Faculty of Geosciences and Environment (FGSE), Institute of Earth Surface Dynamics (IDYST), Université de Lausanne, Quartier UNIL-Mouline – Bâtiment Géopolis, 1015 Lausanne, Switzerland
Invited contribution by Janbert Aarnink, recipient of the EGU Geomorphology Outstanding Student and PhD candidate Presentation Award 2022.
Faculty of Geosciences and Environment (FGSE), Institute of Earth Surface Dynamics (IDYST), Université de Lausanne, Quartier UNIL-Mouline – Bâtiment Géopolis, 1015 Lausanne, Switzerland
Expertise Center for Climate Extremes, Université de Lausanne, 1015 Lausanne, Switzerland
Marceline Vuaridel
Faculty of Geosciences and Environment (FGSE), Institute of Earth Surface Dynamics (IDYST), Université de Lausanne, Quartier UNIL-Mouline – Bâtiment Géopolis, 1015 Lausanne, Switzerland
Virginia Ruiz-Villanueva
Faculty of Geosciences and Environment (FGSE), Institute of Earth Surface Dynamics (IDYST), Université de Lausanne, Quartier UNIL-Mouline – Bâtiment Géopolis, 1015 Lausanne, Switzerland
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
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2,047
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BibTeX: 72
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Total article views: 2,852 (including HTML, PDF, and XML)
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Total article views: 2,330 (including HTML, PDF, and XML)
Thereof 2,254 with geography defined
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Total article views: 522 (including HTML, PDF, and XML)
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This study presents a novel convolutional-neural-network approach for detecting instream large wood in rivers, addressing the need for flexible monitoring methods across diverse data sources. Using a database of 15 228 fully labelled images, the model achieved a weighted mean average precision of 67 %. Fine-tuning parameters and sampling techniques can improve performance by over 10 % in some cases, offering valuable insights into ecosystem management.
This study presents a novel convolutional-neural-network approach for detecting instream large...
