make building results authority-first after V74 evaluation
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Jens
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||||
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||||
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||||
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@@ -0,0 +1,732 @@
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||||
"evaluation_samples": [
|
||||
"v72-assenede-field-cal-bg",
|
||||
"v72-ganshoren-lowrise-cal",
|
||||
"v72-havelange-field-cal-bg",
|
||||
"v72-laeken-park-cal-bg",
|
||||
"v72-libramont-lowrise-cal",
|
||||
"v72-zandhoven-lowrise-cal"
|
||||
],
|
||||
"evaluation_summary_path": "/app/storage/operator-data/building-be-v72-fresh-calibration-r1/diagnostic-yolo512-r2/yolo_tile_dataset_summary.json",
|
||||
"evaluation_summary_sha256": "78e16f46fa2a313a147404a19f2ce99417d2cb384b5db5f6ba33e934ff99c631",
|
||||
"independent_for_all_supplied_lineage_corpora": true,
|
||||
"interpretation": "No evaluation AOI occurs in any split of any supplied ancestral corpus.",
|
||||
"lineage_corpora": [
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 19,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-expanded-minpx4vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "1887e2ba5c719d33675a9ed85db99a274fd66d98d5f6ad44558d673620917201",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 22,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "49b2a07d2105d08356431757b83eafc1498eaf1fb76965b1efe05b776824942a",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 28,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-reviewedexp6-minpx3vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "7c917e31216d1df2174c0f9c736f88a81f3835aa991971f8fb8665e17ddf5c9c",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 12,
|
||||
"lineage_summary_path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/yolo512/experimental_dataset_summary.json",
|
||||
"lineage_summary_sha256": "047874a4f9362c82263ffe11c389a2f65168c449d3f6cbcc77cea709c0ba4c59",
|
||||
"overlapping_evaluation_samples": []
|
||||
}
|
||||
],
|
||||
"overlapping_evaluation_samples": [],
|
||||
"status": "independent"
|
||||
},
|
||||
"pure_background_confidence": 0.15,
|
||||
"pure_background_prefixes": [],
|
||||
"ranking": [
|
||||
{
|
||||
"map50": 0.05677865740374553,
|
||||
"map50_95": 0.02175035531261916,
|
||||
"model_path": "/app/models/geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt",
|
||||
"model_sha256": "a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1",
|
||||
"precision": 0.1576550586330707,
|
||||
"recall": 0.11781609195402298,
|
||||
"size_bytes": 22516074,
|
||||
"status": "ok"
|
||||
},
|
||||
{
|
||||
"map50": 0.026181668883665236,
|
||||
"map50_95": 0.009758453459274275,
|
||||
"model_path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/runs/v74-rehearsal-controlled-e10/candidate.experimental.pt",
|
||||
"model_sha256": "8473f09234bbad9175d5540e2f298e57c902c332fa5729561774b01a98c355d3",
|
||||
"precision": 0.07101814254884525,
|
||||
"recall": 0.07758620689655173,
|
||||
"size_bytes": 22513578,
|
||||
"status": "ok"
|
||||
}
|
||||
],
|
||||
"schema_version": 3,
|
||||
"status": "ok",
|
||||
"successful_candidate_count": 2,
|
||||
"torch_version": "2.11.0+cu128",
|
||||
"validation_image_count": 24
|
||||
}
|
||||
+116
@@ -0,0 +1,116 @@
|
||||
{
|
||||
"attempts": [
|
||||
{
|
||||
"map50": 0.05677865740374553,
|
||||
"map50_95": 0.02175035531261916,
|
||||
"model_path": "/app/models/geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt",
|
||||
"model_sha256": "a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1",
|
||||
"precision": 0.1576550586330707,
|
||||
"recall": 0.11781609195402298,
|
||||
"size_bytes": 22516074,
|
||||
"status": "ok"
|
||||
},
|
||||
{
|
||||
"map50": 0.03488615176663095,
|
||||
"map50_95": 0.013585769267074197,
|
||||
"model_path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/runs/v74-rehearsal-low-lr-e6/candidate.experimental.pt",
|
||||
"model_sha256": "be9881ad734f60f8779e9a08dc9112b993cf87e7cc888709c6f695bcf200dffc",
|
||||
"precision": 0.08391893066046782,
|
||||
"recall": 0.10344827586206896,
|
||||
"size_bytes": 22513066,
|
||||
"status": "ok"
|
||||
}
|
||||
],
|
||||
"candidate_count": 2,
|
||||
"claim_boundary": "Non-protected validation ranking only; no test, challenge or promotion claim.",
|
||||
"dataset_overlap_evidence": {
|
||||
"interpretation": "Tiles do not overlap, but spatial/statistical independence is not established because adjacent tiles share AOI context and edge objects can remain split.",
|
||||
"overlap_pixels": 0,
|
||||
"statistical_independence_established": false,
|
||||
"status": "non_overlapping",
|
||||
"stride": 512,
|
||||
"summary_path": "/app/storage/operator-data/building-be-v72-fresh-calibration-r1/diagnostic-yolo512-r2/yolo_tile_dataset_summary.json",
|
||||
"summary_sha256": "78e16f46fa2a313a147404a19f2ce99417d2cb384b5db5f6ba33e934ff99c631",
|
||||
"tile_size": 512,
|
||||
"validation_tiles_non_overlapping": true
|
||||
},
|
||||
"dataset_yaml": "/app/storage/operator-data/building-be-v72-fresh-calibration-r1/diagnostic-yolo512-r2/dataset.yaml",
|
||||
"dataset_yaml_sha256": "d1e85ffb6776d92a4534830f2214fa6444f5a9160329aca0a68d0ea0a1a40144",
|
||||
"device": "cuda:0",
|
||||
"generated_at": "2026-08-10T02:32:19.973651+00:00",
|
||||
"model_lineage_independence_evidence": {
|
||||
"evaluation_samples": [
|
||||
"v72-assenede-field-cal-bg",
