• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.41-44
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• 2008

In this paper, major species of tree were discriminated in compartment by using LiDAR data and optic imagery. This is an important work in forest field. A current digital stock map has created the aerial photo and collecting survey data. Unlike high resolution imagery, LiDAR data is not influenced by topographic effects since it is an active sensory system. LiDAR system can measure three dimension information of individual tree. And the main methods of this study were to extract reliable the individual tree and analysis techniques to facilitate the used LiDAR data for calculating tree crown 2D parameter. We should estimate the forest inventory for calculating parameter. 2D parameter has need of area, perimeter, diameter, height, crown shape, etc. Eventually, major species of tree were determined the tree parameters, compared a digital stock map.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.543-554
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• 2002

GIS (Geographic Information Systems) need faster access and better visualization. For faster access and better visualization in GIS, map generalization and levels of detail are needed. Existing spatial indexing methods do not support map generalization. Also, a few existing spatial indexing methods supporting map generalization do not support ail map generalization operations. We propose a new index structure, i.e. the LR-tree, supporting ail map generalization operations. This paper presents algorithms for the searching and updating the LR-tree and the results of performance evaluation. Our index structure works better than other spatial indexing methods for map generalization.

• Spatial Information Research
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• v.16 no.1
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• pp.113-127
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• 2008

This paper proposes an efficient spatial index, named $AR^*$-tree(Area $R^*$-tree) which is a variant of the $R^*$-tree, for mobile software. A MBR(Minimum Bounding Rectangle) structure of the $AR^*$-tree has additional min and max values of area axis as well as x and y axes. The value of area axis is used to determine the significance of a spatial data. If area of a spatial data is large, then it is significant when drawing a map. To reduce complexity of a map on a small screen of mobile device, only significant spatial data can be found by the $AR^*$-tree. The result of a series of tests indicates that the $AR^*$-tree provides a method for control of readability of a map and guarantees an efficient performance at the same time.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.200-203
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• 2008

The objective of this study is to produce the $CO_2$ (carbon dioxide) absorption map using KOMPSAT-2 imagery. For estimating the amount of $CO_2$ absorption, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated by the crown width extracted from KOMPSAT-2 imagery. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the IPCC (Intergovernmental Panel on Climate Change) guideline, was used to convert the stand biomass into the amount of $CO_2$ absorption. Thereafter, the KOMPSAT-2 imagery was classified with the SBC (segment based classification) method in order to quantify $CO_2$ absorption by tree species. As a result, the map of $CO_2$ absorption was produced and the amount of $CO_2$ absorption was estimated by tree species.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.47-54
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• 2010

The purpose of this study is finding examine the Thinking Maps and how to use Thinking Maps effectively in Science Education. The result of this study were as follows: First, There are 8 type Maps, Circle Map, Tree Maps, Bubble Map, Double Bubble Map, Flow Map, Multi Flow Map, Brace Map, Bridge Map. Each Maps are useful in the following activities ; Circle Map-Express their thoughts. Tree Map-Activities as like determine the structure, classification, information organization. Bubble Maps-Construction. Double Bubble Map-Comparison of similarities and differences. Flow Map-Set goals, determine the result of changes in time or place. Multi Flow Map-Analysis cause and effect, expectation and reasoning. Brace Map-Analysis whole and part. Bridge Map-Activities need analogies. Second, each element of inquiry has 1~2 appropriate type of Thinking Maps. So student can choose the desired map. Third, the result of analysing of Science Curriculum Subjects, depending on the subject variety maps can be used. Therefore the Thinking Maps can be used for a variety on activities and subject. And student can be selected according to their learning style. So Thinking Maps are effective to improve student's Self-Directed Learning.

• The Korean Journal of Ecology
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• v.10 no.4
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• pp.183-193
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• 1987

An investigation was performed to draw the decy map of litters on the floors of pine and oak forests in Korea and to reveal the turnover cycles of N, P, K, Ca and Mg in litters. Isodecay constant lines of litter organic matter, which are depended upon the altitude, latitude and orientation, were drawn on the Korean map. The decay constants of organic matter of litters were higher in the broadleaf tree forests than in the needleleaf tree forests, and in the grasslands than in the forests. The amount of mineral nutrients such as N, P, K, Ca and Mg returned annually to soils is higher in the broadleaf tree forests than in the needle leaf tree forests, and highest in the Quercus mongolica forest of the forests.

• Korean Journal of Environment and Ecology
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• v.22 no.4
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• pp.396-408
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• 2008

Gwangtan-myeon, Paju-si, Gyeonggi-do was classified as a 4 grade age-class deciduous tree forest, however as a result of vegetation survey, this site was found to consist of natural forest with deciduous trees, thus causing difficulty in judging which age class it belongs to. Subsequently, the necessity of drawing up a detailed stock map plan was raised. For this reason, this research was designed to propose a mapping method for a detailed stock map plan based on a detailed survey on actual vegetation, vegetation structure, and analysis data on tree rings. The detailed analysis of actual vegetation pattern showed that there exist 22 patterns of vegetation, in which the natural forest has 11 patterns, such as Quercus mongolica forest and Q. variabilis forest, etc. while the artificial forest was found to have 6 patterns including Castanea crenata, etc. In order to verify their age-class, this research measured a tree age by collecting 42 quadrats and 89 specimen tree cores on the basis of a detailed actual vegetation map; as a result, an artificial forest and oak trees with small diameters located at low-lying areas, was categorized as 2-grade age class(covering 29.8%), and other areas were judged to be available for land use as 3-grade age-class(covering 57.6%) while the areas judged to be 4-or-more grade age-class (covering 8.8%) was impossible for land use because they are located on a steep slope ridge line on a boundary. In case a proposed site for a small-scale development is judged as a natural forest with deciduous trees as mentioned above, it is necessary that a detailed stock map plan should be drawn up through a detailed investigation into actual vegetation and analysis of plant gathering structure & specimen trees. A detailed stock map plan includes the data that makes it possible to comprehensively judge natural property, scarcity, and diversity of vegetation; thus, it is considered that a detailed stock map plan will be useful in judging the development propriety of a small-scale site.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.15-22
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• 2004

Map services for cellular phone have problem for implementation, which are the limitation of a screen size. To effectively represent map data on screen of celluar phone, it need a process which translate a detailed map data into less detailed data using map generalization, and it should manipulate zoom in out quickly by leveling the generalized data. However, current spatial indexing methods supporting map generalization do not support all map generalization operations. In this paper, We propose a leveled spatial indexing method, LMG-tree, supporting map generalization and presents the results of performance evaluation.