ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911176441462784 |
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| author | Sheppard, Anja Smithline, Tyler Scheffer, Andrew Smith, David Sethuraman, Advaith V. Bird, Ryan Lin, Sabrina Skinner, Katherine A. |
| author_facet | Sheppard, Anja Smithline, Tyler Scheffer, Andrew Smith, David Sethuraman, Advaith V. Bird, Ryan Lin, Sabrina Skinner, Katherine A. |
| contents | In this paper, we introduce ShipwreckFinder, an open-source QGIS plugin that detects shipwrecks from multibeam sonar data. Shipwrecks are an important historical marker of maritime history, and can be discovered through manual inspection of bathymetric data. However, this is a time-consuming process and often requires expert analysis. Our proposed tool allows users to automatically preprocess bathymetry data, perform deep learning inference, threshold model outputs, and produce either pixel-wise segmentation masks or bounding boxes of predicted shipwrecks. The backbone of this open-source tool is a deep learning model, which is trained on a variety of shipwreck data from the Great Lakes and the coasts of Ireland. Additionally, we employ synthetic data generation in order to increase the size and diversity of our dataset. We demonstrate superior segmentation performance with our open-source tool and training pipeline as compared to a deep learning-based ArcGIS toolkit and a more classical inverse sinkhole detection method. The open-source tool can be found at https://github.com/umfieldrobotics/ShipwreckFinderQGISPlugin. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_21386 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data Sheppard, Anja Smithline, Tyler Scheffer, Andrew Smith, David Sethuraman, Advaith V. Bird, Ryan Lin, Sabrina Skinner, Katherine A. Computer Vision and Pattern Recognition Robotics Image and Video Processing In this paper, we introduce ShipwreckFinder, an open-source QGIS plugin that detects shipwrecks from multibeam sonar data. Shipwrecks are an important historical marker of maritime history, and can be discovered through manual inspection of bathymetric data. However, this is a time-consuming process and often requires expert analysis. Our proposed tool allows users to automatically preprocess bathymetry data, perform deep learning inference, threshold model outputs, and produce either pixel-wise segmentation masks or bounding boxes of predicted shipwrecks. The backbone of this open-source tool is a deep learning model, which is trained on a variety of shipwreck data from the Great Lakes and the coasts of Ireland. Additionally, we employ synthetic data generation in order to increase the size and diversity of our dataset. We demonstrate superior segmentation performance with our open-source tool and training pipeline as compared to a deep learning-based ArcGIS toolkit and a more classical inverse sinkhole detection method. The open-source tool can be found at https://github.com/umfieldrobotics/ShipwreckFinderQGISPlugin. |
| title | ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data |
| topic | Computer Vision and Pattern Recognition Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2509.21386 |