|
||||
"v72-ganshoren-lowrise-cal",
|
||||
"v72-havelange-field-cal-bg",
|
||||
"v72-laeken-park-cal-bg",
|
||||
"v72-libramont-lowrise-cal",
|
||||
"v72-zandhoven-lowrise-cal"
|
||||
],
|
||||
"evaluation_summary_path": "/app/storage/operator-data/building-be-v72-fresh-calibration-r1/diagnostic-yolo512-r2/yolo_tile_dataset_summary.json",
|
||||
"evaluation_summary_sha256": "78e16f46fa2a313a147404a19f2ce99417d2cb384b5db5f6ba33e934ff99c631",
|
||||
"independent_for_all_supplied_lineage_corpora": true,
|
||||
"interpretation": "No evaluation AOI occurs in any split of any supplied ancestral corpus.",
|
||||
"lineage_corpora": [
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 19,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-expanded-minpx4vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "1887e2ba5c719d33675a9ed85db99a274fd66d98d5f6ad44558d673620917201",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 22,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "49b2a07d2105d08356431757b83eafc1498eaf1fb76965b1efe05b776824942a",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 28,
|
||||
"lineage_summary_path": "/app/storage/operator-data/yolo-building-aoi1024-reviewedexp6-minpx3vis035/yolo_tile_dataset_summary.json",
|
||||
"lineage_summary_sha256": "7c917e31216d1df2174c0f9c736f88a81f3835aa991971f8fb8665e17ddf5c9c",
|
||||
"overlapping_evaluation_samples": []
|
||||
},
|
||||
{
|
||||
"exposure_roles": {},
|
||||
"lineage_sample_count": 12,
|
||||
"lineage_summary_path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/yolo512/experimental_dataset_summary.json",
|
||||
"lineage_summary_sha256": "047874a4f9362c82263ffe11c389a2f65168c449d3f6cbcc77cea709c0ba4c59",
|
||||
"overlapping_evaluation_samples": []
|
||||
}
|
||||
],
|
||||
"overlapping_evaluation_samples": [],
|
||||
"status": "independent"
|
||||
},
|
||||
"pure_background_confidence": 0.15,
|
||||
"pure_background_prefixes": [],
|
||||
"ranking": [
|
||||
{
|
||||
"map50": 0.05677865740374553,
|
||||
"map50_95": 0.02175035531261916,
|
||||
"model_path": "/app/models/geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt",
|
||||
"model_sha256": "a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1",
|
||||
"precision": 0.1576550586330707,
|
||||
"recall": 0.11781609195402298,
|
||||
"size_bytes": 22516074,
|
||||
"status": "ok"
|
||||
},
|
||||
{
|
||||
"map50": 0.03488615176663095,
|
||||
"map50_95": 0.013585769267074197,
|
||||
"model_path": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/runs/v74-rehearsal-low-lr-e6/candidate.experimental.pt",
|
||||
"model_sha256": "be9881ad734f60f8779e9a08dc9112b993cf87e7cc888709c6f695bcf200dffc",
|
||||
"precision": 0.08391893066046782,
|
||||
"recall": 0.10344827586206896,
|
||||
"size_bytes": 22513066,
|
||||
"status": "ok"
|
||||
}
|
||||
],
|
||||
"schema_version": 3,
|
||||
"status": "ok",
|
||||
"successful_candidate_count": 2,
|
||||
"torch_version": "2.11.0+cu128",
|
||||
"validation_image_count": 24
|
||||
}
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"base_model": "/app/models/geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt",
|
||||
"base_model_sha256": "a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1",
|
||||
"batch": 8,
|
||||
"candidate_model": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/runs/v74-rehearsal-low-lr-e6/candidate.experimental.pt",
|
||||
"candidate_model_sha256": "be9881ad734f60f8779e9a08dc9112b993cf87e7cc888709c6f695bcf200dffc",
|
||||
"dataset_yaml_sha256": "2fa0c66ddb7105629b7873e45030e6977da941130cb032c63e6449033ed42409",
|
||||
"device": "NVIDIA GeForce RTX 4080 SUPER",
|
||||
"epochs": 6,
|
||||
"freeze": 10,
|
||||
"human_review_pending": true,
|
||||
"imgsz": 640,
|
||||
"learning_rate": 0.0001,
|
||||
"optimizer": "AdamW",
|
||||
"promotion_allowed": false,
|
||||
"release_claim_allowed": false,
|
||||
"required_next_gate": "independent frozen V72 calibration evaluation",
|
||||
"schema_version": 1,
|
||||
"status": "trained_experimental_only",
|
||||
"workers": 0
|
||||
}
|
||||
+3795
File diff suppressed because it is too large
Load Diff
+4426
File diff suppressed because it is too large
Load Diff
+17
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"dataset_yaml": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/rehearsal-v74/dataset.yaml",
|
||||
"dataset_yaml_sha256": "2fa0c66ddb7105629b7873e45030e6977da941130cb032c63e6449033ed42409",
|
||||
"human_review_pending": true,
|
||||
"promotion_allowed": false,
|
||||
"protected_v72_used": false,
|
||||
"rehearsal_summary": "/app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035/yolo_tile_dataset_summary.json",
|
||||
"rehearsal_summary_sha256": "49b2a07d2105d08356431757b83eafc1498eaf1fb76965b1efe05b776824942a",
|
||||
"rehearsal_train_tile_count": 180,
|
||||
"release_claim_allowed": false,
|
||||
"remediation_repeat": 2,
|
||||
"remediation_summary": "/app/storage/training/experimental/building-be-v73-fresh-flanders-train-r1/yolo512/experimental_dataset_summary.json",
|
||||
"remediation_summary_sha256": "047874a4f9362c82263ffe11c389a2f65168c449d3f6cbcc77cea709c0ba4c59",
|
||||
"remediation_unique_train_tile_count": 48,
|
||||
"schema_version": 1,
|
||||
"status": "experimental_rehearsal_only"
|
||||
}
|
||||
@@ -1712,6 +1712,12 @@ Returns persisted detections for a dataset as a GeoJSON FeatureCollection. Optio
|
||||
|
||||
Compares persisted detection geometries from an analysis run against persisted `vector_features` from a reference vector dataset.
