The Australian Geoscience Data Cube — Foundations and lessons learned

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

“…This paradigm shift is currently represented by EO Data Cubes (Baumann, Mazzetti, et al, 2016;Purss et al, 2015), an approach that is receiving increasing attention as a new solution to store, organize, manage, and analyze EO data in a way that was not possible before. Data Cubes (DC) are aiming to realize the full potential of EO data repositories by addressing Volume, Velocity, and Variety challenges, providing access to large spatio-temporal data in an analysis ready form (Baumann, 2017;Lewis et al, 2017). Currently, there are various operational DC like the Australian Geoscience Data Cube (AGDC -http://www.datacube.org.au), the Earth Observation Data Cube (EODC -http://eodatacube.eu), the Earth System Data Cube (ESDC -http://earthsystemdatacube.net), Earth on Amazon Web Services (EAWS -https://aws.amazon.com/earth/), and Google Earth Engine (GEE -https://earthengine.google.com).…”

Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model

“…The DEA is an instance of the Open Data Cube (https://www.opendatacube.org/) and has been developed from an earlier prototype referred to as the Australian Geoscience Data Cube (AGDC) [5,25].…”

“…Pixel-based quality (PQ) metadata (e.g., cloud, cloud shadow, saturation) was calculated for all scenes and stored as bitmasks, as described in [27]. This approach is pixel-based, rather than the traditional scene-based approach, and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement [5]. Following the failure of the ETM+ scan line corrector on Landsat 7 [28], we have opted to exclude post-May 2003 Landsat 7 data from the work we describe in this paper.…”

“…Leveraging an increased ability to automate scientific processing of standardised image products [2,3] coupled with new computing, data storage and analytical capabilities and infrastructures [4,5], the Landsat archive offers a temporal dimension for decades long retrospective analysis and ongoing monitoring capabilities. This capability is further enhanced by the potential to integrate data with spatial and spectral compatibility from sensors, such as Sentinel-2 [6], into these new analytical infrastructures [5].…”

A view-based model of data-cube to support big earth data systems interoperability

“…There are already three valuable examples of existing infrastructures based on such a technological framework: the Australian, Colombian, and Swiss national Data-Cube infrastructures (Ariza-Porras et al, 2017;Giuliani et al, 2017;Lewis et al, 2017). CEOS vision is that more than 20 countries will be implementing their own Data-Cube infrastructure by 2022 (Killough, 2017).…”