Extracting Mediterranean Hidden Fishing Hotspots Through Big Data Mining

Created March 19, 2026

Updated on May 14, 2026

approved

Monitoring fishing activity is crucial for fisheries management and governments to ensure regulatory compliance and sustainable marine ecosystems. Analysing vessel movements provides insights into fishing dynamics, aiding decision-making. Additionally, measuring unmonitored fishing activity (hidden fishing) helps counteract the underestimation of fishing pressure. Big data analysis can reveal fishing patterns and hidden activities from vessel position and speed data, such as those transmitted by fleets carrying Automatic Identification Systems (AIS). We used an Open Science-compliant (reproducible, repeatable, and reusable) cloud computing-based big data analysis to estimate the manifest, total, and hidden fishing distributions of AIS-carrying vessels in the Mediterranean Sea from 2017 to 2022, processing about 1.6 billion vessel speed and position data. We estimated the principal hotspots of hidden fishing over the years and the potentially involved stocks from these data. We also assessed whether the hotspots corresponded to illegal fishing or AIS communication issues and concluded that most hotspots potentially corresponded to illegal fishing. Our manifest fishing distribution agreed with another produced through machine learning by the Global Fishing Watch. We developed a fast and reusable approach that can produce new information to help management authorities understand the extent of hidden fishing.

itineris-hub
Tags:
Automatic Information System
Big Data
Cloud Computing
Data Mining
Fisheries
Publication - Article
Basic
Language
English
MainTitle
Extracting Mediterranean Hidden Fishing Hotspots Through Big Data Mining
Original ids
10.1109/ACCESS.2024.3416389
Type
publication
bestAccessRight
OPEN
contributors
  • Institute of Information Science and Technologies ‘‘A. Faedo’’ (ISTI), National Research Council of Italy (CNR), 56124 Pisa, Italy;
  • Institute of Marine Environmental Research (ISMAR), National Research Council of Italy (CNR), 19032 Lerici, Italy;
  • Department of Computer Engineering, University of Pisa, 56124 Pisa, Italy;
  • Department of Computer Engineering, University of Pisa, 56124 Pisa, Italy
countries
Italy
Creator/Author
Full name
  • Coro G., orcid: 0000-0001-7232-191x;
  • Pavirani L.;
  • Ellenbroek A.
Other
Description
Monitoring fishing activity is crucial for fisheries management and governments to ensure regulatory compliance and sustainable marine ecosystems. Analysing vessel movements provides insights into fishing dynamics, aiding decision-making. Additionally, measuring unmonitored fishing activity (hidden fishing) helps counteract the underestimation of fishing pressure. Big data analysis can reveal fishing patterns and hidden activities from vessel position and speed data, such as those transmitted by fleets carrying Automatic Identification Systems (AIS). We used an Open Science-compliant (reproducible, repeatable, and reusable) cloud computing-based big data analysis to estimate the manifest, total, and hidden fishing distributions of AIS-carrying vessels in the Mediterranean Sea from 2017 to 2022, processing about 1.6 billion vessel speed and position data. We estimated the principal hotspots of hidden fishing over the years and the potentially involved stocks from these data. We also assessed whether the hotspots corresponded to illegal fishing or AIS communication issues and concluded that most hotspots potentially corresponded to illegal fishing. Our manifest fishing distribution agreed with another produced through machine learning by the Global Fishing Watch. We developed a fast and reusable approach that can produce new information to help management authorities understand the extent of hidden fishing.
Publication Date
2024-01-01
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subjects
  • Big Data;
  • 0106 biological sciences;
  • Monitoring;
  • Cloud Computing;
  • Fisheries;
  • Biological system modeling;
  • Data models;
  • Automatic information system;
  • Data mining;
  • 01 natural sciences;
isGreen
false
isInDiamondJournal
false
Software
Publication
Ending page
85483
Name
IEEE Access
Publication
Article
Starting page
85465
issnOnline
2169-3536
vol
12
Other Research Product
Detailed informations
system:type
Research Product
Management Info
Author
Catalogue Manager ITINERIS
Version
1
Last Updated
May 14, 2026, 08:31 (UTC)
Created
March 19, 2026, 00:32 (UTC)
Data and Resources

This item has no data

To access the resources you must log in