• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1291-1302
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• 2020

To verify the field-effectiveness of Terrestrial Laser Scanner (TLS), a terrestrial LiDAR was deployed to examine the damage properties of woods in forest fire area, then the data was compared with the results surveyed by a forestry expert. Four sample plots (30 m × 50 m, 0.15 ha) were set from the foot to the top of the mountain, and DBH, height, clear length, burned height, and crown length were investigated. Next, TLS collected information on damage characteristics found in the sample plots. This information was then compared with that amassed by the expert. The expert and the TLS survey results showed 30.8 cm and 29.9 cm for DBH, 15.8 m and 17.5 m for tree height, 8.4 m and 8.4 m for clear length, 4.0 m, 3.5 m for burned height, and 7.4 cm and 9.1 cm for crown length. With the exceptions of height and clear length, no notable discrepancy was observed between two methods. H/D ratio, CL/H ratio, and BH/CL ratio, all of which contribute to stability and decay rate of the stand, from the two methods were also compared. The human survey rated each ratio (H/D, CL/H, BH/CL in order) 51.3%, 47.1%, and 53.6%, while the TLS presented the results of 58.8%, 52.0%, and 38.7%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.136-145
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• 2009

The objective of the Intelligent Excavation System (IES) is to recognize the work environment and produce work plan and automatically control the excavator through integrating sensor and robot technologies. This paper discusses one of the core technologies of IES development project, development of 3D work environment modeling. 3D laser scanner is used for 3-dimensional mathematical model that can be visualized in virtual space in 3D. This paper describes (1) how the most appropriate 3D imaging system has been chosen; (2) the development of user interface and customization of the s/w to control the scanner for IES project; (3) the development of the mobile station for the scanner; (4) and the algorithm for the automatic registration of laser scan segments for IES project. The development system has been tested on the construction field and lessons learned and future development requirements are suggested.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.341-352
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• 2016

Manual 3D pipeline modeling from LiDAR scanned point cloud data is laborious and time-consuming process. This paper presents a method to extract the pipe, elbow and branch information which is essential to the automatic modeling of the pipeline connection. The pipe geometry is estimated from the point cloud data through the Hough transform and the elbow position is calculated by the medial axis intersection for assembling the nearest pair of pipes. The branch is also created for a pair of pipe segments by estimating the virtual points on one pipe segment and checking for any feasible intersection with the other pipe's endpoint within the pre-defined range of distance. As a result of the automatic modeling, a complete 3D pipeline model is generated by connecting the extracted information of pipes, elbows and branches.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.63-72
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• 2006

정보화 시대에 있어 급변하는 기술발전과 함께 인간의 문화적 욕구가 증대됨에 따라 3차원 공간정보에 대한 수요가 증가하고 있다. 하지만 현재 3차원 공간정보를 구축하는 과정은 일반적으로 수작업 또는 반자동에 의해 이루어지고 있다. 이는 데이터의 구축 및 표현에 많은 시간과 비용이 소요된다는 제약을 가지고 있다. 따라서 본 연구에서는 3차원 symbol 라이브러리를 구축하여 분류된 객체에 부합되는 3차원 symbol을 자동으로 선택하며, 선택된 3차원 symbol을 파라메타를 이용하여 자동으로 mapping 하는 과정을 구축하였다. 이를 통해 3차원 공간데이터의 구축 및 표현에 요구되는 시간과 오류를 최소화할 수 있었다. 또한 LiDAR(Light Detection And Range) 데이터의 3차원 정보를 활용하여 symbol의 자동 mapping을 위한 파라메타들을 산출하였고, symbol의 분류를 위한 기본 데이터로 활용하였다. 구축된 알고리즘의 평가를 위하여 실재 데이터의 3차원 공간데이터베이스를 구축하였다. 구축된 데이터에 대해 symbol선택 및 자동 mapping 과정에 대한 오류 검사를 수행하였고, 더불어 구축된 3차원 데이터의 활용 가능성을 평가하였다. 그 결과 본 연구로부터 구축된 알고리즘들은 3차원 공간정보를 표현함에 있어 신속하고 안정적으로 기여할 수 있을 것으로 판단되었다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.631-642
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• 2012

This study aims to predict inundation and flood-stricken areas more accurately by simulating flood damage caused by reversible flow of rain water in the upper water system through precise 3D terrain model and backwater output. For the upstream of the South Han-River, precise 3D terrain model was established by using aerial LiDAR data and backwater by area was output by applying the storm events of 2002 including the history of flood damage. The 3D flood simulation was also performed by using GIS Tool and for occurrence of related rainfall events, inundation events of the upriver region of water system was analyzed. In addition, the results of flood simulation using backwater were verified by making the inundation damage map for the relevant area and comparing it with flood simulation's results. When comparing with the results of the flood simulation applying uniformly the gauging station's water surface elevation used for the existing flood simulation, it is found that the results of the flood simulation using backwater are close to the actual inundation damage status. Accordingly, the causes of flood occurred in downstream of water system and upstream that has different topographic characteristics could be investigated and applying the simulation with backwater is proved more proper in order to procure accuracy of the flood simulation for the upriver region.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.1-14
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• 2024

An image-reconstruction technology, involving the deployment of an unmanned mobility equipped with high-speed LiDAR (Light Detection And Ranging) has been proposed to reconstruct the shape of abandoned mine. Unmanned mobility operation is remarkably useful in abandoned mines fraught with operational difficulties including, but not limited to, obstacles, sludge, underwater and narrow tunnel with the diameter of 1.5 m or more. For cases of real abandoned mines, quadruped robots, quadcopter drones and underwater drones are respectively deployed on land, air, and water-filled sites. In addition to the advantage of scanning the abandoned mines with 2D solid-state lidar sensors, rotation of radiation at an inclination angle offers an increased efficiency for simultaneous reconstruction of mineshaft shapes and detecting obstacles. Sensor and robot posture were used for computing rotation matrices that helped compute geographical coordinates of the solid-state lidar data. Next, the quadruped robot scanned the actual site to reconstruct tunnel shape. Lastly, the optimal elements necessary to increase utility in actual fields were found and proposed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.459-469
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• 2011

There are various factors for determining object shape, such as size, slope and its direction, curvature, length, surface, angles between lines or planes, distribution of the model key points, and so on. Most of the object description and recognition methods are for the 2D space not for the 3D object space where the objects actually exist. In this study, 3D chain code operator, which is basically extension of 2D chain code, was proposed for object description and analysis in 3D space. Results show that the sequence of the 3D chain codes could be basis of a top-down approach for object recognition and modeling. In addition, the proposed method could be applicable to segment point cloud data such as LiDAR data.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.374-381
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• 2023

The travel time of flash floods along mountain streams is mainly governed by reach-average velocity, rather than by the point velocity of the locations of interest. Reach-average velocity is influenced by various factors such as stream geometry, streambed materials, and the hydraulic roughness of streams. In this study, the reach-average velocity in mountain streams was measured for storm periods using rhodamine dye tracing. The point cloud data obtained from a LiDAR survey was used to extract the average hydraulic roughness height, such as R_a, R_max, and R_z. The size distribution of the streambed materials (D₅₀, D₈₄) was also considered in the estimation of the roughness height. The field experiments revealed that the reach-average velocities had a significant relationship with flow discharges (v = 0.5499Q^0.6165 ), with an R² value of 0.77. The root mean square error in the roughness height of the R_a-based estimation (0.45) was lower than those of the other estimations (0.47-1.04). Among the parameters for roughness height estimation, the R_a -based roughness height was the most reliable and suitable for developing the reach-average velocity equation for estimating the travel time of flood waves in mountain streams.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.107-118
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• 2021

Scan-to-BIM can be precisely mod eled by measuring structures with Light Detection And Ranging (LiDAR) and build ing a 3D BIM (Building Information Modeling) model based on it, but has a limitation in that it consumes a lot of manpower, time, and cost. To overcome these limitations, studies are being conducted to perform semantic segmentation of 3D point cloud data applying deep learning algorithms, but studies on how segmentation result changes depending on learning data are insufficient. In this study, a parametric study was conducted to determine how the size and track type of railroad tunnels constituting learning data affect the semantic segmentation of railroad tunnels through deep learning. As a result of the parametric study, the similar size of the tunnels used for learning and testing, the higher segmentation accuracy, and the better results when learning through a double-track tunnel than a single-line tunnel. In addition, when the training data is composed of two or more tunnels, overall accuracy (OA) and mean intersection over union (MIoU) increased by 10% to 50%, it has been confirmed that various configurations of learning data can contribute to efficient learning.