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  3. Driving dataset: San Francisco
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Driving dataset: San Francisco

This large-scale dataset features over 1.7 million unannotated dash-cam images from the San Francisco Bay Area. Spanning a wide range of times of day and weather conditions, this ready-to-deploy dataset ensures that safety-critical AV models are adaptable to real-world complexities.

Specifications

Modalities
2D images
Location
Mountain View and South Bay areas (San Francisco)
Licensable
Yes
Sensor setup
1x dash-camera
Data volume
15 hours 52 mins of recording @30 FPS
Total images
1,715,209 unannotated images
Year of collection
2024 and 2025
Time of day
Afternoon, evening, morning, noon, night, dusk, dawn

Accelerate model development & training processes

  • Contextual driving intelligence

    Equip your AI with the ability to navigate complex urban infrastructure, traffic flows and real-time driving events with greater precision.

  • Scene perception and understanding

    Train models to identify and classify road elements such as pedestrians, vehicles, traffic signs and more, under varied environmental conditions.

  • Anomaly and edge-case detection

    Surface rare and unpredictable driving situations to increase model robustness and reduce operational failures.

  • Scenario-rich training for on-road reliability

    Prepare AI systems to generalize across day/night cycles, weather conditions and different traffic densities, from highways to dense city streets.

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Case Study

Explore our success stories

  • Curating high-quality data for the training and validation of ADAS and AV models

    An autonomous vehicle shown driving on a busy highway.

    Discover how TELUS Digital used our proven field operations testing (FOT) experience to create a high-quality dataset for training advanced driver assistance systems (ADAS) and autonomous vehicles (AV).

    • 12TBdata captured daily

    • 7500kmapproximate total distance covered

    Download case study

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