• Journal of the Korean Institute of Rural Architecture
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• v.14 no.3
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• pp.85-92
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• 2012

This study aims to evaluate the residential exterior environment and ascertain the factors having an effect on the visual environment viewed through the living room window. This study is based on a questionnaire on the exterior environment targeting residents living in the area of Nohyunng in Jeju city, Jeju-do. The results are as follows. First, residents are satisfied with the exterior environment as a whole; however, the longer-term residents' level of satisfaction is found to be relatively low while dissatisfaction is high. Additionally, there is a difference in satisfaction and dissatisfaction levels between the residents in their own housing and those in rental housing. The residents living in rental housing were found to have a relatively low level of satisfaction and high dissatisfaction. Second, in the case where the living room window faces roads, low satisfaction and high dissatisfaction levels with the visual environment were found, where the living room is on the first or second floor. Third, satisfaction and dissatisfaction with the exterior environment have a close correlation to the impression or nature elements of 26 questions but they show low correlation in size, height, color, design, traffic, artifact elements. Finally, as a result of extracting the factors influencing satisfaction and dissatisfaction with the visual environment, four factors were extracted including "Impression & Nature factor", "Building factor", "Design factor", and "Traffic volume & Artifacts factor". "Impression & Nature factors" is the most influencing factor with satisfaction and dissatisfaction and "Traffic volume & Artifacts factor" was found to have an effect on satisfaction, but not as clearly on dissatisfaction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.4
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• pp.335-341
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• 2006

This paper aims at developing an efficient method that extracts building using local spatial association of raw LiDAR data without setting up empirical variables such as a minimum building area, and applying the method to survey data to evaluate the efficiency of that. To do this, LISA(Local Indicatiors of Spatial Association) statistics are used which reflect local variations that can be appeared in the research area. It can be also a preprocess that detects spatial outliers through the significance test of LISA statistics and interpolate using kernel estimation. Boundaries of buildings as well as buildings can be extracted based on quadrant of Moran Scatterplot. Experimental results show that the proposed method is promising in extracting buildings from LiDAR data automatically.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.575-583
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• 2007

Surface segmentation aims to represent the terrain as a set of bounded and analytically defined surface patches. Many previous segmentation methods have been developed to extract planar patches from LIDAR data for building extraction. However, most of them were not fully satisfactory for more general applications in terms of the degree of automation and the quality of the segmentation results. This is mainly caused from the limited information derived from LIDAR data. The purpose of this study is thus to develop an automatic method to perform surface segmentation by combining not only LIDAR data but also images. A region-based method is proposed to generate a set of planar patches by grouping LIDAR points. The grouping criteria are based on both the coordinates of the points and the corresponding intensity values computed from the images. This method has been applied to urban data and the segmentation results are compared with the reference data acquired by manual segmentation. 76% of the test area is correctly segmented. Under-segmentation is rarely founded but over-segmentation still exists. If the over-segmentation is mitigated by merging adjacent patches with similar properties as a post-process, the proposed segmentation method can be effectively utilized for a reliable intermediate process toward automatic extraction of 3D model of the real world.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.307-317
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were 8.1cm, 24.7cm, 35.9cm, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.595-603
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• 2015

With the advances of the big-data process technology, acquiring the real-time information from the massive image data taken by a mobile device inside a vehicle will be possible in the near future. Among the information that can be found around the vehicle, the route information is needed for safe driving. In this study, the automatic extraction of route information from the road sign imagery was dealt with. The scope of the route information in this study included the route number, route type, and their relationship with the driving direction. For the recognition of the route number, the modified Tesseract OCR (Optical Character Recognition) engine was used after extracting the rectangular-road-sign area with the Freeman chain code tracing algorithm. The route types (expressway, highway, rural highway, and municipal road) are recognized using the proposed algorithms, which are acquired from colour space analysis. Those road signs provide information about the route number as well as the roads that may be encountered along the way. In this study, such information was called “OTW (on the way)” or “TTW (to the way)” which between the two should be indicated is determined using direction information. Finally, the route number is matched with the direction information. Experiments are carried out with the road sign imagery taken inside a car. As a result, route numbers, route number type, OTW or TTW are successfully recognized, however some errors occurred in the process of matching TTW number with the direction.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.11-20
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• 2010

In situation that carbon dioxide emissions are being increased as urbanization, urban green space is being promoted as an alternative to find solution for these problems. In urban areas, trees have the ability to reduce carbon dioxide as well as to be aesthetic effect. In this study, we proposed the methodology which uses only LIDAR data in order to extract these trees information effectively. To improve the operational efficiency according to the extraction of trees, the proposed methodology was carried out using multiple data processing such as point, polygon and raster. Because the existing NDSM(Normalized Digital Surface Model) contains both the building and tree information, it has the problems of high complexity of data processing for extracting trees. Therefore, in order to improve these problems, this study used modified NDSM which was removed estimate regions of building. To evaluate the performance of the proposed methodology, three different zones which coexist buildings and trees within urban areas were selected and the accuracy of extracted trees was compared with the image taken by digital camera.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.27-33
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• 2017

GPS provides the positioning solution in most areas of the world. However, the position error largely occurs in the urban area due to signal attenuation, signal blockage, and multipath. Although many studies have been carried out to solve this problem, a definite solution has not yet been proposed. Therefore, research is being conducted to solve the vehicle localization problem in the urban environment by converging sensors such as cameras and Light Detection and Ranging (LIDAR). In this paper, the precise vehicle localization using 3D LIDAR (Velodyne HDL-32E) is performed in the urban area. As there are many tall buildings in the urban area and the outer walls of urban buildings consist of planes generally perpendicular to the earth's surface, the outer wall of the building meets at a vertical corner and this vertical corner can be accurately extracted using 3D LIDAR. In this paper, we describe the vertical corner extraction method using 3D LIDAR and perform the precise localization by combining the extracted corner position and GPS/DR information. The driving test was carried out in an about 4.5 km-long section near Teheran-ro, Gangnam. The lateral and longitudinal RMS position errors were 0.146 m and 0.286 m, respectively and showed very accurate localization performance.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.93-98
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• 2013

Multi-temporal high resolution satellite images are essential data for efficient city analysis and monitoring. Yet even when acquired from the same location, identical sensors as well as different sensors, these multi-temporal images have a geometric inconsistency. Matching points between images, therefore, must be extracted to match the images. With images of an urban area, however, it is difficult to extract matching points accurately because buildings, trees, bridges, and other artificial objects cause shadows over a wide area, which have different intensities and directions in multi-temporal images. In this study, we analyze a shadow effect on image matching of high resolution satellite images in urban area using Scale-Invariant Feature Transform(SIFT), the representative matching points extraction method, and automatic shadow extraction method. The shadow segments are extracted using spatial and spectral attributes derived from the image segmentation. Also, we consider information of shadow adjacency with the building edge buffer. SIFT matching points extracted from shadow segments are eliminated from matching point pairs and then image matching is performed. Finally, we evaluate the quality of matching points and image matching results, visually and quantitatively, for the analysis of shadow effect on image matching of high resolution satellite image.

• Journal of the Korean housing association
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• v.24 no.6
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• pp.199-208
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• 2013

Developing a modern Han-style design and providing support for the commercialization development model in recent years has been propelled by the Han-style Support Strategies of the central government in conjunction with Han-style revitalization related projects that reflect the efforts of local governments. Han-style revitalization, the rekindling and revaluing of human behavior and interest in local governments following the social and cultural changes of the past decades, has emerged as an increasingly traditional area of concern in Han-style design. The purpose of the study was to provide a method which clarifies the design rules of the Han-style bathroom based on an evaluation of sensibilities and a rough set theory, and to give the components meaning and to systematize the method. Essentially, the Han-style bathroom design evaluation is a complex multi-criteria decision making process that seeks to improve the effectiveness and objectively of the Han-style bathroom design. Han-style bathroom design can be displayed in a graphical representation in response to input from the evaluation concerning sensibilities. Because the graphical representation is composed of 3D data, it is possible to display the Han-style bathroom design form in any desired perspective and also to perform shading and other operations. With the proposed method, it is possible to obtain a combination of several contributory components which can be referred to as Reducts, Covering Index and Column Score. Han-Style/Non Han-Style Bathroom Designs were identified by the combination of several components.