TY - JOUR
T1 - The Australian Geoscience Data Cube — Foundations and lessons learned
AU - Lewis, Adam
AU - Oliver, Simon
AU - Lymburner, Leo
AU - Evans, Ben
AU - Wyborn, Lesley
AU - Mueller, Norman
AU - Raevksi, Gregory
AU - Hooke, Jeremy
AU - Woodcock, Rob
AU - Sixsmith, Joshua
AU - Wu, Wenjun
AU - Tan, Peter
AU - Li, Fuqin
AU - Killough, Brian
AU - Minchin, Stuart
AU - Roberts, Dale
AU - Ayers, Damien
AU - Bala, Biswajit
AU - Dwyer, John
AU - Dekker, Arnold
AU - Dhu, Trevor
AU - Hicks, Andrew
AU - Ip, Alex
AU - Purss, Matt
AU - Richards, Clare
AU - Sagar, Stephen
AU - Trenham, Claire
AU - Wang, Peter
AU - Wang, Lan Wei
N1 - Publisher Copyright:
© 2017
PY - 2017/12/1
Y1 - 2017/12/1
N2 - The Australian Geoscience Data Cube (AGDC) aims to realise the full potential of Earth observation data holdings by addressing the Big Data challenges of volume, velocity, and variety that otherwise limit the usefulness of Earth observation data. There have been several iterations and AGDC version 2 is a major advance on previous work. The foundations and core components of the AGDC are: (1) data preparation, including geometric and radiometric corrections to Earth observation data to produce standardised surface reflectance measurements that support time-series analysis, and collection management systems which track the provenance of each Data Cube product and formalise re-processing decisions; (2) the software environment used to manage and interact with the data; and (3) the supporting high performance computing environment provided by the Australian National Computational Infrastructure (NCI). A growing number of examples demonstrate that our data cube approach allows analysts to extract rich new information from Earth observation time series, including through new methods that draw on the full spatial and temporal coverage of the Earth observation archives. To enable easy-uptake of the AGDC, and to facilitate future cooperative development, our code is developed under an open-source, Apache License, Version 2.0. This open-source approach is enabling other organisations, including the Committee on Earth Observing Satellites (CEOS), to explore the use of similar data cubes in developing countries.
AB - The Australian Geoscience Data Cube (AGDC) aims to realise the full potential of Earth observation data holdings by addressing the Big Data challenges of volume, velocity, and variety that otherwise limit the usefulness of Earth observation data. There have been several iterations and AGDC version 2 is a major advance on previous work. The foundations and core components of the AGDC are: (1) data preparation, including geometric and radiometric corrections to Earth observation data to produce standardised surface reflectance measurements that support time-series analysis, and collection management systems which track the provenance of each Data Cube product and formalise re-processing decisions; (2) the software environment used to manage and interact with the data; and (3) the supporting high performance computing environment provided by the Australian National Computational Infrastructure (NCI). A growing number of examples demonstrate that our data cube approach allows analysts to extract rich new information from Earth observation time series, including through new methods that draw on the full spatial and temporal coverage of the Earth observation archives. To enable easy-uptake of the AGDC, and to facilitate future cooperative development, our code is developed under an open-source, Apache License, Version 2.0. This open-source approach is enabling other organisations, including the Committee on Earth Observing Satellites (CEOS), to explore the use of similar data cubes in developing countries.
KW - Australian Geoscience Data Cube
KW - Big data
KW - Collection management
KW - Data cube
KW - Geometric correction
KW - High performance computing
KW - High performance data
KW - Landsat
KW - Pixel quality
KW - Time-series
UR - http://www.scopus.com/inward/record.url?scp=85017413041&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2017.03.015
DO - 10.1016/j.rse.2017.03.015
M3 - Article
SN - 0034-4257
VL - 202
SP - 276
EP - 292
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -