We start with raw urban reality.
High-entropy streets where structure must be discovered, not assumed.
We capture complex urban environments as they are.
Noise, ambiguity, and human behavior are not filtered out — they are the data.
Our core team coordinates field data collection, quality control, and dataset preparation across multiple urban environments.
Why OriginDataLab exists
Most datasets are collected to fit assumptions. We collect reality that breaks them.
High-entropy urban environments are noisy, inconsistent, and human-driven. That is exactly why they matter for next-generation AI — and why simulation alone is not enough.
We focus on real streets in South & Southeast Asia, where structure must be discovered, not assumed.
Our field collectors capture raw urban behavior as it happens, using mobile and helmet-mounted camera systems across complex traffic environments.
Built around reality, not benchmarks
We don’t optimize for clean samples. We optimize for learning under uncertainty.
High-Entropy First
We prioritize dense, non-lane-based, mixed-traffic environments where behavior dominates rules.
Continuity Over Clips
Long, continuous sequences matter more than isolated frames — context is where models learn.
Metadata Is the Product
Video is just the surface. Structure lives in segmentation, QC outcomes, and usable metadata.
In Dhaka, our workshop teams train collectors in capture standards, equipment handling, and field-ready data collection workflows.
How raw reality becomes usable training data
Capture → Segment → Quality Control → Metadata
Capture
We collect real-world urban motion as it happens — without filtering out the complexity.
Segment + QC
Footage is structured into segments and evaluated through consistent quality checks. QC results remain traceable to the source.
Metadata
Each segment is packaged with metadata that makes it searchable, auditable, and training-ready.
We do not inflate public numbers. We continuously collect, and share the current coverage (locations, scenarios, and availability) upon request.
Our data collection network continues expanding across Southeast Asia, capturing real-world traffic environments in rapidly growing cities.
Where we operate
Headquarters
South Korea
Product direction, governance, and customer delivery.
Data Collection
Asia
High-entropy city environments across South & Southeast Asia.
Infrastructure
Singapore
Cloud-first storage and processing for fast, reliable delivery.
Who This Data Is Built For
This dataset is designed for teams building and validating autonomous driving, ADAS, and perception systems in complex, high-entropy urban environments. It is commonly used during early-stage evaluation, edge-case testing, and pre-production validation where real-world variability matters more than scale.
Data coverage snapshot: dense urban POV capture across South Asian cities, segmented into 30–90s clips with structured metadata and QC summaries.
Built for
Teams that need models to handle messy urban reality—not curated benchmarks.
• Autonomous driving & ADAS perception teams
• Robotics / embodied AI teams in dense cities
• Applied research groups validating failure modes
• Data & ML engineers running feasibility checks
Common evaluation moments
Where this data typically proves fit (or quickly reveals mismatch).
• PoC to confirm data fit and schema compatibility
• Robustness testing in non-lane-based, congested traffic
• Pre-production validation (high variability, low assumptions)
• Internal benchmarks against real-world edge cases
Why no logos here
Due to NDAs and early-stage evaluations, specific customer names are not publicly disclosed. We instead publish clear schemas, quality summaries, and non-binding sample structures before any purchase.
Who evaluates this data
Typical evaluators include perception engineers, data/ML engineers, robotics teams, and applied research groups. Most early-stage evaluations run under NDA, so we don’t publish customer names. Instead, we provide transparent schemas, QC summaries, and non-binding sample structures before any commitment.
Trust is designed, not claimed
Consent, provenance, and usage boundaries belong in the pipeline — not in a PDF after the fact.
We build with clear boundaries for data sourcing, permissions, and intended use. When policies evolve, our structure remains traceable and auditable.
Current Coverage Snapshot
A high-level view of our current operational dataset coverage. Detailed scenario breakdowns, validation samples, and release-specific materials are shared on request.
Recorded Volume
font-size:0.85rem; font-weight:700; letter-spacing:0.08em; text-transform:uppercase; text-align:left;"> Recorded Volume1,800+ hours of continuous urban footage
Captured through continuous daily collection operations and organized into structured monthly snapshot releases.
• Continuous daily data collection
• Structured monthly snapshot (versioned release)
• Continuous source footage preserved for segment extraction
Scenario Coverage
25+ tagged high-entropy scenario categories
Coverage is designed around complex real-world urban failure cases that are difficult to reproduce in simulation-only pipelines.
• Non-lane-based traffic environments
• Dense pedestrian and mixed-actor interactions
• Motorbike-heavy urban traffic flows
• Occlusion-rich and interaction-dense scenes
Geographic Coverage
Operational coverage across South & Southeast Asia
Active collection infrastructure is already operating across key cities, with expansion capacity extending into Africa and Central Asia.
• Operational coverage across key cities in South & Southeast Asia
• Active collection infrastructure extending into Africa and Central Asia
• Regional expansion executed based on partner demand and project scope