Shipwreck detection in bathymetry data using semi-automated methods: combining machine learning and topographic inference approaches
Shipwreck detection in bathymetry data using semi-automated methods: combining machine learning and topographic inference approaches
This research presents a workflow that integrates emerging machine learning methods with geospatial mapping techniques to improve the identification of shipwrecks in bathymetry data. By first refining the study area into high-potential units, machine learning algorithms can be applied more efficiently. This approach accelerates the process, reduces computational demands, and offers an adaptive method that can eventually be tailored to survey needs and different seabed environments. This paper contributes to the current discourse surrounding the discovery and management of underwater cultural heritage (UCH) in the context of global seabed mapping, developments in autonomous marine survey, and continued offshore development. Shipwrecks constitute a significant proportion of UCH sites that are increasingly likely to be discovered and impacted by these developments, and thus archaeologists need adequate tools for their rapid detection and monitoring to keep pace with the rate of data generation.
The proposed workflow uses a raster extraction method as a filtering process to identify areas of seabed with high shipwreck potential, based on their topographic signature in three different visualisations of bathymetry (slope, curvature, and topographic position index). Using these results, several different machine learning algorithms were tested on their ability to identify both intact, visible shipwrecks (‘conspicuous’ wrecks) as well as smaller, possible wreck sites. These methods were tested over an area of 3,131 km2 from the south coast of England. Results show that the Raster Extraction method was able to filter out 96% of the test data, while still detecting 78% of the test shipwrecks (n=253). Machine learning models trained on different data visualisations (Hillshade, Shaded Relief, Curvature) and algorithms (Single Shot Detector, Faster R-CNN, and Mask R-CNN) had varied performances in terms of recall and precision.
ShipwrecksMachine learningBathymetryRemote sensingMaritime archaeologyGeographic Information Systems (GIS), Shipwrecks, Machine learning, Bathymetry, Geographic Information Systems (GIS), Remote sensing, Maritime archaeology
Pols, Cal Tariq
306fc140-3ac8-4117-9127-f1ab154a776e
Sturt, Fraser
442e14e1-136f-4159-bd8e-b002bf6b95f6
El Safadi, Crystal
262bdcd0-1f88-41b9-915f-819dec8143dd
Marcu, Antonia
e970d626-8d70-4af0-aee3-40a0635a7ad1
5 July 2025
Pols, Cal Tariq
306fc140-3ac8-4117-9127-f1ab154a776e
Sturt, Fraser
442e14e1-136f-4159-bd8e-b002bf6b95f6
El Safadi, Crystal
262bdcd0-1f88-41b9-915f-819dec8143dd
Marcu, Antonia
e970d626-8d70-4af0-aee3-40a0635a7ad1
Pols, Cal Tariq, Sturt, Fraser, El Safadi, Crystal and Marcu, Antonia
(2025)
Shipwreck detection in bathymetry data using semi-automated methods: combining machine learning and topographic inference approaches.
Journal of Archaeological Science, 181, [106297].
(doi:10.1016/j.jas.2025.106297).
Abstract
This research presents a workflow that integrates emerging machine learning methods with geospatial mapping techniques to improve the identification of shipwrecks in bathymetry data. By first refining the study area into high-potential units, machine learning algorithms can be applied more efficiently. This approach accelerates the process, reduces computational demands, and offers an adaptive method that can eventually be tailored to survey needs and different seabed environments. This paper contributes to the current discourse surrounding the discovery and management of underwater cultural heritage (UCH) in the context of global seabed mapping, developments in autonomous marine survey, and continued offshore development. Shipwrecks constitute a significant proportion of UCH sites that are increasingly likely to be discovered and impacted by these developments, and thus archaeologists need adequate tools for their rapid detection and monitoring to keep pace with the rate of data generation.
The proposed workflow uses a raster extraction method as a filtering process to identify areas of seabed with high shipwreck potential, based on their topographic signature in three different visualisations of bathymetry (slope, curvature, and topographic position index). Using these results, several different machine learning algorithms were tested on their ability to identify both intact, visible shipwrecks (‘conspicuous’ wrecks) as well as smaller, possible wreck sites. These methods were tested over an area of 3,131 km2 from the south coast of England. Results show that the Raster Extraction method was able to filter out 96% of the test data, while still detecting 78% of the test shipwrecks (n=253). Machine learning models trained on different data visualisations (Hillshade, Shaded Relief, Curvature) and algorithms (Single Shot Detector, Faster R-CNN, and Mask R-CNN) had varied performances in terms of recall and precision.
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1-s2.0-S0305440325001463-main
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Pols et al. 2025 shipwreck deteciton
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More information
Accepted/In Press date: 13 June 2025
e-pub ahead of print date: 5 July 2025
Published date: 5 July 2025
Keywords:
ShipwrecksMachine learningBathymetryRemote sensingMaritime archaeologyGeographic Information Systems (GIS), Shipwrecks, Machine learning, Bathymetry, Geographic Information Systems (GIS), Remote sensing, Maritime archaeology
Identifiers
Local EPrints ID: 504110
URI: http://eprints.soton.ac.uk/id/eprint/504110
ISSN: 0305-4403
PURE UUID: 9399c16e-437a-4bce-8400-7eb65c221089
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Date deposited: 26 Aug 2025 16:42
Last modified: 20 Sep 2025 02:18
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Author:
Cal Tariq Pols
Author:
Antonia Marcu
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