• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.475-482
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• 2007

Most golfers believe that knowing yardages will improve their score. Certainly it helps with club selection. But, simple "Graphic" yardage guides being notorious for error and inaccuracies, which a serious golfer will pick immediately, only serve to erode the players enjoyment and ultimately, golf course satisfaction. Someone believes with low-level aerial photographic images, golfer will be impressed with the accuracy of the depiction, helping them play a more confident game. But, there are no mapping products in true 3-D available in the world that allows a golfer to determine shot distances in yards or meters. So, we suggest an lenticular technology for real 3-D display as a viable alternative to conventional image map solution. This technology is an image display method for the generation of multi-image effects like 3D visualization or animation. This methodology is cutting edge stereoscopic image which overcomes the limitation of conventional photo tech by recomposing and producing 3 dimensional images. A significant strength of this methods its versatility concerning display effects. The main use of the hardcopy 3-D lenticular displays is in the fields of science, education, planning, and representation. This paper gives a concise overview of the lenticular foil technology and describes the production of the true 3-D yardage book of golf courses. For this study, 3-D effects are achieved and evaluated with the lenticular display by incorporation multiple synthetic images based on digital topographic terrain model and by using the two images of the actual stereopair.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.257-264
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• 2020

Vectorization is currently the main method in feature collection (extraction) during digital mapping using UAV-Photogrammetry. However, this method is time consuming and prone to gross elevation errors when extracted from a DSM (Digital Surface Model), because three-dimensional feature coordinates are vectorized separately: plane information from an orthophoto and height from a DSM. Consequently, the demand for stereo plotting method capable of acquiring three- dimensional spatial information simultaneously is increasing. However, this method requires an expensive equipment, a Digital Photogrammetry Workstation (DPW), and the technology itself is still incomplete. In this paper, we evaluated the accuracy of low-cost stereo plotting system, Menci's StereoCAD, by analyzing its three-dimensional spatial information acquisition. Images were taken with a FC 6310 camera mounted on a Phantom4 pro at a 90 m altitude with a Ground Sample Distance (GSD) of 3 cm. The accuracy analysis was performed by comparing differences in coordinates between the results from the ground survey and the stereo plotting at check points, and also at the corner points by layers. The results showed that the Root Mean Square Error (RMSE) at check points was 0.048 m for horizontal and 0.078 m for vertical coordinates, respectively, and for different layers, it ranged from 0.104 m to 0.127 m for horizontal and 0.086 m to 0.092 m for vertical coordinates, respectively. In conclusion, the results showed 1: 1,000 digital topographic map can be generated using a stereo plotting system with UAV images.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.73-82
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• 2012

In order to carry out efficient investment and successful business in national geo-spatial industry, economic assessment on the field of 3D geo-information has recently emerged as a serious issue. Therefore, this study is intended to offer cost-effective evaluation scheme which are proper for 3D geo-spatial information, especially focusing on development of orthophoto and DEM. The study is organized as follows. The first section clarifies preliminary rules for feasibility by defining target work and category in order to estimate benefit. Then, this paper will be limited to consideration of production of digital mapping for target business which is expected to create high value and its benefit from cost reduction is suggested. Drawing from the AHP(Analytic Hierarchy Process) methods, this study comprehensively described final result and implication to examine business value. Consequently, this study can suggest economical evaluation methods on 3D geo-spatial information industry, which takes up a considerable part of immaterial benefit and has difficulties in economic assessment and estimation. preventing a variety of errors in system operation in advance.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.115-125
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• 2017

In current, as the release of national base map with an equivalent scale and accuracy for the whole territory areas in South Korea, rapid spatial information industry such as national land development, GIS, and car navigation are used in a variety of spatial information industry as decision making method, and a lot of research and policies are proposed for the wide expansion of spatial information industry. For this, as of 2013, it contributes to the latest trend of spatial information field in order to solve the problems for the latest trend of spatial information, replacing modification of base maps as dividing the whole territory to zone with policy transformation by ordinary modifications. Therefore, this paper evaluates the possibility of modification and renewal of national base maps(scale: 1:5,000) using drones which currently get the limelight from a variety of research fields and industries. In particular, as a result of overlapping orthophoto, 3D point clouds extracted from images acquired by low-cost drones, and digital maps which are applied for the tasks of modification and renewal, it presents 0.2m precision and 0.1m accuracy. This means that drone-based photorgammetry technique can be fully utilized in the tasks of digital map modification and renewal because it conforms the error range of work regulation in making the national base maps(scale 1: 5000).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.225-234
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• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.289-297
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• 2009

In the paper, we propose the methodology to extract training dataset automatically for supervised classification of road networks. For the preprocessing, we co-register the airborne photos, LIDAR data and large-scale digital maps and then, create orthophotos and intensity images. By overlaying the large-scale digital maps onto generated images, we can extract the initial training dataset for the supervised classification of road networks. However, the initial training information is distorted because there are errors propagated from registration process and, also, there are generally various objects in the road networks such as asphalt, road marks, vegetation, cars and so on. As such, to generate the training information only for the road surface, we apply the Expectation Maximization technique and finally, extract the training dataset of the road surface. For the accuracy test, we compare the training dataset with manually extracted ones. Through the statistical tests, we can identify that the developed method is valid.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.223-233
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• 2018

In this study was classified the cadastral control points and parcel boundary points into 40m, 100m by flight altitude of UAV (Unmanned Aerial Vehicle) which compared the coordinates extracted from the orthophoto with the parcel boundary point coordinates by GNSS (Global Navigation Satellite System) ground survey. As a results of this study, first, in the spatial resolution analysis that the average error of the orthoimage by flight altitude were 0.024m at 40m, and 0.034m at 100m which were higher 40m than 100m for spatial resolution of orthophotos and position accuracy. Second, in order to analyze the accuracy of image recognition by airmark of flight altitude that was divided into three cases of nothing, green, and red of RMSE (Root Mean Square Error) were X=0.039m, Y=0.019m and Z=0.055m, the highest accuracy. Third, the result of the comparison between orthophotos and field survey results that showed the total RMSE error of the cadastral control points were X=0.029m, Y=0.028m, H=0.051m, and the parcel boundary points were X=0.041m, Y=0.030m. In conclusion, based on the results of this study, it is expected that if the average error of flight altitude is limited to less than 0.05m in the legal regulations related to orthophotos for cadastral surveying, it will be an economical and efficient method for cadastral survey as well as spatial information acquisition.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1731-1738
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• 2021

The objective of this study was to extract individual trees and tree heights using UAV drone images. The study site was Gongju national university experiment forest, located in Yesan-gun, Chungcheongnam-do. The thinning intensity study sites consisted of 40% thinning, 20% thinning, 10% thinning and control. The image was filmed by using the "Mavic Pro 2" model of DJI company, and the altitude of the photo shoot was set at 80% of the overlay between 180m pictures. In order to prevent image distortion, a ground reference point was installed and the end lap and side lap were set to 80%. Tree heights were extracted using Digital Surface Model (DSM) and Digital Terrain Model (DTM), and individual trees were split and extracted using object-based analysis. As a result of individual tree extraction, thinning 40% stands showed the highest extraction rate of 109.1%, while thinning 20% showed 87.1%, thinning 10% showed 63.5%, and control sites showed 56.0% of accuracy. As a result of tree height extraction, thinning 40% showed 1.43m error compared with field survey data, while thinning 20% showed 1.73 m, thinning 10% showed 1.88 m, and control sites showed the largest error of 2.22 m.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.