st-insar 0.1.1

⚠️ Important Note

This project is actively under development. While the core functionality is production-ready and thoroughly tested, some advanced features are still being refined.

Traditional InSAR processing tells you where the ground has subsided. st-insar tells you where it's going to — by fusing high-resolution Sentinel-1 displacement time-series with coarse hydrological reanalysis and optical foundation-model embeddings on a single hierarchical graph, and forecasting forward with a physics-informed Spatio-Temporal Graph Neural Network that is constrained, by construction, to respect Terzaghi's principle of effective stress.

It exists because groundwater-driven land subsidence — in the Central Valley, in Mexico City, in Jakarta — is one of the few climate-adjacent hazards that is genuinely predictable months in advance, if the model is given the right physics and the right multi-modal signal. Most InSAR tooling stops at the interferogram. st-insar starts there.

Why this exists

Land subsidence from groundwater over-extraction is slow, cumulative, and expensive to reverse — by the time a well field, a rail line, or a coastal levee shows visible damage, the compaction that caused it may already be irreversible. InSAR gives geodesists a precise retrospective record of that compaction, at millimeter precision, every 6–12 days. What it doesn't give them, on its own, is a forecast a water manager can act on before the damage happens.

st-insar closes that gap with three design decisions that most subsidence-modeling pipelines skip:

• No naive resampling. Displacement (PS points, ~5 m) and hydrology (GRACE/ERA5, ~30 km+) live at wildly different resolutions. Instead of upsampling the coarse grid to fake spatial detail it doesn't have, st-insar builds a two-tier hierarchical graph — hydrology cells as super-nodes, PS points as leaf-nodes — and lets graph attention learn the coupling between scales.
• Physics as a loss term, not a post-hoc filter. A TerzaghiPhysicsLoss penalizes the model, during training, for predicting uplift while groundwater storage is measurably depleting — the one direction effective-stress theory actually constrains. It does not penalize continued subsidence during recharge, since a large fraction of compaction in clay/silt aquitards is inelastic and permanent.
• Uncertainty you can act on. Every forecast ships with both aleatoric uncertainty (from a Mixture Density Network head, which can represent genuinely multi-modal futures — "extraction continues" vs. "a moratorium kicks in") and epistemic uncertainty (from Monte Carlo Dropout), reported separately, not collapsed into one number.

How it works

                    InSAR (ps-gnn / pyunwrap)     Optical (Clay / Prithvi)     Hydrology (GRACE / ERA5 / SMAP / CHIRPS)
                    6–12 days · ~5 m              ~5 days · ~10 m              monthly · 30 km+
                            │                             │                             │
                            └──────────────┬──────────────┴──────────────┬──────────────┘
                                           ▼                             ▼
                              hierarchical graph harmonizer   (leaf-nodes ↔ parent super-nodes)
                                           │
                                           ▼
                    ┌──────────────────────────────────────────────────────────────┐
                    │                      ST-GNN core                             │
                    │  TCN / LSTM / frozen-MLP encoders → GATv2 spatial coupling    │
                    │  → explicit temporal attention → Mixture Density Network head │
                    └──────────────────────────────────────────────────────────────┘
                                           │
                          trained under TerzaghiPhysicsLoss, spatial-block +
                          temporal-roll-forward CV, two-phase curriculum
                                           │
                                           ▼
                    scenario "what-if" forecasting  →  explainability + calibration
                    (ONNX or native PyTorch, MC Dropout CIs)   (temporal SHAP, coupling maps, Moran's I)
                                           │
                                           ▼
                                 automated HTML report

Each stage is its own module, independently usable:

Validation sites

Three sites ship pre-configured, each with an independent ground-truth source the forecasts are checked against — not just internal cross-validation:

Installation

git clone https://github.com/st-insar/st-insar.git
cd st-insar
pip install -e ".[dev]"

GPU-accelerated scatter/sparse ops (recommended for graphs beyond a few thousand nodes):

pip install -e ".[gpu]"

