The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
The Australian Geoscience Data Cube — Foundations and lessons learned
Open accessAbstract
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.
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Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. D. S. N., Pozza, L. E., Ugbaje, S. U., Jephcott, T. G., Paterson, S. E., Whelan, B. M., & Bishop, T. F. A. (2019) · Precision Agriculture · doi:10.1007/s11119-018-09628-4 · Cited by 294
UAV & satellite synergies for optical remote sensing applications: A literature review
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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Mohammadi Foumani, N., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3649448 · Cited by 268
Metadata
- Authors
- Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P., & Wang, L.-W.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 202
- Pages
- 276-292
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2605433586
- Cited by
- 351
- References
- 54
- FWCI
- 26.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., . . . Wang, L.-W. (2017). The Australian Geoscience Data Cube — Foundations and lessons learned. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.03.015
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