3D City Models Research Articles

AN INTEROPERABLE DIGITAL TWIN TO SIMULATE SPATIO-TEMPORAL PHOTOVOLTAIC POWER OUTPUT AND GRID CONGESTION AT NEIGHBOURHOOD AND CITY LEVELS IN LUXEMBOURG

Abstract. This paper focuses on the importance of Geographical Digital Twins to increase renewable energy production in urban areas and cities for decarbonization and reduction of climate change impacts. The study demonstrates the role of interoperable geographical digital twins based on Free and Open-Source software and geospatial software technologies in the simulation, monitoring, and management of renewable-based energy systems. We simulated the potential for building-integrated and building-attached solar photovoltaic (PV) electricity generation for the use case in cities to allow at a later stage for scale up to a nationwide level. The platform provides a tool for estimating PV power generation at high resolution across entire neighbourhoods and districts. Tools for the identification of cost-efficient PV placement or integration in buildings on roof-tops and facades were implemented to allow for interactive selection of optimal PV placement. The developed platform serves multiple beneficiaries, including municipalities, urban planners, developers, citizens, investors, and energy communities. Overall, the study highlights the advantage of an interoperable geographical urban digital twin, which provides the flexibility necessary to simulate and test scenarios for rapid, integrated urban planning for decarbonization under climate change. The platform is based on open-source, open standards and APIs, using open 3D city models, and allowing seamless integration of simulation and assessment methods and tools to provide a 3D real-world environment to assess and develop energy transition approaches.

A semantic 3D city model for underground land administration: Development and implementation of an ADE for CityGML 3.0

Current practices of managing and communicating underground physical and cadastral (legal) data are based on fragmented and silo-based 2D methods, which cannot provide unambiguous, coordinated, and reliable representations of assets and their rights, restrictions, and responsibilities (RRRs) in subterranean environments. There is also no link between underground physical and legal data in current practices, resulting in ineffective Underground Land Administration (ULA). A single 3D integrated model of underground assets associated with the cadastral data at a city-scale can improve understanding and interpretation of underground legal and physical aspects. Extending a 3D city model to include underground legal data requirements can potentially provide a viable approach for developing a 3D integrated model. This research aims to develop a new Application Domain Extensions (ADE) for CityGML 3.0 (called VicULA ADE) to manage underground data using a single 3D integrated legal-physical model at a city-scale. The VicULA ADE proposes semantic entities for underground legal spaces, enabling the model to define spatial and semantic relationships between different types of legal spaces. The proposed approach comprises identifying requirements based on the Victorian state of Australia, developing the conceptual and logical data models in UML, and implementing the XML schema as the physical data model. The viability of the VicULA ADE is tested in a complex real-world case study that has various underground legal and physical objects, shifting this data model from a conceptual level to the implementation level. The proposed ADE of the CityGML 3.0 standard revealed how underground legal data elements can be logically embedded in this 3D data model.

Input data requirements for daylight simulations in urban densifications

One of the biggest challenges in urban densifications is securing adequate daylight access. This study examines the potential of using semantic 3D city models as input to daylight simulations. It is focusing on investigating input data requirements to these simulations from a geodata, 3D city model specification and measuring guideline perspective. To achieve this, geodata simulation input requirements for the most common daylight metrics are documented. Next, 3D city model data from two Swedish municipalities along with 3D data constructed by ourselves in CAD- and GIS-environments are utilized to explore the impact of using 3D city models of different levels of detail (LOD) and positional accuracy in daylight simulations linked to Swedish and European laws and recommendations. Similarly, the measuring guidelines and 3D city model specification requirements related to balconies and other façade accessories are also evaluated along with the utilization of façade reflectance properties and colour. It is found that LOD1 is sufficient for the obstruction angle metric for most roof types but for e.g., gabled roofs LOD2 should be used. A positional accuracy on a decimetre-level is accurate enough for the aforementioned metric. Daylight factor simulations require that balconies and façade accessories protruding more than a couple of decimetres must be represented in the 3D city model along with information on façade material and colour. The outcome of the study is expressed in the form of a list of recommendations for the creation of national profiles of 3D city models supporting daylight simulations.

Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

Joint semantic–geometric learning for polygonal building segmentation from high-resolution remote sensing images

As a fundamental task for geographical information updating, 3D city modeling, and other critical applications, the automatic extraction of building footprints from high-resolution remote sensing images has been substantially explored and received increasing attention over recent years. Among different types of building extraction methods, the polygonal segmentation methods produce vector building polygons that are in a more realistic format compared with those obtained from pixel-wise semantic labeling and contour-based methods. However, existing polygonal building segmentation methods usually require a perfect segmentation map and a complex post-processing procedure to guarantee the polygonization quality, or produce inaccurate vertex prediction results that suffer from wrong vertex sequence, self-intersections, fixed vertex quantity, etc. In our previous work, we have proposed a method for polygonal building segmentation from remote sensing images that addresses the above limitations of existing methods. In this paper, we propose PolyCity, which further extends and improves our previous work in terms of the application scenario, methodology design, and experimental results. Our proposed PolyCity contains the following three components: (1) a pixel-wise multi-task network for learning the semantic and geometric information via three tasks, i.e., building segmentation, boundary prediction, and edge orientation prediction; (2) a simple but effective vertex selection module (VSM), which effectively bridges the gap between pixel-wise and graph-based models via transforming the segmentation map into valid polygon vertices; (3) a graph-based vertex refinement network (VRN) for automatically adjusting the coordinates of VSM-generated valid polygon vertices, producing the final building polygons with more precise vertices. Results on three large-scale building extraction datasets demonstrate that our proposed PolyCity generates vector building footprints with more accurate vertices, edges, shapes, etc., achieving significant vertex score improvements while maintaining high segmentation and boundary scores compared with the current state-of-the-art. The code of PolyCity will be released at https://github.com/liweijia/polycity.

Robust registration of aerial and close‐range photogrammetric point clouds using visual context features and scale consistency

AbstractPoint cloud registration is of great significance to the reconstruction of high‐precision 3D city models. There are some challenges when aligning aerial and close‐range photogrammetric point clouds, such as huge view differences caused by the different sights of the sensors, massive noisy points due to the error in dense matching, and scale uncertainty since there are no control points for absolute orientation. To achieve complementary advantages of aerial and close‐range point clouds, in this paper, a robust cross‐source point clouds registration method is proposed using image visual context features and scale consistency. First, a cross‐view image matching method based on image visual context features is proposed to obtain corresponding points. Second, to overcome the challenges of noisy points and scale differences, an outlier filtering method is designed based on scale consistency. Finally, the dual quaternions model considering the scale factor is introduced to solve the spatial transformation model. To analyze the feasibility of this method qualitatively and quantitatively, experiments are conducted using the public scene dataset of Dortmund, Germany, and the scene dataset of Zhengzhou City, China. Three kinds of cross‐source point clouds registration experiments are conducted in this paper, including aerial and close‐range point clouds registration in Dortmund, and aerial and ground point clouds registrations in both Dortmund and Zhengzhou. The chamfer distances of the three sets of experiments are 4.48 m, 5.97 m and 4.78 m, respectively. The ablation study shows that the outlier filtering method and the dual quaternions model improve the accuracy by at least 14% and 25%, respectively. The experiments demonstrate that the method accomplishes the cross‐source point clouds registration in large‐scale scenes accurately and efficiently, providing a solid foundation for subsequent fine 3D reconstruction.

Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset

Abstract Three-dimensional (3D) city modeling is an essential component of 3D geoscience modeling, and window detection of building facades plays a crucial role in 3D city modeling. Windows can serve as structural priors for rapid building reconstruction. In this article, we propose a framework for detecting window lines. The framework consists of two parts: an improved stacked hourglass network and a point–line extraction module. This framework can output vectorized window wireframes from building facade images. Besides, our method is end-to-end trainable, and the vectorized window wireframe consists of point–line structures. The point–line structure contains both semantic and geometric information. Additionally, we propose a new dataset of real-world building facades for window-line detection. Our experimental results demonstrate that our proposed method has superior efficiency, accuracy, and applicability in window-line detection compared to existing line detection algorithms. Moreover, our proposed method presents a new idea for deep learning methods in window detection and other application scenarios in current 3D geoscience modeling.

Climate-neutral municipal building stock - life cycle assessment of large residential building stocks based on semantic 3D city models

This publication presents the results of applying Life Cycle Assessment (LCA) methods to the entire residential building stock of Germany's third largest city, the City of Munich. The focus of the LCA is on the technical components of the building heating system. For the analysis, we use our developed tool urbi+, which takes 3D city models in the CityGML standard enriched with building specific information as input parameters. Thus, we investigate both the status quo of Munichs' building stock and two different development scenarios with the goal of climate neutrality by the year 2035. Development scenario 1 focuses on the use of heat pumps and scenario 2 on the expansion of the district heating network. The year 2035 has been defined by the City of Munich as the official goal for achieving climate neutrality. Within the scope of the assessment, we address the parameters of energy demand, greenhouse gas emissions and costs. The results show a multidimensional assessment space between these parameters and how ambitious it is to achieve the goal of climate neutrality by 2035. The results are intended to help political and urban planning decision-makers validate climate targets and make informed decisions about measures to achieve climate neutrality.

Remote Sensing Data Preparation for Recognition and Classification of Building Roofs

Buildings are among the most significant urban infrastructure that directly affects citizens’ livelihood. Knowledge about their rooftops is essential not only for implementing different Levels of Detail (LoD) in 3D city models but also for performing urban analyses related to usage potential (solar, green, social), construction assessment, maintenance, etc. At the same time, the more detailed information we have about the urban environment, the more adequate urban digital twins we can create. This paper proposes an approach for dataset preparation using an orthophoto with a resolution of 10 cm. The goal is to obtain roof images into separate GeoTIFFs categorised by type (flat, pitched, complex) in a way suitable for feeding rooftop classification models. Although the dataset is initially elaborated for rooftop classification, it can be applied to developing other deep-learning models related to roof recognition, segmentation, and usage potential estimation. The dataset consists of 3617 roofs covering the Lozenets district of Sofia, Bulgaria. During its preparation, the local-specific context is considered.

Visual Perception of Property Rights in 3D

Despite the already advanced work on the construction of jurisdictional 3D cadastre models in many parts of the world and the technical feasibility of building very detailed 3D models of cities, relatively few specialists have focused on the aspects of visualizing property rights in three dimensions. Therefore, to complement the analyses carried out so far in this area, this research aims to investigate the perception of the visualization of multidimensional real estate data using different visual variables and by different audiences. The main contribution of the conducted research to the development of 3D cadastre visualizations is to start a discussion on the differences in their perception among real estate professionals and those who have no experience in this area and may have to use multidimensional property data. The research was conducted using a questionnaire-based survey method with the computer-assisted web interview (CAWI) technique. The questionnaire was completed by students of a course related to real estate law (geodetic science) and those who do not have regular contact with it (environmental engineering, medicine, sports, mechanics, and management). As a result of the survey, it emerged that the group studying geodetic science performed better on average than students in other fields of study. Additionally, the conducted survey confirmed the existing knowledge of the perception of the visualization of property rights in three dimensions. According to it, visualizations of property rights in 3D should use color. The use of transparency helps in visualisations made in grayscale but interferes with more complex colorful objects.

