A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds

Autores
Gallo, Leandro César; Cristallini, Ernesto Osvaldo; Svarc, Marcela
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite its significance, few studies have addressed the uncertainties of the analysis, which can adversely affect the reproduction of results one of the cornerstones of scientific endeavor. Aiming to contribute toward the neglected issue of the moment of inertia precision, we have developed a bootstrap resampling scheme to empirically discover the distribution of uncertainties in the orientation of best fit planes. Dispersion of the bootstrapped normal vectors to the best fit plane is regarded as a measure of precision, evaluated with the maximum angular distance from the optimal solution. This rationale was tested using Monte Carlo-generated samples covering a comprehensive range of shape parameters to assess the dependence between eigen parameters and their inherent bias. Our results show that the oblateness of the point cloud is a robust parameter to assess the reliability of the best fit plane. Given this, the method was then applied to a publicly available lidar data set. We argue that georeferenced point clouds with an oblateness parameter greater than 3 and 1.5 may be placed at 95% confidence levels of 5° and 10°, respectively. We propose using these values as thresholds to obtain robust best fit planes, guaranteeing reproducible results for scientific research.
Fil: Gallo, Leandro César. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; Argentina
Fil: Cristallini, Ernesto Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
Materia
BEST FIT PLANE
BOOTSTRAP STATISTICS
MOMENT OF INERTIA ANALYSIS
MONTE CARLO SIMULATION
ORIENTATION OF STRUCTURAL HETEROGENEITIES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/87672
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/87672
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable ThresholdsGallo, Leandro CésarCristallini, Ernesto OsvaldoSvarc, MarcelaBEST FIT PLANEBOOTSTRAP STATISTICSMOMENT OF INERTIA ANALYSISMONTE CARLO SIMULATIONORIENTATION OF STRUCTURAL HETEROGENEITIEShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite its significance, few studies have addressed the uncertainties of the analysis, which can adversely affect the reproduction of results one of the cornerstones of scientific endeavor. Aiming to contribute toward the neglected issue of the moment of inertia precision, we have developed a bootstrap resampling scheme to empirically discover the distribution of uncertainties in the orientation of best fit planes. Dispersion of the bootstrapped normal vectors to the best fit plane is regarded as a measure of precision, evaluated with the maximum angular distance from the optimal solution. This rationale was tested using Monte Carlo-generated samples covering a comprehensive range of shape parameters to assess the dependence between eigen parameters and their inherent bias. Our results show that the oblateness of the point cloud is a robust parameter to assess the reliability of the best fit plane. Given this, the method was then applied to a publicly available lidar data set. We argue that georeferenced point clouds with an oblateness parameter greater than 3 and 1.5 may be placed at 95% confidence levels of 5° and 10°, respectively. We propose using these values as thresholds to obtain robust best fit planes, guaranteeing reproducible results for scientific research.Fil: Gallo, Leandro César. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Cristallini, Ernesto Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; ArgentinaFil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaAmerican Geophysical Union2018-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87672Gallo, Leandro César; Cristallini, Ernesto Osvaldo; Svarc, Marcela; A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds; American Geophysical Union; Journal of Geophysical Research: Solid Earth; 123; 11; 11-2018; 10,297-10,3082169-93132169-9356CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JB016319info:eu-repo/semantics/altIdentifier/doi/10.1029/2018JB016319info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:07:26Zoai:ri.conicet.gov.ar:11336/87672instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:07:27.185CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
title A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
spellingShingle A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
Gallo, Leandro César
BEST FIT PLANE
BOOTSTRAP STATISTICS
MOMENT OF INERTIA ANALYSIS
MONTE CARLO SIMULATION
ORIENTATION OF STRUCTURAL HETEROGENEITIES
title_short A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
title_full A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
title_fullStr A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
