3D Building Research Articles

Addressing Differential Axial Deformation Effect and Sway in 3D Building Frame Analysis

The Differential Axial Deformation Effect (DADE) is a significant consideration in the 3D analysis of building frames, where elements like columns and walls experience compression forces leading to differential shortening. Traditional design methods often overlook DADE, yet the existing building stock seems serviceable despite this omission. However, modern 3D analysis inherently introduces DADE, prompting designers to seek methods to approximate traditional design forces while utilizing advanced analysis techniques. The aim is to achieve design forces close to those historically used, ensuring structural integrity without disregarding DADE entirely. The methods outlined in this report demonstrate how to reconcile 3D analysis results with traditional design principles while also addressing concerns about sway deflections and the need for a conservative approach. While there's a debate about the economic viability of designing for a wider envelope of design conditions, the report illustrates that the additional reinforcement needed is typically minimal. Notably, the DADE phenomenon is universal across Finite Element Method (FEM) analysis software, and the strategies discussed here can be applied across different platforms. The notion that staged construction analysis effortlessly resolves DADE issues is debunked. Staged construction analysis is complex, doesn't fully eliminate DADE, and can yield unreliable results if not used carefully. In summary, this report offers practical insights into navigating DADE in 3D structural analysis, emphasizing the importance of balancing modern techniques with traditional design considerations

Effect of sand gradations on the fresh properties of 3D printable concrete

Sand is frequently used as a fine aggregate in concrete mixtures, but the performance of concrete can be considerably impacted by regional variations in sand gradation. In this work, the effect of sand gradations on the fresh properties of three-dimensionally printable concrete (3DPC) mixtures was systematically investigated. The binder combinations (cement, fly ash, silica fume, limestone powder, ground granulated blast-furnace slag) and the water/binder ratio were kept constant throughout the trials. The sands used in the mixtures were standard Ennore sand (ES) (grades I, II and III) and locally available river sand (RS). The mini-slump heights and flow values of mixes made with RS and with optimal packing of binary and ternary combinations of the standard ESs were evaluated and compared with those of the unary packing of ESs and RS. A correlation between the fineness modulus of different sand gradations and printable flow time was determined. The influence of sand gradations and fineness modulus on buildability, shape retention, open time and extrudability were evaluated. It was found that the performance of the fresh 3DPC was influenced by the aggregate's gradation and grain size in addition to its fineness. In conclusion, different fine aggregate specifications must be chosen depending on the various 3D printing building conditions and design requirements.

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

Integration of BIPV design and energy efficient technologies for low energy building in meeting net zero target

Based on the World Energy Transition Outlook, almost two thirds of the CO2 emissions released in the world are related to energy emissions with 36 Gigatonnes of energy related CO2 released in 2015. To reduce CO2 emissions by 70 % before 2050, energy efficiency parameters and renewable energy systems will have to be incorporated together to achieve a more sustainable future. The synergy between these two systems could potentially decrease the total final energy consumption of the world by 25EJ, which is by 5 % by the year 2030. One of those synergy methods would be the incorporation of energy efficient building designs and technologies with BIPV systems. Energy efficient building design is the process of upgrading or constructing buildings or elements that are able to maximize the consumption of energy supplied to them by reducing energy losses, through various innovative and effective systems and technologies. BIPV systems are PV products and components that are integrated into the buildings structure. In Malaysia, research and applications of BIPV are just emerging and is still in early development. In the final report submitted to the United Nations Development Program, Malaysia aimed to achieve an increase of 330 % in installed BIPV capacity. This paper aims to simulate an energy efficient building through a specific 3D modelling software, Autodesk Revit, that incorporates BIPV design systems. The method used to create this model will be performed in four stages. First, the development of a comprehensive 3D model of a suitable building. Second, analysis of photovoltaic system of the model. Third, an intricate energy analysis of 3D building and BIPV system via Revit and Autodesk Insight. Lastly, evaluation of the energy consumption of the building and CO2 emissions. This research has managed to reduce the Final Energy Use Intensity of the building from 194 kwh/m2/year to 140 kwh/m2/year and the CO2 emissions from 258 tnes CO2e to 186 tnes CO2e a year. This research presents the integration of energy efficient designs and technologies, with BIPV systems toward low energy building target.

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Building facade structure extraction method based on three-dimensional laser point cloud by considering semantic information

ABSTRACT Building facade structures serve as important data support for three-dimensional (3D) building modelling. A building facade structure extraction method based on 3D Laser Point Cloud Data (LPCD) by considering semantic information is proposed. The proposed method mainly involves three steps. First, an improved 3D LPCD semantic segmentation method is introduced to extract and label point clouds as the building structure and walls. Second, structure line segments are recognized from the initial building facade structure based on the Regulated Block RANSAC (RB-RANSAC) algorithm. Third, geometric information such as distance, vector and positional relationship between structure line segments is considered to optimize the initial building facade structure. The proposed method can effectively extract and fit segment structures by considering the semantic and geometric information, obtaining significantly accurate and concise building facade structures. Case studies on 3D LPCD of local buildings and open-source datasets (Semantic3D) prove the extraction accuracy and efficiency of the proposed method.

Hand drawing-based daylight analysis using deep learning and augmented reality

The utilization of artificial intelligence (AI) in daylight analysis has recently experienced an increase. Its capabilities have been demonstrated through different studies. While using AI for daytime simulation can reduce time, it also presents significant challenges. Whether we utilize the conventional approach of simulating daylight or employ artificial intelligence, the process of going from hand drawing to obtaining the daylight analysis result involves intricate procedures associated with modeling, programming, and integrating AI models. This paper introduces an efficient approach to rapidly predict daylight performance directly from the user's hand-drawn sketches. The core idea is to leverage a deep learning model for predicting directly from hand drawing and augmented reality (AR) to seamlessly display results in an AR environment, overlaying them on the original drawings. This method not only increases user engagement and accessibility but also eliminates the dependency on traditional daylight simulation software and workflow. Artificial neural network (ANN) was exploited using data derived from 3D building models and Useful Daylight Illuminance simulations created by Rhino3D and Honeybee. The color code method, various imaging, and data processing techniques were implemented to ensure compatibility between the hand-drawn input and the simulation model. Unity3D is used as the development platform with the AR engine Vuforia. Notably, the ANN model yields promising results, with R2 ranging from 0.872 to 0.985. This is achieved by efficiently capturing and converting grid-based hand-drawn images into suitable input for the ANN. The obtained results demonstrate the feasibility and emphasize the concept's practicality.

Characterizing urban spatial structure through built form typologies: A new framework using clustering ensembles

Prior research on urban form typologies has largely relied on qualitative classification methods, resulting in subjective and limited analyses. Recently, the emerging data-intensive studies often use a single clustering algorithm and parameter setting, raising concerns about the reliability of the findings. This paper introduces a novel clustering analytical framework for conducting typological studies on urban form that yield stable and reliable results. We employ clustering ensembles, which can combine multiple clustering algorithms to further provide a comprehensible output that facilitates interpretation and knowledge generation. By applying the new framework using 3D building data in Guangzhou, we identify eight typologies of urban built forms and reveal a consistent polycentric pattern across different clustering algorithms and parameter settings. The findings have implications for urban land use planning and regulations by integrating 3D representations of urban form.

Fire and smoke digital twin – A computational framework for modeling fire incident outcomes

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens. CCS conceptsComputer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering.

A process to reproduce repeatable 2D material-based hierarchical enhanced surface area structures

We report a repeatable method for fabrication of a 2D material-based hierarchical enhanced surface area structures (HESAS), which relies on a titanium substrate as the base for nucleation and growth and a titania-acid solution. Since the initial documentation of these formations in 2020, numerous experimental iterations have ensued, culminating in the establishment of a straightforward, replicable procedure for their fabrication. Subsequent to the inaugural report, this manuscript offers a comprehensive overview of the synthesis procedure and structural attributes. Furthermore, it summarizes various pivotal discoveries attained during the course of process refinement, including the significance of substrate preparation in particle synthesis. Specifically, the removal (or lack thereof) of the oxide layer dictates whether (or not) the 2D building blocks form, which corroborates that the composition and level of reactivity of the base surface—where the heterogenous nucleation events take root—appear to impact the ability for crystallization to occur. Nonclassical nucleation and crystallization theories could explain some of the observed patterns in the fabricated HESAS. This report briefly discusses some of the factors which affect supersaturation for the chosen synthesis system and the critical role supersaturation plays in the development of HESAS. Future work should include advancing understanding on the kinetics (and hence dynamic behavior) of the supersaturation parameters; this will be critical to develop controlled fabrication protocols of HESAS, enabling their broader adoption.