|
||||
|
||||
The map-driven building workflow immediately loads the persisted evidence from
|
||||
the resulting quality check. For current Flemish building validation it labels
|
||||
strict matches as GRB-confirmed, unmatched detections as unconfirmed AI-only
|
||||
proposals and unmatched reference footprints as official GRB buildings missed
|
||||
by AI. AI-only proposals are never promoted to authoritative building results.
|
||||
|
||||
Request:
|
||||
|
||||
```json
|
||||
|
||||
@@ -1,3 +1,23 @@
|
||||
## 2026-08-10 - V74 root-cause remediation en authority-first uitvoer
|
||||
|
||||
### Opgeleverd
|
||||
|
||||
- De volledige V72 failure gallery gerenderd en AI-assisted geïnspecteerd;
|
||||
terugkerende foutpositieven omvatten spoorinfrastructuur, voertuigen, bomen
|
||||
en schaduwen.
|
||||
- Een hard-negative proposal-filter en twee rehearsal-kandidaten getraind op
|
||||
de Tower RTX 4080 SUPER en alle drie afgewezen op de onafhankelijke V72-set.
|
||||
- De experimentele trainer bindt nu expliciet AdamW, zodat Ultralytics niet
|
||||
stilzwijgend de gevraagde learning rate vervangt via `optimizer=auto`.
|
||||
- De kaartwerkstroom laadt het GRB-QA-bewijs automatisch en onderscheidt
|
||||
GRB-bevestigde, AI-only en door AI gemiste gebouwobjecten.
|
||||
|
||||
### Grens
|
||||
|
||||
- Het productiecheckpoint bleef ongewijzigd. Er is geen landelijke of
|
||||
100%-accuratesseclaim gedaan en de visuele review blijft AI-assisted.
|
||||
- Evidence: `artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/`.
|
||||
|
||||
## 2026-07-29 - V31 closed-loop iteration 3 assessment and semantic correction
|
||||
|
||||
- Iteration 3 stopped deterministically after epoch 21 with epoch 3 retained as
|
||||
|
||||
@@ -1,5 +1,15 @@
|
||||
# GeoIntel TODO
|
||||
|
||||
## V74 authority-first gebouwresultaten (2026-08-10)
|
||||
|
||||
- [x] Voer een V72-foutanalyse, hard-negative proposal-filter en rehearsal
|
||||
training uit; wijs alle regresserende kandidaten af en behoud het actieve
|
||||
checkpoint.
|
||||
- [x] Laad na gebouwdetectie automatisch het bewaarde GRB-QA-bewijs en splits
|
||||
de kaartuitvoer in GRB-bevestigd, AI-only en door AI gemist.
|
||||
- [ ] Laat een onafhankelijke menselijke reviewer de bevroren contact sheets
|
||||
en labels ondertekenen voordat een kandidaat ooit promoveerbaar wordt.
|
||||
|
||||
## PyTorch-modelprogramma
|
||||
|
||||
Uitvoeringsbord: `docs/PYTORCH_TRAINING_ROADMAP_BELGIUM.md`.
|
||||
|
||||
@@ -0,0 +1,71 @@
|
||||
# V74 foutoorzaak en authority-first resultaten
|
||||
|
||||
## Uitkomst
|
||||
|
||||
V74 heeft geen nieuw productiemodel opgeleverd. De actieve checkpoint blijft
|
||||
ongewijzigd omdat zowel de experimentele proposal-filter als twee
|
||||
rehearsal-kandidaten op de bevroren V72-calibratie slechter presteren. GeoIntel
|
||||
presenteert de automatische gebouwanalyse daarom niet langer als één uniforme
|
||||
resultaatlaag: de GRB-vergelijking maakt voortaan zichtbaar welke AI-kandidaten
|
||||
officieel bevestigd zijn, welke alleen modelvoorstellen zijn en welke
|
||||
GRB-gebouwen door AI gemist zijn.
|
||||
|
||||
## Visuele foutanalyse
|
||||
|
||||
De volledige V72-portfolio van 24 onafhankelijke tegels is met het actieve model
|
||||
op confidence 0,30 gerenderd. De contact sheet toont terugkerende foutpositieven
|
||||
op spoorlijnen, voertuigen, bomen, schaduwen en andere contrastrijke structuren.
|
||||
Daarnaast ontbreken veel gebouwen. Dit ondersteunt de conclusie dat extra
|
||||
epochs op alleen de nieuwe Vlaamse shard de hoofdoorzaak niet oplossen.
