Landvex transforms unstructured field observations into structured decision intelligence for organisations that own, manage or invest in physical assets. This document describes the complete methodology — from field capture through AI review, normalization and aggregation to the delivery of actionable intelligence outputs.
The core principle is evidence-based intelligence: every score, index and forecast is traceable to source observations. Every conclusion can be challenged. Every assumption is explicit.
| Metric | Target |
|---|---|
| Submission approval rate | ≥ 85% |
| GPS accuracy (standard) | < 50 metres |
| GPS accuracy (precision) | < 10 metres |
| Data freshness (standard) | Observations within 90 days |
| Data freshness (premium) | Observations within 30 days |
| Data residency | EU only (AWS eu-north-1, Stockholm) |
Landvex intelligence is powered by the quiXzoom contributor network — verified field observers who photograph infrastructure, buildings and urban environments on defined missions. Contributors are referred to as Zoomers.
Every Zoomer undergoes one-time KYC (Know Your Customer) verification before their first payout. Verification confirms identity, residency and bank account ownership. Contributor identities are stored as pseudonymised hashes in the intelligence pipeline — they are not included in delivered outputs.
| Type | Description | Typical use case |
|---|---|---|
| Infrastructure | Bridges, roads, utility assets, drainage | Condition scoring, maintenance prioritisation |
| Facade | Building exteriors, storefronts, signage | Property assessment, compliance monitoring |
| Commercial survey | Business activity, vacancy, signage | Retail intelligence, commercial vitality |
| Post-event | Damage documentation after weather events | Insurance claims, disaster assessment |
| Verification | Address, access, asset existence | Data quality assurance |
Each submission includes: high-resolution photograph, GPS coordinates (locked at capture start), EXIF metadata (device, timestamp, focal length), mission ID, and a device fingerprint for anti-fraud purposes.
Submitted coordinates are validated against mission target coordinates. Submissions outside tolerance (50m standard / 10m precision) are auto-rejected or flagged for human review. A GPS accuracy class is assigned: coarse (>50m), balanced (10–50m), or precise (<10m).
Each image is analysed for specification compliance (correct object, correct angle, correct detail level), technical quality (resolution, blur, exposure), and content relevance (target present in frame). A quality score 0–100 is assigned. Submissions below threshold receive a rejection code.
AI-borderline submissions (score 40–60) are routed to a human reviewer who applies the mission specification. Decisions are logged with reviewer ID and timestamp. An appeals process is available for contested rejections.
Approved submissions are converted to structured data points. Entity extraction identifies: location (polygon/point), object type, condition class (1–5), and change signal (improving/stable/deteriorating). Each data point retains its source attribution throughout the pipeline.
Observations are aggregated by geographic unit (district, city, region). Five index scores are calculated per unit, each 0–100:
| Index | What it measures |
|---|---|
| Opportunity | Commercial and investment opportunity concentration |
| Growth | Rate of positive change in commercial and physical conditions |
| Commercial Vitality | Density and health of commercial activity |
| Infrastructure Stability | Observed infrastructure condition vs maintenance expectations |
| Investment Confidence | Composite score for investment decision support |
Aggregated observations are compared against official data sources (national statistics, municipal open data, infrastructure registers). Where observed conditions diverge significantly from official narratives, a Narrative Conflict Alert is generated with a confidence score and supporting/contradicting evidence count.
| Format | Description | Delivery |
|---|---|---|
| Index scores | 0–100 per dimension, per geographic unit | Dashboard, API, CSV |
| Contradiction report | Flagged narrative conflicts with evidence | PDF, JSON |
| City intelligence report | District-level scoring with trend analysis | |
| Raw data export | Structured observations with source attribution | JSON, CSV, GeoJSON |
| API access | Programmatic access to scores and reports | REST API (enterprise) |
Landvex targets a minimum 85% approval rate on submitted observations. Below this threshold, additional human review is activated. Enterprise SLAs specify minimum observation counts per geographic unit before scores are considered reliable.
Every data point and aggregate score includes a confidence score (0–100) based on: observation count (higher = more confident), recency (fresher = more confident), cross-validation against multiple submissions of the same object, and reviewer consensus where human review was applied.
Every intelligence output is traceable to its source observations. On request, Landvex provides a complete audit package: observation timestamps, GPS accuracy classes, AI quality scores, human review decisions, normalization logs, and aggregation parameters. Full data lineage documentation: landvex.com/data-lineage/
All data is stored exclusively in the EU (AWS eu-north-1, Stockholm, Sweden). No data is transferred outside the EU without explicit client instruction and appropriate safeguards (Standard Contractual Clauses where applicable).
Contributor personal data is processed under GDPR Article 6(1)(b) (contract performance) and 6(1)(f) (legitimate interest). Contributor identities are pseudonymised before inclusion in intelligence outputs. Right to erasure is supported — deletion confirmed within 30 days of request.
Landvex uses two infrastructure sub-processors: Amazon Web Services (EU region, SOC 2 Type II / ISO 27001) for infrastructure, and Stripe Inc. (EU data residency, SCC-covered) for payment processing. Full sub-processor list: landvex.com/subprocessors/
Enterprise clients receive a Data Processing Agreement (DPA) under GDPR Article 28. Standard Contractual Clauses are available for non-EU data transfers. Service Level Agreements specify data freshness, uptime and quality commitments. Contact: legal@landvex.com
| Enquiry type | Contact |
|---|---|
| Enterprise intelligence enquiries | enterprise@landvex.com |
| Methodology questions | contact@landvex.com |
| Data protection / DPA | legal@landvex.com |
| Security / responsible disclosure | security@landvex.com |
| Pilot programme | landvex.com/pilot/ |