Conference Agenda

Cities are increasingly growing in size and becoming denser. This situation calls for strategic planning of green infrastructure in the urban planning process. Safeguarding the green infrastructure is important for maintaining urban ecosystem services and increasing the well-being of urban populations. To facilitate appropriate urban planning and enabling cities to grow sustainably, it is important that the geospatial community provides adequate vegetation information. In this study, we investigate the need for vegetation information in urban planning applications such as modelling ecosystem services and noise, as well as performing case studies of using vegetation information in daylight and solar energy simulations. Based on these investigations, we formulate a recommendation of how vegetation information should be included in 3D city models. The study is focused on the development of a Swedish national profile of CityGML, but many of the conclusions are general. The recommendations are, in short, that: (1) the vegetation theme should follow CityGML 3.0 with some additional attributes (e.g., popular name of tree species) added as an application domain extension, (2) no LOD division is required for the vegetation information stored (but rather derived if necessary), (3) the vegetation theme should only contain 3D vegetation objects while the 2D vegetation is part of the land cover theme, and (4) the building specification (and city furniture specification) must include the possibility to store information if the roof and facades (and walls) are covered with vegetation.

Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact in the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this extended abstract, we present our method to perform shadowing calculations in urban areas using semantic 3D city models. Our initial results allow the identification of locations that are shadowed by nearby buildings at a given epoch. For the final version of the paper, we expect to compare our results with existing works regarding timing and accurancy assesment.

Natural disasters, conflicts and vulnerable supply chains are challenging conditions for the humanitarian aid delivery. This study analyses how a multimodal 3D situational awareness map displaying remote sensing and other geo data could support teleoperated truck missions in this difficult environment, e.g. for route planning, terrain analysis or evaluation purposes. A key focus of this work is the additional value of a 3D visualization compared to established 2D mapping applications. Structured interviews were conducted with end users, scientists and engineers to identify their needs and requirements for a 3D situational awareness map. Based on the outcomes an exemplary 3D web application integrating geodata and crisis information from remote sensing, governmental and open sources was designed and implemented for test sites in Bavaria. In situ drone imagery was captured and added to increase the local situational awareness. The live position of the truck was transmitted to the application during operation and displayed as 3D model. The created web-based application was very well received by the end users. Especially the inclusion of drone imagery draped on the derived surface model in combination with the available satellite data provided a high additional value by highlighting steep slopes or other blockages in the truck’s path.

Solid waste management is a crucial challenge for achieving city sustainability [1]. In 2020, it was estimated that around 2.24 billion metric tons of municipal solid waste were generated worldwide [2]. This waste increased later due to medical waste produced during the COVID-19 pandemic [3, 4]. Approximately 33% of the overall waste generated is not n environmentally safe ways [5]. This has several negative impacts, including health risks, sewage system blockages, soil contamination, and potential disease vectors [6–9]. Despite not being included as a primary Sustainable Development Goal (SDG), addressing solid waste management is related to 12 out of 17 SDGs, making it essential to achieve city sustainability [10, 11].

In South Africa, 30.5 million tons of solid waste were generated in 2017, with only 34.5% being recycled and 11% not having adequate final disposal [12, 13]. The country has an estimated generation of 1.48 kg/capita/day of solid waste, which is higher than the Sub-Saharan average and at similar levels to some countries in Europe and Central Asia [5]. One of the primary challenges in South Africa is reducing the waste disposed in landfills [13], which is hindered by littering, illegal dumping, lack of regular collection services, incomplete coverage, and historical spatial and service delivery inequalities [14, 15].

The growing demand for sustainable mobility has led to an increased focus on the development and improvement of bicycle infrastructure, especially within cities. However, evaluating the quality of existing or planned bicycle paths is a complex task mostly done manually. This paper presents a novel approach for automatically evaluating the service quality of bicycle paths using parameters derived from semantic 3D city and streetspace models compliant with the international OGC standard CityGML version 3.0. These models contain detailed 3D information with lane-level accuracy, including precise outlines of individual surfaces. This allows for accurate and high-resolution evaluations of changing bicycle path widths and slopes, as well as information on adjacent surfaces and local disturbances such as bus stops. Additionally, estimated, measured or simulated bicycle traffic volumes are considered. Based on these parameters a method for calculating the Bicycle Levels of Service (BLOS) described in a national technical regulation is adapted and implemented for a microscopic analysis. Results of this analysis are then transferred back to the original semantic 3D city objects, allowing for the attributive description of BLOS values for bicycle paths. In addition, results are visually represented by coloring corresponding bicycle path segments according to evaluation results and integrating the colored objects within a web-based Cesium visualization of a semantic 3D city model.

Point clouds significantly differ from other geodata in terms of their computational nature rendering efficient processing of point clouds in traditional geoinfrastructures such as relational database management systems (RDBMS) or distributed key value stores complex. The core reason is easily captured from the concept of identity: if we consider a moderate point cloud with 100 million points, a key value store would have to organize 100 million keys that do not really contribute to the system as they do not have a proper meaning in the beginning and RDBMS would as well have to organize as many identities (e.g., primary keys) in addition to the point data.

