# BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts

arXiv: https://arxiv.org/abs/2502.18844 · HTML: https://mlsanity.com/paper/2502.18844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Now You See Me (CME): Concept-based Model Extraction](https://mlsanity.com/paper/2010.13233/) (2020)
- [Tree Species Identification from Bark Images Using Convolutional Neural Networks](https://mlsanity.com/paper/1803.00949/) (2018)
- [Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis](https://mlsanity.com/paper/2404.05997/) (2024)
- [Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods](https://mlsanity.com/paper/2201.07495/) (2022)
- [Tree bark re-identification using a deep-learning feature descriptor](https://mlsanity.com/paper/1912.03221/) (2019)
- [Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images](https://mlsanity.com/paper/2211.13126/) (2022)
- [Leaf-Based Plant Disease Detection and Explainable AI](https://mlsanity.com/paper/2404.16833/) (2023)

## Built on by (most cited first, 1 of 1)

- [A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models](https://mlsanity.com/paper/2604.13240/) (2026)

## Abstract (arXiv)

The precise identification of tree species is fundamental to forestry, conservation, and environmental monitoring. Though many studies have demonstrated that high accuracy can be achieved using bark-based species classification, these models often function as "black boxes", limiting interpretability, trust, and adoption in critical forestry applications. Attribution-based Explainable AI (XAI) methods have been used to address this issue in related works. However, XAI applications are often dependent on local features (such as a head shape or paw in animal applications) and cannot describe global visual features (such as ruggedness or smoothness) that are present in texture-dominant images such as tree bark. Concept-based XAI methods, on the other hand, offer explanations based on global visual features with concepts, but they tend to require large overhead in building external concept image datasets and the concepts can be vague and subjective without good means of precise quantification. To address these challenges, we propose a lightweight post-hoc method to interpret visual models for tree species classification using operators and quantifiable concepts. Our approach eliminates computational overhead, enables the quantification of complex concepts, and evaluates both concept importance and the model's reasoning process. To the best of our knowledge, our work is the first study to explain bark vision models in terms of global visual features with concepts. Using a human-annotated dataset as ground truth, our experiments demonstrate that our method significantly outperforms TCAV and Llama3.2 in concept importance ranking based on Kendall's Tau, highlighting its superior alignment with human perceptions.