a.i. solutions and USGS Bring AI Into Landsat Flight Operations
March 8, 2026 - Lanham, Maryland. a.i. solutions has entered a Cooperative Research and Development Agreement (CRADA) with the U.S. Geological Survey (USGS) Technology Transfer Office to integrate AI and ML into Landsat flight operations.
The goal: increase mission reliability and operational efficiency by automating anomaly triage, telemetry trending, and complex orbital mechanics analysis. This builds on USGS's December 2024 Sources Sought Notice signaling a push to algorithmic automation across the Landsat program.
Why this matters for operations
This partnership moves flight dynamics from manual-heavy workflows to model-assisted operations. Expect faster triage, earlier detection of component degradation, and tighter loops between analysis and action-without removing humans from the loop.
It's a practical path to scale: automate the repetitive, surface the exceptions, and keep controllers focused on decisions that move risk down and uptime up.
Mission parameters at a glance
- Orbit: Sun-synchronous, global land-use monitoring
- Velocity: ~17,000 mph
- Altitude: ~438 miles (705 km)
- Focus areas: Telemetry analysis and trending to predict degradation before failures, anomaly triage, and advanced orbital mechanics support
Technical scope and tooling
The CRADA centers on telemetry-driven model development using extensive data from Landsat 8 and Landsat 9. Models will progress from training environments into integrated flight operations testbeds, with periodic updates planned.
a.i. solutions brings deep Landsat heritage, including flight dynamics support for Landsat 9 launch and commissioning. Its FreeFlyer software is widely used for high-fidelity orbit determination and mission analysis across civilian and military sectors.
What changes on the console (expected)
- Automated triage cues that rank anomalies by risk and likely root cause
- Predictive telemetry trends that flag components drifting from nominal well before thresholds are crossed
- Faster, model-assisted orbital analysis to inform maneuver planning and conjunction risk review
- Operator visibility into model confidence, alerts, and rationale to avoid alert fatigue and blind spots
Ops checklist to make this work
- Data pipeline hygiene: Validate feeds, schemas, time sync, and unit consistency. Garbage in, false positives out.
- Label quality: Curate a gold set of anomalies and near-misses; review drifted labels quarterly.
- Human-in-the-loop gates: Define clear thresholds for when models suggest vs. when operators decide.
- Model governance: Version control, reproducible training, and rollback plans per model.
- Shadow mode first: Run models alongside current tools to compare alerts and tune thresholds.
- Playbooks: Tie each alert class to a response checklist and escalation path.
- Post-incident reviews: Score model performance after anomalies; feed learnings back into training.
Timeline
The initial research phase is in motion, training on Landsat 8 and Landsat 9 telemetry libraries. As models mature, USGS and a.i. solutions plan to share updates and move from lab training into integrated flight operations testbeds.
Context and further reading
USGS's Technology Transfer Office facilitates partnerships like this CRADA to speed practical innovation in mission ops. Learn more via the USGS pages below.
Want more ways to apply AI to ops workflows-telemetry trending, anomaly triage, and predictive maintenance? Explore AI for Operations.
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