• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.59-65
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• 2012

In this paper, we proposed a method for automatic detection of a updating object from spatial data sets of different scale and updating cycle by using areal feature matching based on shape similarity. For this, we defined a updating object by analysing matching relationships between two different spatial data sets. Next, we firstly eliminated systematic errors in different scale by using affine transformation. Secondly, if any object is overlaid with several areal features of other data sets, we changed several areal features into a single areal feature. Finally, we detected the updating objects by applying areal feature matching based on shape similarity into the changed spatial data sets. After applying the proposed method into digital topographic map and a base map of Korean Address Information System in South Korea, we confirmed that F-measure is highly 0.958 in a statistical evaluation and that significant updating objects are detected from a visual evaluation.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.356-359
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• 2009

최근 센서와 모바일 기술의 발달에 따라 대용량 데이터 처리가 가능해지고, 유비쿼터스와 텔레매틱스 등의 도입으로 공간 데이터가 다양한 환경에 응용되거나 활용 분야가 점차 증가하고 있다. 특히 사용자에게 다양한 공간 데이터를 제공하는 수치지도의 활용성이 점차 증가하고 있다. 기존의 수치지도 관리 시스템은 이력에 대한 체계적인 관리방법과 공간 객체의 변화를 분석 또는 이력에 대한 질의 처리에 대한 구체적인 방안이 없는 실정이다. 따라서 이 논문에서는 효율적인 이력 관리를 위해 시공간 데이터 모델을 설계하고 그 모델을 기반으로 공간 객체의 이력 관리 기법을 제안한다. 제안된 모델을 통해 효율적인 이력 관리 및 시간에 대한 질의 처리가 가능하며, 사용자에게 정확한 이력 정보를 제공할 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.152-161
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• 2017

Making precise 3D maps using Mobile Mapping System (MMS) sensors are essential for the development of self-driving cars. This paper conducts research on the extraction of 3D objects around the roads using the point cloud acquired by the MMS Light Detection and Ranging (LiDAR) sensor through the following steps. First, the digital surface model (DSM) is generated using MMS LiDAR data, and then the slope map is generated from the DSM. Next, the 3D objects around the roads are identified using the slope information. Finally, 97% of the 3D objects around the roads are extracted using the morphological filtering technique. This research contributes a plan for the application of automated driving technology by extracting the 3D objects around the roads using spatial information data acquired by the MMS sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.1-9
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• 2013

This paper proposes a matching algorithm to find corresponding polygon feature sets between heterogeneous digital maps. The algorithm finds corresponding sets in terms of optimizing their shape similarities based on the assumption that the feature sets describing the same entities in the real world are represented in similar shapes. Then, by using a binary code, it is represented that a polygon feature is chosen for constituting a corresponding set or not. These codes are combined into a binary string as a candidate solution of the matching problem. Starting from initial candidate solutions, a genetic algorithm iteratively optimizes the candidate solutions until it meets a termination condition. Finally, it presents the solution with the highest similarity. The proposed method is applied for the topographical and cadastral maps of an urban region in Suwon, Korea to find corresponding polygon feature sets for block areas, and the results show its feasibility. The results were assessed with manual detection results, and showed overall accuracy of 0.946.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.831-837
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• 2009

This paper suggests the method for saving the digital map which was made through aerial photography to ORDBMS (Object Relational Database Management System) and analyze its efficiency through experiments. The digital map has been used by file units because of managing or providing it to others. But this way can not get sequential graphic entities and just use it which was included in only one map. In this paper, we saved the digital map to ORDBMS at a time after converted the digital map entities based on the tile to the things can be inserted to ORDBMS. And, we also proved the possible methods to extract the graphic entities what we need from entire blueprint through experiments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.157-165
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• 2021

Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.183-183
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• 2010

