• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.545-552
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• 2017

Aeolian dunes are typical sand dunes which are maintained and developed by interactions of earth surface, wind and vegetation. Developing a model which can predict the changing phenomena of these sand dunes is vital in enhancing the efficiency of understanding and management of terrains such as land degradation. In the existing models, however, there is lack of studies on the long - term behaviors of the sand dunes and application to actual topography. Therefore, this study applied the wind-vegetation model considering vegetation to the actual topography and analyzed the applicability of the wind-vegetation model by analyzing the long-term behaviors and comparing them with actual data. Through analysis, study found out that use of wind-vegetation model and data from unmanned aerial vehicle is effective in analyzing the changes of actual dune topography. Except for the boundary, the error of about 1m was generated compared with the change of the actual dune topography.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.279-288
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• 2021

In the era of the 4th industrial revolution, smart construction, in which new technologies such as UAV (Unmanned Aerial Vehicle) are fused, is attracting attention in the construction field. However, the method of estimating earthwork volume using DEM generated by UAV survey according to practical regulations such as construction design guidelines or standard product counting is not officially recognized and needs to be improved. In this study, different types of UAV were measured and DEM was obtained using this data. The DEM (Digital Elevation Model) thus obtained was analyzed for changes in the amount of earthworks according to the size of the GSD (Ground Sample Distance). In addition, the amount of earthwork by DEM and the amount of earthwork by existing design drawings were compared and analyzed. As a result of the study, it was suggested that images with a GSD of 5cm or less are effective to generate a high-quality DEM. Next, as a result of comparing the earthwork volume calculation method using DEM and the earthwork volume based on the existing 2D design drawings, a difference of about 1% was shown. In addition, when the design earthwork amount calculated by the double-section averaging method was compared with the designed earthwork amount using DEM data by UAV survey, a difference of about 1% was found. Therefore, it is suggested that the method of calculating the amount of earthworks using UAV is an efficient method that can replace the existing method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.555-563
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• 2018

Thermal infrared images have the characteristic of being able to detect objects that can not be seen with the naked eye and have the advantage of easily obtaining information of inaccessible areas. However, TIR (Thermal InfraRed) images have a relatively low spatial resolution. In this study, the applicability of the pansharpening algorithm used for satellite imagery on images acquired by the UAV (Unmanned Aerial Vehicle) was tested. RGB image have higher spatial resolution than TIR images. In this study, pansharpening algorithm was applied to TIR image to create the images which have similar spatial resolution as RGB images and have temperature information in it. Experimental results show that the pansharpening algorithm using the PC1 band and the average of RGB band shows better results for the quantitative evaluation than the other bands, and it has been confirmed that pansharpening results by ATWT (${\grave{A}}$ Trous Wavelet Transform) exhibit superior spectral resolution and spatial resolution than those by HPF (High-Pass Filter) and SFIM (Smoothing Filter-based Intensity Modulation) pansharpening algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2593-2600
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• 2013

The main purpose of this study is to carry out the performance evaluation of Ultra-light Fixed Wing UAV(Unmanned Aerial Vehicle) photogrammetry which is being, currently, applied for various fields such as cultural assets, accident survey, military reconnaissance work, and disaster management at home and abroad. Firstly, RMSE estimation of Aerial Triangulation (AT) are within approximately 0.10 cm in position (X, Y). And through the comparison of parcel's boundary points coordinates by terrestrial surveying and by UAV photogrammetry, the analysis shows that RMSE are shifted approximately 0.174~0.205 m in X-direction, 0.294~0.298 m in Y-direction respectively. Lastly, parcel's area by orthophoto of UAV photogrammetry and by that of cadastre register has been shown the difference by 0.118 m2. The results presented in this study is just one case study of orthophoto accuracy assessment of Ultra-light fixed wing UAV photogrammetry, hereafter various researches such as AT, direct-georeferencing, flight planning, practical applications, etc. should be necessary continuously.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.453-464
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• 2019

