Visualization of Horizontal Positional Accuracy Assessment Results for Digital Topographic Maps at Scale 1:25000

Kostic, Miodrag

doi:10.15308/sinteza-2017-308-314

Cited by 1 publication

(1 citation statement)

References 8 publications

(9 reference statements)

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“…ISO 19157 standard (ISO 2013) defines that the basic quality elements to be observed and evaluated in cartographic products are: completeness, logical consistency, positional accuracy, temporal accuracy, thematic accuracy and usability. It should be noted that positional accuracy is the most used element to verify spatial data quality (Jakobsson and Vauglin 2002;Drobnjak et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Proposal of a method for evaluating the spatial distribution pattern of linear features

Cunha,

Santos,

Nero

et al. 2024

Bol. Ciênc. Geod.

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Positional accuracy of cartographic products is typically evaluated using positional discrepancies and point-based techniques. However, using linear features has some advantages over the point-based method, such as a greater amount of geometric and positional information and the fact that approximately 80% of the features on a cartographic basis are lines. Despite these advantages, important parameters for evaluating accuracy using lines have not yet been established or determined, such as the spatial distribution pattern, although it is a relevant factor that can affect the results and determine the validity of an evaluation process. This study proposes a method based on the modification of the Nearest Neighbor Method for points, which can be used to evaluate the spatial distribution pattern of linear features. Instead of the traditional Euclidean distance used by the method for points, the method proposes using the Hausdorff Distance as a measure of the spacing between lines. The proposed method, called Nearest Neighbor Method for Linear Features (NNMLF), was applied to simulated and real data. All experiments with simulated data showed that the NNMLF was effective in estimating spatial distribution pattern up to the third order. Its use on real data showed NNMLF is simple to apply.

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Section: Introductionmentioning

confidence: 99%