• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.154-156
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• 2001

공간정보 및 속성정보를 저장 및 관리하여 서비스하는 지리정보시스템은 최근 네트워크 및 분산환경의 기술개발과 더불어 급격히 변화하고 있다. 이러한 지리 정보시스템은 기존의 패키지, 지역적 클라이언트/서버환경의 기술에서 컴포넌트 기반 기술로 자리메김하고 있는 실정이다. 이러한 컴포넌트 GIS의 기술발전을 위해서 OGC(OpenGIS Consortium)에서는 다양한 설계 및 구현 사양과 토픽을 제시하고 있으며, 대부분의 GIS 기술개발시장에서는 이러한 사양과 토픽에 맞게끔 시스템을 설계 및 구현하고 있다. 지리정보시스템에서 다루는 공간데이터는 크게 기하정보(Geometry)와 위상정보(Topology)로 나뉜다. 지리정보시스템에서 기본석인 질의나 분석에 있어서 기하정보 뿐만 아니라 위상정보도 시스템 전체의 성능에 큰 영향을 미치며, 이러한 위상 정보의 저장, 관리는 필수적이다. 본 논문에서는 개방형 컴포넌트 GIS에서의 기본 기하 데이터 모델인 OGC Simple Feature Geometry 모델과 위상정보 (Topolygy Information)를 나타내는 위상 데이터 모델의 통합 및 연동방법에 대해서 살펴보고, 이러한 데이터모델을 포함하고 있는, OGC 구현사항에 맞게 설계 및 구현된 전체시스템 아키텍쳐를 살펴 볼 것이다.

• Spatial Information Research
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• v.19 no.1
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• pp.93-105
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• 2011

In recent years, large and complex three-dimensional building has been constructed by the development of building technology and advanced IT skills, and people have lived there and spent a considerable time so far. Accordingly. in this sophisticatcd three-dimensional space, emergencies services or convenient information services have been in demand. In order to provide these services efficiently, understanding of topological relationships among the complex space should be supported naturally. Not on1y each method of understanding the topological relationships but also its efficiency can be different depending on different topological data models. B-rep based data model is the most widely used for storaging and representing of topological relationships. And from early 2000s, many researches on a network based topological data model have been conducted. The purpose of this study is to verify the efficiency of performance on spatial queries. As a result, Network-based topological data model is more efficient than B-rep based data model for determining the spatial relationship.

• Spatial Information Research
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• v.18 no.5
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• pp.93-105
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• 2010

Spatial neighborhoods are spaces which are relate to target space. A 3D spatial query which is a function for searching spatial neighborhoods is a significant function in spatial analysis. Various methodologies have been proposed in related these studies, this study suggests an adjacent based methodology. The methodology of this paper implements topological data for represent a adjacency via using network based topological data model, then apply modifiable Dijkstra's algorithm to each topological data. Results of ordering analysis about an adjacent space from a target space were visualized and considered ways to take advantage of. Object of this paper is to implement a 3D spatial query for searching a target space with a adjacent relationship in 3D space. And purposes of this study are to 1)generate adjacency based 3D network data via network based topological data model and to 2)implement a 3D spatial query for searching spatial neighborhoods by applying Dijkstra's algorithms to these data.

• Spatial Information Research
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• v.22 no.2
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• pp.31-44
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• 2014

According to the purpose of applications, the application program will utilize the most suitable data model and 3D modeling data would be generated based on the selected data model. In these reasons, there are various data sets to represent the same geographical features. The duplicated data sets bring serious problems in system interoperability and data compatibility issues, as well in finance issues of geo-spatial information industries. In order to overcome the problems, this study proposes a spatial data fusion method using topological relationships among spatial objects in the feature classes, called Topological Relation Model (TRM). The TRM is a spatial data fusion method implemented in application-level, which means that the geometric data generated by two different data models are used directly without any data exchange or conversion processes in an application system to provide indoor LBSs. The topological relationships are defined and described by the basic concepts of IndoorGML. After describing the concepts of TRM, experimental implementations of the proposed data fusion method in 3D GIS are presented. In the final section, the limitations of this study and further research are summarized.

