3D Urban Models Research Articles

Digital interpretation of the botanical heritage through biodesign

The paper presents two digital studies through the study of botanical heritage and plant morphology as inspiration for biodesign. The goal was to create conceptual urban 3D models as potential tools for solving climate challenges faced by selected cities, as well as for raising awareness of botanical heritage. For the first study carried out for the city of Nairobi from Kenya, the inspiration for biodesign was the species Spathodea campanulata P. Beauv. This plant species is recognized as a symbol of the battle for the environment in Kenya. The inspiration for the second conceptual model of biodesign was the protected sycamore (Platanus x acerifolia (Aiton) Willd.) in Topcider Park in Belgrade (Serbia). As a natural monument of botanical character, this plane tree is about 200 years old. The same methodology was applied in both studies, which included field research, identification of the desired morphological pattern of plants, and digital modeling and visualization using appropriate digital (computer) tools. Two conceptual solutions are presented as the results of this research.

Dynamic cabin model of an urban bus in real driving conditions

The transport sector is a key sector to reduce the emission reduction targets. The main auxiliary load, the HVAC system, contributes significantly to the energy consumption and affects the driving range in electric vehicles. Accurate and dynamic models are needed to optimize these systems in urban environments. This research presents a dynamic thermal model of a cabin, including a detailed 3D urban model, a consistent weighted stochastic kinematic model, a climate model accounting for all bus surfaces and environment, and a transient thermal model of the cabin. A validation was performed against dynamic experimental tests. The most demanding mode is for cooling, with a mean cooling demand of 105 kWh/100 km in a warm summer day. The heating demand on a cold winter day is around 22 kWh/100 km. The components analysis reveals that the occupancy contributes to 33–45 % of the cooling demand in summer and the solar components account for 20–42 %. Air changes contribute to 20 % of the heating demand in winter, and conduction, convection, and internal infrared components represent 40 % of the negative load, except for summer when they account for 10–20 % of the positive load. A sensitivity analysis has also been performed to quantify the impact of different strategies.

Research and Application of Urban 3D Modeling Technology in Virtual Reality Scenes

Abstract In this paper, an automatic building extraction process based on MVS point clouds is proposed to automatically extract building point clouds from urban MVS dense point clouds of complex scenes by projection, morphological expansion and contour extraction techniques. Aiming at the deficiency of Poisson surface reconstruction, this paper proposes a surface model optimization method based on RANSAC fast fitting. The method generates the optimized surface model through the filter denoising process and chunked RANSAC fast fitting. Finally, a workflow for the 3D reconstruction of urban buildings based on the MVS point cloud is proposed. In the analysis for the urban 3D modeling technique, the average error of the model after reconstruction is only 0.731%, and the measurement errors in the three-dimensional directions of length, width, and height are less than 5 cm. and the time consumed before and after the optimized method in this paper is reduced by an average of 3.09 s. Therefore, this study provides a simple and efficient method for the automatic extraction and 3D reconstruction of urban buildings.

Use of digital 3D urban models for view evaluation in building envelope design

A feature of digital 3D urban models is that they provide semantic information on various objects in urban space. This paper proposes a basic framework for obtaining visual information about the external conditions of a building to be designed using a 3D urban model. Ray emission and collision detection are used in a game engine to identify the type and distance of objects surrounding the building in the 3D urban model. Tagging the objects with their semantics identifies their type such as sky, building, road, greenery and so on. This paper also proposes setting two different viewpoints at the façade and the interior to consider the building design and the indoor visual environment design from both aspects of the whole building and individual rooms.

A large‐scale urban 3D model organisation method considering spatial distribution of buildings

AbstractThe rendering of urban 3D scenes involves a large number of models. In order to render scenes more efficiently, the main solution is to build a level of detail model (LOD). This may have the problem of building fragmentation, while relying on building a level of detail model (LOD) alone cannot meet the accuracy and fluency of large‐scale scene visualisation. Effective and reasonable data organisation has important research significance for the authors to achieve accurate and fast rendering of scenes. Therefore, the authors propose a large‐scale city model data organisation method considering building distribution to solve the above problems. This method first classifies the buildings in the scene at macro‐, meso‐ and microscales and records the classification using R‐trees. Then an adaptive quadtree is used to construct the data index of the city model. Finally, the data organisation of the large‐scale 3D city model is achieved by using the information of each node of the R‐tree as a constraint and combining with the adaptive quadtree. The results show that the method not only ensures the integrity of the user's area of interest but also can improve the efficiency of 3D scene construction.

