• Journal of the Korean Geographical Society
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• v.47 no.4
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• pp.606-624
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• 2012

The main objective of this paper is to provide an adequate and comprehensive review of what has been done in South Korea in the field of geospatial analysis and modeling. This review focuses on spatial data analysis and spatial statistics, spatial optimization, and geosimulation among various aspects of the field. It is recognized that geospatial analysis and modeling in South Korea got through the initial stage during the 1990s when computer and analytical cartography and GIS were introduced, moved to the growth stage during the first decade of the $21^{st}$ century when there was a surge of relevant researches, and now is heading for its maturity stage. In spatial data analysis and spatial statistics, various topics have been addressed for spatial point pattern data, areal data, geostatistical data, and spatial interaction data. In spatial optimization, modeling and applications related to facility location problems, districting problems, and routing problems have been mostly researched. Finally, in geosimulation, while most of research has focused on cellular automata, studies on agent-based model and simulation are in beginning stage. Among all these works, some have fostered methodological advances beyond simple applications of the standard techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.405-416
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• 2019

Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of internet and geospatial information technology. In the field of geospatial information, machine learning is also being applied to classify or recognize objects using images and point clouds. In this study, the problem of manually constructing training data using existing digital map version 1.0 was improved, and the technique of classifying roads, buildings and vegetation using image and point clouds were proposed. Through experiments, it was possible to classify roads, buildings, and vegetation that could clearly distinguish colors when using true ortho-image with only RGB (Red, Green, Blue) bands. However, if the colors of the objects to be classified are similar, it was possible to identify the limitations of poor classification of the objects. To improve the limitations, random forest and support vector machine techniques were applied after band fusion of true ortho-image and normalized digital surface model, and roads, buildings, and vegetation were classified with more than 85% accuracy.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.67-71
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• 2004

Increasing number and acquisition rate of satellite imagery promoted researches related with DEM generation based on satellite imagery. SPOT image gave us advantage to generate DEM which covers wide area of $60km{\times}60km$. In the case of rigorous sensor model of SPOT imagery, ephemeris data and several ground control points are need and requires arduous computational costs to produce DEM. In this study, using alternative sensor model based on Direct Linear Transform, we generated DEM using small number of ground control points. As a result, it was possible to acquire the DEM with suitable accuracy.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.18-25
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• 2024

This study proposes the utilization of a model registry to secure interoperability among urban domain information models. It reviews urban information and data sharing along with related standards and standardization activities. By analyzing registry use cases in the context of Spatial Data Infrastructure (SDI), it presents the concept of a model registry for facilitating information sharing and exchange among urban domain information models. An use case linking urban domain information models using the model registry is developed with a prerequisite of seamless implementation of smart city services. The paper discusses technical requirements and outlines technology implementation and validation through prototype development, highlighting them as future research tasks.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.57-66
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• 1996

For the effective use of spatial data, it is necessary to translate the diverse digital map data (here, digital map for car navigation system) into common GIS data structure(here, ARC/INFO). For this purpose, analysis on the structure of the established digital map was fulfilled, the data model for effective translation tool was designed. As a result, the possibility to translate diverse digital map structures (database) into GIS tools and to share them, with the users' point of view, was successfully verified.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.126-131
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• 1998

Spatial data warehouse, whose importance is being increased, is composed of huge amounts of historical spatial data for organizational decision making and it also allows users to obtain useful geospatial information through analyzing and summmarizing spatial data. In this paper, we survey effective spatial multidimensional model which is based on virtual scenario for spatial data warehouse modelling. Therefore, we describe spatial multidimensional analytical query which provide multiple analytical functions according tom user's requests.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.93-101
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• 2016

Reservoir area and reservoir capacity must be evaluated for reservoir management such a water supply, water-purity control and so on. In this paper, the reservoir area and reservoir capacity according to the level of storage range of water(149~156 El.m) could be calculated by using TIN data model of study area, Gyoyeon reservoir, TIN data model was made of DSM which was created by using UAV and GCP survey. From the results of applying the various functions to reservoir area and capacity, reservoir area and reservoir capacity according to the level of storage range of water showed the highest coefficient of determination of 0.97 in fourth-order polynomial, and 0.99 in second-order polynomial, respectively. Thus, it could be expected the efficient reservoir management by estimating reservoir area and capacity curve equation through UAV photogrammetry.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.41-50
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• 2008

Recent progress in state-of-the-art geospatial information technologies such as digital mapping, LiDAR(Light Detection And Ranging), and high-resolution satellite imagery provides various data sources fer Digital Elevation Model(DEM). DEMs are major source to extract elements of the hydrological terrain property that are necessary for efficient watershed management. Especially, watersheds extracted from DEM are important geospatial database to identify physical boundaries that are utilized in water resource management plan including water environmental survey, pollutant investigation, polluted/wasteload/pollution load allocation estimation, and water quality modeling. Most of the previous studies related with watershed extraction using DEM are mainly focused on the hydrological elements analysis and preprocessing without considering grid size of the DEMs. This study aims to analyze accuracy of the watersheds extracted from DEMs with various grid sizes generated by LiDAR data and digital map, and appropriate preprocessing methods.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.115-130
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• 1995

The National Geographic Institute(NGI), a national mapping agency, has begun to digitalize national base maps to vitalize nation-wide GIS implementations. However, the NGI's cartographic database design reflects only paper map production and is considered inflexible for various applications. In order to suggest an appropriate data model and database implementation method, approaches of two mapping agencies are analyzed: the United State Geological Survey and Ordnance Survey in the United Kingdom One important finding from the analysis is that each data model is designed to achieve two production purposes in the same time : map and data. By taking advantageous features from the two approaches, an ideal model is proposed. To adapt the ideal model to tile present situation in Korean GIS community, a realistic model is generated, which is an 'SDTS-oriented' data model. Because SDTS will be a Korean data transfer standard, it will be a common basis in developing other data models for different purposes.