• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.23 no.4
• /
• pp.375-384
• /
• 2005

Three dimensional urban models are being increasingly applied for various purposes such as city planning, telecommunication cell planning, traffic analysis, environmental monitoring and disaster management. In recent years, technologies from CAD and GIS are being merged to find optimal solutions in three dimensional modeling of urban buildings. These solutions include modeling of the interior building space as well as its exterior shape visualization. Research and development effort in this area has been performed by scientists and engineers from Computer Graphics, CAD and GIS. Computer Graphics and CAD focussed on precise and efficient visualization, where as GIS emphasized on topology and spatial analysis. Complementary research effort is required for an effective model to serve both visualization and spatial analysis purposes. This study presents an efficient way of using the CAD plans included in the building register documents to reconstruct the internal space of buildings. Topological information was built in the geospatial database and merged with the geometric information of CAD plans. as well as other attributal data from the building register. The GIS network modeling method introduced in this study is expected to enable an effective 3 dimensional spatial analysis of building interior which is developing with increasing complexity and size.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3966-3991
• /
• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.920-927
• /
• 2009

Many schedule delays and cost overruns in interior construction are caused by a lack of understanding in detailed and complicated interior works. To minimize these potential impacts in interior construction, a systematic approach for project managers to detect discrepancies at early stages and take corrective action through use of visualized data is required. This systematic implementation is still challenging: monitoring is time-consuming due to the significant amount of as-built data that needs to be collected and evaluated; and current interior construction progress reports have visual limitations in providing spatial context and in representing the complexities of interior components. To overcome these issues, this research focuses on visualization and computer vision techniques representing interior construction progress with photographs. The as-planned 3D models and as-built photographs are visualized in a 3D walk-through model. Within such an environment, the as-built interior construction elements are detected through computer vision techniques to automatically extract the progress data linked with Building Information Modeling (BIM). This allows a comparison between the as-planned model and as-built elements to be used for the representation of interior construction progress by superimposing over a 3D environment. This paper presents the process of representing and detecting interior construction components and the results for an ongoing construction project. This paper discusses implementation and future potential enhancement of these techniques in construction.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2015.05a
• /
• pp.130-131
• /
• 2015

Accurate construction progress measurement is an important issue for successful project delivery. This paper presents a method that keeps track of the progress measurement involved in construction operations and facilities visualization of the data using BIM and IOT. To verify the method, a residential house project was used for the case study. Test case verifies the usability and validity of the method implemented in the system.

• Journal of Multimedia Information System
• /
• v.4 no.1
• /
• pp.1-8
• /
• 2017

In recent years, the quixotic nature and concentration of rainfall due to global climate change has intensified. To monitor localized heavy rainfalls, a reliable disaster monitoring and warning system with advanced remote observation technology and high-precision display is important. In this paper, we propose a GIS-based intuitive and realistic 3D radar data display technique for accurate and detailed weather analysis. The proposed technique performs 3D object modeling of various radar variables along with ray profiles and then displays stereoscopic radar data on detailed geographical locations. Simulation outcomes show that 3D object modeling of weather radar data can be processed in real time and that changes at each moment of rainfall events can be observed three-dimensionally on GIS.

• Journal of Digital Contents Society
• /
• v.19 no.3
• /
• pp.521-528
• /
• 2018

Data visualization has become a useful tool to effectively communicate information and is widely used not only by experts but also at a general level. However, it is dangerous that it is as efficient as it is to transmit false information. All data visualizations have hidden intent with powerful messages by editor. Building a system that grasps these intentions helps to understand the thoughts of groups and individuals. Most of the existing research focuses on effective data visualization methods and methods of expression. The more various visualization methods, the more likely the data will be distorted. In this paper, we present an analysis of deceptive data visualization in a goal-oriented environment. Based on the vulnerability of human cognitive processing, we classify the attack types and identify what tricks occur in the context of data visualization. This study suggests the first step in studying the case of aggressive visualization and opens the way for further research.

• 한국가시화정보학회:학술대회논문집
• /
• 2005.12a
• /
• pp.46-49
• /
• 2005

Vents at outer walls of a large factory building are very important for natural ventilation. But, if a full-open vent is used, rain comes through the vents. We tried to utilize the natural ventilation effectively using a louver. A 1/120 scale-down building model was placed inside an atmospheric boundary layer simulated in a wind tunnel test section. The effect of louver angle on the ventilation flow inside the factory building was investigated experimentally. Instantaneous velocity fields inside the building model were measured using a 2-frame PIV system with varying the louver angles ($\theta=20^{\circ},\;40^{\circ},\;60^{\circ}$). For the case of $\theta=60^{\circ}$, as the incoming flow into the factory building increases, the inside velocity distribution becomes uniformly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.6_1
• /
• pp.507-516
• /
• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• Smart Structures and Systems
• /
• v.13 no.6
• /
• pp.901-925
• /
• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Journal of KIBIM
• /
• v.12 no.4
• /
• pp.1-18
• /
• 2022

New Fabrication Facility (FAB) construction is performed with Building Information Modeling (BIM) based design. The BIM design data keep updated during the FAB construction. To improve fast-track construction management, a Fabrication Facility Augmented Reality (FABAR) was developed. This study introduces a FABAR system development process and shows performance evaluation results of the FABAR prototype system. The FABAR is implemented with three major modules: Augmented Reality (AR) visualization unit (Room-box) to transfer big BIM data to AR data, AR registration and tracking unit to match AR with real scape and to keep AR coordination in real, and AR data management unit to enhance usability. The prototype performance results were as follows: visualization of design BIM data via AR within 24 hours, precise AR registration and tracking registration, and appropriate usability to support FAB construction management at site. The results indicate that the FABAR is applicable for FAB construction management. Especially, the BIM data transformation method using Room-box in this study signifies a new construction management approach using fluctuating BIM design data in the fast track construction condition.