{"refrec":{"BRefID":396324,"RR":"<b>Quaade, S.; Vallebueno, A.; Alcabes, O.D.N.; Rodolfa, K.T.; Ho, D.E.</b> (2024). Remote sensing and computer vision for marine aquaculture. <i>Science Advances 10(42)</i>: eadn4944. <a href=\"https://dx.doi.org/10.1126/sciadv.adn4944\" target=\"_blank\">https://dx.doi.org/10.1126/sciadv.adn4944</a>","BEntID":394112,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Science Advances 10(42)</i>: eadn4944. <a href=\"https://dx.doi.org/10.1126/sciadv.adn4944\" target=\"_blank\">https://dx.doi.org/10.1126/sciadv.adn4944</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Quaade, S.; Vallebueno, A.; Alcabes, O.D.N.; Rodolfa, K.T.; Ho, D.E.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Quaade, S. <i>et al.</i>","Englishabstract":"Aquaculture, the cultivation of aquatic plants and animals, has grown rapidly since the 1990s, but sparse, self-reported, and aggregated production data limit the effective understanding and monitoring of the industry’s trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000 to 2021 including 4010 cages (average cage area, 69 square meters). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production and develop a flexible framework to quantify uncertainty in these estimates. 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