3D Urban Models Research Articles

ESMUST: EnergyPlus-driven surrogate model for urban surface temperature prediction

This research presents a new urban surface temperature prediction model called the EnergyPlus-driven surrogate model for urban surface temperature (ESMUST). Estimating the urban landscape and building surface temperature is the main challenge for 3D urban structures, especially given the challenge of preparing thousands of specific inputs for the simulations. This research conducted feature engineering to identify informative input features that are mostly related to urban surface temperature sensitivity, and then generated synthetic data using EnergyPlus simulations. ESMUST was built using artificial neural networks with 42 million entries of a synthetic dataset and thirteen input features. During the validation process, ESMUST achieved 0.964% bias and efficiently predicted the surface temperature of 3D urban models with fewer inputs. The main contribution of ESMUST is the development of a surrogate model to predict 3D urban surface temperature with fewer input features and less modeling complexity. This process has great potential to save building energy and improve the outdoor thermal environment in the context of climate change. Moreover, the framework developed for ESMUST is expected to be further applied in the future to evaluate nature-based solutions such as green façades for urban comfort and sustainability.

GIS Based Procedural Modeling in 3D Urban Design

Traditional urban design is time-consuming and laborious. We propose a computer-generated architecture (CGA)-based workflow in this work, with the goal of allowing designers to take advantage of a high level of automation. This workflow is based on procedural modeling. A three-step CGA rule was applied to implement 3D urban procedural modeling, (1) parcel subdivision and clustering, (2) building extrusion, and (3) texture mapping. Parcel subdivision and clustering is the key step of layout modeling, giving the modeler flexibility to adjust the placement and size of the inner building lots. Subsequently, a land-use-based combination of eight common building types and layouts was used to generate various urban forms for different urban functional zones. Finally, individual buildings were decorated by creating texture maps of a planar section of the building facade or, alternatively, decomposing facades into sets of repeating elements and texture maps. We employed the proposed workflow in the H-village urban redevelopment program and an air–rail integration zone development program in Guangzhou. Three design proposals were generated for each project. The results demonstrated that this workflow could generate multiple layout proposals and alternative facade textures quickly and, therefore, address most of the collaborative issues with its analysis functions, including a flexible adjustment mechanism and real-time visualization.

Analysis of Requirements for Achieving Carbon Neutrality in a Cold Dense City Using GIS

Photovoltaics (PV) can make a significant contribution to achieving carbon neutrality in buildings. Geographic information systems (GIS) make it easy to model cities and simulate PV with high accuracy. However, the computational burden from 3D urban models remains high. In this study, the complex geometry of a city was represented in 2D images, and we propose a method for performing multiple PV simulations in a short amount of time by using these 2D images. The ratio of electricity generation to primary energy consumption (EG/E) was calculated for 415 office buildings in Sapporo, Japan, and conditions for achieving carbon neutrality in these buildings were explored. For buildings with a small gross floor area, EG/E can be higher than one, and carbon neutrality can be achieved by improving the performance of the exterior walls and windows and by increasing the area of the PV installed. However, to improve EG/E in buildings with a large gross floor area, it is necessary to consider improving the performance of the equipment used and the efficient operation of the HVAC system. The method is useful for considering ways to achieve carbon neutrality in various cities.

Visual impact assessment of urban developments around heritage landmarks using ULVIA method: (The case of Ark-e-Alishah monument in Tabriz)

Urban intensification and erection of high-rise buildings can affect the visibility of urban landmarks and pedestrians’ viewshed toward important monuments. Current 2D and 3D Isovist techniques use static rather than dynamic and cumulative view sheds to analyze visibility. The purpose of this research is to develop a method called ULVIA 1 to assess the average visibility degree of urban landmarks in urban design process. Several factors such as observer and environmental characteristics as well as the concept of cumulative viewshed (using Nurbs data and ray casting in Grasshopper) have been considered to develop this method. Ark-e-Alishah Mosque in Tabriz was selected as a case study and three alternative 3D urban models were reproduced based on data and aerial photos of the monument and its urban context in 2003, 2020 and a proposed model. The ULVIA is executed in sequential steps. The findings reveal that the 2003 urban fabric creates visibility with normal intensity and distribution in all paths, the 2020 option does not have this balance, and the difference between riding and pedestrian mode is higher. The final proposed alternative has a higher visibility intensity and better distribution in both pedestrian and rider modes than other alternatives and therefore UlVIA has the potential to be integrated into urban design process to assess various development alternatives to achieve the best results in terms of historical landmarks’ visibility from surrounding environments.

