• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.179-189
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• 2019

Korea's transportation paradigm is shifting from a vehicle-centered to a pedestrian-oriented society. Therefore, the interest in pedestrian safety and the improvement of pedestrian environment is also increasing. However, the level of traffic safety in Korea is still severe. It is needed to improve pedestrian safety and pedestrian environment. This study studied pedestrian-vehicle accident data provided by the Traffic Accident Analysis System(TAAS) for 2013-2015 to build a safe walking environment around school zones, and the relation between the school zones and pedestrian-vehicle traffic accidents were identified through the geographic information system(GIS) and spatial regression model. The main results are as follows. First, both road and public transportation factors are likely to increase pedestrian traffic accidents in school zones. Second, regarding land-use factors, residential, commercial, and industrial areas are found to increase pedestrian traffic crashes. On the other hand, mixed use is likely to play a role on the reduction of pedestrian traffic accidents. Finally, it has been shown that high development density also has a positive effect on pedestrian traffic accidents in school zones.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.388-393
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• 2003

도시화 속도의 증가와 더불어 3차원 자료 획득의 출처가 다양해지연서, 도로 및 건물경계선과 같은 선형 GIS 정보에 대한 신속한 갱신 또한 요구되고 있다. 임의의 출처 자료로부터 대상 자료를 갱신하기 위해서는 가장 먼저 두 자료간의 위치 관계를 결정하여야 하며, 특히 영상정보와 같은 출처 자료와 GIS 자료와 같은 대상 자료간의 위치 관계를 결정하기 위하여 기존에 제시되어온 대부분의 방법들은 두 개 자료간의 관계를 정의 할 수 있는 기준정과 같은 정확한 점 정합 요소(point matching entities)를 요구하고 있다. 따라서 정확한 정합 요소들을 획득할 수 없는 경우 영상과 GIS 자료간의 위치 관계를 결정할 수 없을뿐더러 위치 관계 정립의 결과는 정합 요소들의 분포 및 정확도에 매우 의존하게 된다. 또한 이러한 점 정합 요소들을 정의하기 위해서는 대부분의 경우 수동적으로 이루어질 수밖에 없다. 따라서 본 연구에서는 영상 및 GIS 자료의 선형 정보를 이용하여 정확한 점 정합 요소들을 모르더라도 영상과 GIS 자료간의 위치 관계를 결정할 수 있는 기법을 제시하고자 한다. 사용된 알고리즘은 개선된 Hough 변환(Modified Hough Transform)을 기반으로 다수의 선형 정보 중에 정합되는 요소들을 자동으로 찾아내고 이들을 최소제곱법으로 풀이함으로써 두 데이터간의 기하학적 변환 관계를 결정하는 기법이다. 본 연구에서는 이와 같은 접근을 통해 데이터간의 기하학적 변환 관계를 결정한 후, 영상 상에는 존재하지만 GIS 자료에는 존재하지 않는 선형 정보에 대한 갱신 여부를 확인하고 갱신함으로써 3차원 위치 자료의 자동 생성에 대한 가능성을 제시하고자 한다.로 갈수록 퇴적이 우세한 것으로 관측되었다.보체계의 구축사업의 시각이 행정정보화, 생활정보화, 산업정보화 등 다양한 분야와 결합하여 보다 큰 시너지 효과와 사용자 중심의 서비스 개선을 창출할 수 있는 기반을 제공할 것을 기대해 본다.. 이상의 결과를 종합해볼 때, ${\beta}$-glucan은 고용량일 때 직접적으로 또는 $IFN-{\gamma}$ 존재시에는 저용량에서도 복강 큰 포식세로를 활성화시킬 뿐 아니라, 탐식효율도 높임으로써 면역기능을 증진 시키는 것으로 나타났고, 그 효과는 crude ${\beta}$-glucan의 추출조건에 따라 달라지는 것을 알 수 있었다.eveloped. Design concepts and control methods of a new crane will be introduced in this paper.and momentum balance was applied to the fluid field of bundle. while the movement of′ individual material was taken into account. The constitutive model relating the surface force and the deformation of bundle was introduced by considering a representative prodedure that stands for the bundle movement. Then a fundamental equations system could be simplified considering a steady state of th

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.146-157
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• 2019

