In order to classify the raw data of ground surface and non-ground surface acquired from a terrestrial LiDAR, this study developed an automatic module for a complex filtering of global and regional areas by using an algorithm derived from multiple linear regression analysis ; and, by using this developed module, this study conducted the filtering of the raw data acquired from observing the actual sloped terrain using a terrestrial LiDAR and arrived at the following conclusions.
In the case of a sloped terrain, when a complex filtering was conducted by dividing the sloped terrain with the topographical line as a reference, it resulted in an improvement in filtering accuracy of 92%.
As such, when filtering was applied by dividing the observation areas into areas A and B with the topographical line as a reference in order to improve the filtering accuracy, it was seen that the filtering accuracy improved by about 8?9% as compared to when filtering was applied without dividing the observation area.
In addition, considering the fact that the accuracy improved by 5?7% when the sloped sides of a multi-curvature terrain were divided and a complex filtering applied as compared to when filtering was applied for the entire area or by regions, it can be asserted that the accuracy was higher when a complex filtering was conducted by dividing the sloped areas where the slope is not constant due to the multi-curvature of terrain.
Also, by applying the automatic module for filtering developed using Visual Basic Application on the raw data acquired from a terrestrial LiDAR in a sloped terrain, it was possible to efficiently classify the ground surface and non-ground surface data from a terrestrial LiDAR.
Therefore, it is deemed that a performance of high accuracy for terrain analysis can be expected when this is applied in the production of maps for areas of landslide hazard caused by the precipitation levels and heavy rains that have increased sharply of recent and the production of digital elevation models for the implementation of geo-spatial information.