• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.73-81
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• 2010

In recent years, there have been diverse researches and utilizations of creating geo-spatial information with high resolution satellite images. However thematic maps made with middle or low resolution satellite images have low location accuracy and precision of thematic information. This study set out to propose a method of making a precision thematic map with high resolution satellite images by examining the conversion from the conventional method based on middle or low resolution satellite images to the automatic method based on high resolution satellite images of GSD 1m or lower, extracting thematic information of middle or large scale of 1/5,000 or lower, and analyzing its accuracy. Seven classification classes were categorized according to the object-oriented classification in order to automatically extract thematic information with high resolution satellite images. And the classification results were compared and analyzed with the old middle scale land cover map and 1/1000 digital map.

• Journal of the Korea Society of Computer and Information
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• v.7 no.3
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• pp.23-29
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• 2002

The design of data processing of GIS requires efficient method with analysis procedure. This system is easy to be used and managed for presenting route according to conditions as a graphic user interface environmental window system by applying three tiers based object-oriented method. The tier of data is in charge of a class for the exchange, extraction and conservation of data between GeoMedia and application tiers. A route selection algorithm was applied to application tiers, considering all conditions which are necessary for the route selection between a beginning point and an end point, and it was added by module such as data handing, road condition, buffering, clothoid and AHP to select the alternative route followed by new condition. The user tier can express the data acquired by an application tier. Thus three tiers based architecture was presented by implementing design of GIB data processing for its efficiency.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.23-29
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• 2019

Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Journal of the Korean GEO-environmental Society
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• v.20 no.2
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• pp.41-48
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• 2019

Soil compaction is one of the most important activities in the area of civil works, including road construction, airport construction, port construction and backfilling construction of structures. Soil compaction, particularly in road construction, can be categorized into subgrade compaction and roadbed compaction, and is significant work that when done poorly can serve as a factor causing poor construction due to a lack of compaction. Currently, there are many different types of compaction tests, and the plate bearing test and the unit weight of soil test based on the sand cone method are commonly used to measure the degree of compaction, but many other methods are under development as it is difficult to secure economic efficiency. For the purpose of this research, a portable penetration meter called the Free-Fall Penetration Test (FFPT) was developed and manufactured. In this study, a homogeneous sample was obtained from the construction site and soil was classified through a sieve analysis test in order to perform grain size analysis and a specific gravity test for an indoor test. The principle of FFPT is that the penetration needle installed at the tip of an object put into free fall using gravity is used to measure the depth of penetration into the road surface after subgrade or roadbed compaction has been completed; the degree of compaction is obtained through the unit weight of soil test according to the sand cone method and the relationship between the degree of compaction and the depth of the penetration needle is verified. The maximum allowable grain size of soil is 2.36 mm. For $A_1$ compaction, a trend line was developed using the result of the test performed from a drop height of 10 cm, and coefficient of determination of the trend line was $R^2=0.8677$, while for $D_2$ compaction, coefficient of determination of the trend line was $R^2=0.9815$ when testing at a drop height of 20 cm. Free fall test was carried out with the drop height adjusted from 10 cm to 50 cm at increments of 10 cm. This study intends to compare and analyze the correlation between the degree of compaction obtained from the unit weight of soil test based on the sand cone method and the depth of penetration of the penetration needle obtained from the FFPT meter. As such, it is expected that a portable penetration tester will make it easy to test the degree of compaction at many construction sites, and will lead to a reduction in time, equipment, and manpower which are the disadvantages of the current degree of compaction test, ultimately contributing to accurate and simple measurements of the degree of compaction as well as greater economic feasibility.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.11-17
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• 1998

Recently, 3D GIS based on 3D geo-processing methodology and Internet environment are emerging issues in GIS fields. To design and implement 3D GIS, the strategic linkage of Java and VRML is first regarded: 3D feature format definition in the passion of conventional GIS including aspatial attributes, 3B feature indexing, 3D analytical operators such as selection, buffering, and Near, Metric operation such as distance measurement and statistical description, and 3D visualization. In 3D feature format definition, the following aspects are implemented: spatial information for 3D primitives extended from 2D primitives, multimedia data, object texture or color of VRML specification. DXF-format GIS layers with additional attributes are converted to 3D feature format and imported into this system. While, 3D analytical operators are realized in the form of 3D buffering with respect to user-defined point, line, polygon, and 3D objects, and 3D Near functions; furthermore, 'Lantern operator' is newly introduced in this 3D GIS. Because this system is implemented by Java applet, any client with Java-enable browser including VRML browser plug-in can utilize the new style of 3D GIS function in the virtual space. Conclusively, we present prototype of WWW-based 3D GIS, and this approach will be contribute to development of core modules on the stage of concept establishment and of real application model in future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Spatial Information Research
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• v.23 no.5
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• pp.41-52
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• 2015

MOLIT (Minister of Land, Infrastructure and Transport) authorized Indoor Spatial Information as Basic spatial information in 2013. It became a legal evidence for constructing and managing Indoor Spatial Information. Although it has a little advantage to utilize as service level that Indoor Spatial Information by laser scan or measurement, it has a lot of problems such as consuming many resources, requiring additional progresses for inputting Object Information. In conclusion, it is inefficient to utilize for the maintenance and domestic AEC/FM field. The purposes of this study is to output Indoor Spatial Information by operating IFC model which based on open BIM and to improve availability of Indoor Spatial Information with data visualization. The open-sources of IFC Exporter, a inner program of Revit (Autodesk Inc), is used to output Indoor Spatial Information. Directs 3D Library is also operated to visualize Indoor Spatial Information. It is possible to inter-operate between XML format and the objects of Indoor Spatial Information. It can be utilized in various field as well. For example COBie linkage in facility management, construction of geo-database using air-photogrammetry of UAV (Unmaned Areal Vehicle), the simulation of large-scale military operations and the simulation of large-scale evacuation. The method that is purposed in this study has outstanding advantages such as conformance with national spatial information policy, high level of interoperability as indoor spatial information objects based on IFC, convenience of editing information, light level of data and simplifying progress of producing information.

• Spatial Information Research
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• v.23 no.2
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• pp.49-57
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• 2015

Recently, Through the linkage and opening, the fusion of the spatial information, it is necessary for productive ecosystem to provide a variety of information and to increase the civil use. Depending on the economic growth, demand for quality of life and well-being has been on the increase. Spatial information service contents for the public convenience has emerged to solve the problem such as health, safety, welfare and discomfort of daily life This study aims to implement search services for a tailored residence space through the three-dimensional data modeling on cadastral information. To achieve this goal, we established the requirements for deriving a registered object by investigating recent trend with respect to existing cadastral data model and defined property and relationship. Focusing on Songpa-gu, Jamsil station in Seoul, we implemented search services for a tailored residence space for three-dimensional right analysis in conjunction with residential and commercial complex building. As a result, we derived a way to supply 3D cadastre information through open platforms (VWorld) and to represent efficiently, which is able to improve the quality of spatial information service contents for the public convenience as well as to widen utilization of information.