  Recent developments and research in modern machine learning have led to
substantial improvements in the geospatial field. Although numerous deep
learning architectures and models have been proposed, the majority of them have
been solely developed on benchmark datasets that lack strong real-world
relevance. Furthermore, the performance of many methods has already saturated
on these datasets. We argue that a shift from a model-centric view to a
complementary data-centric perspective is necessary for further improvements in
accuracy, generalization ability, and real impact on end-user applications.
Furthermore, considering the entire machine learning cycle - from problem
definition to model deployment with feedback - is crucial for enhancing machine
learning models that can be reliable in unforeseen situations. This work
presents a definition as well as a precise categorization and overview of
automated data-centric learning approaches for geospatial data. It highlights
the complementary role of data-centric learning with respect to model-centric
in the larger machine learning deployment cycle. We review papers across the
entire geospatial field and categorize them into different groups. A set of
representative experiments shows concrete implementation examples. These
examples provide concrete steps to act on geospatial data with data-centric
machine learning approaches.
