• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.331-340
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• 2005

Digital map is very important digital geographic information which is the base for various fields such as building and using the geographic information system (GIS), planing the regional development, and etc. Therefore, it needs high accuracy. Then we offer the advanced technique which minimizes errors on digital maps, using the automated inspection through the whole figures. In addition this new technique raises the economical efficiency as well as accuracy applying the automated error detection method which can recognize, search and classify errors automatically.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.158-160
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• 1998

지도 제작에 있어서, 기존의 구축된 대축척의 원천 데이터로부터 소축척의 목적 데이터를 추출해 냄으로 데이터 구축을 중복되지 않고 효율적으로 할 수 있게 하는 것을 지도 일반화라고 한다. 초기의 선을 단순화하는 알고리즘 개발과 향상에 대한 연구로부터, 최근에는 자동화를 위한 지식 기반 일반화 및 데이터 품질에 대한 많은 관심과 연구가 진행되고 있다. 최근에 지리 정보 시스템의 발전으로 다양한 공간 분석이 필요하고, 그 성능 향상을 위하여 위상 정보를 구축하게 된다. 그러므로, 본 논문에서는 위상 정보를 가진 원천 데이터 베이스에서, 일반화 연산자가 적용됨으로 발생하게 되는 위상 데이터의 손실과 불일치를 해결하기 위하여 일반화 연산자들이 위상 정보에 미치는 영향과 이를 해결하기 위한 규칙들을 제시한다. 그리고, 지도 일반화 과정에서 위상 정보의 일관성을 유지한 목적 데이터 베이스를 구축하는 시스템을 구현하는 것이 본 논문의 목적이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.691-702
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• 2019

Flood inundation map has various important roles in terms of municipal planning, timely dam operation, economic levee design, and building flood forecasting systems. Considering that the riparian areas adjacent to national rivers with high potential flood vulnerability conventionally imposed special cares to justify applications of recently available two- or three-dimensional flood inundation numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. On the contrary, local streams usually could not have benefits from recent technological advances, instead they inevitably have relied upon time-consuming manual drawings or have accepted DEMs with poor resolutions or inaccurate 1D numerical models for producing inundation maps due mainly to limited budgets and suitable techniques. In order to efficiently and cost-effectively provide a series of flood inundation maps dedicatedly for the local streams, this study proposed an OpenGIS-based flood mapping tool named Open Flood Mapper (OFM). The spatial accuracy of flood inundation map derived from the OFM was validated throughout comparison with an inundation trace map acquired after typhoon Nari in Hancheon basin located in Jeju Island. Also, a series of inundation maps from the OFM were comprehensively investigated to track the burst of flood in the extreme flood events.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.313-322
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• 2005

Rapid advances in technology and changes in human and cultural activities bring about changes to the earth surface in terms of spatial extension as well as time frame of the changes. Such advances introduce shorter updating frequency of maps and geospatial database. To satisfy these requirements, recent research efforts in the geoinformatics field have been focused on the automation and speeding up of the mapping processes which resulted in products such as the digital photogrammetric workstation, GPSIINS, applications of satellite imagery, automatic feature extraction and the LiDAR system. The possibility of automatically extracting buildings and generating contours from airborne LiDAR data has received much attention because LiDAR data produce promising results. However, compared with the manually derived building footprints using traditional photogrammetric process, more investigation and analysis need to be carried out in terms of accuracy and efficiency. On the other hand, generation of the contours with LiDAR data is more efficient and economical in terms of the quality and accuracy. In this study, the effects of various conditions of the pre-processing phase and the subsequent building extraction and contour generation phases for digital mapping have on the accuracy were investigated.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.486-490
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• 2017

