Data processing for forestry applications is challenged by the increasing availability of multi-source and multi-temporal data. The advancements of Deep Learning (DL) algorithms have made it a prominent family of methods for machine learning and artificial intelligence. This review determines the current state-of-the-art in using DL for solving forestry problems. Although DL has shown potential for various estimation tasks, the applications of DL to forestry are in their infancy. The main study line has related to comparing various Convolutional Neural Network (CNN) architectures between each other and against more shallow machine learning techniques. The main asset of DL is the possibility to internally learn multi-scale features without an explicit feature extraction step, which many people typically perceive as a black box approach. According to a comprehensive literature review, we identified challenges related to (1) acquiring sufficient amounts of representative and labelled training data, (2) difficulties to select suitable DL architecture and hyperparameterization among many methodological choices and (3) susceptibility to overlearn the training data and consequent risks related to the generalizability of the predictions, which can however be reduced by proper choices on the above. We recognized possibilities in building time-series prediction strategies upon Recurrent Neural Network architectures and, more generally, re-thinking forestry applications in terms of components inherent to DL. Nevertheless, DL applications remain data-driven, in contrast to being based on causal reasoning, and currently lack many best practices of conventional forestry modelling approaches. The benefits of DL depend on the application, and the practitioners are advised to ex ante subject their requirements to operational data availability, for example. By this review, we contribute to the technical discussion about the prospects of DL for forestry and shed light on properties that require attention from the practitioners.Peer reviewe
