Roof Planes Research Articles

Using Dynamic Software in Teaching Roof Design

The geometric design of roofs is a key element in the design and construction of buildings. Roofs play an important role in protecting buildings from the weather and providing esthetic appearance and functionality. Theoretically, to design a roof over a given plan means to construct the projections of the intersections of the roof planes. All this usually takes place in only one (orthogonal) projection. In simpler cases, for example if the roof plan is a rectangle, students can visualize the roof. If the floor plan is more complex, students often don’t know what the roof actually looks like and what the different steps of construction actually should be. In the recent period, the need for digital teaching materials has arisen in the context of distance education. Materials created using GeoGebra software have been shown to be suitable for use in mainstream teaching and to help students with homework. Their advantage is that they can facilitate self-directed study, as students can pace their progress. A further advantage is the combination of the solution in a orthogonal projection with the visualization in 3D. Based on this experience, type problems have been created in GeoGebra software by teachers, that show the solution of roofs in orthogonal projection, as is customary in this subject, and also show the solution in 3D. In addition, the documents created in GeoGebra can be used for 3D printing. In this paper, we present selected tasks and demonstrate the possibilities of using GeoGebra for graphical solutions.

A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Roof plane segmentation from airborne light detection and ranging (LiDAR) point clouds is an important technology for three-dimensional (3D) building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features, such as point-to-plane distance, normal vector, etc., to extract roof planes. However, the abilities of these features are relatively low, especially in boundary areas. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch multi-task network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point towards its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near the plane instance boundary. Therefore, we first robustly group plane points into many clusters in Euclidean and embedding spaces to find candidate planes. Then, we assign the rest boundary points to their closest clusters to generate the final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, to train the network and evaluate the performance of our approach, we prepare a synthetic dataset and two real datasets. The experiments conducted on synthetic and real datasets show that the proposed approach significantly outperforms the existing state-of-the-art approaches in both qualitative evaluation and quantitative metrics. To facilitate future research, we will make datasets and source code of our approach publicly available at https://github.com/Li-Li-Whu/DeepRoofPlane.

Glubam roof truss with riveted glubam connections adopting thin-walled steel tube: Experiment, modeling, and model-updating

Bio-based laminated structures offer a timely solution to meet the pressing demand for resource-efficient and environmentally responsible construction practices in civil engineering. Riveted connections are a kind of commonly used joint and significantly impact the mechanical behaviors of global structure. However, existing studies on the complex behavior of such joints for bamboo-based structures are insufficient. This study investigated the hysteretic behavior of riveted Glued Laminated Bamboo (glubam) connections using thin-walled steel tube and corresponding planar roof truss with thin-walled steel tube connections by experimental and numerical methods. Firstly, cyclic tests were conducted on the riveted glubam connections with thin-walled steel tube to investigate their hysteretic behavior. The test matrix encompasses four distinct connection configurations, carefully selected to assess the connection behavior of glubam. After the investigation of connection behavior, cyclic tests were further conducted on glubam planar roof truss structures to evaluate their global and local hysteretic behavior. Subsequently, the numerical hysteretic models for joints and hybrid roof trusses were developed within the OpenSeesPy framework. To accurately and efficiently calibrate the model parameters, a novel parallel genetic algorithm (PGA) was proposed to carry out an initial calibration on model parameters. Based on PGA, a more comprehensive model updating framework combining the neural network, prior knowledge and PGA was developed to obtain optimal parameters. Finally, the numerical models were successfully validated against the experimental data, demonstrating the effectiveness of the established numerical model and model-updating framework.

Roof plane parsing towards LoD-2.2 building reconstruction based on joint learning using remote sensing images

Building models are important for urban studies. Remote sensing multi-spectral (MS) images are widely used for its rich semantic information. The lack of geometry features is fulfilled by introducing photogrammetry derived digital surface models (DSMs), resulting in pairs of DSMs and MS images. Utilizing such pairs and a convolutional neural network, level of detail (LoD) 2.2 building models, which contain roof planes and major roof elements (e.g. dormers), are reconstructed in this work. Leveraging both raster and vector predictions, 3-D building models with straight edges and sharp corners are obtained. The proposed two-stage method first extracts vectorized roof lines from pairs of DSMs and RGB images, followed by generation of detailed 2-D and 3-D polygonal building models. We conducted our experiments based on two datasets: a custom dataset in Landsberg am Lech in Germany, and an open dataset named Roof3D. For the custom dataset, our proposed model achieved mean average precision (mAP) for building roof vertices of 64.3% and for building roof lines of 54.5% at highest. Mean precision and recall for reconstructed 2-D building roof plane polygons are 52.2% and 54.7% respectively. For the Roof3D dataset, mAP is reported to be 25.3% and 12.4% for the extracted building roof lines and roof plane polygons respectively.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

Modeling of glubam roof truss, parameter identification and updating based on parallel genetic algorithm

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model’s predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices.

PLANES4LOD2: Reconstruction of LoD-2 building models using a depth attention-based fully convolutional neural network

Level of detail (LoD)-2 reconstruction is an inevitable task in digital twin-related applications such as disaster management, flood simulation, landslide simulation and solar panel recommendation. However, there is a lack of capable methods that can exploit fine details in RGB imagery and mitigate noise in photogrammetric digital surface models (DSMs). Our investigation is focused on the use of roof planes to achieve a geometrically complete and correct, and topologically consistent LoD-2 building reconstruction. Using UNet with the EfficientNet-B3 backbone, the developed approach starts with jointly predicting building sections and roof planes from the orthorectified RGB imagery and a photogrammetric DSM. The detected sections and planes are then vectorized by employing tree search and simplified with the Douglas Peucker algorithm. Subsequently, height values from the noisy input DSM and the vectorized image-based (and simplified) roof planes are used to derive 3D-planes. Finally, the building model is formed by computing plane intersections as the ridge lines. This study demonstrates that a well-designed depth attention module (DAM), which is the bottleneck of the UNet, can achieve a very good use of both spectral and depth features. The resultant 1-to-n correspondence between building section and roof plane benefits accurate and consistent building model reconstruction. Furthermore, it leads to a superior generalization capability of the proposed method. Experiments with 1437 buildings from the cities Cologne and Braunschweig, Germany, demonstrate the success of the proposed workflow in reconstructing compound buildings with complex roof structures. The achieved geometric mean absolute error (MAE) is 1.06m and 0.24m respectively. Comprehensive comparative evaluations showcase the superiority of the approach in terms of geometric completeness and accuracy, and topological consistence with. The improvement over SAT2LOD2 (Gui and Qin, 2021) is 1.12m in Cologne (data accessible at https://github.com/dlrPHS/GPUB) and 0.47m in Braunschweig in geometrical MAE.

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.

Pengaruh Campuran Fasad Bangunan Arsitektur Eropa Dan Lokal Terhadap Tampilan Bangunan

The BJB Syariah Building is a Colonial Building with a European Architectural style, this building combine European and local elements, the façade of this building really reflects the European style. This research focuses on the mixture of local and European culture seen through buildings such as BJB Syariah Bandung which is the result of a combination of European and Indonesian architectural style. The type of research used in this study is qualitative research. Because this research uses a qualitative approach, the data results will be focused in the form of descriptive statements and do not review a hypothesis and do not correct variables. The result of his research is the composition of the façade that considers functional elements such as windows, doors, sun protection, and roof planes, as well as the emphasis on creating harmonious unity through good proportions, vertical and horizontal structures, rhythm of materials, colors, and decorative elements being the focal point.

Experimental and numerical investigation of bending performance of prestressed purlins having different longitudinal web opening

Variable cross-section pre-stressed precast concrete purlin (PPCP) members are frequently used in industrial buildings. Lightening these elements, which create a significant weight on the roof plane, is extremely important to reduce both concrete consumption and the weight that will affect the earthquake force. The voids left in the purlin bodies can make them even more economical. A series of experimental and numeric studies were carried out to find an answer to this research question. In the study, 8 PPCP beams with different 1/1 geometric scale web opening ratios (ranging between 7.5%∼35% depending on the beam length) were tested. At the end of the experimental study, the openings created in the beams did not significantly reduce the load carrying capacity. In addition, bearing capacity of the reference beam was obtained approximately 17% greater than the calculated analytical value. The results of the tests were validated utilizing ABAQUS FEM. Then, a parametric study was performed on 48 models according to three different pre-stressing levels (0.3 P, 0.6 P and 0.9 P) and three different concrete strengths (30 MPa, 40 MPa and 50 MPa). According to results, especially in PPCP with web opening ratios of 27.5% and 32.5%, both initial stiffness and ductility values increased compared to the reference beam. From the numerical models, it was revelead that the decrease in concrete strength caused a capacity loss of up to 17%, especially in beams with the highest opening ratio. It was discovered that the increase in the pre-stress level increased the stiffness and capacity, but the least increase was in the beam with the highest opening ratio.

Airborne LiDAR Point Cloud Roof Plane Segmentation Method Based on Voxel and Point Hybrid Growth

Airborne LiDAR Point Cloud Roof Plane Segmentation Method Based on Voxel and Point Hybrid Growth

Vectorizing planar roof structure from very high resolution remote sensing images using transformers

ABSTRACT Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from 87.1 % , 76.2 % to 89.1 % , 78.1 % , and from 69.7 % , 68.8 % to 71.2 % , 69.5 % , respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure.

