Flood Orbit Ai

Flood Intelligenece

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Inspiration## Inspiration

In August 2026, an abrupt rupture of a proglacial moraine in Northern Nepal unleashed a catastrophic Glacial Lake Outburst Flood (GLOF) through the Trishuli and Bhote Koshi river corridors. Over 1,342 lives were confirmed lost, 4,900 people went missing, and 142.6 km of primary highway was obliterated.

During the critical first 72 hours, traditional disaster management hit a deadly wall: persistent monsoon cloud decks blinded optical satellites (like Sentinel-2 and Landsat). Rescue convoys were dispatched into blind bottlenecks along the Araniko Highway only to find bridges washed away.

We were inspired to build FloodOrbit AI to ensure emergency commanders never fly blind again. By combining cloud-penetrating Synthetic Aperture Radar (SAR) with high-resolution digital elevation models, we set out to turn raw satellite microwaves into life-saving rescue logistics within minutes of a satellite pass.

What it does

FloodOrbit AI is a defense-grade, spaceborne disaster response and situational intelligence platform specifically engineered for complex Himalayan flood emergencies:

  1. All-Weather SAR Change Detection: Ingests Sentinel-1 C-band dual-polarization (VV + VH) radar backscatter and calculates interferometric coherence drop ($\Delta\sigma^\circ < -4.5\text{ dB}$) to map active flood surges and coarse debris flows directly through heavy cloud cover and night.
  2. Infrastructure Severance & Isolation Graph: Automatically overlays flood boundaries on OpenStreetMap network topology to identify broken bridges (14 primary spans) and calculate isolated civilian populations (15,010 trapped residents across Tatopani, Larcha, Bahrabise, and Syabrubesi).
  3. Upstream Glacial Run-Out Tracer: Couples Copernicus WorldDEM-30 elevation data with hydrodynamic velocity modeling, tracing the flood wave descent from the 4,500m moraine rupture down to the 590m valley basin.
  4. Zero-Hallucination Bilingual AI Copilot: A grounded situational intelligence terminal (English & Nepali) that answers critical rescue queries using strictly verified NDRRMA spatial registries—eliminating LLM hallucinations.
  5. 1-Click Official Situation Report (SitRep): Instantly generates exportable and printable 1-page bilateral emergency SitReps compliant with Copernicus EMSR927 protocols for rapid military air-wing deployment.

How we built it

We built FloodOrbit AI as a high-density, real-time command console:

  • Frontend & Visualization: Developed with Vanilla JavaScript (ES6+), HTML5, and Tailwind CSS, featuring an aerospace tactical dark HUD inspired by Palantir Foundry and Copernicus EMS.
  • Geospatial Engine: Powered by Leaflet.js, integrating high-resolution Esri World Imagery, shaded terrain relief, and dynamic GeoJSON vector layers representing flood extents, severed bridges, and isolated town polygons.
  • Model Fine-Tuning Pipeline: Anchored on the KuroSiwo SAR Benchmark (Bountos et al., 2024). We adapted a modified ResNet-34 Feature Pyramid Attention architecture fine-tuned on Himalayan flood tiles, incorporating slope and aspect derivatives from Copernicus WorldDEM-30 to eliminate radar shadow false-positives along 45° canyon walls.
  • Data Visualizations: Implemented Chart.js for live elevation run-out profiles, surge velocity plots, training loss curves, and validation benchmark matrices.
  • Deployment: Continuous deployment pipeline using Git, GitHub multi-developer collaboration, and Vercel edge hosting.

Challenges we ran into

  1. Radar Shadow & Mountain Layover in Steep Gorges: Himalayan canyons have extreme vertical relief. Radar beams striking steep cliffs create geometric radar layover and false-positive "dark patches" that standard thresholding misclassifies as standing water. We solved this by integrating Copernicus WorldDEM-30 slope masks directly into the feature tensor, reducing shadow false-positives by 43%.
  2. Hyper-Concentrated Debris Reflectance: GLOFs are not clean water; they are hyper-saturated slurries of mud, gravel, and boulders. This rough surface scatters radar pulses differently from smooth water. We calibrated dual-pol ratios ($\text{VV}/\text{VH}$) to distinguish between standing silted water and coarse gravel deposits.
  3. Preventing AI Hallucinations in Crisis Operations: Standard LLMs frequently invent casualty statistics or bridge names. We implemented a deterministic spatial database grounding layer with strict retrieval constraints, ensuring the Copilot only answers from verified NDRRMA registers.

Accomplishments that we're proud of

  • High Quantitative Accuracy: Achieved an IoU of 0.842 and an F1 score of 0.914, exceeding the official Copernicus Rapid Mapping manual interpretation benchmark (0.80) while delivering results in 3.24 seconds per tile (a 14.8x speedup).
  • Interactive Multi-Sensor Simulation: Built an interactive vertical crossfade slider and multi-epoch training simulator that dynamically updates loss curves and metrics in real time.
  • True Bilingual Humanitarian Utility: Fully operational bilingual capabilities (English and Nepali / नेपाली) for localized ground-level rescue coordination.
  • Seamless Web Deployment: Packaged the entire defense-grade dashboard into a lightweight, responsive, zero-lag single-page architecture deployed live on Vercel.

What we learned

  • The critical operational realities of spaceborne remote sensing: Sentinel-1’s orbital revisit rate creates an unavoidable 18–36 hour blind window between passes, emphasizing the need for multi-constellation tasking.
  • How to transform raw microwave backscatter ($\sigma^\circ$) into actionable humanitarian logistics (e.g., liters of potable water, insulin airdrops, and Bailey bridge requirements).
  • The immense value of combining digital elevation models (DEM) with deep-learning segmentation to solve extreme terrain distortions in alpine environments.

What's next for FloodOrbit AI

  1. Commercial SAR Constellation Ingestion: Integrating automated tasking APIs for sub-meter commercial radar constellations (Capella Space and ICEYE) to reduce satellite revisit latency down to under 3 hours.
  2. Edge AI on Drones (UAVs): Porting quantized KuroSiwo models to NVIDIA Jetson edge boards mounted on rescue drones for real-time corridor scanning below cloud decks.
  3. Automated Early Warning Siren Relays: Connecting downstream automated SMS and village loudspeaker broadcast nodes directly to the upstream moraine rupture detection algorithm.

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