Data-Centric Machine Learning for Geospatial Remote Sensing Data
Ribana Roscher Marc Rußwurm Caroline Gevaert Michael Kampffmeyer Jefersson A. dos Santos Maria Vakalopoulou Ronny Hänsch Stine Hansen Keiller Nogueira Jonathan Prexl Devis Tuia
Recent developments and research in modern machine learning have led to
substantial improvements in the geospatial field. Although numerous deep
learning models have been proposed, the majority of them have been 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 shifting the focus towards a complementary data-centric perspective is
necessary to achieve further improvements in accuracy, generalization ability,
and real impact in end-user applications. This work presents a definition and
precise categorization 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.