umbra-py
Search, preview, load, and convert Umbra open SAR data.
Umbra publishes 16–25 cm SAR as CC BY 4.0 open data, but no search API —
only a 17+ TB S3 bucket and a static STAC tree. umbra-py is that layer:
search, preview, download, and analysis-ready arrays without the usual 500
lines of glue. A community STAC API (umbra serve) and MCP server sit on
the same host, so pystac-client and Claude can query the archive with
nothing installed.
📖 Docs: umbra-py.space · Showcase: browse the archive in the browser (no install)
Status: v0.1.2. Discovery, download, xarray loading, SICD → geocoded COG, change/timescan composites, chips, a STAC API (
umbra serve, with a community host), and an MCP server all ship. This is not an InSAR toolbox (phase is not preserved through convert). Not affiliated with Umbra Lab, Inc.
Install
pip install umbra-py # core: search + download + metadata
pip install "umbra-py[load]" # + xarray / rasterio
pip install "umbra-py[viz]" # + quicklooks, maps, galleries
pip install "umbra-py[convert]" # + SICD → geocoded COG
pip install "umbra-py[all]" # convert + load + viz + export
Python 3.10+. Other extras (dask, serve, mcp, ai, langchain,
llamaindex) are listed in the install guide.
Five minutes to a scene
Fetch the weekly catalog snapshot, then search and preview offline. A live
walk of the bucket (umbra search without --local) works but is slow.
pip install "umbra-py[viz,load]"
umbra index fetch
umbra search --local --area Centerfield --product GEC --limit 3
umbra gallery --local --area Centerfield --limit 6 --out gallery.html --db
from umbra_py import CatalogIndex, to_xarray
with CatalogIndex.from_release() as index:
item = next(iter(index.search(area="Centerfield", product_types=["GEC"], limit=1)))
# Stream a downsampled window over HTTP — no multi-GB download. Needs [load].
da = to_xarray(item, max_size=1024, db=True)
print(item.summary())
If the snapshot is missing, the same search against the live bucket is
UmbraCatalog().search(...) / umbra search --area Centerfield.
What you can do
More detail, options, and caveats live in the docs.
Search by bbox, place name, polygon, or Umbra task (area=).
--local reads the snapshot; omit it to walk S3.
from umbra_py import UmbraCatalog
for item in UmbraCatalog().search(area="Centerfield", product_types=["GEC"], limit=5):
print(item.summary())
Preview without downloading the scene: umbra gallery, umbra quicklook <stac-url> --out scene.png --db, umbra view <stac-url> (full-res tiles),
or umbra change --area Centerfield --out change.png.
Load a geocoded GEC into xarray or a GeoTIFF (to_xarray, to_geotiff,
to_stack). Needs [load].
Convert a SICD to a north-up COG (sicd_to_geocoded_cog, umbra convert).
Needs [convert]. Open products generally have no radiometric metadata, so
--calibrate / --noise-model measured refuse rather than invent numbers.
See limitations.
Chip scenes into georeferenced ML tiles: umbra chips --area Centerfield --out chips/.
Drive it from an agent. Zero-install MCP server:
uvx --from 'umbra-py[mcp]' umbra-mcp
{
"mcpServers": {
"umbra": {
"command": "uvx",
"args": ["--from", "umbra-py[mcp]", "umbra-mcp"]
}
}
}
That command is published to the MCP registry
as io.github.reesehammer/umbra-mcp. A community host serves the same tools
over Streamable HTTP (POST /mcp) and a read-only STAC API (/search,
/docs) on one URL — umbra serve --public. Point pystac-client at the
Railway public domain; see Deploy.
docker compose up is the one-command self-host.
What the data looks like
| Asset | What it is | Use it for |
|---|---|---|
GEC |
Geocoded cloud-optimized GeoTIFF | Map-ready imagery. Start here. |
CSI |
Color sub-aperture GeoTIFF | Quick-look RGB, not a measurement |
SIDD |
Geocoded detected image (NITF) | Detected imagery in a standard format |
SICD |
Complex data in the radar slant plane (NITF) | Phase-preserving work, InSAR inputs |
CPHD |
Compensated phase history | Custom image formation |
umbra-py downloads SICD/CPHD and can geocode a SICD to amplitude. It does
not form interferograms or compute coherence.
Data license & attribution
Umbra's imagery is CC BY 4.0. If you use or redistribute the data or derived products you must attribute Umbra, e.g.:
Contains Umbra open data, licensed under CC BY 4.0.
umbra-py itself is Apache 2.0 (LICENSE). The two licenses
are independent and compatible.
Citing umbra-py
Machine-readable metadata lives in CITATION.cff. GitHub renders it as a "Cite this repository" button. Please also honor the CC BY 4.0 line above for any Umbra data you use.
Community
Acknowledgements
Built on the SAR open-source community, including
sarpy and Umbra's open data program.
Not affiliated with or endorsed by Umbra Lab, Inc.
Metadata
Release files for umbra-py 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| umbra_py-0.1.2.tar.gz | 1.2 MB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| umbra_py-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.9 MB
Release files / umbra_py-0.1.2.tar.gz
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