  Recent work has shown that deep learning models can be used to classify
land-use data from geospatial satellite imagery. We show that when these deep
learning models are trained on data from specific continents/seasons, there is
a high degree of variability in model performance on out-of-sample
continents/seasons. This suggests that just because a model accurately predicts
land-use classes in one continent or season does not mean that the model will
accurately predict land-use classes in a different continent or season. We then
use clustering techniques on satellite imagery from different continents to
visualize the differences in landscapes that make geospatial generalization
particularly difficult, and summarize our takeaways for future satellite
imagery-related applications.
