Scientific collaboration to support a sustainable ocean

Advances in machine learning and satellite technologies can transform human management of the ocean, but taking advantage of these technologies requires effective partnerships and an interdisciplinary team.

At Global Fishing Watch, we connect machine learning experts with leaders in the scientific community to produce new datasets, publish impactful research and empower others to use our data. 

Our collaborations contribute to discoveries and solutions critical to marine conservation, global economics and human welfare. These partnerships help us better understand the role commercial fishing plays in the physical, biological, economic and political factors of the ocean and ensures that we are addressing the most urgent ocean challenges.

Results generated from these collaborations can be found on our publications page.


 In this video, research partners Kristina Boerder of Dalhousie University and Quentin Hanich of the University of Wollongong, speak to the value of Global Fishing Watch data to support research into pressing questions in fisheries research.

R package to support data analysis

gfwr logo

Global Fishing Watch enables the research community to access data from its application programming interfaces, or APIs, directly through R—a specialized programming language for data processing, statistical analysis and data visualizations. The package, called gfwr, allows R users to request data from Global Fishing Watch’s APIs and receive data in a tidy format suitable for incorporation into new or existing R workflows. Installing gfwr gives academics, researchers, students and journalists the ability to easily pull data for analysis without any prior API experience.

See how we empower others to use our data here.

Global Fishing Activity 2016
In 2018, we published the first high-resolution map of global fishing activity. Our findings revealed that commercial fishing takes place across more than 55 percent of the ocean—a footprint, by area, greater than four times that of agriculture.
Combining four satellite technologies—automatic identification system, optical imagery, nighttime optical imagery and radar—with machine learning, we identified the largest reported case of illegal fishing.
By combining satellite data, machine learning and on-the-ground expertise from human rights practitioners, we identified vessels with a high risk of engaging in human rights abuses.
Research Partners
Recent Work
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