Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation

Automated individual tree crown (ITC) delineation plays an important role in forest remote sensing. Accurate ITC delineation benefits biomass estimation, allometry estimation, and species classification among other forest-related tasks, all of which are used to monitor forest health and make important decisions in forest management. In this article, we introduce neuro-symbolic DeepForest, a convolutional neural network (CNN)-based ITC delineation algorithm that uses a neuro-symbolic framework to inject domain knowledge (represented as rules written in probabilistic soft logic) into a CNN. We create rules that encode concepts for competition, allometry, constrained growth, mean ITC area, and crown color. Our results show that the delineation model learns from the annotated training data as well as the rules and that under some conditions, the injection of rules improves model performance and affects model bias. We then analyze the effects of each rule on its related aspects of model performance. We find that the addition of domain data can improve
$F1$
by as much as four
$F1$
points, reduce the Kullback–Leibler divergence (KL-divergence) between ground-truth and predicted area distributions, and reduce the aggregate error in area between ground-truth and predicted delineations.