Abstract: The present invention relates to an apparatus for editing geological elements using a touch-based interface for retrieving geological information of a survey area, thereby mapping and displaying geometric elements of points, lines, and areas. The apparatus for editing geological elements according to the present invention includes a geological symbol storage module configured to store point symbols, line symbols, and area symbols; a geographic information storage module; a geological element edit module configured, by a user's operation, to read geological information on a specific map from the geological information storage module, to read the symbols from the geological symbol storage module, to rotate the point symbols, to connect the line symbols to each other, to automatically transform the line symbols to area symbols, and to automatically create outlines for the area symbols; a controller; and a display module.

Abstract: The present invention relates to an apparatus for editing geological elements using a touch-based interface for retrieving geological information of a survey area, thereby mapping and displaying geometric elements of points, lines, and areas. The apparatus for editing geological elements according to the present invention includes a geological symbol storage module configured to store point symbols, line symbols, and area symbols; a geographic information storage module; a geological element edit module configured, by a user's operation, to read geological information on a specific map from the geological information storage module, to read the symbols from the geological symbol storage module, to rotate the point symbols, to connect the line symbols to each other, to automatically transform the line symbols to area symbols, and to automatically create outlines for the area symbols; a controller; and a display module.

Abstract: The present inventions generally relate to a leaf node ranking method in decision trees for spatial prediction and its recording medium. The leaf node ranking method in decision trees includes a learning step to form a decision tree having one root node, in which each parent node has multiple child nodes, using training data sets for spatial prediction; and a leaf node ranking step from the decision tree that finishes the learning. In the learning step, each node of the decision tree stores both the number of classes according to class distribution of training data and structures for storing the number. In the leaf node ranking step, a rank of a leaf node is determined using the number of classes according to class distribution, which is stored in each node on a path from the root node to the leaf node.

Abstract: The present inventions generally relate to a leaf node ranking method in decision trees for spatial prediction and its recording medium. The leaf node ranking method in decision trees includes a learning step to form a decision tree having one root node, in which each parent node has multiple child nodes, using training data sets for spatial prediction; and a leaf node ranking step from the decision tree that finishes the learning. In the learning step, each node of the decision tree stores both the number of classes according to class distribution of training data and structures for storing the number. In the leaf node ranking step, a rank of a leaf node is determined using the number of classes according to class distribution, which is stored in each node on a path from the root node to the leaf node.