Spatially resolved indoor overheating evaluation using microscale meteorological simulation as input for building simulation – opportunities and limitations

To assess the spatial heat resilience of buildings in urban development we test the suitability of a toolchain approach from microscale meteorological simulations, resolving the spatial influences on local urban climate, to building performance simulations, evaluating the indoor overheating risk in buildings. This approach makes it possible to investigate how much microscale effects (e.g. buildings, trees e.g. roads) in open space influence the overheating intensity in a building depending on its location within a district. In this context, the question arises how realistic the microscale meteorological simulation is to be used as input for indoor overheating evaluation. In this context, we applied a 3D urban climate model (ENVI-met) and a 1D boundary layer model (HIRVAC) for two urban districts in Germany as meteorological input for an indoor thermal comfort evaluation of two representative buildings. The results demonstrate that ENVI-met simulations without using measured temperature data create unrealistically low diurnal variations in outdoor air temperature despite an overestimated solar irradiance. The implementation to building simulation leads to a significant underestimation of the heat resilience for both buildings and to wrong conclusions about the efficacy of passive heat adaptation measures. In contrast, the HIRVACsimulations show a more realistic representation of the meteorological variables (when measured data is used for calibration) but are not able to resolve urban 3D structures. Our findings point out that an adjusted boundary layer representation in microscale meteorological simulations is crucial to provide meteorological input suitable for realistic spatially resolved indoor overheating analysis.

Camera and LiDAR Fusion for Urban Scene Reconstruction and Novel View Synthesis via Voxel-Based Neural Radiance Fields

3D reconstruction of urban scenes is an important research topic in remote sensing. Neural Radiance Fields (NeRFs) offer an efficient solution for both structure recovery and novel view synthesis. The realistic 3D urban models generated by NeRFs have potential future applications in simulation for autonomous driving, as well as in Augmented and Virtual Reality (AR/VR) experiences. Previous NeRF methods struggle with large-scale, urban environments. Due to the limited model capability of NeRF, directly applying them to urban environments may result in noticeable artifacts in synthesized images and inferior visual fidelity. To address this challenge, we propose a sparse voxel-based NeRF. First, our approach leverages LiDAR odometry to refine frame-by-frame LiDAR point cloud alignment and derive accurate initial camera pose through joint LiDAR-camera calibration. Second, we partition the space into sparse voxels and perform voxel interpolation based on 3D LiDAR point clouds, and then construct a voxel octree structure to disregard empty voxels during subsequent ray sampling in the NeRF, which can increase the rendering speed. Finally, the depth information provided by the 3D point cloud on each viewpoint image supervises our NeRF model, which is further optimized using a depth consistency loss function and a plane constraint loss function. In the real-world urban scenes, our method significantly reduces the training time to around an hour and enhances reconstruction quality with a PSNR improvement of 1–2 dB, outperforming other state-of-the-art NeRF models.

Bridging the gap between pedestrian and street views for human-centric environment measurement: A GIS-based 3D virtual environment

Street View Image (SVI) data has gained popularity as a methodological tool for assessing pedestrian-level environments across various disciplines. Several studies in transportation and public health fields posit that environmental features measured through SVIs are similar to those perceived by pedestrians, whereas, in fact, SVIs are typically collected from vehicles. As a result, the SVI data may reflect significant differences from an actual pedestrian’s view. Hence, this study investigates the errors in measuring neighborhood environmental features resulting from the difference between pedestrian and vehicle views. To accomplish this goal, a three-dimensional (3D) urban model is developed, utilizing multiple Geographic Information Systems (GIS) data. This model accurately represents the spatial positioning and dimensions of various entities. From the 3D model, virtual SVIs for each perspective are collected. Subsequently, we quantify environmental features in a manner similar to semantic segmentation for the images. Our findings reveal significant measurement errors for sidewalks, greenery, and roads between pedestrian and vehicle views. These errors are more pronounced with certain environment characteristics and parameter settings for image acquisition. For example, narrow road segments with few on-street parked vehicles exhibit increased measurement errors for greenery. Overall, the study provides guidelines for researchers on utilizing SVI to achieve reliable measurement of environmental features and move toward human-centric measurements.

Point Cloud-Based Historical Building Information Modeling (H-BIM) in Urban Heritage Documentation Studies

Integrating building information modeling (BIM) technology into historical and cultural heritage structures has resulted in historic building information modeling (H-BIM), an effective method for managing and documenting invaluable historical artifacts. H-BIM has the potential to aid with the design, restoration, and maintenance activities of historical buildings by properly integrating parametric data and deliberately assessing semantic information, thus progressing the 3D model’s performance. H-BIM also aims to conduct cultural heritage (CH) studies in a systematic manner by combining 3D modeling techniques and information management. The first step in this process is to conduct a suitable geomatic survey to build accurate 3D models. The most efficient modeling method is to employ 3D point cloud data that are acquired using the terrestrial laser scanning method. This study proposes a manual modeling strategy for urban historical buildings that falls within the scope of the H-BIM methodology. The proposed modeling strategy follows a workflow that inserts point cloud data into the BIM environment in order to create a 3D urban cultural heritage model using ontologies applied with the H-BIM methodology. With this objective in mind, the H-BIM model development platform Revit was used to model the object elements of the inheritance structure in 3D and save them in the BIM library. It is very important to model building elements separately in 3D modeling studies. The resulting BIM product sheds light on fundamental data for the future planning, design, operation, and maintenance processes of the building. Users can obtain data suitable for H-BIM by adding graphical and non-graphical data.

A methodology for urban planning generation: A novel approach based on generative design

The construction sector is undergoing a digital transformation that aims to increase productivity, improve processes and take advantage of the new advances in digitalization. Urban planning is particularly susceptible of benefiting from these advances due to its complexity and the large amount of data and disciplines that come together. In this paper, we propose a novel methodology that aims to enhance current urban planning design methods, which are mainly designed by a planner, to an optimized process where the planner interacts with a software that automates many of the tasks. This methodology based on generative design principles, develop urban design solutions by subdividing a given plot and assigning different housing typologies on it. Our proposed software requires 3D urban models datasets as a reference to create solutions within a specific shape, style, proportions, among others, as well as input from the planner to guide the program according to project requirements and existing local norms. Higher automation in the design process eases project changes and allows for more and varied design testing, which in the end, contributes to better analysis and decision making. We tested our proposal in a case study in the city of Vienna to illustrate the design process, obtain several urban planning solutions and validate our methodology.

3D Virtual Art Design Method Based on ARCS Model

Abstract The author proposes the use of 3D virtual VR technology in the ARCS model to understand the real and specific environment design of the ARCS model in 3D virtual art design, to check the application of 3D virtual VR technology in the design of the urban environment, the application is 3D virtual VR technology, - the construction of 3D urban model, exploring specific applications. Check out specific applications of 3D virtual VR technology in urban park art design, automotive ambient art design, and landscape ambient art design. Experiments have shown that 3D virtual VR technology can improve environmental design results with high user satisfaction and economic results in the construction industry.

PPK PROCESSING TO BOOST UAS ACCURACY IN CADASTRAL MAPPING

Abstract. Unmanned Aerial Systems (UAS) are increasingly used in different applications, including 3D urban modelling, cadastral mapping, urban planning, GIS information system and other fields because of their advantages. As a consequence, UAS equipment is constantly developed to provide more accurate results in a more reliable mode. This paper aims to evaluate the performances of a low-cost UAS system, namely DJI Phantom 4 Pro v2 equipped with a TeoKIT GNSS PPK (post-processing kinematic) module for cadastral mapping purposes. Two fights (oblique and nadir) over a residential area at 60 m height were performed and some 100 ground points were used to derive RMSE accuracies. Comparison between GNSS-aided with PPK processing and indirect georeferencing processes are performed. Given a mobile laser scanner (MLS) point cloud as ground truth, comparison with UAS point clouds and manually digitized features are also performed and reported.

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.

Leveraging LiDAR for smart cities climate change resilient: A solar potential case study in a developing area

Smart cities and solar energy are crucial components in climate change adaptation and sustainability. The interrelation with United Nations Sustainable Development Goals (SDGs) based on the 7th SDG goal, Affordable and Clean Energy for solar energy, and the 11th SDG goal, Sustainable Cities and Communities for smart cities, catalyze the research in this field. Unfortunately, there are some gaps in integrating smart cities and solar energy, especially in the elevation model and solar potential accuracy factors. It is essential because the highly accurate integrated model is enormously beneficial, especially for stakeholders. Therefore, this study is leveraging the Light Detection and Ranging (LiDAR) technology to provide high-resolution datasets for elevation models and create a precise 3D urban environment model for resilient integrated smart cities climate change. Digital terrain, including Digital Terrain Model and Digital Surface Model, were developed based on precise LiDAR point clouds before two vital surfaces, building rooftops and land parcels, were treated as a fundamental surface for solar potential calculation. The solar potential Return of Investment rate was classified and integrated with the 3D urban environment model. This 3D urban environment model helps visualize and provides a more specific and coherent picture of reality. The potential application of LiDAR data in climate change adaptation and sustainability that can be linked using various technologies and scales is also demonstrated in this study.

