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Improving Rare Tree Species Classification using Domain Knowledge

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posted on 2022-12-24, 05:41 authored by Ira HarmonIra Harmon, Sergio Marconi, Ben Weinstein, Yang Bai, Daisy Zhe Wang, Ethan P. WhiteEthan P. White, Stephanie BohlmanStephanie Bohlman

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.


NSF 1926542


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University of Florida

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  • United States of America