Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation

Fabre Ferber, Frédérick; Gay, Dominique; Soulié, Jean-Christophe; Diatta, Jean; Maillard, Odalric Ambrym

Type de document :

Pré-publication ou Document de travail

Titre :

Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation

Auteur(s) :

Fabre Ferber, Frédérick [Auteur]
Recyclage et risque [UPR Recyclage et risque]
Laboratoire d'Informatique et de Mathématiques [LIM]
Gay, Dominique [Auteur]
Laboratoire d'Informatique et de Mathématiques [LIM]
Soulié, Jean-Christophe [Auteur]
Recyclage et risque [UPR Recyclage et risque]
Diatta, Jean [Auteur]
Laboratoire d'Informatique et de Mathématiques [LIM]
Maillard, Odalric Ambrym [Auteur]

Scool [Scool]

Mot(s)-clé(s) en anglais :

Gaussian Process
Kriging
Interpolation

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced ...
Lire la suite >Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La Réunion. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts.Lire moins >

Langue :

Anglais

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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