🛰️ CritterScope

Turn a drone survey into georeferenced animal/object detections on a 3D map.

A drone flies a survey → thermal+RGB frames + telemetry → an AI detector emits warm-blob detections → each is projected onto terrain as (lat, lon, type, confidence, temp, time) → this app plots them on a 3D terrain globe with a timeline you can replay.

The dashboard runs today off a built-in simulator, so the whole UI is done and testable before any drone arrives. Real footage feeds the same dashboard via POST /api/ingest.

Stack

• Cloudflare Worker + Hono (src/index.js)
• MapLibre GL JS — satellite imagery + Terrarium DEM for real 3D terrain (no API key)
• D1 (optional) — stores real surveys/detections; schema in schema.sql
• Deploys to critterscope.theradicalparty.com

Run locally

npm install
npm run dev            # http://localhost:8787

Open / for the dashboard. Point it anywhere:

/?lat=-33.73&lon=150.31&r=1000        # center, radius (m)
/?seed=42                             # different simulated survey

Data contract (what the drone pipeline must produce)

GET /api/survey returns:

{
  "survey":     { "id","name","center_lon","center_lat","radius_m","sensor","drone","started_at","ended_at","simulated" },
  "flight":     [ { "seq","lon","lat","agl_m","t" } ],
  "detections": [ { "id","type","lon","lat","confidence","temp_c","t","frame" } ],
  "stats":      { "total","counts","area_km2","duration_min" }
}

type ∈ deer | fox | rabbit | boar | kangaroo | person | vehicle | unknown.

Feeding real drone data

1. wrangler d1 create critterscope, paste the id into wrangler.toml, uncomment the binding.
2. npm run db:init
3. POST /api/ingest with { survey, flight, detections }.
4. View it at /?survey=<id>.

The georeferencing step (frame + drone GPS + gimbal angle + terrain → lat/lon) lives in the drone pipeline, not here — this app just needs the resulting detections.

Drone ingest pipeline

See pipeline/ — turns real drone footage + DJI telemetry into georeferenced detections and POSTs them here. Runs end-to-end today via a synthetic flight + stub detector; drop in a trained YOLO model for real thermal.

Roadmap

• Drone ingest pipeline (frames + SRT telemetry → detector → georeferencer → ingest)
• Wire a trained thermal-wildlife YOLO model into the ONNX detector
• Remote D1 (needs a D1-capable Cloudflare token; live site runs the simulator until then)
• Live mode (stream detections during flight, not just post-survey)
• Track clustering (merge repeat sightings of the same animal across passes)
• DEM-based georeferencing (ray/terrain intersection instead of flat ground)