• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.103-112
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• 2017

Due to the large size and high complexity of modern buildings, the interest and the studies about indoor spatial information are increasing. Previous studies related to indoor spatial information were mostly about relevant technologies, and the application of indoor spatial information has been less studied. In the present study, the public administrative work areas where indoor spatial information may be applied were identified by using a modified delphi technique. And the indoor LOD (Level of Detail) and indoor positioning accuracy for indoor spatial information applications considering user requirements was established as standards for efficiently establishing and providing services. The required LOD and positioning accuracy for services was established by reestablishing indoor LOD and positioning accuracy and classifying services with reference to those. The indoor LOD was reestablished from LOD 0 to 4 by focusing on service utilization and general recognition, and the positioning accuracy was reestablished in three levels by considering the accuracy of the present positioning technology and service utilization status.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.138-140
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• 2003

The aim of this study is to evaluate the effect of spatial resolutions on the accuracy to landslide susceptibility mapping. For this, landslide locations were identified in the Boun, Korea from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, linearment and land use data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The 15 factors that influence landslide occurrence were extracted and calculated from the spatial database with 5m, 10m, 30m, 100m and 200m spatial resolutions. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability model, likelihood ratio, for the five cases spatial resolutions. The results of the analysis were verified using the landslide location data. In the cases of spatial resolution 5m, 10m and 30m, the verification results was similar, but in the cases of 100m and 200m the results worse than the others. Because the scale of input data was 1:5,000 ? 1:50,000, so the cases of 5m, 10m and 30m have similar accuracy but the cases of 100m and 200m have the lower accuracy. From this, there is an effect of spatial resolutions on accuracy and landslide susceptibility mapping the result is dependent on input map.

• Journal of KIBIM
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• v.4 no.4
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• pp.13-23
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• 2014

After being introduced, Building Information Modeling (BIM) has been actively applied to the cost estimation of construction projects, and various studies on BIM based quantity take-off have been carried out. In practice, however, these calculations take considerable time, because BIM based quantity take-off is further conducted along with 2D-based quantity take-off. Studies on the quantity take-off using BIM spatial objects have been carried out on early stages of projects, but how this method differs from the existing quantity take-off method and how accurate it is in comparison have rarely been verified. Therefore, by comparing 2D based quantities with quantities through BIM spatial objects, this study analyzed the accuracy of quantity take-off using BIM spatial objects. To this end, the properties of BIM spatial objects and quantity calculable spatial types were analyzed, and existing 2D-based quantities and quantities extracted from BIM spatial objects were compared through a case study. As a result, the quantity of spatial objects found to be more by about 7.13% in 0.05% and therefore, this difference should be considered during quantity take-off using BIM spatial objects. Through the results of this study, we can improve the accuracy of quantity take-off using BIM spatial objects in the early stage of a construction project.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.669-678
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• 2023

Recently, spatial information has been applied to various fields and its usability is increasing day by day. In particular, in the field of civil engineering and construction, BIM based on spatial information is being applied to all construction industries and related research has been conducted. BIM is a technology that utilizes spatial information from the design phase and aids in the construction and maintenance of buildings, including the management of their attributes. However, to apply BIM technology to existing buildings, it takes a lot of time and money to produce models based on design drawings along with current surveying. In this study, quantitative and qualitative analysis was conducted to determine the applicability of the acquired data and the applicability of BIM by generating data and analyzing the accuracy using UAV images and ground lidar, which are representative spatial information acquisition methods. Quantitative analysis revealed that TLS (Terrestrial Laser Scanner) showed reliable accuracy in both planar and elevation measurements, whereas unmanned aerial images exhibited lower accuracy in elevation measurements, resulting in reduced reliability. Qualitative analysis indicated that neither TLS nor unmanned aerial images alone provided perfect completeness. However, the combination of both spatial information sources, tailored to specific needs, resulted in the most comprehensive completeness. Therefore, it is concluded that the appropriate utilization of spatial information acquired through unmanned aerial images and TLS holds the potential for application in the fields of BIM and reverse engineering.

• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.521-532
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• 2015

Purpose: The purpose of this study is to compare two multivariate cumulative sum (MCUSUM) charts designed for spatio-temporal surveillance in terms of not only temporal detection performance but also spatial detection performance. Method: Experiments under various configurations are designed and performed to test two CUSUM charts, namely SMCUSUM and RMCUSUM. In addition to average run length(ARL), two measures of spatial identification accuracy are reported and compared. Results: The RMCUSUM chart provides higher level of spatial identification accuracy while two charts show comparable performance in terms of ARL. Conclusion: The RMCUSUM chart has more flexibility, robustness, and spatial identification accuracy when compared to those of the SMCUSUM chart. We recommend to use the RMCUSUM chart if control limit calibration is not an urgent task.

• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.587-607
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• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8370-8377
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• 2015

In spatial data stream management systems, it is needed appropriate load shedding algorithm because real-time input spatial data streams could exceed the limitation of main memory. However previous researches, lack regard for input ratio and spatial utilization rates of spatial data streams, or the characteristics of data source which generates data streams with spatial information efficiently, can lead to decrease the performance and accuracy of spatial data stream query. Therefore, multi-level load shedding scheme for spatial data stream management systems is proposed to increase the spatial query performance and accuracy. This proposed scheme limits overloads in relation to the input rate and the characteristics of data source first, and then, if needed, query data representing low query participation probability based on spatial utilizations are dropped relatively. Our experiments show that the proposed method could decrease load shedding frequency for previous researches by more than 11% despite query results accuracy and query performance are superior at 0.04% and 3%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.301-308
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• 2003

As the results of this study, the proposed method of this study which is increased to accuracy of DEM by classification of terrain is better than accuracy of DEM which is automatically generated by digital photogrammetry workstation system(DPWS). And, the edge detection method which is proposed by this study is established to extraction of geo-spatial information in ortho image.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.81-81
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• 2002

Classification is considered as one of the processes of extracting attributes from satellite imagery and is one of the usual functions in the commercial satellite image processing software. Accuracy of classification plays a key role in deciding the usage of its results. Many tremendous efforts far the higher accuracy have been done in such fields; training area selection, classification algorithm. Our research is one of these effort in different manners. In this research, we conduct classification using multiple satellite image data and evidential approach. We statistically consider the posterior probabilities and certainty in maximum likelihood classification and methodologically Dempster's orthogonal sums. Unfortunately, accuracy for the whole data sets has not assessed yet, but accuracy assessments in training fields and check fields shows accuracy improvement over 10% in overall accuracy and over 0.1 in kappa index.