High-Def Map Automation
An HD-map annotation team was spending the majority of their day on repetitive, low-judgment labeling work. The team layered automation into their existing workflow — not replacing expertise, but eliminating the parts that didn't need it.
8×
throughput increase
92%
segments auto-processed
28h
hours returned / week
A workflow built around people doing everything
The team had a well-defined process. Every annotator knew their role. The problem wasn't skill or commitment — it was that the workflow demanded human attention at every step, including the ones that didn't actually require a human decision.
Download & Prep
Engineers manually pulled scan batches, converted formats, and staged files for each annotator.
Hand-Label Every Element
Annotators traced lane boundaries, road edges, signs, and barriers one-by-one inside a 3-D point cloud viewer.
Internal QA Review
A second reviewer checked every label for geometric accuracy before the segment could move to the map database.
Manual Ingest
Approved segments were hand-merged into the map tile system, a process prone to version conflicts and re-work.
~500 meters of road annotated per annotator, per hour
Of that time, roughly 80% was spent on geometry that follows predictable rules — straight sections, standard lane widths, common signage. The other 20% was genuinely complex: unusual intersections, construction zones, ambiguous markings. That 20% was where their expertise actually mattered.
Automation layered into the existing workflow
The workflow wasn't redesigned. Each manual step was mapped with one question: does this decision actually require a human? Where the answer was no, it was automated. Where the answer was sometimes, tools were built to help humans decide faster.
Automated Ingestion Pipeline
Scan batches are pulled, validated, and pre-processed the moment they land in the data lake — no engineer touch required.
Feature Extraction Engine
A trained segmentation model detects lane markings, road boundaries, and fixed infrastructure across standard road geometries, producing draft annotations in seconds.
Uncertainty Flagging
Segments where model confidence falls below threshold — intersections, construction zones, novel road types — are surfaced to the review queue with bounding highlights.
Conflict-Free Tile Merge
Approved segments are diffed and merged into the map database atomically, eliminating the manual ingest step and version-conflict rework entirely.
“The goal was never to replace the annotators. It was to make sure that when they showed up to work, every minute was spent on something only they could do.”
Time returned to the people who earned it
BEFORE
- Annotators labeled every element in every segment
- QA reviewers re-checked all labels, not just edge cases
- Engineers managed file prep and ingest manually
- Team velocity capped by the slowest manual step
AFTER
- Annotators review AI drafts and correct errors in flagged segments only
- QA focus shifted to model improvement, not routine re-checking
- Engineers spend time on pipeline accuracy, not file management
- Throughput scales with data volume, not headcount
8×
Map throughput
Same team, same hours. Road coverage scaled through automated segment processing.
92%
Auto-processed
Of all road segments are now handled end-to-end without annotator intervention.
28h
Returned per person / week
Hours previously spent on routine labeling, now available for high-value review work.
< 2s
Draft annotation time
From data landing to draft annotation ready for review — down from 40+ minutes.
Similar work happening in your team?
If your team is spending skilled hours on repetitive process steps, there's likely a fit. We start by mapping what people actually do all day, then find the parts that don't need them.