Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach

TL;DR

This paper introduces a Bayesian predictive stacking framework for geospatial data analysis, enabling rapid, automated inference on massive datasets with minimal human intervention.

Contribution

It presents a novel transfer learning approach that efficiently handles large-scale spatial data through Bayesian stacking, reducing computational costs and human effort.

Findings

Effective analysis of massive vegetation index datasets.

Inference results comparable to traditional statistical methods.

Automated process requiring minimal hardware resources.

Abstract

Building artificially intelligent geospatial systems requires rapid delivery of spatial data analysis on massive scales with minimal human intervention. Depending upon their intended use, data analysis can also involve model assessment and uncertainty quantification. This article devises transfer learning frameworks for deployment in artificially intelligent systems, where a massive data set is split into smaller data sets that stream into the analytical framework to propagate learning and assimilate inference for the entire data set. Specifically, we introduce Bayesian predictive stacking for multivariate spatial data and demonstrate rapid and automated analysis of massive data sets. Furthermore, inference is delivered without human intervention without excessively demanding hardware settings. We illustrate the effectiveness of our approach through extensive simulation experiments and in producing inference from massive dataset on vegetation index that are indistinguishable from traditional (and more expensive) statistical approaches.