When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables, mapping closely the CityGML architecture. In this paper, we propose an alternative (called ‘cjdb’) for storing CityGML models efficiently in PostgreSQL with a much simpler table structure and data model design (only 3 tables are necessary). This is achieved by storing the attributes and geometries of the objects directly in JSON. In the case of the geometries we thus adopt the Simple Feature paradigm and we use the structure of CityJSON. We compare our solution against 3DCityDB with large real-world 3D city models, and we find that cjdb has significantly lower demands in storage space (around a factor of 10), allows for faster import/export of data, and has a comparable data retrieval speed with some queries being faster and some slower. The accompanying software (importer and exporter) is available at https://github.com/cityjson/cjdb/ under a permissive open-source license.@en

We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on CityJSON and JSON Text Sequences. CityJSONSeq was added to the CityJSON specifications version 2.0 to allow us to stream very large 3D city models. The main idea is to decompose a CityJSON dataset into its individual city objects (each building, each tree, etc.) and create several independent JSON objects of a newly defined type: CityJSONFeature. We elaborate on the engineering decisions that were taken to develop CityJSONSeq, we present the open-source software we have developed to convert to and from CityJSONSeq, and we discuss different aspects of the new format, eg filesize, usability, memory footprint, etc. For several use-cases, we consider CityJSONSeq to be a better format than CityJSON because: (1) once serialised it is about 10% more compact; (2) it takes an order of magnitude less time to process; and (3) it uses significantly less memory.@en

This document provides a description of the project “Computing volumes and surface areas including party walls for the 3DBAG dataset” which has been carried out between the 3D Geoinformation group at TU Del6, 3DGI, and RVO in the timeframe between November 2022 and October 2023. The goal of this project is to derive parameters from the 3DBAG that are relevant for energy consumption estimation, i.e. the enclosed volume of each building, as well as the party wall areas, the exterior wall areas, the ground floor areas and the roof areas. As the detection of the party wall (i.e. the portion of the building shell that is shared between two buildings (BAG-panden)) is the most complex task to solve, specifically for a large data set, the main goal of the project is to define, evaluate and implement a methodology to compute the area extents of party walls between adjacent buildings from the 3DBAG data set. The 3DBAG dataset was first released in March 2021. A first revised version was released in September 2021 which we used for our analysis carried out during the first part of this project. The latest (5th) version has been released in October 2023 based on which we generated the final data for this project. In this last version AHN4 has been incorporated. 3DBAG is a country-wide dataset containing all buildings in the Netherlands, modelled in multiple LoDs, and based on the international standard CityGML. According to CityGML, a building can be modelled as a single-part unique object, or as an aggregation of building parts, each one having its own geometry. Additionally, each building is a geographical feature that can have several aTributes (e.g. year of construction, number of storeys, etc.) and different geometries representing each one a specific Level of Detail (LoD). A graphical overview of the different LoDs according to CityGML v. 2.0 is given in Figure 1. In particular, the LoD2 allows differentiating between different thematic surfaces composing the building envelope. The geometries are semantically enriched and classified into GroundSurfaces, WallSurfaces and RoofSurfaces. In the case of LoD2, RoofSurfaces represent the main planar surface(s) of the roofs. Smaller roof structures like chimneys and dormers are generally absent if their size is too small with regard to the surveyed data used for the 3D reconstruction process (e.g. the Lidar point cloud density). The GroundSurfaces generally correspond to the planar extents of the roof surfaces projected onto the horizontal ground but they can also correspond to footprints. WallSurfaces connect vertically the Roof- and GroundSurfaces. This means that overhanging geometries (e.g. of roofs) lead to larger GroundSurfaces as in reality. But roof overhangs can also be represented. A graphical example can be seen comparing LoD2 and LoD3 in Figure 1. Finally, in LoD2 buildings there are no openings, i.e. neither doors nor windows. More details about CityGML v. 2.0 and the modelling rules for the buildings can be found in the technical specifications of the standard published by the Open Geospatial Consortium.@en

