STAC-geoparquet

Convert STAC items to GeoParquet.

Purpose

This library helps convert STAC Items to GeoParquet. While STAC Items are commonly distributed as individual JSON files on object storage or through a STAC API, STAC GeoParquet allows users to access a large number of STAC items in bulk without making repeated HTTP requests.

Usage

stac_geoparquet.to_dataframe does it all. You give it a list of (STAC Item) dictionaries. It just converts them to a geopandas.GeoDataFrame, which can be written to parquet with .to_parquet.

>>> import requests
>>> import stac_geoparquet
>>> item = requests.get("https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items/ia_m_4209150_sw_15_060_20190828_20191105").json()
>>> df = stac_geoparquet.to_geodataframe([item])
>>> df.to_parquet("naip.parquet")

Note that stac_geoparquet lifts the keys in the item properties up to the top level of the DataFrame, similar to geopandas.GeoDataFrame.from_features.

>>> list(df.columns)
['type',
 'stac_version',
 'stac_extensions',
 'id',
 'geometry',
 'bbox',
 'links',
 'assets',
 'collection',
 'gsd',
 'datetime',
 'naip:year',
 'proj:bbox',
 'proj:epsg',
 'naip:state',
 'proj:shape',
 'proj:transform']

We also provide stac_geoparquet.to_dict and stac_geoparquet.to_item_collection helpers that can be used to convert from DataFrames back to the original STAC items.

pgstac integration

stac_geoparquet.pgstac_reader has some helpers for working with items coming from a pgstac.items table. It takes care of

• Rehydrating the dehydrated items
• Partitioning by time
• Injecting dynamic links and assets from a STAC API