# nukirk

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [V Matthew King-Yarde](https://cerebralvalley.ai/u/nukirk)
- **GitHub:** https://github.com/nufuturepro/street-scan
- **Demo video:** https://youtu.be/BT4tYagtKo4
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5076

StreetScan: Automated Urban Hazard & Infrastructure Auditor

StreetScan converts passive video footage—from dashcams, action cams, and handlebar cameras—into verified, geo-tagged municipal work orders. Instead of requiring citizens to manually stop and fill out complex 311 forms while navigating traffic, StreetScan automates physical street auditing through a multi-stage perception and reasoning pipeline.

Core Capabilities:

Ingest & Extraction: Streams raw MP4/MOV footage, parses embedded GPS tracks via ExifTool, or extracts coordinates directly from burned-in video timestamps and overlays using OCR.

Temporal Hazard Tracking: Employs SAM 3.1 to generate pixel-accurate polygon masks across continuous frames, discarding transient optical noise with an IoU temporal persistence filter.

Multimodal Classification & Triage: Routes segmented hazard crops to Muse Spark, which reasons across depth cues, edge deterioration, surrounding scene context, and visible street signs to classify defects (potholes, cave-ins, cracking, debris, blocked lanes) and assign municipal urgency ratings (1–5).

Multi-Pass Consensus & RQSI: Reconciles overlapping route footage using maximum bipartite matching to automatically verify corroborated defects and flag disputes. Computes a continuous Route Quality & Safety Index (RQSI) across 100-meter segments and exports standards-compliant Open311 JSON and GIS payloads.


Civic Realism: Distinguishes roadway defects from sidewalk accessibility barriers, ensuring a cracked pedestrian curb is recorded without improperly degrading driving route safety scores.

The core architectural pipelines—temporal segmentation filtering, multi-pass bipartite consensus, and dual-rate transcoding—are fully built and verified across 169 automated assertions. Live video ingestion hit an active pipeline regression right at the deadline window, but the architecture, telemetry, and pre-seeded audit inspection represent the working system state.

## More from Meta Global AI Developer Hackathon

- [AtMax_28](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5073)
- [Chinmay](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5074)
- [LearnMore](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5075)
- [name-agent](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5077)
- [iwaka](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5078)
- [Rypha](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5079)

---

Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5076. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt