mlsanityData to 14 Oct 2026

ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data

arXiv 2509.21386 ↗First posted 23 Sep 2025cs.CV cs.RO eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 23 Sep 2025.

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4 of 24 references.

References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  3. 2024Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmarkcited by 13
  4. 2023STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagerycited by 4

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