arrow
Return

The Australian Geoscience Data Cube - Foundations and lessons learned

delete2017-12-01
delete222
delete
OA
AI
A
Adam Lewis *
S
Simon Oliver
L
Leo Lymburner
B
Ben Evans
L
Lesley Wyborn
N
Norman Mueller
G
Gregory Raevksi
J
Jeremy Hooke
R
Rob Woodcock
J
Joshua Sixsmith
W
Wenjun Wu
P
Peter Tan
F
Fuqin Li
B
Brian Killough
S
Stuart Minchin
D
Dale Roberts
D
Damien Ayers
B
B. K. Bala
J
John L. Dwyer
A
Arnold G. Dekker
T
Trevor Dhu
A
Andrew Hicks
A
Alex Ip
P
Purss, Matt
C
Clare Richards
S
Stephen Sagar
C
Claire Trenham
P
Peter Wang
L
Lanwei Wang
DOI:10.1016/j.rse.2017.03.015delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
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. Crown Copyright (C) 2017 Published by Elsevier Inc.
Keywords:
Landsat
Time-series
Big data
Data cube
High performance computing
High performance data
Collection management
Geometric correction
Pixel quality
Australian Geoscience Data Cube
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Remote Sensing of Environment cover
Remote Sensing of Environment
IF:
11.4
Papers:
1.1W
Citations:
9.4W

Organization

A
Australian National University
Scholars:
2.1W
Papers: 2.3W
Citations: 3.9W
N
national aeronautics & space administration (nasa)
Scholars:
3.1W
Papers: 2.6W
Citations: 46
N
nasa langley research center
Scholars:
1.5K
Papers: 1.1K
Citations: 1
G
Geoscience Australia
Scholars:
493
Papers: 401
Citations: 683
researcher View more organizations