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deepT investigates times series of stem radius changes with deep neural network (DNN) algorithms in order to recognize tree species, growth patterns and tree water deficit-induced stem shrinkage characteristics. deepT tests different types of DNN algorithms, develops a Python environment for extensive data sets and quantifies the reliability of different approaches to recognize species-specific patterns and judge their potential for making predictions.

deepT is a collaboration bewtween EMPA (Mirko Lukovic, Cellulose & Wood Materials, Empa Dübendorf) and WSL.



deepT - deep neural network algorithms to recognize species and their specific response patterns in stem growth data of TreeNet