Spatio-temporal machine learning is critically needed for a variety of
societal applications, such as agricultural monitoring, hydrological forecast,
and traffic management. These applications greatly rely on regional features
that characterize spatial and temporal differences. However, spatio-temporal
data often exhibit complex patterns and significant data variability across
different locations. The labels in many real-world applications can also be
limited, which makes it difficult to separately train independent models for
different locations. Although meta learning has shown promise in model
adaptation with small samples, existing meta learning methods remain limited in
handling a large number of heterogeneous tasks, e.g., a large number of
locations with varying data patterns. To bridge the gap, we propose
task-adaptive formulations and a model-agnostic meta-learning framework that
ensembles regionally heterogeneous data into location-sensitive meta tasks. We
conduct task adaptation following an easy-to-hard task hierarchy in which
different meta models are adapted to tasks of different difficulty levels. One
major advantage of our proposed method is that it improves the model adaptation
to a large number of heterogeneous tasks. It also enhances the model
generalization by automatically adapting the meta model of the corresponding
difficulty level to any new tasks. We demonstrate the superiority of our
proposed framework over a diverse set of baselines and state-of-the-art
meta-learning frameworks. Our extensive experiments on real crop yield data
show the effectiveness of the proposed method in handling spatial-related
heterogeneous tasks in real societal applications.

提出了一种模型无关的元学习框架，采用任务自适应的公式将地域异质性数据集合成位置敏感的元任务，使模型适应大量异质性任务，并在实际社会应用中处理了空间相关的异质性任务。