• Journal of the Korean earth science society
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• v.39 no.1
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• pp.46-52
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• 2018

This paper is to study the development of three dimensional Digital Surface Model (DSM) using photogrammetry technique based on self-developed Drone (Unmanned Aerial Vehicle (UAV)). To develop DSM, we selected a study area in Jeju island and took 24 pictures from the drone. The three dimensional coordinates of the photos were made by Differential Global Positioning System (DGPS) surveying with 10 ground control points (GCP). From the calculated three dimensional coordinates, we produced orthographic image and DSM. The accuracy of DSM was calculated using three GCPs. The average accuracy of X and Y was from 8.8 to 14.7 cm, and the accuracy of Z was 0.8 to 12.4 cm. The accuracy was less than the reference accuracy of 1/1,000 digital map provided by National Geographic Information Institute (NGII). From the results, we found that the self-developed drone and the photogrammetry technique are a useful tool to make DSM and digital map of Jeju.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.195-202
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• 2017

Ortho images created by aerial photogrammetry have been used in large areas but they are uneconomical for small areas and continuous change observation. The drones have been developed for military purposes, and recently they are being used crop management and analysis, broadcast relay, meteorological observation and disaster investigation and so on. Also there were a lot of studies of expensive commercial drone. In this paper, lower price self-assembly drone usable for in small areas, Obtained images and produced Orthomosaic and DSM using mission planner which is a normal digital camera and open source program, and postprocessing was used Pix4d software. GCP errors are X-coordinate 3.4cm, Y-coordinate 2.4cm, Z-coordinate 4.2cm. It seems like the self-assembly drone can be used for various fields.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.249-256
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• 2022

In the case of Drone Photogrammetry, the "pixel to point tool" module of Metashape, Pix4D Mapper, ContextCapture, and Global MapperGIS, which is a simple software, are widely used. Each SW has its own logic for the analysis of aerial triangulation, but from the user's point of view, it is necessary to select a SW by comparative analysis of the coordinate values of geospatial information for the result. Taking aerial photos for drone photogrammetry, surveying GCP reference points through VRS-GPS Survey, processing the acquired basic data using each SW to construct ortho image and DSM, and GCPSurvey performance and acquisition from each SW The coordinates (X,Y) of the center point of the GCP target on the Ortho-Image and the height value (EL) of the GCP point by DSM were compared. According to the "Public Surveying Work Regulations", the results of each SW are all within the margin of error. It turned out that there is no problem with the regulations no matter which SW is included within the scope.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1063-1071
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• 2023

Unmanned aerial vehicles (UAVs, drones) are becoming increasingly useful in a variety of fields. Advances in UAV and camera technology have made it possible to equip them with ultra-high resolution sensors and capture images at low altitudes, which has improved the reliability and classification accuracy of object identification on the ground. The distinctive contribution of this study is the derivation of sensor-specific performance metrics (GRD/GSD), which shows that as the GSD increases with altitude, the GRD value also increases. In this study, we identified the characteristics of various onboard sensors and analysed the image quality (discrimination resolution) of aerial photography results using UAVs, and calculated the shooting conditions to obtain the discrimination resolution required for reading ground objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.131-140
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• 2020

Recently the importance of BIM (Building Information Modeling) that enables 3D location-based design and construction work is being highlighted around the world. In Korea, the road map has been established to settle the design based on BIM using drone survey results by 2025. As the first step, BIM would be applied to road construction projects worth more than 50 billion Korean Won from 2020. On the other hand, drone survey regulation has been enacted and the data for drone survey cost were also included on Standard of construction estimate in 2020. However, more careful improvement is required to reflect drone survey results in BIM design and construction. Currently, Engineering instructions and Standard of construction estimate specifies that earthwork volume must be calculated by cross section method only. So it is required to add the method of DEM (Digital Elevation Model) based volume calculation on these regulations to realize BIM application. In order for that, this study verified the method of DEM based earthwork volume calculation. To get an accurate DEM for accurate volume computation, drone survey was carried out according to the drone survey regulation and then could get an accurate DEM data which have errors less than 3cm in X, Y and 6.8cm in H. As each DEM cell has 3D coordinate component, the volume of each cell can be calculated by obtaining the height of area of the cell then total volume is calculated by multiplying total number of cells by volume of each cell for the construction area. Verification for the new calculation method compare with existing method was carried out. The difference between DEM based volume by drone survey and cross section based volume by traditional survey was less than 1.33% and it can be seen that new DEM method will be able to be applied to BIM design and construction instead of cross section method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.605-614
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• 2015

