Abstract
Tree species identification and mapping is crucial for forest management, biodiversity conservation, and ecological research. Bark images can be captured easily from the ground-level and can provide large amount of information about the tree species and its health. Yet, existing datasets for tree bark images are often limited in scope, lacking diversity in species representation and temporal attributes. To address these limitations, we present BarkVisionAI, a comprehensive dataset of 156001 tree bark images for 13 species collected from diverse forest types across India. Each image is labeled with location, species name, device attributes, and timestamp, providing a robust foundation for studying species identification and the variability of bark characteristics. We are providing detailed metadata information about each image, encouraging its use in ecological research, machine learning model training, and environmental monitoring. Benchmarking experiments using standard image classification models demonstrate the dataset’s utility and effectiveness, highlighting its potential as a valuable resource for developing reliable, real-world applications in automated tree species identification and environmental change monitoring.
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Data availability
The dataset is available on Figshare: https://doi.org/10.6084/m9.figshare.28427246.
Code availability
The code to reproduce the model results are available on: GitHub.
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Acknowledgements
We sincerely acknowledge the Forest Guards, Range Officers, and Divisional Forest Officers of the Himachal Pradesh Forest Department for their crucial support in data collection. We extend our gratitude to our research team and field experts for their dedicated efforts in conducting field surveys and ensuring data accuracy. We gratefully acknowledge the International Land and Forest Tenure Facility, Sweden, for providing essential funding support that enabled the design, field implementation, and curation of the BarkVisionAI dataset. Their commitment to advancing community-centered forest governance and evidence-based research made it possible to undertake large-scale data collection across diverse forest landscapes in India. This support was instrumental in establishing the technical and organizational foundation required to generate an open, high-quality dataset for the global scientific community.
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A.C.: supervision, conceptualization, writing—review and editing, and funding acquisition. N.S.: methodology, data acquisition, and formal analysis. A.K.P.: conceptualization, methodology, data acquisition, analysis, and writing—original draft. P.R.: data acquisition, and writing—review and editing. M.J.: methodology, writing—review and editing.
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Chhatre, A., Saini, N., Parmar, A.K. et al. BarkVisionAI: Novel dataset for rapid tree species identification.
Sci Data (2026). https://doi.org/10.1038/s41597-026-06711-8
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DOI: https://doi.org/10.1038/s41597-026-06711-8
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