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synapse-sr synapse-sr

Sentinel-2 at 2 m, with every pixel accounted for.

RV University, Bengaluru: Sentinel-2 10 m versus synapse-sr 2 m

synapse-sr turns a Sentinel-2 L2A scene into a 2.0 m red, green, blue and near-infrared GeoTIFF. A physical model of the instrument pins everything the satellite measured. The network only adds what 10 m pixels cannot show, and every result says which is which.

import synapse_sr

r = synapse_sr.super_resolve("sentinel2_l2a.tif")
r.save("sentinel2_2m.tif")

Open in Colab Open in Kaggle

What you can do with it

  • Crop monitoring

    NDVI, SAVI, EVI and red-edge NDRE at 2 m, field-boundary maps, statistics restricted to observation-determined pixels.

    r.indices()["ndvi"] · synapse_sr.boundaries(r, "field")

  • Urban analysis

    Building and road edges, built-up index, per-building detection.

    synapse_sr.boundaries(r, "urban") · r.indices()["ndbi"]

  • Water and floods

    2 m water masks, shoreline strength, flooded area between two dates in km².

    synapse_sr.change(before, after, "ndwi")

  • Disaster assessment

    Burn scars, landslides and debris. Change is flagged only where both dates are trustworthy.

    synapse_sr.change(before, after, "nbr")

Worked examples: Applications.

Flash or Pro

Flash (default) Pro
Network ~0.6 M-parameter CNN, distilled from Pro 14.4 M-parameter Mamba state-space model
Speed ~1 s per 1.28 km scene on a laptop CPU ~5 s per scene on a GPU
Runs best on anything: CPU, laptops, integrated graphics, Apple silicon, ARM, GPU GPU (Colab, Kaggle, workstations)
Guarantees observation-consistent, support map, calibrated uncertainty the same

More: Choosing a model and a device.

Why trust it
Observation-consistent by construction x_hat = x_base + P_N(delta): the network's contribution lies in the null space of the Sentinel-2 forward model, so re-observing the output reproduces the measurement.
Accountable per-pixel support (observation-determined versus prior-dominated), calibrated uncertainty, validity mask and a measured round-trip consistency on every result.
Geospatially exact GeoTIFF in, GeoTIFF out, CRS and bounds preserved, direct x5 onto a grid that shares the input origin.
Runs anywhere CUDA, Triton, or pure PyTorch on CPU; live progress bars in terminals and notebooks; fully offline once the weights are local.