The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, massive amount of data about Earth and its environment is now continuously being generated by a large number of Earth observing satellites as well as physics-based earth system models running on large-scale computational platforms. These massive and information-rich datasets offer huge potential for understanding how the Earth's climate and ecosystem have been changing and how they are being impacted by humans actions. We discuss the challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change.
|Original language||English (US)|
|Title of host publication||KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||2|
|State||Published - Aug 13 2017|
|Event||23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada|
Duration: Aug 13 2017 → Aug 17 2017
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Other||23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017|
|Period||8/13/17 → 8/17/17|
Bibliographical noteFunding Information:
This work was supported by NSF grant IIS-1029771 and NASA awards 14-CMAC14-0010 and NNX12AP37G.
© 2017 Copyright held by the owner/author(s).
- Climate science
- Earth observation data
- Machine learning