• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.23-32
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• 2018

The purpose of this study is to establish a walking energy weighted ERAM model that can predict the pedestrian volume by the connection structure of the vertical and horizontal spaces within a three-dimensional building. The process of building a walking-energy weighted ERAM model is as follows. First, the spatial graph was used to reproduce three-dimensional buildings with vertical and horizontal spatial connection structures. Second, the walking energy was measured on the spatial graph. Third, ERAM model was used to apply weights with spatial connection properties in random walking environment, and the walking energy weights were applied to the ERAM model to calculate the walk energy weighted ERAM values and visualize the distribution of pedestrian flow. To verify the validation of the established model, existing and proposed spatial analysis models were compared to real space. The results of this study are as follows : The model proposed in this study showed as much elaborated estimation of pedestrian traffic flow in real space as in traditional spatial analysis models, and also it showed much higher level of forecasting pedestrian traffic flow in real space than existing models.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.67-77
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• 2021

Spatial data is becoming very important as a medium that connects various data produced in smart cities, digital twins, autonomous driving, smart construction, and other applications. In addition, the rapid construction and update of spatial information is becoming a hot topic to satisfy the diverse needs of consumers in this field. This study developed a software prototype that can match the position of an image-based 3D building model produced without Ground Control Points using existing spatial data. As a result of applying this software to the test area, the 3D building model produced based on the image and the existing spatial data show a high positional matching rate, so that it can be widely used in applications requiring the latest 3D spatial data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.347-354
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• 2007

Since large portal service providers started web services for 3D city models around the world using spatial imagery, the competition has been getting intense to provide the models with the higher quality and accuracy. The building models are the most in number among the 3D city model objects, and it takes much time and money to create realistic model due to various shapes and visual appearances of building object. The aforementioned problem is the most significant limitation for the service and the update of the 3D city model of the large area. This study proposed a method of generating realistic 3D building models with quick and economical texture mapping using multiple spatial imagery such as aerial photos or satellite images after reconstructed geometric models of buildings from building layers in digital maps. Based on the experimental results, the suggested method has effectiveness for the generation of the 3D building models using various air-borne imagery and satellite imagery quickly and economically.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.186-192
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• 2021

Recently, the demand for more precise and demand-oriented customized spatial information is increasing due to the 4th industrial revolution. In particular, the use of 3D spatial information and digital twins which based on spatial information, and research for solving social problems in cities by using such information are continuously conducted. Globally, non-face-to-face services are increasing due to COVID-19, and the national policy direction is also rapidly progressing digital transformation, digitization and virtualization of the Korean version of the New Deal, which means that 3D spatial information has become an important factor to support it. In this study, physical objects for cities defined by world organizations such as ISO, OGC, and ITU were selected and the target of the 3D object model was limited to buildings. Based on CityGML2.0, the data collected using a drone suitable for building a 3D model of a small area is selected to be updated through road name address and building ledger, which are administrative information related to this, and LoD2.5 data is constructed and urban space. It was intended to suggest an object update method for a 3D building among data.

• Journal of the Korean Society for Advanced Composite Structures
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• v.6 no.1
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• pp.14-20
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• 2015

In this study, we propose an innovative lateral force distribution building system between tall buildings by utilizing the difference of moment of inertia, as the alternative design for highly integrated city area. Considering a tri-axial symmetric conditions and boundary conditions for the three-dimensional building structure system, a two-dimensional model is composed. In the proposed indeterminate structural model, important design variables are determined for obtaining minimum horizontal deflections, reactions and bending moments at the ground level of the buildings. Regarding a case of the provided two spatial structures connected to 4 buildings, the optimum location of middle located spatial structure is 45% from the top of the building, which minimize the end moments at the bottom of the buildings. In the considered verification examples, reduced drifts at the top location of the building systems are validated against static wind pressure loads and static earthquake loads. The suggested hybrid building system will improve the safety and reliability of the system due to the added internal truss-dome structures in terms of more than 30% reduced drift and vibration through the development of convergence of tall buildings and spatial structures.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.6
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• pp.3-14
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• 2018

The aim of this study is to propose a method for calculating the weight of walking energy in ERAM model by calculating it for the analysis of vertical and horizontal spaces in a building. Conventional theories on the space analysis in the field of architectural planning predict the pedestrian volume of network spaces in urban street or in two-dimensional plane within a building, however, for vertical and horizontal spaces in a building, estimates of the pedestrian volume by those theories are limited. Because in the spatial syntax and ERAM model have been applied weights such as the spatial depth, adjacent angles, and physical distances available only to the two-dimensional same layer or plane. Therefore, the following basic assumptions and analysis conditions in this study were established for deriving a predictor of pedestrian volume in vertical and horizontal spaces of a building. The basic premise of space analysis is not to address the relationship between the pedestrian volume and the spatial structure itself but to the properties of spatial structure connection that human beings experience. The analysis conditions in three-dimensional spaces are as follows : 1) Measurement units should be standardized on the same scale, and 2) The connection characteristics between spaces should influence the accessibility of human beings. In this regard, a factor of walking energy has the attributes to analyze the connection of vertical and horizontal spaces and satisfies the analysis conditions presented in this study. This study has two implications. First, this study has shown how to quantitatively calculate the walking energy after a factor of walking energy was derived to predict the pedestrian volume in vertical and horizontal spaces. Second, the method of calculating the walking energy can be applied to the weights of the ERAM model, which provided the theoretical basis for future studies to predict the pedestrian volume of vertical and horizontal spaces in a building.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.16-41
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• 2020

Geospatial input setting to represent the reality of spatial distribution or quantitative property within model has become a major interest in earth system simulation. Many studies showed the variation of grid resolution could lead to drastic changes of spatial model results because of insufficient surface property estimations. Hence, in this paper, the authors proposed Monte Carlo Integration (MCI) to apply spatial probability (SP) in a spatial-sampling framework using a three-dimensional point cloud (3DPC) to keep the optimized spatial distribution and area/volume property of buildings in urban area. Three different decision rule based building identification results were compared : SP threshold, cell size, and 3DPC density. Results shows the identified building area property tend to increase according to the spatial sampling grid area enlargement. Hence, areal building property manipulation in the sampling frameworks by using decision rules is strongly recommended to increase reliability of geospatial modeling and analysis results. Proposed method will support the modeling needs to keep quantitative building properties in both finer and coarser grids.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.151-159
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• 2013

The building information should be up-to-date information and propagated rapidly for urban modeling, terrain analysis, life information, navigational system, and location-based services(LBS), hence the most recent and updated data of the building information have been required of researchers. This paper presents the developed system to extract the 3-dimension spatial information from aerial orthoimage and LiDAR data of HSI color model. In particular, this paper presents the image processing algorithm to extract the outline of specific buildings and generate the building polygon from the image using HIS color model, recursive backtracking algorithm and the search maze algorithm. Also, this paper shows the effectivity of the HIS color model in the image segmentation.