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Toward a Collective Agenda on AI for Earth Science Data Analysis

Tuia, Devis; Roscher, Ribana; Wegner, Jan Dirk; Jacobs, Nathan; Zhu, Xiaoxiang; Camps-Valls, Gustau (2021). Toward a Collective Agenda on AI for Earth Science Data Analysis. Proceedings of the IEEE, 9(2):88-104.

Abstract

In past years, we have witnessed the fields of geosciences and remote sensing and artificial intelligence (AI) become closer. Thanks to the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to help advance the modeling and understanding of the Earth system. Despite such great opportunities, we have also observed a worrisome tendency to remain in disciplinary comfort zones, applying recent advances from AI on well-resolved remote sensing problems. Here, we take a position on the research directions for which we think the interface between these fields will have the most significant impact and become potential game changers. In our declared agenda for AI in Earth sciences, we aim to inspire researchers, especially the younger generations, to tackle these challenges for a real advance of remote sensing and the geosciences.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute for Computational Science
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:530 Physics
Scopus Subject Areas:Physical Sciences > General Computer Science
Physical Sciences > Instrumentation
Physical Sciences > General Earth and Planetary Sciences
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering, General Earth and Planetary Sciences, Instrumentation, General Computer Science
Language:English
Date:1 June 2021
Deposited On:02 Nov 2021 16:43
Last Modified:26 Oct 2024 01:38
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0018-9219
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/mgrs.2020.3043504
Project Information:
  • Funder: National Science Foundation
  • Grant ID:
  • Project Title:
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