https://github.com/gjoseph92/stackstac

Turn a STAC catalog into a dask-based xarray
https://github.com/gjoseph92/stackstac

cog dask geospatial rasterio stac xarray

Last synced: 4 days ago
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Turn a STAC catalog into a dask-based xarray

• Host: GitHub
• URL: https://github.com/gjoseph92/stackstac
• Owner: gjoseph92
• License: mit
• Created: 2021-03-06T04:29:22.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-08-10T04:34:02.000Z (5 months ago)
• Last Synced: 2024-12-20T09:03:03.519Z (11 days ago)
• Topics: cog, dask, geospatial, rasterio, stac, xarray
• Language: Python
• Homepage: https://stackstac.readthedocs.io
• Size: 56.2 MB
• Stars: 250
• Watchers: 20
• Forks: 51
• Open Issues: 54
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-earthobservation-code - stackstac - Turn a list of STAC items into a 4D xarray DataArray `Python` (`Python` processing of optical imagery (non deep learning) / Cloud Native Geospatial)

README

        
# StackSTAC

[![Documentation Status](https://readthedocs.org/projects/stackstac/badge/?version=latest)](https://stackstac.readthedocs.io/en/latest/?badge=latest) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/gjoseph92/stackstac/main?labpath=%2Fdocs%2Fbasic.ipynb%3Ffile-browser-path%3D%2Fexamples)

Turn a list of [STAC](http://stacspec.org) items into a 4D [xarray](http://xarray.pydata.org/en/stable/) DataArray (dims: `time, band, y, x`), including reprojection to a common grid. The array is a lazy [Dask array](https://docs.dask.org/en/latest/array.html), so loading and processing the data in parallel—locally or [on a cluster](https://coiled.io/)—is just a `compute()` call away.

For more information and examples, please [see the documentation](https://stackstac.readthedocs.io).

```python

import stackstac

import pystac_client

URL = "https://earth-search.aws.element84.com/v1"

catalog = pystac_client.Client.open(URL)

stac_items = catalog.search(

    intersects=dict(type="Point", coordinates=[-105.78, 35.79]),

    collections=["sentinel-2-l2a"],

    datetime="2020-04-01/2020-05-01"

).get_all_items()

stack = stackstac.stack(stac_items)

print(stack)

```

```

dask.array

Coordinates: (12/24)

  * time                        (time) datetime64[ns] 2020-04-01T18:04:04 ......

    id                          (time)