Quickstart

from st_insar.data.harmonizer import MultiModalHarmonizer, VALIDATION_SITES

site = VALIDATION_SITES["central_valley"]
harmonizer = MultiModalHarmonizer(site=site)

harmonizer.register_insar(ps_points_path="data/central_valley_ps.geojson")
harmonizer.register_hydrology(era5_path="data/era5_tws.nc", grace_path="data/grace_tws.nc")
harmonizer.register_optical_embeddings(embeddings_path="data/clay_embeddings.nc")
harmonizer.register_static(topo_path="data/dem.tif", geology_path="data/soil_type.tif")

graphs = harmonizer.build_dynamic_graph_sequence(start="2016-01-01", end="2020-12-31", freq="MS")

from st_insar.inference.forecaster import ScenarioForecaster

forecaster = ScenarioForecaster(schema=harmonizer.schema, checkpoint_path="checkpoints/best.pt")
result = forecaster.forecast(x_seq, edge_index, mc_samples=50)   # mean, 95% CI, aleatoric + epistemic variance

# "What if extraction increases 20% over the next 6 months?"
scenario = ScenarioForecaster.scale_hydro_channel(x_seq, harmonizer.schema, factor=1.2, steps=6)
what_if = forecaster.forecast(scenario, edge_index)

Training from raw files, with the full spatial-block CV × curriculum pipeline, runs from the CLI:

st-insar-train \
  --site central_valley \
  --ps-points data/central_valley_ps.geojson \
  --grace data/grace_tws.nc --era5 data/era5.nc \
  --history-len 24 --horizon 12 \
  --phase1-epochs 20 --phase2-epochs 40

Project layout

st_insar/
├── data/            harmonization: multi-modal alignment onto a dynamic hierarchical graph
├── models/          encoders, ST-GNN core (GAT + temporal attention + MDN), TerzaghiPhysicsLoss
├── training/        spatial-block + temporal-roll-forward CV, curriculum, trainer, CLI
├── inference/       scenario forecasting, MC Dropout uncertainty, ONNX export/serving
├── analytics/       temporal SHAP, coupling maps, Moran's I, calibration, HTML report generator
├── visualization/   animated maps, 3D space-time cubes, forecast/SHAP/loss charts
└── utils/           shared helpers
tests/               unit, model, leakage, ONNX, and end-to-end integration tests
assets/              logo source files

Testing

pytest tests/ -m "not slow"     # unit, model, and leakage tests — a few seconds
pytest tests/                    # add the full harmonize → train → forecast → report integration test

48 tests, 76% line coverage, zero mypy errors. The suite is unusually paranoid about two things on purpose: the leakage tests construct a mock dataset where the future has a deliberately different distribution than the past and assert, by inspecting actual tensor values, that no training window ever touched it; the physics tests assert the Terzaghi loss's asymmetry directly (penalized: uplift during depletion; not penalized: continued subsidence during recharge).

Roadmap

• Package scaffold, pyproject.toml, multi-modal harmonizer
• Modality encoders, GATv2 spatial coupling, temporal attention, MDN forecast head
• TerzaghiPhysicsLoss, scenario "what-if" forecasting API, ONNX export
• Spatial-block + temporal-roll-forward CV, two-phase curriculum, training CLI
• Temporal SHAP, spatial-attention coupling maps, spatio-temporal Moran's I, calibration
• Animated maps, 3D space-time cubes, automated HTML reporting
• Unit, model, leakage, ONNX, and integration test suite; CI
• Pretrained checkpoints for all three validation sites
• Direct pygeofetch ingestion (currently: bring your own harmonized files)
• Multi-GPU / distributed training for continental-scale graphs

Related projects

st-insar is designed to sit downstream of two companion packages and, eventually, feed into a third:

• ps-gnn — Persistent Scatterer identification
• pyunwrap — AI-based InSAR phase unwrapping
• pygeofetch — multi-source Earth observation ingestion (planned integration)

Citation

If st-insar is useful in your research, please cite it:

@software{stinsar2026,
  title  = {st-insar: Physics-Informed Spatio-Temporal Deformation Forecasting},
  author = {{st-insar contributors}},
  year   = {2026},
  url    = {https://github.com/st-insar/st-insar}
}

Contributing

Issues and pull requests are welcome. Before opening a PR: pytest tests/ -m "not slow", ruff check st_insar tests, and black st_insar tests should all be clean — CI runs the same checks, plus the full slow suite, on every push.

License

MIT — see LICENSE.