Projects
Detection of Fishing Impacts
Better models to evaluate ocean harvest
Algorithms can help determine when and where vessels are fishing. When we apply them to vessel movement data, we can more accurately assess fishing activity and help decision makers manage global fisheries more equitably and sustainably.
A truer measure of fishing costs
The Food and Agriculture Organization of the United Nations estimates that over a billion people rely on fish as their primary source of protein. To truly understand the impact of fishing at this scale and better direct conservation efforts, we need to measure the presence and intensity of fishing activity around the globe.Â
Data from the automatic identification system (AIS) provides detailed tracks of tens of thousands of fishing vessels. However, this data only reveals fishing vessel presence. For an accurate assessment of impact, we need to more completely characterize fishing activity and differentiate it from non-fishing activity. There are many knowledge gaps to fill, such as impacts by fishing gear type, seabird bycatch during line-setting and ocean floor habitat disturbance by bottom trawling vessels. To better understand the problems and potential solutions, it is important to more closely measure where, when and how much fishing occurs, particularly with high-impact gear types.
Connecting the dots on fishing activityÂ
To differentiate vessel activity, we developed a model using a convolutional neural network—an algorithm for arranging imagery and data—that classifies each AIS position as either fishing or not fishing for an accurate representation of global fishing activity. To address the need for detailed classification of fishing behavior, we also developed a specialized model that estimates where and when a longline vessel is starting and finishing its set, haul and transit activity. This information can be used to inform management measures and help monitor compliance. We are developing similar models for bottom-trawl fishing and pilot projects are in progress using alternative technology solutions to track small-scale fishing vessels that do not broadcast AIS.
Research Lead
Contributing Team
Papers
Datasets
Recent Work
Emerging technology gives first ever global view of hidden vessels
Satellite radar and machine learning publicly reveal previously unseen vessel activity around the world Washington, D.C. – Global Fishing Watch has developed and publicly released the first ever global map of previously undetected dark fleets,
Satellite Technology Can Reveal Collision Risks for Whale Sharks
Global Fishing Watch data helps researchers link shipping traffic to whale shark fatalities The whale shark is the world’s largest fish, with adults weighing up to 5,000 pounds and reaching up to 20 meters in
Satellite Data Casts Light on Seamounts at Risk
Emerging tools and datasets help quantify fishing pressure and can inform management at remote, unmonitored seamounts Seamounts—large underwater mountains— hold vital biological diversity, but they also contend with heavy exploitation. Numbering in the tens of
New Techniques Reveal Fishing Vessel Identities in the Dark of Night
New Global Fishing Watch technology merges nighttime images with GPS datasets to observe vessels not broadcasting their positions When the sun sets, human activity on the ocean goes on. And every night, satellites snap a