Noise simulations are an important part of noise studies that investigate the impact of noise sources on the environment. In noise simulation, noise levels at receiver points are calculated based on the noise propagation paths between the receiver and source points. These paths are derived from the height of the terrain. In current calculation approaches implemented in noise simulation software, 3D polylines are used as input to describe the height of the terrain. These 3D polylines are semi-automatically generated to meet the highly demanding computing performance of simulation software. In addition, previous research showed that the reconstruction of appropriate height lines as used in noise simulation is very difficult to automate, if not impossible As a solution, this research investigates how noise propagation paths between receiver and source points can directly be generated from a Triangulate Irregular Network (TIN) without creating the height lines. This would allow us to use the automatically generated TIN as input for noise simulation instead of the height lines. In addition, a TIN enables better control of the quality of the data than height lines do. This study uses the 3D noise modeling guidelines of Common Noise Assessment Methods in Europe (CNOSSOS-EU). Algorithms have been developed and implemented in a prototype to generate and validate the paths between receiver and source points using a TIN that includes the buildings as well as the noise absorption properties of the terrain. The prototype is successfully tested on two scenarios from the Netherlands. Since CNOSSOS-EU guidelines were used, the prototype is applicable to the entire European Union and can be the first step in improving the automation of 3D noise modeling using currently available techniques and data.@en

De 3D BAG bevat automatisch gereconstrueerde LoD2-modellen van alle panden in Nederland, en is voor het eerst gereconstrueerd in het voorjaar van 2021 op basis van AHN3.1 Op basis van AHN4 is een nieuwe versie van de 3D BAG gereconstrueerd, in een samenwerking tussen 3DGI en de onderzoeksgroep 3D Geoinformation (TU Delft). AHN4 is niet alleen van hogere actualiteit, maar heeft ook andere kenmerken dan AHN3. Voor de geactualiseerde versie van 3D BAG hebben we daarom onderzocht hoe beide datasets optimaal gebruikt kunnen worden.@en

3D-representaties van onze leefomgeving zijn belangrijk in toepassingen die helpen bij de planning, de inrichting en het beheer van de openbare ruimte. Met 3D-modellen kunnen simulaties worden uitgevoerd voor bijvoorbeeld geluid, energie, luchtkwaliteit, windcomfort, zicht en wateroverlast. Om deze simulaties te bedienen met dezelfde 3D-gegevens, zijn standaarden essentieel. Voor bruikbaarheid in de praktijk moet een standaard voor 3D-data zowel specifiek als generiek zijn, eenvoudig gebruik van 3D-data mogelijk maken, en niet te grote bestanden opleveren. Dat is de motivatie geweest om CityJSON te ontwikkelen, een JSON-encoding (JavaScript Object Notation) van de OGC-standaard CityGML.@en

In this paper, we present our workflow to automatically reconstruct three-dimensional (3D) building models based on two-dimensional building polygons and a lidar point cloud. The workflow generates models at different levels of detail (LoDs) to support data require-ments of different applications from one consistent source. Specific attention has been paid to make the workflow robust to quickly run a new iteration in case of improvements in an algorithm or in case new input data become available. The quality of the reconstructed data highly depends on the quality of the input data and is monitored in several steps of the process. A 3D viewer has been developed to view and download the openly available 3D data at different LoDs in different formats. The workflow has been applied to all 10 million buildings of the Netherlands. The 3D ser-vice will be updated after new input data becomes available.