We design and implement an efficient in-memory processing library compatible with the Python buffer protocol and Numpy to seamlessly do queries similar to (but not limited to)

- Computing the radius of the k nearest neighbors
- Computing Structure Tensor Features
- Simple Range Queries (2D Polygon, 3D Box)
- 4D queries in the spatiotemporal neighborhood
- Building up the full neighborhood graph

We show how this approach is highly scalable and flexible and hope to influence the community to consider these techniques in their research software.

Point clouds and high-resolution 3D data have become increasingly important in a variety of fields, including surveying, construction, and virtual reality.
However, simply having this data is not enough; to extract useful information, semantic labeling is crucial.
In this context, we propose a method to transfer annotations from a labeled to an unlabeled point cloud using an octree structure.
The structure also analyses changes between the point clouds. Our experiments confirm that our method effectively transfers annotations while addressing changes.

Data fusion is a method to integrate various datasets (from multisensor or multiscale) by combining single survey data with other acquisition techniques. Currently, multisource data integration can be conducted at point cloud level by using Iterative Closest Point (ICP) algorithm. It is suggested to be used due to its highly accurate produced data. However, the ICP process has a limitation, i.e., the gaps after co-registration.

This paper evaluated the results of amalgamated data to determine the gaps between the data by using CloudCompare software. It fused three datasets from three different techniques: lidar points by drone, terrestrial laser scanning points, and image-based points by drone. The quality of each data was observed by its surface density and roughness value. For data integration, the ICP registration was applied twice which used TLS points as a reference. Then, for integration assessment, the multiscale model-to-model cloud comparison (M3C2) distance was calculated.

This initial work produced high accuracy of image-based point as indicated by the roughness figure. Along with laser scanning point by drone, they covered the rooftop part of the TLS based model. However, the fusion of both data pairs showed the gaps in term of distance as indicated by the STD figure. Thus, the future work will focus on the refinement of the gaps for generating a better fused 3D point clouds dataset.

Low-cost lidar mounted on unmanned aircraft systems (UAS) can be applied for the acquisition of small-scale forestry applications providing many advantages such as flexibility, low flight altitude and small laser footprint as well as the advantages of a far‐reaching field of view. Compared to 3D data generated from dense image matching using photogrammetry, lidar has the advantage of penetration through the canopy gaps, resulting in a better representation of the vertical structure of the vegetation. We analyse the effect of different flight altitudes on the penetration rate of heathland vegetation in the Blue Mountains, Australia using a Phoenix system based on a Velodyne Puck 16 scanner and a GreenValley LiAir X3-H system based on a Livox scanner. The different sensors achieve quite different performances, especially for the mid-vegetation layer between the canopy and the ground layer. Representation of this layer is especially important when investigating fuel availability for bushfire analyses. In this layer, the LiAir system achieves a quite complete picture at an altitude of 65 m above ground, whereas the Phoenix system needs to be flown as low as 40 m to get a comparable result.

As an initial stage in change detection and spatiotemporal analysis with point clouds, point distance calculations are frequently performed. There are various methods for calculating inter-point distance or the distance between two corresponding point clouds. These methods can be classified from simple to complex, with more steps and calculations required for the latter. Generally, it is assumed that a more complex method will result in a more precise calculation of inter-point distance, but this assumption is rarely evaluated. This paper compares eight commonly used methods for calculating the inter-point distance. The results indicate that the accuracy of distance calculations depends on the chosen method and a characteristic related to the point density, the intra-point distance, which refers to the distance between points within the same point cloud. The results are helpful for applications that analyze spatiotemporal point clouds for change detection. The findings will be useful for future applications, including analyzing spatio-temporal point clouds for change detection.

This study explores the implementation of point cloud visualization in virtual reality environments to evaluate immersion based on point cloud characteristics. Two visualization methods were utilized, including points and meshes, and the method was tested on point clouds from three popular datasets: Paris-Carla-3D, Toronto-3D, and Stanford 3D Indoor Scene. The point-based visualization better preserved the original point cloud's geometries and colors, but visualization through surfaces formed by points added greater realism to the experience. However, the illumination options of the points resulted in a loss of realism. Mesh visualization modified both the geometry and colors, producing models that were less realistic than those created by the point visualization. Occlusions affected the realism of all scenes, causing a loss of information and influencing the generation of erroneous illumination. Immersion was hindered by the large number of points displayed on the screen simultaneously, which significantly reduced Frames Per Second. Despite this, no problems related to Virtual Reality sickness were observed beyond a brief initial adaptation period.