Recently, as the demand for 3D geospatial information increases, the importance of rapid and accurate data construction has increased. Although many studies have been conducted to register UAV (Unmanned Aerial Vehicle) imagery based on LiDAR (Light Detection and Ranging) data, which is capable of precise 3D data construction, studies using LiDAR data embedded in MMS (Mobile Mapping System) are insufficient. Therefore, this study compared and analyzed 9 matching algorithms based on feature points for registering reflectance image converted from LiDAR point cloud intensity data acquired from MMS with image data from UAV. Our results indicated that when the SIFT (Scale Invariant Feature Transform) algorithm was applied, it was able to stable secure a high matching accuracy, and it was confirmed that sufficient conjugate points were extracted even in various road environments. For the registration accuracy analysis, the SIFT algorithm was able to secure the accuracy at about 10 pixels except the case when the overlapping area is low and the same pattern is repeated. This is a reasonable result considering that the distortion of the UAV altitude is included at the time of UAV image capturing. Therefore, the results of this study are expected to be used as a basic research for 3D registration of LiDAR point cloud intensity data and UAV imagery.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.355-362
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• 2017

Recently, UAV(Unmanned Aerial Vehicle) have been utilized in various fields, including surveys, mapping, and spatial analysis, depending on the increase in demand for spatial information and UAV is receiving a lot of attention due to rapid data acquisition and economic viability. In this study, the applicability of UAV image images was analyzed for urban disaster prevention. UAV images were acquired for the study area and digital surface model and ortho image were generated through data processing. Also, the process using PPK(Post Processed Kinematic) GNSS method is compared with existing method. Through the research, it was able to effectively deploy urban disaster prevention information about the target area, and displayed the effectiveness of the methods for efficient comparison with existing unmanned aerial photogrammetry. If the PPK technique is applied to thethe disaster prevention field, it is expected that the work flow in the field of rapid data acquisition and disaster prevention data construction can be greatly improved.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.379-388
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• 2019

Recently, a technique for acquiring spatial information data using UAV (Unmanned Aerial Vehicle) has been greatly developed. It is a very crucial issue of the GIS (Geographic Information System) mapping system that passes way point in the unmanned airframe and finally measures the accurate image and stable localization to the desired destination. Though positioning using DGPS (Differential Global Navigation System) or RTK-GPS (Real Time Kinematic-GPS) guarantee highly accurate, they are more expensive than the construction of a single positioning system using a single GPS. In the case of a low-priced single GPS system, the stability of the positioning data deteriorates. Therefore, it is necessary to supplement the uncertainty of the absolute position data of the UAV and to improve the accuracy of the current position data economically in the operating state of the UAV. The aim of this study was to present an algorithm enhancing the stability of position data in a single GPS mode of UAV with multiple GPS. First, the arrangement of multiple GPS receivers through the center of gravity of the UAV were examined. Next, MD (Mahalanobis Distance) is applied to detect instantaneous errors of GPS data in advance and eliminate outliers to increase the accuracy of previously collected multiple GPS data. Processing procedure for multiple GPS reception data by applying the center of the triangular method were presented to improve the position accuracy. Second, UAV navigation systems integrated multiple GPS through configuration of the UAV specifications were implemented. Using the unmanned airframe equipped with multiple GPS receivers, GPS data is measured with the TCM (Triangular Center Method). In addition, UAV equipped with multiple GPS were operated in study area and locational accuracy of multiple GPS of UAV with VRS (Virtual Reference Station) GNSS surveying were compared. The result showed that the error factors are compensated, and the error range are reduced, resulting in the reliability of the corrected value. In conclusion, the result in this paper is expected to realize high-precision position estimation at low cost in UAV using multiple low-cost GPS receivers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.349-356
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• 2017

In this study, we propose a method to investigate topographic characteristics by applying machine learning which is an artificial intelligence analysis method based on the spatial data constructed using UAV and the training data created through spatial analysis. This method provides an alternative to the subjective judgment and accuracy of spatial data, which is a problem of existing topographic characteristics survey for officially assessed land price. The analysis method of this study is expected to improve the problems of topographic characteristics survey method of existing field researchers and contribute to more accurate decision of officially assessed land price by providing more objective land survey method.