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

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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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

Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure are benchmarked to determine the best solution for storing and querying large MD hydrologic datasets. Expert consultancy was conducted to establish benchmark sets, with the HydroNET-4 system being utilized to provide the benchmark environment. In the final benchmark tests, the effect of data storage configurations, elaborating chunk size, dimension order (spatio-temporal clustering) and compression on the query performance, is explored. Results indicate that for big hydrologic MD data management, the properly chunked NetCDF-4 solution without compression is, in general, more efficient than the SciDB DBMS. However, benefits of a DBMS should not be neglected, for example, the integration with other data types, smart caching strategies, transaction support, scalability, and out-of-The-box support for parallelization.@en

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

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.@en

Er zijn veel thema’s in de Omgevingswet die een 3D-component hebben. Er is dan ook een groeiende vraag om 3D geo-informatie in het DSO (Digitaal Stelsel Omgevingswet) te faciliteren. Een goede basis hiervoor is 3D BGT. IMGeo, het informatiemodel dat de inhoud van de Basisregistratie Grootschalige Topografie (BGT) vastlegt, is voorbereid op 3D en zou daarmee een basis kunnen zijn voor deze 3D informatiebehoefte. Hoe werkt de implementatie van 3D IMGeo in de huidige praktijk? Is de 2D BGT, nu deze bijna landsdekkend beschikbaar is, eenvoudig uit te breiden naar 3D? Zijn de 3D IMGeo specificaties eenduidig en geschikt voor 3D-toepassingen? En hoe kan een 3D-versie van de BGT worden geïmplementeerd inclusief beheer en ontsluiting?@en

Standaarden voor het beheer, de modellering, uitwisseling en ontsluiting van 3D informatie zijn essentieel bij 3D ontwikkelingen: standaarden voor 3D data bieden softwarebedrijven een solide basis voor investeringen. Bovendien maken 3D standaarden hergebruik van 3D data mogelijk.@en