Reduction of measurement data before Digital Terrain Model generation vs. DTM generalisation

Błaszczak-Bąk, Wioleta; Poniewiera, Marian; Sobieraj-Żłobińska, Anna; Kowalik, Michał

doi:10.1080/00396265.2018.1474685

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The time required for the implementation of the OptD method can be considered as negligible in the whole process of preparing the data for the DTM construction. In the work, [35] has shown that the approach based on the OptD method is less time-and labour-consuming than the approach based on DTM generalization. It results from the fact that before the DTM is generalized, it must first be built from the original point cloud.…”

Section: Theoretical Background Of Optd Methodsmentioning

confidence: 99%

On the Use of the OptD Method for Building Diagnostics

et al. 2020

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Terrestrial laser scanner (TLS) measurements can be used to assess the technical condition of buildings and structures; in particular, high-resolution TLS measurements should be taken in order to detect defects in building walls. This consequently results in the creation of a huge amount of data in a very short time. Despite high-resolution measurements typically being needed in certain areas of interest, e.g., to detect cracks, reducing redundant information on regions of low interest is of fundamental importance in order to enable computationally efficient and effective analysis of the dataset. In this work, data reduction is made by using the Optimum Dataset (OptD) method, which allows to significantly reduce the amount of data while preserving the geometrical information of the region of interest. As a result, more points are retained on areas corresponding to cracks and cavities than on flat and homogeneous surfaces. This approach allows for a thorough analysis of the surface discontinuity in building walls. In this investigation, the TLS dataset was acquired by means of the time-of-flight scanners Riegl VZ-400i and Leica ScanStation C10. The results obtained by reducing the TLS dataset by means of OptD show that this method is a viable solution for data reduction in building and structure diagnostics, thus enabling the implementation of computationally more efficient diagnostic strategies.

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Section: Theoretical Background Of Optd Methodsmentioning

confidence: 99%

On the Use of the OptD Method for Building Diagnostics

et al. 2020

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“…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.…”

Section: Data Reduction and Dtm Generationmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Błaszczak-Bąk

Sobieraj-Żłobińska

2018

E3S Web Conf.

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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.

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“…It can be used in the OptD-single variant, when there is one optimization criterion, or in the OptD-multi one when there are more criteria. The OptD-single method was tested, among others, in [1,9], while the OptD-multi method in [2]. In the case of processing by means of the OptD-single method, one solution is obtained, while the OptD-multi gives as a result more than one solution.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Błaszczak-Bąk

Sobieraj-Żłobińska

Kowalik

2019

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New measurement technologies, e.g. Light Detection And Ranging (LiDAR), generate very large datasets. In many cases, it is reasonable to reduce the number of measuring points, but in such a way that the datasets after reduction satisfy specific optimization criteria. For this purpose the Optimum Dataset (OptD) method proposed in [1] and [2] can be applied. The OptD method with the use of several optimization criteria is called OptD-multi and it gives several acceptable solutions. The paper presents methods of selecting one best solution based on the assumptions of two selected numerical optimization methods: the weighted sum method and the ε-constraint method. The research was carried out on two measurement datasets from Airborne Laser Scanning (ALS) and Mobile Laser Scanning (MLS). The analysis have shown that it is possible to use numerical optimization methods (often used in construction) to obtain the LiDAR data. Both methods gave different results, they are determined by initially adopted assumptions and -in relation to early made findings, these results can be used instead of the original dataset for various studies.

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Reduction of measurement data before Digital Terrain Model generation vs. DTM generalisation

Cited by 4 publications

References 9 publications

On the Use of the OptD Method for Building Diagnostics

On the Use of the OptD Method for Building Diagnostics

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

Metrology and Measurement Systems

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