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.

翻译：构建人工智能地理空间系统需要在最小人工干预下实现大规模空间数据分析的快速交付。根据其预期用途，数据分析还可能涉及模型评估与不确定性量化。本文设计了适用于人工智能系统的迁移学习框架，将海量数据集分割为多个子数据集，使其流式输入分析框架，从而在整个数据集上传播学习成果并融合推断结果。具体而言，我们针对多元空间数据提出了贝叶斯预测堆叠方法，并展示了对海量数据集的快速自动化分析能力。此外，该推断过程无需人工干预，且对硬件配置要求适中。我们通过大量模拟实验证明该方法的有效性，并在植被指数海量数据集上实现了与传统（且成本更高）统计方法无显著差异的推断结果。