Navigating challenges in spatial machine learning

Spatial machine learning has matured considerably in recent years, providing powerful approaches for mapping and prediction. However, many challenges remain in achieving methodological robustness, transparency, and comparability. This paper examines six key methodological themes – validation, assessment, uncertainty quantification, algorithm design, software implementation, and reporting – and highlights the need for greater rigor to ensure reliable spatial predictions. We emphasize the importance of domain knowledge, appropriate sampling design, and prediction-domain adaptive cross-validation for realistic model evaluation. When training data fail to represent the prediction domain, areas beyond the model’s area of applicability should be masked, and performance should be reported only within unmasked regions. Uncertainty remains a prominent challenge, requiring methodological innovation to account for spatial dependencies and improved strategies for quantifying and communicating it to diverse audiences. Furthermore, while numerous methods now incorporate spatial information into machine learning, the lack of theoretical grounding and systematic benchmarking continues to constrain comparability and progress. Finally, we highlight the critical role of open, reproducible software tools and call for standardized reporting protocols tailored to spatial prediction workflows. Addressing these methodological and practical gaps will enhance reliability, transparency, and reproducibility in spatial machine learning applications

Benchmarking · Comparability · Domain (mathematical analysis) · Key (lock) · Geographic Information Systems Studies · Land Use and Ecosystem Services · Software · Spatial and Panel Data Analysis