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3D-toepassingen gaan vaak gepaard met de wens om gebouwen met dakvormen te modelleren. Na jaren onderzoek en ontwikkeling hebben we in Delft een methode gerealiseerd die volledig automatisch dakvormen (LoD2) reconstrueert uit puntenwolken en 2D-pandpolygonen. Met deze methode hebben we 3D-modellen gegenereerd voor alle 10 miljoen BAG-panden in Nederland, de eerste open 3D-dataset op dit detailniveau. Niet alle toepassingen zijn gebaat bij dit detailniveau. Daarom reconstrueren we in hetzelfde proces ook andere detailniveaus. Het volledig automatisch proces zorgt ook in de toekomst voor consistentie als nieuwe modellen worden geconstrueerd met actuele input-data. Bovendien monitoren we verschillende kwaliteitsparameters die gebruikers kunnen helpen bij de juiste toepassing van de data. @en

Three-dimensional city models are essential to assess the impact that environmental factors will have on citizens, because they are the input to several simulation and prediction software. Examples of such environmental factors are noise (Stoter et al., 2008), wind (Garcı́a-Sánchez et al., 2014), air pollution (Ujang et al., 2013), and temperature (Hsieh et al., 2011; Lee et
al., 2013). However, those 3D models, which typically contain buildings and other man-made objects such as roads, overpasses, bridges, and trees, are in practice complex to obtain, and it is very time-consuming and tedious to reconstruct them manually. The software 3dfier addresses this issue by automating the 3D reconstruction process. It takes 2D geographical datasets (e.g., topographic datasets) that consist of polygons and “3dfies” them (as in “making them three-dimensional”). The elevation is obtained from an aerial point cloud dataset, and the semantics of the polygons is used to perform the lifting to the third dimension, so that it is realistic. The resulting 3D dataset is semantically decomposed/labelled based on the input polygons, and together they form one(many) surface(s) that aim(s) to be error-free: no self-intersections, no gaps, etc. Several output formats are supported (including
the international standards), and the 3D city models are optimised for use in different software.@en

Overheden moeten kunnen beoordelen of de geluidsbelasting op bijvoorbeeld een woonwijk binnen wettelijke limieten valt. Daarvoor moeten geluidsniveaus worden gesimuleerd. Het Kadaster, RIVM, RWS, IPO en TU Delft (3D Geoinformation) zijn in 2017 begonnen met een project om vanuit bestaande gegevens 3D-inputdata voor geluidsstudies te genereren, aansluitend op onze andere 3D-projecten (zoals bijvoorbeeld beschreven in de vorige Geo-Info). De algoritmes waren vorig jaar goed genoeg voor een landsdekkende uitrol. Sinds februari 2021 is dit 3D-omgevingsmodel voor Geluid als open data via PDOK beschikbaar.@en

Fully automated reconstruction of high-detail building models on a national scale is challenging. It raises a set of problems that are seldom found when processing smaller areas, single cities. Often there is no reference, ground truth available to evaluate the quality of the reconstructed models. Therefore, only relative quality metrics are computed, comparing the models to the source data sets. In the paper we present a set of relative quality metrics that we use for assessing the quality of 3D building models, that were reconstructed in a fully automated process, in Levels of Detail 1.2, 1.3, 2.2 for the whole of the Netherlands. The source data sets for the reconstruction are the Dutch Building and Address Register (BAG) and the National Height Model (AHN). The quality assessment is done by comparing the building models to these two data sources. The work presented in this paper lays the foundation for future research on the quality control and management of automated building reconstruction. Additionally, it serves as an important step in our ongoing effort for a fully automated building reconstruction method of high-detail, high-quality models.

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As in many countries, in The Netherlands governmental organisations are acquiring 3D city models to support their public tasks. However, this is still being done within individual organisation, resulting in differences in 3D city models within one country and sometimes covering the same area: i.e. differences in data structure, height references used, update cycle, data quality, use of the 3D data etc. In addition, often only large governmental organisations can afford investing in 3D city models (and the required knowledge) and not small organisations, like small municipalities. To address this problem, the Dutch Kadaster is collaborating with the 3D Geoinformation research group at TU Delft to generate and disseminate a 3D city model covering the whole of the Netherlands and to do this in a sustainable manner, i.e. with an implementation that ensures periodical updates and that aligns with the 3D city models of other governmental organisations, such as large cities. This article describes the workflow that has been developed and implemented.

