Fishing Vessels Activity Detection from Longitudinal AIS Data
Saeed Arasteh, Mohammad A. Tayebi, Zahra Zohrevand, Uwe Glässer, Amir Yaghoubi Shahir, Parvaneh Saeedi, Hans Wehn
Abstract
The impact of marine life on the oceans of our planet is undeniable and overfishing is a serious threat to marine ecosystems worldwide. Maritime domain awareness calls for continuous monitoring and tracking of fisheries using data from maritime intelligence sources to detect illegal fishing activities. Marine traffic data from vessel tracking services is a promising source for identifying, locating, and capturing vessel information. Given the volume of such data, manual processing is impossible, raising an immediate need for autonomous and smart systems to follow the footprints of vessels and detect their activity types in near real-time. To achieve this goal, we propose FishNET, a simple yet effective convolutional neural network (CNN) model for vessel trajectory classification. The model is trained using a set of invariant spatiotemporal feature sequences extracted from the behavioral characteristics of vessel movements.