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.
GeoAI
Turn imagery into answers, detections, and review workflows
Anchored in the NAIPAI pattern: ask aerial, drone, or satellite imagery useful questions, keep confidence and caveats visible, and turn answers into map layers, reports, APIs, or analyst review queues.
Damage and responseAsset inventoryInspection and compliance workflows
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
Why infrastructure AI teams need GIS-native partners
- Generic annotation services miss spatial context — coordinates, projections, and asset classifications matter
- Off-the-shelf remote sensing tools don't fit your specific imagery, AOI, or feature schema
- Your ML team is burning engineering hours on data labeling instead of model development
What we deliver
What we deliver
98%F1 target
On infrastructure asset classes, validated per delivery
Street Asset Annotation
Bounding boxes, segmentation masks, and point labels for road signs, utility poles, manholes, and street furniture.
02
Satellite & Drone Imagery
Multi-spectral analysis from Landsat, Sentinel, Planet, and UAV captures — feature extraction with sub-meter precision.
03
Solar & Aerial Detection
Panel detection, rooftop segmentation, and vegetation encroachment from satellite and drone imagery.
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GIS-Aware QA
Spatial validation, coordinate accuracy checks, and classification QA against authoritative GIS databases.
05
ML Implementation
Deploy trained models into production pipelines with TensorFlow, PyTorch, and cloud-native inference.
Proof-led positioning
What this page needs to make obvious
GeoAI implementation, imagery chat, remote sensing AI workflow, satellite imagery analysis, and aerial image assistant.
Damage and response
Ask what changed, where damage appears likely, and which parcels or assets need field review first.
Asset inventory
Count solar panels, tanks, equipment yards, vehicles, utility features, or visible site assets from imagery.
Inspection and compliance workflows
Convert repetitive visual review into answer schemas, confidence cues, QA checklists, and exportable notes.
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
A sample image set and the 3-5 questions your team asks repeatedly.
Review surface
We configure model behavior, answer structure, confidence cues, evidence panels, exports, and user path.
Evidence
Each answer shows what was found, how confident it is, what needs review, and what data was used.
Output
Private demo, embedded app, API endpoint, analyst dashboard, map layer, or client-ready report workflow.
Source and limits
Technical trust should stay visible.
Confidence
Answers should include confidence, caveats, and human review points.
Caveat
Imagery assistants should not hide uncertainty or replace field confirmation where required.
Source
NAIP, Sentinel, Landsat, drone orthomosaics, inspection photos, thermal, multispectral, and customer archives.
QA boundary
Prompt rules, answer schema, confidence language, examples, thresholds, and reviewer workflow.
Export path
Map layer, report, ticket, CSV, GeoJSON, dashboard card, API response, or analyst queue.
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
- Sub-meter accuracy (0.3m) with multi-spectral analysis across 15+ bands
- CVAT, Labelbox, Roboflow, TensorFlow, PyTorch, GeoPandas, GDAL
- Operated by Geospatial Solutions LLC — Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
Do you build the ML models, or just provide training data?
Both. We can deliver labeled datasets alone, or build the model on top — TensorFlow, PyTorch, or your team's framework — and integrate it into your inference pipeline.
What model types do you typically deploy?
Object detection (bounding box and segmentation) for street assets, semantic segmentation for land use and pavement, change detection for satellite imagery, and graph neural networks for routing and network analysis.
Where does the trained model actually run?
Depends on your need. Edge inference (on-device) for field workflows, batch inference on cloud for archive processing, or real-time serving via Cloud Run / Lambda / SageMaker for production pipelines.
How do you handle model drift in production?
Monthly retraining on accumulated data with automatic drift detection — we surface accuracy degradation before it shows up in operations. Ship a model card with every release so you can audit performance over time.
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Book a GeoAI implementation review
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.
Scope an imagery assistant or GeoAI pilot