The damage of subsurface infrastructure under the auspices of excavation is a long-standing global problem, which causes great financial losses as a consequence of project delays, disruptions of public supply, and the increased life-cycle costs of utility lines. The primary causes of excavation damage are attributed to the lack of reliable utility information and inadequate approaches to communicating the positional uncertainties to the end users. Accordingly, this study presents a deterministic uncertainty-aware approach for visualising subsurface infrastructure in 3D using augmented reality (AR). The prototype was presented and evaluated in a focus group interview with five respondents with experience from the utility sector. The participants agreed, that the insufficient availability of vertical coordinates for the cables at present constitutes the biggest challenge. However, they emphasised the future potential of the AR solution in the prospect of ongoing improvements in data quality prompted by the new Danish data model for exchanging utility information.

Immersive virtual reality (IVR) enables the possibility of viewing abstract concepts and entities in a three dimensional (3D) visuospatial environment. In this paper, we innovatively introduced IVR technology into the verification of the as-built state of electric line networks in buildings. On the one hand, using the reasoning-based estimation of electric line networks as a starting point, we demonstrated IVR technology's usability for verifying installed utilities in buildings. On the other hand, we established the communication between the Reasoner and the practitioner and also simulated the verification action of electric line networks in buildings in the real world. The principal findings of this work pave the way for a subsequent and systematic evaluation of the different reasoning strategies for estimating and generating the as-built state of building utilities.

This paper discusses two experimental Augmented Reality (AR) projects conducted by a partnership between a history museum and a university research center in the City of Charlotte, USA. These projects employ 2D mapping, 3D procedural modeling, and marker-based AR techniques for data visualization focused on social and economic issues in Charlotte on a neighborhood scale. AR offers an interactive method to expand visualization capabilities in GIS. These projects show that AR can support local community events that are aimed at expanding overall public participation with a goal of increasing awareness of neighborhood changes over time through 3D data visualization.

Open Geospatial Consortium Testbeds are an annual research and development initiative that explore geospatial technology. For Testbed-19 we generate a multipurpose 3D dataset in support of the development of Augmented Reality/Metaverse architectures and infrastructure prototypes: Road Hazard Monitoring, Mixed Euclidean and Minkowski GeoPose Graphs, and GeoPose Graphs in a Minkowski Bubble.

The data comprises: LiDAR, position and orientation, imagery from a survey vehicle, drone imagery, and imagery from static cameras and “GoPros”. This is extended by a simulated orbiting platform with high-resolution sensor. Our coverage is a bubble of radius 256m, centered near the Ordnance Survey headquarters in Southampton, UK.

The data will be published in an open-source repository, supporting OGC projects, and a prototype of parts of the Ride Hailing use case of the Metaverse Standards Forum Real/Virtual World Integration Domain Working Group. Open availability of a standard dataset will enable independent testing and benchmarking of architectures and algorithms for a variety of applications.

With the availability and low cost of RGB-D sensors, indoor
3D modeling from RGB-D data has gained more interest in the research
community. However, this topic is still challenging because of the com-
plexity of indoor environments and the poor quality of RGB-D data. To
deal with this problem, a focus on semantic segmentation as a first and
crucial step in 3D modeling process is primordial. The main purpose of
this paper is to offer a review of recent researches carried out on RGB-D
semantic segmentation. Especially approaches based on deep neural net-
work, their datasets, their metrics, and their challenges and limits are
presented. Based on this state of the art, guidelines to improve research
in this field are proposed.

Due to its efficiency and effectiveness, image data is widely used in many fields to express indoor space. However, most of them are limited to visualizing the indoor space because combining image with topology data is difficult. To overcome this limitation, this study proposes a framework for generating topology data from image data. In detail, this paper presents the methods of capturing image data from indoor space, detecting spatial entities and spatial relationships from omnidirectional images, and generating NRG (Node-Relation Graph). The methodologies proposed in this study can create topology data using only images without additional data and build topology data at a low cost. Using the suggested framework, we expect to be able to provide a variety of services for more indoor spaces.

There is a growing trend toward relying on the strength of the existing WiFi signal for indoor localization. The fact that WiFi's received signal strength (RSS) is vulnerable to multipath, signal attenuation, and environmental variations is a major roadblock to accurate indoor localization. Because of this, RSS is a poor measure of signal strength. In this study, WiFi signals from all around a region are combined to build a localization system accurate to within a few meters. The characteristics of WiFi propagation are used as a sort of location fingerprinting. This study aims to provide a method for indoor localization that uses Wi-Fi RSSI fingerprinting. In order to adapt to new environments, our system uses a Variational Autoencoder to disseminate WiFi signal properties, an LSTM network to extract temporal relations of Wi-Fi signals, and a feature backpropagating refinement module to update neural network weights during inference. Together, they help the system accomplish its primary objective—domain adaptability. The localization accuracy was increased by around 18 percentage points when compared to the neural network utilized as a baseline.

Model-based LiDAR localization systems provide accurate pose estimation but they highly rely on the accuracy of 3D models. The inaccurate parts of 3D models will introduce localization errors. This paper presents a novel LiDAR relocalization method using synthetic LiDAR scans generated from a LiDAR generative adversarial network. Synthetic LiDAR scans are generated in a 3D model using the poses of a set of real LiDAR scans and input into a change detection network together with the corresponding real LiDAR scans to detect differences between the 3D models and the real environments. The synthetic and real data, and the differences are input into a generative adversarial network to correct the difference in synthetic LiDAR scans. A pose regression network is then trained using the corrected synthetic LiDAR scans and tested using new real LiDAR data. Experimental results show the proposed method achieves a higher accuracy than previous model based pose regression methods.

This research investigates interactions between delivery robots and pedestrians in urban settings to enhance safety and efficiency. We developed a 3D digital-twin environment model that simulates robot-human and robot-cityscape interactions, adopting the Pedestrian Aware Model (PAM) for robot simulations to ensure effective and safe navigation. Using agent-based modeling, we analyzed various scenarios involving pedestrians, wheelchair users, and robots sharing sidewalk spaces. Our findings reveal that robots do not inherently contribute to sidewalk congestion and maintain a larger buffer zone for safety and efficiency, suggesting their potential for smooth coexistence with pedestrians. We observed that robots caused most collisions, while pedestrians were primarily responsible for proximity violations, emphasizing the need for further research and strategies to reduce risks associated with these incidents. This study underscores the importance of examining pedestrian and sidewalk robot interactions in urban settings and presents a framework for designing more innovative, secure, and efficient environments. The results suggest that with careful planning and continued research, robots can safely and comfortably share sidewalks with pedestrians, contributing to a more harmonious and efficient urban landscape. Our proposed simulation model, incorporating PAM, can assist urban planners, policymakers, and researchers in evaluating the influence of various design interventions and policies on human-robot coexistence in cities, marking a crucial step toward accommodating both humans and robots in urban spaces.

For useful applications of 3D city models, a ruleset is needed to unambiguously define 3D objects at differentLoDs. Biljecki et al (2016) refined the LoD specification of the CityGML conceptual model to define such a ruleset for buildings.This has indeed reduced the vagueness of the four main CityGML LoDs. However, this refined framework was defined when 3D city models were mainly generated from measurements and observations. In recent years, new ways have been developed to generate 3D data, such as the automated abstraction of BIM models into generalised buildings, which can be integrated in 3D city models to study the impact of their design on the environment and vice versa.

BIM derived output does not fit neatly into the LoD framework of Biljecki et al (2016).The goal of this paper is therefore to evaluate how suitable the framework
of Biljecki et al. (2016) is for the integration of BIM derived models in 3D city
models over LoD0 to LoD3 and to propose refinements accordingly. This is done
by comparing the output of two BIM envelope extractors with respect to the
existing framework. This research is a first step to standardise converted
BIM models at different LoDs to be used in urban applications

The final paper will extend the evaluation and propose refinements of the LoD framework to make it better suitable for BIM-derived models at different LoDs, also looking from the perspective of data requirements of use cases, such as digital building permit and 3D Cadastre.

Digital building models are increasingly available and used as sources to inform geospatial data sets. This poses the requirement to spatially locate building models in the geospatial context. In this paper, we present a case study on transferring georeference during conversion from digital building models in IFC (Industry Foundation Classes) to OpenStreetMap (OSM). First, we provide a condensed overview of how coordinates in IFC's local engineering coordinate systems are related to geospatial reference systems with examples for different constellations. Second, we demonstrate a simple method to enrich IFC datasets with georeferences using existing OpenStreetMap outlines. In the third part we describe two substantially different methods to convert engineering coordinates into geospatial coordinates and show how these methods are implemented in two opensource software packages. Finally, we verify and compare the methods with a set of sample IFC and OSM data.

Digital Twin technology is the tool for monitoring, management and intervening in a timely manner to prevent disasters in urban areas, transforming them into smart cities. By enabling real-time data flow through utilizing a vast network of interconnected sensors and smart devices the functioning of buildings, facilities and utility networks can be monitored, managed and optimized to achieve a more stabilized, fair and sustainable urban environment. Good land management and optimal exploitation can also be achieved. The paper presents an innovative crowdsourced methodology for a fast, low cost and reliable structuring of the necessary geospatial infrastructure for a digital twin of an urban neighborhood by merging and visualizing individual BIMs with available open data, such as open orthophotos and cadastral maps, planning and building regulations, as well as existing land uses, smart devices and metric data derived from the Google Earth Pro and Street View platforms. The above-mentioned data if transferred into an open-source platform, such as Tandem, are ideal for tracking, managing and improving the residents’ living, from an economic, hygienic, safety and ecological point of view. Data derived from smart devices is a prerequisite for predicting and preventing various problems and setbacks.

The AI4TWINNING project aims at the automated generation of a system of interrelated digital twins of the built environment spanning multiple resolution scales providing rich semantics and coherent geometry. To this end, an interdisciplinary group of researchers develops a multi-scale, multi-sensor, multi-method approach combining terrestrial, airborne, and spaceborne acquisition, different sensor types (visible, thermal, LiDAR, Radar) and different processing methods integrating top-down and bottom-up AI approaches. The key concept of the project lies in intelligently fusing the data from different sources by AI-based methods, thus closing information gaps and increasing completeness, accuracy and reliance of the resulting digital twins. To facilitate the process and improve the results, the project makes extensive use of informed machine learning by exploiting explicit knowledge on the design and construction of built facilities. The final goal of the project is not to create a single monolithic digital twin, but instead a system of interlinked twins across different scales, providing the opportunity to seamlessly blend city, district and building models while keeping them up-to-date and consistent. As testbed and demonstration scenario serves an urban zone around the city campus of TUM, for which large data sets from various sensors are available.