위치기반서비스(Location Based Service)는 갈수록 고도화 되어 가고 있다. 특히 최근의 대형 포털을 중심으로 지오웹 서비스가 활성화 되어 있고 이를 스마트폰과 같은 개인용 이용기기를 통해 연속적으로 제공하려는 경향이 뚜렷하다. 이와 같은 시점에서 정부와 민간에서 구축 중이거나 보유 중인 전국적 규모의 데이터 간 상호 연동과 융합을 도모하려는 시도 또한 불가결하다. 이는 고도화된 LBS를 위하여 반드시 필요한 과정이기 때문이다. 이에 따라 몇 가지 주요한 전국 데이터를 대상으로 상호 연동과 융합을 위한 기술개발을 시도하였다. 우선 도로명주소기본도와 수치지형도 간 POI의 연계를 위한 연구를 수행하고 있다. 이 연구에서는 두도면 내의 POI를 대상으로 다양한 매칭과 이에 기반 한 의사결정 방법론을 이용하여 자동으로 상호 인식 및 연계가 될 수 있도록 하고 있다. 다음으로 지적도와 수치지형도 간의 객체 매칭에 관한 연구이다. 수치지형도와 지적도의 불부합으로 인하여 그 동안 지적도를 수치지형도에 맞춘 형태의 편집지적도를 지속적으로 생산하여 왔고 앞으로도 그럴 것이다. 문제는 여기에 필요한 많은 예산이다. 만일 수치지형도와 지적도를 자동으로 매칭하여 편집지적도를 자동으로 생산할 수 있게 된다면 많은 예산 절감과 함께 편집지적도의 현시성을 확보할 수 있게 될 것이다. 다음으로 항공사진과 도로망도의 매칭이다. 현재 주요 포털에서 제공하고 있는 항공사진 기반의 도로망도는 기복변위와 같은 문제로 인하여 시각적으로 많은 위치오차를 보이고 있다. 만일 항공사진의 도로영역을 자동으로 추출하여 벡터 도로망도와 매칭을 할 수 있다면 보다 시각적으로 안정된 항공사진 상의 도로망도를 제공할 수 있게 되고 나아가 이는 차량이나 보행자 네비게이션에 매우 요긴하게 이용될 수 있을 것이다. 다음으로 서로 LOD가 다른 도로망도의 매칭 문제이다. 많은 기관에서 독자적으로 생산한 도로망도는 LOD의 상이에 기인한 문제가 많아 서로 연계 활용되지 않는다. 이를 자동으로 매칭하여 서로 연계할 수 있다면 두 도로망도가 보유하고 있는 속성정보를 공동으로 이용할 수 있는 이익을 얻게 된다. 다음으로 지도 일반화 기술이다. 지도일반화는 지적도내 수치지형도와 같은 대규모 데이터를 스마트폰과 같은 저용량 사양의 기기에 서비스 할 때 불가결한 기술이다. 지도상 객체들의 기하학적 정보 손실을 최소화하면서 메모리 측면에서 경량의 지도를 자동으로 만들어 낸다면 이는 매우 요긴하게 이용될 것이다. 마지막으로 보행자 네트워크의 생성기술이다. 보행자 네트워크는 그 상세함과 정보용량에 있어서 차량용 네트워크에 견줄 수 없다. 이를 현행의 차량용 네트워크와 같이 수동으로 생성하는 데에는 경제적으로나 시간적으로 막대한 투자가 필요하다. 따라서 이를 기존의 공간정보들을 활용하여 자동으로 생성해 낼 수 있다면 그 파급효과는 매우 크리라 판단된다. 본 발표에서는 위와 같은 주제에 관하여 그간의 연구 성과를 개략적으로 소개해본다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.249-255
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• 2007

The building of spatial database such as digital maps has been extensively achieved as a base of geographic information system. However, in the GIS system built by the organizations including local autonomous entities, there are some problems technically and systematically for a promptly updating of GIS DB. This research is to develop the object-oriented database updating system to efficiently update the spatial data at any time for a small area. It could be managed the history of spatial database such as creation, modification and deletion at a object level using the UFID of spatial data. This system could help to modify or update the changed geographical features in an office or in-situ promptly. Finally, it could be made the efficient updating manner of geographic data and the assurance of newest database.

• 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.