In 2012 verloren we Jean Jacques Peters, voormalig ingenieur van het Waterbouwkundig Laboratorium (1964 tot 1979) en internationaal expert in sedimenttransport, rivierhydraulica en -morfologie. Als eerbetoon aan hem hebben we potamology (http://www.potamology.com/) gecreëerd, een virtueel gedenkarchief dat als doel heeft om zijn manier van denken en morfologische aanpak van rivierproblemen in de wereld in stand te houden en te verspreiden.
Het merendeel van z’n werk hebben we toegankelijk gemaakt via onderstaande zoekinterface.
Semantic segmentation of AIS trajectories for detecting complete fishing activities
Wu, S.; Zimányi, E.; Sakr, M.; Torp, K. (2022). Semantic segmentation of AIS trajectories for detecting complete fishing activities, in: 2022 23rd IEEE International Conference on Mobile Data Management (MDM): Proceedings. pp. 419-424. https://dx.doi.org/10.1109/MDM55031.2022.00092
In: (2022). 2022 23rd IEEE International Conference on Mobile Data Management (MDM): Proceedings. IEEE: [s.l.]. ISBN 978-1-6654-5177-2; e-ISBN 978-1-6654-5176-5. xxxix, 529 pp. https://dx.doi.org/10.1109/MDM55031.2022
Detection of fishing activities in trajectory data is important for authorities to develop fishery management policies and combat illegal, unreported, and unregulated (IUU) fishing at sea. However, the complex movement patterns of fishing activities challenge existing trajectory segmentation approaches, which may not identify complete fishing activities. In light of this, we propose a window-based trajectory segmentation algorithm which aims to detect fishing activities as completely as possible. Firstly, we introduce a visualization-based technique TPoSTE to help design features characterizing different movement patterns. Secondly, a window-based segmentation algorithm WBS-RLE is proposed to split a trajectory into fishing and non-fishing segments. WBS-RLE first utilizes a pre-trained classifier to label windows in a trajectory as fishing or non-fishing, then it uses the run-length encoding technique to merge those labeled windows into complete fishing activities. The effectiveness of our approach and its advantages over existing approaches are evaluated on a real-world trajectory dataset.
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