# Barad-dûr

- **Event:** [National Security Hackathon (by Army xTech)](https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon)
- **When:** May 2 at 9:00 AM – May 3 at 4:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Christine Baek](https://cerebralvalley.ai/u/cbaek)
- **GitHub:** https://github.com/viirii/barad_dur
- **Demo video:** https://youtu.be/FehKX-S4I00
- **Gallery:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/63

Airfields are high-signal locations where subtle visual changes can indicate meaningful shifts in activity, but manually reviewing satellite imagery over time is slow, inconsistent, and easy to miss. This project turns the open-ended question “let me know when there is cause for concern” into a structured surveillance and analysis pipeline for designated locations of interest, starting with airports and airfields. It combines proven computer vision and object detection methods to identify aircraft, model-assisted analysis to classify aircraft type and assess visual reliability, and generative AI to summarize changes, uncertainty, and potential anomalies in analyst-readable language. As AI capabilities advance, their value still depends on the quality of the data and context provided; the goal here is not just to count planes, but to understand how aircraft composition, concentration, and surrounding context evolve over time, then surface unusual changes for timely human intervention

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Markdown version of https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/63. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt