---
id: pystac
version: "1.15.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# pystac — Python library for working with the SpatioTemporal Asset Catalog (STAC) specification

License: permissive · Maintenance: active · Downloads: 4.6M/mo

## What it is and what it does

PySTAC is a production-grade Python implementation of the SpatioTemporal Asset Catalog (STAC) specification, which is a standardized format for organizing and describing geospatial and temporal data assets. The library lets you create, read, validate, and manipulate STAC catalogs, collections, and items—the core data structures used to catalog satellite imagery, raster data, and other earth observation assets. It handles JSON serialization, metadata validation, and navigation of hierarchical catalog structures.

The package is built on pystac-core and includes 23 extension packages that add domain-specific metadata schemas for specialized data types (e.g., SAR, point clouds, data cubes, labels). Most users start with the core library and add extensions as needed. PySTAC is widely used in geospatial workflows to standardize how imagery and sensor data are described and discovered, making it easier to build data pipelines and search tools that work across different data providers.

Use it for:

- Build or maintain a searchable catalog of satellite or aerial imagery with standardized metadata.
- Ingest and validate earth observation data into a STAC-compliant repository.
- Write tools that read STAC catalogs and extract assets for analysis or processing.
- Integrate geospatial data from multiple sources into a unified STAC collection.
- Automate metadata generation and catalog updates for remote sensing workflows.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

PySTAC is a Python library for reading, writing, and working with SpatioTemporal Asset Catalog (STAC) specifications, enabling standardized organization and discovery of geospatial and temporal data.

Yes. PySTAC is actively maintained, production-stable, permissively licensed, and widely adopted in geospatial data workflows. Install it if you work with satellite imagery, earth observation data, or need to organize and catalog geospatial assets according to the STAC standard. The core library has low install friction; add optional extras (validation, orjson, urllib3, jinja2) only if you need their specific features.

## Install

pip install pystac
uv add pystac
poetry add pystac

## Installing pystac

Before you install: Installation is straightforward with low friction; the package is actively maintained with a recent release (18 days old) and supports current Python versions (3.10–3.13). The core library is pure Python, though optional extras like validation, orjson serialization, and Jupyter display are available.

License in practice: Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute PySTAC freely in commercial and private projects without copyleft obligations.

Quickstart:

pip install pystac

import pystac

# Read a STAC catalog
catalog = pystac.read_dict(stac_dict)
for item in catalog.get_items():
    print(item.id)

Requires Python 3.10 or later; optional validation requires jsonschema, orjson serialization requires orjson, and network retry support requires urllib3.

Verify before relying:

- Whether the 23 extension packages (pystac-ext-*) are all required for typical use or if most users only need a subset.
- Performance characteristics when working with large catalogs or deeply nested STAC structures.
- Compatibility with STAC specification versions beyond the current release.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 4.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

STAC catalog python library, spatiotemporal asset catalog, geospatial metadata management, earth observation data catalog, raster imagery catalog, STAC specification implementation, geospatial data discovery, geospatial, earth-observation, metadata-catalog

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