This paper presents the lessons learnt from the integration of open datasets in the Netherlands for the creation of a country-wide enriched semantic 3D city model for urban building energy modelling. Although the Netherlands provides open access to building data up to the dwelling level, several challenges still remain related to data fragmentation, inconsistency, and incompleteness. The resulting dataset uses the CityGML with the Energy ADE data model since they offer a robust framework for integrating geospatial and non-geospatial data for energy applications. Our research highlights the need for significant preprocessing, harmonisation pipelines, and enrichment strategies to address gaps in data completeness and reliability. Finally, we identify critical missing data (e.g., renovation history, thermal zoning, and detailed HVAC specifications) and propose directions for improvement.

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt, and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas—the Santana district in São Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands—were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

In the abstract, there is a typo in the name of the study area in the Netherlands: It is “Henio,” it should be “Heino.” [...]

The urban socio-ecological transformation requires pathways for an urban energy transition, including the establishment of Positive Energy Districts (PEDs). Technical solutions and simulation tools for urban energy systems are needed for the planning, management and implementation of PEDs. In addition, the involvement of all societal stakeholders is needed to achieve the EU's ambitious target of 100 PEDs by 2025. To this end, innovative research, information and communication strategies must be developed.The transnational funded research project DigiTwins4PEDs focuses on developing an Urban Digital Twin as a dynamic digital representation of urban energy systems using advanced modelling tools. The framework facilitates the integrated energy demand-supply analysis at district scale. It enables the construction and analysis of future development scenarios to simulate the performance of PEDs.It supportsinformed decision-making by citizens and urban administration for a sustainable urban energy transition. The transnational project applies innovative methods and develops implementation strategies supported by a participatory process involving key stakeholders and citizens in co-design, co-creation and co-learning stages of research. Through the framework of living labs in four different case studies, citizens are continuously engaged throughout the project so that citizen-driven actions towards Positive Energy Districts can be considered and implemented more efficiently. New tools and methods are developed and adapted using Urban Digital Twins based on the CityGML data format to enhance public participation in advancing clean energy transition. These toolsenable citizens to actively engage in shaping the future energy transition of their communities and thus supporting informed decision-making.The developed and implemented urban digital twin framework is tested in different urban case study areas (Vienna, Stuttgart, Rotterdam, Wroclaw)within an innovative public participatory process to address the multifaceted aspects crucial for establishing PEDs together with the citizens. This paper discusses the concept and first prototype of the developedparticipatory planning framework, a shared urban data and modelling scheme,utilizing a digital twin, as well as itsimplementation.It will show how the developed frameworkenables the simulation of urban energy systems with integrated local socio-economic and demographic parameters to identify and visualise current and future energy demand, renewable potential and different energy flexibility strategies in a district. It will discuss how the developed framework can be integrated/combined with other citizens’ engagement tools, focussing on the ones used in the case study sites. Furthermore, we draw conclusions on how this framework can be used to support co-design, co-creation and co-learning of community-driven solutions for energy transformation.

Digital Twins (DTs) are transforming construction and energy management sectors by integrating 3D surveying, monitoring, Building Performance Simulation (BPS), and Building Energy Simulation (BES) from the earliest design or retrofit stages. Moreover, dynamic thermal simulations further support energy performance assessments by modeling indoor conditions to meet comfort and efficiency targets. However, their reliability depends on accurate, standards-compliant 3D building models, which are costly to create. This research introduces a complete framework for automatically generating energy-focused Digital Twins (EDTs) directly from unstructured point clouds. Combining Deep Learning-based instance detection, Scan-to-BIM techniques, and computational geometry, the method produces simulation-ready models without manual intervention. The resulting EDTs streamline early-stage performance evaluation, enable scenario testing, and enhance decision making for energy-efficient retrofits, advancing smart-building design through predictive simulation.

With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to better understand the energy performance of the building stock and can contribute to defining refurbishment strategies. However, UBEM applications require lots of heterogeneous data, eventually advocating for standards for data interoperability. The Energy Application Domain Extension has been created to cope with UBEM data requirements and offers a standardised data model that builds upon the CityGML standard. The Energy ADE 1.0, released in 2018, creates new classes and adds new properties to existing classes of the CityGML 2.0 Core and Building modules. CityGML 3.0, released in 2021, has introduced several changes to the data model and its ADE mechanism. These changes render the Energy ADE incompatible with CityGML 3.0. This article presents how the Energy ADE has been ported to CityGML 3.0 to allow, on the one hand, for a lossless data conversion and, on the other hand, to exploit the new characteristics of CityGML 3.0 while keeping a logical symmetry between the ADE classes of both CityGML versions. The article describes the chosen methodology, the mapping strategies, the implementation steps, as well as the data conversion tests to check the validity of the “new” Energy ADE for CityGML 3.0.

