• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.73-79
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• 2012

This study attempts to analyze changes in land use patterns in a compound urban and agricultural city Kimje-si, using LANDSAT TM imagery and to forecast future changes accordingly. As a new approach to supervised classification, HSB(Hue, Saturation, Brightness)-transformed images were used to select training zones, and in doing so classification accuracy increased by more than 5 percent. Land use changes were forecasted by using a cellular automaton algorithm developed by applying Markov Chain techniques, and by taking into account classification results and GIS data, such as population of the pertinent region by area, DEMs, road networks, water systems. Upon comparing the results of the forecast of the land use changes, it appears that geographical features had the greatest influence on the changes. Moreover, a forecast of post-2030 land use change patterns demonstrates that 21.67 percent of mountain lands in Kimje-si is likely to be farmland, and 13.11 percent is likely to become city areas. The major changes are likely to occur in small mountain lands located in the heart of the city. Based on the study result, it seems certain that forecasting future land use changes can help plan land use in a compound urban and agricultural city to procure food resources.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.52-60
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• 2008

A simple Time Division Multiplex(TDM) switching system which has been widely in satellite networks provides any size of bandwidth for a number of low bandwidth subscribers by allocating proper number of time slots in a frame. In this paper, we propose a new approach based on graph coloring model for efficient time slot assignment algorithm in contrast to network flow model in previous works. When the frame length of an initial matrix of time slot requests is 2's power, this matrix is divided into two matrices of time slot requests using binary divide and conquer method based on the graph coloring model. This process is continued until resulting matrices of time slot requests are of length one. While the most efficient algorithm proposed in the literature has time complexity of $O(N^{4.5})$, the time complexity of the proposed algorithm is $O(NLlog_2L)$, where N is the number of input/output links and L is the number of time slot alloted to each link in the frame.

• The Plant Pathology Journal
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• v.28 no.2
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• pp.123-136
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• 2012

Diseases caused by begomoviruses (family Geminiviridae, genus Begomovirus) constitute a serious constraint to tropical and sub-tropical agro-ecosystems worldwide. In recent years, they have also introduced in temperate regions of the world where they have great impact and are posing a serious threat to a variety of greenhouse crops. Begomoviral diseases can in extreme cases reduce yields to zero leading to catastrophic losses in agriculture. They are still evolving and pose a serious threat to sustainable agriculture across the world, particularly in tropics and sub-tropics. Till recently, there have been no records on the occurrence of begomoviral disease in South Korea, however, the etiology of other plant viral diseases are known since last century. The first begomovirus infected sample was collected from sweet potato plant in 2003 and since then there has been gradual increase in the begomoviral epidemics specially in tomato and sweet potato crops. So far, 48 begomovirus sequences originating from various plant species have been submitted in public sequence data base from different parts of the country. The rapid emergence of begomoviral epidemics might be with some of the factors like evolution of new variants of the viruses, appearance of efficient vectors, changing cropping systems, introduction of susceptible plant varieties, increase in global trade in agricultural products, intercontinental transportation networks, and changes in global climatic conditions. Another concern might be the emergence of a begomovirus complex and satellite DNA molecules. Thorough understanding of the pathosystems is needed for the designing of effective managements. Efforts should also be made towards the integration of the resistant genes for the development of transgenic plants specially tomato and sweet potato as they have been found to be widely infected in South Korea. There should be efficient surveillance for emergence or incursions of other begomoviruses and biotypes of whitefly. This review discusses the general characteristics of begomoviruses, transmission by their vector B. tabaci with an especial emphasis on the occurrence and distribution of begomoviruses in South Korea, and control measures that must be addressed in order to develop more sustainable management strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.882-885
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• 2014

The duration and frequency of flooding and not last long, by the time climate change drought. The increased accordingly by reducing stream flow and year variation. This trend is expected to continue, and change towards a comprehensive analysis of such quantity, quality and management of water resources are managed. Flood warning system is called to perform them electronically to the management of water resources such as these to be in the organic water-related basic data acquisition, storage, processing and utilization. Can be divided into hydrological observations and flood warning systems alert system broadcast system. Hydrological observation system is the measurement from the hydrological stations (water level, rainfall, water) that can be observed hydrological status of the dam basin hydrological observation data transmitted to the central office, located at the dam monitoring and control system through a variety of networks including satellite, and the collected defined as the system that sent the K-water head office in 1 minute increments hydrological observation data. Headquartered in support of this decision. Dimensions of the dam are provided in addition to inward. Channeled through various hydrologic analysis and leveraging the data transfer. This paper looks at ways to build out hydrological observation system.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.58-66
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• 2023

The latest global issues are the Space economy and low-orbit Space communication. 3GPP announced Release 17 standardization in June 2022, and in this regard, the United States prepared a strategy to enhance the competitiveness of the low-orbit 5G-NTN Space industry, and create an ecosystem at the national level in March 2023. Global smartphone semiconductor manufacturers have announced the development and verification results of standard-based chip technology, and satellite communication operators are launching low-orbit 5G-NTN Space communication services and rate products through convergence between terrestrial communication networks. This study diagnoses the current status of Korea's low-orbit 5G-NTN space communication ecosystem. We present our ecosystem creation strategy in terms of fair competition in the market, the service legal system, and the national R&D governance system.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the Korean Geographical Society
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• v.37 no.4
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• pp.293-316
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• 2002

This study aims to identify changes that have occurred in the regional structure and locational characteristics of the Najin-Seonbong Economic and Trade Zone established in North Korea in 1991. In order to analyze land use patterns as variables of change in the regional structure, an field trip data, satellite imagery and other materials about the region are examined. In terms of its location as a major regional transit hub, the Najin-seonbong Economic and Trade Zone has not been supported by the required infrastructural developments and the establishment of the export processing zones has exposed the lack of vital links with local networks and industry. Thus, despite the fact that the local government has made a lot of effort in attracting foreign investment over the past decade, little progress has been made and the region has not changed. By and large, its operational efficiency and potential for development as a major export processing zone has been relatively limited. In the long w, prospects for the region's emergence as a major economic player will depend on the North Korean Govemment's policy in tackling the various infrastructural deficiencies.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.