STACD: STAC Extension with DAGs for Geospatial Data and Algorithm Management

Geospatial datasets have complex lineages that are crucial for reproducibility and understanding data provenance, yet current metadata standards like STAC (SpatioTemporal Asset Catalog) provide limited support for capturing complete processing workflows. We propose STACD (STAC extension with DAGs), an extension to STAC specifications that incorporates Directed Acyclic Graph (DAG) representations along with defining algorithms and version changes in the workflows. We also provide a reference implementation on Apache Airflow to demonstrate STACD capabilities such as selective recomputation when some datasets or algorithms in a DAG are updated, complete lineage construction for a dataset, and opportunities for improved collaboration and distributed processing that arise with this standard.

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Publication Details

Published
2025-10-09
DOI
https://doi.org/10.1145/3759536.3763803
Citations
8
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
33.35
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STACD: STAC Extension with DAGs for Geospatial Data and Algorithm Management

Saurabh Y. Joshi, Abhilash Jindal, Tarun Mangla, Aaditeshwar Seth et al.
8 citations
Scientific Computing and Data Management
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article

STACD: STAC Extension with DAGs for Geospatial Data and Algorithm Management

Saurabh Y. Joshi, Abhilash Jindal, Tarun Mangla, Aaditeshwar Seth, Saharsh Laud
article en
8 citations

Abstract

Geospatial datasets have complex lineages that are crucial for reproducibility and understanding data provenance, yet current metadata standards like STAC (SpatioTemporal Asset Catalog) provide limited support for capturing complete processing workflows. We propose STACD (STAC extension with DAGs), an extension to STAC specifications that incorporates Directed Acyclic Graph (DAG) representations along with defining algorithms and version changes in the workflows. We also provide a reference implementation on Apache Airflow to demonstrate STACD capabilities such as selective recomputation when some datasets or algorithms in a DAG are updated, complete lineage construction for a dataset, and opportunities for improved collaboration and distributed processing that arise with this standard.

Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 0%
Scientific Computing and Data Management
33.35
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