Synthetic Data for
Safer, Smarter Cities
Smart city AI systems depend on robust computer vision training for traffic monitoring, road maintenance, crime detection, and signage recognition. Real world capture struggles to keep pace with evolving cityscapes, diverse weather, and rare events. Synthetic images and synthetic datasets for CV ensure safe, scalable, and highly varied training across multiple smart city applications from autonomous navigation to public safety monitoring.
Why Traditional Approaches Fall Short
Real world data is costly, slow, and incomplete. Road damage, traffic violations, weapon incidents, and rare signage changes are hard to capture at scale. Camera angles and lighting are often fixed, limiting generalization for background segmentation synthetic, depth prediction with synthetic data, and normal estimation synthetic. Annotation for millions of diverse scenarios is resource-intensive and inconsistent.
How Synthera™ Solves It
Synthera™ uses simulation based data generation and photorealistic image synthesis to create smart city datasets at scale. We simulate roads, vehicles, street signs, and human activity with full control over lighting, weather, and camera pipelines. Configurable actors, synthetic backgrounds, and procedural object generation provide unmatched diversity. Outputs include two dimensional and three dimensional boxes, semantic segmentation, and rich metadata for multi modal AI with synthetics.
Use Case Examples
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Street sign recognition and text detection
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Road crack and pothole detection
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Vehicle type classification by body shape
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Number plate recognition across regions
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Knife detection and public safety alerts
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Gun detection with occlusion handling
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Traffic flow and congestion monitoring
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Suspicious object tracking in public areas
