• Journal of the Korean association of regional geographers
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• v.21 no.2
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• pp.342-354
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• 2015

We suggested definition and classification scheme of uninhabited islands in Taeanhaean National Park for efficient management. Islands (seom) and reefs (yeo) were classified based on approximate HHW. Uninhabited islands were categorized into vegetated islands and rocky islands depending on presence of woody vegetation cover for physical geographic and ecological value and importance. Reefs were also divided into an underwater reef and a reef which covers and uncovers based on approximate LLW. We excluded those areas which are not separated from main land by waterbody even though it is in approx. HHW from islands. We considered several divided areas which adjoin geographically and ecologically one another under the condition of approx. LLW as an island. By using above schemes, we categorized 50 uninhabited islands in Taeanhaean National Park into three groups; 24 vegetated islands, 14 rocky islands, and 12 reefs. If the public institutions adopt these schemes as national standards, it will be useful in managing uninhabited islands nationwide.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.147-153
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• 2017

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It's five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• Land and Housing Review
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• v.11 no.4
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• pp.51-65
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• 2020

The purpose of this study was to identify the problems related to the amendment and preservation of the lien system and to suggest a feasible and efficient improvement plan. The Analytic Hierarchy Process (AHP) and descriptive statistical analyses were used in this study. The survey subjects were divided into three groups based on their expertise: "Administrative Experts, Practical Experts, and Financial Institution Experts" and compared to find the results. The results show that 1) the procedural aspect was the most important for the large classification, 2) the supplementary disclosure system was the most important for the legislative aspect, and 3) the supplementation of the abuse of the lien report was the most important for the intermediate classification. Furthermore, the study showed that the most important finding was the reinforcement of punishment for the right of false reporting and illegal acts, followed by the registration order system (the creditor alone application), and the registration system (bond, debtor, joint application). The implications and suggestions of this study are as followed. With regard to the current lien system, illegal acts such as false liens and the misuse of multiple reporting were considered as the biggest problems. In terms of the effective improvement plan of the lien system, the misuse of reporting liens and improvement in the procedural aspects of reporting rights should be given top priority. Therefore, the most important course of action is to strengthen the punishment for false liens, improve the disclosure system and make it mandatory to report rights.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.5
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• pp.31-41
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• 2010

This research aims to present a methodological approach for repurposing small pockets of national/public lands, which can be constructed as parks, through an investigation of the present status of these areas of national/public lands that are scattered around Busan Megalopolis as well as the suitability of their construction. In order to attain this, this study looked at the present status of these small areas of national/public lands by utilizing a national land, city land list (lot number), land registration map and satellite image of Busan Megalopolis, and evaluating their suitability as parks through GIS analysis and classification. As a result, these small areas of lands with the potential to be turned into parks include 516 spots($375,934m^2$). Geographically, 39% of these areas are located on flat land and are the most scattered. 260 places met the requirements for optimal placement for conversion, while convenience included 305 places, and availability 267 places. The most optimal of the places meeting such standards include 61 spots. The characteristics of these areas of national/public lands include being below $500m^2$, with flatlands and open areas above a 5' grade occupy the highest ratio, accounting for 25.4% of the land studied. These results have offered a methodology for a GIS DB, which can visualize the data for a positive utilization be yond the simple level of the maintenance/preservation of national/public lands and provide basic data for the utilization and management of these types of areas in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.250-257
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• 2020

It is difficult for farmers to select new crops for cultivation to increase income. So we conducted land suitability assessment of grapes with soil and climate information related to crop growth. At first, land suitabilities for grapes were classified into three categories (most suitable, suitable, low productive & not suitable areas) according to soil and climate conditions, respectively. In details, land suitability with respect to soil was assessed by soil morphological and physical properties including soil texture, drainage class, available soil depth, slope and gravel content, whereas one in accordance with climate was evaluated by average annual temperature, temperature during the growing season, temperature during maturation, the lowest temperature, chilling requirement and precipitation during the growing season. Secondly, we combined both soil and climate classification results using a most-limiting characteristic method. Maps showing the suitable land for grapes cultivation were drawn. The results indicate that the most suitable area of cultivation for grapes in south Korea was 3.43% and suitable (possible) area was 10.61%. This study may help to preserve land and increase the productivity through providing valuable information regarding where more suitable areas for grapes are located.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.221-229
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• 2011

Road design needs to ensure the economic justification and the preservation of nature by adapting road alignment to the natural terrain. Though current road design guideline only defines a flat and a mountainous terrain, classification including rolling terrain should be needed while considering the fact that about 25.8% of our land can be classified as rolling and the road design guideline of developed countries such as United States and Australia has a terrain classification including rolling in order to take a deep consideration on the natural environment. The study attempts to draw a criterion to classify the assumed three individual terrains in a quantitative way by using a index like the undulation of the original ground profile. The study carried out a case study based on a conceptual frame developed in the study as an approach to differentiate each terrain. As a result, the study suggests a criterion in that a flat terrain has less than 40 meters in the difference between the highest and the lowest point of original ground from 40 to 60 meters for rolling terrain, and greater than 60 meters for mountainous respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.73-81
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• 2010

In recent years, there have been diverse researches and utilizations of creating geo-spatial information with high resolution satellite images. However thematic maps made with middle or low resolution satellite images have low location accuracy and precision of thematic information. This study set out to propose a method of making a precision thematic map with high resolution satellite images by examining the conversion from the conventional method based on middle or low resolution satellite images to the automatic method based on high resolution satellite images of GSD 1m or lower, extracting thematic information of middle or large scale of 1/5,000 or lower, and analyzing its accuracy. Seven classification classes were categorized according to the object-oriented classification in order to automatically extract thematic information with high resolution satellite images. And the classification results were compared and analyzed with the old middle scale land cover map and 1/1000 digital map.

• Korean Journal of Environment and Ecology
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• v.18 no.2
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• pp.231-235
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• 2004

Landsat satellite images were analyzed to study vegetation change patterns of warm-temperate forests from 1991 to 2002 in Wando. For this purpose, Landsat TM satellite image of 1991 and Landsat ETM image of 2002 were used for vegetation classification using ENVI image processing software. Four different forest types were set as a classification criteria; evergreen broadleaf, evergreen conifer, deciduous broadleaf, and others. Unsupervised classification method was applied to classily forest types. Although it was impossible to draw exact forest types in rocky areas because of differences in data detection time and rough resolution of image, 2002 data revealed that total 2,027ha of evergreen broadleaf forests were growing in Wando. Evergreen broadleaves and evergreen conifers increased in total areas compared to 11 years ago, but there was sharp decrease in deciduous broadleaves. GIS-based management system for warm-temperate forest was done using Arc/Info. Geographic and attribute database of Wando such as vegetation, soils, topography, land owners were built with Arc/Info and ArcView. Graphic user interface which manages and queries necessary data was developed using Avenue.