rss_da_stac - Python utilities for interacting with common STAC providers

Remote Sensing Scientists associated with the JRSRP have several established work practices that have been developed over many years, and predate STAC and cloud-native Earth Observation (EO) data.

This package is designed To help integrate their current work approaches with modern data providers via STAC. Of particular interest is the interoperability of the qvf name convention and how that relates to STAC. Several utilities are designed to work with qvf style names, such as

• qvf2item: Convert one or more QVF files to a STAC ItemCollection
• item2qvf: Given a stac item or itemcollection, provide the equivalent qvf compliant name for each item.

see notes for details on the included scripts and the API.

Installation

Use pip or uv, for example, as part of a uv project:

uv add rss-da-stac

Using as a command-line tool

The quickest way to run rss-stac without setting up a project is with uvx:

uvx rss-da-stac qvf2item cfmsre_t56kkv_20221223_adbm6.tif

This downloads and runs the package in a temporary environment — no installation needed.

If you want the rss-stac command permanently available on your system:

uv tool install rss-da-stac
rss-stac qvf2item cfmsre_t56kkv_20221223_adbm6.tif

Citation

If you use this software in your research, please cite it as:

Denham, Robert (2026). rss_da_stac_source_code: Python utilities for interacting with common STAC providers for remote sensing scientists. Version 0.1.5. The University of Queensland. Data Collection. https://doi.org/10.48610/d3a13bb

BibTeX

@misc{denham_2026_rss_da_stac,
  author    = {Denham, Robert},
  title     = {rss\_da\_stac\_source\_code: Python utilities for interacting
               with common STAC providers for remote sensing scientists},
  year      = {2026},
  version   = {0.1.5},
  publisher = {The University of Queensland},
  type      = {Data Collection},
  doi       = {10.48610/d3a13bb},
  url       = {https://doi.org/10.48610/d3a13bb}
}