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Identification of black reef shipwreck sites using AI and satellite multispectral imagery

Identification of black reef shipwreck sites using AI and satellite multispectral imagery
Identification of black reef shipwreck sites using AI and satellite multispectral imagery

UNESCO estimates that our planet’s oceans and lakes are home to more than three million shipwrecks. Of these three million, the locations of only 10% are currently known. Apart from the historical and archaeological interest in finding wrecks, there are other reasons why we need to know their precise locations. While a shipwreck can provide an excellent habitat for marine life, acting as an artificial reef, shipwrecks are also potential sources of pollution, leaking fuel and corroding heavy metals. When a vessel runs aground on an iron-free environment, changes in the chemistry of the surrounding environment can occur, creating a discoloration called black reef. In this work, we examine the use of supervised deep learning methods for the detection of shipwrecks on coral reefs through the presence of this discoloration using satellite images. One of the main challenges is the limited number of known locations of black reefs, and therefore, the limited training dataset. Our results show that even with relatively limited data, the simple eight-layer, fully convolutional network has been trained efficiently using minimal computational resources and has identified and classified all investigated black reefs and consequently the presence of shipwrecks. Furthermore, it has proven to be a useful tool for monitoring the extent of discoloration and consequently the ecological impact on the reef by using time series imagery.

artificial intelligence, black reefs, coral reefs, environment, remote sensing, shipwreck
2072-4292
Karamitrou, Alexandra
25acd266-3030-4958-b5c5-72d4c6b74caf
Sturt, Fraser
442e14e1-136f-4159-bd8e-b002bf6b95f6
Bogiatzis, Petros
8fc5767f-51a2-4d3f-aab9-1ee9cfa9272d
Karamitrou, Alexandra
25acd266-3030-4958-b5c5-72d4c6b74caf
Sturt, Fraser
442e14e1-136f-4159-bd8e-b002bf6b95f6
Bogiatzis, Petros
8fc5767f-51a2-4d3f-aab9-1ee9cfa9272d

Karamitrou, Alexandra, Sturt, Fraser and Bogiatzis, Petros (2023) Identification of black reef shipwreck sites using AI and satellite multispectral imagery. Remote Sensing, 15 (8), [2030]. (doi:10.3390/rs15082030).

Record type: Article

Abstract

UNESCO estimates that our planet’s oceans and lakes are home to more than three million shipwrecks. Of these three million, the locations of only 10% are currently known. Apart from the historical and archaeological interest in finding wrecks, there are other reasons why we need to know their precise locations. While a shipwreck can provide an excellent habitat for marine life, acting as an artificial reef, shipwrecks are also potential sources of pollution, leaking fuel and corroding heavy metals. When a vessel runs aground on an iron-free environment, changes in the chemistry of the surrounding environment can occur, creating a discoloration called black reef. In this work, we examine the use of supervised deep learning methods for the detection of shipwrecks on coral reefs through the presence of this discoloration using satellite images. One of the main challenges is the limited number of known locations of black reefs, and therefore, the limited training dataset. Our results show that even with relatively limited data, the simple eight-layer, fully convolutional network has been trained efficiently using minimal computational resources and has identified and classified all investigated black reefs and consequently the presence of shipwrecks. Furthermore, it has proven to be a useful tool for monitoring the extent of discoloration and consequently the ecological impact on the reef by using time series imagery.

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Accepted/In Press date: 2 April 2023
Published date: 11 April 2023
Additional Information: Funding Information: This work was supported by the National Environment Research Council and the Daphne Jackson Trust. Publisher Copyright: © 2023 by the authors.
Keywords: artificial intelligence, black reefs, coral reefs, environment, remote sensing, shipwreck

Identifiers

Local EPrints ID: 477377
URI: http://eprints.soton.ac.uk/id/eprint/477377
ISSN: 2072-4292
PURE UUID: aae507b7-5f89-4f84-b0d7-57a2b1e285da
ORCID for Alexandra Karamitrou: ORCID iD orcid.org/0000-0002-4142-1958
ORCID for Fraser Sturt: ORCID iD orcid.org/0000-0002-3010-990X
ORCID for Petros Bogiatzis: ORCID iD orcid.org/0000-0003-1902-7476

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Date deposited: 05 Jun 2023 16:49
Last modified: 13 Jun 2024 01:58

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Contributors

Author: Alexandra Karamitrou ORCID iD
Author: Fraser Sturt ORCID iD
Author: Petros Bogiatzis ORCID iD

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