• Journal of the Korea Safety Management & Science
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• v.4 no.1
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• pp.69-80
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• 2002

최근 컴퓨터 기술의 발전과 함께 Mobile 컴퓨터 환경도 함께 급속히 발전하고 있다. 이로 인해 오프라인 컴퓨팅 및 네트워킹을 이용하던 사용자들이 Mobile 컴퓨팅 환경을 자주 이용하게 되고, Mobile 솔루션에 대한 사용자의 요구도 다양화되고 있는 추세이다. 이 중 Mobile 지도 서비스는 휴대하기 편한 Pocket PC라고도 불리는 PDA(Personal Digital Assistants)는 자신의 현재 위치를 확인할 수 있는 GPS(Global Positioning System)가 급속도로 보급됨으로써 Mobile 환경에서 가장 활용도가 높은 서비스로 부각되고 있다. 가까운 미래의 IT기술은 GPS와 PDA 등을 연계한 e포지션의 폭발적인 이용 증대와 국내 원천기술에 의한 세계적인 사업모델의 탄생이 눈앞에 현실로 다가오고 있다는 점을 감안하면, GPS를 활용한 Mobile 기술력 확보는 국가적 차원에서 대단히 중요하다는 견지를 같이하는 전문가들이 많다. 이러한 시대적 요구 환경 하에서, 본 논문에서는 JAVA VM 기반의 무선위치추적 제어미들웨어와 GIS 프로그램(Vector MAP Viewer)을 구축함으로써 APTS의 효율성에 관한 연구를 제안하고, 앞으로의 향후 개선방향을 제시하고자 한다.

• The Journal of Engineering Geology
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• v.26 no.1
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• pp.23-32
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• 2016

This study evaluates landslide susceptibility using statistical analysis by SVM (support vector machine) and the illite index of clay minerals extracted from ASTER(advanced spaceborne thermal emission and reflection radiometer) imagery which can be use to create mineralogical mapping. Landslide locations in the study area were identified from aerial photographs and field surveys. A GIS spatial database was compiled containing topographic maps (slope, aspect, curvature, distance to stream, and distance to road), maps of soil properties (thickness, material, topography, and drainage), maps of timber properties (diameter, age, and density), and an ASTER satellite imagery (illite index). The landslide susceptibility map was constructed through factor correlation using SVM to analyze the spatial database. Comparison of area under the curve values showed that using the illite index model provided landslide susceptibility maps that were 76.46% accurate, which compared favorably with 74.09% accuracy achieved without them.

• Spatial Information Research
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• v.3 no.1
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• pp.47-53
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• 1995

The weed survey in lowland rice fields through Korea was conducted in 1992 to determine a change of the weed communities based on different regions, soil types, planting methods, and cultural practices. GIS was applied to identify a spatial analysis of predominant weed species in specific region. On behalf of vegetatine analysis such as absolute and relative density, absolute and relative frequency, importance value, and summed dominance ratio(SDR), there was highly dominant with a perennial weed species, Eleocharis kuroguwai Ohwi over whole country. However, in particular it was most predominant at southem area of Gyunggi province in Korea. Thus, rice farmers of this area have to introduce a specific comperhensive control strategy against this predominant weed species.

• Spatial Information Research
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• v.3 no.2
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• pp.179-189
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• 1995

To make vector data from a map which has no negative plates by using vectoring tool, it is necessary that we can extract objects to be vectorized from a scanned map. In this paper, we studied on extracting vectoring objects from scanned color maps. To do this, we classified vectoring objects into three types : line type, filled - area type and character/symbol type. To make the extraction method effective, we analyzed characteristics of vectoring objects and color distribution in scanned color maps. Then, we applied these characteristics to designing process of the extraction method. To extract the line type object, our line tracing method was designed by using the masks which considered connectivity and geometrical characteristics of lines. By using the local thresholding method and the similarity function for comparing the color distribution between two NxN blocks, we extracted character/symbol and the filled-area objects effectively. The method proposed in this paper can be used for constructing the small scale GIS application economically using existing color maps.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.379-387
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• 2006

The real world is spatially modelled conceptually either as discrete objects or earth surface. The generated data models are then usually represented as vector and raster respectively. Although there are limited cases where only one data model is sufficient to solve the spatial problem at hand, it is now generally accepted that GIS should be able to handle various types of data model. Recent advances in spatial technology introduced even more variety of heterogeneous data models and the need is ever growing to handle and manage efficiently these large variety of spatial data. The OGC (Open GIS Consortium), an international organization pursuing standardization in the geospatial industry. recently introduced the GMLJP2 (Geographic Mark-Up Language JP2) format which enables store and handle heterogeneous spatial data. The GMLJP2 format, which is based on the JP2 format which is an abbreviation for JPEG2000 wavelet image compression format, takes advantage of the versatility of the GML capabilities to add extra data on top of the compressed image. This study takes a close look into the GMLJP2 format to analyse and exploit its potential to handle and mange hetergeneous spatial data. Aerial image, digital map and LIDAR data were successfully transformed end archived into a single GMLJP2 file. A simple viewing program was made to view the heterogeneous spatial data from this single file.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.283-287
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• 2007

기존 국토환경성펴가지도는 1:25,000 축적을 기본으로 하고 있어 전국이나 광역 차원의 환경성 평가나 개발 가능지 분석, 거시적인 지역의 확인 및 중첩분석시 용이하나 지역 차원의 개발계획 수립부문에서의 활용도를 높일 필요성이 제기 되었다. 본 연구의 목적은 연구지역을 선정하여 기 구축된 국토환경성평가지도의 동일 방법론 및 주제도를 활용하여 1:5,000 축적의 국토환경성평가지도를 재구축고, Vector형태 및 필지단위론 재평가를 실시하여, 1:25,000 축적의 국토환경성평가지도와의 평과 결과를 비교${\cdot}$분석하는데 있다. 연구결과, 기 구축된 연구지역의 1:25,000축적 국토환경성평가지도 등급별 면적 비율은 1등급 23.3등급 29.4%, 3등급 23.9% 4등급 11.7%, 5등급 11.8%를 보이고 있으며, 신규 구축된 연구지역의 1:5,000 축적 국토환경성평가지도 등급별 면적 비율은 1등급 29.3%, 2등급 21.7%, 3등급 17.2%, 4등급 7.1%, 5등급 24.7%이다.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.