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Reduction of the measurement dataset is one of the current issues related to constantly developing technologies that provide large datasets, e.g. laser scanning. It could seems that presence and evolution of processors computer, increase of hard drive capacity etc. is the solution for development of such large datasets. And in fact it is, however, the “lighter” datasets are easier to work with. Additionally, reduced datasets can be exchange/transfer/download faster via internet or cloud stored. Therefore the issue of data reduction algorithms/methods is continuously relevant. In this paper authors presented the results of the study whether the standard deviation of measurement data can be used as optimization criterion in the process of dataset reduction conducted by means of the OptD method. The OptD is based on the cartographic generalization methods. In iterative process irrelevant points are being removed and those that characteristic are being preserved, what in results means more points in complex fragments of scanned object/surface and less in flat/uncomplicated area. Obtained reduced datasets were then the basis for DTMs generation. For DTMs assessment RMSE was calculated.

Standard deviation as the optimization criterion in the OptD method and its influence on the generated DTM