Proposed Technology of lidar data processing to build DTM

Light Detection and Ranging (LiDAR) is a sensing technology whieh has application in building the Digital Terrain Model (DTM). A point cloud generated from laser scanning makes up the so-called large dataset, which is difficult and sometimes even impossible to use directly. Import of LiDAR point cloud into appropriate software and its processing is time-consuming and demands high computing power. Therefore, it is advisable to optimize the volume of observation results which make up the point cloud. The following paper presents operation of a modified algorithm for optimization of points' number in a large dataset [Błaszczak W., 2006]. The optimization involves reduction and uses existing cartographic generalization methods. The optimized dataset was filtered, and during the process the points representing the terrain were separated from data representing non-ground elements. Filtration was carried out with the application of a proposed new method including trend line in search belts, and the laser power used to register points. The optimized and filtered data set was then used to build a DTM. The results obtained encourage further detailed study of theoretical and empirical character.