• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.283-294
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• 2021

Solar radiation is an important meteorological factor in the agricultural sector. The ground exposed to sunlight is highly influenced by the surrounding terrains especially in South Korea where the topology is complex. The solar radiation on an inclined surface is estimated using a solar irradiance correction factor for the slope of the terrain along with the solar radiation on a horizontal surface. However, such an estimation method assumes that there is no barrier in surroundings, which blocks sunlight from the sky. This would result in errors in estimation of solar radiation because the effect of shading caused by the surrounding terrain has not been taken into account sufficiently. In this study, the shading effect was simulated to obtain the brightness value (BV), which was used as a correction factor. The shaded relief images, which were generated using a 30m-resolution digital elevation model (DEM), were used to derive the BVs. These images were also prepared using the position of the sun and the relief of the terrain as inputs. The gridded data where the variation of direct solar radiation was quantified as brightness were obtained. The value of cells in the gridded data ranged from 0 (the darkest value) to 255 (the brightest value). The BV analysis was performed using meteorological observation data at 22 stations installed in study area. The observed insolation was compared with the BV of each point under clear and cloudless condition. It was found that brightness values were significantly correlated with the solar radiation, which confirmed that shading due to terrain could explain the variation in direct solar radiation. Further studies are needed to accurately estimate detailed solar radiation using shaded relief images and brightness values.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.26-40
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• 2021

This study aims to propose improvement plans and directions of existing policies to build a Barrier-Free city for the socially underprivileged. We conducted correlation analysis and T-test based on the results of a survey of the general public including the socially underprivileged to understand how their satisfaction with the use of facilities close to their daily lives, such as housing, sales·business facilities, affects their happiness. The results are as follows: First of all, the socially underprivileged experienced discomfort and barriers in urban life more than the privileged, and there was a negative(-) correlation between the degree of discomfort and barrier that the socially underprivileged experienced and happiness in urban life. Second, as a result of analyzing the difference in satisfaction with the life·welfare·leisure environment indicators, the satisfaction with the life environment showed a significant difference between the socially underprivileged and the privileged, and it was found that satisfaction in the life environment had the greatest effect on happiness to the socially underprivileged. We examined the current status and the spatial distribution of BF-certified facilities, one of the representative policies for the socially underprivileged to support their urban life without any obstacles, based on the above outcome, and most certified facilities were concentrated in welfare environment. In addition, we used ArcGIS to analyze that BF-certified facilities have been being supplied to people who really need them and saw that the spatial distribution of the socially underprivileged (elderly, children, and infant) and the distribution of BF certification facilities were somewhat consistent, but were intensively supplied to urban areas.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.73-82
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• 2021

According to the government's announcement of the safety management enhancement policy for small and medium-sized private construction sites, the subject of mandatory CCTV installation has been expanded from large construction sites to small and medium-sized construction sites. However, since the existing CCTV at construction sites has been used for simple control for safety management, so research is needed for monitoring of construction sites. Therefore, in this study, three vanishing points were calculated based on a single image taken with a monocular camera, and then a camera matrix containing interior orientation parameters information was determined. And the accuracy was verified by calculating the height of the target object from the height of the reference object. Through height determination experiments using vanishing points based on a monocular camera, it was possible to determine the height of target objects only with a single image without separately surveying of ground control points. As a result of the accuracy evaluation, the root mean square error was ±0.161m. Therefore, it is determined that the progress of construction work at the construction sites can be monitored through the single image taken using the single camera.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.1-17
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• 2022

This study analyzed the PM_2.5 pattern by using data measured for one year from June 2020 to May 2021 by 21 low-cost sensors installed near the Changwon National Industrial Complex in Changwon, Gyeongsangnam-do. For the PM_2.5 pattern, the land use types around the measuring points and meteorological factors such as air temperature and wind speed were considered. The PM_2.5 concentration was high from November to March in winter, and from 1 to 9 in the morning and early in the morning by time zone. The concentration of PM_2.5 was higher as it got closer to the industrial area, but the concentration was lower in the residential area and public facility area. In terms of meteorological factors, the higher the air temperature and wind speed, the lower the concentration of PM_2.5. As a result of this study, it was possible to identify the PM_2.5 patter near Changwon National Industrial Complex. This result will be useful data that can be used in urban and environmental planning to improve air quality including PM_2.5 in urban area in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.985-994
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• 2022

