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Used in research

Independent research has already used Umbra's open archive as an ISR-style training source. This page points at that work and at the umbra-py path that gets you from catalog filters to trainable chips — discovery → arrays, not the model.

ProSR

ProSR (Kim & Kim, KAIST-VICLab) curated about 502 Umbra SLC acquisitions into roughly 132k patches at 0.25 m as an ISR super-resolution benchmark. Code and data notes live at KAIST-VICLab/ProSR.

umbra-py does not reimplement ProSR or any diffusion / SR model. It covers the data path those papers need: filtered search, size-aware download, and chips. ProSR tiled slant-range SLC amplitude (not map geometry) — sicd_to_amplitude_geotiff is the matching convert; sicd_to_geocoded_cog warps into ground-range and is a different product. GEC chips are the fast smoke stand-in, not a ProSR-equivalent training tensor.

How umbra-py helps

  • Index search with polarization and incidence filters (CatalogIndex.from_release() / index.search(...)) so multi-filter corpus assembly does not crawl S3.
  • Download (download_item) after confirming asset size (e.g. HTTP HEAD / Content-Length on the asset href).
  • Convert (sicd_to_amplitude_geotiff / umbra convert --slant-plane) for slant-plane SLC amplitude (the ProSR-like tensor); sicd_to_geocoded_cog / umbra convert when you need that amplitude on a map instead.
  • Chips (write_chips / umbra chips) for georeferenced training tiles from open GEC — a fast smoke stand-in, not ProSR's slant-range SLC patches.

Walkthrough: examples/09_isr_training_set.ipynb (search → size-check → chip). Chip mechanics alone: examples/05_detection_chips.ipynb.

License & affiliation

Umbra open imagery is CC BY 4.0 — attribute "Contains Umbra open data, licensed under CC BY 4.0." when you publish derived products. This is an independent, unofficial toolkit and is not affiliated with Umbra Lab, Inc.