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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., Landscape Architecture, Urban Data Science

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., Urban Data Science

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., Urban Data Science

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, Urban Data Science

This paper focuses on GIS-based visibility analysis to explore landscape architecture com-positions as a means to understand visual-spatial characteristics and identify related design principles. More specifically, the paper elaborates a practical method to employ high-resolution data acquired by terrestrial LiDAR (Light Detection and Ranging or Laser Imaging Detection and Ranging) for this pur-pose. Though LiDAR provides a powerful means to digitally capture the real-world, methods like GIS-based visual landscape research that utilize viewshed analysis ideally require a continuous Digital Landscape Model conduct visibility computation. Therefore, one of the obstacles before the visual analysis is to process the points into surface or solid models. Voxel-based algorithms are powerful means to process LiDAR data. There are many applications known of voxel-based visibility analysis but requires often specialist software that is hard to handle or unavailable to landscape architects in practice. This paper showcases an attempt to use standard software for voxel-based visibility analysis. It presents a practical method for applications in landscape architecture analysis. Jichang Garden (Wuxi, China) is used as an example. The historical garden is an evocative example of a landscape architecture compo-sition that displays skilful applications of spatial-visual design principles and therefore worthy of ana-lyse., Landscape Architecture, Urban Data Science

In this paper, five commonly used software tools to estimate solar radiation in the urban context (GRASS GIS, ArcGIS, SimStadt, CitySim and Ladybug) are run on the same test site and are compared in terms of input data requirements, usability, and accuracy of the results. Spatial and weather data have been collected for an area located in the Brazilian city of São Paulo, in the district of Santana. The test area surrounds a weather station, for which meteorological data of the last 15 years have been collected and used as ground truth when analysing and comparing the simulation results. In terms of spatial data, raster-and vector-based models of the study area have been generated in order to comply with the different input requirements. More specifically, in the case of the vector-based tools (SimStadt, CitySim and Ladybug), a common 3D model based on CityGML and containing buildings, vegetation (trees) and terrain has been generated and used as a common urban model. The paper presents the findings and discusses the results not only from a numerical point of view, but also from the perspective of the overall usability of the software in terms of data requirements, simulation time and task automatisation., Urban Data Science

Data on the number of floors is required for several applications, for instance, energy demand estimation, population estimation, and flood response plans. Despite this, open data on the number of floors is very rare, even when a 3D city model is available. In practice, it is most often inferred with a geometric method: elevation data is used to estimate the height of a building, which is divided by an assumed storey height and rounded. However, as we demonstrate in this paper with a large dataset of residential buildings, this method is unreliable: <70% of the buildings have a correct estimate. We demonstrate that other attributes and characteristics of buildings can help us better predict the number of floors. We propose several indicators (e.g. construction year, cadastral attributes, building geometry, and neighbourhood census data), and we present a predictive model that was trained with 172,000 buildings in the Netherlands. Our model achieves an accuracy of 94.5% for residential buildings with five floors or less, which is an improvement of about 25% over the geometric approach. Above five floors, our model has only a slight improvement on the geometric approach (5%). The main culprit is the lack of training data for tall buildings, which is uncommon in the Netherlands., Urban Data Science

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., Urban Data Science

This document introduces the process for the creation of a testbed for energy applications based on a semantic 3D city model for the municipality of Rijssen-Holten in The Netherlands. The creation of this dataset requires the consolidation from multiple data sources as well as a lot of manual work so the authors can warranty as much as possible the quality of the dataset so in can be used in several use cases. The data is stored following the OGC standard CityGML v2.0 and contain the geometrical and semantical information of CityObjects from the thematic modules Building, Vegetation and Relief. This data set consolidates the open weather data from the closest weather station to the study area located in Heino in the Netherlands. We discuss the decisions taken during the manual data collection process and we present some use cases that have already consume the dataset at the time of writing this document., Urban Data Science

