用高斯过程与克里金法增强地理数据，提升作物预测模型性能。
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation
- 采用不同核函数的高斯过程与多种变异函数的克里金法进行空间插值
- 组合核高斯过程（GP-COMB）在少量新增数据下显著提升回归模型表现
- 克里金法空间覆盖更均匀，适合对分布一致性要求高的场景
数据增强是构建稳健监督学习模型的关键步骤，尤其在数据量有限时。本研究探讨了地理参考数据的插值方法，旨在预测留尼汪岛甘蔗田中布氏宽叶草（Commelina benghalensis L.）的分布。鉴于数据的空间特性及采集成本高昂，评估了两种插值方法：使用不同核函数的高斯过程（GPs）和采用多种变异函数的克里金法。研究目标包括：(i) 确定不同回归算法下最优的插值方法；(ii) 分析添加观测数量对性能的影响；(iii) 评估增广数据集的空间一致性。结果表明，基于高斯过程的方法（特别是组合核方法，GP-COMB）显著提升了回归算法性能，且所需新增数据更少。尽管克里金法性能略低，但其空间覆盖更均匀，具有特定应用场景的优势。
原文摘要 · Abstract (English)
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.
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