  As geospatial machine learning models and maps derived from their predictions
are increasingly used for downstream analyses in science and policy, it is
imperative to evaluate their accuracy and applicability. Geospatial machine
learning has key distinctions from other learning paradigms, and as such, the
correct way to measure performance of spatial machine learning outputs has been
a topic of debate. In this paper, I delineate unique challenges of model
evaluation for geospatial machine learning with global or remotely sensed
datasets, culminating in concrete takeaways to improve evaluations of
geospatial model performance.
