2020
DOI: 10.1016/j.rse.2019.111617
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SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery

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Cited by 32 publications
(39 citation statements)
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“…The roughness of the terrain in the Antarctic pack ice and the available quality of satellite images still makes it difficult to discern between two seals lying next to each other and to distinguish pups from adults. Although making use of citizen scientist votes and comparing it to an expert voter is biased, automated image recognition only detects 30% of seals, even during summer when the ice floes are smaller and flatter, and seals haul out in high numbers close to each other (Gonçalves, Spitzbart, & Lynch, 2020).…”
Section: Caveats Of the Datamentioning
confidence: 99%
“…The roughness of the terrain in the Antarctic pack ice and the available quality of satellite images still makes it difficult to discern between two seals lying next to each other and to distinguish pups from adults. Although making use of citizen scientist votes and comparing it to an expert voter is biased, automated image recognition only detects 30% of seals, even during summer when the ice floes are smaller and flatter, and seals haul out in high numbers close to each other (Gonçalves, Spitzbart, & Lynch, 2020).…”
Section: Caveats Of the Datamentioning
confidence: 99%
“…While remote sensing is a promising tool to better monitor these predators, it does not currently yield as detailed information as traditional visual surveys made from ship or manned aircraft as it is still difficult to reliably detect or distinguish species from remote sensing platforms. Nonetheless, further advances in remote sensing platforms and automated image processing (Gonçalves et al 2020) will make regional and Southern Ocean-wide spatiotemporal monitoring of pack-ice seal population trends possible.…”
Section: Discussionmentioning
confidence: 99%
“…Remote observation might improve this (e.g., Labrousse et al, 2019) and satellite surveillance can enable remote area assessment, at large scale and much reduced cost (LaRue and Knight, 2014). Counting animals from satellite images manually can be time-consuming and researchers are harnessing Deep Learning (e.g., Gonçalves et al, 2020) and citizen science through crowdsourcing, where online volunteers identify animals in the images (LaRue et al, 2019).…”
Section: Population Monitoringmentioning
confidence: 99%