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Improving Rare Tree Species Classification using Domain Knowledge
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  • Ira Harmon ,
  • Sergio Marconi ,
  • Ben Weinstein ,
  • Yang Bai ,
  • Daisy Zhe Wang ,
  • Ethan P. White ,
  • Stephanie Bohlman
Ira Harmon
University of Florida

Corresponding Author:[email protected]

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Sergio Marconi
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Ben Weinstein
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Daisy Zhe Wang
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Ethan P. White
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Stephanie Bohlman
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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.
2023Published in IEEE Geoscience and Remote Sensing Letters volume 20 on pages 1-5. 10.1109/LGRS.2023.3278170