Evaluation of the Impact of 3D Space Building Learning Using Augmented Reality Based on Android Applications

Mathematics is a subject that is considered difficult by many students, especially geometric materials. Grade 5 students at SDN Bugel 2 had a low average score in learning geometric shapes with an achievement of 40,7. The aim of this research is to increase students' understanding of spatial construction materials in class 5 through learning applications. This research was conducted using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) method. The result of this research is to create a 3D space building learning application based on Augmented Reality which can make it easier for students to understand space building learning. This learning application is effective and can improve student understanding. This is proven by the average quiz score increasing to 81,9.

Exploring the influence of building form parameters on three-dimensional ventilation potential in urban centers: A case study of Nanjing, China

In urban areas, variations in the functional usage of buildings or their clusters directly influence the 2D/3D morphology of buildings which can lead to changes in the ventilation potential (VP) at different heights, but there is a lack of refined quantitative relationship analysis between 3D building form parameters (BFPs) and VP at different heights within a certain urban area. The objective of our study is to obtain simulated wind field data from different heights, considering distances from roads, and transverse and longitudinal sections. We employ partial correlation analysis, simple correlation analysis, and Geographic detectors to uncover the extent and mechanisms of the influence of BFPs on the ventilation effectiveness of neighborhoods in a major urban center in China, specifically Nanjing. The findings demonstrate that, at all heights, there is an inverse relationship between wind speed and building density (BD), with the location of higher wind speed generally aligning with the intersection points. The correlation coefficients between BD and simulated wind speed are 0.040 (1.5 ​m), −0.475 (10 ​m), and −0.314 (30 ​m), while the correlation coefficients between building height (BH) and simulated wind speed are 0.237. However, no significant correlation is observed between floor area ratio (FAR) and simulated wind speed. Geographic detector analysis reveals differences in the correlation between BFPs and simulated wind speed at different heights within various building functional areas. Our study provides some practical information for urban planners and designers to carry out wind sensitivity and adaptation planning and urban design.

Compact rover surveying and laser scanning for BIM development.

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details.

Regulating nitrogen/sulfur terminals on 3D porous Ti3C2 MXene with enhanced reaction kinetics toward high-performance alkali metal ion storage

In this paper, we have developed a simple and efficient sulfur-amine chemistry strategy to prepare a three-dimensional (3D) porous Ti3C2Tx composite with large amounts of N and S terminal groups. The well-designed 3D macroporous architecture presents enlarged interlayer spacing, large specific surface area, and unique porous structure, which successfully solves the re-stacking issue of MXene during storage and electrode fabrication. It is the amount of concentrated hydrochloric acid added to the S-EDA (ethylenediamine)/MXene colloidal suspension that is critical to the formation of 3D morphology. In addition, N and S terminals on MXene could improve the adsorption ability of K+. Owing to the synergistic effect of the structure design and terminal modification, the N, S codoped three-dimensional porous Ti3C2Tx (3D-NSPM) material shows a high surface capacitive contribution and rapid diffusion kinetics for K+ and Na+. As a result, the as-prepared 3D-NSPM delivers high reversible capacity (237 and 273 mAh g−1 at 0.1 A g−1 for PIBs and SIBs, respectively), superb cycling stability (84.9% capacity retention after 10,000 cycles at 1 A g−1 in PIBs and 74.0% capacity retention after 2200 cycles at 1 A g−1 in SIBs), and excellent rate capability (111 and 196 mAh g−1 at 5 A g−1 for PIBs and SIBs, respectively), which are superior to other MXene-based anodes for PIBs and SIBs. Moreover, the described strategy provides a new insight for constructing the 3D porous structure from 2D building blocks beyond MXene.

Energy-efficient indoor hybrid deployment strategy for 5G mobile small-cell base stations using JAFR Algorithm

In the context of 5th-generation (5G) mobile communication technology, deploying indoor small-cell base stations (SBS) to serve visitors has become common. However, indoor SBS is constrained by factors such as service capacity, signal interference, and structural layout. Merchants within large buildings frequently host diverse activities to attract visitors, significantly increasing indoor traffic and crowd-gathering phenomenon. Consequently, SBS faces challenges of excessive energy consumption, compromised communication quality, and an inability to provide service to all visitors. Merchants aim to deploy SBS that can effectively curtail energy consumption costs while fulfilling visitor needs. However, due to the intermittent nature of high footfall situations, employing additional fixed SBS is not economically viable. Therefore, we address the challenge of maintaining service quality and mitigating energy consumption of SBS during footfall fluctuations by proposing an SBS model with a dynamic sleep mechanism. We simulate the internal structure of a three-dimensional (3D) building and the footfall over time. Within this model, we leverage the flexibility of mobile small-cell base stations (MSBS) to seamlessly traverse service regions. We compute the transmission power and location of SBS and MSBS based on energy efficiency (EE), combining their strengths to tackle the challenge. This approach maintains SBS communication quality while curbing energy consumption. We attain the optimal hybrid deployment strategy by enhancing the adaptive differential evolution with optional external archive (JADE) algorithm and incorporating the final fitness formula, the adaptive ranking mutation operator strategy, and the disorder replacement strategy (DRS) in it to form the proposed joint adaptive fusion with ranking (JAFR) algorithm. Our comparative simulation experiments demonstrate the effectiveness of JAFR in addressing the challenges against conventional methods, recent differential evolution algorithms, and mobile base station (MBS) deployment approaches posed by this model. The results indicate that the JAFR algorithm yields superior SBS deployment strategies in most cases.

