• Journal of Korea Spatial Information System Society
• /
• v.11 no.1
• /
• pp.55-62
• /
• 2009

In u-GIS environment, we collect spatial data needed through sensor network and provide them with information real-time processed or stored. When information through Internet is requested on Web based applications, it is transmitted in XML. Especially, when requested information includes spatial data, GML, S-XML, and other document that can process spatial data are used. In this processing, real-time stream data processed in DSMS is transformed to S-XML document type and spatial information service based on web receive S-XML document through Internet. Because most of spatial application service use existing spatial DBMS as a storage system, The data used in S-XML and SDBMS needs transformation between themselves. In this paper, we propose S-XML a transformation method using caching of spatial data. The proposed method caches the spatial data part of S-XML to transform S-XML and relational spatial database for providing spatial data efficiently and it transforms cached data without additional transformation cost when a transformation between data in the same region is required. Through proposed method, we show that it reduced the cost of transformation between S-XML documents and spatial information services based on web to provide spatial information in u-GIS environment and increased the performance of query processing through performance evaluation.

• Journal of Korea Multimedia Society
• /
• v.4 no.3
• /
• pp.197-204
• /
• 2001

In order to increase the performance of spatial database systems, a spatial indexing method is necessary to manage spatial objects efficiently in both dynamic and static environments. A spatial indexing method considering a spatial locality is required to increase the retrieval performance. And the spatial locality is related to the location property of objects. The previous spatial indexing methods did not consider the circular location property of objects. In this paper, we introduce the CR-Tree that is a spatial index structure for clustering spatially adjacent objects in which a search space is constructed with the circular and linear domains. Using a spatial index structure considered a circular location property of objects, we show that high hit ratio and bucket utilization are increased through the simulation.

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

• Korean Journal of Remote Sensing
• /
• v.24 no.2
• /
• pp.117-124
• /
• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.42-45
• /
• 2002

A novel characteristic value extraction method based on morphological spatial frequency is proposed. Morphological spatial frequency defined by morphological pattern distribution function is introduced. Superiority of the method was proved for various images by experiment. Furthermore the fact that the proposed method does not need threshold to obtain binary image provides its applicability to content-based image retrieval.

• Journal of Cadastre & Land InformatiX
• /
• v.47 no.2
• /
• pp.185-199
• /
• 2017

This study is on a recombination method for the construction of spatial information products which demands are unpredictably various. The present production method of digital maps is not flexible enough for their reusability because it is not object-oriented but top-down. Each spatial object needs to have particular attributes to be recombined. The demand changes the production method through the reclassification of data and changing the properties. In a user perspective, the bottom-up method can produce on-demand spatial information products including existing digital maps. The method is derived from case studies and theoretical reviews and compared with the existing production method. In the method spatial information products are reclassified by their geometry objects such as point, line, and polygon, with basic attributes, and other related domain attributes. The geometry objects and domain attributes are connected by adding new attributes for their later relationship and management, which make the recombination possible. To prove its usability of the method it is tested for current and future user demands including the national base map, thematic maps and the future spatial information products.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.4
• /
• pp.1317-1325
• /
• 2014

The study focused on analyzing spatial sampling by minimizing autocorrelation of spatial based on spatial and geographical data. The study concluded two different ways of minimizing autocorrelation. First, it was important to use suitable spatial sampling method to alienate spatial autocorrelation from spatial or geographical data. The shear distribution rate of public transportation in Seoul resulted in high rate of autocorrelation. However, the study showed samples eliminated autocorrelation when samples were extracted with reasonable distance(above 400m) apart. Without spatial sampling the distortion of spatial data leads to false results; therefore, spatial sampling is indispensable. Second, factors which fluctuates shear distribution of public transportation spatial sampling changed before and after spatial sampling. This was caused by incapable of controling inherent spatial autocorrelation of the data.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1851-1863
• /
• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.361-364
• /
• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• Spatial Information Research
• /
• v.13 no.4 s.35
• /
• pp.355-364
• /
• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.