• Spatial Information Research
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• v.10 no.3
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• pp.455-468
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• 2002

Toward the systematic and efficient management of national land, National Geography Institute(NGI, National mapping agency) has been producing national basemap in automated process since middle of 1980's. Under the National Geographic Information System(NGIS) Development Plan, NGI began to produce digital maps in the scales of 1:1,000, 1:5,000, 1:25,000 since 1995. However, those of digital maps that have been generated under NGIS Development Plan need to be modified and corrected due to lack of technology and experience in making digital maps. In this context, those digital maps generated are currently in great need for improving the data dictionary. It is fully appreciated in previous research that data dictionary will be a key element far users and generators of digital maps to rectify the existing problems in digital maps as well as to maximize the application of digital maps. In this paper, we analyzed existing problems in digital maps based on previous researches and interviews with engineers in different fields of geospatial engineering. And then, the existing data dictionary has been redefined and modified. In the line of modification process, a relational matrix was established fur each topographic feature defined in the existing feature classification system. This paper presents newly proposed data dictionary which conforms to newly defined feature classification system from previous research performed by NGI.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.119-129
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• 2018

The Ministry of Land, Transport and Maritime Affairs has promulgated the mandatory design of BIM for road projects of more than 50 billion won by 2020 under the Basic Plan for the Sixth Construction Technology Promotion. As a result, major public clients are attempting to implement BIMs that are appropriate to the situation of each institution. On the other hand, it is difficult to design and construct a proper BIM and accumulate BIM information of the ordering organization because the technical guidelines and standard classification system that can perform BIM effectively have not been presented sufficiently. The characteristics of the road should be managed systematically, e.g., atypical objects, such as earthworks, which are constantly changing along a line; large objects, such as bridges and tunnels; and facilities, such as signs and soundproof walls. To achieve this, a multitude of standard systems should be developed and disseminated, but there have been insufficient studies on practical methods. To solve this problem, this study developed a BIM standard object classification system in the road sector to meet the international standard, accommodate a multi-dimensional information system, and provide a more effective BIM standard information environment that can be utilized easily by practitioners.

• Journal of Korean Society of Rural Planning
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• v.18 no.4
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• pp.13-26
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• 2012

The purpose of this study is to select the study area, which will be formed into Daegu Science Park as an national industrial complex, and to assess the landscape value based on biotop classification with different polygon forms, and to develop and computerize Biotop Value Assessment Tool (B-VAT) based on GIS. The result is as follows. First, according to the result of biotop classification based on an advanced analysis on preliminary data, a field study, and a literature review, total 13 biotop groups such as forrest biotop groups and total 63 biotop types were classified. Second, based on the advanced research on landscape value assessment model of biotop, we development biotop value assessment tool by using visual basic programming language on the ArcGIS. The first application result with B-VAT showed that the first grade was classified into 19 types including riverside forest(BE), the second grade 12 types including artificial plantation(ED), and the third class, the fourth grade, and the fifth grade 12 types, 2 types, and 18 types respectively. Also, according to the second evaluation result with above results, we divided a total number of 31 areas and 34 areas, which had special meaning for landscape conservation(1a, 1b) and which had meaning for landscape conservation(2a, 2b, 2c). As such, biotop type classification and an landscape value evaluation, both of which were suggested from the result of the study, will help to scientifically understand a landscape value for a target land before undertaking reckless development. And it will serve to provide important preliminary data aimed to overcome damaged landscape due to developed and to manage a landscape planning in the future. In particular, we expect that B-VAT based on GIS will help overcome the limitations of applicability for of current value evaluation models, which are based on complicated algorithms, and will be a great contribution to an increase in convenience and popularity. In addition, this will save time and improve the accuracy for hand-counting. However, this study limited to aesthetic-visual part in biotop assessment. Therefore, it is certain that in the future research comprehensive assessment should be conducted with conservation and recreation view.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.2
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• pp.77-84
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• 2001

