GNSS datasets for benchmarking, validation and AI.
Curated logging runs for real-world environments — designed to support signal-quality research, RTK validation, and autonomous platform testing.
What’s inside
Built to be useful for engineering, not just storage.
Raw measurements
Multi-constellation observables and timing needed for repeatable analysis and algorithm development.
RTCM streams
Correction and message-flow captures for encoder/decoder testing, transport validation and observability.
Metadata
Run structure, device context, environment notes and configuration snapshots to make datasets re-usable.
Typical workflows
Quality monitoring
Detect multipath/LOS-NLOS behaviour and correction anomalies using interpretable metrics.
Model training
Train and evaluate models that predict reliability and degrade gracefully in real-time autonomy loops.
RTK validation
Benchmark convergence, fix stability and robustness across environments and motion profiles.
Designed for AI and signal-quality studies
We focus on explainable, engineering-friendly signals: degradation, consistency, and reliability over time.