Vectorized dataset of roadside noise barriers in China using street view imagery

Abstract. Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6×106 Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08 % ± 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).

Repairing geometric errors in 3D urban models with kinetic data structures

3D urban models created either interactively by human operators or automatically with reconstruction algorithms often contain geometric and semantic errors. Correcting them in an automated manner is an important scientific challenge. Prior work, which traditionally relies on local analysis and heuristic-based geometric operations on mesh data structures, is typically tailored-made for specific 3D formats and urban objects. We propose a more general method to process different types of urban models without tedious parameter tuning. The key idea lies on the construction of a kinetic data structure that decomposes the 3D space into polyhedra by extending the facets of the imperfect input model. Such a data structure allows us to re-build all the relations between the facets in an efficient and robust manner. Once built, the cells of the polyhedral partition are regrouped by semantic classes to reconstruct the corrected output model. We demonstrate the robustness and efficiency of our algorithm on a variety of real-world defect-laden models and show its competitiveness with respect to traditional mesh repairing techniques from both Building Information Modeling (BIM) and Geographic Information Systems (GIS) data.

Urban Building Mesh Polygonization Based on Plane-Guided Segmentation, Topology Correction and Corner Point Clump Optimization

The lightweight representation of 3D building models has played an increasingly important role in the comprehensive application of urban 3D models. Polygonization is a compact and lightweight representation for which a fundamental challenge is the fidelity of building models. In this paper, we propose an improved polyhedralization method for 3D building models based on guided plane segmentation, topology correction, and corner point clump optimization. Improvements due to our method arise from three aspects: (1) A plane-guided segmentation method is used to improve the simplicity and reliability of planar extraction. (2) Based on the structural characteristics of a building, incorrect topological connections of thin-plate planes are corrected, and the lamellar structure is recovered. (3) Optimization based on corner point clumps reduces redundant corner points and improves the realism of a polyhedral building model. We conducted detailed qualitative and quantitative analyses of building mesh models from multiple datasets, and the results show that our method obtains concise and reliable segmented planes by segmentation, obtains high-fidelity building polygonal models, and improves the structural perception of building polygonization.

A Fast Scheduling Method for Massive Oblique Photography 3D Models

In view of the large amount of oblique photography 3D model data and the fact that high-performance hardware devices cannot load and render massive oblique photography 3D models at one time, this paper provides a fast scheduling method for massive oblique photography 3D models: get the tile radius, side length, scheduling range, and other parameters from the original data, normalize the original tile to a hundred grid, use a regular grid + quadtree + hexadecimal tree to build a tile spatial index, resample the tile data at each level to generate the elevation model DEM and the real radiographic image TDOM, and use the DEM+TDOM method to reconstruct the layered and block top-level 3D model to achieve rapid scheduling and rendering of massive oblique photography 3D models. Experiments show that the method is efficient and feasible and can meet the visualization and real-time interaction requirements of massive urban 3D models.

Comparison of precise and approximated building height: Estimation from number of building storeys and spatial variations in the Tokyo metropolitan region

Precise building height is indispensable for evaluating variability in building heights. However, relevant data are not always available. Conventionally, building height is approximated as the product of the number of building storeys and floor height, called approximated building height. However, there is no consensus on how floor height, a key determinant of approximated building height, should be set. In Japan, increasingly precise building height data are becoming available as an open 3D urban model. This provides the motivation for answering the following research questions, in the context of the Tokyo metropolitan region: (1) What is the difference between approximated and precise building height? (2) How should we set floor height to minimise the difference between approximated and precise building height? The results show that (1) the average difference is −3.46 m if floor height, c, is 5 m, −0.87 m if c is 4 m and 1.71 m if c is 3 m; (2) c = 4 can effectively estimate building height from the number of storeys; and (3) the greatest difference between approximated and precise building height is spatially clustered where commercial zones and industrial zones are allocated, with their degree being dependent on how floor height is set. Furthermore, it is found that (1) in commercial zones and industrial districts, the optimal floor height, defined as the optimal solution which minimises the sum of squared differences between approximated and precise building height, is greater than 4 m; (2) in mid/high rise residential districts, the optimal floor height ranges from 3 to 3.4 m; and (3) in low-rise residential districts, the optimal floor height ranges from 3.5 to 3.9 m. The findings of this paper can help urban planners find the optimal floor height in each district and understand spatial variations in building and floor heights.

Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm

With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.

A Novel Framework for Road Data Surveying Based on Deep-learning and GIS

This paper proposes a novel framework for road data surveying and 3D urban visualisation based on deep-learning technologies, open data, and GIS techniques. Existing road inventory preparation methods are time-consuming, labour-intensive, inefficient, and lack an acceptable method for 3D urban visualisation in Sri Lanka. Thus, modern approaches are not applicable due to a lack of resources, technology, and financial capacity; the proposed framework will enable us to overcome these weaknesses. The study comprised of three main stages: a literature review, development of the framework, and its validation in Ranna area. The framework was applied to two consecutive model validation events: the first related to the road data surveying model and the second to the 3D urban visualisation model. Its proposed returned KAPPA Accuracy Scores were 92% and 90% respectively. The findings of this study further the use of cutting-edge deep learning and mapping techniques in transport and urban planning, making the preparation of road inventory surveys and 3D urban visualisations more cost-effective and efficient. Transport engineers, urban and regional planners, geographers, and GIS experts can employ the proposed framework for road data collection and 3D urban visualisation.

Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.

A microscale three-dimensional model of urban outdoor thermal exposure (TUF-Pedestrian).

Urban street design choices relating to tree planting, building height and spacing, ground cover, and building façade properties impact outdoor thermal exposure. However, existing tools to simulate heat exposure have limitations with regard to optimization of street design for pedestrian cooling. A microscale three-dimensional (3D) urban radiation and energy balance model, Temperatures of Urban Facets for Pedestrians (TUF-Pedestrian), was developed to simulate pedestrian radiation exposure and study heat-reducing interventions such as urban tree planting and modifications to building and paving materials. TUF-Pedestrian simulates the spatial distribution of radiation and surface temperature impacts of trees and buildings on their surroundings at the sub-facet scale. In addition, radiation absorption by a three-dimensional pedestrian is considered, permitting calculation of a summary metric of human radiation exposure: the mean radiant temperature (TMRT). TUF-Pedestrian is evaluated against a unique 24-h observational dataset acquired using a mobile human-biometeorological station, MaRTy, in an urban canyon with trees on the Arizona State University Tempe campus (USA). Model evaluation demonstrates that TUF-Pedestrian accurately simulates both incoming directional radiative fluxes and TMRT in an urban environment with and without tree cover. Model sensitivity simulations demonstrate how modelled TMRT and directional radiative fluxes respond to increased building height (ΔTMRT reaching -32°C when pedestrian becomes shaded), added tree cover (ΔTMRT approaching -20°C for 8m trees with leaf area density of 0.5 m2m-3), and increased street albedo (ΔTMRT reaching + 6°C for a 0.21 increase in pavement albedo). Sensitivity results agree with findings from previous studies and demonstrate the potential utility of TUF-Pedestrian as a tool to optimize street design for pedestrian heat exposure reduction.

Investigation of Using Sky Openness Ratio as Predictor for Navigation Performance in Urban-like Environment to Support PBN in UTM.

One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.

ESTIMATION OF PHOTOVOLTAIC POTENTIAL AT THE URBAN LEVEL FROM 3D CITY MODEL (SOLAR CADASTER): CASE OF CASABLANCA CITY, MOROCCO

Abstract. Nowadays, the use of solar energy in buildings, especially photovoltaic energy, has undergone a great evolution in the world, thanks to various technological advances and to incentive programs. Related to this topic, the solar cadaster is an important interactive tool to predict the solar potential in an urban environment. The main objective of this research work is to estimate the photovoltaic energy potential of roofs based on aerial photogrammetry and GIS processing. The location chosen for the study is the Maarif district located in the city of Casablanca in order to raise awareness of the public and decision makers to this energy potential through a geoportal that will be developed for this purpose. The tool proposed in this research work makes it possible to evaluate the solar irradiation on a part of the territory of Casablanca with a sufficiently satisfactory precision and reliability, this thanks to the precise reconstruction of the territory in 3D urban model called digital surface model (DSM) at 50 cm resolution by techniques known as photogrammetry which makes it possible to carry out measurements extracted from a stereoscopic pairs, by using the parallax and the correlation between the digital images taken from various points of view. The analysis was used on the basis of specific algorithms and several factors including geographical location, shade, tilt, orientation, roof accessibility and topography which are the main factors influencing the productivity of solar panels.

The effect of courtyard buildings on the ventilation of street canyons: A wind-tunnel study

Using controlled tests in a wind tunnel, we simulated the pollution of four different street canyons formed by four 3D urban array models. The urban models differed by the geometry of roofs (pitched and flat roofs) and buildings (courtyard and solid buildings). We simulated traffic pollution from a ground-level source positioned in the middle of the street canyons. We show that the courtyard buildings significantly improve (by a factor of 1.3) the ventilation of the street canyons only in the cases with pitched roofs. We explain the differences between the ventilation performances of the street canyons by analysing the dynamics of the coherent structures. The buildings at the roof level shed two main vortex structures into the flow. However, the street canyon with pitched roofs and the courtyard buildings shed more stable structures that collide and penetrate deeper downstream near the wind-facing eaves. Near the pedestrian zone, ventilation is driven by advection, manifested as corner vortices at the street ends and flow convergence from the windward to the leeward side in the middle of the street canyons. The corner vortices are more pronounced in the courtyard buildings regardless of roof shape, resulting in higher concentrations than solid buildings.

Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.

Proposing a sustainable urban 3D model to minimize the potential risk associated with green infrastructure by applying engineering tools

The goal of this study is to estimate the potential risk of exposure to urban green infrastructure by calculating and improving of AIROT index, adding meteorological factors as wind direction and updating the index to be more accurate for smaller urban green areas. To achieve this objective, BIM methodology has been applied by creating a 3D BIM model from the reality capture of a street with LiDAR. The BIM model contains the parametric data needed to apply AIROT index and it allows to map results in a graphic environmental sustainability study. The importance of location of green infrastructure is one of main conclusions obtained in order to minimize aerobiological risks in future new buildings or even in maintenance tasks of urban green infrastructure. A valuable result obtained from the developed methodology are walk simulations in the 3D model with the aim to identify high risk of potential exposure of urban green infrastructure with allergenic interest for allergic patients in order to supply health itineraries of pedestrians in a proposal of Smart City.

Size-Adaptive Texture Atlas Generation and Remapping for 3D Urban Building Models

A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.

A new workflow for detailed urban scale building energy modeling using spatial joining of attributes for archetype selection

Cities play an essential role in energy consumption and its environmental impacts. Urban Building Energy Modeling (UBEM) can help optimize the built environment's energy efficiency and improve the design and operation of building energy systems. In order to develop UBEM, individual buildings' characteristics such as constructions, internal loads, energy systems, etc., are required. To develop a comprehensive urban building energy model requires detailed 3D urban building geometry information, comprehensive building attribute libraries and a detailed archetype selection to automatically assign parameters to the building construction and usage. Most of the already developed tools do not take full advantage of external data sources of building characteristics with different formats and integrate them into the UBEM workflow. This study introduces a workflow to automatically extract, collect, and pre-process the energy-related parameters from open-source data to enrich the UBEM using spatial joining of attributes and detailed archetype selection. Two different 3D urban models (low and high resolution) are proposed to be used for urban building energy simulation. The workflow using the high-resolution model is demonstrated by applying it to the downtown Montreal buildings as a Canadian case study. A novel method for assigning the building's attributes to the building surfaces and thermal zones is developed, which is based on a detailed and automatic archetype selection. The archetype-selected data and other required information for urban building energy demand calculation are fed into EnergyPlus by introducing a hierarchical concept. The high-resolution enriched UBEM is calibrated using monthly measured data of a reference building, which resulted in an acceptable root mean square error. The method was then applied to the whole district, and it could be shown that ventilation and infiltration rates have the highest impact on energy demand. This study shows that using high-resolution UBEM allows detailed urban building energy analysis, which helps decision-makers to better understand their built environment.