Life Cycle Assessment of building energy systems on neighbourhood level based on semantic 3D city models

This study presents a method which allows conducting an energy and emission related Life Cycle Assessment (LCA) and cost-related Life Cycle Cost Assessment (LCC) of large residential building stocks focusing on selected heating system components. Using this method the status quo as well as two refurbishment scenarios for an entire neighbourhood with 196 residential buildings in Munich, Germany, is assessed. Within the status quo and the refurbishment scenarios, among other things, the use of different energy systems and energy standards of the building envelope are investigated. CityGML semantic 3D city models serve as a source of building-specific information for these assessments. The results show that even if all residential buildings are refurbished to passive house standard and powered by renewable energies by 2035, life cycle-based energy demands and emissions will still be present, that counteract the goal of achieving climate neutrality. In addition, the life cycle-based costs exceed the savings achieved by the refurbishment. The developed method is implemented in Java which provides on the one hand a new approach to assessing building stocks on a life cycle basis and on the other hand its transferability. This results in innovative evaluation and assessment possibilities for political and municipal decision-makers.

Improving Semantic Segmentation of Roof Segments Using Large-Scale Datasets Derived from 3D City Models and High-Resolution Aerial Imagery

Advances in deep learning techniques for remote sensing as well as the increased availability of high-resolution data enable the extraction of more detailed information from aerial images. One promising task is the semantic segmentation of roof segments and their orientation. However, the lack of annotated data is a major barrier for deploying respective models on a large scale. Previous research demonstrated the viability of the deep learning approach for the task, but currently, published datasets are small-scale, manually labeled, and rare. Therefore, this paper extends the state of the art by presenting a novel method for the automated generation of large-scale datasets based on semantic 3D city models. Furthermore, we train a model on a dataset 50 times larger than existing datasets and achieve superior performance while applying it to a wider variety of buildings. We evaluate the approach by comparing networks trained on four dataset configurations, including an existing dataset and our novel large-scale dataset. The results show that the network performance measured as intersection over union can be increased from 0.60 for the existing dataset to 0.70 when the large-scale model is applied on the same region. The large-scale model performs superiorly even when applied to more diverse test samples, achieving 0.635. The novel approach contributes to solving the dataset bottleneck and consequently to improving semantic segmentation of roof segments. The resulting remotely sensed information is crucial for applications such as solar potential analysis or urban planning.

Joint Deep Learning and Information Propagation for Fast 3D City Modeling

In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling.

Urban 5G MmWave Networks: Line-of-Sight Probabilities and Optimal Site Locations

In this work, we implemented line-of-sight (LoS) ray tracing functionality to investigate problems in millimetre-wave propagation modelling and network planning in 3D city model environments. First, we validated an existing LoS propagation probability model expressed as an exponential rule with the link distance. By fitting ray tracing simulation results under different scenarios to the model, the relationships between key parameters in the model and factors including the building density and the transmitter height were qualitatively analysed. Next, we developed a network planning framework for a multi-hop outdoor urban network by formulating a mixed-integer linear programming problem which minimises the overall deployment cost through optimal site selection. Taking the sets of potential site locations and potential links as inputs, we selected a subset of the sites that comprise a tree-structured network that satisfies all the user demands at a minimum deployment cost. We also analysed the time required for solving this optimisation problem in order to provide a prediction of the execution time for larger-sized problems.

A Proposal for a Parametric 3D City Model Semantic Format

This research concerns the development of a parametric format for the semantic description of 3D city models. Following a brief review of the state of the art, the case study is introduced (an historical city block in Catania) whose geometries will be semantically organised through the VPL Grasshopper.

Glass façade segmentation and repair for aerial photogrammetric 3D building models with multiple constraints

Aerial photogrammetric three-dimensional (3D) city models provide photorealistic virtual scenes for various applications, such as urban planning, autonomous driving, emergency management, etc. With the rapid progress of urbanization, glass façades commonly exist on buildings, which cannot be well reconstructed with geometric deformation and distortion due to the limitation of the image-based Multi-View Stereo (MVS) modeling pipeline. The distorted glass façades make building models visually unreal and messy. This paper proposes a workflow to segment and repair glass façades in aerial photogrammetric 3D building models with multiple constraints to resolve the problem, mainly including two aspects: 1) projection-based glass façade segmentation with geometric information and multi-view constraints, and 2) glass façade model repair via mesh smoothing and texture mapping. First, an efficient approach for façade information extraction is proposed, which utilizes the density of the reprojected point clouds and elevation differences because the typical structure of building façades is usually vertically aligned to the ground surface. Second, we project the façade mesh onto the multi-view oblique aerial images. The glass façades are predicted by a deep learning-based semantic segmentation method to distinguish the glass façades. Finally, based on the extracted glass façades, we remedy the geometric deformation using a mesh denoising method by grouping strategy with edge constraints. After smoothing the mesh structure of glass façades, we remap the texture to generate the completed 3D models with optimized glass façades. Our method leverages multiple constraints, including multi-view constraints, façade structure constraints, grouping strategy, and boundary constraints. The multi-view and façade structure constraints help decrease the adverse effects caused by false segmentation; the grouping strategy and boundary constraints can avoid disorders in some specific areas and preserve the sharp feature in the adjacent regions. Thus, our approach is robust and does not worsen the 3D model. Experiments demonstrate that our workflow is feasible for glass façade segmentation and repair for aerial photogrammetric 3D building models.

Management of Climate Resilience: Exploring the Potential of Digital Twin Technology, 3D City Modelling, and Early Warning Systems.

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future.

Semantic 3D City Model Data Visualization for Smar t City Digital Twin

Digital Twin has gotten much attention from the industry and academics worldwide. Thank the achievements in the 4.0 industry have facilitated many chances in other fields such as smart city, disaster recovery, etc. 3D Cities geospatial infrastructure is an increasingly widely used technique for storing, operating, analyzing, and managing digital data for smart cities. Based on those things, we discuss applying the Digital Twin technique to Smart Cities for simulation and management purposes. In this field, CityGML is an emerging concept, thanks to its strength in exchanging as well as representing the attributes of smart city data, also known as smart cities, also known as a 3D geospatial information data standard of OGC(Open Geospatial Consortium). We present a scenario for visualizing a smart village in Busan, South Korea, in 3D by using Cesium, the platform for 3D Geospatial. In this study, we propose a method for converting 3D city models from RVT/IFC to Cesium 3D Tiles format for streaming on the Cesium 3D globe.

Future Swedish 3D City Models—Specifications, Test Data, and Evaluation

Three-dimensional city models are increasingly being used for analyses and simulations. To enable such applications, it is necessary to standardise semantically richer city models and, in some cases, to connect the models with external data sources. In this study, we describe the development of a new Swedish specification for 3D city models, denoted as 3CIM, which is a joint effort between the three largest cities in Sweden—Stockholm, Gothenburg, and Malmö. Technically, 3CIM is an extension of the OGC standard CityGML 2.0, implemented as an application domain extension (ADE). The ADE is semantically thin, mainly extending CityGML 2.0 to harmonise with national standards; in contrast, 3CIM is mainly based on linkages to external databases, registers, and operational systems for the semantic part. The current version, 3CIM 1.0, includes various themes, including Bridge, Building, Utility, City Furniture, Transportation, Tunnel, Vegetation, and Water. Three test areas were created with 3CIM data, one in each city. These data were evaluated in several use-cases, including visualisation as well as daylight, noise, and flooding simulations. The conclusion from these use-cases is that the 3CIM data, together with the linked external data sources, allow for the inclusion of the necessary information for the visualisation and simulations, but extract, transform, and load (ETL) processes are required to tailor the input data. The next step is to implement 3CIM within the three cities, which will entail several challenges, as discussed at the end of the paper.

Designing and Building an Intelligent Pavement Management System for Urban Road Networks

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant.