ESTABLISHING A NATIONAL 3D GEO-DATA MODEL FOR BUILDING DATA COMPLIANT TO CITYGML: CASE OF TURKEY

This paper presents the generation of the 3D national building geo-data model of Turkey, which is compatible with the international OGC CityGML Encoding Standard. We prepare an ADE named CityGML-TRKBIS.BI that is produced by extending existing thematic modules of CityGML according to TRKBIS needs. All thematic data groups in TRKBIS geo-data model have been remodelled in order to generate the national large scale 3D geo-data model for Turkey. Specific attention has been paid to data groups that have different class structure according to related CityGML data themes such as building data model. Current 2D geo-information model for building data theme of Turkey (TRKBIS.BI) was established based on INSPIRE specifications for building (Core 2D and Extended 2D profiles), ISO/TC 211 standards and OGC web services. New version of TRKBIS.BI which is established according to semantic and geometric rules of CityGML will represent 2D-2.5D and 3D objects. After a short overview on generic approach, this paper describes extending CityGML building data theme according to TRKBIS.BI through several steps. First, building models of both standards were compared according to their data structure, classes and attributes. Second, CityGML building model was extended with respect to TRKBIS needs and CityGML-TRKBIS Building ADE was established in UML. This study provides new insights into 3D applications in Turkey. The generated 3D geo-data model for building thematic class will be used as a common exchange format that meets 2D, 2.5D and 3D implementation needs at national level.

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HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-689-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 689-695

Author(s):

Z. Li ◽

W. Zhang ◽

J. Shan

Keyword(s):

Large Scale ◽

Traditional Approach ◽

Holistic Approach ◽

Point Clouds ◽

Airborne Lidar ◽

Reconstruction Method ◽

Optimization Strategy ◽

Building Roof ◽

Building Model ◽

Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10120798 ◽

2021 ◽

Vol 10 (12) ◽

pp. 798

Author(s):

Xuequan Zhang ◽

Wei Liu ◽

Bing Liu ◽

Xin Zhao ◽

Zihe Hu

Keyword(s):

Large Scale ◽

Texture Mapping ◽

Graphics Hardware ◽

High Fidelity ◽

Blank Space ◽

Web Based ◽

Vertex Number ◽

Building Model ◽

Building Models ◽

Atlas Method

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.

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Use of topographic LiDAR point clouds for building reconstruction

10.32920/ryerson.14661030 ◽

2021 ◽

Author(s):

Yipeng Yuan

Keyword(s):

Large Scale ◽

Three Dimensional ◽

Point Clouds ◽

Building Roof ◽

Building Model ◽

Building Models ◽

Urban Models ◽

Elevation Data ◽

3D Building Model ◽

Building Reconstruction

Demand for three-dimensional (3D) urban models keeps growing in various civil and military applications. Topographic LiDAR systems are capable of acquiring elevation data directly over terrain features. However, the task of creating a large-scale virtual environment still remains a time-consuming and manual work. In this thesis a method for 3D building reconstruction, consisting of building roof detection, roof outline extraction and regularization, and 3D building model generation, directly from LiDAR point clouds is developed. In the proposed approach, a new algorithm called Gaussian Markov Random Field (GMRF) and Markov Chain Monte Carlo (MCMC) is used to segment point clouds for building roof detection. The modified convex hull (MCH) algorithm is used for the extraction of roof outlines followed by the regularization of the extracted outlines using the modified hierarchical regularization algorithm. Finally, 3D building models are generated in an ArcGIS environment. The results obtained demonstrate the effectiveness and satisfactory accuracy of the developed method.

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Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

Remote Sensing ◽

10.3390/rs11141660 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1660

Author(s):

Partovi ◽

Fraundorfer ◽

Bahmanyar ◽

Huang ◽

Reinartz

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Large Scale ◽

Missing Values ◽

Model Reconstruction ◽

Large Area ◽

Building Model ◽

Building Models ◽

D Model ◽

Very High

Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.

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Parameter-Free Half-Spaces Based 3D Building Reconstruction Using Ground and Segmented Building Points from Airborne LiDAR Data with 2D Outlines

Remote Sensing ◽

10.3390/rs13214430 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4430

Author(s):

Marko Bizjak ◽

Borut Žalik ◽

Niko Lukač

Keyword(s):

Large Scale ◽

Airborne Lidar ◽

Lidar Data ◽

New Approach ◽

Building Model ◽

Building Models ◽

3D Building Model ◽

Two Stages ◽

Building Level ◽

Airborne Lidar Data

This paper aims to automatically reconstruct 3D building models on a large scale using a new approach on the basis of half-spaces, while making no assumptions about the building layout and keeping the number of input parameters to a minimum. The proposed algorithm is performed in two stages. First, the airborne LiDAR data and buildings’ outlines are preprocessed to generate buildings’ base models and the corresponding half-spaces. In the second stage, the half-spaces are analysed and used for shaping the final 3D building model using 3D Boolean operations. In experiments, the proposed algorithm was applied on a large scale, and its’ performance was inspected on a city level and on a single building level. Accurate reconstruction of buildings with various layouts were demonstrated and limitations were identified for large-scale applications. Finally, the proposed algorithm was validated on an ISPRS benchmark dataset, where a RMSE of 1.31 m and completeness of 98.9 % were obtained.

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Fragility Evaluation in Building-Piping Systems: Effect of Piping Interaction With Buildings

Journal of Pressure Vessel Technology ◽

10.1115/1.4039004 ◽

2018 ◽

Vol 141 (1) ◽

Cited By ~ 2

Author(s):

Yong Hee Ryu ◽

Abhinav Gupta ◽

Bu Seog Ju

Keyword(s):

Large Scale ◽

Nonlinear Models ◽

Time History ◽

Piping System ◽

Nonstructural Components ◽

Building Model ◽

Building Models ◽

Piping Systems ◽

Story Building ◽

Critical Locations

Many studies assessing the damage from 1971 San Fernando and 1994 North Ridge earthquakes reported that the failure of nonstructural components like piping systems was one of the significant reasons for shutdown of hospitals immediately after the earthquakes. This paper is focused on evaluating seismic fragility of a large-scale piping system in representative high-rise, midrise, and low-rise buildings using nonlinear time history analyses. The emphasis is on evaluating piping's interaction with building and its effect on piping fragility. The building models include the effects of nonlinearity in the performance of beams and columns. In a 20-story building that is detuned with the piping system, critical locations are on the top two floors for the linear frame building model. For the nonlinear building model, critical locations are on the bottom two floors. In an eight-story building that is nearly tuned with the piping system, the critical locations for both the linear frame and nonlinear models are the third and fourth floors. It is observed that building nonlinearity can reduce fragility due to reduction in the tuning between building and piping systems. In a two-story building, the nonlinear building frequencies are closer to the critical piping system frequencies than the linear building frequency; the nonlinear building is more fragile than the linear building for this case. However, it is observed that the linear building models give excessively conservative estimates of fragility than the nonlinear building models.

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Use of topographic LiDAR point clouds for building reconstruction

10.32920/ryerson.14661030.v1 ◽

2021 ◽

Author(s):

Yipeng Yuan

Keyword(s):

Large Scale ◽

Three Dimensional ◽

Point Clouds ◽

Building Roof ◽

Building Model ◽

Building Models ◽

Urban Models ◽

Elevation Data ◽

3D Building Model ◽

Building Reconstruction

Demand for three-dimensional (3D) urban models keeps growing in various civil and military applications. Topographic LiDAR systems are capable of acquiring elevation data directly over terrain features. However, the task of creating a large-scale virtual environment still remains a time-consuming and manual work. In this thesis a method for 3D building reconstruction, consisting of building roof detection, roof outline extraction and regularization, and 3D building model generation, directly from LiDAR point clouds is developed. In the proposed approach, a new algorithm called Gaussian Markov Random Field (GMRF) and Markov Chain Monte Carlo (MCMC) is used to segment point clouds for building roof detection. The modified convex hull (MCH) algorithm is used for the extraction of roof outlines followed by the regularization of the extracted outlines using the modified hierarchical regularization algorithm. Finally, 3D building models are generated in an ArcGIS environment. The results obtained demonstrate the effectiveness and satisfactory accuracy of the developed method.

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Scheming

10.3366/edinburgh/9781474440561.001.0001 ◽

2018 ◽

Cited By ~ 2

Author(s):

Seán Damer

Keyword(s):

Social Class ◽

Housing Policy ◽

Large Scale ◽

Class Structure ◽

Local Council ◽

Housing Scheme ◽

Post War ◽

First Time ◽

The Voice ◽

House Building

This book seeks to explain how the Corporation of Glasgow, in its large-scale council house-building programme in the inter- and post-war years, came to reproduce a hierarchical Victorian class structure. The three tiers of housing scheme which it constructed – Ordinary, Intermediate, and Slum-Clearance – effectively signified First, Second and Third Class. This came about because the Corporation uncritically reproduced the offensive and patriarchal attitudes of the Victorian bourgeoisie towards the working-class. The book shows how this worked out on the ground in Glasgow, and describes the attitudes of both authoritarian housing officials, and council tenants. This is the first time the voice of Glasgow’s council tenants has been heard. The conclusion is that local council housing policy was driven by unapologetic considerations of social class.

