Precise BPO provides outsourced polyline annotation services for lane, road, GIS boundary and utility datasets, from pilot batches to high-volume production. Our in-house team of 540+ annotators in Pune, India has labeled 810M+ images across all annotation types since 2008. Our processes are aligned with ISO 27001, HIPAA and GDPR.
Security, Compliance & Working Terms
Polyline annotation is the process of tracing linear structures (lanes, roads, boundaries, cables, pipelines and pathways) in images or video, using sequences of connected points called vertices. Each polyline records the path, direction and class of a line, so a model knows where it runs and what it connects to.
It is one of the core techniques in computer vision data labeling, used heavily for lane detection, GIS mapping, utility inspection and autonomous driving. A bounding box encloses an object; a polyline follows the path of a line, which is why it suits navigation, geospatial and infrastructure models.
Polyline outputs are ordered lists of vertex coordinates, usually delivered as GeoJSON, Shapefile, COCO-style JSON or a custom schema, so the geometry loads directly into GIS tools, training frameworks and path-planning systems.
Since 2017, our team has delivered polyline annotation as part of our data labeling services, covering lane extraction in mobility AI, roofline tracing for utilities and GIS platforms, aisle mapping for retail and route annotation for logistics. The work is done from our delivery center in Pune, which runs around the clock to cover US, UK and EU time zones, and every dataset is built to your model specification.
Our annotators specialize in line-based geometry. They apply vertex spacing standards, continuity rules and topology checks so each dataset can go straight into training. We handle footage from dashcams, drones and CCTV, and imagery from satellites and aerial surveys. We work in your annotation platform and deliver in your output schema.
For autonomous vehicle programs, we label lane boundaries at volume with frame-accurate polylines, covering solid lines, dashed markings, merge zones and complex intersections. Mobility teams can move from a pilot to full production without building their own labeling operation.
GIS and geospatial teams use us for polyline labeling of utility corridors, property boundaries and hydrological features in aerial and satellite imagery. We take on long-running mapping programs and one-off infrastructure surveys, and we work inside your existing tools.
We also take on online data entry and data conversion work under the same NDA.
Polyline datasets are used wherever a model has to follow a line rather than find an object: mapping, mobility, utilities, inspection and retail analytics.
Lanes, curbs, road edges, barriers and drivable paths, tracked consistently from frame to frame for AV perception and navigation models.
Rooflines, boundaries, pipelines, utilities, river courses and contour lines from satellite and aerial imagery. Outputs load into QGIS, ArcGIS and cloud mapping engines.
Powerlines, cables and pipelines traced from aerial and drone imagery for predictive maintenance, grid management and asset-integrity models.
Aisles, walkways and customer movement paths traced from CCTV footage, used for flow modeling, heatmaps and store layout analysis.
Road network updates, asset tracing, excavation outlines and progress measurement from drone and ground-level imagery, for civil engineering and infrastructure monitoring models.
Warehouse pathways, route constraints, waypoints and yard movements, used for route optimization, fleet management and last-mile delivery models. We also handle e-BOL data entry and driver's log data entry for logistics clients.
Field boundaries, irrigation lines and farm paths traced from drone imagery, for precision farming, yield prediction and land-use monitoring.
Vessel centerlines, nerve pathways and other tubular structures in medical imaging, for radiology, surgical navigation and clinical decision support models. We also handle medical claims data entry for healthcare clients.
The right annotation type depends on what you are labeling and what the model has to output. Here is how the three compare for line-based features. If bounding boxes are on your shortlist, our guide to common bounding box mistakes shows where they tend to go wrong.
| Criteria | Polyline Annotation | Bounding Box | Semantic Segmentation |
|---|---|---|---|
| Shape | Multi-point connected line / path | Rectangle (axis-aligned or rotated) | Pixel-level mask per class |
| Best for | Linear structures — lanes, roads, cables, rivers, field boundaries | Bounded objects — vehicles, people, products, animals | Scene understanding: roads, sky, buildings |
| Annotation Speed | Fast — path-tracing workflow | Fastest — single drag | Slowest — pixel-by-pixel |
| Cost Efficiency | High — scales well with volume | Highest — minimal effort per object | Lowest — intensive per image |
| Boundary Precision | Follows the line vertex by vertex | Object-level (includes background) | Pixel-level |
| Video / Temporal | Strong — frame-by-frame path tracking | Fast frame tracking | Very high effort per frame |
| Common Use Cases | Autonomous driving, GIS mapping, utilities, agriculture, medical | Retail, ADAS, medical, sports analytics, surveillance | Urban scene parsing, surgical vision, land cover mapping |
| Precise BPO Service | This page — Polyline Annotation | Bounding Box Annotation → | Semantic Segmentation → |
Not sure which annotation type fits your project? Talk to our specialists — we'll recommend the right approach based on your feature types, model architecture and dataset requirements.