|
||||
|
||||
De review is AI-assisted en niet menselijk. Het bewijs mag dus geen menselijke
|
||||
acceptatie of promotiegate vervangen.
|
||||
|
||||
## Afgewezen proposal-filter
|
||||
|
||||
Een experimentele ResNet18-filter werd op afzonderlijke V73-AOI's getraind om
|
||||
building proposals van moeilijke negatieve patches te onderscheiden. De interne
|
||||
V73-validatie behaalde F1 0,8068, maar die score generaliseerde niet naar V72.
|
||||
De beste globale V72-combinatie behaalde precision 0,2161, recall 0,2241 en F1
|
||||
0,2200. Het actieve model zonder filter behaalt op confidence 0,30 F1 0,2504.
|
||||
De filter kan precision verhogen bij een vaste detectordrempel, maar halveert
|
||||
dan de recall. Hij blijft daarom experimenteel en wordt niet geladen in
|
||||
productie.
|
||||
|
||||
## Afgewezen rehearsal-training
|
||||
|
||||
De trainingsset combineert 180 historische trainingstegels met de 48 nieuwe
|
||||
V73-tegels, waarbij V73 tweemaal voorkomt. V72 is alleen evaluatie en komt niet
|
||||
in de fitdata voor.
|
||||
|
||||
| Model | Precision | Recall | mAP50 | mAP50-95 | Besluit |
|
||||
|---|---:|---:|---:|---:|---|
|
||||
| Actief | 0,1577 | 0,1178 | 0,0568 | 0,0218 | behouden |
|
||||
| Rehearsal, optimizer auto, 10 epochs | 0,0710 | 0,0776 | 0,0262 | 0,0098 | afgewezen |
|
||||
| Rehearsal, AdamW 0,0001, 6 epochs | 0,0839 | 0,1034 | 0,0349 | 0,0136 | afgewezen |
|
||||
|
||||
De eerste run bracht bovendien een reproduceerbaarheidsfout aan het licht:
|
||||
Ultralytics `optimizer=auto` negeerde de gevraagde learning rate en koos AdamW
|
||||
0,002. De wrapper bindt daarom nu expliciet `optimizer=AdamW` en legt dit vast
|
||||
in het trainingsmanifest. De gecorrigeerde low-learning-rate-run bleef eveneens
|
||||
onder de actieve baseline.
|
||||
|
||||
## Productcorrectheid
|
||||
|
||||
Voor actuele gebouwvalidatie in Vlaanderen is GRB de gezagsbron binnen de
|
||||
beschikbare editie en dekking. De kaartwerkstroom laadt na detectie automatisch
|
||||
het bewaarde QA-bewijs en toont afzonderlijk:
|
||||
|
||||
- GRB-bevestigde AI-kandidaten;
|
||||
- AI-only, onbevestigde onderzoeksvoorstellen;
|
||||
- officiële GRB-gebouwen die het model miste.
|
||||
|
||||
AI-only voorstellen worden niet als officiële gebouwen voorgesteld. Dit maakt
|
||||
de productuitvoer aantoonbaar correcter zonder de zwakke modelscore te verbergen
|
||||
of een niet-bewezen nauwkeurigheidsclaim te maken.
|
||||
|
||||
## Evidence
|
||||
|
||||
Alle ruwe contact sheets, foutmanifesten, filtermatrices, trainingssamenvattingen
|
||||
en modelhashes staan onder
|
||||
`artifacts/evidence/accuracy/model-training/20260810-v74-root-cause/`.
|
||||
@@ -580,6 +580,7 @@ function WorkbenchApp({ username, accessMode, loggingOut, onLogout }: WorkbenchA
|
||||
prepareAndRunDetection: (datasetId) => prepareAndRunDetection(datasetId, 'yolo-configured'),
|
||||
compareDetectionRunWithReference: (analysisRunId, referenceDatasetId, iouThreshold) =>
|
||||
compareDetectionRunWithReference(analysisRunId, referenceDatasetId, false, iouThreshold),
|
||||
loadQualityEvidence: (qualityCheckId) => loadQualityEvidenceGeoJson(qualityCheckId),
|
||||
onAnalysisReady: () => {
|
||||
setMapContentMode('analysis')
|
||||
setMapLayerVisible(true)
|
||||
|
||||
@@ -2759,13 +2759,17 @@ export function MapWorkspace({
|
||||
{orthophotoAnalysisQuality ? (
|
||||
<>
|
||||
<div className="geo-image-quality-metrics" aria-label="Gemeten kwaliteit van de beeldanalyse">
|
||||
<div><span>Kandidaten</span><strong>{orthophotoAnalysisDetectionCount?.toLocaleString('nl-BE') ?? 'n.v.t.'}</strong></div>
|
||||
<div><span>Strikte matches</span><strong>{orthophotoAnalysisQuality.matches.toLocaleString('nl-BE')}</strong></div>
|
||||
<div><span>Precision</span><strong>{formatPercentage(orthophotoAnalysisQuality.precision)}</strong></div>
|
||||
<div><span>Herkenningsgraad</span><strong>{formatPercentage(orthophotoAnalysisQuality.recall)}</strong></div>
|
||||
<div><span>AI-kandidaten</span><strong>{orthophotoAnalysisDetectionCount?.toLocaleString('nl-BE') ?? 'n.v.t.'}</strong></div>
|
||||
<div><span>GRB-bevestigd</span><strong>{orthophotoAnalysisQuality.matches.toLocaleString('nl-BE')}</strong></div>
|
||||
<div><span>AI-only, onbevestigd</span><strong>{orthophotoAnalysisQuality.false_positives.toLocaleString('nl-BE')}</strong></div>
|
||||
<div><span>GRB, gemist door AI</span><strong>{orthophotoAnalysisQuality.false_negatives.toLocaleString('nl-BE')}</strong></div>
|
||||
<div><span>AI-precision</span><strong>{formatPercentage(orthophotoAnalysisQuality.precision)}</strong></div>
|
||||
<div><span>AI-herkenningsgraad</span><strong>{formatPercentage(orthophotoAnalysisQuality.recall)}</strong></div>
|
||||
<div><span>F1</span><strong>{formatPercentage(orthophotoAnalysisQuality.f1_score)}</strong></div>
|
||||
<div><span>Fout / gemist</span><strong>{orthophotoAnalysisQuality.false_positives.toLocaleString('nl-BE')} / {orthophotoAnalysisQuality.false_negatives.toLocaleString('nl-BE')}</strong></div>
|
||||
</div>
|
||||
<p className="geo-image-quality-context">
|
||||
GRB is hier de gezagsbron voor geregistreerde gebouwen. AI-only vormen zijn onderzoeksvoorstellen en worden niet als officieel resultaat voorgesteld. De bewijslaag op de kaart toont elke categorie afzonderlijk.