title_full_unstemmed A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
title_sort A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds
dc.creator.none.fl_str_mv Gallo, Leandro César
Cristallini, Ernesto Osvaldo
Svarc, Marcela
author Gallo, Leandro César
author_facet Gallo, Leandro César
Cristallini, Ernesto Osvaldo
Svarc, Marcela
author_role author
author2 Cristallini, Ernesto Osvaldo
Svarc, Marcela
author2_role author
author
dc.subject.none.fl_str_mv BEST FIT PLANE
BOOTSTRAP STATISTICS
MOMENT OF INERTIA ANALYSIS
MONTE CARLO SIMULATION
ORIENTATION OF STRUCTURAL HETEROGENEITIES
topic BEST FIT PLANE
BOOTSTRAP STATISTICS
MOMENT OF INERTIA ANALYSIS
MONTE CARLO SIMULATION
ORIENTATION OF STRUCTURAL HETEROGENEITIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite its significance, few studies have addressed the uncertainties of the analysis, which can adversely affect the reproduction of results one of the cornerstones of scientific endeavor. Aiming to contribute toward the neglected issue of the moment of inertia precision, we have developed a bootstrap resampling scheme to empirically discover the distribution of uncertainties in the orientation of best fit planes. Dispersion of the bootstrapped normal vectors to the best fit plane is regarded as a measure of precision, evaluated with the maximum angular distance from the optimal solution. This rationale was tested using Monte Carlo-generated samples covering a comprehensive range of shape parameters to assess the dependence between eigen parameters and their inherent bias. Our results show that the oblateness of the point cloud is a robust parameter to assess the reliability of the best fit plane. Given this, the method was then applied to a publicly available lidar data set. We argue that georeferenced point clouds with an oblateness parameter greater than 3 and 1.5 may be placed at 95% confidence levels of 5° and 10°, respectively. We propose using these values as thresholds to obtain robust best fit planes, guaranteeing reproducible results for scientific research.
Fil: Gallo, Leandro César. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; Argentina
Fil: Cristallini, Ernesto Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
description The fitting of a plane to data points is essential to the geosciences. However, it is recognized that the reliability of these best fit planes depends upon the point set distribution and geometry, evaluated in terms of the eigen-based parameters derived from the moment of inertia analysis. Despite its significance, few studies have addressed the uncertainties of the analysis, which can adversely affect the reproduction of results one of the cornerstones of scientific endeavor. Aiming to contribute toward the neglected issue of the moment of inertia precision, we have developed a bootstrap resampling scheme to empirically discover the distribution of uncertainties in the orientation of best fit planes. Dispersion of the bootstrapped normal vectors to the best fit plane is regarded as a measure of precision, evaluated with the maximum angular distance from the optimal solution. This rationale was tested using Monte Carlo-generated samples covering a comprehensive range of shape parameters to assess the dependence between eigen parameters and their inherent bias. Our results show that the oblateness of the point cloud is a robust parameter to assess the reliability of the best fit plane. Given this, the method was then applied to a publicly available lidar data set. We argue that georeferenced point clouds with an oblateness parameter greater than 3 and 1.5 may be placed at 95% confidence levels of 5° and 10°, respectively. We propose using these values as thresholds to obtain robust best fit planes, guaranteeing reproducible results for scientific research.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/87672
Gallo, Leandro César; Cristallini, Ernesto Osvaldo; Svarc, Marcela; A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds; American Geophysical Union; Journal of Geophysical Research: Solid Earth; 123; 11; 11-2018; 10,297-10,308
2169-9313
2169-9356
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87672
identifier_str_mv Gallo, Leandro César; Cristallini, Ernesto Osvaldo; Svarc, Marcela; A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds; American Geophysical Union; Journal of Geophysical Research: Solid Earth; 123; 11; 11-2018; 10,297-10,308
2169-9313
2169-9356
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JB016319
info:eu-repo/semantics/altIdentifier/doi/10.1029/2018JB016319
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Geophysical Union
publisher.none.fl_str_mv American Geophysical Union
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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