• International conference on construction engineering and project management
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• 2015.10a
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• pp.268-270
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• 2015

Overseas countries' government and facility management industries make efforts to ensure precise and fluent data from building information modeling (BIM). In facility management, a large amount of data and information are necessary to continue the process activities. Facility condition assessment, which is performed to make budget plan for the maintenance and operation requires the related facilities' documentation and information. However, it depends on the owner and the user of the facility to provide accurate and complete information to consultant. The problems as follows: (1) owner and user should provide documents and information, and (2) the consultant cannot verify the provided information. To solve these problems, we suggest a methodology to produce the information for FCA through BIM. First, all of the essential documentation and assessment elements are listed. Next, the documents and elements are separated out, whether they are able to be extracted from BIM or not. Then, the list indicates only the data that is linked with BIM. The suggestion is expected to provide the required information through the connection to BIM with accuracy and completeness and to present another BIM application use for facility management.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.219-227
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• 2012

Daily solar radiation is essential for water resources planning and environmental impact assessment. However, radiation data is not commonly available in Korea other than in big cities, and there has been no direct measurement for rural areas where water resources planning and environmental impact assessment is usually most needed. In general, missing radiation data is estimated from nearby regional stations within a certain distance, and this study compared two dominant methods (modified Angstrom equation and transmittance interpolation method) at six stations in Nakdong River watershed area. Two methods shows a similar level of accuracy but the transmittance interpolation method is likely to be superior in that there is no need for any measurement element since the modified Angstrom equation require the sunshine hour measurement. This study will contribute to improve water resource and water quality management in Nakdong River watershed.

• Smart Structures and Systems
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• v.31 no.1
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• pp.79-88
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• 2023

As the number of deteriorated buildings increases, the importance of safety diagnosis, maintenance, and the repair of buildings also increases. Traditionally, building condition assessments are performed by one person or one company and various inspections are needed. This entails a subjective judgment by the inspector, resulting in different assessment results, poor objectivity and a lack of reliability. Therefore, this study proposed a method to bring about accurate grading results of building conditions. The limitations of visual inspection and condition assessment processes previously conducted were identified by reviewing existing studies. Building defect data was collected using the reverse-engineered three-dimensional (3D) model. The accuracy of the results was verified by comparing them with the actual evaluation results. The results show a 50% time-saving to the same area with an accuracy of approximately 90%. Consequently, defect data with high objectivity and reliability were acquired by measuring the length, area, and width. In addition, the proposed method can improve the efficiency of the building diagnosis process.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.433-438
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• 1999

The land use type of Korea is high-density. So, the image classification using coarse resolution satellite image may not provide land cover classification results as good as expected. The purpose of this paper is to compare the result of visual interpretation with that of digital image classification of 20 m resolution SPOT satellite image at Kwangju-eup, Kyunggi-do, Korea. Classes are forest, cultivated field, pasture, water and residential area, which are clearly discriminated in visual interpretation. Maximum likelihood classifier was used for digital image classification. Accuracy assessment was done by comparing each classification result with ground truth data obtained from field checking. The classification result from the visual interpretation presented an total accuracy 9.23 percent higher than that of the digital image classification. This proves the importance of visual interpretation for the area with high density land use like the study site in Korea.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.319-327
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• 2012

In this paper, we introduce a newly developed photogrammetric software for automatic generation of high quality geo-products and its performance assessment carried out using various satellite images. Our newly developed software provides the latest techniques of an optimized sensor modelling, ortho-image generation and automated Digital Elevation Model (DEM) generation for diverse remote sensing images. In particular, images from dual- and multi-sensor images can be integrated for 3D mapping. This can be a novel innovation toward a wider applicability of remote sensing data, since 3D mapping has been limited within only single-sensor so far. We used Kompsat-2, Ikonos, QuickBird, Spot-5 high resolution satellite images to test an accuracy of 3D points and ortho-image generated by the software. Outputs were assessed by comparing reliable reference data. From various sensor combinations 3D mapping were implemented and their accuracy was evaluated using independent check points. Model accuracy of 1~2 pixels or better was achieved regardless of sensor combination type. The high resolution ortho-image results are consistent with the reference map on a scale of 1:5,000 after being rectified by the software and an accuracy of 1~2 pixels could be achieved through quantitative assessment. The developed software offers efficient critical geo-processing modules of various remote sensing images and it is expected that the software can be widely used to meet the demand on the high-quality geo products.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.2
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• pp.173-181
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• 2001

It is very important to assess spatial data quality of a digital map produced through digital map generalization. In this study, as a aspect of spatial data quality maintenance, we examined the tolerate range of theoretical expectation accuracy and established the quality assessment standard in spatial data for the transformed digital map data do not act contrary to the digital map specifications and the digital map accuracy of the relational scale. And, transforming large scale digital map to small scale, if we reduce complexity through processes as simplification, smoothing, refinement and so on., the spatial position change may be always happened. thus, because it is very difficult to analyse the spatial accuracy of the transformed position, we used the buffering as assessment method of spatial accuracy in digital map generalization procedure. Although the tolerated range of generic positioning error for l/l, 000 and l/5, 000 scale is determined based on related law, because the algorithms adapted to each processing elements have different property each other, if we don't determine the suitable parameter and tolerance, we will not satisfy the result after generalization procedure with tolerated range of positioning error. The results of this study test which is about the parameters of each algorithm based on tolerated range showed that the parameter of the simplification algorithm and the positional accuracy are 0.2617 m, 0.4617 m respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.41-52
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• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.341-344
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• 2007

A major obstacle to classify and validate Land Cover maps is the high cost of generating reference data or multiple thematic maps for subsequent comparative analysis. In case of inaccessible area such as North Korea, the high resolution satellite imagery may be used as in situ data so as to overcome the lack of reliable reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird (0.6m) of North Korea obtained from Google Earth data provided thru internet. Monthly NDVI images of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes; coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water and built-up area. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional in situ data collection on the site where the accessibility is severely limited.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1063-1074
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• 2010

The objective of this study is to evaluate the application of Land Surface Model (LSM) and global spatial and weather data. After selecting the appropriate LSM, we evaluated the calculation ability of the model for dam basins. Based on the global meteorological and topography data, the accuracy of runoff results were analysed to assess the uncertainty of global data. Period analysis was performed to suggest the global data utilization. The model results by using local data are within the acceptable range reflecting the local complex meteorological and topographical characteristics. Although the accuracy of the simulated results from global data is not good by the uncertainty of meteorological data, it indicated that the accuracy can be improved with increasing duration of runoff analysis over 10 days.