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Detection of inconsistencies in geospatial data with geostatistics / Adriana Maria Rocha Trancoso Santos in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)  

Public [article]  inBoletim de Ciências Geodésicas > vol 23 n° 2 (abr - jun 2017) . - pp 296 - 308 Titre : Detection of inconsistencies in geospatial data with geostatistics Type de document : Article/Communication Auteurs : Adriana Maria Rocha Trancoso Santos, Auteur ; Gerson Rodrigues dos Santos, Auteur ; Paulo César Emiliano, Auteur Année de publication : 2017 Article en page(s) : pp 296 - 308 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] cohérence des données [Termes IGN] détection d'anomalie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] géostatistique [Termes IGN] modèle numérique de surface [Termes IGN] valeur aberrante [Termes IGN] variable régionalisée Résumé : (auteur) Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study. Numéro de notice : A2017-395 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1590/S1982-21702017000200019 En ligne : http://dx.doi.org/10.1590/S1982-21702017000200019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85911 [article]