In the last 15 years semantic 3D city models have seen a steady growth in terms of creation and adoption. Many cities world-wide have now at least one city model which can be used for several applications. Energy- and sustainability-related topics are among those that have experienced a noteworthy increase of interest from the Geomatics community. 3D city models have become a steady component of Urban Energy Modelling, in which bottom-up approaches are developed to assess, for example, the energy efficiency of the building stock and to explore different scenarios of building refurbishment. Within this context, this paper focuses on investigating how much party walls can contribute to the energy demand estimation of a building. For this reason, two approaches to compute party walls are described and compared. The nature and the magnitude of their differences, as well as their possible impact on downstream applications, are considered in order to shed light on whether discrepancies in the amount of computed party wall area might lead to significant differences in terms energy demand of the residential building stock. The case study area is located in the Netherlands and encompasses the municipality of Rijssen-Holten., Urban Data Science

3D city models are playing a growing role worldwide as sources of integrated information upon which different urban applications are developed. In the context of urban planning and design, semantic 3D city models can provide plenty of qualitative and quantitative information about the urban context and of the area(s) to be transformed. This paper takes inspiration and continues a work recently published in which several design parameters and Key Performance Indicators are computed from a semantic 3D city model, and later used in a GIS-supported urban design process to develop a new area. As many of such parameters are derived from the gross volume of the building stock, this paper investigates whether and to which extent different building stock models might affect the estimation of the gross volume. The study is carried out in anticipation of the upcoming LoD2-based, country-wide model of the Netherlands that is being finalised by our team. At the same time, the paper investigates whether and which information can be obtained regarding the quality of the LoD2 model from a comparison with the LoD1 one, with a focus on volume calculation., Urban Data Science

Worldwide, cities are nowadays formulating their own sustainability goals, including ambitious targets related to the generation and consumption of energy. In order to support decision makers in reaching these goals, energy experts typically rely on simulation models of urban energy systems, which provide a cheap and efficient way to analyze potential solutions. The availability of high-quality, well-formatted and semantically structured data is a crucial prerequisite for such simulation-based assessments. Unfortunately, best practices for data modelling are rarely utilized in the context of energy-related simulations, so data management and data access often become tedious and cumbersome tasks. However, with the steady progress of digitalization, more and more spatial and semantic city data also become available and accessible. This paper addresses the challenge to represent these data in a way that ensures simulation tools can make use of them in an efficient and user-friendly way. Requirements for an effective linking of semantic 3D city models with domain-specific simulation tools are presented and discussed. Based on these requirements, a software prototype implementing the required functionality has been developed on top of the CityGML standard. This prototype has been applied to a simple yet realistic use case, which combines data from various sources to analyze the operating conditions of a gas network in a city district. The aim of the presented approach is to foster a stronger collaboration between experts for urban data modelling and energy simulations, based on a concrete proof-of-concept implementation that may serve as an inspiration for future developments., Urban Data Science

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., Physical and Space Geodesy, Urban Data Science

The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow.

The 3D BAG v. 2.0 dataset has been recently released: it is a country-wide dataset containing all buildings in the Netherlands, modelled in multiple LoDs (LoD1.2, LoD1.3 and LoD2.2). In particular, the LoD2.2 allows differentiating between different thematic surfaces composing the building envelope. This paper describes the first steps to test and use the 3D BAG 2.0 to perform energy simulations and characterise the energy performance of the building stock. Two well-known energy simulation software packages have been tested: SimStadt and CitySim Pro. Particular care has been paid to generate a suitable, valid CityGML test dataset, located in the municipality of Rijssen-Holten in the central-eastern part of the Netherlands, that has been then used to test the energy simulation tools. Results from the simulation tools have been then stored into the 3D City Database, additionally extended to deal with the CityGML Energy ADE. The whole workflow has been checked in order to guarantee a lossless dataflow. The paper reports on the proposed workflow, the issues encountered, some solutions implemented, and what the next steps will be., Urban Data Science

