# Himalayan Sentinel

- **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:** [Dhruv Dashora](https://cerebralvalley.ai/u/dhruv_dashora)
- **GitHub:** https://github.com/Dhruv-Dashora/Meta-Hackathon
- **Demo video:** https://youtu.be/3wRiErRSe3w
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1982

Himalayan Sentinel — Turning Disaster Images into Actionable Intelligence

When floods, landslides, or other natural disasters strike vulnerable communities, responders face a critical challenge: understanding the scale of damage, identifying threatened infrastructure, and determining where attention is most urgently needed.

The Problem: Disaster response often depends on fragmented photographs and visual reports that require time-consuming manual interpretation. Identifying hazards, locating affected structures, distinguishing confirmed damage from possible risks, and translating observations into actionable incident reports can delay situational awareness. Existing image-analysis tools may identify objects or describe scenes, but often fail to connect visual evidence with structured impact assessments and practical response decisions.

Our Solution: Inspired by the disaster risks faced by Himalayan communities, Himalayan Sentinel is designed as a broadly applicable AI-powered disaster intelligence platform. It transforms disaster photographs into structured, evidence-grounded incident intelligence, helping responders understand what is visible, where potential impacts exist, what needs urgent human verification, and what actions should be considered - all through one integrated workflow.

Rather than building another image-captioning tool, we developed a four-stage AI pipeline powered by Meta's multimodal models:

1. Understand: Muse Spark analyzes disaster imagery to identify visible hazards, infrastructure, people, and environmental conditions.
2. Locate: SAM 3.1 generates precise segmentation masks and bounding boxes, guided by automatically planned or user-specified object prompts.
3. Assess: An evidence-aware assessment layer connects visual observations with potential impacts, safety concerns, and verification priorities.
4. Report: The platform produces structured incident dossiers containing visual evidence, impact summaries, recommended verification actions, uncertainty disclosures, and downloadable PDF/JSON reports.

What Makes It Different? Himalayan Sentinel combines semantic reasoning with pixel-level visual grounding, evidence traceability, and human-in-the-loop verification. It distinguishes directly observable facts from inferred impacts instead of presenting uncertain AI predictions as confirmed disaster information.

Built as a functional React and FastAPI application, the platform demonstrates an end-to-end workflow using real disaster imagery, with Nepal floods 2026 scenario serving as its initial demonstration.

The Impact: Himalayan Sentinel aims to reduce the effort required to interpret disaster imagery and prepare incident assessments, helping responders and local authorities identify areas requiring attention, prioritize verification, and communicate findings more effectively—particularly in remote, resource-constrained, or disaster-affected regions worldwide.

Our Vision: Turn multimodal AI from a passive image-understanding technology into a practical, accountable tool that supports faster, clearer, and better-informed disaster-response decisions.

## More from Meta Global AI Developer Hackathon

- [SRIJALABS](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1979)
- [SellerChat AI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1980)
- [Youngenz](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1981)
- [netops RL env](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1983)
- [Teamvejimil](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1984)
- [Proof of Turnover](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1985)

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