Dataset: Automation of tree-ring detection and measurements using deep learning
Authors/Creators
- 1. Gregor Mendel Institute, Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030, Vienna, Austria; Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 129,165 00, Prague, Czech Republic
- 2. Gregor Mendel Institute, Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030, Vienna, Austria
- 3. Gregor Mendel Institute, Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030, Vienna, Austria; Faculty of Biology, Ludwig-Maximilians-University Munich, 82152 Martinsried, Germany
- 4. Gregor Mendel Institute, Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030, Vienna, Austria; Max Perutz Labs, Department of Structural and Computational Biology, University of Vienna, Campus-Vienna-Biocenter 5, 1030, Vienna, Austria
Description
Datasets used to train and evaluate neural network-based implementation that automates detection and measurement of tree-ring boundaries from coniferous species.
SplitTrainValDatasets files contain hand-annotated squared images of Picea abies core samples with over 8000 ring boundaries.
RealWorldEvaluation.zip contains real-world core samples of 4 conifer species (Picea abies, Abies alba, Pinus sylvestris and Larix decidua) used to evaluate our application and tables with the results of these evaluations.
Full code of the application is available at https://github.com/Gregor-Mendel-Institute/TRG-ImageProcessing/tree/master.
More details about the datasets and the application can be found in our publication: Poláček, M., Arizpe, A., Hüther, P., Weidlich, L., Steindl, S., & Swarts, K. (2022). Automation of tree-ring detection and measurements using deep learning (p. 2022.01.10.475709). bioRxiv. https://doi.org/10.1101/2022.01.10.475709.
Files
EvaluationRealWorldData.zip
Files
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