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 the observation of changes made to the geo model on the BIM model. Challenges addressed during development include georeferencing, data format import, handling different geometries, hierarchy of attributes, code optimization, user-friendliness, and enhanced visualization. Future developments involve generating semantically correct models and incorporating the functionality to reproject CityJSON geometries based on acquired coordinate reference system information. The plugin contributes to the seamless integration of geospatial and BIM data, enhancing interoperability and supporting informed decision-making in the AEC and urban domains.

The recent rapid rise in the demand for digital methods for planning and management of road infrastructure has led to the development of new concepts for structuring the targeted applications where digital methods shall be applied in the future to improve overall efficiency. This transition towards digital planning, maintenance and even coupling digital representations with the asset in the real world requires a clear structure so that existing potential can be optimally exploited. In this paper, we propose an improved LOAD concept for the digital representation of roads for the use in digital twins. Since digital planning is just emerging in road construction, the road infrastructure sector currently faces the issue of existing roads not having a digital representation. Accounting for the current situation, we especially highlight the path from reality capturing towards the establishment of the digital representation as-is, in cases, there is no digital representation of the targeted road segment available yet.

The surveying and mapping department of the City of Vienna has been working on the Digital geoTwin project since the end of 2019. The new strategy focuses on both the creation of semantic 3D objects and other geodata products, which completely restructures existing workflows. The core of the strategy is to process the three-dimensional measurement data of the surveying and mapping department from existing as well as potentially 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 this article, we try to explain the concept of a digital twin in the context of a large city. Subsequently, the development and goals of the Digital geoTwin will be discussed and the need for new data models will be assessed. CityGML as an international standard for 3D city models offers a foundation for the development of such data models. The results of a prototypical development of a data model for the Digital geoTwin based on CityGML form the main part of this article.

The paper presents a new hierarchy of multiple levels of detail (LOD) designed for the modeling of underground utility networks. The hierarchy of LODs is applied to water management possible scenarios in the context of determining the aging of distribution networks. The hierarchy of LODs proposes four levels, two sub-levels, and three quality levels. The various LODs are defined based on the dimension of the space and the geometric primitives, the topology, semantic aspects, contextual information, and data quality (regarding 3D data collecting). It is believed that this number of levels is sufficient and appropriate to cover the modeling of underground utility networks suitable for various applications.

Representations of an object in different granularity using the Level of Detail concept are usually separated without links between corresponding elements. Extension of a 3D model to the fourth dimension opens new possibilities for spatial analysis. Integration of scale using additional spatial dimension can help in scale-dependant analysis and preserving consistency, especially in case of model updates. In this paper, an extended version of the dual half-edge structure for topological representation of 4D cell complexes is proposed. This feasibility study shows implementation of the Poincaré duality theorem in practice. Thanks to that, the data structure remains simple, where only two atomic elements are used in a construction process. i.e. nodes and edges. This solution lays the groundwork for future research, where topological links in the fourth dimension will be used to connect consecutive object representations of different granularity.

Topology can be defined as properties that describe how objects in a space are related which includes containment, adjacencies and connectivity information. The topological information may seem simple yet it is fundamental to facilitate more complex analysis such as 3D data validation and applications. As a spatial property, the preservation of topology within a spatial database is important. Topology rules are a set of conditions that define topological relationships between objects stored in a spatial database. This study attempted to implement 3D topology rules for determining topological relationships between 3D objects stored in an Oracle spatial database. A set of 3D topology rules are implemented based on the 36IM. Ten topological groups can be tested in the 36IM which consists of point-to-point, point-to-line, point-to-region, point-to-volume, line-to-line, line-to-region, line-to-volume, region-to-region, region-to-volume and volume-to-volume. The 36-IM is represented by a 12×3 intersection matrix that can be simplified into a 3×3 intersection matrix that holds the highest dimension of intersection. As a result, topological relationships between 3D objects could be determined without any decomposition into lower dimension objects. This is due to the nature of 36IM that handles ten topological groups which includes equal and cross dimension objects. The geometrical integrity of objects is preserved while maintaining accurate 3D topological information.

