Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce

Training geospatial machine learning models is challenging when field data is scarce, expensive, and logistically difficult to collect. The recommended approach involves maximizing information from each sample through feature engineering and integrating data from sources like optical sensors, LiDAR, and radar. Model selection should prioritize robust, regularized algorithms like Random Forest or XGBoost to control variance and prevent overfitting. It is critical to use spatial cross-validation for an honest performance assessment, as random validation inflates metrics due to spatial autocorrelation. Ultimately, uncertainty maps must be treated as a primary product to transparently communicate where the model's predictions are reliable and where they are extrapolating.