|
||||
</p>
|
||||
{orthophotoAnalysisQuality.box_to_footprint_diagnostics ? (
|
||||
<p className="geo-image-quality-context">
|
||||
Rechthoekcontrole: {orthophotoAnalysisQuality.box_to_footprint_diagnostics.envelope_matches.toLocaleString('nl-BE')} matches,
|
||||
|
||||
@@ -32,6 +32,7 @@ interface MapOrthophotoAnalysisOptions {
|
||||
referenceDatasetId: string,
|
||||
iouThreshold: number,
|
||||
) => Promise<DetectionQaResult | null>
|
||||
loadQualityEvidence: (qualityCheckId: string) => Promise<unknown>
|
||||
onAnalysisReady: () => void
|
||||
}
|
||||
|
||||
@@ -79,6 +80,7 @@ export function useMapOrthophotoAnalysis({
|
||||
loadProjectData,
|
||||
prepareAndRunDetection,
|
||||
compareDetectionRunWithReference,
|
||||
loadQualityEvidence,
|
||||
onAnalysisReady,
|
||||
}: MapOrthophotoAnalysisOptions) {
|
||||
const [stage, setStage] = useState<MapOrthophotoAnalysisStage>('idle')
|
||||
@@ -180,9 +182,12 @@ export function useMapOrthophotoAnalysis({
|
||||
MAP_BUILDING_QA_IOU_THRESHOLD,
|
||||
)
|
||||
setLastQuality(quality)
|
||||
if (quality) {
|
||||
await loadQualityEvidence(quality.quality_check_id)
|
||||
}
|
||||
setStatus(
|
||||
quality
|
||||
? `Analyse klaar: ${detection.detection_count.toLocaleString('nl-BE')} kandidaten, ${quality.matches.toLocaleString('nl-BE')} gekoppeld aan GRB. Precision ${formatQualityPercentage(quality.precision)}, recall ${formatQualityPercentage(quality.recall)}.`
|
||||
? `Controle klaar: ${quality.matches.toLocaleString('nl-BE')} door GRB bevestigde AI-kandidaten, ${quality.false_positives.toLocaleString('nl-BE')} onbevestigde AI-kandidaten en ${quality.false_negatives.toLocaleString('nl-BE')} officiële GRB-gebouwen gemist door AI. AI-precision ${formatQualityPercentage(quality.precision)}, herkenningsgraad ${formatQualityPercentage(quality.recall)}.`
|
||||
: `Analyse klaar: ${detection.detection_count.toLocaleString('nl-BE')} kandidaten; kwaliteitscontrole kon niet afronden.`,
|
||||
)
|
||||
} else {
|
||||
|
||||
@@ -0,0 +1,141 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Compose legacy rehearsal and fresh remediation tiles for an experiment."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
SCRIPT_DIR = Path(__file__).resolve().parent
|
||||
if str(SCRIPT_DIR) not in sys.path:
|
||||
sys.path.insert(0, str(SCRIPT_DIR))
|
||||
|
||||
from export_experimental_yolo_train_shard import EXPERIMENTAL_ROOT, file_sha256 # noqa: E402
|
||||
|
||||
|
||||
def _inside(path: Path, root: Path) -> bool:
|
||||
try:
|
||||
path.resolve().relative_to(root.resolve())
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
|
||||
def validate_sources(rehearsal_summary: Path, remediation_summary: Path, output_dir: Path) -> tuple[dict, dict]:
|
||||
if not _inside(output_dir, EXPERIMENTAL_ROOT):
|
||||
raise ValueError(f"output must remain below {EXPERIMENTAL_ROOT}")
|
||||
rehearsal = json.loads(rehearsal_summary.read_text(encoding="utf-8"))
|
||||
remediation = json.loads(remediation_summary.read_text(encoding="utf-8"))
|
||||
marker_path = remediation_summary.parent / "EXPERIMENTAL_ONLY.json"
|
||||
if not marker_path.is_file():
|
||||
raise ValueError("fresh remediation source has no EXPERIMENTAL_ONLY marker")
|
||||
marker = json.loads(marker_path.read_text(encoding="utf-8"))
|
||||
if marker.get("promotion_allowed") is not False:
|
||||
raise ValueError("fresh remediation marker does not prohibit promotion")
|
||||
if remediation.get("source_manifest_sha256") != marker.get("source_manifest_sha256"):
|
||||
raise ValueError("fresh remediation marker/summary binding mismatch")
|
||||
rehearsal_tiles = [tile for tile in rehearsal.get("tiles") or [] if tile.get("kept", True)]
|
||||
remediation_tiles = [tile for tile in remediation.get("tiles") or [] if tile.get("kept", True)]
|
||||
if not rehearsal_tiles or not remediation_tiles:
|
||||
raise ValueError("both rehearsal and remediation summaries require retained tiles")
|
||||
if any(tile.get("split") not in {"train", "val"} for tile in rehearsal_tiles):
|
||||
raise ValueError("rehearsal source contains a protected or unsupported split")
|
||||
if any(tile.get("split") != "train" for tile in remediation_tiles):
|
||||
raise ValueError("fresh remediation source must be train-only")
|
||||
return rehearsal, remediation
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--rehearsal-summary", type=Path, required=True)
|
||||
parser.add_argument("--remediation-summary", type=Path, required=True)
|
||||
parser.add_argument("--output-dir", type=Path, required=True)
|
||||
