DeepKriging on the global Data

TL;DR

This paper introduces a Spherical DeepKriging framework that improves large-scale global spatial predictions on spherical domains by integrating intrinsic TPS basis functions, addressing limitations of classical models.

Contribution

It presents a novel deep learning-based kriging method specifically designed for spherical data, enhancing scalability and prediction accuracy.

Findings

Demonstrates superior predictive performance over traditional methods.

Effective on both simulated and real global datasets.

Addresses computational challenges of massive spatial data.

Abstract

The increasing availability of large-scale global datasets has generated a demand for scalable spatial prediction methods defined on spherical domains. Classical spatial models that rely on Euclidean distance representations are inappropriate for spherical data because planar projections distort geodesic distances and spatial neighborhood structures, while traditional kriging-based prediction methods are often computationally prohibitive for massive datasets. To address these challenges, we propose a Spherical DeepKriging framework for spatial prediction on $\mathbb{S}^2$. The proposed approach constructs a flexible prediction model by integrating thin-plate spline (TPS) basis functions defined intrinsically on the sphere. Simulation studies and real data analyses are presented to demonstrate the superior predictive performance of the proposed method.