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# QA/QC Engine
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## 1. Doel
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De QA/QC Engine maakt GeoIntel professioneel. AI-resultaten zijn pas waardevol als ze controleerbaar zijn tegenover referentiedata zoals GRB.
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## 2. Primaire use case
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Gebouwdetecties vergelijken met GRB-gebouwpolygonen.
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## 3. Input
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- analysis_run_id met detecties of segmentaties
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- reference_dataset_id met referentiepolygonen
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- IoU threshold
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- class mapping
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## 4. Output
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- true positives
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- false positives
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- false negatives
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- precision
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- recall
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- F1
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- mean IoU
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- kaartlagen met fouten
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## 5. Matching-regels
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1. Bereken intersects tussen AI-output en referentieobjecten.
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2. Bereken IoU per kandidaatpaar.
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3. Match hoogste IoU boven threshold.
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4. Voorkom dubbele matches.
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5. AI-output zonder match = false positive.
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6. Referentie zonder match = false negative.
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7. Matched objecten = true positive.
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## 6. IoU
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```text
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IoU = area(intersection) / area(union)
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```
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## 7. Metrics
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```text
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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F1 = 2 * precision * recall / (precision + recall)
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```
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## 8. Kaartlagen
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QA/QC moet minstens drie lagen kunnen tonen:
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- true positives
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- false positives
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- false negatives
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Later:
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- low IoU matches
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- confidence vs IoU scatter
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- spatial error clusters
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## 9. Rapportage
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QA/QC-output moet exporteerbaar zijn als:
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- GeoJSON
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- JSON summary
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- later PDF/HTML report
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## 10. Belangrijk
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GRB is een referentie, geen perfecte waarheid. UI moet spreken over “referentievergelijking” of “ground-truth proxy”, niet absolute waarheid.
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