We label lane boundaries, GIS features, road edges and route paths, and we are set up for high-volume, multi-class datasets where topology accuracy matters.
Every project follows the same six steps, from requirements through to delivery.
We agree annotation goals, line classes, vertex spacing rules, topology standards, continuity logic and output schemas with your AI or GIS team before any labeling begins.
Images and video arrive through encrypted transfer, are converted to standard formats, geo-referenced where required and split into batches, all under NDA.
Specialized annotators trace polylines with client-defined vertex density, spacing standards and class rules, using annotation platforms of your choice or our internal tooling. Video data is annotated frame by frame for temporal continuity.
Every batch passes three QA tiers: peer review first, then automated geometry and topology checks, then an expert audit and reviewer sign-off.
Annotated batches go to you for review. Feedback goes into structured revision rounds, and guideline changes are tracked so later batches match the corrected ones.
Outputs are delivered in your required format through secure transfer. We also support active learning pipelines, retraining cycles and longer annotation engagements.
Here are polyline projects we have delivered across lane detection, aerial mapping, utility inspection, retail layout and infrastructure.
We work inside your existing tools, or we can recommend a stack for your project. Our annotators are trained on CVAT, Labelbox, SuperAnnotate, V7, Label Studio and other major platforms, so there is no lock-in and no re-tooling.
We have labeled polylines for AI teams since 2017, covering autonomous driving, HD maps, GIS vector mapping, utility cable tracing and medical contour work. All of it is done by our in-house team.
Start a Free Pilot →Nine of those years have gone into line-based work, from simple lane markings to complex multi-vertex GIS polylines.
Our annotators are our own employees, not crowd workers, and they work in dedicated teams.
Access is permission-scoped, every annotator signs an NDA, and security audits run across project environments.
Three-tier QA combining topology validation, vertex-precision checks, peer review and expert audit, so geometry errors are caught before delivery.
Delivery from India runs 40–60% below US/UK rates, and you get a free pilot batch before you commit.
We annotate within your preferred tooling (CVAT, Labelbox, V7, SuperAnnotate) and deliver in whatever schema you define.
Every batch passes three QA tiers before delivery. The system catches vertex placement, topology and path continuity errors early, so defects do not carry through into your training data.
The annotator checks their own work first, then a senior peer cross-checks it. This catches vertex placement errors, path discontinuities, class mismatches and guideline deviations before any automated scoring.
Automated checks validate polyline geometry and vertex density, flag broken or overlapping paths and surface statistical outliers for human re-review.
The QA lead conducts random sampling, plus full-batch review, on high-stakes projects. Client feedback loops are built in: corrections are applied and re-verified before final sign-off and delivery.
A side-by-side look at the three ways teams usually handle polyline labeling, for AI leads, ML engineers and procurement teams making the case for outsourcing. In-house and generic BPO figures are our own estimates.
| Criteria | In-House Team | Generic BPO | Precise BPO |
|---|---|---|---|
| Annotation Accuracy | 85–92% (fatigue, limited geometry QC) | 90–94% (inconsistent topology checks) | ✔ 99.8% — 3-tier geometry pipeline |
| Setup Time | 6–10 weeks (hire, train, tool) | 3–5 weeks | ✔ Pilot-ready in days, no hiring or tooling |
| Scalability for Surge Volumes | ❌ Fixed headcount, slow ramp | ⚠ Limited, delays common | ✔ 540+ team, fast ramp-up |
| Cost vs US/UK Rates | Baseline (salary + infra) | 25–35% savings | ✔ 40–60% Below US/UK Rates |
| Security & Compliance | ⚠ Varies by team | ⚠ Varies by vendor | ✔ Compliance-aligned workflows, NDA on every engagement |
| GIS & Geo-Referenced Output | ⚠ Limited capability | ⚠ Often not specialized | ✔ Geo-referenced delivery in GIS formats |
| Video / Frame Polyline Tracking | ⚠ Possible but slow | ⚠ Varies by vendor | ✔ Full temporal polyline tracking |
| Free Trial / Pilot | No equivalent | Varies | ✔ Free pilot batch, no commitment |
Pricing depends on volume, line complexity and timeline. There are no platform fees, and every engagement starts with a free pilot batch.
