Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/1728
Title: Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods
Authors: Ruiz-Lendínez, Juan J.
Ariza-López, Francisco J.
Ureña-Cámara, Manuel A.
Abstract: first_pagesettingsOrder Article Reprints Open AccessArticle Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods by Juan José Ruiz-Lendínez *,Francisco Javier Ariza-López andManuel Antonio Ureña-CámaraORCID Department of Cartographic Engineering, University of Jaén, 23071 Jaén, Spain * Author to whom correspondence should be addressed. ISPRS Int. J. Geo-Inf. 2019, 8(12), 552; https://doi.org/10.3390/ijgi8120552 Submission received: 18 September 2019 / Revised: 27 November 2019 / Accepted: 2 December 2019 / Published: 2 December 2019 Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Point-based standard methodologies (PBSM) suggest using ‘at least 20’ check points in order to assess the positional accuracy of a certain spatial dataset. However, the reason for decreasing the number of checkpoints to 20 is not elaborated upon in the original documents provided by the mapping agencies which develop these methodologies. By means of theoretical analysis and experimental tests, several authors and studies have demonstrated that this limited number of points is clearly insufficient. Using the point-based methodology for the automatic positional accuracy assessment of spatial data developed in our previous study Ruiz-Lendínez, et al (2017) and specifically, a subset of check points obtained from the application of this methodology to two urban spatial datasets, the variability of National Standard for Spatial Data Accuracy (NSSDA) estimations has been analyzed according to sample size. The results show that the variability of NSSDA estimations decreases when the number of check points increases, and also that these estimations have a tendency to underestimate accuracy. Finally, the graphical representation of the results can be employed in order to give some guidance on the recommended sample size when PBSMs are used.
Keywords: spatial data accuracy
automatic assessment
standards
NSSDA estimations
Issue Date: 2019
metadata.dc.description.sponsorship: This research was funded by the Ministry of Education and Culture of Spain, Grant number CAS18/00024 (“José Castillejo” Mobility Support for Stay Abroad Program).
Publisher: MDPI
Appears in Collections:DICGF-Artículos



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