Automated BIM generation for large-scale indoor complex environments based on deep learning

Large volumes of 3D parametric datasets, such as building information modeling (BIM), are the foundation for developing and applying smart city and digital twin technologies. Those datasets are also considered valuable tools for efficiently managing rebuilt structures during the operation and maintenance stages. Nevertheless, current approaches developed for the scan-to-BIM process rely on manual or semi-automatic procedures and insufficiently leverage semantic data in point clouds. These methods struggle to accurately represent large-scale indoor complex layouts and extract details from irregular-shaped unstructured elements, causing inefficiencies in BIM model generation. To address these issues, we propose an innovative scan-to-BIM framework based on deep learning algorithms and raw point cloud data, enabling the automatic generation of 3D models for both structured and unstructured indoor elements. Initially, we propose an enhanced deep learning neural network to improve the point clouds' semantic segmentation accuracy. Subsequently, an efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. The proposed workflow can reconstruct large-scale data with multiple room layouts of Manhattan or non-Manhattan structures and reconstruct 3D models automatically by using a BIM parametric algorithm implemented in Revit software. Moreover, we introduce a robust method for unstructured elements to automatically generate corresponding 3D BIM models, even when the incorporating semantic information is incomplete. The proposed approach was evaluated on synthetic and real data for different scales and complexities of indoor scenes. The results of the experiments demonstrate that the improved model significantly enhances the overall semantic segmentation accuracy compared to the baseline models. The proposed scan-to-BIM framework is efficient for indoor element 3D reconstruction, achieving precision, recall, and F-score values ranging from 96% to 99%. The generated BIM models are competitive with traditional methods regarding model completeness and geometric accuracy.

SEMANTIC LABELING OF 3D BUILDINGS BY USING GRAPH NEURAL NETWORK (GNN)

Abstract. Understanding building semantics is crucial for comprehending their structure, components, and functions. This study investigates the utilization of Graph Neural Networks (GNN) to semantically label 3D building models, aiming to optimize these labels' output within Geographic Information Systems (GIS). The methodology leverages diverse datasets comprising 3D building models, utilizing BuildingGNN algorithms to iteratively refine the labeling process. The resultant labeled components, after thorough validation, are translated into the CityGML format. The generated CityGML dataset holds promise for a wide array of 3D city model applications, heightening the utility of labeled components within GIS analyses. While exploring GNN's capabilities may reduce manual effort and time and provide standardized data representation, this study also addresses challenges in ensuring output reliability. While automation remains a goal, future research endeavors may focus on refining automatic labeling techniques and semantic translation processes to further improve accuracy and applicability.

Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

Reconstruction of 3D Information of Buildings from Single-View Images Based on Shadow Information

Accurate building geometry information is crucial for urban planning in constrained spaces, fueling the growing demand for large-scale, high-precision 3D city modeling. Traditional methods like oblique photogrammetry and LiDAR prove time consuming and expensive for low-cost 3D reconstruction of expansive urban scenes. Addressing this challenge, our study proposes a novel approach to leveraging single-view remote sensing images. By integrating shadow information with deep learning networks, our method measures building height and employs a semantic segmentation technique for single-image high-rise building reconstruction. In addition, we have designed complex shadow measurement algorithms and building contour correction algorithms to improve the accuracy of building models in conjunction with our previous research. We evaluate the method’s precision, time efficiency, and applicability across various data sources, scenarios, and scales. The results demonstrate the rapid and accurate acquisition of 3D building data with maintained geometric accuracy (mean error below 5 m). This approach offers an economical and effective solution for large-scale urban modeling, bridging the gap in cost-efficient 3D reconstruction techniques.