This paper reports stages of 3D GIS research and development by the community from 2006 to the year 2022 (in two decades) as presented at the 3D Geoinfo conference series. The first event was held in 2006 in Kuala Lumpur, Malaysia. In general, 3D GIS domain has been investigated in four main aspects, namely, data acquisition, data processing, database, and visualization as what was being researched thus far in the 2D domain. Issues related to the four aspects generate and trigger for more advanced theories and scientific techniques of spatial data modelling and visualization. Interoperability and standard of 3D spatial data also being investigated in parallel by the community as ways of data sharing and dissemination. It is the aim of this paper to share how the 3D Geoinfo conference addressed those geospatial disciplines annually from 2006 to 2022. This paper describes few early works on 3D GIS works such as by Zlatanova (1998), Pilouk (1996) and Abdul Rahman (2000). 3D geoinformation research has been investigated in academia and private entities (with research facility). This paper also will describe the other remaining 3D Geoinfo events till 2022 which was organized in Sydney. Two decades of research discussions by the experts and researchers in 3D geoinformation. Then reflections and conclusion also to be presented in this paper.

Digital Twins (DTs) for cities represent a new trend for city planning and management, enhancing three-dimensional modeling and simulation of cities. While progress has been made in this research field, the current scientific literature mainly focuses on the use of semantically segmented point clouds to develop 3D city models for DTs. However, this study discusses a new reflection that argues on directly integrating the results of semantic segmentation to create the skeleton of the DTs and uses enriched semantically segmented point clouds to perform targeted simulations without generating 3D models. The paper discusses to what extent enriched semantic 3D point clouds can replace semantic 3D city models in the DTs scope. Ultimately, this research aims to reduce the cost and complexity of 3D modeling to fit some DTs requirements and address its specific needs. New perspectives are set to tackle the challenges of using semantic 3D point clouds to implement DTs for cities.

This paper discusses the reconstruction of LoD2 building models from 2D and 3D data for large-scale urban environments. Traditional methods involve the use of LiDAR point clouds, but due to high costs and long intervals associated with acquiring such data for rapidly developing areas, researchers have started exploring the use of point clouds generated from (oblique) aerial images. However, using such point clouds for traditional plane detection-based methods can result in significant errors and introduce noise into the reconstructed building models. To address this, this paper presents a method for extracting rooflines from true orthophotos using line detection for the reconstruction of building models at the LoD2 level. The approach is able to extract relatively complete rooflines without the need for pre-labeled training data or pre-trained models. These lines can directly be used in the LoD2 building model reconstruction process. The method is superior to existing plane detection-based methods and state-of-the-art deep learning methods in terms of the accuracy and completeness of the reconstructed building. Our source code will be released when the paper is accepted.

3D spatial data are widely used to simulate various urbanistic phenomena, thanks to their valuable semantic, geometric and topologic information. CityGML is a highly adopted data standard for semantic 3D city models, providing a standardized description of the cityscape that enables interoperability across different stakeholders. When future scenarios for urban development are simulated, the simulation results can be visualized and further analyzed in synthetically generated 3D city models. However, land use regulations are often overlooked when generating synthetic 3D city models for simulation purposes, despite some regulatory urban constraints having a direct impact on simulation results. For instance, the roof shape is highly correlated with building solar energy potential, while the zoning maximum allowed number of apartments directly influences the buildings' urban density estimation. Therefore, integrating such constructability knowledge within 3D city models is crucial. This paper proposes a framework for mapping urban planning rules defined in the German XPlanung standard onto 3D city models structured in compliance with CityGML to ensure legislative validity and real-life applicability. We review related work, discuss the structure of CityGML and the main elements concerned by urbanistic laws, explain the main concepts of XPlanung, and investigate the mapping of regulatory information with CityGML entities. In conclusion, this paper provides a conceptual framework for bridging the gap between urban planning regulations and 3D city models, highlighting the importance of integrating land use regulations into synthetic 3D city models to improve the accuracy of urban simulations. The proposed approach contributes to ensuring that the generated 3D City model satisfies the binding constructability rules by incorporating important urban planning regulations defined in XPlanung.

Visual, autonomous, object-based outdoor vehicle localization premise detailed object-based maps. Semantic-rich and qualitative Level of detail 3 (LOD3) building models fulfill these requirements, but only a few real-world city models are available. These models are mainly reconstructed by LiDAR, have a cost benefit, scalability and reconstruction complexity
problem. Challenging the issues, we propose an alternative data acquisition approach for LOD3 model reconstruction primarily based on images. Thus, we created a moveable handcart mounted height-adjustable, high-precision gimbal. The gimbal enables 360°poses of camera, LiDAR rangefinder, inertial measuring unit. Additionally, GNSS RTK is used for absolute positioning. Our system is capable to record data and ground truth data in one step. The paper is about the system design, data processing and validation of the proposed reconstruction approach. Resulting real-world reconstruction accuracies are in the millimeter to low centimeter range. So, our system is compared in discussion with competing systems.

Urban Digital Twins have received significant attention in recent years due to their economic and research importance. Although many definitions exist, the general consensus agrees on a continuous two way data flow between a physical entity and its virtual counterpart in a digital twin. In the context of smart cities and semantic 3D city models, however, no major breakthrough in realizing such complex change detection and analysis systems has yet been achieved. While several methods for change detection in semantic 3D city models have been proposed, the analysis of found changes, especially the identification of patterns among a large number of changes, has not been given as much attention. Without a proper handling of patterns, it is difficult to provide useful interpretation of changes with respect to stakeholders. Therefore, this research proposes a framework to define, detect and decipher complex semantic change patterns in semantic 3D city models. The approach provides a central rule network to describe aggregation relations between changes as well as methods to identify and capture detected change patterns directly in the graph representation of a city model.