도심지에서의 침수피해는 이상홍수 및 국지성 호우 시 우수관거 시설기준 미달, 펌프장 등 배수시설이 설치되지 않아 하천의 계획홍수위보다 제내지의 지반고가 낮은 저지대 지역에서 많이 발생하고 있다. 특히, 내수침수의 경우는 외수에 따른 범람보다는 국민의 재산과 인명피해에 직접적인 영향을 미치므로 침수피해 위험도가 높은 지역의 주민에게 그 지역의 침수빈도와 범위를 인지시키고 사전대응 능력을 향상시킬 필요가 있다. 따라서 연구의 목적으로 매년 피해가 발생한 이력이 있는 위험지구에 대해 전국단위 시군구별 침수피해 지도를 작성하여 침수심 산정과 피해액 예측할 수 있는 기초자료로 활용하고, 주민들의 신속한 대처를 통해 그들의 생명과 재산을 보호하여 재난 안전 국가 이미지 제고에 기여하고자 한다. 본 연구에서는 도심지 유출모형인 XP-SWMM을 활용하여 내수재해 위험요인에 대한 전국을 해석하는 것에 한계가 있어 풍수해저감종합계획에 수록된 XP-SWMM모의 분석 결과 값을 활용하고자 하였다. 기 수립된 전국 풍수해저감종합계획의 과거 피해 자료를 바탕으로 이상 집중호우나 태풍의 내습 시 풍수해 피해 발생 가능성이 제일 높은 지역을 연구범위 대상지역으로 선정하였다. 그 중 풍수해의 주요 원인으로서 태풍, 집중호우 및 해일로 인한 피해발생 빈도가 높은 지역이면서 하천재해 및 내수침수 피해가 많은 경기도 동두천시를 연구대상 지역으로 선정하였으며, 대상지 유역 현황과 지형정보 및 빈도별 침수심을 조사하였다. 수록된 내용에 따르면 경기도 동두천시는 우수관망의 밀도가 높은 4개 위험지구를 내수재해 발생가능성 지역으로 선정하여 10년, 20년, 30년, 50년, 100년, 200년 6개 빈도에 대해 XP-SWMM 모의를 실시하였다. 이와 같이 수록된 각 빈도에 대한 모의 결과 값을 GIS기술을 이용하여 디지털화 하고 부가적인 분석을 위한 GIS데이터화 하는 내삽법을 선정하여 침수면적 및 침수심을 산출하였다. 그러나 면적비교를 통해 모의 결과 값을 디지털화 하는 과정에서 많은 오차가 발생되는 것을 확인하였고, 이를 보완하기 위해 좌표보정 자동화 프로그램을 개발하여 이러한 문제점을 제거하여 신뢰도를 향상시켰다. 이렇게 계산된 연구 대상지역의 침수심과 침수면적을 활용하여 지도제작 표준 지침서 및 가이드라인을 제시하여 한국형 호우피해 지도제작 기술개발에 기여하고, 비구조적 대책으로서 이상홍수에 대한 위험도를 파악하여 지역별 도심침수 방지를 위한 대비체계를 구축하는 등 위험지역에 대한 사전분석 및 활용에 기초자료로 도움이 되고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1372-1375
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• 2021

최근 산업 현장에서 많은 안전사고가 일어나고 있고, 현장 노동력의 부족으로 무인 로봇 시스템들을 도입하는 등 다양한 변화를 맞이하고 있다. 이에 차세대 자동화 시스템은 보다 유연하고 지능적이어야 한다. AGV(Automatic Guided Vehicle)의 경우 실시간으로 변하는 현장에 대응하기 어렵고, 새로운 어플리케이션에 대한 제품개발의 어려움이 따른다. 이에 대한 대안으로 AGV 인식 스택을 재구축하여 인간과 동일한 공간인식 능력을 갖춘 AMR(Autonomous Mobile Robot)이 대두되고 있다. 본 연구에서는 SLAM과 ROS를 이용하여 AMR의 기능을 구축하였다. YD Lidar 센서와 SLAM을 이용하여 주변 환경을 지도화 하여 로봇의 현재 위치를 파악할 수 있도록 제작하였고, 직접 지도상의 최적 경로를 파악하여 주변 장애물을 회피하며 주행할 수 있음을 확인하였다. DC 모터의 응답 특성에 따라 주행 속도, 조향각 등을 제어할 수 있도록 구현하였다.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.637-645
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• 2019

Due to the transformation to the intelligent information society, the rapid change of our life and environment is expected. The Ministry of Science and ICT (MSIT) and the National Research Council of Science and Technology (NST) introduced a five-year government supported research institution's planning and evaluation based on the mid-to long-term perspective. This study collects international benchmarking information including industry, academia, and research fields by collecting mid- and long-term strategy reports from public research institutes, surveys by experts from abroad universities and research institutes, and analyzing overseas market information reports. The British Geological Survey (BGS), the U.S. Geological Survey (USGS) and the japanese geological survey related institutes (AIST-GSJ) plans for three-dimensional national geological information, predictions of geological environmental disasters, and development of important metals and material in the low carbon economic transformation and in the era of the Fourth Industrial Revolution. The mid- and long-term program emphasizes basic and public research on geological information through abroad experts survey such as the IPGP-CNRS etc. The market analysis of the mining automation and digital map sectors has been able to derive the fields in which the role of public research institutes by the market is expected such as data collection on land and in the air, mobile or three-dimensional information production, smooth/fast/real-time maps, custom map design, mapping support to various platforms, geological environmental risk assessment and disaster management information and maps.