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πŸ”¬Active

Disease Control

Satellite screens for standing water, drones confirm breeding sites, precision spray response.

Our most mature, furthest-along implementation. Nepal's Terai districts carry the highest malaria and dengue burden in the country, and manual surveillance takes 2–4 weeks to find a breeding site β€” by which point an outbreak is already underway. Drishti closes that gap: satellite screening flags candidate water bodies weekly at zero marginal cost, autonomous drones verify only the flagged zones, and confirmed sites get a precision larvicide response β€” all with a full georeferenced audit trail from pixel to intervention.

72h
End-to-end cycle vs. 2–4 weeks manually
70–80%
Reduction in drone flight hours vs. blanket survey
60–80%
Less larvicide use vs. blanket spraying
4–6 weeks
Outbreak prediction horizon
How it works
🌍01

Sentinel-2 acquisition

ESA Copernicus satellite passes over target districts weekly. Cloud-masked Sentinel-2 L2A tiles are ingested automatically via Google Earth Engine at zero cost.

πŸ“‘02

NDWI water detection

Normalized Difference Water Index isolates standing water. Week-over-week change detection flags new or growing water bodies. Permanent rivers and reservoirs are excluded via historical mask.

πŸ—ΊοΈ03

Mission planning

Flagged candidate zones are ranked by area, proximity to settlements, and historical case burden. Top N zones are queued for drone validation. Operator approves the mission in ~15 minutes.

🚁04

Survey drone flight

Autonomous drone flies only to flagged zones at 30m altitude, capturing 5–10cm GSD imagery. YOLOv8 detects standing water, containers, blocked drains, and tire piles across the full zone.

πŸ”¬05

Nano-shot confirmation

For each high-confidence water surface, the drone descends to 2–5m. EfficientNet-B0 classifier analyzes macro close-ups for larval signatures β€” turbidity, organic film, container type, visible larvae.

πŸ’§06

Intervention dispatch

larvae_confirmed detections trigger an intervention mission. Drone returns to base β€” camera payload swapped for larvicide tank in <5 minutes. Precision spray applied at exact confirmed coordinates.

πŸ“Š07

Risk prediction

XGBoost model fuses satellite trends, confirmed habitat density, IoT sensor readings, and historical case data to generate ward-level outbreak risk scores 4–6 weeks ahead.

βœ…08

Closed-loop audit

Every intervention traces back through detection β†’ flight β†’ mission β†’ satellite pixel. Full georeferenced audit trail with timestamp. FCHVs receive alerts in Nepali. Risk scores updated.

VERIFY
Steps 1–3 Β· Satellite
VALIDATE
Steps 4–5 Β· Drone
EXECUTE
Steps 6–8 Β· Intervention + Predict