Drone imaging, which is more cost-effective and controllable compared to airborne LiDAR, requires a low-cost camera and is used for capturing color images. From the overlapped color images, we produced two high-resolution digital surface models over different test areas. After segmentation, we performed tree identification according to the method proposed by , and computed the tree height and the canopy crown size. Compared with the field measurements, the computed results for the tree height in test area 1 (coniferous trees) were found to be accurate, while the results in test area 2 (deciduous coniferous trees) were found to be underestimated. The RMSE of the tree height was 0.84 m, and the width of the canopy crown was 1.51 m in test area 1. Further, the RMSE of the tree height was 2.45 m, and the width of the canopy crown was 1.53 m in test area 2. The experiment results validated the use of drone images for the extraction of a tree structure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.93-101
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• 2021

Recently, with the urban redevelopment and the spread of the planned cities, there is increasing interest in the wind environment, which is related not only to design of buildings and landscaping but also to the comfortability of pedestrians. Numerical analysis for wind environment prediction is underway in many fields, such as dense areas of high-rise building or composition of the apartment complexes, a precisive 3D building model is essentially required in this process. Many studies conducted for wind environment analysis have typically used the method of creating a 3D model by utilizing the building layer included in the GIS (Geographic Information System) data. These data can easily and quickly observe the flow of atmosphere in a wide urban environment, but cannot be suitable for observing precisive flow of atmosphere, and in particular, the effect of a complicated structure of a single building on the flow of atmosphere cannot be calculated. Recently, drone photogrammetry has shown the advantage of being able to automatically perform building modeling based on a large number of images. In this study, we applied photogrammetry technology using a drone to evaluate the flow of atmosphere around two buildings located close to each other. Two 3D models were made into an automatic modeling technique and manual modeling technique. Auto-modeling technique is using an automatically generates a point cloud through photogrammetry and generating models through interpolation, and manual-modeling technique is a manually operated technique that individually generates 3D models based on point clouds. And then the flow of atmosphere for the two models was compared and analyzed. As a result, the wind environment of the two models showed a clear difference, and the model created by auto-modeling showed faster flow of atmosphere than the model created by manual modeling. Also in the case of the 3D mesh generated by auto-modeling showed the limitation of not proceeding an accurate analysis because the precise 3D shape was not reproduced in the closed area such as the porch of the building or the bridge between buildings.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.141-148
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• 2020

A drone platform built by Korea Institute of Geoscience and Mineral Resources (KIGAM) is introduced. The platform consists of various drone systems developed at KIGAM for photogrammetry, remote exploration, physical exploration, field operation methods, a vehicle-based drone control center, as well as a drone data platform for data storage, sharing, analysis, and visualization of the acquired data. The performance of the drone platform is verified using results obtained with the various systems, which are tested individually and in various combined applications. Finally, the possibility of using the KIGAM drone platform for geological surveys and mineral resource exploration is discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.127-134
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• 2018

Electric transmission towers are facilities to transport electrical power from a plant to an electrical substation. The towers are connected using power lines that are installed with a proper sag by loosening the cable to lower the tension and to secure the sufficient clearance from the ground or nearby objects. The power line sag may extend over the tolerance due to the weather such as strong winds, temperature changes, and a heavy snowfall. Therefore the periodical mapping of the power lines is required but the poor accessibility to the power lines limit the work because most power lines are placed at the mountain area. In addition, the manual mapping of the power lines is also time-consuming either using the terrestrial surveying or the aerial surveying. Therefore we utilized multiple overlapping images acquired from a low-cost drone to automatically reconstruct the power lines in the object space. Two overlapping images are selected for epipolar image resampling, followed by the line extraction for the resampled images and the redundant images. The extracted lines from the epipolar images are matched together and reconstructed for the power lines primitive that are noisy because of the multiple line matches. They are filtered using the extracted line information from the redundant images for final power lines points. The experiment result showed that the proposed method successfully generated parabolic curves of power lines by interpolating the power lines points though the line extraction and reconstruction were not complete in some part due to the lack of the image contrast.