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Open AccessArticle

Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

1
Australian Institute of Marine Science, 4810 Cape Cleveland, QLD, Australia
2
Global Change Institute, The University of Queensland, 4072 St Lucia, QLD, Australia
3
School of Biological Sciences and ARC CoE for Coral Reef Studies, The University of Queensland, 4072 St Lucia. QLD, Australia
4
Berkeley Artificial Intelligence Research, University of California, Berkeley, CA 94720, USA
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Instituto de Ecologia y Zoologia Tropical, Universidad Central de Venezuela, Caracas, Miranda 1051 Venezuela
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Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA
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ARC Centre of Mathematical and Statistical Frontiers, Queensland University of Technology, and School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, 4000 Brisbane, QLD, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 489; https://doi.org/10.3390/rs12030489
Received: 28 December 2019 / Revised: 24 January 2020 / Accepted: 1 February 2020 / Published: 4 February 2020
Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas. View Full-Text
Keywords: coral reefs; monitoring; artificial intelligence; automated image analysis coral reefs; monitoring; artificial intelligence; automated image analysis
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MDPI and ACS Style

González-Rivero, M.; Beijbom, O.; Rodriguez-Ramirez, A.; Bryant, D.E.P.; Ganase, A.; Gonzalez-Marrero, Y.; Herrera-Reveles, A.; Kennedy, E.V.; Kim, C.J.S.; Lopez-Marcano, S.; Markey, K.; Neal, B.P.; Osborne, K.; Reyes-Nivia, C.; Sampayo, E.M.; Stolberg, K.; Taylor, A.; Vercelloni, J.; Wyatt, M.; Hoegh-Guldberg, O. Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach. Remote Sens. 2020, 12, 489.

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