• Spatial Information Research
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• v.21 no.2
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• pp.11-21
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• 2013

W ith the recent increasing build and application for 3D spatial information, the importance of management and application for spatial information based on indoor space has been increased. Especially, Due to the increasing of the scale and complexity of the building according to the development of construction technologies several studies have been conducted to provide the services based on indoor space such as indoor navigation for disaster. Therefore, to efficient manage and service for information of complicated indoor space, it is necessary to extend and develop 3D spatial model and services that have been developed for outdoor space. In this paper, Indoor Spatial Data Model(ISDM) is developed to support building spatial information for complicated indoor space and location based services through topological information. ISDM contains a feature model which is a CityGML Application Domain Extension(ADE) model and a topology model that refers the IndoorGML.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.353-356
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• 2000

최근 복잡한 실제 사물을 가상 공간상에 표현하기 위해 삼차원 모델을 많이 이용하고 있다. 기존의 삼차원 데이터 처리는 주로 정지 모델에 대해 기하학 정보와 위상학 정보를 표현하거나 다중 해상도(Level of Details, LOD)로 나타내는데 역점을 두었다. 그러나 네트웍을 통한 가상 공간에서 삼차원 애니메이션에 대한 응용이 점차 늘어남에 따라 이러한 데이터를 효율적으로 압축하여 전송하거나 저장할 필요가 생겼다 본 논문에서는 삼차원 애니메이션 모델의 공간적 또는 시간적 상관 관계를 이용하여 삼차원 모델 정보를 부호화하는 방법을 제안한다. 먼저 주어진 모델의 움직임을 분석하고 이를 (r,θ,ø)의 구 좌표계로 변환한 후 (θ,ø)의 분포에 따라 모델을 분할(Segmentation)한다. 그리고 움직임 벡터는 Affine 변환을 이용하여 삼차원 공간에서의 움직임을 정의한다. Key프레임에 해당하는 정지 모델의 기하학 정보와 위상학 정보를 압축하고, LOD 기술을 적용하여 손실 혹은 무손실로 부호화하여 전송한다. 또한 Key프레임 사이의 화면에서는 선형 또는 비선형 보간법으로 각 분할 부분을 복원하고, 이를 조합하여 전체적인 삼차원 모델을 복원한다.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1589-1605
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• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.873-884
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• 2003

So far, significant achievements have been studied on the development of models for spatial and spatiotemporal objects with indeterminate boundaries which are found in many applications for geographic analysis and image understanding. Therefore, in this paper we propose the spatiotemporal data model which is applicable for spatial and spatiotemporal objects with uncertainty. Based on this model, we defined topological relationships among the indeterminate spatiotemporal objects and designed the algorithm for the operations. For compatibility with existing spatial models, the proposed model has been designed by extending the spatiotemporal object model which is based on the open GIS specification. We defined indeterminate spatial objects, such as the objects whose position and the shape change discretely over time, and the objects whose shape changes continuously as well as the position. We defined topological relationships among these objects using the extended 9-IM. The proposed model can be efficiently applied to the management systems of natural resource data, westher information, geographic information. and so on.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.188-194
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• 2006

Three-dimensional topological data is essential for 3D modeling and application such as emergency management and 3D network analysis. This paper reviewed current 3D topological data model and developed a method to construct 3D topological node-relation data structure from 2D computer aided design (CAD) data. The method needed two steps with medial axis-transformation and topological node-relation algorithms. Using a medial-axis transformation algorithm, the first step is to extract skeleton from wall data that was drawn polygon or double line in a CAD data. The second step is to build a topological node-relation structure by converting rooms to nodes and the relations between rooms to links. So, links represent adjacency and connectivity between nodes (rooms). As a result, with the conversion method 3D topological data for micro-level sub-unit of each building can be easily constructed from CAD data that are commonly used to design a building as a blueprint.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.79-81
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• 2001

2차원 공간데이터를 시간차원으로 확장한 데이터모델은 최상위의 TGeometry 컴포넌트를 갖는다. TGeometry 컴포넌트는 시공간에 관한 연산뿐만 아니라 다양한 연산들을 제공한다. 2차원 시공간 데이터모델에 대한 시공간 연산자를 설계하기 위하며, 공간에 대한 방법으로서 차원으로 확장된 9IM 방법을 이용하였는데, 이것은 공간 객체를 내부, 경계, 그리고 외부로 구분하고, 이것들간의 관계를 차원으로 나타내는 방법이고, 또한 시간에 대한 방법으로서, OpenGIS에서 제안한 8개의 연산들을 시간에 적용했으며, 이들 두 가지 방법을 통합하여 시공간 객체에 대한 위상관계 tContains, tWithin, tOverlaps, tlntersects, tTouches, tEquals, tDisjoint, tCrosses 연산을 설계하였으며, 실제 예를 통해 적용하였다.