parser.add_argument("--remediation-repeat", type=int, default=2)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
if args.remediation_repeat < 1 or args.remediation_repeat > 4:
|
||||
raise SystemExit("remediation-repeat must be between 1 and 4")
|
||||
if args.output_dir.exists():
|
||||
raise SystemExit(f"refusing to overwrite rehearsal dataset: {args.output_dir}")
|
||||
try:
|
||||
rehearsal, remediation = validate_sources(
|
||||
args.rehearsal_summary, args.remediation_summary, args.output_dir
|
||||
)
|
||||
except (OSError, ValueError, json.JSONDecodeError) as exc:
|
||||
raise SystemExit(str(exc)) from exc
|
||||
rehearsal_yaml = Path(str(rehearsal["dataset_yaml"]))
|
||||
remediation_yaml = Path(str(remediation["dataset_yaml"]))
|
||||
rehearsal_root = rehearsal_yaml.parent
|
||||
remediation_root = remediation_yaml.parent
|
||||
for directory in (
|
||||
rehearsal_root / "images" / "train",
|
||||
rehearsal_root / "images" / "val",
|
||||
remediation_root / "images" / "train",
|
||||
):
|
||||
if not directory.is_dir():
|
||||
raise SystemExit(f"rehearsal dataset directory is missing: {directory}")
|
||||
args.output_dir.mkdir(parents=True)
|
||||
train_sources = [str(rehearsal_root / "images" / "train")] + [
|
||||
str(remediation_root / "images" / "train")
|
||||
] * args.remediation_repeat
|
||||
yaml_path = args.output_dir / "dataset.yaml"
|
||||
yaml_path.write_text(
|
||||
"\n".join(
|
||||
[
|
||||
"path: /",
|
||||
"train:",
|
||||
*[f" - {path}" for path in train_sources],
|
||||
f"val: {rehearsal_root / 'images' / 'val'}",
|
||||
"names:",
|
||||
" 0: building",
|
||||
"",
|
||||
]
|
||||
),
|
||||
encoding="utf-8",
|
||||
)
|
||||
summary = {
|
||||
"schema_version": 1,
|
||||
"status": "experimental_rehearsal_only",
|
||||
"promotion_allowed": False,
|
||||
"release_claim_allowed": False,
|
||||
"human_review_pending": True,
|
||||
"dataset_yaml": str(yaml_path),
|
||||
"dataset_yaml_sha256": file_sha256(yaml_path),
|
||||
"rehearsal_summary": str(args.rehearsal_summary),
|
||||
"rehearsal_summary_sha256": file_sha256(args.rehearsal_summary),
|
||||
"remediation_summary": str(args.remediation_summary),
|
||||
"remediation_summary_sha256": file_sha256(args.remediation_summary),
|
||||
"remediation_repeat": args.remediation_repeat,
|
||||
"rehearsal_train_tile_count": int(rehearsal.get("train_tile_count") or 0),
|
||||
"remediation_unique_train_tile_count": int(remediation.get("tile_count") or 0),
|
||||
"protected_v72_used": False,
|
||||
}
|
||||
(args.output_dir / "experimental_rehearsal_summary.json").write_text(
|
||||
json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8"
|
||||
)
|
||||
marker = {
|
||||
key: summary[key]
|
||||
for key in (
|
||||
"schema_version",
|
||||
"status",
|
||||
"promotion_allowed",
|
||||
"release_claim_allowed",
|
||||
"dataset_yaml_sha256",
|
||||
"rehearsal_summary_sha256",
|
||||
"remediation_summary_sha256",
|
||||
)
|
||||
}
|
||||
(args.output_dir / "EXPERIMENTAL_ONLY.json").write_text(
|
||||
json.dumps(marker, indent=2, sort_keys=True) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps(summary, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,211 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Train a non-promotable second-stage building/background proposal filter."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import random
|
||||
import shutil
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
SCRIPT_DIR = Path(__file__).resolve().parent
|
||||
if str(SCRIPT_DIR) not in sys.path:
|
||||
sys.path.insert(0, str(SCRIPT_DIR))
|
||||
|
||||
from train_building_proposal_filter import ( # noqa: E402
|
||||
choose_threshold,
|
||||
materialize_split,
|
||||
sha256,
|
||||
)
|
||||
|
||||
EXPERIMENTAL_ROOT = Path("/app/storage/training/experimental")
|
||||
|
||||
|
||||
def _inside(path: Path, root: Path = EXPERIMENTAL_ROOT) -> bool:
|
||||
try:
|
||||
path.resolve().relative_to(root.resolve())
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
|
||||
def validate_inputs(summary_path: Path, output_dir: Path) -> dict:
|
||||
if not _inside(summary_path) or not _inside(output_dir):
|
||||
raise ValueError(f"summary and output must remain below {EXPERIMENTAL_ROOT}")
|
||||
marker_path = summary_path.parent / "EXPERIMENTAL_ONLY.json"
|
||||
if not marker_path.is_file():
|
||||
raise ValueError("experimental source marker is missing")
|
||||
marker = json.loads(marker_path.read_text(encoding="utf-8"))
|
||||
if marker.get("promotion_allowed") is not False or marker.get("release_claim_allowed") is not False:
|
||||
raise ValueError("experimental marker does not prohibit promotion")