In both research and practice, data interoperability is considered essential to support a rising number of applications that need data from different domains. Therefore, standard data formats and models are developed by standardization organizations such as Open Geospatial Consortium (for the Geo domain) and buildingSMART (for the BIM domain). However, from practical experiences, problems were noticed (standards implementation and use of standardized data). Nevertheless, it was hard to discover what the most serious issues were and what was their reason. For GeoBIM (integration of geoinformation with building information models), the CityGML standard, by Open Geospatial Consortium, and Industry Foundation Classes (IFC) by buildingSMART, were considered and tested in this initiative. A sample of datasets in the two formats were provided. External volunteers were asked to import the datasets in tools supposed to support the standards, check relevant aspects for the use of data (geometry, semantics, georeferencing, functionalities), report on them, and re-export the datasets in the standard format. Other tasks were intended to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. This benchmark was useful to gather best practices and data about the functioning of useful tools to manage standardized data. Common behaviors and potential problems were pointed out and the discussion about the use of standards was further pushed., Urban Data Science

In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This article proposes a methodology to estimate the average size of a dwelling in existing urban areas from available open data, and to use it as one of the design parameters for new urban-development projects. The proposed unit of measure, called “living space”, includes outdoor and indoor spaces. The idea is to quantitatively analyze the city of today to help design the city of tomorrow. First, the “typical”-dwelling size and a series of Key Performance Indicators are computed for all neighborhoods from a semantic 3D city model and other spatial and non-spatial datasets. A limited number of neighborhoods is selected based on their similarities with the envisioned development plan. The size of the living space of the selected neighborhoods is successively used as a design parameter to support the computer-assisted generation of several design proposals. Each proposal can be exported, shared, and visualized online. As a test case, a to-be-planned neighborhood in Amsterdam, called “Sloterdijk One”, has been chosen, Urban Data Science, Theory & Territories

The organisational data models that support the information needs of utility network managers are proprietary and domain-specific, while the emerging national standards in this field often lack lifecycle data representation capabilities. However, multiple types of utility networks can be comprehensively represented with the free and open-source Utility Network Application Domain Extension (ADE) of the international standard CityGML. The Operation & Maintenance (O&M) Domain Ontology is a proposed extended version of the Utility Network ADE that allows for consistent and comprehensive processing, storage and exchange of O&M-related utility network data. So far, this ontology has not yet been implemented in a spatial-relational database. Consequently, the support it offers during routine utility asset management tasks has remained untested. This paper, therefore, tests the support of the O&M domain ontology for asset management and proposes a database implementation of this data model. To this end, it models and loads two utility networks from the campus of the University of Twente, the Netherlands. It tests the ontology's support for asset management by simulating a street reconstruction project and retrieving necessary project information in relation to a utility's (a) maintenance history and performance, and (b) site conditions and valve locations. Results show that the implemented model supports projects with rapid, comprehensive, and consistent information about semantic details of utilities. Such data needs yet to be collected and registered systematically to enable future data-driven asset management practices., Urban Data Science

Urban mining aims at reusing building materials enclosed in our cities. Therefore, it requires accurate information on the availability of these materials for each separate building. While recent publications have demonstrated that such information can be obtained using machine learning and data fusion techniques applied to hyperspectral imagery, challenges still persist. One of these is the so-called 'salt-And-pepper noise', i.e.The oversensitivity to the presence of several materials within one pixel (e.g. chimneys, roof windows). For the specific case of identifying roof materials, this research demonstrates the potential of 3D city models to identify and filter out such unreliable pixels beforehand. As, from a geometrical point of view, most available 3D city models are too generalized for this purpose (e.g. in CityGML Level of Detail 2), semantic enrichment using a point cloud is proposed to compensate missing details. So-called deviations are mapped onto a 3D building model by comparing it with a point cloud. Seeded region growing approach based on distance and orientation features is used for the comparison. Further, the results of a validation carried out for parts of Rotterdam and resulting in KHAT values as high as 0.7 are discussed., Environmental Technology and Design, Urban Data Science, Building Physics

