{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"Converts STAC (SpatialTemporal Asset Catalog) metadata into xarray Datasets, with optional Dask support for distributed data loading and computation.","skillfed_tags":["geospatial","stac","dask-distributed"],"use_cases":["Load satellite imagery from a STAC catalog into xarray for time-series analysis or change detection.","Query a STAC source, retrieve matching scenes, and parallelize their loading across a Dask cluster.","Convert STAC metadata into a standardized xarray format for ingestion into geospatial processing pipelines.","Visualize multispectral raster data (e.g., red, green, blue bands) retrieved from STAC items using xarray's plotting methods.","Build reproducible workflows that fetch and process geospatial data from STAC catalogs without manual download steps."],"what_it_does":"odc-stac bridges STAC metadata catalogs and xarray, the standard data model for multidimensional geospatial arrays in Python. It takes STAC items (which describe collections of raster imagery and other spatial data) and loads them into xarray Datasets, making them ready for analysis, visualization, and computation. The package integrates with Dask to parallelize data loading and processing across multiple machines or cores, which is essential for working with large satellite or climate datasets that don't fit in memory.\n\nThe library is built on a foundation of mature geospatial tools: rasterio for raster I/O, affine for coordinate transformations, odc-geo for geospatial utilities, and xarray for the data model itself. It's actively maintained and supports modern Python versions (3.10\u20133.13). The package is designed for researchers, data engineers, and analysts working with cloud-hosted or distributed geospatial datasets.","worth_installing":"Yes, if you work with STAC catalogs or cloud-hosted geospatial data. The package is actively maintained, has low install friction, and fills a clear gap between STAC metadata and xarray workflows. However, verify the license terms before use in proprietary projects, and confirm your workflow's actual dependency requirements."},"id":"odc-stac","links":{"html":"https://skillfed.io/packages/odc-stac","md":"https://skillfed.io/packages/odc-stac.md","pypi":"https://pypi.org/project/odc-stac/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-30","license_spdx":null,"license_treatment":"unclear","name":"odc-stac","python_support":"supports_current","summary":"Tooling for converting STAC metadata to ODC data model"},"popularity":{"monthly_downloads":114501,"position":12293,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.3"}