Datasets and Splits

Proper dataset management is crucial for geospatial ML.

Spatial Autocorrelation

Nearby pixels are highly correlated. Random splits cause spatial data leakage, leading to over-optimistic validation metrics.

Solution: Use block-based splitting.

graph TD
    Raw[Raw Map] --> Grid[Grid Blocks]
    Grid --> Split{Spatial Split}
    Split --> Train[Train Blocks]
    Split --> Val[Val Blocks]

Class Imbalance

Satellite imagery is often dominated by "Background" or "Water" classes.

Strategies: 1. Oversampling: Sample patches centered on rare classes. 2. Loss Weighting: Inversely proportional to class frequency.

w_c = \frac{N_{total}}{K \cdot N_c}