With the current high speed and scale of urbanisation, there is a growing demand for affordable housing – together with all other aspects that are tightly related to it: infrastructure for transportation, utility networks, etc. For this reason, integrated planning is playing more and more a crucial role as the impacts of a new construction project should be investigated, evaluated and minimised from the very early stages of the design process (Josuf et al., 2017; Agugiaro et al., 2020). [...]

Urban areas face increasing pressure due to densification, presenting numerous challenges involving various stakeholders. The impact of densification on human well-being in existing urban areas can be both positive and negative, which requires a comprehensive understanding of its consequences. Computational Urban Design (CUD) emerges as a valuable tool in this context, offering rapid generation and evaluation of design solutions, although it currently lacks consideration for human perception in urban areas. This research addresses the challenge of incorporating human perception into computational urban design in the context of urban densification, and therefore demonstrates a complete process. Using Place Pulse 2.0 data and multinomial logit models, the study first quantifies the relationship between volumetric built elements and human perception (beauty, liveliness, and safety). The findings are then integrated into a Grasshopper-based CUD tool, enabling the optimization of parametric designs based on human perception criteria. The results show the potential of this approach. Finally, future research and development ideas are suggested based on the experiences and insights derived from this study.

This paper presents a process to develop a CityGML-based 3D city model that, together with results from a flood simulation, can be used to investigate direct and indirect effects of floods on a city, its inhabitants and its critical infrastructure, and to quantify such effects by means of a Flood Resilience Score. In addition, the model can be used as a spatial planning support tool for urban planners to prioritise the redevelopment of certain areas and to test new spatial design decisions. First, a semantic 3D city model is prepared and enriched with additional building and infrastructure information. Then a Flood Resilience Score (FReSco) is defined and computed by quantifying the direct and indirect impacts of flooding on buildings, households, and critical infrastructure points using information from both the 3D city model and the flood simulation results. Lastly, a prototype of a spatial planning support tool is proposed to evaluate the flood resilience of a new environmental plan. As a case study, the neighbourhood of “Nieuw Kralingen” in Rotterdam was chosen. Overall, the outcomes of this work are meant to help cities better understand the impacts of flooding and adjust their urban planning activities accordingly. At the same time, the developed methodology also tests the strengths and limits of CityGML-based 3D city models in combination with openly available data and software.

Our built environment is facing critical environmental and societal challenges due to climate change, housing crisis, ageing population, and many other factors. Therefore, large-scale sustainable transitions of the built environment are urgently needed. Digitalization has an immense potential to accelerate a sustainable transition towards the adoption of renewable energy, the use of bio-based and circular materials in construction and renovation projects, and the construction/adaptation of climate-resilient buildings and urban infrastructures for the well-being of the citizens. At the same time, there is still a need for new knowledge covering, for example, topics such as Artificial Intelligence, Robotics, Augmented/Virtual Reality, and Digital Twinning - and how they relate to the transition of the built environment. The annual “Research Day on Digitalization of the Built Environment” is an initiative of the four Dutch universities of technology (4TU) in collaboration with the fourteen Dutch universities of applied sciences (14UAS) to bring together researchers and to foster knowledge exchange in the aforementioned topics and challenges. This open-access book contains the proceedings of the 3rd Research Day on Digitalization of the Built Environment in the form of selected extended abstracts. The event was jointly organized on 25 April 2024 by Inholland University of Applied Sciences, The Hague University of Applied Sciences, and Delft University of Technology.

This paper introduces a new plug-in for QGIS that allows to connect to the free and open-source 3D City Database to load CityGML data, structured as classic GIS layers, into QGIS. The user is therefore not required to be a CityGML specialist, or a SQL expert, as the plug-in takes care of hiding from the user most of the complexity in terms of underlying data model and database schema implementation. The user can therefore load CityGML thematic “layers” (e.g. for buildings, bridges, vegetation, terrain, etc.), explore their geometries in 2D and 3D and access and edit the associated attributes. At the same time, depending on the user privileges, it is possible to delete features from the database using either normal QGIS editing tools, or a “bulk delete” tool, also included. The plug-in is composed of two parts, a server-side one, which must be installed in the 3D City Database instance, and the client-side one, which runs as a QGIS plug-in in strict sense. A GUI-based tool is also provided for database administrators in order to install/uninstall the database-side part of the plug-in, and manage users and their privileges. All in all, the 3DCityDB-Tools plug-in facilitates the access to CityGML data for GIS practitioners from heterogeneous fields and expertise with the common denominator being the well-known QGIS environment.

