Machine Learning Model Specification for Cataloging Spatio-Temporal Models (Demo Paper)
The Machine Learning Model (MLM) extension is a specification that extends the SpatioTemporal Asset Catalogs (STAC) framework to catalog machine learning models. This demo paper introduces the goals of the MLM, highlighting its role in improving searchability and reproducibility of geospatial models. The MLM is contextualized within the STAC ecosystem, demonstrating its compatibility and the advantages it brings to discovering relevant geospatial models and describing their inference requirements.
Authors
- Simone Vaccari (ORCID: https://orcid.org/0000-0002-2757-4165)
- Devis Peressutti (ORCID: https://orcid.org/0000-0002-4660-0576)
- Jia Yu (ORCID: https://orcid.org/0000-0003-1340-6475)
- Joses Omojola (ORCID: https://orcid.org/0000-0001-5807-2953)
- Brian Pondi (ORCID: https://orcid.org/0009-0008-0367-1690)
- Francis Charette-Migneault (ORCID: https://orcid.org/0000-0003-4862-3349)
- Ryan Avery (ORCID: https://orcid.org/0000-0001-7392-1474)
- Jed Sundwall (ORCID: https://orcid.org/0000-0001-9681-230X)
- Parham Membari
Institutions
- University of Arizona (US)
- University of Münster (DE)
- Terradue (Italy) (IT)
- Computer Research Institute of Montréal (CA)
- Planet Biotechnology (United States) (US)
- Presidio Ospedaliero (IT)
- Radiant Research (United States) (US)
- Radiant Earth
Publication Details
- Published
- 2024-10-22
- DOI
- https://doi.org/10.1145/3681769.3698586
- Citations
- 1
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.35