From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

Abstract

In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

Dynamic Geospatial Data Integration: A Case Study of Moving Objects in Munakata City, Japan Using OGC API Moving Features and Sensorthings API

“…It is designed to efficiently process complex queries on moving object trajectories, providing significant functionalities for OGC Moving Features. In the past, it has been used in various fields including transportation, urban planning, environmental monitoring, and location-based services (Sakr et al, 2023, Brandoli et al, 2022, Godfrid et al, 2022.…”

The Big Picture: An Improved Method for Mapping Shipping Activities

“…The majority of these works assume that at least in theory, the trajectory of a ship can be approximated as a straight line for a short time interval. Therefore, linear interpolation is the most widely used gap-filling method [32][33][34][35]. According to this method, the positions of the ship between two points are calculated by interpolating their coordinates (latitude and longitude) according to the desired timestamp.…”

Semantic Segmentation of AIS Trajectories for Detecting Complete Fishing Activities

“…There are mainly two lines of research closely related to our work. The first line of research focuses on trajectory points and try to assign each trajectory point a label of fishing or nonfishing [9], [10]. The second line of research [6], [7], [2], [3], [11], [4], [5] deals with the trajectory segmentation problem and aims to partition trajectories into a series of segments according to some spatiotemporal criteria or a cost function.…”

“…In the first line of research, speed has been used as the main criterion to recognize fishing activities. Such methods detect fishing activities by using fixed speed ranges [10], or by modeling the speed range as a bi-modal distribution [9]. However this assumption mainly works for trawlers and does not apply to other gear types.…”