Measuring Physiological Responses to Visualizations of Urban Planning Scenarios in Immersive Virtual Reality

Stakeholder participation is an important component of modern urban planning processes. It can provide information about potential social conflicts related to specific urban planning scenarios. However, acquiring feedback from stakeholders is usually limited to explicit response types such as interviews or questionnaires. Such explicit response types are not suitable for the assessment of unconscious responses to specific parameters of an urban planning scenario. To address this limitation, we propose an approach for the assessment of affective and stress responses using implicit measures. Using a measure for electrodermal activity (EDA) and a virtual reality (VR)-based 3D urban model, we demonstrate how implicit physiological measurements can be visualized and temporally matched to specific parameters in an immersive representation of an urban planning scenario. Since this approach is supposed to support conventional stakeholder participation processes in urban planning, we designed it to be simple, cost-effective and with as little task interference as possible. Based on the additional insights gained from measuring physiological responses to urban planning scenarios, urban planners can further optimize planning scenarios by adjusting them to the derived implicitly expressed needs of stakeholders. To support simple implementation of the suggested approach, we provide sample scripts for visualization of EDA data. Limitations concerning the evaluation of raw EDA data and potentials for extending the described approach with additional physiological measures and real-time data evaluation are discussed.

Giving Historical Photographs a New Perspective: Introducing Camera Orientation Parameters as New Metadata in a Large-Scale 4D Application

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.

City Information Models (CIMs) as precursors for Urban Digital Twins (UDTs): A case study of Lancaster

City Information Models (CIMs) present several research challenges and are arguably pre-cursors for a roadmap to the transition to Urban or City Digital Twins (UDTs/CDTs) and broader smart city drives. In a period of digital transformation in urban planning, CIMs are primarily designed to intersect and merge urban GIS/BIM in what is termed GeoBIM. CIMs are often cloud-based geospatial services and data repositories with one-way data connections, which differ from integrated and high-fidelity cyber-physical complex systems for digital twins (DTs). However, there is a lack of precision in terms of defining CIMs and UDTs, and this paper examines four broad sociotechnical research challenges regarding the progress of CIMs toward full UDTs/CDTs, including (1) technical training for adoption, (2) acquisition strategies for aerial data and remote sensing, (3) data licensing types and system management, and (4) stakeholder engagement with CIM platforms. These four research challenges and digital transformation are discussed by using back-casting futuring methods, establishing a framework for CIMs through co-designed workshops, and explored in the Lancaster City Information Model (LCIM) prototype. The LCIM, as a resulting case study, was developed for architects, planners, and stakeholders to engage with 3D urban models and urban analytics (view sheds, masterplan scenarios, and solar potentials of buildings) from 2019 to 2021 to address digital inequality in planning and data fragmentation. The framework and prototype results resulted in the most extensive 3D open urban dataset published in the UK, consisting of a baseline of 1″+/− buildings and infrastructure and 7.5 cm aerial imagery and a framework for developing transitions from CIMs to UDTs. The delivery of this case and the resulting published dataset highlights the essential research needed regarding definitions and precision around terminology and systems between CIMs and UDTs and four applied challenges in digital transformation and future socio-technical relationships.

Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

3D Urban Modeling and Blockchain-based Digital Twin Management Tools, Real-Time Internet of Things and Remote Sensing Data, and Spatial Computing and Predictive Control Algorithms in Virtual Urban Environments and Cognitive Smart Cities

3D Urban Modeling and Blockchain-based Digital Twin Management Tools, Real-Time Internet of Things and Remote Sensing Data, and Spatial Computing and Predictive Control Algorithms in Virtual Urban Environments and Cognitive Smart Cities

Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery

Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to recover point clouds and 3D models of buildings. However, such models rely heavily on multi-view images of buildings, which are time-intensive and limit the applicability and practicality of the models. To solve these issues, we focus on designing an efficient DSM estimation-driven reconstruction framework (Building3D), which aims to reconstruct 3D building models from the input single-view remote sensing image. Existing DSM estimation networks suffer from the imbalance between local features and global features, which leads to over-smooth DSM estimates at instance boundaries. To address this issue, we propose a Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the proposed concept of elevation semantic flow to achieve the registration of local and global features. First, in order to make the network semantics globally aware, we propose an Elevation Semantic Globalization (ESG) module to realize the semantic globalization of instances. Further, in order to alleviate the semantic span of global features and original local features, we propose a Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on elevation semantic flow. Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction and surface reconstruction (or CityGML model reconstruction). On this basis, our Building3D can optionally generate CityGML models or surface mesh models of the buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the DSM estimation task show that our SFFDE significantly improves upon state-of-the-art and δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> metrics of our SFFDE are improved to 0.595, 0.897 and 0.970. Furthermore, our Building3D achieves impressive results in the 3D point cloud and 3D model reconstruction process.