Fishing Vessels Activity Detection from Longitudinal AIS Data

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. While existing approaches present point-based classification models, in this paper we not only discuss that a segment-based classification model has more realistic real-world applications but also show, by using expert-labelled data, that FishNET outperforms state-of-the-art fishing activity detection models. Our method does not require information about the fishing vessels type or type of fishing gear which is deployed. To show applications in taking action against illegal fishing, we apply the trained model on large real-world but unlabelled fishing vessel data from the U.S. and Denmark gathered over a period of four years. In this analysis, we show how FishNET can contribute to managing fisheries by learning more about spatiotemporal fishing effort distribution, and to law enforcement agencies by detecting unreported and underreported fishing effort of individual vessels.