Satellites can reveal global extent of forced labor in the world's fishing fleet

McDonald, Gavin G., Costello, Christopher, Bone, Jennifer, Cabral, Reniel B., Farabee, Valerie, Hochberg, Timothy, Kroodsma, David, Mangin, Tracey, Meng, Kyle C., and Zahn, Oliver (2021) Satellites can reveal global extent of forced labor in the world's fishing fleet. Proceedings of the National Academy of Sciences of the United States of America, 118 (3). e2016238117.

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Abstract

While forced labor in the world's fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Item ID: 71260
Item Type: Article (Research - C1)
ISSN: 1091-6490
Keywords: forced labor in fisheries, machine learning, satellite vessel monitoring data
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Copyright Information: This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND).
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This article contains supporting information online at https://www.pnas.org/lookup/suppl/ doi:10.1073/pnas.2016238117/-/DCSupplemental.

Data Availability. All CSV files necessary to reproduce this analysis are found in the supporting information. All CSV files and R code necessary to reproduce this analysis are available in GitHub (https://github.com/emlab-ucsb/slaveryin-fisheries) and Zenodo (DOI: 10.5281/zenodo.3635980)

Date Deposited: 15 Feb 2022 02:27
FoR Codes: 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300505 Fisheries management @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science @ 50%
SEO Codes: 10 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 1003 Fisheries - wild caught > 100305 Wild caught fin fish (excl. tuna) @ 100%
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