To increase the utilization of soil survey results, classification of morphological types of paddy soils which was consisted of land-form, texture, and drainage classes etc. was attempted as an interpretive classification system. The paddy soils could be classified into 37 types. Among the types, the "Lfi(Fine loamy textured semi-wet paddy on local valley and fans)" acreage of about 224 thousand ha, "Lfd(Fine loamy textured dry paddy on local valley and fans)" 160 thousand ha. "Lmi(Coarse loamy textured semi-wet paddy on local valley and fans)" 112 thousand ha, and "Lkd(Loamy skeletal dry paddy on local valley and fans)" 93 thousand ha, respectively were the dominant types. The possibility of double cropping, plastic film house, green manure cropping etc., and that for soil managements such as application of raw straw or compost, deep plowing or adding fine earth materials, mole drainage, susceptibility to erosion or reduction injury etc. for each types were recommended.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.48-63
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• 2015

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.671-679
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• 2020

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.419-431
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• 2009

The core of this study is finding out the efficient band selection or extraction method discovering the optimal spectral bands when applying canonical correlation classifier (CCC) to hyperspectral data. The optimal efficient bands grounded on each separability decision technique are selected using Multispec$^{(C)}$ software developed by Purdue university of USA. Total 6 separability decision techniques are used, which are Divergence, Transformed Divergence, Bhattacharyya, Mean Bhattacharyya, Covariance Bhattacharyya, Noncovariance Bhattacharyya. For feature extraction, PCA transformation and MNF transformation are accomplished by ERDAS Imagine and ENVI software. For the comparison and assessment on the effect of feature selection and feature extraction, land cover classification is performed by CCC. The overall accuracy of CCC using the firstly selected 60 bands is 71.8%, the highest classification accuracy acquired by CCC is 79.0% as the case that executes CCC after appling Noncovariance Bhattacharyya. In conclusion, as a matter of fact, only Noncovariance Bhattacharyya separability decision method was valuable as feature selection algorithm for hyperspectral image classification depended on CCC. The lassification accuracy using other feature selection and extraction algorithms except Divergence rather declined in CCC.

• Journal of Korean Society of Forest Science
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• v.79 no.4
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• pp.388-397
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• 1990

This study was conducted to investigate the changes of land use and stand volume around Mt. Kuem-O by B/W aerial photographs in 1979 and B/W Infrared aerial photographs in 1988. The results obtained in this study were as follow : 1. In classification of forest type on aerial photographs, coniferous stand was dark tone and hardwood stand was light tone and irregularly rounded crowns. 2. In classification of coniferous stand, Pinus densiflora was narraw cone and rounded tip of crowns and rough texture, Pinus rigida was irregulary rounded and broadly conical crowns. 3. To refer to changes of forest land area, mixed forest was changed into P. desiflora (687ha), P. rigida (130ha) and hardwood stand (219ha). 4. The regression equations between crown diameter and DBH were significant at 1% level by F-test in all stands. So the equation, D=a+bCD was used to estimate DBH. 5. The tree height curve equations were significant at 1% level by F-test in all stands. To estimate tree height the equation, logH=loga+blogD was adopted in P. densiflora and L. leptolepis and $H=a-bD+cD^2$ was adopted in P. rigida, hardwood stand and mixed forest. 6. The highest volume per hectare was observed in L. leptolepis and mixed forest showed the greatest growth percentage, while the lowest volume per hectare and growth percentage were observed in hardwood stand.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.4 s.15
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• pp.35-42
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• 2004

Accurate classification of water area is a preliminary step to analyze the flooded area and damages caused by flood. This is essential process for monitoring the region where annually repeating flood is a problem. The accurate estimation of flooded area can ultimately be utilized as a primary source of information for the policy decision. In this paper, flooded areas was classified using 1:25,000 land use map and a RADARSAT image of Ok-Chun and Bo-Eun located in Chung-Book province taken in 12th of August, 1998. Then we analyzed the flood area based on GIS. A RADARSAT image was used to classify the flooded areas with slope theme generated from digital elevation model. In processing on a RADARSAT image, the geometric correction was performed by a backwardgeocoding method based on ephemeris data and one control point for near real time flood area analysis.