Addressing the small data problem in forestry: self-supervised learning for aboveground biomass estimation

Harry Seely, Nicholas C. Coops, J. M. White, David Montwé and Ahmed Ragab

Article (2026)

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Department:	Department of Mathematics and Industrial Engineering
Funders:	NSERC, Natural Resources Canada, National Research Council of Canada
Grant number:	RGPIN-2018-03851, DHGA-119-1
PolyPublie URL:	https://publications.polymtl.ca/73638/
Journal Title:	Forestry An International Journal of Forest Research (vol. 99, no. 2)
Publisher:	Oxford University Press
DOI:	10.1093/forestry/cpag004
Official URL:	https://doi.org/10.1093/forestry/cpag004
Date Deposited:	10 Mar 2026 15:37
Last Modified:	10 Mar 2026 15:39

Cite in APA 7:	Seely, H., Coops, N. C., White, J. M., Montwé, D., & Ragab, A. (2026). Addressing the small data problem in forestry: self-supervised learning for aboveground biomass estimation. Forestry An International Journal of Forest Research, 99(2), 19 pages. https://doi.org/10.1093/forestry/cpag004
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