Bernt
bae705aa97
- Add NFC ePassport roadmap (ICAO 9303, eIDAS) - Add TensorFlow.js edge face detection (BlazeFace) - Add structured audit logger (GDPR-compliant) - Risk scoring support Part of KYC Apple Native UX v1.1.0
143 lines
3.7 KiB
Markdown
143 lines
3.7 KiB
Markdown
# LANDVEX — AI Intelligence Pipeline
|
|
**Beslutad:** 2026-06-21, Erik Svensson
|
|
**Princip:** Lokal first, Claude last. Lägsta möjliga token-kostnad.
|
|
|
|
---
|
|
|
|
## Arkitektur
|
|
|
|
```
|
|
quiXzoom bild (JPEG)
|
|
│
|
|
▼
|
|
[STEG 1 — LOKAL, GRATIS]
|
|
YOLOv8n (5MB modell)
|
|
→ Object detection
|
|
→ Tags: power_pole, cable, graffiti, signage, person, vehicle, building
|
|
→ Körtid: ~80ms/bild på CPU
|
|
|
|
│
|
|
▼
|
|
[STEG 2 — LOKAL, GRATIS]
|
|
MobileNetV3 / CLIP-lite
|
|
→ Condition score 1-10
|
|
→ Vacancy: open/closed/uncertain
|
|
→ Körtid: ~50ms/bild
|
|
|
|
│
|
|
▼
|
|
[STEG 3 — REGEL-BASERAT, GRATIS]
|
|
Python rule engine
|
|
→ Beräknar Infrastructure Risk Index
|
|
→ Beräknar Contradiction potential
|
|
→ Jämför mot officiell data (cache)
|
|
→ Producerar structured JSON
|
|
→ Körtid: ~5ms
|
|
|
|
│
|
|
├─► 95% av bilder → direkt till databas (ingen Claude)
|
|
│
|
|
▼
|
|
[STEG 4 — CLAUDE, BARA VID BEHOV]
|
|
Triggas ENDAST om:
|
|
- Contradiction potential > 0.7
|
|
- Okänd objektkategori (confidence < 0.4)
|
|
- Första bild från ny koordinat/stad
|
|
|
|
Claude-prompt: ultra-kort, strukturerad output
|
|
→ ~150 tokens in, ~100 tokens ut per bild
|
|
→ Kostnad: ~$0.0004 per triggad bild
|
|
```
|
|
|
|
---
|
|
|
|
## Structured Observation Schema
|
|
|
|
```json
|
|
{
|
|
"observation_id": "qx-bkk-20260621-001",
|
|
"lat": 13.7420, "lng": 100.5571,
|
|
"city": "bangkok", "district": "sukhumvit",
|
|
"timestamp": "2026-06-21T12:18:00Z",
|
|
"zoomer_level": "L2",
|
|
"image_hash": "sha256:...",
|
|
|
|
"detected_objects": [
|
|
{"class": "power_pole", "confidence": 0.94, "bbox": [...]},
|
|
{"class": "cable_bundle", "confidence": 0.87, "bbox": [...]},
|
|
{"class": "graffiti", "confidence": 0.71, "bbox": [...]},
|
|
{"class": "commercial_sign", "confidence": 0.89, "bbox": [...]}
|
|
],
|
|
|
|
"computed_scores": {
|
|
"infrastructure_risk": 8.2,
|
|
"condition_score": 3.1,
|
|
"vacancy_status": "occupied",
|
|
"graffiti_density": "low",
|
|
"cable_disorder_severity": "critical"
|
|
},
|
|
|
|
"contradiction": {
|
|
"potential": 0.84,
|
|
"official_classification": "infrastructure_approved",
|
|
"observed_deviation": "critical_cable_disorder",
|
|
"claude_triggered": true,
|
|
"narrative": "Official data: approved. Observed: critical cable bundling, fire risk."
|
|
},
|
|
|
|
"tags": ["electrical_hazard", "cable_disorder", "graffiti", "commercial_active"],
|
|
"source_first": {
|
|
"type": "observation",
|
|
"verified_by": "ai_v1.2",
|
|
"confidence": 0.87
|
|
}
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Claude-prompt (minimalt)
|
|
|
|
Bara triggas vid contradiction_potential > 0.7:
|
|
|
|
```
|
|
SYSTEM: You are a urban intelligence classifier. Output JSON only.
|
|
|
|
USER: Location: Bangkok Sukhumvit. Objects: power_pole, cable_bundle(critical), graffiti(low).
|
|
Official status: infrastructure_approved.
|
|
Output: {"contradiction_score":0-1, "risk_tags":[], "narrative":"<20 words"}
|
|
```
|
|
|
|
Kostnad per prompt: ~$0.0003. Med 5% trigger-rate på 1000 bilder/dag = $0.15/dag.
|
|
|
|
---
|
|
|
|
## Implementation — server-side script
|
|
|
|
Fil: /opt/amos/api/landvex-vision/process_image.py
|
|
|
|
Kräver: ultralytics (YOLOv8), Pillow, numpy
|
|
Installeras en gång: pip install ultralytics pillow numpy anthropic
|
|
|
|
---
|
|
|
|
## Träningsstrategi
|
|
|
|
### Fas 1 — Bootstrap (nu)
|
|
- Labla 500 bilder manuellt (Erik + Johan, 2h)
|
|
- Kategorier: infrastructure_risk, vacancy, condition, graffiti
|
|
- Verktyg: Label Studio (gratis, self-hosted)
|
|
|
|
### Fas 2 — Active learning (vecka 2-4)
|
|
- Modellen flaggar bilder med låg confidence → manuell labeling
|
|
- Varje ny stad bootstrappas med 50-100 bilder
|
|
|
|
### Fas 3 — Temporal learning (månad 2+)
|
|
- Samma koordinat fotograferas igen 30 dagar senare
|
|
- Change detector: delta mellan t0 och t1
|
|
- Automatisk träningsdata utan manuellt arbete
|
|
|
|
### Fas 4 — Contradiction fine-tuning
|
|
- Alla bilder där Claude ändrade klassificeringen → träningsdata
|
|
- Målet: Claude-triggern sjunker från 5% → 1% → 0.1%
|