EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Spatio-Temporal Asset Catalog (STAC) for in-situ data

Justus Magin and Tina Odaka
Justus Magin and Tina Odaka
  • IFREMER, UMR-LOPS, Plouzané, France

In order to make use of a collection of datasets – for example, scenes from a SAR satellite – more efficient, it is important to be able to search for datasets relevant for a specific application. In particular, one might want to search for a specific period in time, for the spatial extent, or perform searches over multiple collections together.

For SAR data or data obtained from optical satellites, Spatio-Temporal Asset Catalogs (STAC) have become increasingly popular in the past few years. Defined as JSON and backed by databases with geospatial extensions, STAC servers (endpoints) have the advantage of being efficient, language-agnostic and following a standardized API.

Just like satellite scenes, in-situ data is growing in size very quickly and thus would benefit from being catalogued. However, the sequential nature of in-situ data and its sparse distribution in space makes it difficult to fit into STAC's standard model.

In the session, we present a experimental STAC extension that defines the most common properties of in-situ data as identified from ArgoFloat and  biologging data.

How to cite: Magin, J. and Odaka, T.: Spatio-Temporal Asset Catalog (STAC) for in-situ data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8096,, 2023.