• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.409-418
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• 2021

As the amount of construction for aerial photography increases, the need for automation of quality inspection is emerging. In this study, an experiment was performed to classify or detect clouds in aerial photos using deep learning techniques. Also, classification and detection were performed by including satellite images in the learning data. As algorithms used in the experiment, GoogLeNet, VGG16, Faster R-CNN and YOLOv3 were applied and the results were compared. In addition, considering the practical limitations of securing erroneous images including clouds in aerial images, we also analyzed whether additional learning of satellite images affects classification and detection accuracy in comparison a training dataset that only contains aerial images. As results, the GoogLeNet and YOLOv3 algorithms showed relatively superior accuracy in cloud classification and detection of aerial images, respectively. GoogLeNet showed producer's accuracy of 83.8% for cloud and YOLOv3 showed producer's accuracy of 84.0% for cloud. And, the addition of satellite image learning data showed that it can be applied as an alternative when there is a lack of aerial image data.

• Journal of Environmental Health Sciences
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• v.48 no.6
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• pp.298-305
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• 2022

Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM_2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM_2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM_2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM_2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.46-55
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• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.60-70
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• 2006

National atlas map for provide various information is one part of National territorial Statatics Survey but National atlas map in 2004 year don't have stability and ability. National territorial Statatics Survey in 2005 years have octuple data compare with data in 2004 years but have only one map. One map is going to provide with stability and ability. We don't use DBMS, But We make a similarly struct in file based program. We programmed system of dynamic-linked data with spatial data. To dynamic-linked system, we make search engine to based index struct and make combobox search system. spatial data only have index codes(year, national terrial indicator, area). If spatial data request specfied data, search engine search index code and provide DB data. New system is middle step of using DBMS. We redraw map for display real Korea area (with dokdo). New map is shape and similar Korea map.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.9-16
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• 2009

In ubiquitous spatial computing environments, GeoSensor generates sensor data streams including spatial information as well as various conventional sensor data from RFID, WSN, Web CAM, Digital Camera, CCTV, and Telematics units. This GeoSensor enables the revitalization of various ubiquitous USN technologies and services on geographic information. In order to service the u-GIS applications based on GeoSensors, it is indispensable to efficiently process sensor data streams from GeoSensors of a wide area. In this paper, we propose a GeoSensor data stream processing system for u-GIS computing over real-time stream data from GeoSensors with geographic information. The proposed system provides efficient gathering, storing, and continuous query processing of GeoSensor data stream, and also makes it possible to develop diverse u-GIS applications meet each user requirements effectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.12-23
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• 2003

The expansion of internet and the development of communication technology have brought about an explosive increasement of data. Further progress has led to the increasing demand for efficient and effective data analysis tools. According to this demand, data mining techniques have been developed to find out knowledge from a huge amounts of raw data. This paper suggests a generalization based classification method which explores rules from real world data appearing repeatedly. Also, it analyzed the relation between weather data and forest fire, and efficiently predicted through it as a prediction model by applying the suggested generalization based classification method to forest fire data. Additionally, the proposed method can be utilized variously in the important field of real life like the analysis and prediction on natural disaster occurring repeatedly, the prediction of energy demand and so forth.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.68-79
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• 2008

Ground vehicle borne system which is named RoSSAV(Road Safety Survey and Analysis Vehicle) developed in KICT(Korea Institute of Construction Technology) can collect road geometric data. This system therefore is able to evaluate the road safety and analyze road deficient sections using data collected along the roads. The purpose of this study is to extract road geometric data for 3D road modeling in dangerous road section and The system should be able to quickly provide more accurate data. Various sensors(circular laser scanner, GPS, INS, CCD camera and DMI) are installed in moving object and collect road environment data. Finally, We extract 3d road geometry(center, boundary), road facility and slope using integrated multi-sensor data.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.4
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• pp.75-85
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• 2002

The introduction of GPS technique in transportation study make real time tracking of driving vehicle's position possible. Position information data gauged with three dimension(X, Y, Z) can be achieved with time-continuity and from more than two driving vehicles. For this field of study, in past day, there were many difficulties in collecting real time data, but now, it becomes easy thanks to this. But for the resonable result analysis, fully understanding of GPS data-accuracy must be proceeded. Because accompanied magnitude of error depends on the study's accuracy. For this reason, this study surveyed the GPS data's error and suggest calibrate technique. This study's result will be helpful for following studies using DGPS data. For this, this study takes two types study in road, and set triangulation coordinates, and compare it with GPS data. DGPS data contains less than 0.6m's error.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.24-33
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• 2004

Advances in remote sensing technologies are resulting in the rapid increase of the number of spectral channels, and thus, growing data volumes. This creates a need for developing faster techniques for processing such data. One application in which such fast processing is needed is the dimension reduction of the multispectral data. Principal component transformation is perhaps the mostpopular dimension reduction technique for multispectral data. In this paper, we discussed the processing procedures of principal component transformation. And we presented and discussed the results of the principal component transformation of the multispectral data. Moreover principal components image data are classified by the Maximum Likelihood method and Multilayer Perceptron method. In addition, the performances of two classification methods and data reduction effects are evaluated and analyzed based on the experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.716-723
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• 2016

A computerized information management system of road bridges as a national key infrastructure is needed to effectively collect data of the current status, improve the reliability of data, and use the results from the analysis of the accumulated data as fundamental resources for supporting the establishment of policies. The Internet-based Bridge Information System (BIS), including a database and geographic information systems (GIS), was designed, and the data items were comprised of essential information, such as GIS-based location coordinates, bridge condition grade information and so on. The BIS was developed to be connected with a related information system, and it is possible to make the current information of traffic volume, address and so on by adopting the GIS. To enhance the reliability of the information of current bridge status, it is also possible to improve the accuracy of data through an information verifying function to prevent entry errors. In addition, the BIS can easily support the establishment of policies offering various types of knowledge information that were available in the past based on an analysis of the accumulated data. The intuitive identification and analysis of the current status is to be feasible through a GIS screen. Improvement of the business efficiency and data accuracy and time-series information analysis are available by managing the information of current status through BIS. In the future, it is expected that BIS can be used effectively for the establishment of reasonable maintenance policies of the nation.