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.
Human Geo Annotations
Human-reviewed geospatial labels for difficult imagery
When generic labelers flatten ambiguity, we keep the human review loop visible: edge-case notes, correction decisions, QA flags, and spatial reasoning your model team can audit.
Ambiguity handled explicitlyCorrection loopsTaxonomy discipline
How we keep the first step easy
Three commitments that come standard
02
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
Where generic annotation services fall short
- Annotators label objects but miss spatial relationships — proximity, orientation, and geographic context
- Coordinate-referenced labels require GIS expertise most labeling teams don't have
- Inconsistent labeling creates noisy training data that compounds into model failures in production
What we deliver
What we deliver
98%F1 target
On infrastructure asset classes, validated per delivery
Road Infrastructure Labeling
Pavement, striping, lanes, boundaries, and surface condition labels — tied to real geography with QA trails.
02
Asset Geolocation
Signs, signals, poles, utilities, streetlights — bounding boxes, segmentation masks, and point labels with coordinate accuracy.
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Imagery Workflows
Roadway, street-level, and LiDAR imagery converted into QA-reviewed features your mapping/AI/asset teams can use immediately.
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GIS-Aware QA
Spatial validation, coordinate-accuracy checks, and asset classification QA against authoritative GIS databases.
05
Schema-Ready Exports
Deliveries in QGIS, ArcGIS, GeoJSON, COCO, KITTI, Mapillary — whatever your pipeline ingests.
Proof-led positioning
What this page needs to make obvious
Human-in-the-loop geospatial annotation, manual GIS annotation, expert map labeling, and QA-reviewed spatial labels.
Ambiguity handled explicitly
Reviewer notes document what was visible, what was uncertain, and why the final label was accepted or corrected.
Correction loops
Flagged records move through review, correction, and re-verification before delivery.
Taxonomy discipline
Class definitions, geometry rules, and edge-case examples prevent silent drift across labeling batches.
Proof workflow
Input, review, evidence, output.
Modeled on the live Geospatial Solutions demos: the page should show what the buyer sends, what they review, what evidence stays visible, and what they receive.
Input
Ambiguous imagery, prior label errors, target classes, and examples of disputed edge cases.
Review surface
Human reviewers compare source imagery, map context, taxonomy rules, and customer acceptance criteria.
Evidence
Each disputed label keeps reviewer notes, confidence, reason codes, and correction status visible.
Output
Reviewed labels, edge-case log, QA scorecard, and updated labeling guidance.
Source and limits
Technical trust should stay visible.
Confidence
Human review focuses on the records most likely to degrade model or GIS quality.
Caveat
Ambiguous imagery still needs explicit acceptance rules, not silent guessing.
Source
Street-level imagery, aerial scenes, LiDAR context, GIS records, and customer review notes.
QA boundary
Reviewer notes, correction logs, class consistency checks, and re-verification.
Export path
QA-reviewed labels, issue logs, correction reports, and GIS-ready exports.
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 with 98%+ QA target before scaling to full volume
- 24/7 follow-the-sun annotation capacity for production pipelines
- CVAT, Labelbox, Roboflow, and custom pipelines — operated by Geospatial Solutions LLC, Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
Why 'human geo' annotations vs automated?
Automated extraction works for the easy 70% of features. The 30% with edge cases — occlusion, ambiguous geometry, novel asset types — needs human judgment validated against geographic context. We focus on that gap.
How does this differ from geospatialannotations.com?
Same underlying team and standards, but humangeoannotations.com is positioned for projects where human QA is the explicit differentiator — regulatory deliverables, training data for safety-critical AI, and high-stakes mapping where automated-only output won't defend.
How do you maintain human consistency at scale?
Per-annotator consensus scoring, weekly calibration sessions against ground-truth, and a senior QA pass on a sampled percentage of every delivery. Annotator drift is detected and corrected before it propagates.
Are your annotators full-time employees or contractors?
Mix. The QA team is full-time. Production annotators are contracted but vetted, trained, and retained — we don't use anonymous task-platform crowds. Annotator continuity is what enables our QA discipline.
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Start a free annotation pilot
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.
Send edge cases for human review