Stage 07

Intelligence

A detection is a fact. Cross-referencing it, watching it recur, fusing it with an independent data stream — that is where a fact becomes something you can act on. Every signal on this page is described for exactly what it proves, and what it does not.

Atmospheric fusion

SO2 and wind, fused into one layer

Two independent, real data streams over the same footprint — a gas concentration and the wind that moves it — rendered as one map layer instead of two datasets you have to align yourself.

Sentinel-5P TROPOMI SO2 column density NOAA GFS 10 m wind · 13×13 grid Fusion regrid · smooth shared source cache Map overlay heat ramp + wind arrows

What it shows

SO2 column density from the Sentinel-5P TROPOMI instrument, regridded onto a regular lat/lon grid and rendered as a six-stop heat ramp (blue through red). NOAA GFS 10 m wind is sampled on a 13×13 grid over the same footprint and drawn as directional arrows — so concentration and drift direction read as one picture, for volcanic SO2, refinery and industrial plumes, or cross-border pollution events.

How it is fetched

Both sources are queried live for the AOI and date requested — the SO2 swath via Copernicus' Data Space Ecosystem (CDSE), the wind field from NOAA's public GFS archive. The source granule is cached once per scene, shared across jobs, so re-rendering the same footprint does not re-download it.

What this is, and what it is not. Both layers are real, live-fetched data — nothing here is synthesized. What it is not: a dispersion or plume-transport model. The wind arrows show the wind field at the time of the pass, not a forecast of where the gas will be in six hours — reading that forward is left to you, the same way it would be with the raw sources.
SO2 concentration rendered as a blue-to-red heat ramp over the Sunda Strait between Sumatra and Java, Indonesia, fused with orange NOAA GFS wind-direction arrows on a hybrid satellite basemap labelled with place names — Serang, Cilegon, Labuan, Pandeglang, Rangkasbitung, the Java Sea and the Indian Ocean.

Atmospheric Fusion · SO2 column density + wind vectors, Sunda Strait

Correlation & tracking

Six ways SatView reasons about what it already has

No new sensor, no new provider — these cross-reference detections, orbital data and tasking history that SatView already collects.

AIS correlation & dark vessels

A ship detection is checked against real AIS broadcast positions within 3 km and ±30 minutes. A match narrows down which vessel it likely is; no match flags it as a dark-vessel candidate. AIS history only goes back as far as SatView has been watching an area — never backfilled.

Recurring entities

Vehicle, ship and aircraft detections are clustered by position (150 m) across repeated passes. One sighting is just that; two or more at the same spot is surfaced as recurring activity worth watching over time.

TLE maneuver watch

Every TLE update is compared against the satellite's previous one. A mean-motion jump past 0.005 rev/day, or an inclination change past 0.05°, is logged as a maneuver candidate — a lead to verify, not a confirmed burn.

Reverse tasking

Give SatView an AOI and it answers a different question: which satellites, across the full 500+ catalog, will pass over it in the next few days — not just the ones you already selected. A quick way to know who else might be watching the same ground.

Archive vs. new

Before a fresh tasking order goes out, SatView checks the last 30 days of archive across your connected providers for the same footprint and cloud constraint. A hit surfaces as a pre-filled shortcut into catalog search — archive imagery typically runs 30-50% cheaper than tasking, a sector rule of thumb SatView surfaces rather than a quoted price.

Confidence, decomposed

A detection's popup does not collapse everything into one invented percentage. It shows the model's own detection score, an AIS-correlation confidence tier where it applies, and a recurrence count — three real signals, kept separate.

Why these stay separate. None of this is calibrated uncertainty quantification — no conformal intervals, no regional calibration. Each signal is exactly what it says: a real distance, a real count, a real threshold crossed. Where SatView is not sure, it says so instead of rounding up to a single confident-looking number.
A ship detected on a harbour channel in Rotterdam, popup showing 'ship, Score: 78%, Method: onnx' — 490 objects detected in this view including 86 ships and 7 harbours, alongside 393 storage tanks.

Detection popup · model score and inference method shown per object

Signals need somewhere to land

Every one of these can trigger a monitoring rule and a notification, not just a map marker.