Three-dimensional City Research Articles

3D City Modeling for Flood Inundation Analysis in Mayjen Sungkono Area, Surabaya

Abstract Floods in urban areas are primarily caused by high rainfall combined with inadequate drainage systems, inappropriate land use changes, and insufficient flood mitigation measures. Surabaya, characterized as a seasonal zone with periodic rainfall, frequently experiences flood inundation due to the loss of water retention areas and an inadequate drainage infrastructure. Three-dimensional (3D) city models provide a detailed representation of urban environments, capturing the three-dimensional geometry of objects and general urban structures. This 3D modeling approach can enhance the understanding of flood depths impacting surrounding buildings and infrastructure and assist in estimating the effects on drainage capacity and catchment areas for long-term planning. In this research, 3D flood inundation modeling was conducted using LiDAR data and aerial photography, integrated with other GIS data. The flood inundation simulation was performed using HEC-RAS, while the 3D modeling was carried out using GIS-based software employing semi-automatic methods. The results of the 3D modeling and visualization indicate potential flood inundation depths reaching up to 5 meters, affecting 1,100 buildings and 30 roads. Further research could focus on improving the accuracy of terrain and channel data used in flood modeling to enhance the precision of the results.

Three-dimensional City Modeling for Microclimate Simulation of Urban Areas

Abstract Increasing urbanization and building density can trigger an increase in environmental temperature, thereby influencing urban climate change. Urban management that responds to climate change is urgently needed, requiring a smarter urban design approach than before. As geospatial science and technology develop, conventional two-dimensional methods will transition to more complex three-dimensional spatial modeling. This approach has great potential in supporting climate change-responsive decision making in urban environments. Three-dimensional city modeling at the LOD 2.0 level was used for the microclimate simulation. The research was conducted in the Central Business District (CBD) of Mayjend Sungkono, Surabaya, which has special urban characteristics related to the urban climate, such as variations in the use of building functions, such as residential, village, commercial, apartment, or hotel, as well as unique types of roofs in the area. The parameters used for the simulation included air temperature (°C), relative humidity (%), wind speed (m/s), and wind direction. Three-dimensional city modeling was performed using a semi-automatic method based on LiDAR data and aerial photos. The simulation was carried out by integrating three-dimensional city modeling with the visualization of microweather parameters in Envi-Met, along with pattern indicators from building facades. In addition, a vegetation scenario was created to reduce the temperature. The second factor is a comparison between the existing conditions and the scenarios that have been created.

Three-Dimensional Web-Based Client Presentation of Integrated BIM and GIS for Smart Cities

Smart cities use technological solutions to reduce the drawbacks of urban living. The importance of BIM and GIS integration has increased with the popularity of smart city and 3D city concepts in recent years. In addition to 3D city models, Building Information Modeling (BIM) is an essential element of smart cities. The 3D city model web client in this study displays three-dimensional (3D) city models created using photogrammetric techniques, BIM, and campus infrastructure projects. The comparison and integration of the aforementioned systems were evaluated. A web-based 3D client framework and implementation for combined BIM and 3D city models are the goals of the submitted work. The Web is a very challenging platform for 3D data presentation. The Cesium engine based on HTML5 and WebGL is an open-source creation and the virtualcityMAP application using the Cesium infrastructure was used in this study.

A framework for fully automated reconstruction of semantic building model at urban-scale using textured LoD2 data

The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-dimensional (3D) city modeling, has been accessible for an extended period. However, its comprehensive implementation remains limited due to challenges such as insufficient automation and inconsistent data quality. This research introduces an innovative and fully automated framework aimed at urban-scale semantic building model reconstruction. The proposed framework addresses three critical challenges: (1) proposing facade layout graph model to formalize the geometry and topological relationships of semantic entities on building facades, thereby promoting the deduction of structural completeness and the reconstruction of semantic facade models; (2) establishing a mapping relationship between texture images, semantic entities, and building shells guided by the facade layout graph to ensure consistent correlations among the geometry, semantics, and topology of building models; (3) developing an efficient representation methodology for semantic building models utilizing a parameter set derived from the facade layout graph. The proposed framework has been successfully validated by reconstructing 8,681 buildings from three different locations in Berlin. The results demonstrate an outstanding reconstruction accuracy of 91%, with a time efficiency of only 3.42 s per building. Visual analysis further confirms that the framework effectively fulfills the application prerequisites of 3D GIS. The code of the proposed framework is available in the repository: https://github.com/wangyuefeng2017/LoD3Framework-.

Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction

Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture. Additionally, we integrate Markov field-based contextual analysis for post-segmentation assessment and cluster analysis algorithms for building instantiation. Training a model to adapt to diverse datasets necessitates a substantial volume of annotated data, encompassing both real data from Quebec City, Canada, and simulated data from Evermotion and Unreal Engine. The experimental results indicate that incorporating simulated data improves segmentation accuracy, especially for under-represented features, and the DBSCAN algorithm proves effective in extracting isolated buildings. We further show that the model is highly sensible for the method of creating 3D meshes.

Automatic Point Cloud Colorization of Ground-Based LiDAR Data Using Video Imagery without Position and Orientation System

With the continuous development of three-dimensional city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of light detection and ranging and cameras in mobile measurement systems provides a new and highly effective solution. Currently, integrated mobile measurement systems commonly require cameras, lasers, position and orientation system and inertial measurement units; thus, the hardware cost is relatively expensive, and the system integration is complex. Therefore, in this paper, we propose a ground mobile measurement system only composed of a LiDAR and a GoPro camera, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. The error of the result is only 0.5–1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the speeded-up robust features algorithm with distance restriction and random sample consensus to select corresponding points. The vertical parallax of the stereo image pair model is less than one pixel, indicating that the accuracy is high. (4) A point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. In the colored point cloud, the textures of the buildings are clear, and targets such as windows, grass, trees, and vehicles can be clearly distinguished. Overall, the result meets the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

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.

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.

An Analysis of the Three-Dimensional Modelling Using LiDAR Data and Unmanned Aerial Vehicle (UAV) (Case Study: Institut Teknologi Sepuluh Nopember, Sukolilo Campus)

The process of modelling a three-dimensional city in the Institut Teknologi Sepuluh Nopember, Sukolilo Campus, can be carried out using a semi-automatic method with a model-driven approach to update the three-dimensional model using LiDAR and orthophoto data in 2016 into a three-dimensional model of several new objects using UAV data acquired in 2021. The application of building footprint segmentation, roof modification, combination of CGA rules, and facade texture can represent a three-dimensional city model according to a certain level of detail or LOD so that 1236 building models are obtained from the 2016 LiDAR data and 104 new building models are added from the UAVs in 2021. The results of the accuracy test analysis of the 3D modelling with LiDAR data sources and UAV data showed that all model accuracy values have met the LOD 2.1 standard according to the refined level of details standard. Based on the level of detail analysis assessment, the average completeness of the model achieved is 87.49%. Meanwhile, the correctness value that was successfully formed by the model was 94.08%, and finally the quality value of the resulting model was 82.79%. Based on SNI 03-1733-2004, the calculation of the completeness and correctness of the model has met the standard, which is more than 85% of the model approach with the original object in the field. Meanwhile, for the quality value of the resulting model, it is still 2.21% of the allowed tolerance value.

Proposed Methodology for Accuracy Improvement of LOD1 3D Building Models Created Based on Stereo Pléiades Satellite Imagery

Three-dimensional city models play an important role for a large number of applications in urban environments, and thus it is of high interest to create them automatically, accurately and in a cost-effective manner. This paper presents a new methodology for point cloud accuracy improvement to generate terrain topographic models and 3D building modeling with the Open Geospatial Consortium (OGC) CityGML standard, level of detail 1 (LOD1), using very high-resolution (VHR) satellite images. In that context, a number of steps are given attention (which are often (in the literature) not considered in detail), including the local geoid and the role of the digital terrain model (DTM) in the dense image matching process. The quality of the resulting models is analyzed thoroughly. For this objective, two stereo Pléiades 1 satellite images over Iasi city were acquired in September 2016, and 142 points were measured in situ by global navigation satellite system real-time kinematic positioning (GNSS-RTK) technology. First, the quasigeoid surface resulting from EGG2008 regional gravimetric model was corrected based on data from GNSS and leveling measurements using a four-parameter transformation, and the ellipsoidal heights of the 142 GNSS-RTK points were corrected based on the local quasigeoid surface. The DTM of the study area was created based on low-resolution airborne laser scanner (LR ALS) point clouds that have been filtered using the robust filter algorithm and a mask for buildings, and the ellipsoidal heights were also corrected with the local quasigeoid surface, resulting in a standard deviation of 37.3 cm for 50 levelling points and 28.1 cm for the 142 GNSS-RTK points. For the point cloud generation, two scenarios were considered: (1) no DTM and ground control points (GCPs) with uncorrected ellipsoidal heights resulting in an RMS difference (Z) for the 64 GCPs and 78 ChPs of 69.8 cm and (2) with LR ALS-DTM and GCPs with corrected ellipsoidal height values resulting in an RMS difference (Z) of 60.9 cm. The LOD1 models of 1550 buildings from the Iasi city center were created based on Pléiades-DSM point clouds (corrected and not corrected) and existing building sub-footprints, with four methods for the derivation of the building roof elevations, resulting in a standard deviation of 1.6 m against high-resolution (HR) ALS point cloud in the case of the best scenario. The proposed method for height extraction and reconstruction of the city structure performed the best compared with other studies on multiple satellite stereo imagery.

3D MODEL OF BUILDING BASED ON MULTI-SOURCE DATA FUSION

Abstract. The process of building a digital city is the construction of a three-dimensional city model. The construction of 3 d data model is the process of multi-source spatial information collection and fusion. The current means of spatial information collection are mainly divided into three-dimensional laser technology and UAV photogrammetry technology. The three-dimensional laser technology is mainly based on ground laser, which has high acquisition accuracy for the bottom part of the building, but it is insufficient for the information collection at the top of the building. UAV photogrammetry has a large area and high efficiency spatial information collection method. At the same time, a large three-dimensional city model of the building can also be constructed. However, due to practical problems such as flight height and ground occlusion, the model constructed by UAV photogrammetry has a perfect top and poor bottom quality. The two methods are combined to build a more perfect point cloud model. Aiming at the above problems, a three-dimensional modeling method of buildings under multi-source data fusion is proposed. Firstly, the point cloud data and image data obtained by 3D laser scanner and UAV are preprocessed, and the two data forms are transformed into high-precision point cloud data. Then two sets of point cloud data are fused to generate the whole point cloud data. Finally, based on the patch generated by the whole point cloud, the point cloud model is reverse modeled. Get the corresponding building entity model, in order to get a more perfect 3D city model.

EXPLORING EXISTING 3D RECONSTRUCTION TOOLS FOR THE GENERATION OF 3D CITY MODELS AT VARIOUS LOD FROM A SINGLE DATA SOURCE

Abstract. The use of three-dimensional (3D) city models has increased in a wide range of applications beyond visualisation. However, generation and maintenance of 3D data comes at a high cost, time, and workload. The purpose of the generalisation where coarser versions are obtained from a source data is of great interest for National Mapping and Cadastral Agencies (NMCA), which would benefit obtaining multiple 3D versions of an area from a single source. The main aim of the exploration presented in this paper is to study the potential of downsizing point clouds as an approach to generate 3D city models at multiple levels of detail from a single source and evaluate the steps required to ensure the output is fit for real world applications from an NMCA context. While interesting results are obtained when testing with sample data, no software managed to semi-automatically reconstruct 3D model for buildings of rather complicated geometry.

Method for 2D-3D Registration under Inverse Depth and Structural Semantic Constraints for Digital Twin City

A digital twin city maps a virtual three-dimensional (3D) city model to the geographic information system, constructs a virtual world, and integrates real sensor data to achieve the purpose of virtual–real fusion. Focusing on the accuracy problem of vision sensor registration in the virtual digital twin city scene, this study proposes a 2D-3D registration method under inverse depth and structural semantic constraints. First, perspective and inverse depth images of the virtual scene were obtained by using perspective view and inverse-depth nascent technology, and then the structural semantic features were extracted by the two-line minimal solution set method. A simultaneous matching and pose estimation method under inverse depth and structural semantic constraints was proposed to achieve the 2D-3D registration of real images and virtual scenes. The experimental results show that the proposed method can effectively optimize the initial vision sensor pose and achieve high-precision registration in the digital twin scene, and the Z-coordinate error is reduced by 45%. An application experiment of monocular image multi-object spatial positioning was designed, which proved the practicability of this method, and the influence of model data error on registration accuracy was analyzed.

Reviewing the literature on the tripartite cycle containing digital building permit, 3D city modeling, and 3D property ownership

There is an increasing trend for digitalization because of the developments in Information and Communications Technologies (ICTs). One of the topics that digitalization might be helpful is the building permit issuing that has some drawbacks in terms of the duration of finalization, a large number of documents, and the transparency of processes. Another topic is the need for updating three-dimensional (3D) city models that are beneficial for the effective management of urban areas in the sense of a wide range of subjects such as land-use planning, disaster response, and underground infrastructure monitoring. Apart from these topics, 3D land administration that copes with the ownership problems due to the ever-increasing existence of multilayered buildings in the built environment is gaining importance. Many studies mention that the interrelation among these topics can be advantageous because of exploiting the common digital building models namely Building Information Models (BIMs). It is therefore focused on a research question as follows: Is there a potential for integrating the digital building permit procedures, 3D city model updating, and 3D registration of property ownership as a tripartite cycle? This paper accordingly aims to review the scientific literature on the vision of the tripartite cycle to provide an insight into possible integration among its parts. The methodology contains an elaborate examination of the previous research that is collected from scientific databases and is categorized based on aspects of the tripartite cycle. Following the discussion of the literature, the recommendations are provided in the sense of the vision of the tripartite cycle.

