• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.27-38
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• 1996

Non-scale digital map have important role in the field of GIS and other application area which using geographical data in recently against conventional map restricted by scale and information. The main objective of this study is to develope the automated map production system for small scale map in conjuction with generalization techniques in a digital environment. We will intend to develope algorithms and programs for each generalization operators based on specific terrain feature with vector data. This study will be performed aspects related to an data model development of generalization process, focussing on priority for processing sequency with maintaining vector topology, and error analysis for generalized digital data.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.141-149
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• 2010

Three-dimensional geo-spatial information is important for the efficient use and management of the country and the three-dimensional expression and analysis of urban projects, such as urban plans devised by local governments and urban management. Thanks to the revitalization of the geo-spatial information service industry, it is now being variously used not only in public but also private areas. For the creation of high-guiltily three-dimensional geo-spatial information, emphasis should be placed on not only the quality of the source image and three-dimensional geo-spatial model but also the level of visualization, such as level of detail and texturing. However, in the case of existing three-dimensional geo-spatial information, its establishment process is complicated and its data are not updated frequently enough, as it uses ready-created digital maps. In addition, as it uses Ortho Images, the images exist Relief displacement. As a result, the visibility is low and the three-dimensional models of artificial features are simplified to reach LoD between 2 and 3, making the images look less realistic. Therefore, this paper, analyzed the quality of three-dimensional geo-spatial information created using the three-dimensional modeling technique were applied using Digital photogrammetry technique, using digital aerial photo images by an existing large-format digital camera and multi-looking camera. The analysis of the accuracy of visualization information of three-dimensional models showed that the source image alone, without other visualization information, secured the accuracy of 84% or more and that the establishment of three-dimensional spatial information carried out simultaneously with filming made it easier to gain the latest data. The analysis of the location accuracy of true Ortho images used in the work process showed that the location accuracy was better than the allowable horizontal position accuracy of 1:1,000 digital maps.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.545-554
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• 2018

As the map paradigm shifts from analog to digital, the LOD (Level Of Detail) of spatial information needs to be redefined. In this study, we propose 4- dimensional indoor LOD model which can be used in digital map environment. For this purpose, the limitation of the previous research is derived through study of related works, and based on this, four different LODs are defined such PLOD (Position accuracy LOD) based on position accuracy, GLOD (Geometric LOD) based on shape representation, CLOD (Complete LOD) based on generalization, and SLOD (Semantic LOD) based on theme accuracy. In addition, we describe the relationships among the four different LODs, and explain how to express the indoor LOD using the four different LODs and show examples. In the future, the case studies of indoor LOD adoption for various indoor services and the study of method for applying CLOD and SLOD to each feature should be performed to verify the feasibility and validity of proposed indoor LOD.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.123-132
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• 1998

This paper presents a road detection technique for spaceborne synthetic aperture radar (SAR) images. Roads are important cartographic features. We incorporate an active contour model called snake as a model for the road and define a new external energy for snake which is appropriate for the road. Detecting roads in spaceborne SAR images is very difficult without other information. In this paper, digital maps are utilized to obtain the initial position and shape for snake. Only approximate geodetic location of roads appearing in SAR images can be known through geocoding process and usual digital maps also have location errors. Therefore, there exist large location offsets between the two data. By introducing initial matching procedure, the errors are reduced significantly. Then we initialize the snake's shape using the roads extracted from digital map and minimize the energies of all snake points to detect roads. We outline two problems in detection and propose a method that mitigates them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.197-205
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• 2020

Compared to aerial photogrammetry, UAV photogrammetry has advantages in acquiring and utilizing high-resolution images more quickly. The production of 3D models using UAV photogrammetry has become an important issue at a time when the applications of 3D spatial information are proliferating. Therefore, this study assessed the feasibility of utilizing 3D models produced by UAV photogrammetry through quantitative and qualitative analyses. The qualitative analysis was performed in accordance with the LODs (Level of Details) specified in the 3D Land Spatial Information Construction Regulation. The results showed that the features on planes have a high LoD while features with elevation differences have a low LoD due to the occlusion area and parallax. Quantitative analysis was performed using the 3D coordinates obtained from the CPs (Checkpoints) and edges of nearby structures. The mean errors for residuals at CPs were 0.042 m to 0.059 m in the horizontal and 0.050 m to 0.161 m in the vertical coordinates while the mean errors in the structure's edges were 0.068 m and 0.071 m in horizontal and vertical coordinates, respectively. Therefore, this study confirmed the potential of 3D models from UAV photogrammetry for analyzing the digital twin and slope as well as BIM (Building Information Modeling).

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.23-37
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• 2024

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.30-36
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• 2008

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we tested of the railway facilities using laser surveying system, then we propose data a generation of spatial images for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation. As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.191-196
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• 2019

The development of open-pit mines leads to large-area topographical changes in highland forests and can lead to severe deterioration of forests, requiring continuous monitoring. The drone photogrammetry is performed at a lower altitude than the existing manned aerial photogrammetry, and thus has a relatively high accuracy. The purpose of this study is to construct spatial information of large open pit mine using drone photogrammetry and to evaluate the accuracy and utilization of the results. The accuracy of the drone photogrammetric results was 0.018 ~ 0.063m in the horizontal direction and 0.027m ~ 0.088m in the vertical direction. These results satisfy the permissible accuracy of 1: 1,000 digital topographic map and it can be used for open mine monitoring. The geospatial information of the open pit mine can be used in various ways, and it can be used to monitor the quantitative change of a specific area for time series change through data management by periodic data acquisition. If drone photogrammetry is applied to open-pit mine monitoring in the future, work time and cost can be greatly reduced compared to the conventional GNSS or total station method, and the work efficiency can be greatly improved because more visible data can be generated.