|
||||
summary = json.loads(summary_path.read_text(encoding="utf-8"))
|
||||
if marker.get("dataset_yaml_sha256") != summary.get("dataset_yaml_sha256"):
|
||||
raise ValueError("experimental marker/summary binding mismatch")
|
||||
if summary.get("status") != "experimental_only_human_review_pending":
|
||||
raise ValueError("source summary is not an experimental train shard")
|
||||
return summary
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--summary", type=Path, required=True)
|
||||
parser.add_argument("--proposal-model", type=Path, required=True)
|
||||
parser.add_argument("--output-dir", type=Path, required=True)
|
||||
parser.add_argument("--val-sample", action="append", required=True)
|
||||
parser.add_argument("--device", default="cuda:0")
|
||||
parser.add_argument("--proposal-confidence", type=float, default=0.01)
|
||||
parser.add_argument("--crop-scale", type=float, default=1.4)
|
||||
parser.add_argument("--negative-match-iou", type=float, default=0.05)
|
||||
parser.add_argument("--max-positive-per-tile", type=int, default=40)
|
||||
parser.add_argument("--max-negative-per-tile", type=int, default=40)
|
||||
parser.add_argument("--epochs", type=int, default=8)
|
||||
parser.add_argument("--batch", type=int, default=64)
|
||||
parser.add_argument("--seed", type=int, default=74)
|
||||
parser.add_argument("--force", action="store_true")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = parse_args()
|
||||
try:
|
||||
summary = validate_inputs(args.summary, args.output_dir)
|
||||
except (OSError, ValueError, json.JSONDecodeError) as exc:
|
||||
raise SystemExit(str(exc)) from exc
|
||||
if not args.proposal_model.is_file():
|
||||
raise SystemExit(f"proposal model is unavailable: {args.proposal_model}")
|
||||
val_samples = {slug.strip() for slug in args.val_sample if slug.strip()}
|
||||
sample_slugs = {str(tile["sample_slug"]) for tile in summary["tiles"]}
|
||||
unknown = val_samples - sample_slugs
|
||||
if unknown:
|
||||
raise SystemExit(f"unknown validation samples: {', '.join(sorted(unknown))}")
|
||||
if not val_samples or val_samples == sample_slugs:
|
||||
raise SystemExit("proposal filter requires disjoint non-empty train and validation AOIs")
|
||||
if args.output_dir.exists():
|
||||
if not args.force:
|
||||
raise SystemExit(f"output already exists: {args.output_dir}")
|
||||
shutil.rmtree(args.output_dir)
|
||||
|
||||
random.seed(args.seed)
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision import datasets
|
||||
from torchvision.models import ResNet18_Weights, resnet18
|
||||
from ultralytics import YOLO
|
||||
|
||||
if not torch.cuda.is_available() or not args.device.startswith("cuda"):
|
||||
raise SystemExit("CUDA is required for experimental proposal-filter training")
|
||||
torch.manual_seed(args.seed)
|
||||
tiles = [tile for tile in summary["tiles"] if tile.get("kept", True)]
|
||||
split_tiles = {
|
||||
"val": [dict(tile, split="val") for tile in tiles if tile["sample_slug"] in val_samples],
|
||||
"train": [dict(tile, split="train") for tile in tiles if tile["sample_slug"] not in val_samples],
|
||||
}
|
||||
proposal_model = YOLO(str(args.proposal_model))
|
||||
counts: dict[str, dict[str, int]] = {}
|
||||
for split, selected in split_tiles.items():
|
||||
results = proposal_model.predict(
|
||||
[tile["image_path"] for tile in selected],
|
||||
conf=args.proposal_confidence,
|
||||
imgsz=640,
|
||||
max_det=1000,
|
||||
device=args.device,
|
||||
verbose=False,
|
||||
)
|
||||
counts[split] = materialize_split(
|
||||
tiles=selected,
|
||||
results=results,
|
||||
output_dir=args.output_dir / "crops",
|
||||
split=split,
|
||||
crop_scale=args.crop_scale,
|
||||
max_positives_per_tile=args.max_positive_per_tile,
|
||||
max_negatives_per_tile=args.max_negative_per_tile,
|
||||
negative_match_iou=args.negative_match_iou,
|
||||
)
|
||||
del results
|
||||
torch.cuda.empty_cache()
|
||||
del proposal_model
|
||||
torch.cuda.empty_cache()
|
||||
if any(counts[split][label] == 0 for split in ("train", "val") for label in ("positive", "negative")):
|
||||
raise SystemExit(f"proposal crop class is empty: {counts}")
|
||||
|
||||
weights = ResNet18_Weights.DEFAULT
|
||||
transform = weights.transforms()
|
||||
data = {
|
||||
split: datasets.ImageFolder(args.output_dir / "crops" / split, transform=transform)
|
||||
for split in ("train", "val")
|
||||
}
|
||||
loaders = {
|
||||
split: DataLoader(dataset, batch_size=args.batch, shuffle=split == "train", num_workers=0)
|
||||
for split, dataset in data.items()
|
||||
}
|
||||
