GIST — GNSS Interference Spatio-Temporal network

▶ Live demo & slides: https://mevichitra.github.io/gist-gnss-interference/

For the presenter
Live demo (full-screen, offline-capable)	docs/demo/, or open presentation/demo/index.html locally
Slides	presentation/GIST_DL_Project.pptx (speaker notes on every slide) · PDF
Presenter notes	presentation/ELI5.md

Results in the deck and demo are interim (seed 0); the final multi-seed run is pending.

Detect, classify (jamming vs spoofing) and localize GNSS interference from crowdsourced ADS-B, with no manual labels. Final-year deep learning project + paper. Full design: PLAN.md; decisions and dead ends: LOG.md.

Pipeline

Step	Command	Output
Download + parse days (adsb.lol, ODbL)	uv run python -m gist.data.fetch_days [YYYY-MM-DD ...]	data_store/points/<day>/
Features + weak labels	uv run python -m gist.labels.build [days]	data_store/steps/, data_store/windows/
Scene datasets (real + simulated emitters)	uv run python -m gist.train.dataset <days> --real 300 --sim 300	data_store/scenes/<split>/
Train GIST	uv run python -m gist.train.train --train train --val val --name gist	runs/gist/
Baselines	uv run python -m gist.train.eval_baselines --train train --test test --out runs/baselines.json	JSON metrics
Tests	uv run pytest

Splits are by day: train ≤ 2025-06, val 2025-07..2025-12, test ≥ 2026-01.

Layout

src/gist/data      download (resumable, throttle-aware), trace parser, regions
src/gist/features  physics-consistency step features, 10-min windows
src/gist/labels    labeling functions, Dawid–Skene label model, build script, event catalogue
src/gist/sim       RF propagation (NumPy/PyTorch), jammer/spoofer injection, scene sampling
src/gist/models    GIST (Transformer + spatial attention + physics localizer), baselines, NLS
src/gist/train     scene tensors, datasets, training, evaluation
paper/             LaTeX (IEEE), bibliography, venues

Data and licences

• adsb.lol globe_history — ODbL-1.0. Derived datasets we release are ODbL; cite "ADSB.lol and contributors".
• ADS-B Exchange samples are not used (evaluation-only terms). See DATA_LICENSE.md.
• OpenSky Network (Trino) — requested 2026-10-27; used only for events before 2023 (e.g. Denver KDEN 2022).

Code: MIT.