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Recent developments and research in modern machine learning have led tosubstantial improvements in the geospatial field. Although numerous deeplearning models have been proposed, the majority of them have been developed onbenchmark datasets that lack strong real-world relevance. Furthermore, theperformance of many methods has already saturated on these datasets. We arguethat shifting the focus towards a complementary data-centric perspective isnecessary to achieve further improvements in accuracy, generalization ability,and real impact in end-user applications. This work presents a definition andprecise categorization of automated data-centric learning approaches forgeospatial data. It highlights the complementary role of data-centric learningwith respect to model-centric in the larger machine learning deployment cycle.We review papers across the entire geospatial field and categorize them intodifferent groups. A set of representative experiments shows concreteimplementation examples. These examples provide concrete steps to act ongeospatial data with data-centric machine learning approaches.

Data-Centric Machine Learning for Geospatial Remote Sensing Data