Bren School of Environmental Science & Management, University of California, Santa Barbara, CA 93106;
Marine Science Institute, University of California, Santa Barbara, CA 93106.
Proc Natl Acad Sci U S A. 2021 Jan 19;118(3). doi: 10.1073/pnas.2016238117. Epub 2020 Dec 21.
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.
虽然世界渔业船队中的强迫劳动已被广泛记录，但具体规模仍不得而知。以前没有任何方法可以远程识别在全球范围内可能从事这些滥用行为的个别渔船。通过结合人权从业者的专业知识和卫星船只监测数据，我们表明，被报告使用强迫劳动的船只的行为与其他船只存在系统差异。我们利用这一见解，通过使用机器学习从 16000 艘工业延绳钓渔船、鱿鱼钓渔船和拖网渔船中识别高风险船只。我们的模型显示，有 14%至 26%的船只属于高风险船只，还揭示了这些船只的捕鱼地点和访问的港口。大约有 57000 至 100000 人在这些船上工作，其中许多人可能是强迫劳动的受害者。这些信息为打击这一人道主义悲剧的新干预措施提供了前所未有的机会。更广泛地说，这项研究证明了使用遥感技术检测强迫劳动滥用的概念验证。
