AI Lab

Artificial intelligence (AI) is high on the agenda of public and scientific discussions due to its important role in the so-called fourth industrial revolution. AI includes systems that exhibit intelligent behaviour by analysing the environment and taking action – with some degree of autonomy – to accomplish defined goals.

AI-enabled solutions have great potential for forest and eco-system monitoring and management. By applying artificial neural networks (ANN) for example, specific or general patterns can be derived to estimate harvest volume or aboveground biomass of forest stands. For this purpose, machine-learning bridges traditional forestry methods and modern information technology. Competitive ANNs distil biometric patterns from terrestrial and remotely sensed inventory data that can be applied to any stand unit for extrapolation.

Recent studies on machine learning and neural networks show that neuronal networks offer the necessary potential to model complex biometric and spatial relationships between various classically sampled forest parameters and modern area-wide remotely sensed data surveys significantly more accurately and robustly.

Windthrown Tree Stems and Convolutional Networks

FIT student Stefan Reder in cooperation with Jan-Peter Mund, Nicole Albert, Lilli...

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Maschine Learning

In this study, Julian Backa, FIT M.Sc. graducate and former team member,...

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