MOBILE MAPPING EXTRACTION & QA

Turn raw street captures into GIS-ready asset inventories.

For mobile-mapping primes, DOT contractors, and infrastructure GIS teams that need a reliable extraction partner. We convert imagery, video, LiDAR, and existing layers into reviewer-audited signs, poles, hydrants, curb ramps, sidewalk defects, pavement assets, utility features, and custom schemas.

Send a sample dataset View annotation demo

Schema-first

Asset fields agreed before extraction starts

Reviewer QA

Human-reviewed batches with spatial checks

GIS-ready

GeoJSON, FileGDB, Shapefile, CSV, or PostGIS

Mobile mapping vehicle capturing a street corridor with GIS extraction overlays

Source

Street imagery + LiDAR

Objects

Points, lines, polygons

Output

QA report + GIS package

WHAT THE BUYER IS REALLY BUYING

Not labels. A defensible asset layer your client can load, inspect, and sign off on.

The useful decision is simple: can this capture become an operational inventory without surprising the prime, DOT reviewer, or GIS lead later? Inspect the delivery evidence: source frame, extracted object, schema fields, QA evidence, and delivery format.

Mobile-mapping primes

You run the truck and the rig. We provide the extraction capacity, QA layer, and GIS packaging behind your delivery name.

DOT contractors

Traffic sign, ADA, pavement, drainage, and ROW inventories that need state-specific schema compliance and documented review.

Infrastructure GIS teams

You already have imagery, LiDAR, or stale inventories. We turn it into a clean refresh package your team can maintain.

SAMPLE EXTRACTION FRAMES

Representative extraction frames.

These are the kinds of objects we turn into GIS records. Each frame should map to attributes, evidence, and a QA status instead of ending as an isolated annotation task.

Signal Pole

Attribute QA

pole typesignal headmountcondition

Curb Ramp

ADA evidence

ramp pointtruncated domescrosswalk tiephoto ref

Hydrant

Field inventory

asset classside of streetvisibilitycondition

Sidewalk cracking sample with defect marker

Sidewalk Distress

Condition QA

defect typeseveritysegmentrepair flag

ASSET CLASSES WE EXTRACT

Right-of-way, ADA, pavement, drainage, utilities. Custom schemas welcome.

Send the data dictionary if you already have one. If you do not, we can propose a practical field list for the pilot and tighten it after the first review pass.

RecordQA state

SIGN_01482verified

RAMP_00831field check

HYD_00217verified

SWLK_00392severity review

Right-of-way assets

Traffic signs and MUTCD attributes
Signals, poles, mast arms, cabinets
Guardrails, barriers, lighting
Bus stops, benches, shelters

Pavement, sidewalk, ADA

Curb ramps and truncated domes
Sidewalk defects and obstructions
Pavement markings and lane features
Condition ratings and repair flags

Utilities and drainage

Hydrants, valves, manholes
Catch basins and inlets
Above-ground utility features
Existing inventory refresh and audit

QA WORKBENCH

Every extracted object needs a reason it can be trusted.

The reviewer sees the source frame, geometry, attribute row, and exception queue together. Each record shows the checks performed, findings, and the next review action.

Source frame linked

Geometry

Point

Asset class

Drainage / utility

Confidence

Reviewer accepted

Action

Deliver

QA checks

Schema completeness
Duplicate and missed asset review
Coordinate and side-of-road plausibility
Cross-source agreement where imagery and LiDAR overlap
Condition and attribute spot checks
Delivery package review before handoff

HOW WE WORK

A five-step extraction lane built for subcontractor delivery.

Scoping & schema

We define the asset classes, field names, geometry type, confidence rules, and target delivery format before anyone starts drawing points.

Imagery and LiDAR ingest

Street-level imagery, point clouds, aerials, or existing GIS layers are organized into a clean extraction queue with route, tile, and source references.

Extraction pipeline

Semi-automated detection where it helps, manual interpretation where accuracy matters, and GIS normalization so records load cleanly downstream.

Reviewer-audited QA

A second reviewer checks missed assets, false positives, attribute completeness, spatial plausibility, and sample evidence before delivery.

Delivery handoff

You get the dataset, schema note, QA summary, open questions, and a clear path to scale the pilot into a recurring delivery lane.

TOOLS AND OUTPUT FORMATS

We fit your stack. We do not make the prime workflow bend around ours.

Annotation and extraction can happen in CVAT, Labelbox, ArcGIS, QGIS, FME, PostGIS, or a custom review pipeline. The important part is that your client receives a clean dataset, schema note, and QA package in a format they already know how to load.

CVATLabelboxArcGIS ProArcGIS OnlineQGISFMEPostGISSurvey123Field Maps

GeoJSON

Esri File Geodatabase

Shapefile

CSV / Excel

PostGIS direct load

Custom schema

Each batch ships with a reviewer note: what was checked, what was accepted, what needs client clarification, and what changed since the prior delivery.

SEE THE DELIVERABLE

The geospatial annotation demo is the public proof point for this workflow.

Source imagery, map-linked review, QA status per asset, schema-validated output, and a delivery panel. This simulated demonstration uses representative records. It illustrates the review and delivery process; it does not establish client results, production accuracy, or throughput.

Open the demo

FAQ

What primes and DOT teams ask first.

Do you work as a subcontractor under our prime?

Yes. We can sit behind your delivery name as a discreet extraction and QA layer, or work front-of-house if your client wants a named partner. NDAs and pass-through invoicing are routine.

What input data can you start from?

Street-level imagery, panoramic or forward-facing video, mobile or terrestrial LiDAR, aerial imagery, drone capture, and existing GIS layers that need refresh or audit.

Can you match our existing schema?

Yes. Send a prior delivery, data dictionary, or client schema. We align the fields, domains, geometry type, coordinate system, and QA thresholds to that target.

How do you handle QA?

Every batch gets reviewer-audited QA: schema validation, spatial plausibility checks, cross-source agreement when available, and a written QA summary attached to the delivery.

What is the smallest engagement you accept?

Pilot batches can be small: one route segment, one tile, or one priority asset class. The goal is to prove the schema, QA, and delivery shape before scaling.

Do you handle MUTCD or state DOT-specific traffic sign attributes?

Yes. MUTCD code, sign assembly, mounting type, condition, visibility flags, and state-specific attributes can be included when you provide the target rules.

Send a sample dataset

Give us a slice of your imagery, LiDAR, or existing inventory. We will reply within one business day with a pilot scope, schema recommendation, and delivery estimate.

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