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Lv, Zhonghui, Nunez, Karinna, Brewer, Ethan, Runfola, Dan (2023) pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks. Computers & Geosciences, 171. 105296 doi:10.1016/j.cageo.2022.105296

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Reference TypeJournal (article/letter/editorial)
TitlepyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks
JournalComputers & Geosciences
AuthorsLv, ZhonghuiAuthor
Nunez, KarinnaAuthor
Brewer, EthanAuthor
Runfola, DanAuthor
Year2023 (February)Volume171
PublisherElsevier BV
DOIdoi:10.1016/j.cageo.2022.105296Search in ResearchGate
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Mindat Ref. ID15664549Long-form Identifiermindat:1:5:15664549:4
GUID0
Full ReferenceLv, Zhonghui, Nunez, Karinna, Brewer, Ethan, Runfola, Dan (2023) pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks. Computers & Geosciences, 171. 105296 doi:10.1016/j.cageo.2022.105296
Plain TextLv, Zhonghui, Nunez, Karinna, Brewer, Ethan, Runfola, Dan (2023) pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks. Computers & Geosciences, 171. 105296 doi:10.1016/j.cageo.2022.105296
In(2023) Computers & Geosciences Vol. 171. Elsevier BV

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