The Optimum Dataset method – examples of the application

Abstract

Abstract. Data reduction is a procedure to decrease the dataset in order to make their analysis more effective and easier. Reduction of the dataset is an issue that requires proper planning, so after reduction it meets all the user's expectations. Evidently, it is better if the result is an optimal solution in terms of adopted criteria. Within reduction methods, which provide the optimal solution there is the Optimum Dataset method (OptD) proposed by Błaszczak-Bąk (2016). The paper presents the application of this method for different datasets from LiDAR and the possibility of using the method for various purposes of the study. The following reduced datasets were presented: (a) measurement of Sielska street in Olsztyn (Airbrone Laser Scanning data -ALS data), (b) measurement of the bas-relief that is on the building in Gdańsk (Terrestrial Laser Scanning data -TLS data), (c) dataset from Biebrza river measurment (TLS data).

Reduction Method for Mobile Laser Scanning Data

“…Our previous method, which was called the Optimum Dataset method, is presented in Błaszczak-Bąk et al [17][18][19]. The OptD method removes those points which do not have relevant effect on the terrain characteristics from a practical point of view.…”

Terrestial laser scaning in forest invetory based on OptD optimization method

“…The OptD is a optimization method described in [12][13][14] and used to reducing the number of points in the processing of Airborne Laser Scanning point cloud. Of course there is a lot of algorithms described in the literature [15][16] that allows to filter and reduce the point clouds obtained from laser scanning, but the proposed algorithm, in comparison to other methods, allows to obtain the optimal solution. The algorithm of this method can reduce the datasets based on the specified optimization criterion.…”

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

“…As optimization criteria in the OptD method parameters like: the number of points in reduced dataset (M) and the percentage of points to be in the dataset after processing (p%) were used and tested so far [13][14][15][16][17]. In this paper it was decided to use the standard deviation estimator (SD) of ALS data.…”

“…In this paper focus is on the influence of the source data, in particular, whether the standard deviation estimator of ALS (Airborne Laser Scanning) data can be used as optimization criterion in dataset reduction and whether using SD has an impact on the generated DTM. Reduction was performed by means of the Optimum Dataset (OptD) method [12], which allows to preserve points representing characteristics elements in reduced dataset [13,14,15]. DTM was generated on the basis of the original dataset (after its filtration) as well as from the datasets obtained after processing by the OptD method.…”