Findable Accessible Interoperable Reusable data are essential to support the ambitious goals of distributed data environments, digital twins, artificial intelligence and so on. Standards are critical to enable them. However, the need of developing very comprehensive standard for domain description often generates data models which are both over-specified (i.e. representing more than what is needed for the single use case) and under-specified (i.e. multiple implementation choices are allowed). Issues arise when this prevents the compliant datasets to be completely similar to each other, and may affect the trust the users have in standardised data and automatic processing. For this reason, the OGC Data Exchange Toolkit is developed, which is presented in this paper. It includes a standard data model profiling tool which exploits the semantic technologies, standing on the OGC Registry for Accessible Identifiers of Names and Basic Ontologies for the Web (Rainbow). The generated machine readable profile can effectively support data validation as well as the implementation of tools supporting the specific profile described. A second part of the toolkit is a human-friendly template intended to support data requirements definition and data modelling. An initial test of the developed toolkit is performed within the project 'Change toolkit for digital building permit' (CHEK), on the digitalisation of building permits.

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 use emph{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 ABCD has significantly lower demands in storage space (around a factor 10), allows for faster import of data, and its data retrieval speed is comparable (some queries are faster and some slower, we attempt to explain why).
The accompanying software (importer and exporter) is available under a permissive open-source license.

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, ”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.

Please refer to the attached file for the extended abstract.

Buildings are certainly the most important urban objects to represent in 3D city models due to their prominent role in the urban tissue and the amount of data available about them. This includes three-dimensional data from aerial Lidar. Lidar provides fast acquisition of abundant high-resolution topographical data, making it valuable for reconstructing detailed building models. However, in an urban context, buildings reconstruction using three-dimensional point clouds can be problematic due to several reasons. The process presents challenges not only in creating detailed and accurate models but also in the processing and computational requirements needed to generate these models. The processing of massive Lidar data is time consuming due to the huge amount of records and the iterative calculations. In this paper, we propose an approach for efficient and scalable 3D generation of large-scale detailed building models from aerial Lidar data. Our method involves data tiling and indexing for enhanced efficiency, along with parallel processing that accelerates the 3D reconstruction process by nearly five times. This overcomes limitations of existing methods that are computationally expensive and lack scalability, especially with large datasets. Additionally, we correct height inaccuracies in the final model to ensure precise and accurate results, leading to reliable applications.

We propose an approach for the reconstruction of 3D fa ̧cade
details. We integrate mobile laser scanning (MLS) point clouds and CAD
models using a Bag of words (BoW) concept, which we augment by
incorporating semi-global features. Our method demonstrates promising
results, improving the conventional BoW approach.

The automation of 3D roof reconstruction has become a critical research topic in the field of GIScience. Existing methods for this purpose needs to segment roof planes and further extract roof vertices and edges after topology analysis. However, the roof plane-based topology analysis may lead to additional errors for the next step’s extraction result of roof vertices and edges. In this study, based on segmented roof planes, roof edges parallel to the x-y plane are extracted at first, and then the topology relationships of these special roof edges are analyzed and corrected by simple rules. This new approach analyzes the roof structures and extracts roof vertices and edges at the same time, which avoid the accumulated errors by the process of “topology analysis – extraction of roof vertices and edges”. The qualitative and the preliminary quantitative experiment results indicate that the proposed approach can achieve the 3D roof reconstruction well.

Although LoD3 building models reveal great potential in various applications, they are scarcely available.
In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds.
Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper.
Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals.

Semantic segmentation is a first step of point cloud processing algorithms that are used for many city management applications, such as detection of building footprints, creation of digital twins and city models, preparation of deformation maps and many others. As a result, its accuracy highly influences the results of further processing.

Recently, deep learning approaches for semantic segmentation gained the attention of the community as they enable high classification accuracy with relatively fast processing after the network training phase. However, usually, the network is trained and tested individually for each data set that is processed. Therefore, in this paper, we would like to show a different approach to this problem that includes using two data sets simultaneously for training a deep network. To achieve this goal, we propose to utilize the SparseCNN network and ALS data sets collected for Vienna and Zurich.

The point clouds were classified into ground and water, vegetation, building and bridges, and other classes. The accuracy was tested based on the median IoU value. The results of the experiments showed that including the data from additional source into the training data enabled to keep the high accuracy of vegetation and ground and water classes (94.7% and 97.2%, respectively) while improving the accuracy of buildings and bridges class by around 1 pp and the accuracy of other class by around 1.5 pp (93.3% and 55.1%, respectively).

Point cloud with classified facade details are key to create digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing combining geometric features with deep learning networks for point cloud classification at facade-level. Our experiment concludes that such early-fused features improve deep learning methods' performance.