This study diagnoses the maturity level of local government's spatial information policy in the face of the digital transformation period. To this end, by using the stratification (AHP) analysis technique, for the spatial information expert group, the importance evaluation is performed along with the selection of measurement indicators of the level of spatial informatization. First, the current status and indicators in terms of spatial information policies of local governments are studied through theoretical considerations. In addition, by examining previous studies, differentiation from this study is presented. In this study, 3 indicators and 18 detailed items of spatial informatization level evaluation indicators could be selected. As a result of the evaluation, the overall importance ranking for the measurement index was 0.125 (12.5%) for the establishment and utilizations of GIS Systems provided by local governments, 0.109 (10.9%) for the quality management of basic spatial information, 0.107 (10.7%) for the establishment and utilization of its own spatial information. 0.073 (7.3%) for the collaboration and sharing within the institution, 0.071 (7.1%) for the operation of education and support system on spatial information, 0.065 (6.5%) for the securing a dedicated organization and professional human resources, and interest at the institutional level were derived in the order of 0.053 (5.3%). The results of this study are expected to be usefully utilized as basic data to evaluate the level of spatial information policies promoted by local governments. In addition, if the local government's spatial information policy is pursued centering on the major indicators derived from the study, it is expected that efficient policy operation will be possible.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.79-89
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• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.55-70
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• 2021

In this study, in the field of remote sensing, where the scope of application is rapidly expanding to fields such as land monitoring, disaster prediction, facility safety inspection, and maintenance of cultural properties, monitoring of rural space and surrounding environment using UAV is utilized. It was carried out to verify the possibility, and the following main results were derived. First, the aerial image taken with an unmanned aerial vehicle had a much higher image size and spatial resolution than the aerial image provided by the National Geographic Information Service. It was suitable for analysis due to its high accuracy. Second, the more the number of photographed photos and the more complex the terrain features, the more the point cloud included in the aerial image taken with the UAV was extracted. As the amount of point cloud increases, accurate 3D mapping is possible, For accurate 3D mapping, it is judged that a point cloud acquisition method for difficult-to-photograph parts in the air is required. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. Fourth, the digital elevation model(DEM) produced with aerial image taken with an UAV can visually express the altitude and shape of the topography of the study site, so it can be used as data to predict the effects of topographical changes due to changes in rural space. Therefore, it is possible to utilize various results using the data included in the aerial image taken by the UAV. In this study, the superiority of images acquired by UAV was verified by comparison with existing images, and the effect of 3D mapping on rural space monitoring was visually analyzed. If various types of spatial data such as GIS analysis and topographic map production are collected and utilized using data that can be acquired by unmanned aerial vehicles, it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.159-170
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• 2022

As the severity of water-related disasters increases in urban watersheds due to climate change, reducing flood damage in urban watersheds is one of the important issues. This study focuses on prioritizing the optimal site for permeable pavement to maximize the efficiency of reducing flood damage in urban watersheds in the future climate environment using multi-criteria decision making techniques. The Mokgamcheon watershed which is considerably urbanized than in the past was selected for the study area and its 27 sub-watersheds were considered as candidate sites. Six General Circulation Model (GCM) of Coupled Model Intercomparison Project 6(CMIP6) according to two Shared Socioeconomic Pathway (SSP) scenarios were used to estimate future monthly precipitation for the study area. The Driving force-Pressure-State-Impact-Response (DPSIR) framework was used to select the water quantity evaluation criteria for prioritizing permeable pavement, and the study area was modeled using ArcGIS and Storm Water Management Model (SWMM). For the values corresponding to the evaluation criteria based on the DPSIR framework, data from national statistics and long-term runoff simulation value of SWMM according to future monthly precipitation were used. Finally, the priority for permeable pavement was determined using the Fuzzy TOPSIS and Minimax regret method. The high priorities were concentrated in the downstream sub-watersheds where urbanization was more progressed and densely populated than the upstream watersheds.