• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1D
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• pp.81-88
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• 2012

This present research was carried out by dividing two parts; field surveying and data processing, in order to analyze changed patterns of a shoreline. Firstly, the shoreline information measured by the precise GPS positioning during long duration was collected. Secondly, the algorithm for detecting an auto boundary with regards to the changed shoreline with multi-image data was developed. Then, a comparative research was conducted. Haeundae beach which is one of the most famous ones in Korea was selected as a test site. RTK-GPS surveying had been performed overall eight times from September 2005 to September 2009. The filed test by aerial Lidar was conducted twice on December 2006 and March 2009 respectively. As a result estimated from both sensors, there is a slight difference. The average length of shoreline analyzed by RTK-GPS is approximately 1,364.6 m, while one from aerial Lidar is about 1,402.5 m. In this investigation, the specific algorithm for detecting the shoreline detection was developed by Visual C++ MFC (Microsoft Foundation Class). The analysis result estimated by aerial photo and satellite image was 1,391.0 m. The level of reliability was 98.1% for auto boundary detection when it compared with real surveying data.

• 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 Broadcast Engineering
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• v.13 no.2
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• pp.236-244
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• 2008

We propose a new building detection and description algorithm for Lidar data and photogrammetric imagery using color segmentation, line segments matching, perceptual grouping. Our algorithm consists of two steps. In the first step, from the initial building regions extracted from Lidar data and the color segmentation results from the photogrammetric imagery, we extract coarse building boundaries based on the Lidar results with split and merge technique from aerial imagery. In the secondstep, we extract precise building boundaries based on coarse building boundaries and edges from aerial imagery using line segments matching and perceptual grouping. The contribution of this algorithm is that color information in photogrammetric imagery is used to complement collapsed building boundaries obtained by Lidar. Moreover, linearity of the edges and construction of closed roof form are used to reflect the characteristic of man-made object. Experimental results on multisensor data demonstrate that the proposed algorithm produces more accurate and reliable results than Lidar sensor.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.28-39
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• 2014

Several studies have been performed globally on the construction of integrated systems that are available for the integrated use of 3D geographic information on terrain and oceans. Research on 3D geographic modeling is also facilitated by the application of Lidar surveying, which enables the highly accurate realization of 3D geographic information for a wide area of land. In addition, a few marine research organizations have been conducting investigations and surveying diverse ocean information for building and applying MGIS(Marine Geographic Information System). However, the construction of integrated geographic information systems for both terrain and oceans has certain limitations resulting from the inconsistency in reference systems and datum levels between two data. Therefore, in this investigation, integrated geospatial information has been realized by using a combined topographical map, after matching the reference systems and datum levels by integration of airborne Lidar data and multi-beam echo sounder data. To verify the accuracy of the integrated geospatial information data, ten randomly selected samples from study areas were selected and analyzed. The results show that the 10 analyzed data samples have an RMSE of 0.46m, which meets the IHO standard(0.5m) for depth accuracy of hydrographic surveys.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.353-362
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• 2012

3D building modeling from airborne Lidar without model regularization may cause positional errors or topological inconsistency in building models. Regularization of 3D building models, on the other hand, restricts the types of models which can be reconstructed. To resolve these issues, this paper modelled 3D buildings from airborne Lidar by building model regularization which considers more various types of buildings. Building points are first segmented into roof planes by clustering in feature space and segmentation in object space. Then, 3D building models are reconstructed by consecutive adjustment of planes, lines, and points to satisfy parallelism, symmetry, and consistency between model components. The experimental results demonstrated that the method could make more various types of 3d building models with regularity. The effects of regularization on the positional accuracies of models were also analyzed quantitatively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3213-3219
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• 2014

Currently, increase of greenhouse gas has had a signigicant impact on climate change in urbanization. As a result, the government has been looking for ways to take advantage of the trees that generate oxygen and reduce carbon dioxide for the prevention of climate change. It is essential to extract individual tree for calculating the amount of carbon dioxide reduction of trees. Aerial LIDAR data have three-dimensional information of building as well as trees as form of point clouds. In this study, automated model was developed to extract individual tree using aerial LIDAR data. For this purpose, we established a methodology for extracting trees and then proceeded the process of developing it as an automated model based on model builder of ArcGIS Software. In order to evaluate the applicability of the developed model, the model was compared with commercial software in study area located in Yongin City. Through the experimental result, the proposed model was extract trees 9.91% higher than commercial software. From this results, it was found that the model effectively extracted trees.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.533-542
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• 2006

Lidar point clouds provide three dimensional information of terrain surface and have a great advantage to generate precise digital elevation model (DEM), particularly over forested area where some laser signals are transmitted to vegetation canopy and reflected from the bare ground. This study initially investigates the characteristics of lidar-derived height information as related to vertical structure of forest stands. Then, we propose a new filtering method to separate ground points from Lidar point clouds, which is a prerequisite process both to generate DEM surface and to extract biophysical information of forest stands. Laser points clouds over the forest stands in central Korea show that the vertical distribution of laser points greatly varies by the stand characteristics. Based on the characteristics, the proposed filtering method processes first and last returns simultaneously without setting any threshold value. The ground points separated by the proposed method are used to generate digital elevation model, furthermore, the result provides the possibilities to extract other biophysical characteristics of forest.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.471-477
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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 ${\pm}$8.1cm, ${\pm}$24.7cm, ${\pm}$35.9cm, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.923-929
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• 2008

Due to the increasing need for 3D spatial data, modeling of topography and artificial structures plays an important role in three-dimensional Urban Analysis. This study suggests a methodology for solving the problem of calculation for the extraction of building boundary, minimizing the user's intervention, and automatically extracting building boundary, using the LIDAR data. The methodology suggested in this study is characterized by combining the merits of the point-based process and the image-based process. The procedures for extracting building boundary are three steps: 1) LIDAR point data are interpolated to extract approximately building region. 2) LIDAR point data are triangulated in each individual building area. 3) Extracted boundary of each building is then simplified in consideration of its area, minimum length of building.The performance of the developed methodology is evaluated using real LIDAR data. Through the experiment, the extracted building boundaries are compared with digital map.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.235-241
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• 2006

The demand of 3D terrain mapping techniques is increasing in many application fields such as CNS(Car Navigation System), web service system, DMB(Digital Multimedia Broadcasting) systems and etc To construct a 3D terrain map, it is a pre-requite step that register data collected from different surveying sources. This Paper Present the methodology to register airborne photo, LIDAR data, and digital map, which are major data sources to create a 3D terrain mao. For this purpose, we developed the generally applicable algorithm that uses linear features to register airborne photos and digital maps to LIDAR data. The algorithm explicitly formulates step-by-step methodologies to establish observation equations for transformation. The results clearly demonstrate the proposed algorithm is appropriate to register these data sources.