Solar energy is becoming increasingly important with the transition towards green and sustainable energy. Predicting solar irradiance is one of the core steps to optimise solar energy utilisation when planning and scheduling power grids. Accurate solar irradiance prediction can also help forecast microclimate conditions, enabling the analysis of citizens and planning of optimal intervention strategies for heating or cooling behaviour. This paper discusses a novel approach to computing the solar potential of buildings at the city level with promising scalability using semantic 3D city models. Experiments are conducted at different locations in the Netherlands. We evaluate our results by comparing them to the statistical Dutch data, and CitySim shows huge discrepancies in summer.

This paper presents a CityGML-based data model developed for the semantic 3D city model of Vienna, Austria. The data model consists in a profile of the CityGML 2.0 standard and has been extended by means of an Application Domain Extension (ADE) developed by the Department for Surveying and Mapping of the City of Vienna in order to comply with the current and future needs of the municipality. The definition and adoption of such data model are a fundamental part of Vienna’s “Digital geoTwin” project. The core of the strategy is to process the 3D measurement data of the surveying and mapping department from existing as well as new measurement methods directly into a Digital geoTwin—a virtual, semantic 3D replica of all objects in the city—and to derive other geodata products (city map, elevation models, etc.) from this 3D model. Furthermore, the Digital geoTwin should serve as a geometric and semantic basis for a digital twin of the City of Vienna. In order to define the data model for the Digital geoTwin, 3D modelling of all city objects has been carried out in a test area of the city, followed by a mapping of the objects to the CityGML data model. In an iterative development process, conceptual gaps have been identified, analysed and eventually formalized into a UML-based Application Domain Extension. Additionally, the free and open-source CityGML 3D City Database (3DCityDB) has been used for storage after being extended accordingly, and FME workbenches have been created to transform and import the original source data into the 3DCityDB and therefore test the suitability of the developed data model.

Historic gardens, regarded as a significant genre of cultural heritage, encapsulate the enduring essence of bygone eras while concurrently transcending temporal boundaries to resonate with the present and future. These gardens provide us vitality and inspiration, holding a collective repository of human memory and serving as a testament to our shared heritage. However, like landscapes, gardens constantly change through natural processes and human interventions. How can we preserve these gardens, though changes are unavoidable? Spatial and visual characteristics are the gardens' essential characteristics, and point-cloud (LiDAR) technologies are powerful tools to reveal and analyze gardens’ spatial-visual relationships and characteristics. Therefore, this paper aims to present a point-cloud-based approach to identifying spatial-visual design principles and making them operational to protect and develop historic gardens. Additionally, several methods have been proposed in this research, including (a) a voxel-based method to transfer points into a solid model for GIS-based computation, (b) a novel method to analyze the field of view (FOV), and (c) a systemic framework to reveal historic gardens’ spatial-visual characteristics based on the voxelized model. Jichang Garden, a historic garden in Wuxi, China, known for its visual design and spatial arrangement, has been selected as a case study to showcase how to apply the methods proposed by this paper. The findings include the design principles for the water body, the arrangement for a route, and the planting strategies of the garden. The conservational strategies have been formed based on the findings, and the appliable potentials and limitations of the methods have also been discussed.

Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact on the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this paper, we present our method to perform shadowing calculations in urban areas using semantic 3D city models, which is split into five sections: Point Grid Generation, Sun-Ray Generation, Nightside Filtering, Bounding Volume Hierarchy and the intersection between the sun rays and the BVH to identify which locations are shadowed at a given moment (epoch). Our tests are performed in Rotterdam’s city center, a dense urban area in The Netherlands. Our initial results indicate that the computational time per 100 k grid points fluctuates within 0.2–0.7s.

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the Architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations.

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.

This paper describes the proposed methodology, the implementation, and the experience resulting from the further development of a tool, embedded in Rhinoceros/Grasshopper, that allows to perform preliminary environmental analyses at district scale in the case of a new planned building. The CAD-based parametric 3D model of a “new” building, generated in Grasshopper, is enriched with and embedded into a 3D urban scene of the block/district where it is planned to be built. The resulting 3D scene is then used to perform shadowing, solar and wind analyses that are used by architects and engineers in their preliminary development phases of the project. The work stems from a preliminary analysis in terms of data and software requirements carried out between practitioners from both the GIS and AEC domain.

More in detail, a series of modules in Grasshopper have been developed that allow to import GIS “surrounding” data at district scale (e.g. buildings, terrain) and to blend them with the “new” building model, in order to perform environmental analyses in (near) real time while the designer interactively changes the design parameters of the building and its position. The paper presents the results and discusses the inherent limitations.

Current GIS software offer tools to perform the solar irradiance calculations. However, these computations based their work on data assumptions or generalisations to speed up their processing time. In this work, a method is shown to perform the calculation using very high and very low spatial resolution open datasets. The results show that there too detailed raster data like 50cm horizontal spatial resolution DSM does not improve the calculations compared to lower resolution datasets.