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QGIS AI Vectorizer

AI-powered raster-to-vector map digitization

QGIS plugin that uses neural networks to automatically trace lines and polygons from raster maps, providing AI-assisted autocomplete for faster digitization of scanned maps, engineering drawings, and geologic surveys.

$500K raisedEarly Stage
Founded 2022 · 30+ government agencies (US, UK, Brazil, India, Australia); 4-person team; $440K ARR (as of 2025)
Website →← All Tools
WHY IT EXISTS

Why This Tool Exists

The Problem

GIS professionals manually digitize raster maps and scanned documents by clicking each vertex, taking 2-3 hours per map. Geological surveys, utility plans, historical maps, and engineering drawings remain locked in paper format or PDFs, requiring extensive manual tracing.

The Solution

AI Vectorizer learns what you're tracing and autocompletes lines and polygons in real-time. Users click twice along a feature, and the AI predicts and draws the complete line, reducing digitization time by ~50% while handling curved, dashed, and complex line styles.

USAGE

How You Use It

Delivery Method
Plugin
Integrations
QGISArcGISCAD (export)
Disciplines
Civil Engineering, Landscape Architecture, Facility Management
Workflows
Data & Automation, Operations & Facility Management
Project Phases
Pre-Design, Design Development
Project Types
Infrastructure, Institutional
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Raster map or image (GeoTIFF, PNG, PDF, scanned document), QGIS vector layer (LineString, MultiLine, or Polygon)
Optional:User-drawn partial traces, Style reference data
Formats:PNG, JPG, GeoTIFF, PDF, Scanned raster images
Output
What you get
Format:Vector features (lines or polygons)
Fields:Digitized line features, Polygon boundaries and fills, Geometry coordinates, Feature attributes (manual entry by user)
Algorithm
How it works
Model:Proprietary neural network (architecture details not publicly disclosed)
Accuracy:Claims 4x faster digitization than manual tracing; accuracy not quantified but tool allows real-time user corrections
Privacy
How your data is protected
Retention:Raster context around cursor sent to Bunting Labs servers during vectorization; Organizations plan users may request immediate deletion after processing; Personal/Professional tiers: data retained per privacy policy (specific retention period not specified)
Training:Not explicitly stated whether vectorization data used for AI model training; pricing page indicates 'Zero Data Retention' and 'no model training' for Professional tier, suggesting opt-in training for Personal tier
Compliance:GDPR (EU/EEA rights), CCPA (California consumer rights), CalOPPA (California online privacy), PCI-DSS (payment processing)
API
Integration
Endpoint:Contact vendor for API access
Method:Plugin-based (REST API for third-party integration not publicly documented)
USE CASES

Use Cases

  • ·Digitizing historical topographic maps for GIS databases
  • ·Converting utility infrastructure maps (water, sewer, electrical) from paper to vector
  • ·Automating as-built engineering drawing digitization
  • ·Geological survey digitization (fault lines, soil boundaries, mineral deposits)
  • ·Cadastral map digitization for land records
PRICING

Pricing

Free
Free trial (email signup, no credit card required)
Pro
Personal tier $20/seat/month (40 vectorization chunks/day, 10GB storage); Professional tier $99/seat/month (unlimited vectorization, zero data retention, no model training)
Enterprise
Organizations plan with custom pricing; on-premises deployment available
Sources & Research NotesResearch date: 2026-02-01
Data Sources (10)
Fields Checked (18)
problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, yearFounded, maturityStage, customerBase, fundingTotal, vendor, website, pricing, control-features, useCases, input-output-specs, compliance
Not Found (7)
algorithm-architecture-details, accuracy-quantified-metrics, soc2-certification, data-retention-period-specific, training-data-usage-explicit, webhook-support, third-party-api-documentation
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.
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