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Semantically enriched 3D city models have the potential to be powerful hubs of integrated information for computer-based urban spatial analysis. This article presents the state of the art in 3D city modelling in the context of broader developments such as smart cities and digital twins, and outlines six challenges that must be overcome before 3D data as a platform becomes a reality.@en

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.@en

The 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.@en

Improving waste and resource management entails working on interrelations between different material flows, territories and groups of actors. This calls for new decision support tools for translating the complex information on flows into accessible knowledge usable by stakeholders in the spatial planning process. This article describes an open source tool based on the geodesign approach, which links the co-creation of design proposals together with stakeholders, impact simulations informed by geographic contexts, systems thinking, and digital technology—the Geodesign Decision Support Environment. Though already used for strategic spatial planning, the potential of geodesign for waste management and recycling is yet to be explored. This article draws on empirical evidence from the pioneering application of the tool to promote spatially explicit circular economy strategies in the Amsterdam Metropolitan Area.
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Before the interpretation of any text can start, the original wording of the text itself must be critically established. Conventionally, this is done following qualitative criteria. This article, however, explores the application of spatial analyses to New Testament textual criticism by demonstrating how the Levenshtein edit distance could be adapted to calculate confusion distances for variant readings in New Testament manuscripts, i.e. the possibility that a (combination of) letter(s) is confused by another (combination of) letter(s). Subsequently the outcomes are translated to Euclidian space using classical Multi-Dimensional Scaling, which enables visualisation and spatial analyses (in this case not related to geographical space). The article focuses on the data preparation and algorithm to make the data suitable for spatial analyses, thus providing the New Testament textual critic with new analytical tools.

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The 3D representation of buildings with roof shapes (also called LoD2) is popular in the 3D city modelling domain since it provides a realistic view of 3D city models. However, for many application block models of buildings are sufficient or even more suitable. These so called LoD1 models can be reconstructed relatively easily from building footprints and point clouds. But LoD1 representations for the same building can be rather different because of differences in height references used to reconstruct the block models and differences in underlying statistical calculation methods. Users are often not aware of these differences, while these differences may have an impact on the outcome of spatial analyses. To standardise possible variances of LoD1 models and let the users choose the best one for their application, we have developed a LoD1 reconstruction service that generates several heights per building (both for the ground surface and the extrusion height). The building models are generated for all ~10 million buildings in The Netherlands based on footprints of buildings and LiDAR point clouds. The 3D dataset is updated every month automatically. In addition, for each building quality parameters are calculated and made available. This article describes the development of the LoD1 building service and we report on the spatial analysis that we performed on the generated height values.

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Geluid is een groot probleem in stedelijk gebied. Om geluidsoverlast te verminderen, voeren vele overheidsorganisaties op allerlei niveaus geluidstudies uit. Geluidssimulatie is hierin een belangrijk onderdeel. De reken methode voor de simulatie van geluid is in Nederland gestandaardiseerd. Maar de benodigde 3D-inputgegevens worden voor iedere geluidstudie opnieuw gecreëerd. Dit is niet efficiënt, maar evenzo belangrijk is dat verschillen in inputgegevens kunnen leiden tot verschillen in de uitkomsten. Daarom bekijken we in een samenwerking van RIVM, Rijkswaterstaat, het Kadaster, IPO en de TU Delft hoe de benodigde inputgegevens over de fysieke ruimte automatisch kunnen worden gegenereerd uit landsdekkende gegevensbronnen zoals de BGT, de BAG en het AHN en voor overheden beschikbaar kan worden gesteld.@en

Different urban issues urgently request up-to-date, valid, accurate, suitable, error-free 3D data of the living environment. A dynamic, virtual copy of cities, landscape, and buildings enables to continuously monitor what is happening in reality and evaluate different scenarios through simulation. These simulations help address issues like flooding, noise pollution, air pollution, and urban heat islands. Methods are developed to reconstruct, validate, and disseminate virtual 3D models. The resulting methods are implemented in open-source software, such as PolyFit, MVStudio, 3dfier, and Val3dity. The resulting semantic rich 3D data of the living environment is structured and tailored for specific uses.@en