model = resnet18(weights=weights)
|
||||
model.fc = nn.Linear(model.fc.in_features, 1)
|
||||
model.to(args.device)
|
||||
positives = counts["train"]["positive"]
|
||||
negatives = counts["train"]["negative"]
|
||||
criterion = nn.BCEWithLogitsLoss(
|
||||
pos_weight=torch.tensor([negatives / positives], device=args.device)
|
||||
)
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)
|
||||
history = []
|
||||
for epoch in range(1, args.epochs + 1):
|
||||
model.train()
|
||||
loss_sum = 0.0
|
||||
for images, labels in loaders["train"]:
|
||||
images = images.to(args.device)
|
||||
labels = labels.float().to(args.device)
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
loss = criterion(model(images).flatten(), labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
loss_sum += float(loss.detach()) * len(images)
|
||||
row = {"epoch": epoch, "train_loss": loss_sum / len(data["train"])}
|
||||
history.append(row)
|
||||
print(json.dumps(row), flush=True)
|
||||
|
||||
model.eval()
|
||||
probabilities: list[float] = []
|
||||
labels: list[int] = []
|
||||
with torch.inference_mode():
|
||||
for images, batch_labels in loaders["val"]:
|
||||
probabilities.extend(torch.sigmoid(model(images.to(args.device)).flatten()).cpu().tolist())
|
||||
labels.extend(batch_labels.tolist())
|
||||
threshold, validation_metrics = choose_threshold(probabilities, labels)
|
||||
args.output_dir.mkdir(parents=True, exist_ok=True)
|
||||
model_path = args.output_dir / "proposal-filter.experimental.torchscript.pt"
|
||||
torch.jit.script(model.cpu()).save(str(model_path))
|
||||
evidence = {
|
||||
"schema_version": 1,
|
||||
"status": "trained_experimental_only",
|
||||
"promotion_allowed": False,
|
||||
"release_claim_allowed": False,
|
||||
"human_review_pending": True,
|
||||
"architecture": "resnet18_binary_proposal_filter",
|
||||
"source_summary": str(args.summary),
|
||||
"source_summary_sha256": sha256(args.summary),
|
||||
"proposal_model": str(args.proposal_model),
|
||||
"proposal_model_sha256": sha256(args.proposal_model),
|
||||
"train_samples": sorted(sample_slugs - val_samples),
|
||||
"validation_samples": sorted(val_samples),
|
||||
"counts": counts,
|
||||
"epochs": args.epochs,
|
||||
"history": history,
|
||||
"selected_threshold_source": "internal_v73_validation_only",
|
||||
"selected_threshold": threshold,
|
||||
"validation_metrics": validation_metrics,
|
||||
"model": str(model_path),
|
||||
"model_sha256": sha256(model_path),
|
||||
"required_next_gate": "independent V72 detector-plus-filter calibration",
|
||||
}
|
||||
(args.output_dir / "proposal-filter.experimental.json").write_text(
|
||||
json.dumps(evidence, indent=2, sort_keys=True) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps(evidence, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -84,6 +84,7 @@ def main() -> int:
|
||||
seed=73,
|
||||
amp=False,
|
||||
val=False,
|
||||
optimizer="AdamW",
|
||||
lr0=args.learning_rate,
|
||||
lrf=0.1,
|
||||
freeze=args.freeze,
|
||||
@@ -114,6 +115,7 @@ def main() -> int:
|
||||
"imgsz": args.imgsz,
|
||||
"batch": args.batch,
|
||||
"learning_rate": args.learning_rate,
|
||||
"optimizer": "AdamW",
|
||||
"freeze": args.freeze,
|
||||
"workers": args.workers,
|
||||
"device": torch.cuda.get_device_name(0),
|
||||
|
||||
@@ -7,6 +7,8 @@ from scripts.export_experimental_yolo_train_shard import (
|
||||
validate_experimental_request,
|
||||
)
|
||||
from scripts.train_experimental_yolo_candidate import validate_paths
|
||||
from scripts.train_experimental_building_proposal_filter import validate_inputs
|
||||
from scripts.compose_experimental_yolo_rehearsal_dataset import validate_sources
|
||||
|
||||
|
||||
def test_export_refuses_non_experimental_output(tmp_path: Path) -> None:
|
||||
@@ -26,3 +28,13 @@ def test_train_shard_rejects_near_duplicate_edge_cover_windows() -> None:
|
||||
edge_cover = {"row_off": 513, "col_off": 0, "height": 512, "width": 512}
|
||||
assert is_canonical_train_window(canonical, 512)
|
||||
assert not is_canonical_train_window(edge_cover, 512)
|
||||
|
||||
|
||||
def test_proposal_filter_refuses_non_experimental_paths(tmp_path: Path) -> None:
|
||||
with pytest.raises(ValueError, match="must remain"):
|
||||
validate_inputs(tmp_path / "summary.json", tmp_path / "output")
|
||||
|
||||
|
||||
def test_rehearsal_composer_refuses_non_experimental_output(tmp_path: Path) -> None:
|
||||
with pytest.raises(ValueError, match="output must remain"):
|
||||
validate_sources(tmp_path / "old.json", tmp_path / "new.json", tmp_path / "out")
|
||||
|
||||
Reference in New Issue
Block a user