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.
Geospatial Annotations
A focused geospatial annotation pilot in days, not weeks
Send a representative sample, lock the taxonomy, review calibration labels, and get a small delivery package before committing to production volume.
Representative sampleCalibration firstPilot handoff
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.
03
Imagery Workflows
Roadway, street-level, and LiDAR imagery converted into QA-reviewed features your mapping/AI/asset teams can use immediately.
04
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.
What you can evaluate
Proof you can inspect before the first call
Geospatial annotation pilot, fast infrastructure AI training data, and sample labeling project.
Representative sample
50-500 frames or features, target classes, output format, and edge-case examples.
Calibration first
Small labeled set and QA review before production rules are locked.
Pilot handoff
Labels, scorecard, edge-case log, revised taxonomy, and production estimate.
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
Representative sample, target feature classes, geometry types, output format, deadline, and accuracy target.
Review surface
We label a calibration set and document disagreements before scale.
Evidence
QA scorecard, edge-case log, source notes, and revised taxonomy make the next decision obvious.
Output
Pilot labels, delivery package, production scope, and pricing path.
Source and limits
What stays visible before you commit.
Confidence
A small pilot exposes class ambiguity before production spend.
Caveat
Pilot results are used to tune rules, not to imply all imagery will be equally simple.
Source
Representative customer imagery, GIS layers, target schema, and review examples.
Review path
Calibration review, edge-case log, QA scorecard, and revised class rules.
Export path
Small labeled dataset, QA notes, edge-case log, and production recommendation.
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 does 'GIS-native' matter for annotation?
Coordinates, projections, and spatial relationships are part of the label — not metadata added after. A road sign label includes its real-world geometry and orientation; lane lines preserve direction-of-travel; assets are validated against authoritative GIS databases before delivery.
What does a 500-sample pilot actually look like?
You send 500 representative frames (imagery + AOI), we return labeled output in your preferred format (GeoJSON, COCO, KITTI, Mapillary) within 2-4 business days, with a QA report showing inter-annotator agreement and edge-case handling.
How do you handle ambiguous or edge-case features?
We document them. Every pilot returns an edge-case log with examples and our interpretation. You sign off on the calls before production scale. Disagreements become explicit guidance, not silent inconsistency.
Can you train custom annotators for our project?
Yes. For embedded engagements we onboard a dedicated team to your taxonomy, run them through your QA standard, and integrate them with your existing tooling. Typical ramp is 2-3 weeks.
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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.
Run a small annotation pilot