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HOME VISITS DURING THE FIRST MONTH OF DELIVERY FOR RECENTLY DELIVERED WOMEN IN UTTAR PRADESH, INDIA

Journal of Advances in Social Science and Humanities ◽

10.15520/jassh.v6i5.483 ◽

2020 ◽

Vol 6 (5) ◽

pp. 1183-1189

Author(s):

Dr. Tridibesh Tripathy ◽

Dr. Umakant Prusty ◽

Dr. Chintamani Nayak ◽

Dr. Rakesh Dwivedi ◽

Dr. Mohini Gautam

Keyword(s):

Large Scale ◽

National Level ◽

Uttar Pradesh ◽

Life Time ◽

Structured Interview ◽

The Other ◽

Registration System ◽

Action Group ◽

Current Article ◽

The Impact

The current article of Uttar Pradesh (UP) is about the ASHAs who are the daughters-in-law of a family that resides in the same community that they serve as the grassroots health worker since 2005 when the NRHM was introduced in the Empowered Action Group (EAG) states. UP is one such Empowered Action Group (EAG) state. The current study explores the actual responses of Recently Delivered Women (RDW) on their visits during the first month of their recent delivery. From the catchment area of each of the 250 ASHAs, two RDWs were selected who had a child in the age group of 3 to 6 months during the survey. The response profiles of the RDWs on the post- delivery first month visits are dwelled upon to evolve a picture representing the entire state of UP. The relevance of the study assumes significance as detailed data on the modalities of postnatal visits are available but not exclusively for the first month period of their recent delivery. The details of the post-delivery first month period related visits are not available even in large scale surveys like National Family Health Survey 4 done in 2015-16. The current study gives an insight in to these visits with a five-point approach i.e. type of personnel doing the visit, frequency of the visits, visits done in a particular week from among those four weeks separately for the three visits separately. The current study is basically regarding the summary of this Penta approach for the post- delivery one-month period. The first month period after each delivery deals with 70% of the time of the postnatal period & the entire neonatal period. Therefore, it does impact the Maternal Mortality Rate & Ratio (MMR) & the Neonatal Mortality Rates (NMR) in India and especially in UP through the unsafe Maternal & Neonatal practices in the first month period after delivery. The current MM Rate of UP is 20.1 & MM Ratio is 216 whereas the MM ratio is 122 in India (SRS, 2019). The Sample Registration System (SRS) report also mentions that the Life Time Risk (LTR) of a woman in pregnancy is 0.7% which is the highest in the nation (SRS, 2019). This means it is very risky to give birth in UP in comparison to other regions in the country (SRS, 2019). This risk is at the peak in the first month period after each delivery. Similarly, the current NMR in India is 23 per 1000 livebirths (UNIGME,2018). As NMR data is not available separately for states, the national level data also hold good for the states and that’s how for the state of UP as well. These mortalities are the impact indicators and such indicators can be reduced through long drawn processes that includes effective and timely visits to RDWs especially in the first month period after delivery. This would help in making their post-natal & neonatal stage safe. This is the area of post-delivery first month visit profile detailing that the current article helps in popping out in relation to the recent delivery of the respondents. A total of four districts of Uttar Pradesh were selected purposively for the study and the data collection was conducted in the villages of the respective districts with the help of a pre-tested structured interview schedule with both close-ended and open-ended questions. The current article deals with five close ended questions with options, two for the type of personnel & frequency while the other three are for each of the three visits in the first month after the recent delivery of respondents. In addition, in-depth interviews were also conducted amongst the RDWs and a total 500 respondents had participated in the study. Among the districts related to this article, the results showed that ASHA was the type of personnel who did the majority of visits in all the four districts. On the other hand, 25-40% of RDWs in all the 4 districts replied that they did not receive any visit within the first month of their recent delivery. Regarding frequency, most of the RDWs in all the 4 districts received 1-2 times visits by ASHAs. Regarding the first visit, it was found that the ASHAs of Barabanki and Gonda visited less percentage of RDWs in the first week after delivery. Similarly, the second visit revealed that about 1.2% RDWs in Banda district could not recall about the visit. Further on the second visit, the RDWs responded that most of them in 3 districts except Gonda district did receive the second postnatal visit in 7-15 days after their recent delivery. Less than half of RDWs in Barabanki district & just more than half of RDWs in Gonda district received the third visit in 15-21 days period after delivery. For the same period, the majority of RDWs in the rest two districts responded that they had been entertained through a home visit.

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