A Biologist's Guide to the Galaxy: Leveraging Artificial Intelligence and Very High-Resolution Satellite Imagery to Monitor Marine Mammals from Space

被引：7

作者：

Khan, Christin B. ^{[1
]}

Goetz, Kimberly T. ^{[2
]}

Cubaynes, Hannah C. ^{[3
]}

Robinson, Caleb ^{[4
]}

Murnane, Erin ^{[5
]}

Aldrich, Tyler ^{[1
]}

Sackett, Meredith ^{[1
]}

Clarke, Penny J. ^{[3
,6
]}

LaRue, Michelle A. ^{[7
,8
]}

White, Timothy ^{[9
]}

Leonard, Kathleen ^{[10
]}

Ortiz, Anthony ^{[4
]}

Ferres, Juan M. Lavista M. ^{[4
]}

机构：

[1] NOAA, Northeast Fisheries Sci Ctr, Natl Marine Fisheries Serv, Woods Hole, MA 02543 USA

[2] NOAA, Marine Mammal Lab, Alaska Fisheries Sci Ctr, Natl Marine Fisheries Serv, Seattle, WA 98115 USA

[3] British Antarctic Survey, Madingley Rd, Cambridge CB3 0ET, England

[4] Microsoft Good Res Lab, 1 Microsoft Way, Redmond, WA 98052 USA

[5] Naval Ctr Space Technol NCST, Naval Res Lab, Washington, DC 20375 USA

[6] Univ Edinburgh, Sch Engn, Sanderson Bldg,Robert Stevenson Rd,Kings Bldg, Edinburgh EH9 3FB, Scotland

[7] Univ Canterbury, Sch Earth & Environm, Christchurch 8140, New Zealand

[8] Univ Minnesota, Dept Earth & Environm Sci, Minneapolis, MN 55455 USA

[9] Bur Ocean Energy Management, Environm Studies Program, Sterling, VA 20166 USA

[10] NOAA, Protected Resources Div, Alaska Reg Off, Natl Marine Fisheries Serv, Anchorage, AK 99513 USA

来源：

JOURNAL OF MARINE SCIENCE AND ENGINEERING | 2023年 / 11卷 / 03期

基金：

英国自然环境研究理事会;

关键词：

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

D O I：

10.3390/jmse11030595

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

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.

引用

页数：30

共 9 条

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