Evaluation Challenges for Geospatial ML
This addresses evaluation issues for researchers and practitioners using geospatial ML in domains like environmental science and policy, but it is incremental as it focuses on refining existing evaluation practices rather than introducing new methods.
The paper tackles the problem of evaluating geospatial machine learning models, which are increasingly used in science and policy, by delineating unique challenges and providing concrete takeaways to improve performance assessments.
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.