Abstract--In this paper, we introduce ShipwreckFinder, an open-source QGIS plugin that detects shipwrecks from multi-beam 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.

Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on data from more structured environments. However, their transfer to the seabed domain is not immediate and efforts to port them are hindered by the lack of bathymetric datasets. Thus, in this paper we propose a neural network architecture aimed to showcase the potential of adapting such techniques to correspondence matching in bathymetric data. We train our framework on real bathymetry from an AUV mission and evaluate its performance on the tasks of loop closure detection and coarse point cloud alignment. Finally, we show its potential against a more traditional method and release both its implementation and the dataset used.

In this notebook, we will use bathymetry data provided by NOAA to detect shipwrecks from the Shell Bank Basin area located near New York City in United States. A Bathymetric Attributed Grid (BAG) is a two-band imagery where one of the bands is elevation and the other is uncertainty (define uncertainty of elevation value). We have applied deep learning methods after pre-processing the data (which is explained in Preprocess bathymetric data) for the detection. One important step in pre-processing is applying shaded relief function provided in ArcGIS which is also used by NOAA in one of their BAG visualizations here. Shaded Relief is a 3D representation of the terrain which differentiate the shipwrecks distinctly from the background and reveals them.