Multi-Task Learning of Relative Height Estimation and Semantic Segmentation from Single Airborne RGB Images

The generation of topographic classification maps or relative heights from aerial or remote sensing images represents a crucial research tool in remote sensing. On the one hand, from auto-driving, three-dimensional city modeling, road design, and resource statistics to smart cities, each task requires relative height data and classification data of objects. On the other hand, most relative height data acquisition methods currently use multiple images. We find that relative height and geographic classification data can be mutually assisted through data distribution. In recent years, with the rapid development of artificial intelligence technology, it has become possible to estimate the relative height from a single image. It learns implicit mapping relationships in a data-driven manner that may not be explicitly available through mathematical modeling. On this basis, we propose a unified, in-depth learning structure that can generate both estimated relative height maps and semantically segmented maps and perform end-to-end training. Compared with the existing methods, our task is to perform both relative height estimation and semantic segmentation tasks simultaneously. We only need one picture to obtain the corresponding semantically segmented images and relative heights simultaneously. The model’s performance is much better than that of equivalent computational models. We also designed dynamic weights to enable the model to learn relative height estimation and semantic segmentation simultaneously. At the same time, we have conducted good experiments on existing datasets. The experimental results show that the proposed Transformer-based network architecture is suitable for relative height estimation tasks and vastly outperforms other state-of-the-art DL (Deep Learning) methods.

INTEGRATION AND WEB-BASED PRESENTATION OF 3D CITY MODELS AND BIM DATA: THE CASE OF THE KÖYCEĞİZ CAMPUS

Abstract. Smart cities increase the quality of life of people living in cities with the help of technology. They are the living spaces where practices that minimize the adverse effects of city life are implemented. The ever-growing and complex structure of cities has increased the need to include advanced information and communication technologies in management processes. In addition, the creation and use of three-dimensional (3D) city models in different areas are becoming widespread. 3D city models can be used for spatial analysis and visualization in various applications such as urban and telecommunications planning, disaster management, real-time simulations for educational purposes, and facility management. New concepts and techniques such as 3D GIS and virtual geographic environments are still under development in this context. In addition, Building Information Modelling (BIM) processes are of great importance for the construction of buildings, from manufacturing to operation and management, to the theme of smart cities.In this study, three-dimensional (3D) city models produced by terrestrial and aerial photogrammetry, BIM, and campus infrastructure projects produced by the theme of smart cities were presented on the web. Evaluations were made on the comparison and integration of the mentioned systems.

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand

Promoting the use of solar photovoltaic (PV) systems in global cities can be an effective way to cope with severe environmental problems caused by the consuming of fossil fuels. However, a complex urban environment challenges the effective use of PV systems for practical applications. Essentially, this is a spatial optimization problem, where the goal is maximizing the harvesting of solar energy while minimizing occupied urban surfaces. To address this problem, this paper proposes three hierarchical optimizations. First, computational optimization provides a parallel architecture for an established 3D solar estimation model to achieve spatially scalable computation with high spatio-temporal resolution. Second, priority optimization determines the use of different urban partitions considering various constraints. Third, capacity optimization analyzes the spatial and quantitative distribution of solar potential, constrained by the smallest solar irradiation and the minimum surface area to be used. The overall optimization framework is then set to obtain the minimum PV installation capacity required to meet the real demand with the identification of urban surfaces to be equipped with PV modules. By using smart meter data with high temporal resolution in the city of Bologna, Italy, our analysis not only provides executable plans to meet the real demand but also reveals that rebalance and storage capacity are needed to achieve a real-time self-supportive architecture. The proposed analytic and optimization framework can promote distributed PV systems in urban areas and facilitate energy transition adapted to a variety of applications.

Interaction and Interpretation of City and Folk Sports Culture

Sports culture of city and folk together build Chinese sports culture, and increasingly become an important part of Chinese culture. This article systematically analyzes the interaction and interpretation of city and folk sports culture by using research methods of literature and logical analysis. The development of city and folk sports culture should adapt to the cultural needs of diversification and multi-level. Cultural identity and participation are the main factors that promote the development of city and folk sports. People's cultural identity is related to cultural needs, and values play a major guiding role. Through the diversification of expression types such as festival folk customs, fitness entertainment, performance competitions, etc., people are encouraged to form a rich and three-dimensional city and folk sports culture identity concept.

Research on Ubiquitous Map Visualization for Ternary Space

In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” with the extension of its object space, expression space and information source, which challenges the theory of cartographic visualization. This paper discusses the ubiquitous map visualization from the object content and expression form. Oriented to the ternary space, it divides the object dimension of ubiquitous map visualization and analyzes the expression characteristics of ubiquitous map visualization. Based on that, it constructs the variable system, symbol system and method system of ubiquitous map visualization. With three cases of the metro roadmap, the tag map, and the three-dimensional (3D) city map, the application of the proposed content is explained to illustrate its effectiveness. The research in this paper is expected to further enrich the theoretical basis of cartographic visualization and provide theoretical support for the expression and application of ubiquitous map visualization.