lazycogs 0.3.1

Lazily load COG assets from STAC items into xarray DataArrays using async-geotiff

Open a lazy (band, time, y, x) xarray DataArray from thousands of cloud-optimized geotiffs (COGs). No GDAL required.

What is lazycogs?

stackstac and odc-stac established the pattern that lazycogs builds on: take a STAC item collection and expose it as a spatially-aligned xarray DataArray ready for dask-parallel computation. Both are excellent tools that cover most satellite imagery workflows well. They rely on the trusty combination of rasterio and GDAL for data i/o and warping operations.

lazycogs takes the same approach but replaces GDAL and rasterio with a Rust-native stack: rustac for STAC queries over stac-geoparquet files, async-geotiff for COG i/o, and obstore for cloud storage access.

The result is a tool that can instantly expose a lazy xarray DataArray view of massive STAC item archives in any CRS and resolution. Each array operation triggers a targeted spatial query on the stac-geoparquet file to find only the assets needed for that specific chunk — no upfront scan of every item required.

One constraint worth naming: lazycogs only reads Cloud Optimized GeoTIFFs. If your assets are in another format, this is not the right tool.

Task	Library
STAC search + spatial indexing	rustac (DuckDB + geoparquet)
COG I/O	async-geotiff (Rust, no GDAL)
Cloud storage	obstore
Reprojection	pyproj + numpy
Lazy dataset construction	xarray BackendEntrypoint + LazilyIndexedArray

Installation

pip install lazycogs

Coordinate convention

lazycogs.open() returns a DataArray whose y coordinates follow the standard north-up raster convention with the origin in the top left (not bottom left). That is, y coordinates are descending from north to south. In other words, y label 0 is the northernmost pixel and y[-1] is the southernmost. This matches the affine transform and is consistent with odc-stac, rioxarray, and GDAL.

Use sel(y=slice(north, south)) (high to low) for spatial subsetting.

x and y keep their RasterIndex-based spatial selection behavior, but the coordinate variables themselves are materialized eagerly so chunked nearest-neighbor spatial selections compute cleanly.

Example

import rustac
import lazycogs
from pyproj import Transformer

# set a target CRS and extent
dst_crs = "EPSG:32615"
dst_bbox = (380000.0, 4928000.0, 420000.0, 4984000.0)

# transform to 4326 for STAC search
transformer = Transformer.from_crs(dst_crs, "epsg:4326", always_xy=True)
bbox_4326 = transformer.transform_bounds(*dst_bbox)

# Search a STAC API and cache results to a local stac-geoparquet file.
await rustac.search_to(
    "items.parquet",
    "https://earth-search.aws.element84.com/v1",
    collections=["sentinel-2-l2a"],
    datetime="2023-06-01/2023-08-31",
    bbox=bbox_4326,
)

# Open a fully lazy (band, time, y, x) DataArray. No COGs are read yet.
da = lazycogs.open(
    "items.parquet",
    bbox=dst_bbox,
    crs=dst_crs,
    resolution=10.0,
)

Async loading

When you are already inside an async context (for example, a Jupyter notebook running on an asyncio loop), you can trigger chunk reads without blocking the event loop:

# Fetch data asynchronously and load into memory in-place.
subset = await da.isel(x=slice(0, 10), y=slice(0, 10), time=slice(0, 10)).load_async()

load_async uses xarray's async protocol, which dispatches through MultiBandStacBackendArray.async_getitem and stays on the caller's event loop. Multiple concurrent chunk reads overlap naturally, so the async path can be faster than the synchronous da.compute() when reading many chunks inside an already-running loop.

Documentation

• Home — quickstart and full usage guide
• Example: Midwest US daily Sentinel-2 array
• Example: Southwest US monthly low-cloud Sentinel-2 array
• Architecture
• Contributing