations across the fields of city planning, transportation, climate, energy, and urban data science. Characterising buildings with a set of numerical metrics is fundamental to studying the urban form. Despite the rapid developments in 3D geoinformation science, and the growing 3D data availability, most studies simplify buildings to their 2D footprint, and when taking their height into account, they at most assume one height value per building, i.e. simple 3D. We take the first step in elevating building metrics into full/true 3D, uncovering the use of higher levels of detail, and taking into account the detailed shape of a building. We set the foundation of the new research line on 3D urban morphology by providing a comprehensive set of 3D metrics, implementing them in openly released software, generating an open dataset containing 2D and 3D metrics for 823,000 buildings in the Netherlands, and demonstrating a use case where clusters and architectural patterns are analysed through time. Our experiments suggest the added value of 3D metrics to complement existing counterparts, reducing ambiguity, and providing advanced insights. Furthermore, we provide a comparative analysis using different levels of detail of 3D building models.@en

3D city models are frequently used to acquire and store energy-related information of buildings for energy applications. In this context, CityGML is the most common data model, and the Energy ADE, one of its most complex extensions, provides a systematic way of storing detailed energy-related data in XML format. Contrarily, even though CityGML's JSON-based encoding, CityJSON, has an extension mechanism, an energy-related CityJSON Extension is missing. This paper, therefore, presents the first results of the development of a CityJSON Energy Extension and space heating demand calculation is utilized as the use case. The simplified version of the Energy ADE, called the Energy ADE KIT profile, is used to create a semi-direct translation to the CityJSON Energy Extension. This Extension is then validated through the official validator of CityJSON and the use case, and improvements are made considering the validation results. The space heating demand is calculated according to the Dutch standard NTA 8800 for a subset of Rijssen-Holten in the Netherlands although the solar gains calculation requires further review. The results show that the final CityJSON Energy Extension provides full support for space heating demand calculations based on the NTA 8800 and eliminates the deep hierarchical structure of the Energy ADE. A comparison on CityJSON file sizes shows a 25.2 MB increase after the required input data is stored in a CityJSON + Energy Extension file, which is not significant considering the high amount of data stored in the file. Overall, this paper shows that the CityJSON Energy Extension could provide an easy-to-use alternative to the CityGML Energy ADE.

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To support building permit issuing with automatic digital tools, the reuse of models produced by designers would make the process quicker and more objective. However, current studies and pilots often leave a gap with respect to the models as actually provided by architects, having varying quality and content. In this study, rather than taking a top down approach, we started from the available data and made the necessary inferences, which gave the opportunity to tackle basic and common issues often preventing smooth automatic processing. Specific characteristics of the IFC models were outlined and a tool was developed to extract the necessary information from them to check representative regulations. While the case study is specific in location, regulations and input models, the type of issues encountered are a generally applicable example for automated code compliance checking. This represents a solid base for future works towards the automation of building permits issuing.

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Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3m to 1.94m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.

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In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change detection based on traditional classification algorithms is difficult due to the high level of noise in the data, such as introduced by temporary changes (e.g. cars being parked). This paper describes a method to detect changes between two time steps using 2.5D data and to transfer these insights to a digital map. For every polygon in the map, several attributes are collected from the input data, which are used to train a machine-learning model based on gradient boosting. A case study in Haarlem, in the Netherlands, was conducted to test the performance of this proposed approach. Results show that this methodology can recognize a substantial amount of changes and can support - and speed up - the manual updating process.

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Industry Foundation Classes (IFC), the buildingSMART open standard for BIM, is underused with respect to its promising potential, since, according to the experience of practitioners and researchers working with BIM, issues in the standard’s implementation and use prevent its effective use. Nevertheless, a systematic investigation of these issues has never been carried out, and there is thus insufficient evidence for tackling the problems. The GeoBIM benchmark project is aimed at finding such evidence by involving external volunteers, reporting on various aspects of the behavior of tools (geometry, semantics, georeferencing, functionalities), analyzed and described in this article. Interestingly, different IFC software programs with the same standardized data sets yield inconsistent results, with few detectable common patterns, and significant issues are found in their support of the standard, probably due to the very high complexity of the standard data model. A companion article (Part II) describes the results of the benchmark related to CityGML, the counterpart of IFC within geoinformation.

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The concept of the digital twin has been gaining significant momentum in the geospatial domain since 2016. Although there is some consensus on the basic features of a digital twin, some critical reflection is still necessary to make the concept tangible. Only then can the digital twin be implemented as a technical solution that scales to real-world problems rather than being an abstract vision for partial solutions. In this article, the authors reflect on the practical digital twin-related challenges in the geospatial domain. @en

Industry Foundation Classes (IFC) is a complete, wide and complex open standard data model to represent Building Information Models. Big efforts are being made by the standardization organization buildingSMART, to develop and maintain this standard in collaboration with researchers, companies and institutions. However, when trying to use IFC models from practice for automatic analysis, some issues emerge, as a consequence of a misalignment between what is prescribed by, or available in, the standard with the data sets that are produced in practice. In this study, a sample of models produced by practitioners for aims different from their explicit use within automatic processing tools is inspected and analyzed. The aim is to find common patterns in data set from practice and their possible discrepancies with the standard, in order to find ways to address such discrepancies in a next step. In particular, it is noticeable that the overall quality of the models requires specific additional care by the modellers before relying on them for automatic analysis, and a high level of variability is present concerning the storage of some relevant information (such as georeferencing).@en

De term Digital Twin wordt op allerlei manieren gebruikt sinds dit concept een aantal jaren geleden haar intrede deed in het geodomein als veelbelovende technologie om grote maatschappelijke opgaven en omgevingsvraagstukken op te lossen. Hoewel er consensus bestaat over de basisfunctionaliteiten van een Digital Twin, is het risico van de diversiteit aan definities dat alles en niets hieronder valt. Hierdoor kunnen implementaties beperkt blijven tot afzonderlijke – veelal technische – onderdelen, en wordt het beloofde einddoel van een fundamentele oplossing voor maatschappelijke en omgevingsvraagstukken nooit behaald. In dit artikel reflecteren wij op het concept Digital Twin binnen de geo-informatie. We beschrijven de potenties en uitdagingen om Digital Twin in al haar facetten te implementeren in de praktijk, daarbij gebruikmakend van bestaande ervaringen en kennis in het geodomein.@en

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.@en