• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1930-1936
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• 2007

Electronic Navigational Charts(ENCs) are official nautical charts which are equivalent to paper charts with supplementary information. Although their main purpose is to be used for the safe navigation of ships, they also contain much information on coasts and seas which may be interesting to ordinary people. However, there is no easy way to access them because of their specialized data format, access method and visualization. This paper proposes m implementation of SVG for the access and services of ENCs. SVG(Scalable Vector Graphic) makes it possible to make use of Vector graphics for map services in basic internet browsing environment. Implementation of SVG for ENC applications by this research is free of special server side GIS mapping system and client side extra technology. The Implementation of SVG for ENC Applications can be summarized as follows: Firstly, SVG provides spatial information to possess searching engine to embody SVG map. Secondly SVG can provide high-quality vector map graphics and interactive facility without special Internet GIS system. It makes it possible to use services with very low cost. Thirdly, SVG information service targeting on maritime transportation can be used as template, so it can be used dynamically any other purpose such as traffic management and vessel monitoring. Many good characteristics of SVG in mapping at computer screen and reusability of SVG document provide new era of visualization of marine geographic information.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.30-36
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• 2008

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. There it is needed to apply laser measurement technique in the spatial target object to obtain accuracy. Currently, the LiDAR data which combines the laser measurement skill and GPS has been introduced to obtain high resolution accuracy in the altitude measurement. In this paper, we tested of the railway facilities using laser surveying system, then we propose data a generation of spatial images for the optimal manage and synthesis of railway facility system in our 3-D spatial terrain information. For this object, LiDAR based height data transformed to DEM, and the realtime unification of the vector via digital image mapping and raster via exactness evaluation is transformed to make it possible to trace the model of generated 3-dimensional railway model with long distance for 3D tract model generation. As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents.

• Journal of the Korean Regional Science Association
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• v.37 no.3
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• pp.19-36
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• 2021

This study examined the determinants of change in residential size according to the patterns of residential mobility in the Seoul Metropolitan Area. Particularly, this study examined the upward and downward in residential size, which is emerging as an important factor in the era of increasing non-face-to-face environment. For the empirical analysis, I used 「2018 Korea Housing Survey」 and employed binary logistic regression model. The empirical analysis shows the change of residential size is statistically significant depending on the direction of geographic. In addition, there are differences in the determinants of change in residential size. When people move within Seoul, housing factors, accessibility, age of residents, and the number of household members can be the determinants. When people move from Seoul to Gyeonggi or Incheon, housing factors, safety, gender, and the number of household members work as determinants. On the other hand, when moving from Gyeonggi or Incheon to Seoul, whether it is studio or not, housing type, accessibility, the number of household members, and the disability of homeownership are the determinants. When moving within Gyeonggi or Incheon, housing factors, Accessibility to green areas, safety, age of resident, income, and the number of household members, are the determinants.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.975-987
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• 2021

Potential habitat mapping of Meretrix lyrata which is found in large parts of South East Asian tidal flat was carried out to find out causes of collective death. Frequency Ratio (FR) method, one of geospatialstatistical method, was employed with some benthic environmental factors; Digital elevation model (DEM) made from Landsat imagery, slope, tidal channel distance, tidal channel density, sedimentary facesfrom WorldView-02 image. Field survey was carried out to measure elevation of each station and to collect surface sediment and benthos samples. Potential habitat maps of the all clams and the juvenile clams were made and accuracy of each map showed a good performance, 76.82 % and 69.51 %. Both adult and juvenile clams prefer sand dominant tidal flat. But suitable elevation of adult clams is ranged from -0.2 to 0.2 m, and that of juvenile clams is ranged from 0 to 0.3 m. Tidal channel didn't affect the habitat of juvenile clams, but it affected the adult clams. In the furtherstudy, comparison with case of Korean tidal flat will be carried out to improve a performance of the potential habitat map. Change in the benthic echo-system caused by climate change will be predictable through potential habitat mapping of macro benthos.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Korean journal of applied entomology
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• v.60 no.1
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• pp.135-143
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• 2021

