WildCity: Real-World Multimodal Street-View Data from U.S. Cities

WildCity is a real-world city-scale multimodal dataset for street-view reconstruction, simulation, and spatial intelligence. It was collected from autonomous-driving fleet logs across multiple U.S. cities and contains surround-view RGB images, LiDAR, calibration, ego and sensor poses, object annotations, semantic masks, and processed reconstruction assets. The dataset was authored by Neptune615 and last updated on 2026-06-30.

Use Cases

• Street-view reconstruction based on surround-view RGB images and LiDAR data.
• Autonomous driving simulation based on ego and sensor poses.
• Object detection and segmentation training based on object annotations and semantic masks.
• Spatial intelligence analysis based on processed reconstruction assets.

Strengths

• Contains multiple synchronized sensor modalities including RGB images, LiDAR, and poses.
• Covers a city-scale geographic area across multiple U.S. cities.
• Includes processed reconstruction assets for direct application.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Neptune615 via Hugging Face

Collection Method

Collected from autonomous-driving fleet logs.

Freshness

Last updated 2026-06-30 23:03:13; freshness should be verified.

Geography

Multiple U.S. cities.