A bottom-up building energy modelling at the urban scale based on Geographic Information System and semantic 3D city models can provide quantitative insights to tackle critical urban energy challenges. Nevertheless, incomplete information is a common obstacle to produce reliable modelling results. The residential building heating demand simulation performance gap caused by input uncertainties is discussed in this study. We present a data-driven urban scale energy modelling framework from open-source data harmonization, sensitivity analysis, heating demand simulation at the postcode level to Bayesian calibration with six years of training data and two years of validation data. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity in the study area have significantly improved from 25.0% to 8.3% and from 19.9% to 7.7% in two validation years, while CVRMSE2016=11.5% and CVRMSE2017=13.2%. The overall methodology is extendable to other urban contexts., Intelligent Electrical Power Grids, Urban Data Science

GIS has become an important part of many disciplines and supports a vast range of applications. It is used everywhere, from agriculture to public health care. Furthermore, with the advances in technologies, the availability of GIS data and software support has grown exponentially. In this paper, we present the results of our international survey to investigate the adoption of geospatial data, standards, and software by the practitioners in different application domains. The results demonstrate a clear trend towards the increased use of GIS in a number of application domains including architecture, geosciences, hydrology, and so on. We also explore the expectations of the users from the GIS technologies and provide some insight into the current status of 3D GIS data and its applications., Urban Data Science

In urban planning, a common unit of measurement for population density is the number of households per hectare. However, the actual size of the households is seldom considered, neither in 2D nor in 3D. This paper proposes a method to calculate the average size of the household in existing urban areas from available open data and to use it as a design parameter for new urban development. The proposed unit of measurement includes outdoor and indoor spaces, the latter comprising both residential and non-residential spaces. As a test case, a to-be-planned neighbourhood in Amsterdam, called Sloterdijk One, was chosen. First, the sizes of “typical” households, as well as a series of KPIs, were computed in existing neighbourhoods of Amsterdam, based on their similarities with the envisioned Sloterdijk One plan. Successively, the resulting size of the household was used as a design parameter in a custom-made tool to generate semi-automatically several design proposals for Sloterdijk One. Additionally, each proposal can be exported as a CityGML model and visualised using web-based virtual globes, too. Significant differences among the resulting proposals based on this new unit of measurement were encountered, meaning that the average size of a household plays indeed a major role., Urban Data Science, Theory & Territories

Urban Data Science

Smart management of urban built environment relies on the availability of data supporting sound policy making and guiding city renovation processes toward more sustainable and performant models. Nevertheless, public managers are unlikely to have comprehensive information on the existing building stock. In addition, tools providing effective insights on potential costs and benefits of retrofit strategies at city/district scale are hardly available. This article describes how data related to existing buildings may be effectively combined together into a so-called Building Information System, and discusses the advantages and shortcomings related to this process. At the same time, the implementation on a real case study in northern Italy demonstrates how the effort due to data harmonization and integration is able to foster applications to support policy makers in the management of the built environment and in the definition of urban sustainability strategies. Building data were harmonized according to the requirements of the international open standard CityGML, therefore facilitating the exchange of building information. The whole project was carried out while considering the characteristics of data sources that are available for each public body in Italy and, as a consequence, it may be replicated to other Italian municipalities., Urban Data Science

An investigation into the implementation state of open standards in software is currently ongoing through the ISPRS/EuroSDR ‘GeoBIM benchmark 2019’ initiative, which kicked off earlier this year. The benchmark activity provides a way of assessing and comparing the functionality of different software packages in GIS and BIM in terms of their ability to handle standardised data (IFC and CityGML) and undertake various tasks using this data. Approximately 65 people have registered to participate so far, with participants from a wide range of backgrounds and proposing to test a variety of software packages. This confirms that the issues under investigation are of interest, and also meets the wider benchmark aim of having a variety of participants, since the project is conceived as using a bottom-up approach with cross-disciplinary and cross-expertise participation. While full benchmark results are not due to be submitted until later this year, interim results have highlighted a number of common issues across multiple software packages, and a web meeting for participants held in July 2019 also led to some improvements in how the benchmark results are being captured., Urban Data Science