Pay per annotated image. Suits fixed datasets and one-off projects where you want a predictable per-unit cost.
Priced per video frame. Suits autonomous driving, CCTV movement tracking and dashcam lane work, where frame count is the natural unit.
Hourly pricing for high-complexity work such as thin powerline tracing, dense vertex requirements and multi-layer GIS features, where a per-image rate would not reflect the effort involved.
A dedicated polyline annotation team at fixed monthly capacity. Suits enterprises and AI labs with continuous labeling needs, active learning pipelines or production GIS update workflows.
Our Pune delivery team works around the clock, so we can match your working hours in the US, UK, Europe, APAC, the Middle East, Australia, Canada and LATAM.
What AI, GIS and mobility teams say after working with us.
"Precise BPO now runs lane annotation for our ADAS training data end to end. Lines stay continuous from frame to frame and the vertices land where they should. When we need more volume they add people without any fuss, and batch quality has held steady."
"We gave them roofline and boundary annotation for about 300K satellite images. The files dropped straight into our GIS pipeline with no format problems, it came back quickly, and the quality was excellent."
"Our retail movement models improved noticeably once annotation moved to Precise BPO. On our CCTV footage the paths came back as clean polylines with the right class tag on every frame, which is exactly what we needed."
"Powerline tracing across 120K drone images for our grid maintenance AI was a big job. Their thin-line guidelines made the difference, and the work arrived accurate and on schedule."
"They work inside our own platform like part of our team. Four years in, they still hit their SLAs, and their accuracy is the best we have had from any outsourced annotation provider."
"We have relied on Precise BPO for HD map polyline updates for a long time. Accuracy holds steady, the cost works for us, and our mapping pipeline depends on them."
Answers to the questions teams usually ask before starting a project.
Polyline annotation is used to label linear features such as roads, lanes, boundaries, cables and pipelines in images or video. It is mainly used to train models that need to follow a line: lane detection in driving systems, boundary extraction in mapping, and cable or pipeline tracing in utilities. For the broader picture, our guide to data labeling covers the fundamentals.
We label aerial and satellite imagery, drone footage, dashcam video, CCTV footage and scanned maps. Any source that contains linear features such as lanes, edges, borders or paths can be annotated, although thin features like cables need high-resolution imagery.
Lines labeled with direction and connectivity let a model learn where a road continues, where a lane ends and how routes join. That feeds path planning, map generation and routing in autonomous driving, GIS platforms and mobility analytics.
We split large datasets into batches, each labeled under the same written rules and reviewed before the next one starts. This lets you scale volume, add data in stages and retrain over several months without the labels drifting.
Most of our polyline work comes from autonomous driving, digital mapping, utilities, infrastructure monitoring, logistics, agriculture and retail layout analysis.
Before labeling starts we agree written guidelines covering vertex rules, spacing and class definitions. Reviewers then check alignment, continuity and topology on samples from every batch, so similar features are labeled the same way throughout. See our annotation governance framework for how we enforce these standards on every project.
We typically deliver GeoJSON, Shapefile, CSV, COCO-style JSON or a custom schema. These load into QGIS and ArcGIS and into PyTorch and TensorFlow training pipelines, and the structure lets your team validate geometry before using the data.
Pricing depends on data volume, line complexity, vertex density and how deep the review needs to be. We quote per image, per frame, per hour or as a monthly retainer, and our rates typically run 40–60% below US/UK providers. If you are comparing providers, our comparison of data annotation companies puts pricing and strengths side by side. You can also see our data labeling pricing guide or request a tailored quote.
Our workflows are aligned with ISO 27001, HIPAA and GDPR. Every annotator signs an NDA before getting project access, roles are permission-scoped, and automated security audits run across all project environments.
Guides on annotation quality, pricing, vendor selection and industry workflows, for AI engineers, ML teams and data leads.
Our in-house team handles polyline work for lane detection, GIS mapping, utilities and infrastructure AI. We can also run the wider data labeling work under the same engagement. Send us a request below for a free pilot or a project quote.
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