Abstract
Forest inventory forms the foundation of forest management. Remote
sensing (RS) is an efficient means of measuring forest parameters at
scale. Remotely sensed species classification can be used to estimate
species abundances, distributions, and to better approximate metrics
such as above ground biomass. State of the art methods of RS species
classification rely on deep learning models such as convolutional neural
networks (CNN). These models have 2 major drawbacks: they require large
samples of each species to classify well and they lack explainablity.
Therefore, rare species are poorly classified causing poor
approximations of their associated parameters. We show that the
classification of rare species can be improved by as much as 8 F1-points
using a neuro-symbolic (NS) approach that combines CNNs with a NS
framework. The framework allows for the incorporation of domain
knowledge into the model through the use of mathematically represented
rules, improving model explainability.