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：构建人工智能地理系统需要以最少的人工干预，在超大规模上快速交付空间数据分析。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，其核心思想是将大规模数据集拆分为多个小规模子数据集，这些子数据集流式进入分析框架，以传播学习成果并整合整个数据集的推断过程。具体而言，我们针对多元空间数据引入贝叶斯预测堆叠方法，并展示了对大规模数据集的快速自动化分析。此外，该方法无需人工干预，也无需过高的硬件配置。通过大量模拟实验，以及在植被指数大规模数据集上生成与传统（且更昂贵）统计方法难以区分的推断结果，我们展示了该方法的有效性。