Today we are announcing the largest reliability upgrade in LaunchDetect’s history. A new detection model is now live in production across our entire monitoring network – and it changes what an alert from our service means.

The headline numbers:

  • Over 100x fewer false alerts. Quiet skies now stay quiet. When LaunchDetect notifies you, it is because a launch happened.
  • 99% agreement with human analysts, benchmarked against expert review of every event in our corpus of more than 1,500 real, human-verified space-launch event sequences.
  • Detections timestamped to the liftoff frame. Each detection identifies the exact image, time, and place a launch occurred – with the supporting evidence attached.

Why This Matters for Program Managers

If you run a program that consumes launch-event data – whether through the Unified Data Library, a direct feed, or the LaunchDetect dashboard – the practical value of an automated detection service comes down to one question: can my team act on an alert without independently re-verifying it?

Alert fatigue is the quiet failure mode of most automated monitoring. A system that cries wolf ten times for every real event trains its consumers to ignore it, and the operational value goes to zero no matter how good the underlying sensing is. The single most important metric we optimized in this upgrade was trust: driving false alerts down by more than two orders of magnitude while holding detection performance to the standard of expert human review.

Three properties of the new model are worth building into your program’s expectations:

1. Alerts are decision-grade. With false alerts reduced over 100x, a LaunchDetect notification is no longer a cue to start an investigation – it is the investigation’s first confirmed data point. Watch floors and duty officers can treat an alert as a high-confidence event report, not a lead to be screened.

2. Every detection is auditable. Each detection is anchored to a specific satellite image frame, timestamped to liftoff, and delivered with the evidence behind the call. When your stakeholders ask “how do we know?”, the answer is attached to the record itself – no black box, no unexplained score.

3. Honest negatives are a feature. The new model reports what the imagery actually shows. When a launch produces no observable signature from geostationary orbit – heavy cloud cover, for example – the platform says so rather than manufacturing a detection. For programs that fuse multiple sources, a truthful “no observable signature” is far more useful than a flattering false positive.

Validated Against Humans, Not Just Metrics

The benchmark behind the 99% figure is deliberately conservative. Every event sequence in our corpus – over 1,500 of them, collected from real launches observed by GOES-18, GOES-19, and Himawari-9 – was independently reviewed by a human analyst. The new model’s verdicts were then scored against that expert baseline, event by event, on the same data. The previous model agreed with human review 35% of the time. The new model agrees 99% of the time.

We also went back and re-evaluated every historical event on the platform under the new model, so the record your team sees today is internally consistent: past events, current events, and future events are all held to the same standard.

What Stays the Same

Nothing about your integration changes. The same unclassified civil geostationary constellation provides coverage at a 5-10 minute global cadence. Detections continue to flow into the Unified Data Library in the same schemas, the dashboard at launchdetect.com works exactly as before, and the operator-verified review process remains in the loop. This capability was prototyped through our work in the USSF SDA TAP Lab, and the upgrade path was designed so that consumers experience only one difference: dramatically fewer alerts that turn out to be nothing.

The Bottom Line

For a program office, the pitch is one sentence: LaunchDetect alerts now carry the reliability of an expert analyst, delivered at machine speed, with the evidence attached. If your team has been discounting automated launch-detection feeds because of noise, this is the release that changes the math.

Questions about integrating LaunchDetect into your program – UDL access, direct feeds, or evaluation datasets? Reach out at launchdetect.com.