• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.95-110
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• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.127-133
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• 2014

The purpose of this study is to evaluate the accuracy of Network-RTK(VRS) survey for applying to Ground Control Points(GCPs) survey required for mapping aerial photographs. Network-RTK has been serviced by National Geographic Information Institute since 2007. On the basis of the global coordinates system(ITRF2000), the coordinates of GCPs determined by Static GNSS survey with relative positioning techniques were regarded as accurate values. The coordinates of GCPs were also determined by Network-RTK survey using two kinds of receivers, and then they were converted into the global coordinates system(ITRF2000) by applying suitable geoid model and coordinate transformation. These coordinates of GCPs were compared with those from Static GNSS survey. The root mean squares error (RMSE) of coordinate differences between Network-RTK and Static GNSS was ${\pm}2.0cm$ in plane and ${\pm}7.0cm$ in height. Therefore, Network-RTK survey that enables single GNSS receiver to measure positions in short time is a practical alternative in positioning GCPs to either RTK survey that uses more than two sets of GNSS receivers or Static GNSS survey that requires longer observation time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.19-27
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• 2014

Kompsat-3 is an optical high-resolution earth observation satellite launched in May 2012. In addition to its 0.7m spatial resolution, Kompsat-3 is capable of in-track stereo acquisition enabling quality Digital Elevation Model(DEM) generation. Typical DEM generation procedure requires accurate control points well-distributed over the entire image region. But we often face difficult situations especially when the area of interests is oversea or inaccessible area. One solution to this is to use existing geospatial data even though they only cover a part of the image. This paper aimed to assess accuracy of DEM from Kompsat-3 with different scenarios including no control point, Rational Polynomial Coefficients(RPC) relative adjustment, and RPC adjustment with control points. Experiments were carried out for Kompsat-3 stereo data in USA. We used Digital Orthophoto Quadrangle(DOQ) and Shuttle Radar Topography Mission(SRTM) as control points sources. The generated DEMs are compared to a LiDAR DEM for accuracy assessment. The test results showed that the relative RPC adjustment significantly improved DEM accuracy without any control point. And comparable DEM could be derived from single control point from DOQ and SRTM, showing 7 meters of mean elevation error.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.55-63
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• 2019

Recently, the use of real-world image data has been increasing to express realistic virtual environments in various application fields such as education, manufacturing, and construction. In particular, with increasing interest in digital twins like smart cities, realistic 3D urban models are being built using real-world images, such as aerial images. However, the captured aerial image includes shadows from the sun, and the 3D city model including the shadows has a problem of distorting and expressing information to the user. Many studies have been conducted to remove the shadow, but it is recognized as a challenging problem that is still difficult to solve. In this paper, we construct a virtual environment dataset including the height map of buildings using 3D spatial information provided by VWorld, and We propose a new shadow detection method using height map and deep learning. According to the experimental results, We can observed that the shadow detection error rate is reduced when using the height map.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.197-210
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• 2012

The KOrea Multi-Purpose SATellite(KOMPSAT)-2 has a capability to provide a cross-track stereo imagery using two different orbits for generating various spatial information. However, in order to fully realize the potential of the KOMPSAT-2 stereo imagery in terms of mapping, various tests are necessary. The purpose of this study is to evaluate the possibility of mapping using the KOMPSAT-2 stereo imagery. For this, digital plotting was conducted based on the stereoscopic images. Also the Digital Elevation Model(DEM) and an ortho-image were generated using digital plotting results. An accuracy of digital plotting, DEM, and ortho-image were evaluated by comparing with the existing data. Consequently, we found that horizontal and vertical error of the modeling results based on the Rational Polynomial Coefficient(RPC) was less than 1.5 meters compared with the Global Positioning System(GPS) survey results. The maximum difference of vertical direction between the plotted results in this study and the existing digital map on the scale of 1/5,000 was more than 5 meters according as the topographical characteristics. Although there were some irregular parallax on the images, we realized that it was possible to interpret and plot at least seventy percent of the layer which was required the digital map on the scale of 1/5,000. Also an accuracy of DEM, which was generated based on the digital plotting, was compared with the existing LiDAR DEM. We found that the ortho-images, which were generated using the extracted DEM in this study, sufficiently satisfied with the requirement of the geometric accuracy for an ortho-image map on the scale of 1/5,000.

• Journal of Navigation and Port Research
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• v.34 no.3
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• pp.175-180
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• 2010

In the almost application parts, GNSS being used the primary navigation system on world-widely. However, some of nations attempt or deliberate to enhance current Loran system, as a backup to satellite navigation system because of the vulnerability to the disturbance signal. Loran interests in supplemental navigation system by the development and enhancement, which is called eLoran, and that consists of advancement of receiver and transmitter and of differential Loran in order to increase the accuracy of current Loran-C. A significant factor limiting the ranging accuracy of the eLoran signal is the ASF in the TOAs observed by the receiver. The ASF is mostly due to the fact that the ground-wave signal is likely to propagate over paths of varying conductivity and topography. This paper presents comparison results between the predicted ASF and the measured ASF in a southern east region of Korea. For predicting ASF, the Monteath model is used. Actual ASF is measured from the legacy Loran signal transmitted Pohang station in the GRI 9930 chain. The test results showed the repeatability of the measured ASF and the consistent characteristics between the predicted and the measured ASF values.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.502-517
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• 2019

Global warming and rapid climate change have long affected the characteristics of typhoons in the Northwest Pacific, which has induced increasing devastating disasters along the coastal regions of the Korean peninsula. Synthetic Aperature Radar (SAR), as one of the microwave sensors, makes it possible to produce high-resolution sea surface wind field around the typhoon under cloudy atmospheric conditions, which has been impossible to obtain the winds from satellite optical and infrared sensors. The Geophysical Model Functions (GMFs) for sea surface wind retrieval from SAR data requires the input of wind direction, which should be based on the accurate estimation of the center of the typhoon. This study estimated the typhoon centers using Sentinel-1A images to improve the problem of typhoon center detection method and to reflect it in retrieving the sea surface wind. The results were validated by comparing with the typhoon best track data provided by the Korea Meteorological Administration (KMA) and Japan Meteorological Agency (JMA), and also by using infrared images of Himawari-8 satellite. The initial center position of the typhoon was determined by using VH polarization, thereby reducing the possibility of error. The detected center showed a difference of 23.76 km on average with the best track data of the four typhoons provided by the KMA and JMA. Compared to the typhoon center estimated by Himawari-8 satellite, the results showed an average spatial variation of 11.80 km except one typhoon located near land with a large difference of 58.73 km. This result suggests that high-resolution SAR images can be used to estimate the center and retrieve sea surface wind around typhoons.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.