• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.4
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• pp.52-63
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• 2009

WTO/TBT aims to reduce impediments to trade resulting from differences between national regulations and standards. Where international standards exist or their completion is imminent, the Code of Good Practice says that standardizing bodies should use them, or the relevant parts of them, as a basis for any standards they develop. Drawing is a formal and precise way of communicating information about the shape, size and, features. In addition, drawing is a part of the universal language of engineering. However there are many differences between international landscape drawing standard ISO 11091 and Korean landscape drawing practice(KLDP). The result of a comparison of ISO 11091 with KLDP and suggestions for international standardization of KLDP are summarized as follows. First, Among the 33 kinds of conventions from ISO 11091, 2 similar kinds and 15 different kinds from KLDP and 16 kinds of conventions which exist only in ISO 11091 appeared-for the international standardization of KLDP, it is necessary to make an extensive alteration of KLDP. Second, Europe Unity countries accepted ISO 11091 and are using it as their national standard for landscape drawing. Even Japan has accepted ISO 11091 on their civil engineering drawings and is using it as their national standard. Therefore, we need to hasten KS standard enactment based on ISO 1091. Third, For the KS standard of construction drawings, the degree of international standardization is rising even though there are still differences from the ISO standard. Therefore, since the burden on the international standardization of KLDP is expected to be weighed, preparations should be quickly brought about in the practice fields. Fourth, Since in the landscape planting ordinances of local independent governments is the standard presented by categorizing trees into evergreen and deciduous, such parts should be modified and introduced when enacting the KS standard based on ISO 11091. Fifth, For the enactment of the KS standard for landscape drawings, a wide range of opinions should be collected by the relevant landscape organization by installing a committee, and based on its recommendation, an application for the KS standard enactment of landscape drawing should be made to the chief of Ministry of Knowledge Economy.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.396-400
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• 2007

Various ways of automated guideway transit construction are being planned recently owing to the policies of the national government and local municipalities as well as increasing investment from the private sector. Particularly, the increase in the private investment is increasing greatly in SOC (Social Overhead Cost). This trend of promoting private sector investment must be conducted on the basis of a thorough analysis of the economic feasibility of the project from the government and construction companies in the private sector. In other words, an accurate cost analysis of initial investment cost (Construction cost), maintenance/repair cost, profit making through the operation of the concerned facilities, cost of dissolution, etc. in terms of the life cycle is very much in need. Nevertheless, the analysis of uncertainty factors and its probabilistic theory are in need of development so that they can be used in the analysis of the economic feasibility of a construction project. First of all, the actual studies on maintenance/repair cost of automated guideway transit are scarce as of yet, prohibiting an accurate computation of the cost and its economic analysis. Accordingly, this study focused on the uncertainty analysis of the economic feasibility for civil engineering structures among automated guideway transit construction projects based on the rapidly increasing investment on such structures from the private sector. For this research purpose, a cost classification system for the automated guideway transit is proposed, first of all, and the data On the cost cycle of the civil structure facilities and their unit cost are collected and analyzed. Then, the uncertainty in the cost is analyzed from the perspective of LCC. In consideration of the current status with almost no. studies on maintenance/repair of such facilities, it is expected that the cost classification system and the uncertainty analysis technique proposed in this study will greatly enhance LCC analysis and economic feasibility studies for automated guideway transit projects in the future.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.