• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.53-60
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• 2006

The purpose of this paper is forcasting of flooding area using LiDAR surveying data, and flood map for damage prevention is established for this purpose. Teahwa river at Ulsan city was chosen as test area and the flood simulation was produced in this area. For the flood simulation, each DEM using LiDAR data and digital map was established and then HEC model program and MIKE program was used to decide the amount of flood flowing and flood height. To improve the rainfall-overflow simulation confidence using inspection comparison of LiDAR data this paper analyzed and compared the LiDAR DEM accuracy and 1/5000 digital map DEM. The height accuracy is important factor to make flood map, however, LiDAR survey execution of all river area is not economic so, LiDAR survey execution of only important area is possible to be make high accuracy and economic flood map. The expectation effect of flood simulation is flood damage prevention and economic savings of recovery cost by forcasting of rainfall-overflow area and establishment of counter-measure.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.235-238
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• 2010

Since the mid 1990s airborne LiDAR data have been widely used, automation of building modeling is getting a central issue. LiDAR data processing for building modeling is involved with extracting surface patch elements by segmentation and surface fitting with optimal mathematical functions. In this study, simulation LiDAR data were generated with complex polyhedral roofs of buildings and an automatic modeling approach was proposed.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.97-104
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• 2010

The important factors in a flood simulation are hydrologic data (such as the rainfall and intensity), a threedimensional terrain model, and the hydrologic inundation calculation matrix. Should any of these factors lack accuracy, flood prediction data becomes unreliable and imprecise. The three-dimensional terrain model is constructed based on existing digital maps, current map updates, and airborne LiDAR data. This research analyzes and offers ways to improve the model's accuracy by comparing flood weakness areas selected according to the existing data on flood locations and design frequency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.91-102
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• 2017

Backpack-mounted mapping system is firstly introduced for flexible movement in indoor spaces where satellite-based localization is not available. With the achieved advances in miniaturization and weight reduction, use of LiDAR (Light Detection and Ranging) sensors in mobile platforms has been increasing, and indeed, they have provided high-precision information on indoor environments and their surroundings. Previous research on the development of backpack-mounted mapping systems, has concentrated mostly on the improvement of data processing methods or algorithms, whereas practical system components have been determined empirically. Thus, in the present study, a simulator for a LiDAR sensor (Velodyne VLP-16), was developed for comparison of the effects of diverse conditions on the backpack system and its operation. The simulated data was analyzed by visual inspection and comparison of the data sets' statistics, which differed according to the LiDAR arrangement and moving speed. Also, the data was used as input to a point-cloud registration algorithm, ICP (Iterative Closest Point), to validate its applicability as pre-analysis data. In fact, the results indicated centimeter-level accuracy, thus demonstrating the potentials of simulation data to be utilized as a tool for performance comparison of pointdata processing methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.199-205
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• 2007

In this study, we have intended to precisely analyze the aspect of propagation and the extent of damage due to the traffic noise as hon as a main source of noise in urban area. The propagation of traffic noise has a strong relationship between distance and shape of surface. Thus, it is necessary to consider the distribution of buildings for estimating effects of noise in urban area because noise propagations will be affected by buildings. For this, we developed the DEM and DBM using the airborne LiDAR data in the study area and compared with results from the noise simulations using the each model. The extent of damage occurred by the traffic noise above 60 dB(A) from the case of DEM were shown at the 60% of a whole study area, whereas the extent from other case of DBM were shown at the 30% of a whole study area. Also, the extent of the noise levels between 45 dB(A) and 50 dB(A) will be generally recognized as calm environment was increased(the 0% to the 43%) in the case which simulated with building informations. These results indicated that the shape informations of buildings like a DBM is a essential source to simulate the propagation of traffic noise in urban area especially. With results in this study, the effect of traffic noise at a specific area will be easily and precisely estimated if we have the LiDAR data and a traffic census for Korea. Furthermore specific area's traffic noise simulation could be possible using only road traffic information once we have DBM data from LiDAR surveying. This also could be applied as a base data for noise pollution petitioning, traffic planning, construction, etc. in huge city planning projects like a U-City.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.3
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• pp.191-197
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• 2017

Light detection and ranging (LiDAR) is one of the most efficient technologies to obtain the topographic and bathymetric map of coastal zones, superior to other technologies, such as sound navigation and ranging (SONAR) and synthetic aperture radar (SAR). However, the measurement results using LiDAR are vulnerable to environmental factors. To achieve a correspondence between the acquired LiDAR data and reality, error sources must be considered, such as the water surface slope, water turbidity, and seafloor slope. Based on the knowledge of those factors' effects, error corrections can be applied. We concentrated on the effect of the seafloor slope on LiDAR waveforms while restricting other error sources. A simulation regarding in-water beam scattering was conducted, followed by an investigation of the correlation between the seafloor slope and peak timing of return waveforms. As a result, an equation was derived to correct the depth error caused by the seafloor slope.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1865-1873
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• 2017

The feature-based visual SLAM requires 3D positions for the extracted feature points to perform 3D-2D motion estimation. LiDAR can provide reliable and accurate 3D position information with low computational burden, while stereo camera has the problem of the impossibility of stereo matching in simple texture image region, the inaccuracy in depth value due to error contained in intrinsic and extrinsic camera parameter, and the limited number of depth value restricted by permissible stereo disparity. However, the sparsity of LiDAR data may increase the inaccuracy of motion estimation and can even lead to the result of motion estimation failure. Therefore, in this paper, we propose three interpolation methods which can be applied to interpolate sparse LiDAR data. Simulation results obtained by applying these three methods to a visual odometry algorithm demonstrates that the selective bilinear interpolation shows better performance in the view point of computation speed and accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.227-239
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• 2008

Processing LiDAR (Light Detection And Ranging) data obtained from ALS (Airborne Laser Scanning) systems mainly involves organization and segmentation of the data for 3D object modeling and mapping purposes. The ALS systems are viable and becoming more mature technology in various applications. ALS technology requires complex integration of optics, opto-mechanics and electronics in the multi-sensor components, Le. data captured from GPS, INS and laser scanner. In this study, digital image processing techniques mainly were implemented to gray level coded image of the LiDAR data for building extraction and superstructures segmentation. One of the advantages to use gray level image is easy to apply various existing digital image processing algorithms. Gridding and quantization of the raw LiDAR data into limited gray level might introduce smoothing effect and loss of the detail information. However, smoothed surface data that are more suitable for surface patch segmentation and modeling could be obtained by the quantization of the height values. The building boundaries were precisely extracted by the robust edge detection operator and regularized with shape constraints. As for segmentation of the roof structures, basically region growing based and gap filling segmentation methods were implemented. The results present that various image processing methods are applicable to extract buildings and to segment surface patches of the superstructures on the roofs. Finally, conceptual methodology for extracting characteristic information to reconstruct roof shapes was proposed. Statistical and geometric properties were utilized to segment and model superstructures. The simulation results show that segmentation of the roof surface patches and modeling were possible with the proposed method.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.71-78
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• 2022

Recently, mountain disasters such as landslides and debris flows have flowed along mountain streams and hit residential areas and roads, increasing damage. In this study, in order to reduce damage and analyze causes of mountain disasters, field surveys and Terrestrial LiDAR terrain analysis were conducted targeting debris flow areas, and debris flow flow processes were simulated using FLO-2D and RAMM models, which are numerical models of debris flows. In addition, the debris flow deposition area was calculated and compared and analyzed with the actual occurrence section. The sedimentation area of the debris flow generation section of the LiDAR scan data was estimated to be approximately 21,336 ㎡, and was analyzed to be 20,425 ㎡ in the FLO-2D simulation and 19,275 ㎡ in the case of the RAMMS model. The constructed topographical data can be used as basic data to secure the safety of disaster risk areas.