Spatio-temporal trajectory data modeling for fishing gear classification

• Published in: Pattern analysis and applications : PAA Vol. 27; no. 2
• Main Authors: Rodriguez-Albala, Juan Manuel, Peña, Alejandro, Melzi, Pietro, Morales, Aythami, Tolosana, Ruben, Fierrez, Julian, Vera-Rodriguez, Ruben, Ortega-Garcia, Javier
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2024; Springer Nature B.V
• Subjects: Ablation; Accuracy; Algorithms; Classification; Computer Science; Feature extraction; Fishing; Fishing equipment; Gears; International organizations; Kinematics; Machine learning; Oceans; Organizations; Original Article; Pattern Recognition; Sampling; Sea vessels; Strategic planning; Supervised learning; Trawling; Climate change; Spatio-temporal trajectory data modeling; Database; Illegal fishing; Fishing gear classification; Biodiversity; AI for social good
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-024-01263-2

International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels’ fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to 90 % . Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of 99 % . We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.