Digital twins that serve as virtual representations of real-world objects and structures, are used in various applications for urban environments. Challenges for creating and maintaining digital twins involve data acquisition, fusion of heterogeneous data types, AI-based data analysis, and the integration into existing applications and workflows. In this paper, we present the concept and implementation of a dynamic digital twin from a city’s perspective. The concept avoids explicit modeling to simplify the creation of a comprehensive data basis that can be easily updated frequently. 3D point clouds with semantics are used as representations for static objects and structures, such as buildings, infrastructure and subsurface structures. Dynamic aspects are represented through a time series of sensor data to enable real-time monitoring and change detection applications. A centralized data repository for applications such as infrastructure monitoring, condition assessment, and inventory management represent a basis to support decisions. We present typical use cases and challenges from the perspective of a city and how the dynamic digital twins can create significant added value.

Digital twins have gained increasing attention, regarded as a tool to facilitate decision-making in the cities. However, the current discourse predominantly focuses on technical aspects while overlooking the human aspect in urban digital twins. This work is intended to propose a conceptual framework that addresses the role of humans in relation to the urban environment, therefore highlighting the social value of urban digital twins. The proposed framework is subsequently implemented in a specific case study, validating its feasibility in practice. By incorporating human sensing data, such as participatory data, urban digital twins have the potential to represent the dynamic interaction between people and environments, generating a holistic physical-social-virtual system.

Urban Digital Twins (UDTs) have emerged as essential tools for managing city operations, forming the basis of smart city solutions. They offer a digital representation of the physical urban environment, which supports various city applications such as monitoring mobility, air quality, and modelling simulations. To accurately represent the physical world, UDTs need to be updated continuously to reflect the changes in the urban environment on time. The Internet of Things (IoT) enables real-time data collection to capture these changes. Combined with 3D city models, IoT allows the interactive visualisation of patterns and trends in UDTs. In this study, we conduct investigations on the requirements for the web visualisation of semantic 3D city models enriched with time-dependent properties from IoT and simulation data. We explore the 3D models and IoT data integration requirements, 4D web visualisation design considerations, and the technical implementation requirements for rendering dynamic properties for UDTs applications. The results from our initial experiments form the basis for the next steps towards creating compelling 4D visualisations catering to different user and application needs.

Rapid urbanisation and limited land availability have led to increased consideration of underground spaces. This increased utilisation of subterranean space has created a rise in its value, highlighting the significance of ownership of underground areas. A fully-integrated digital model that effectively represents underground space ownership is vital for communicating rights, restrictions, and responsibilities (RRRs) in underground spaces. Underground assets are often built and developed vertically at different time slots, resulting in changes to the legal ownership of underground spaces. Therefore, underground RRRs change over time due to factors such as new subdivisions, consolidations, boundary reconstructions, and land acquisitions. Current practices use 2D survey plans and property base maps to manage and communicate underground RRRs, with some textual notations as well as attributes providing temporal information. However, these methods have limitations, and studies have explored the use of 3D models to address these issues, though they often neglect the temporal aspects. Some studies have investigated 4D cadastre (3D cadastre + time) in different jurisdictions, but these studies mainly remain at a conceptual level for above-ground parcels and buildings. CityGML 3.0 is a data model that provides 3D geometries, topologies, semantics, and entities for modelling temporal aspects such as representing dynamic data and time series and maintaining spatial objects’ history and versions. This paper explores the temporal aspects of underground land administration (ULA) and investigates CityGML 3.0 entities for modelling these temporal aspects, with a synthetic prototype implemented as a proof of concept to demonstrate the applicability of a 4D ULA model. The result indicates that considering the temporal aspects of ULA using CityGML 3.0 entities can improve the functionality and capability of a land administration model in managing and communicating ownership information in underground areas.

Semantic 3D city models have been widely used to solve problems that affect the human-built environment. Due to the complexity of the city and its dynamic aspect, these models should allow studying the evolution of a phenomena in real time. Therefore, 3D city models are evolving towards Digital Twin of cities to allow handling the dynamic aspect of the city. For instance, such evolution requires real-time data to be collected using Internet of Things devices (IoT). This kind of dynamic data requires specific tools that should allow its particular exploitation and manipulation. To model the urban environment, CityGML and CityJSON for instance, as international 3D standards, allow creating, storing and exchanging 3D city models in an interoperable way. This paper aims to propose and implement a methodology for integrating real-time data in a city model by using the “Dynamizer” extension of CityGML based on CityJSON. We then have developed a web interface plugin to handle IoT data and their interactive visualization as a first step for future simulations.

This solution allows the understanding of the 3D city model encoded with JSON, also the simplification and the easy comprehension of the Dynamizer extension compared to the CityGML solution which is difficult to decipher. The solution also makes it possible to establish the connection between the physical entities and their digital twins, to follow the evolution of their real characteristics over time, to represent them, analyze them and take the appropriate decision to preserve the components of the territory, to manage it correctly and ensure its resilience and sustainability.