async-geotiff 0.3.0b1

Async GeoTIFF reader for Python

async-geotiff

Fast, async GeoTIFF and Cloud-Optimized GeoTIFF (COG) reader for Python, wrapping the Rust-based Async-TIFF library.

Documentation website.
Release post for v0.1

Features

• Asynchronous, read-only support for GeoTIFF and COG formats.
• High-level, familiar, easy to use API.
  • Load from full-resolution or reduced-resolution overviews as 3D NumPy arrays.
  • Simplify handling of nodata values and nodata masks with NumPy masked arrays.
  • Interpret Coordinate Reference Systems as PyProj CRS objects.
  • Find pixels with geotransforms exposed as Affine matrices.
  • Represent internal COG tile grids as TileMatrixSets via Morecantile integration.
• Performance-focused:
  • Rust core ensures compiled performance.
  • CPU-bound image decoding happens in a thread pool, without blocking the async executor.
  • Buffer protocol integration for zero-copy data sharing between Rust and Python.
• Lightweight with no GDAL dependency.
• Access data from AWS S3, Google Cloud Storage, and Azure Storage via integration with obstore.
• Full type hinting for all operations.
• Broad decompression support: Deflate, JPEG, JPEG2000, LERC, LERC_DEFLATE, LERC_ZSTD, LZMA, LZW, WebP, ZSTD.
• Support for any arbitrary backend via obspec protocols.

Anti-Features

Features explicitly not in scope:

• No pixel resampling.
• No warping/reprojection.
• Automatic resolution/overview selection

Resampling and warping bring significant additional complexity and are out of scope for this library. Consider using Async-GeoTIFF to load data, then Rasterio's In-Memory Files to resample or reproject data, if needed.

Example

First create a "store", such as an S3Store, GCSStore, AzureStore, or LocalStore for reading data from AWS S3, Google Cloud, Azure Storage, or local files. Refer to obstore documentation for more information.

from obstore.store import S3Store

store = S3Store("sentinel-cogs", region="us-west-2", skip_signature=True)
path = "sentinel-s2-l2a-cogs/12/S/UF/2022/6/S2B_12SUF_20220609_0_L2A/TCI.tif"

Then open a GeoTIFF:

from async_geotiff import GeoTIFF

geotiff = await GeoTIFF.open(path, store=store)

On the GeoTIFF instance you have metadata about the image, such as its affine transform and Coordinate Reference System:

geotiff.transform
# Affine(10.0, 0.0, 300000.0,
#        0.0, -10.0, 4100040.0)

geotiff.crs
# <Projected CRS: EPSG:32612>
# Name: WGS 84 / UTM zone 12N

For a COG, you can access the overviews, or reduced resolution versions, of the image:

# Overviews are ordered from finest to coarsest resolution
# In this case, access the second-coarsest resolution version of the image
overview = geotiff.overviews[-2]

Then we can read data from the image. This loads a 512-pixel square from the upper-left corner of the selected overview.

from async_geotiff import Window

window = Window(col_off=0, row_off=0, width=512, height=512)
array = await overview.read(window=window)

This Array instance has data, mask, and some other metadata about the fetched array data.

Plot, using rasterio.plot.show (requires matplotlib):

import rasterio.plot

rasterio.plot.show(array.data)