ROOF3D: A REAL AND SYNTHETIC DATA COLLECTION FOR INDIVIDUAL BUILDING ROOF PLANE AND BUILDING SECTIONS DETECTION

Abstract. Deep learning is a powerful tool to extract both individual building and roof plane polygons. But deep learning requires a large amount of labeled data. Hence, publicly available level of detail (LoD)-2 datasets are a natural choice to train fully convolutional neural networks (FCNs) models for both building section and roof plane instance segmentation. Since publicly available datasets are often automatically derived, e.g. based on laser scanning, they lack on annotation accuracy. To complement such a dataset, we introduce manually annotated and synthetically generated data. Manually annotated data is domain-specific and has a high annotation quality but is expensive to obtain. Synthetically generated data has high-quality annotations by definition, but lacks domain-specificity. Moreover, we not only detect individual building section instances, but also roof plane instances. We predict separations not only between individual buildings, but also by a class that describes the line which separates roof planes. The predicted building and roof plane instances are polygonized by a simple tree search algorithm. To achieve more regular polygons, we utilize the Douglas-Peucker polygon simplification algorithm. We describe our dataset in detail to allow comparability between successive methods. To facilitate future works in building and roof plane prediction, our Roof3D dataset is accessible at <code>https://github.com/dlrPHS/GPUB</code>.

A bottom-up method for roof plane extraction from airborne LiDAR point clouds

Accurate roof plane extraction is a crucial step in constructing a three-dimensional model for buildings. Due to the significant differences in size and shape of building roofs in airborne light detection and ranging point clouds, many existing plane extraction methods are struggling to achieve good performance. To solve the above problem, a bottom–up method for roof plane extraction is proposed in this paper. Starting with the division of the roof point cloud into voxels, the initial planes are obtained in the voxels. The initial planes are then expanded by a parameter-adaptive region growing algorithm. Then, the grown planes are merged according to predefined constraints. Finally, an energy minimization-based method is applied to optimize the results of roof plane extraction. The performance of our proposed method is evaluated on the Vaihingen dataset and the DALES dataset. Experiments demonstrate that our proposed method achieves a superior roof plane extraction result.

Detecting vertices of building roofs from ALS point cloud data

ABSTRACTRoof vertex information is vital for 3D roof structures. Reconstructing 3D roof structures from point cloud data using traditional methods remains a challenge because their extracted roof vertices are affected by uncertainty and additional errors from roof plane segmentation and supplementary sub-steps for extracting primitives. In this study, instead of segmenting roof planes and then extracting primitives based on them, a flexible rule-based method is proposed to directly detect the vertices of building roofs from point cloud data without the requirement of training data. The point cloud data is first voxelized with a dominant direction-based rotation. Based on the different features of the interior roof points and vertices, rules for voxel filtering and structure line determination are defined to extract the roof vertices. The experimental results on a custom dataset in Trondheim, Norway demonstrate that the proposed method can effectively and accurately extract roof vertices from point cloud data. The comparative experimental results with an unfine-tuned deep learning-based method on custom and benchmark datasets with different point densities further show that the proposed method has good generalization and can adapt to changes of datasets.

Evaluation of a Measurement Turbulence Model of the Wind Pressure on the Ruin of a Fortified Tower

Abstract An analysis of the external pressure coefficient on the surface of a ruin in different flow directions is presented. The ruin has almost cube-like proportions with an open roof plane and a destroyed corner. Flow simulations were performed using 3D Time Steady RANS and compared with experimental results from the boundary layer wind tunnel at the Slovak University of Technology in Bratislava. The optimal turbulence model and internal mesh settings were selected based on their statistical evaluation. For an evaluation of the critical directions of the wind flow around the ruin, the values of the external wind pressure coefficient were obtained from the selected calculation model and settings.

