Inference contract
Use QL1 for the 100-ft crop contract and QL2 for 200 ft. Inputs must already use projected coordinates in feet; inference does not reproject LAS files. The segmentation decoder tile path runs in FP32. Each forward consumes 20,480 sampled points; windows overlap by 50 ft or 100 ft in the examples.
Source LAS attributes, classification, header/CRS, and extra dimensions are preserved. The following dimensions are added:
| Field | Meaning |
|---|---|
pred_semantic |
0–4 semantic index; 5 low confidence; 255 uncovered |
pred_las_class |
LAS 2–6; 1 low confidence; 0 uncovered |
pred_confidence |
Maximum fused softmax probability; 0 uncovered |
coverage_count |
Number of accumulated support observations (uint32) |
Sampling does not ensure every raw point is covered. Inspect coverage before using products. No prediction is propagated to unsampled points.
Examples use fusion_mode: paper_legacy to retain the paper pipeline's
NumPy buffered repeated-index accumulation. Repeated indices within a padded
window effectively update once. sum_occurrences uses np.add.at to sum
and count every occurrence correctly. This changes weighting when support
indices repeat; re-evaluate products before comparing scores with the paper.
Both modes reject non-finite predictions.
Crop NPZ output keeps raw semantic index 0–4 and separately thresholds
semantic_las_class to 1 below 0.60. Tile pred_semantic also encodes low
confidence as 5. Neither introduces an additional learned class.