Data Delivery Structure

Data Overview

This is a practical summary of what you actually receive in a PoC pack: scenes, metadata, QC signals, and delivery structure. Start small, validate fit, then scale.

Review real-world failure scenarios before committing to a larger scope.

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Includes sample video clips, JSON metadata, and schema preview.

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No payment. No commitment. Reply within 24 hours.

Goal: reduce integration friction. You should be able to run your pipeline end-to-end within days, not weeks.

What you get in a PoC pack

  • MP4 clips (short segments suitable for fast iteration)
  • JSON metadata per clip (scene type + operational notes)
  • High-level QC summary (accept/reject signals and reasons)
  • Coverage notes (what is included, what is excluded)
  • Non-binding delivery structure preview for validation

We keep the format simple: video + metadata. You can add labels later if your team wants labeling or evaluation tasks.

Download full documentation: Technical Summary (PDF) ↓ · License Summary (PDF) ↓

Exactly what you receive

A practical breakdown of what is included in a PoC delivery, what can be added after validation, and what is not included by default.

Included in PoC

  • MP4 sample sequences
  • Segment-level JSON metadata
  • QC summary
  • Schema preview / manifest

Optional Add-ons

  • Annotation / labeling
  • Custom scenario expansion
  • Custom delivery format

Not Included

  • Exclusive rights
  • Resale rights
  • Full production licensing

Dataset snapshot

120+

hours of raw footage

18,000+

video segments

4

cities

3

countries

Coverage is continuously expanding as new collection cycles are completed.

Coverage Table

A simplified snapshot of current collection coverage. Detailed breakdowns and sample packs are shared during qualification.

Swipe horizontally to view full coverage details.

City / Country Collection Window Primary Scenarios Day / Night Weather Traffic Density Segment / Footage Scale
Dhaka, Bangladesh Q4 2025 – Q1 2026 Urban intersections, mixed traffic Mostly Day, Partial Night Clear, Overcast, Light Rain Medium to Heavy 10,000+ segments
Chattogram, Bangladesh Q4 2025 – Q1 2026 Dense mixed vehicle flow Day Humid, Overcast Medium to Heavy Growing weekly
Secondary Urban Coverage Rolling collection Market streets, narrow roads Day / Select Night Variable Medium Available on request

Representative public snapshot. Detailed breakdowns available during buyer qualification.

Coverage at a glance

Our core focus is high-entropy traffic environments and edge-like everyday reality: mixed agents, dense interactions, and non-lane-based flow.

Representative scene categories

  • Unstructured urban flow (mixed agents, non-lane-based)
  • Dense intersections and merges
  • Near-field pedestrian interactions
  • Motorcycle-heavy corridors
  • Night, rain, glare, occlusion-heavy moments

Need a specific city pattern or traffic behavior? Ask for a Coverage Snapshot and we will reply with what is feasible and how fast.

Dataset schema overview

Each delivery includes structured video segments and consistent metadata so your team can filter, audit, and integrate quickly. The schema is intentionally simple for PoC speed while remaining stable enough for larger training pipelines.

Core fields

  • segment_id — stable ID used for joins and traceability
  • scene_type — high-level environment category
  • short_description — short natural language description
  • capture_context — optional context such as time_of_day or platform
  • qc_status — basic QC result for the segment
  • qc_notes — brief QC reviewer notes

Example segment metadata (illustrative)

{
  "segment_id": "IN-BLR-2026-02-000123",
  "scene_type": "unstructured_urban_flow",
  "short_description": "Dense mixed traffic with frequent cut-ins and near-field pedestrians.",
  "capture_context": {
    "time_of_day": "day",
    "weather": "clear",
    "platform": "two_wheeler"
  },
  "qc_status": "pass",
  "qc_notes": "Stable exposure, minimal obstruction, continuous forward view."
}

This example illustrates the structure. Final schema definitions and allowed values are shared during the PoC stage so your pipeline can validate exact keys.

Quality control summary

Quality control focuses on technical usability rather than visual perfection. Each segment is checked for video integrity, basic visibility, continuity, and metadata completeness to ensure the data can be reliably ingested into training pipelines.