Roof Segmentation From Airborne LiDAR Using Octree-Based Hybrid Region Growing and Boundary Neighborhood Verification Voting

Building roof segmentation is a key step in the process of three-dimensional (3D) building reconstruction using airborne Light Detection and Ranging (LiDAR) point cloud data. Voxel-based region growing is one of the most widely used methods to segment planes because of its high efficiency and easy implementation, but it is easy to omit roof planes due to the unreasonable voxel size and the complex roof structures. In addition, boundaries between adjacent roof planes are inaccurate. To solve the issues, a roof segmentation method using octree-based hybrid region growing and boundary neighborhood verification voting is proposed. First, an octree-based voxelization is performed on the raw building points to generate two basic units: planar voxels and individual points (i.e., points that are not in the planar voxels). Then, the hybrid region growing is conducted on these two basic units to segment coarse roof planes. A parameter-free boundary neighborhood verification voting strategy is used to assign the boundary points to the correct roof planes by verifying the neighborhoods of the boundary points and using reliable neighborhood information. Experimental results of four datasets, including two datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), and two high-density datasets provided by OpenTopography, verify that roof planes can be successfully segmented by the proposed method with over 96.8% completeness and a minimum 93.2% correctness. In addition, boundary points are assigned to the correct roof planes by the neighborhood verification voting strategy. Thus, the segmented roof planes can be used in various applications.

Principles of processing and three-dimensional modelling through lidar data for applied research of the urban environment

Introduction. The 3D modeling technology of the urban environment using LiDAR survey data expands the possibilities of urban research. With proper use of various methods, models and algorithms for processing and analyzing LiDAR data, they can significantly facilitate and open up new opportunities for many applications discussed in this paper. The main research objective of the paper is to review methods for analyzing LiDAR survey data in urban studies and to present individual elements of the author’s optimization of these methods. Results. LiDAR data obtained as a result of laser scanning of the earth's surface from a certain vehicle form a three-dimensional terrain model in the point cloud form of varying density degrees. The post-processing of such data can branch out into many applications, which are discussed in this paper. The building extraction from a cloud of LiDAR points is performed using complex computational operations, the essence of which is to calculate the points of separate planes of the buildings roofs and then extract these points for 3D building modeling. There are many approaches to building extraction that aim to either improve the quality and accuracy of the extracted models or to speed up the data processing. Finding the optimal solution for 3D modeling of the urban environment is an urgent task in this area of research. Tracking changes in urban buildings involves comparing digital models of urban areas for different time periods in order to obtain the changes volume for each building. In a similar fashion, LiDAR data is used to assess damage to buildings by creating random points on the buildings walls and comparing their displacements before and after the damage. The population estimate using LiDAR data is based on a comparison of population data for census tracts with data on the number, area and volume of buildings in the same tracts obtained from processed LiDAR data. As a result, the expected population in each individual building can be calculated. Roads extraction from LiDAR data is performed by creating an image of the LiDAR laser pulse intensity and then comparing this image with a digital surface model. The article provides an example of a scheme for such road extraction. In addition, methods for extracting and mapping power lines by filtering the corresponding points are also considered. The ability to determine the exact size, slope, and exposure of a building's roof plane also makes it possible to estimate the potential level of solar radiation received by the roof, which can contribute to the optimal placement of solar power plants. Such an assessment may cause some difficulties, which are discussed in the article. The article proposes various optimization solutions for the considered methods, which were partially implemented in the ELiT software. In addition to effective tools for automatic data processing, the ELiT Project also provides an environment for high-quality visualization of results in a standard web-GIS interface. Conclusions. LiDAR data, in combination with efficient algorithms for processing and filtering data, greatly facilitates the solution of a number of tasks related to area monitoring and urban planning. In the future, the high accuracy of LiDAR data and the possibility of their visualization in GIS will make it possible to analyze the urban development features in order to identify the urban geosystemic properties of the city.

Object-based building instance segmentation from airborne LiDAR point clouds

ABSTRACT Building instance segmentation is of very importance to parallel reconstruction, management and analysis of building instance. Previous studies of building instance segmentation mainly focused on the building scenes where the building spacing is much larger than the point spacing, while the accuracy of building instance segmentation for complex buildings scenes and the building point clouds where the space between buildings is similar with point spacing is low. To improve the accuracy of building instance segmentation for complex building scenes, we propose a novel object-based building instance segmentation (OBBIS) method from airborne light detection and ranging (LiDAR) point clouds. Firstly, our proposed method divides building point clouds into objects, and then the objects are classified according to the characteristics of building roof plane objects, roof accessory objects and building facade objects. Secondly, we use node to represent object and then a fix-size feature vector is inferred for each node. Thirdly, vertical cylinder neighbour node graph is constructed. Finally, the energy function is constructed according to the relationship between the nodes, and then the objects are merged according to the energy minimum (that is, objects are merged with a minimum energy to obtain the building instances). Comprehensive experiments on benchmark datasets demonstrate that the proposed OBBIS method performs better than eight state-of-the-art building instance segmentation methods.

An Improved Multi‐Task Pointwise Network for Segmentation of Building Roofs in Airborne Laser Scanning Point Clouds

AbstractRoof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data‐sets or they are not automatic and efficient. To tackle these issues, an improved multi‐task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean‐shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data‐set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data‐set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task.