Dataset: Automation of tree-ring detection and measurements using deep learning

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