One of the most distinctive features of the South Korean rural environment is that the variation of weather or climate is large even within a small area due to complex terrains. The Geospatial Schemes based on Topo-Climatology (GSTP) was developed to simulate such variations effectively. In the present study, we reviewed the progress of the geospatial schemes for production of farm-scale agricultural weather data. Efforts have been made to improve the GSTP since 2000s. The schemes were used to provide climate information based on the current normal year and future climate scenarios at a landscape scale. The digital climate maps for the normal year include the maps of the monthly minimum temperature, maximum temperature, precipitation, and solar radiation in the past 30 years at 30 m or 270 m spatial resolution. Based on these digital climate maps, future climate change scenario maps were also produced at the high spatial resolution. These maps have been used for climate change impact assessment at the field scale by reprocessing them and transforming them into various forms. In the 2010s, the GSTP model was used to produce information for farm-specific weather conditions and weather forecast data on a landscape scale. The microclimate models of which the GSTP model consists have been improved to provide detailed weather condition data based on daily weather observation data in recent development. Using such daily data, the Early warning service for agrometeorological hazard has been developed to provide weather forecasts in real-time by processing a digital forecast and mid-term weather forecast data (KMA) at 30 m spatial resolution. Currently, daily minimum temperature, maximum temperature, precipitation, solar radiation quantity, and the duration of sunshine are forecasted as detailed weather conditions and forecast information. Moreover, based on farm-specific past-current-future weather information, growth information for various crops and agrometeorological disaster forecasts have been produced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.40-47
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• 2020

Various technologies, systems, and legal systems are applied to prevent and quickly respond to fire disaster; nevertheless, the damages to life and property caused by fires are not reduced every year. For managing fire disaster, generating spatial information-based safety status map and procuring suitability of attribute information for each position information are essential. The safety status map is generated by deriving the fire safety status assessment factors, indexing, and locating the surveying results through various methods. In this paper, we deal with derivation of building fire safety assessment factors for 3D safety status map. At first, we survey the foreign and domestic fire assessment model cases and its factors, and analyze the applicability of Korean 3D fire safety status map. Next, assessment factors for fire safety assessment model are derived. Assessment factors are derived and categorized by their information collecting activity; factors that can be accessed through basic building information and factors that can be accessed through field survey. As a derivation result, 14 assessment factors were derived over five categories(Industry Risk, Structural Risk, Fire Fighting Facility, Fire Dangerousness, Fire Response Status).

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.101-117
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• 2004

Standards are essential to facilitate the efficient use of GIS data. International Standards such as ISO TC211's 19100 series and various technical specifications from OpenGIS Consortium are some of the examples of efforts to maintain the interoperability among GIS applications. Marine GIS is no exception to this rule and in this context. developing standards for marine GIS is also in urgent needs. Using the same meaning and definition for the features commonly found in marine GIS applications is one of the ways to increase the interoperability among systems. One of the key requirements for maintaining the standard meanings for features is to build a common feature catalogue. This paper examines the concept of feature catalogue and describe the ways in which the feature catalogue can be organized. To identify the common features found in various marine GIS applications, a comprehensive search has been made to collect and analyze the features used in various applications. To maintain the interoperability with the National GIS (NGIS) system, the features used in various NGIS applications have been analyzed as well. The result of these analyses are used to create a comprehensive list of common features for marine GIS. This paper then explains the common feature catalogue for marine GIS and the provides the appropriate classification and coding systems for the common features. In addition, a registration tool for registering the common features into the standard registry has been developed in this study. This Web-based tool can be used to input features into the feature catalogue by various applications and also to maintain a standard-compliant feature catalogue by standard agencies.

• Spatial Information Research
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• v.18 no.5
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• pp.37-45
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• 2010

AT(Aerial Triangulation) is the essential procedure for creating orthophoto and transforming coordinates on the photographs into the real world coordinates utilizing GCPs (Ground Control Point) which is obtained by field survey and the external orientation factors from GPS/INS as a reference coordinates. In this procedure, all of the GCPs can be collected from field survey using GPS and Total Station, or obtained from digital maps. Collecting GCPs by field survey is accurate than GCPs from digital maps; however, lots of manpower should be put into the collecting procedure, and time and cost as well. On the other hand, in the case of obtaining GCPs from digital maps, it is very difficult to secure the required accuracy because almost things at each stage in the collecting procedure should rely on the subjective judgement of the performer. In this study, the results from three methods have been compared for the accuracy assessment in order to know if the results of each case is within the allowance error: for the perceivable objects such as road boarder, speed bumps, constructions etc., 1) GCPs selection utilizing the unique LiDAR intensity value reflected from such objects, 2) using LiDAR DSM and 3) GCPs from field survey. And also, AT and error analysis have been carried out w ith GCPs obtained by each case.