• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.653-666
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• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.527-532
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• 2012

Terrestrial LiDAR emerges as a main mapping technology for indoor 3D cadastre, cultural heritage conservation and, building management in that it provides fast, accurate, and reliable 3D data. In this paper, a new 3D modeling method consisting of segmentation stage and outline extraction stage is proposed to develop indoor 3D model from the terrestrial LiDAR. In the segmentation process, RANSAC and a refinement grid is used to identify points that belong to identical planar planes. In the outline tracing process, a tracing grid and a data conversion method are used to extract outlines of indoor 3D models. However, despite of an improvement of productivity, the proposed approach requires an optimization process to adjust parameters such as a threshold of the RANSAC and sizes of the refinement and outline extraction grids. Furthermore, it is required to model curvilinear and rounded shape of the indoor structures.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.285-287
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• 2010

It is recently enlarged to necessity of 3D spatial information model in urban areas. and in order to that, It is increased to use the terrestrial LiDAR. The Point clouds which are received by terrestrial LiDAR take a relateive coordinate. For transform into absolute coordinate, it carry out GPS surveying. However, it is difficult to geo-referencing of point clouds using the GPS due to high buildings and facilities in urban area. This study suggests a methodology, that is geo-referencing of point clouds which is received from terresstrial LiDAR in urban area and then verified accuracy of geo-referencing of point clouds. In order to geo-Referencing of point clouds which are received in Engineering building of Yonsei Univ., it was be setout through GPS surveying, and then obtained absolute coordinate of real building. Using this coordinate, It was operated geo-referencing of point clouds, verified accuracy between check point and geo-referenced point clouds. As a result, RMSE of check point shows that GPS surveying is 6.9~8.0cm.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.467-470
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• 2007

A golf course provides comfortable leisure space, but construction of it demands eco-friendly design which minimizes the environmental spoil and harmonizes its surroundings. Therefore, it is highly recommended that appropriate understanding of existing golf course, accurate estimation of new golf course design and precise construction. In this study, data for golf course design were researched using LiDAR intensity. Consequently, a sectional registration data of a golf course was generated efficiently.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.439-440
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• 2013

본 논문은 라이다 데이터를 활용하여 2.5차원 지적도의 구축에 대한 것으로 라이다데이터를 사용하여 지형의 수치표고모델 및 수치표면모델을 기반으로 필지에 대한 지적도의 중첩으로 입체적인 지적도 구축을 의미한다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.183-190
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• 2007

To make use of surveying data obtained from different sensors and different techniques, it is a pre-requite step that register them in a common coordinate system. For this purpose, we developed methodologies to register IKONOS-2 Satellite Imagery using LiDAR(Light Detection And Ranging) data. To achieve this, conjugate features from these data should be extracted in advance. In this study, linear features are chosen as conjugate features. Then, to register them, observation equations are established from similarity measurements of the extracted features and the results was evaluated statistically. The results clearly demonstrate that the proposed algorithms are appropriate to register these data.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1009-1024
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• 2018

With the increased use of LiDAR (Light Detection and Ranging) that can obtain over millions of point dataset, methodologies for efficient search and dimensionality reduction for the point cloud became a crucial technique. The existing octree-based "parametric algorithm" has proved its efficiency and contributed as a part of PCL (Point Cloud Library). However, the implementation of the algorithm on GPU (Graphics Processing Unit) is considered very difficult because of structural constraints of the octree implemented in PCL. In this paper, we present a method for the parametric algorithm on GPU environment and implement a projection of the queried points on four directions with an improved noise reduction.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.7-7
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• 2018

1차원 하수관로 해석 모형과 2차원 지표면 유출 해석 모형을 연계한 1D-2D 결합 도시침수 모델은 도시지역의 유출 현상과 침수 모의에 널리 이용되고 있다. 그러나 도시 지역의 복잡한 지형이 지표면 유출 흐름에 미치는 영향을 보다 자세히 파악하기 위해서는 보다 높은 해상도의 지형자료를 활용한 모의가 필요하다. 본 연구에서는 도시침수 해석을 위한 1D-2D 결합 하이브리드(Hybrid) 병렬화 코드(H12)를 개발하여 넓은 도시 유역에 대해서 고해상도 지형자료를 활용한 모의가 유역단위로 가능하도록 하였다. H12는 Open Multi-Processing(OpenMP)와 Message Passing Interface(MPI) 병렬 계산을 동시에 수행하여 매우 넓은 지역에 대해서도 도로의 형태를 확인 할 수 있는 수준의 고해상도 침수 해석 모의가 가능하다. 또한 도시지역의 복잡한 지형을 자세히 재현하고 계산의 효율을 높이기 위하여 격자세분화 기법이 적용되었다. H12의 적용성을 평가하기 위하여 미국 텍사스 알링턴 지역의 Johnson Creek 유역(${\sim}40km^2$)유역에 대한 시범 모의를 수행하였으며 도시유역의 지형을 표현하기 위하여 1m 해상도의 LiDAR자료를 사용하여 침수발생시 보다 자세한 유출수의 흐름을 해석할 수 있도록 하였다. 모의 결과 하이브리드 병렬 계산은 순차적 계산에 비하여 최고 79배 이상 빠른 계산속도 효율 향상을 보여주었으며, OpenMP나 MPI를 단독으로 사용하는 것에 비하여 더욱 효율적인 계산속도 효율 향상을 보여주었다.