A biologist’s guide to the galaxy: Leveraging artificial intelligence and very high-resolution satellite imagery to monitor marine mammals from space

Khan, Christin B.; Goetz, Kimberly T.; Cubaynes, Hannah C. ORCID: https://orcid.org/0000-0002-9497-154X; Robinson, Caleb; Murnane, Erin; Aldrich, Tyler; Sackett, Meredith; Clarke, Penny J. ORCID: https://orcid.org/0000-0002-2648-9639; LaRue, Michelle A.; White, Timothy; Leonard, Kathleen; Ortiz, Anthony; Lavista Ferres, Juan M.. 2023 A biologist’s guide to the galaxy: Leveraging artificial intelligence and very high-resolution satellite imagery to monitor marine mammals from space [in special issue: Advanced Research Techniques for Cetacean Conservation] Journal of Marine Science and Engineering, 11 (3), 595. 30, pp. 10.3390/jmse11030595

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Official URL: https://www.mdpi.com/2077-1312/11/3/595

Abstract/Summary

Monitoring marine mammals is of broad interest to governments and individuals around the globe. Very high-resolution (VHR) satellites hold the promise of reaching remote and challenging locations to fill gaps in our knowledge of marine mammal distribution. The time has come to create an operational platform that leverages the increased resolution of satellite imagery, proof-of-concept research, advances in cloud computing, and machine learning to monitor the world’s oceans. The Geospatial Artificial Intelligence for Animals (GAIA) initiative was formed to address this challenge with collaborative innovation from government agencies, academia, and the private sector. In this paper, we share lessons learned, challenges faced, and our vision for how VHR satellite imagery can enhance our understanding of cetacean distribution in the future.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/jmse11030595
Additional Keywords:	: very high-resolution satellite imagery; artificial intelligence; machine learning; remote sensing; marine mammal; cetacean; annotation; collaborative innovation; open-source; Geospatial Artificial Intelligence for Animals
Date made live:	13 Mar 2023 16:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534212

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.3390/jmse11030595

Additional Keywords:

: very high-resolution satellite imagery; artificial intelligence; machine learning; remote sensing; marine mammal; cetacean; annotation; collaborative innovation; open-source; Geospatial Artificial Intelligence for Animals

Date made live:

13 Mar 2023 16:46 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/534212