| # Inference contract |
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|
| 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. |
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| Source LAS attributes, classification, header/CRS, and extra dimensions are |
| preserved. The following dimensions are added: |
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| | 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) | |
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| Sampling does not ensure every raw point is covered. Inspect coverage before |
| using products. No prediction is propagated to unsampled points. |
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| 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. |
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| 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. |
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