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Transfer learning for distance classification of marine vessels using underwater sound
Decrop, W.; Deneudt, D.; Parcerisas, C.; Schall, E.; Debusschere, E. (2025). Transfer learning for distance classification of marine vessels using underwater sound. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 18: 19710-19726. https://dx.doi.org/10.1109/jstars.2025.3593779
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE: Piscataway. ISSN 1939-1404; e-ISSN 2151-1535
Peer reviewed article  
Available in  Authors 
    Vlaams Instituut voor de Zee: Open access 416272 [ download pdf ]
Keyword
    Marine/Coastal
Project Top | Authors 
  • Imaging data and services for aquatic science
Authors  Top 
  • Decrop, W.
  • Deneudt, D.
  • Parcerisas, C.
  • Schall, E.
  • Debusschere, E.
Abstract
    Marine environments are increasingly affected by human activities, which generate underwater noise as a by-product. Acoustic data from these environments can offer valuable insights for tracking human activity and improving the monitoring of sensitive areas, such as marine protected areas (MPAs) and offshore wind farms. This study presents a convolutional neural network (CNN) trained to classify vessel distances from passive acoustic recordings. We constructed an open-source, diverse dataset by integrating 116 days of acoustic data from two stations in the Belgian part of the North Sea with automatic identification system data. The CNN was trained to classify acoustic clips into discrete distance bins, representing the proximity of the nearest vessel. Our results demonstrate that the model can effectively distinguish between distance categories using underwater sound alone, confirming the feasibility of passive acoustic monitoring for vessel activity. This technology provides an innovative approach to enhance MPA oversight and represents a first step in a promising pathway for conservation efforts.
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