Use of semantic segmentation for mapping Sargassum on beaches

被引:11
作者
Arellano-Verdejo, Javier [1 ]
Santos-Romero, Martin [2 ]
Lazcano-Hernandez, Hugo E. [3 ]
机构
[1] El Colegio Frontera Sur, Dept Observat & Study Earth Atmosphere & Ocean, Chetmal, Quintana Roo, Mexico
[2] Univ Vinci, Mexico City, DF, Mexico
[3] El Colegio Frontera Sur, CONACYT, Dept Observat & Study Earth Atmosphere & Ocean, Chetmal, Quintana Roo, Mexico
来源
PEERJ | 2022年 / 10卷
关键词
Deep learning; Beach monitoring; Ecological application; Citizen science; Sargasso;
D O I
10.7717/peerj.13537
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The unusual arrival of Sargassum on Caribbean beaches is an emerging problem that has generated numerous challenges. The monitoring, visualization, and estimation of Sargassum coverage on the beaches remain a constant complication. This study proposes a new mapping methodology to estimate Sargassum coverage on the beaches. Semantic segmentation of geotagged photographs allows the generation of accurate maps showing the percent coverage of Sargassum. The first dataset of segmented Sargassum images was built for this study and used to train the proposed model. The results demonstrate that the currently proposed method has an accuracy of 91%, improving on the results reported in the state-of-the-art method where data was also collected through a crowdsourcing scheme, in which only information on the presence and absence of Sargassum is displayed.
引用
收藏
页数:27
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