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A Semi-Automated, Hybrid GIS-AI Approach to Seabed Boulder Detection Using High Resolution Multibeam Echosounder.

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Title: A Semi-Automated, Hybrid GIS-AI Approach to Seabed Boulder Detection Using High Resolution Multibeam Echosounder.
Authors: Downing, Eoin1 (AUTHOR) eoin.downing@greenrebel.ie, O'Reilly, Luke1 (AUTHOR), Majcher, Jan1 (AUTHOR), O'Mahony, Evan1 (AUTHOR), Peters, Jared1 (AUTHOR)
Source: Remote Sensing. Aug2025, Vol. 17 Issue 15, p2711. 25p.
Subjects: Boulders, Multibeam mapping, Benthic ecology, Offshore wind power plants, Hazards, Random forest algorithms
Geographic Terms: Long Island Sound (N.Y. & Conn.)
Abstract: The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km2 site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km2 study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A Semi-Automated, Hybrid GIS-AI Approach to Seabed Boulder Detection Using High Resolution Multibeam Echosounder.
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  Data: <searchLink fieldCode="DE" term="%22Boulders%22">Boulders</searchLink><br /><searchLink fieldCode="DE" term="%22Multibeam+mapping%22">Multibeam mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Benthic+ecology%22">Benthic ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Offshore+wind+power+plants%22">Offshore wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Hazards%22">Hazards</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Data: The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km2 site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km2 study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17152711
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        Text: English
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      – SubjectFull: Boulders
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      – SubjectFull: Benthic ecology
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      – SubjectFull: Offshore wind power plants
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      – SubjectFull: Hazards
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      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Long Island Sound (N.Y. & Conn.)
        Type: general
    Titles:
      – TitleFull: A Semi-Automated, Hybrid GIS-AI Approach to Seabed Boulder Detection Using High Resolution Multibeam Echosounder.
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            NameFull: Downing, Eoin
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              M: 08
              Text: Aug2025
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              Y: 2025
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