• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.2
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• pp.15-30
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• 2014

Systematic management of wetlands should be a priority to build the data for the extent and distribution of wetlands all over the country. However there are no clear guidelines for the wetland boundary delineation, so researchers have to determine the boundary of wetlands in each different way. As a result, it is very difficult to identify the extent and distribution of wetlands. This study proposes applicable criteria of setting boundary of wetlands which consider their wetland vegetation and geographical characteristics, according to wetland classification. The proposed site in this study is selected wetlands that represent each wetland type and have been ecologically well preserved like the wetland protected areas. GIS data for setting the boundary of wetlands selected were land-cover maps, aerial photographs, high resolution satellite images, and digital topographic maps. In this study, 'wetland unit determination' of the Washington State Wetlands Rating System(WSDE, 1993) and the concept of 'Wetland and Deep-water Habitats' was suggested by Wetland Delineation Manual(USACE, 1987) were used as criteria for setting the boundary of wetlands. As a result, it was found that the boundary of wetlands could be, in general, set consistently. Also, it seemed possible to set systematic and standardized boundary of wetlands and to provide more objective data for establishing national wetland policies, if maps of wetlands are made and an investigation of wetlands is implemented according to the criteria.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.2
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• pp.59-67
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• 1993

Since society rapidly change, we need accurate and rapid information. Due to complication and rapid change of national infrastructure system, we meet a limitation of 2-D information management. Currently most digital cartographic data is acquired by manual digitizing with a tablet. Recently high cost scanner is widely used and preprocessing and postprocessing software of scanning are developed, so we expect its availability. In this study, we know that scanning is more convenient than digitizing with a tablet for digital mapping, also, possibility of 3-D modeling of vectorized document is suggested. Because information rapidly provided in the planning and implementation, operation efficiency and advance are archived in water supply project. Improvement of service for need of citizen and possibility combined information system connected with other system is presented.

• Journal of Korean Library and Information Science Society
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• v.30 no.1
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• pp.219-241
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• 1999

For an efficient access to Internet resources, these resources need to be organized systematically for accessibility. and their cataloging technique has been developing. Consequently, after I compared and analyzed OCLC manual applying AACR2 techniques and procedures, with Dublin core metadata used for cataloging Internet resources, I examined their mapping into MARC format. Dublin core metadata elements should be transformed to MARC format fields. In methodology of integration into different types of the metadata, an update of MARC format and its new field should be provided. Especially, Internet resources cataloging experts familiar with these technology will be really wanted nowadays.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Korean Journal of Computational Design and Engineering
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• v.20 no.4
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• pp.431-442
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• 2015

Currently, a BIM-based quantity takeoff (QTO) system is mainly focused on architectural projects. To perform this, diverse quantity takeoff methods such as an object-based automatic quantity takeoff, manual quantity and base functions of calculation have widely been utilizing. However, since BIM library for road projects includes structural elements associated with alignment, it is necessary to establish cost estimation system interlocked with historical cost using 3D library by each unit length. Accordingly, the aim of this study is to develop cost estimation model with using a historical cost approach so that it can be utilized in construction planning based on the BIM library for road projects. For this, based on the BIM library for road, the standardized quantity is estimated, and a process for calculating historical cost and a verification model with a 5D simulation was developed by mapping a WBS code with each BIM library object. This can be applied during the approximate cost estimation process in a project planning and an initial design phase for road projects. Besides, it is expected that these results will be utilized in constructing an optimal historical cost estimation process for project libraries.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.189-197
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• 2012

This research attempted to find possibilities of the practical use utilizing geospatial methods for the balanced promotion of sustainable coastal development and preservation through a case study of Jekyll Island, one of Georgia's barrier islands. In response, this research provided ways for practical use in sustainable development and preservation plans. First this research thoroughly investigated the 1996 master plan of Jekyll Island and tried to recalculate developed and undeveloped areas. Second, new estimations for developed areas were investigated through field survey. Third, this research proposed the use of the satellite images with different levels of spatial resolutions and tested different classification schemes to find possibilities for practical use. For these purposes, first, we classified developed and undeveloped features by manual digitization using an aerial photo image with 0.5m spatial resolution. Second, a Landsat 7 ETM+ and a QuickBird satellite images with mid- and high-levels of spatial resolutions were applied to identify developed and undeveloped areas using both the National Land Cover Data (NLCD) and the Coastal Change Analysis Program (CCAP) classification schemes. Also, GEOBIA (Geographic Object-Based Image Analysis) was conducted to accurately identify developed and undeveloped areas.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.61-66
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• 2003

The change detection using the satellite imagery and airphotos has been carried out in the application of terrain mapping, environment, forestry, facility detection, etc. The low-spatial resolution data such as Landsat, NOAA satellite images is generally used for automatic change detection, while on the other hand the high-spatial resolution data is used for change detection by image interpretation. The research to integrate automatic method with manual change detection through the high-spatial resolution satellite image is performed. but the problem such as shadow, building 'lean' due to perspective geometry and precision geocorrection was found. In this paper we performed change detection using the IKONOS satellite images, and present the concerning problem.

• Computers and Concrete
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• v.10 no.4
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• pp.331-347
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• 2012

This paper describes the varying material model, the analysis method and the software development for reinforced concrete circular columns confined by spiral or hoop transverse steel reinforcement and subjected to eccentric loading. The widely used Mander model of concentric loading is adapted here to eccentric loading by developing an auto-adjustable stress-strain curve based on the eccentricity of the axial load or the size of the compression zone to generate more accurate interaction diagrams. The prediction of the ultimate unconfined capacity is straight forward. On the other hand, the prediction of the actual ultimate capacity of confined concrete columns requires specialized nonlinear analysis. This nonlinear procedure is programmed using C-Sharp to build efficient software that can be used for design, analysis, extreme event evaluation and forensic engineering. The software is equipped with an elegant graphics interface that assimilates input data, detail drawings, capacity diagrams and demand point mapping in a single sheet. Options for preliminary design, section and reinforcement selection are seamlessly integrated as well. Improvements to KDOT Bridge Design Manual using this software with reference to AASHTO LRFD are made.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.25 no.3
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• pp.316-321
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• 2023

Developmental venous anomalies (DVAs) are composed of mature venous vessels that lack malformed or neoplastic elements. Although the hemorrhage risk is considered negligible, some patients may have neurological symptoms attributable to acute infarction or intracranial hemorrhage secondary to thrombosis, in the absence of a coexisting cavernous malformation. We report the case of a 42-year-old patient who presented with acute left-hand paresis secondary to a subcortical hemorrhage. This bleeding originated from a DVA in the corticospinal tract area and was surgically drained through an awake craniotomy. To accomplish this, we used a trans-precentral sulcus approach. After the complete removal of the coagulum, small venous channels appeared, which were coagulated. No associated cavernoma was found. Although the main DVA trunk was left patent, no signs of ischemia or venous infarction were observed after coagulating the small venous channels found inside the hematoma cavity. Two weeks after the procedure, the patient's hand function improved, and he was able to resume desktop work. DVA-associated hemorrhage within the cortico-spinal tract could be safely removed with modern awake mapping techniques. This technique allowed the patient to rapidly improve his hand function.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.