Geospatial annotation and QA/QC
Turn raw captures into trusted GIS data.
This simulated workflow uses representative records to show how automation proposes repeatable labels, a reviewer changes ambiguous geometry or attributes, and GIS validation checks schema, CRS, topology, duplicates, omissions, and exceptions. It does not establish production accuracy or throughput.
Image-space annotations and real-world coordinates are separate evidence states. Representative exports use EPSG:4326 and retain source-frame provenance; a live project must confirm its own CRS, datum, source fitness, taxonomy, and acceptance sample.
A transparent demonstration of how labels can move through imagery review, infrastructure features, catch QA failures, and package the output for GIS, asset inventories, and machine-learning teams.
7
sample records
4
export-ready
3
QA holds
label
Bounding box
status
Export ready
confidence
94%
Interactive proof workspace
Review imagery, map position, attributes, and QA in one place.
Switch scenarios to see how different infrastructure annotation rules change the review decision. Selecting a marker, source frame, or table row keeps the imagery, GIS position, and QA panel synchronized.
Mobile-mapping primes and DOT sign-inventory teams
Road Signs
Invalid signs, repeated labels, loose boxes, and misplaced geolocation points make downstream inventories hard to trust.
Regulatory sign with geolocation
Forward-facing roadway imagery plus map-linked sign points
QA decision
RS-0184
Accepted. Box encloses the visible sign, and map position is tied to the post location instead of the road centerline.
MUTCD review
Required
Condition
Good
Geometry
Point plus image box
Duplicate check
Map position checked
Review flags
Error taxonomy: geometry, attribute, taxonomy, duplicate, omission, provenance, and unresolved-source exceptions. Reviewer decisions remain attached to the representative record.
Production workflow
Built for repeatable QA, not one-off labeling.
01
Ingest sample data
Imagery, frames, point clouds, labels, or existing GIS layers are organized by route, tile, source, and expected output.
02
Calibrate labels
Classes, geometry rules, visibility thresholds, condition definitions, and required attributes are locked before production.
03
Annotate
Human reviewers label source imagery, place points or boxes, and capture the attributes needed for GIS and model training.
04
QA/QC
A second review checks false positives, missed features, duplicates, spatial plausibility, and missing fields.
05
Export
Accepted records move into GeoJSON, FileGDB, Shapefile, CSV, PostGIS, COCO, or custom schema delivery.
Centroid placement
Points land at the visual center of the visible object footprint, not on an edge or arbitrary image location.
Geolocation check
Signs and assets are tied to real-world position using road alignment, side of street, poles, and nearby landmarks.
Visibility threshold
Annotate clear assets with enough visible evidence; skip ambiguous, distant, or off-scope objects instead of guessing.
Duplicate prevention
Map-linked review catches repeated signs, repeated sidewalk defects, and duplicate assets across nearby frames.
Mandatory attributes
Condition, class, geometry type, confidence, review status, and client-specific fields must be complete before export.
Lead QA acceptance
Flagged records pass through calibration or lead review before they become production delivery data.
Handoff package
The output is data your team can load, audit, and reuse.
The demo stays static for reliability, but the delivery shape is the same one buyers ask for: clean features, review notes, source references, and schema-ready exports.
Start with one sample
Send imagery, frames, LiDAR-derived views, or an existing asset layer.
Sample ingest
One corridor, tile, route, or priority class is enough.
QA rules
We confirm classes, geometry, attributes, and review thresholds.
GIS handoff
The pilot ends in a delivery shape your team can inspect.
Scale path
Accepted rules become the production lane for larger batches.
Prefer a conversation first? Book a scoping call.
Send a sample annotation dataset
Share a sample link and the output you need. We will respond with a pilot scope, QA approach, and delivery estimate.