Author

S. Bohlman

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 7

Signature Assays, Organisms & Methods

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Assay:imaging· 7 Approach:deep learning· 6 Research Focus:ecology & biodiversity· 4 Research Focus:benchmarking· 3 Research Focus:cell type annotation· 2 Approach:benchmark study· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1

Frequent Co-Authors

B. Weinstein· 7 shared S. Marconi· 7 shared A. Zare· 6 shared A. Singh· 4 shared E. White· 4 shared S. Graves· 4 shared E. P. White· 3 shared L. Magee· 2 shared

Activity by Year

2019-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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7 shared papersEcology

B. Weinstein

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Author profile →

7 papers

Recent·Most cited

Individual tree crown maps for the National Ecological Observatory Network

B. Weinstein, S. Marconi, A. Zare, S. Bohlman, A. Singh, S. J. Graves, L. Magee, D. J. Johnson, S. Record, V. E. Rubio, N. G. Swenson, T. Veblen, P. Townsend, R. A. Andrus, E. P. White

The paper uses computer vision and deep neural networks to predict individual tree species, crown characteristics, and locations across 24 sites in the National Ecological Observatory Network.

bioRxiv · Oct 25, 2023Ecology

Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model

B. Weinstein, S. Marconi, S. Graves, A. Zare, A. Singh, S. Bohlman, L. Magee, D. Johnson, P. Townsend, E. White

The paper develops a multi-temporal hierarchical model to improve rare tree species classification in airborne imagery, achieving 75% accuracy for 14 species and generating species maps for over 670,000 trees.

bioRxiv · Dec 7, 2022 · PreprintEcology

Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, E. White

This paper develops a semi-supervised deep learning method to detect tree crowns in RGB imagery using unsupervised LIDAR data and a small number of hand-annotated images, achieving accurate predictions in natural landscapes.

bioRxiv · Feb 12, 2021Ecology

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

B. Weinstein, S. Graves, S. Marconi, A. Singh, A. Zare, D. Stewart, S. Bohlman, E. P. White

The paper introduces a benchmark dataset for evaluating individual tree crown delineation algorithms using co-registered remote sensing data and provides an R package for standardized evaluation.

bioRxiv · Nov 16, 2020Ecology

NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, A. Singh, S. Graves, E. White

The paper introduces a dataset of 100 million individual tree crowns derived from remote sensing and deep learning, enabling large-scale forest analysis.

bioRxiv · Sep 8, 2020 · PreprintEcology

Estimating individual level plant traits at scale

S. Marconi, S. Graves, B. Weinstein, S. Bohlman, E. White

This paper develops a method to estimate individual tree traits at scale using remote sensing data, achieving moderate accuracy for several traits and demonstrating improved performance over pixel-level predictions.

bioRxiv · Aug 4, 2020Ecology

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, E. P. White

This paper evaluates the generalization of RGB deep learning tree detection models across different forest types using data from the National Ecological Observatory Network, finding that a universal model performs as well as site-specific models.

bioRxiv · Oct 2, 2019 · PreprintEcology