We introduce Domain Shift across Geographic Regions (DSGR), a new large-scale dataset designed to study the effects of real-world geospatial distribution shifts in satellite imagery classification. DSGR captures variability across diverse geographic regions, with particular emphasis on underrepresented areas such as Africa and Oceania, enabling systematic analysis of how regional differences impact model performance. This dataset is motivated by a fundamental limitation of deep learning models: they are typically trained under the i.i.d. assumption and often suffer significant performance degradation when deployed in environments that differ from the training data. Domain Generalisation (DG) aims to address this challenge by improving model robustness to Out-Of-Distribution data without access to target domains during training. By explicitly modelling geographic domain shifts, DSGR provides a valuable benchmark for advancing DG research in satellite imagery classification.

Find out more about DSGR here: https://rwgai.com/dsgr/