01
See the work before you contract
Send 25-50 representative frames. We label them at our cost, return the output and a per-class QA scorecard. You decide whether to scope a pilot after you have seen the labels, not before.
Annotation Services
Outsource geospatial labeling without losing GIS discipline
Production annotation teams for infrastructure AI, remote sensing, mobile mapping, and GIS data programs, with batch delivery, QA reporting, and export formats your pipeline can ingest.
Service tiersVolume handlingSupported delivery formats
How we keep the first step easy
Three commitments that come standard
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Per object or per hour, your call
Bill per labeled object when scope and volume are predictable. Bill per labeling hour when the workflow is exploratory or the schema is still firming up. Both models are on the table from the first scoping call.
03
Your labeling platform, our labor
We operate in CVAT, Labelbox, Roboflow, V7, Scale AI workflows, and most in-house labeling stacks. No platform migration on your end. If you have a custom tool, we learn it on the pilot.
The status quo
Why teams outsource to us
- Hiring and training in-house annotators with GIS knowledge is slow and expensive
- You need flexible capacity — ramp up for training runs, scale down between releases
- Annotation accuracy directly impacts model performance and the credibility of your delivery to customers
What we deliver
Service tiers
98%F1 target
On infrastructure asset classes, validated per delivery
Rapid Pilot (2-4 days)
Small-batch pilot to benchmark our quality against your current annotation pipeline before scaling.
02
Managed Annotation
End-to-end labeling projects with dedicated QA, project management, and delivery SLAs.
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Embedded Teams
GIS-trained annotators integrated into your existing labeling pipeline and tools.
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QA & Reporting
Inter-annotator agreement scores, label distribution reports, and quality trend dashboards.
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Schema-Ready Delivery
GeoJSON, COCO, KITTI, OSM-ready, and custom schema exports — wired into the format your pipeline expects.
What you can evaluate
Proof you can inspect before the first call
Outsource geospatial annotation, GIS annotation services, and production data labeling vendor.
Service tiers
Calibration, pilot, production, and embedded QA support for teams that need predictable delivery.
Volume handling
Structured batches, progress tracking, issue logs, and feedback loops keep production consistent.
Supported delivery formats
GeoJSON, shapefiles, masks, point layers, classified point clouds, CSV, and customer-specific schemas.
Proof workflow
Input, review, evidence, output.
Bring the closest real workflow. We map what you send, what your team reviews, what evidence stays visible, and what you receive at handoff.
Input
Volume estimate, sample data, taxonomy, delivery cadence, security limits, and output format.
Review surface
We define service tier, batch process, QA acceptance, issue reporting, and delivery schedule.
Evidence
Progress, QA reports, issue logs, source context, and export status stay visible.
Output
Production annotation batches with QA reports and agreed GIS/model-ready formats.
Source and limits
What stays visible before you commit.
Confidence
Production quality depends on clear rules, batch QA, and feedback loops.
Caveat
Volume commitments should follow a calibration phase.
Source
Satellite, aerial, drone, LiDAR, orthomosaic, GIS vector, and time-series data.
Review path
Batch QA, duplicate checks, class consistency, geometry checks, and export verification.
Export path
Recurring delivery batches, QA reports, data dictionaries, and GIS-ready files.
Before the first call
What you send · What you get
No vague discovery phase. You bring four or five things, we return a specific plan you can evaluate.
→ What you send
- 1A representative sample (50-500 frames) from your imagery source
- 2Target feature classes and geometry types (point, line, polygon, mask)
- 3Required output format (GeoJSON, COCO, KITTI, Mapillary, custom)
- 4Approximate volume, deadline, and accuracy requirement
- 5Security or NDA constraints (we sign mutual NDA up front)
← What you get back
- 1Calibration set with QA scores returned in 2-4 business days
- 2Documented edge-case log with our interpretation of every ambiguous class
- 3Schema-locked production scope with per-frame pricing
- 4Inter-annotator agreement report (kappa, F1 by class)
- 5Sample report with feature layer, QA notes, and exports
Class library
83 documented asset classes across 4 categories
Every class has a labeled definition, edge-case examples, and QA rules calibrated against authoritative GIS databases. Add custom classes during pilot and we extend the taxonomy.
