Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916564466401280 |
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| author | Ferber, Frédérick Fabre Gay, Dominique Soulié, Jean-Christophe Diatta, Jean Maillard, Odalric-Ambrym |
| author_facet | Ferber, Frédérick Fabre Gay, Dominique Soulié, Jean-Christophe Diatta, Jean Maillard, Odalric-Ambrym |
| contents | 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 R{é}union. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation Ferber, Frédérick Fabre Gay, Dominique Soulié, Jean-Christophe Diatta, Jean Maillard, Odalric-Ambrym Artificial Intelligence 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 R{é}union. 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. |
| title | Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07183 |