QC checks (overview)

  • Video integrity — corruption, missing frames, encoding errors
  • Exposure and visibility — severe blur or unusable lighting
  • Obstruction — camera blocked or heavy handling artifacts
  • Segment continuity — stable forward motion within the clip
  • Metadata completeness — required fields present

For PoC deliveries, QC remains lightweight and transparent. For larger production programs, QC can be extended with additional checks and sampling.

QC Structure, Metadata Preview, and Integration Notes

This section is designed for technical buyers who want to review filtering principles, example scenario structure, metadata format, and a simple ingestion path before requesting a PoC.

QC filtering principles

  • Technical usability first — segments are reviewed for visibility, continuity, and ingestion readiness.
  • Low-information segments — repetitive or operationally weak clips are excluded from delivery priority.
  • High-entropy interactions — dense mixed traffic, ambiguity, and near-field interactions are prioritized.
  • Metadata consistency — required keys must be present for segment-level delivery.
  • Delivery rule — rejected segments are excluded from delivery datasets.

For PoC, the goal is transparent filtering structure rather than overengineered scoring. Expanded QC logic can be introduced for larger production programs.

Scenario categories and distribution logic

  • Unsignalized intersections
  • Mixed-agent urban flow
  • Pedestrian negotiation zones
  • Motorbike-heavy corridors
  • Occlusion, glare, rain, and dense compression scenes
Edge-like interactions
Routine low-complexity flow
Occlusion / ambiguity moments

Distribution snapshot is illustrative. The collection strategy is intentionally biased toward scenarios that are more useful for evaluation, failure analysis, and hard-case review.

Sample metadata JSON

{
  "segment_id": "BD-DHK-2026-000182",
  "file_name": "BD-DHK-2026-000182.mp4",
  "duration_sec": 42,
  "scene_type": "unsignalized_intersection",
  "short_description": "Dense mixed traffic with pedestrian negotiation near junction entry.",
  "capture_context": {
    "time_of_day": "day",
    "weather": "overcast",
    "platform": "two_wheeler"
  },
  "qc_status": "pass",
  "qc_notes": "Continuous forward view, acceptable visibility, stable enough for PoC ingestion."
}

Preview structure or test integration using a real sample file.

Preview Metadata → Download JSON →

Use this file to test parsing, mapping, and internal evaluation workflows before requesting a full sample pack.

Integration hint

  • Delivery format — MP4 clips + JSON metadata
  • File naming — one segment ID maps to one video file and one metadata entry
  • Clip-to-metadata mapping — use segment_id as the stable join key
  • Schema simplicity — lightweight fields for fast PoC validation

Typical ingestion flow

  1. Download sample package
  2. Parse JSON metadata
  3. Map segment_id to clip filename
  4. Run ingestion and compatibility checks
  5. Expand coverage after validation

The objective is to reduce internal review friction and make technical validation possible without requiring custom tooling first.

How to evaluate with a PoC

Start with a small PoC dataset, validate integration with your pipeline, then expand coverage once the data proves useful for your model training.

Recommended evaluation steps

  1. Run your ingestion pipeline (MP4 + JSON)
  2. Verify metadata parsing and schema compatibility
  3. Filter by scene_type and inspect environment diversity
  4. Run a small training or evaluation experiment
  5. Decide next data scale and scene coverage targets

Get a sample plan tailored to your evaluation setup.

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We will respond with a practical data plan based on your target environment, licensing constraints, and evaluation workflow.

FAQ

Can you provide labels?

Yes, but PoC usually starts with video + metadata first. After you validate value, we can add labeling or evaluation tasks based on your needs.

Do you support custom scene requests?

Yes. Send a short requirement list and we will reply with feasibility, timeline, and minimum order assumptions.

What about legal and privacy?

Understand how data is collected, structured, and used during PoC and evaluation.

See the Legal and Transparency page for our approach and PoC usage framing.

View Legal & Transparency →

No sensitive personal data. Clear usage scope defined for PoC evaluation.

Ready to validate in your pipeline?

Tell us your target environment and failure cases. We will recommend a PoC tier and a coverage plan.

Start with a small sample before scaling to a larger dataset.

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No payment. No commitment. Reply within 24 hours.