Road infrastructure
28 classes
- Pavement markings
- Striping (single, double, dashed)
- Crosswalks (all types)
- Lane lines (direction-aware)
- Stop bars + yield triangles
- Road boundaries + shoulders
- Surface condition cues (cracking, raveling, rutting)
Asset geolocation
34 classes
- Traffic signs (R-series, W-series, MUTCD-compliant)
- Traffic signals + pedestrian heads
- Utility poles (wood, concrete, steel)
- Streetlights + cobra heads
- Guardrails + crash cushions
- Barriers (Jersey, K-rail, temporary)
- Manholes + catch basins
- Fire hydrants + valves
Training data extraction
12 classes
- Object detection bounding boxes
- Semantic segmentation masks
- Instance segmentation
- Polygon classification
- False-positive cleanup pass
- False-negative recovery (hard-negative mining)
GIS delivery formats
9 classes
- GeoJSON (QGIS / ArcGIS native)
- COCO (training-ready)
- KITTI (AV-research convention)
- Mapillary (street-level standard)
- OpenStreetMap-ready attributes
- Custom JSON schemas
- PostGIS direct write
- Shapefile (legacy support)
Sample deliverable
A single feature, as you would receive it
Every label is a complete GeoJSON feature with geometry, class, confidence, QA trail, and source provenance. Loads directly into your map, your trainer, or your validator — no conversion script.
json
{
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [[[ -77.0364, 38.8951 ], ...]]
},
"properties": {
"class": "crosswalk",
"class_id": "CW_001",
"mutcd_type": "continental",
"confidence": 0.97,
"qa_status": "approved",
"qa_reviewer": "annotator_03",
"qa_timestamp": "2024-08-15T14:23:17Z",
"source_frame": "frame_847.jpg",
"capture_timestamp": "2024-08-12T11:18:04-04:00",
"schema_version": "gss-roads-v2.4"
}
}Deliverables
What you walk away with
- Labeled dataset in your preferred format (GeoJSON, COCO, KITTI, Mapillary, custom)
- QA dashboard with per-annotator scores and inter-annotator agreement
- Documented taxonomy and edge-case handling guide
- Production SLA option or pilot-only delivery — your choice
How we work
A scoped path from sample data to running system
No open-ended retainers. No "discovery phases" that bill for months without producing anything you can evaluate.
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01
Sample
50-100 frames, your schema, your edge cases. We return a calibration set so you can see how we interpret your taxonomy before scale.
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02
Pilot
500 samples in 2-4 business days. Inter-annotator agreement scores, QA dashboard, format in your pipeline (GeoJSON, COCO, KITTI, Mapillary).
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03
Scale
Production volume with SLA. 24/7 follow-the-sun capacity, 98%+ QA target, weekly delivery cadence.
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04
Integrate
Wire into your training pipeline, deploy custom validation rules, build out edge case mining. Optional embedded team.
Live on geospatialsolutions.co
Click into the actual work
These open the real, interactive demos on our main site — not screenshots, not videos. Click around before you decide to talk to us.
Why teams trust us
- 2-4 day pilot turnaround, 98%+ QA target, 24/7 capacity
- Flexible engagement: project-based, embedded, or pilot
- Operated by Geospatial Solutions LLC — Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
How does pricing work for production volume?
Per-sample or per-frame, with volume tiers. Pilots are fixed-fee ($1.5k-$4k). Production runs are typically $0.40-$2.50 per frame depending on schema complexity, QA level, and turnaround.
Can you scale to a million samples per month?
Yes, with notice. Our 24/7 follow-the-sun capacity tops out around 1.5M samples per month at full saturation. We typically ramp from pilot to full volume over 4-6 weeks so QA stays calibrated.
Do you handle confidential or PII-sensitive imagery?
Yes. Mutual NDA, secure delivery channels (private S3, signed URLs), and annotator NDAs for sensitive projects. We've handled redacted defense, medical, and law-enforcement imagery.
Can we hire the annotators trained on our project?
For embedded engagements, yes. The team trained on your project can convert to your direct employment after a minimum 3-month engagement. We charge a transition fee but no buyout.
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Request a free pilot project
Send us 500 frames. Get a labeled pilot in 2 days.
No purchase order, no master service agreement. Send a representative slice and a target schema; we return the labels in the format your pipeline already ingests.
Get production annotation pricing