Aerospace and geospatial technologies have become more accessible by researchers and agricultural practitioners, and these technologies can play a pivotal role in transforming current pest management practices in agriculture and forestry. During the past 20 years, technologies including satellites, manned and unmanned aircraft, spectral sensors, information systems, and autonomous field equipment, have been used to detect pests and apply control measures site-specifically. Despite the availability of aerospace and geospatial technologies, along with big-data-driven artificial intelligence, applications of such technologies to apple IPM have not been realized yet. Using a case study conducted at the Korea Apple Research Institute, this article discusses the advances and limitations of current aerospace and geospatial technologies that can be used for improving apple IPM.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.459-466
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• 2021

The present study was conducted to propose future research directions for weed management by examining the current trends of research on weed occurrence according to cropping systems. The cropping systems are developed for the efficient use of arable land, and the weed flora changes according to the management practices of a given cropping system. In particular, weed occurrence can be reduced by altering the soil environment. In addition, cultivation methods, such as tillage, affect the weed seed bank in the soil, thus altering the pattern of weed occurrence. Here, we propose three weed management practices according to the cropping system. First, it is necessary to develop a model that can classify weed species by analyzing young seedlings and can predict the flora in the field. Second, it is important to manage the cropping system history and establish a database of agricultural information, which can be linked to meteorological and geographic data. Third, it is critical to estimate the weed occurrence and soil seed bank dynamics, based on which a cropping system platform and digitalization technology can be developed. In the future, the prediction of weed occurrence and control according to the cropping system will contribute to sustainable agriculture by reducing the use of herbicides and solving the problems of resistant weeds.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.146-168
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• 2022

Witnessing current military conflicts in South China Sea and Eastern Europe, most defense analysts evaluate one of the most serious security threat toward the US is coming from the superpower competitions with Russia and China. The main means for such super power hegemonic competitions is military power and space power is a key enabler to maximize the efficiency and effectiveness of military employment. Reflecting above circumstances, the space hegemonic competition between the Unites States and China is spreading into all aspects of national powers. Under such an environment, R.O.K needs to significantly develop national space power to preserve life and assets of people in space. On the other hand, the R.O.K has a lot of limitations in launching space assets into orbits by land-based space rockets due to its geographic locations. The limitation of rocket launching direction, the failure to secure a significant area enough to secure safety and the limitation to secure open area enough to build associated facilities are among them. On this paper, I will suggest the need to build the 2nd space rocket launching site after analyzing a lot of short-falls the current 'Naro' space center face, compared to those of advanced space powers around the world.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• Clinical and Experimental Pediatrics
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• v.64 no.12
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• pp.632-641
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• 2021

Background: Over the past few decades, various goals have been defined to reduce the mortality of children caused by acute lower respiratory infections (ALRIs) worldwide. However, few spatial studies to date have reported on ALRI deaths. Purpose: We aimed to assess the spatial modeling of mortality from ALRI in children under 5 years of age during 2000-2017 using a global data. Methods: The data on the mortality of children under 5 years old caused by ALRI were initially obtained from the official website of the World Health Organization. The income status of their home countries was also gathered from the Country Income Groups (World Bank Classification) website and divided into 5 categories. After that, in the ArcGIS 10.6 environment, a database was created and the statistical tests and related maps were extracted. The Global Moran's I statistic, Getis-Ord Gi statistic, and geographically weighted regression were used for the analyses. In this study, higher z scores indicated the hot spots, while lower z scores indicated the cold spots. Results: In 2000-2017, child mortality showed a downward trend from 17.6 per 100,000 children to 8.1 and had a clustered pattern. Hot spots were concentrated in Asia in 2000 but shifted toward African countries by 2017. A cold spot that formed in Europe in 2007 showed an ascending trend by 2017. Based on the results of geographically weighted regression test, the regions identified as the hot spots of mortality from ALRI in children under 5 years old were among the middle-income countries (R²=0.01, adjusted R²=8.77). Conclusion: While the total number of child deaths in 2000-2017 has decreased, the number of hot spots has increased among countries. This study also concluded that, during the study period, Central and Western Africa countries became the main new hot spots of deaths from ALRI.