A Neuro-Symbolic Framework for Tree Crown Delineation and Tree Species Classification

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However,...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (23), p.4365
Main Authors: Harmon, Ira, Weinstein, Ben, Bohlman, Stephanie, White, Ethan, Wang, Daisy Zhe
Format: Article
Language:English
Subjects:
Algorithms
Automation
Classification
Datasets
Deep learning
Delineation
Forests
Hybrids
Logic
Machine learning
neuro-symbolics
Object oriented programming
Optimization
Plant species
Python
Remote sensing
Semantics
Species classification
tree crown delineation
tree species classification
Trees
Tuning
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Summary:Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come at the cost of increased complexity, which may deter the uninitiated from using these models. In this work, we present a framework to simplify the creation of neuro-symbolic models for tree crown delineation and tree species classification via the use of object-oriented programming and hyperparameter tuning algorithms. We show that models created using our framework outperform their non-neuro-symbolic counterparts by as much as two F1 points for crown delineation and three F1 points for species classification. Furthermore, our use of hyperparameter tuning algorithms allows users to experiment with multiple formulations of domain knowledge without the burden of manual tuning.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16234365