# Data Preparation

Fine-tuning inputs are NumPy arrays with at least four columns:
`[x_ft, y_ft, z_ft, las_class]`. Coordinates must use a projected feet-based
coordinate system consistent within a crop. Do not supply geographic degrees.

The public segmentation map is LAS 2 -> ground, 3 -> low vegetation, 4 ->
medium vegetation, 5 -> high vegetation, and 6 -> building. All other classes
map to ignore index `-1`. They remain in sampled encoder input; they must not
be removed merely because they are ignored in loss.

Use non-overlapping train, validation, and test manifests. Record crop size,
normalization, thinning strategy, and class distributions beside each dataset.